The effect of competition on stand, tree, and wood growth and structure in subtropical Eucalyptus grandis plantations

Southern Cross UniversityePublications@SCU

Theses

2007

The effect of competition on stand, tree, and woodgrowth and structure in subtropical Eucalyptusgrandis plantationsFelicity HarrisSouthern Cross University

ePublications@SCU is an electronic repository administered by Southern Cross University Library. Its goal is to capture and preserve the intellectualoutput of Southern Cross University authors and researchers, and to increase visibility and impact through open access to researchers around theworld. For further information please contact [email protected].

Publication detailsHarris, F 2007, 'The effect of competition on stand, tree, and wood growth and structure in subtropical Eucalyptus grandisplantations', PhD thesis, Southern Cross University, Lismore, NSW.Copyright F Harris 2007

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The Effect of Competition on

Stand, Tree, and Wood Growth and Structure in

Subtropical Eucalyptus grandis Plantations

Felicity Catherine Harris

Bachelor of Science (Forestry) with Honours (1st Class), ANU

Bachelor of Economics, ANU

School of Environmental Science and Management

Southern Cross University

A thesis submitted for the Degree of Doctor of Philosophy

within Southern Cross University.

January 2007

i

STATEMENT OF SOURCES

The work presented in this thesis is my own. Specific contributions made by others are

referred to in the text and acknowledgements.

The material of this thesis has not been submitted either in whole, or in part, for a degree at

this or any other University.

Felicity Harris

ii

ABSTRACT

In this study, the effect of competition on stand, tree, and wood structure was examined in the

context of the viability of high-density eucalyptus plantations for the production of quality

timber. The study was based on a 4-year-old Eucalyptus grandis trial planted in south-eastern

Queensland in March 1999 by Greenfield Resource Options P/L. Stocking densities of 250,

1,000, 5,000 and 10,000 stems/ha allowed growth traits in extreme stockings to be compared.

High stocking densities led to greater stand growth and also greater inequality in tree size.

The largest trees in high stocking densities were smaller than those in low stocking densities,

however high stocking densities had more trees in the largest cohort and they were more

uniform in size. Consequently the total stem volume of the largest 1,000 st/ha was similar for

all stocking densities, and the total stem volume of the remaining trees increased with

stocking density.

The allocation of tree biomass skewed away from crown production and towards stem

production as stocking density (general competition) increased and as stem diameter

(competitive status) decreased. Dominant trees in high stocking densities had similar

aboveground biomass accumulation per unit leaf area compared to dominant trees in low

stocking densities, but had a larger proportion of biomass allocated to the stem when

compared to the crown. Dominant trees in high stocking densities therefore had similar tree

growth efficiency but better stem growth efficiency than dominant trees in low stocking

densities. Increased competition appeared to restrict the growth of dominant trees by

restricting resource capture rather than by reducing the efficiency of growth, since the tree

growth efficiency of dominant trees was not affected by competition.

Examination of the stem wood structure revealed that the largest trees in high stocking

densities exhibited more desirable wood properties including more uniform wood density, less

variability in wood anatomy, and better branch-shed (hence low knot content) than the largest

trees in the low stocking densities. This suggests that densely stocked plantations could be of

better value for timber production than lightly stocked plantations.

The results illustrate the importance of including stand structure in forest research since a

failure to do so will underestimate the productivity of the largest trees in densely stocked

stands and does not adequately account for the structural benefits of high stocking density.

iii

The findings are based on a young plantation, however they indicate that densely stocked

plantations could be used to provide an early cash return from a harvested biomass crop with

no detrimental effect on a retained solid hardwood crop of the largest 1,000 st/ha. The results

of this study indicate that the perception that densely stocked plantations cannot produce an

equivalent volume of sawlogs of similar quality wood to that produced from lightly stocked

plantations is incorrect.

iv

ACKNOWLEDGEMENTS

My thanks to Southern Cross University, the Commonwealth Government of Australia, and

industry sponsors Southern Pacific Petroleum Pty/Ltd and Greenfield Resource Options

Pty/Ltd, without whose support through providing scholarships, grants, study material and

equipment this Ph..D. study would not have been possible.

My thanks to my supervisors A. Prof. Alison Specht and Prof. Jerry Vanclay of Southern

Cross University and Dr Nigel Turvey of Greenfield Resource Options, who have provided

constant guidance, support and encouragement throughout the thesis.

My thanks to technical and administrative staff at Southern Cross University, especially

Maxine Dawes, Paul Kelly and Delva Smith, who provided efficient and friendly support

whenever needed.

My thanks to family members who came from Canberra (ACT) to Gin Gin (QLD) to provide

invaluable help with field measurements, including my sisters Cecilia (who came twice) and

Bridget (who came whilst enduring morning sickness), and my father John.

My thanks to my parents, Ruth and John Harris, who provided a laptop computer for my

studies as well as their interest and support.

Finally my thanks to my husband, Gareth Wooler, who has gracefully accepted and

encouraged all the time and effort spent I have spent on the Ph.D.

v

TABLE OF CONTENTS

STATEMENT OF SOURCES...............................................................................................................................I

ABSTRACT .......................................................................................................................................................... II

ACKNOWLEDGEMENTS ................................................................................................................................IV

TABLE OF CONTENTS ..................................................................................................................................... V

1. INTRODUCTION ....................................................................................................................................... 1

1.1 BACKGROUND TO THE STUDY ............................................................................................................... 1

1.2 STRUCTURE OF THE THESIS ................................................................................................................... 4

2. THE SPACING TRIAL .............................................................................................................................. 5

2.1 STUDY SITE........................................................................................................................................... 5

2.2 EXPERIMENTAL DESIGN ........................................................................................................................ 7

2.3 ESTABLISHMENT ................................................................................................................................... 9

2.4 SAMPLE SELECTION ............................................................................................................................ 12

2.4.1 3 Year Old Sample Selection ......................................................................................................... 12

2.4.2 4 Year Old Sample Selection ......................................................................................................... 12

3. STAND GROWTH AND STRUCTURE ................................................................................................. 14

3.1 STATE OF KNOWLEDGE ....................................................................................................................... 14

3.1.1 Stand Growth................................................................................................................................. 14

3.1.2 Stand Structure .............................................................................................................................. 16

3.2 EXPERIMENTAL RATIONALE ............................................................................................................... 20

3.3 METHODOLOGY .................................................................................................................................. 21

3.3.1 Sample Age and Size...................................................................................................................... 21

3.3.2 Data Collection and Calculation................................................................................................... 21

3.4 RESULTS AND DISCUSSION.................................................................................................................. 25

3.4.1 Stand Growth................................................................................................................................. 25

3.4.2 Stand Structure .............................................................................................................................. 28

3.5 SUMMARY ........................................................................................................................................... 39

4. TREE GROWTH AND STRUCTURE.................................................................................................... 40


4.1.1 Tree Growth................................................................................................................................... 40

4.1.2 Tree Structure ................................................................................................................................ 46

4.2 EXPERIMENTAL RATIONALE ............................................................................................................... 55

4.3 METHODOLOGY .................................................................................................................................. 56

4.3.1 Sample Age and Size...................................................................................................................... 56

4.3.2 Data Collection and Calculation................................................................................................... 56

vi

4.3.3 Data Analysis................................................................................................................................. 62

4.4 RESULTS AND DISCUSSION.................................................................................................................. 64

4.4.1 Tree Growth................................................................................................................................... 65

4.4.2 Tree Structure ................................................................................................................................ 74

4.4.3 Implications for Stand Growth and Structure................................................................................ 90

4.5 SUMMARY ........................................................................................................................................... 94

5. WOOD GROWTH AND STRUCTURE ................................................................................................. 97


5.1.1 Wood Growth................................................................................................................................. 97

5.1.2 Wood Structure .............................................................................................................................. 99

5.1.3 Wood Types ................................................................................................................................. 104

5.1.4 Wood Properties .......................................................................................................................... 107

5.2 EXPERIMENTAL RATIONALE ............................................................................................................. 120

5.3 METHODOLOGY ................................................................................................................................ 122

5.3.1 Sample Age, Size and Preparation .............................................................................................. 122

5.3.2 Data Collection and Calculation................................................................................................. 124

5.3.3 Data Analysis............................................................................................................................... 129

5.4 RESULTS AND DISCUSSION................................................................................................................ 131

5.4.1 Sapwood ...................................................................................................................................... 131

5.4.2 Wood Anatomy............................................................................................................................. 133

5.4.3 Stemwood Basic Density.............................................................................................................. 147

5.4.4 Branching Habits......................................................................................................................... 151

5.5 SUMMARY ......................................................................................................................................... 160

6. CONCLUSION ........................................................................................................................................ 164

6.1 SYNOPSIS .......................................................................................................................................... 164

6.2 MAJOR DISCOVERY........................................................................................................................... 168

6.3 MANAGEMENT APPLICATIONS .......................................................................................................... 169

6.4 FURTHER RESEARCH REQUIREMENTS ............................................................................................... 170

REFERENCES .................................................................................................................................................. 172

APPENDICES ................................................................................................................................................... 185

APPENDIX 1: STEM VOLUME MODELS............................................................................................................. 185

Chapter 1 Introduction Page 1

1. INTRODUCTION

1.1 Background to the Study

The vast majority of eucalyptus plantations in Australia are fast-growing plantations managed

to produce pulpwood for paper production (Ferguson et al. 2002; Turner et al. 2004). There is

increasing demand, however, to source additional wood products from hardwood plantations.

Such products include sawn solid hardwood for construction and furniture timber, in which

there is a national trading deficit (Turner et al. 2004), and raw wood biomass for renewable

bio-fuel, in which there is increasing demand due to the global drive for greenhouse gas

reduction (Stucley et al. 2004). The future ability to source such products from eucalyptus

plantations will require either establishing new plantations or converting existing pulpwood

plantations to other uses.

The establishment of new eucalyptus plantations for solid hardwood production is forecast to

increase up to eight-fold over the next 40 years, yet the increased production is not expected

to meet demands for solid hardwood products (Ferguson et al. 2002). Additional new

eucalyptus plantations are clearly required if Australia is to reduce the trade deficit in solid

hardwood products, however there exist substantial impediments to achieving this aim. These

include a lack of expertise in managing eucalyptus plantations for solid wood production and

the long-term nature of the investment. An alternative to establishing new solid hardwood

plantations is to source solid hardwood from current pulpwood plantations. This option,

however, is often discounted as the relatively high stocking rates used in pulpwood

plantations (1,200-1,500 st/ha) are thought to restrict growth of the final sawlog crop and are

considered unlikely to produce good quality solid hardwood (Turner et al. 2004).

Raw wood biomass is readily available from current and future eucalyptus plantations, but is

generally restricted to harvesting residues (Bugg et al. 2002). Few eucalyptus plantations in

Australia are dedicated to producing biomass since it is not a profitable commodity. There is

substantial government and industry funding for research into renewable fuels in Australia

due to the global drive for greenhouse gas reduction, indicating that it is only a matter of time

before bio-fuel markets emerge (Stucley et al. 2004). Biomass may then become a profitable

commodity, providing a return on forest residues and possibly stimulating the establishment

of eucalyptus plantations for biomass production alone.


It is generally accepted that pulpwood plantations cannot produce good quality solid

hardwood within a profitable time period due to their relatively high stocking rates, creating

an impediment to converting pulpwood plantations into solid hardwood plantations. This

perception, however, is challenged by stand development in fast-growing native eucalyptus

forests, in which prolific seedling regeneration results in very highly stocked stands that go on

to produce good quality solid hardwood (Florence 1996). Furthermore, evidence suggests that

increased competition due to higher stocking does not greatly impede the growth of the

largest trees in the stand (Bredenkamp and Burkhart 1990b; Battaglia 2001; Franc 2001;

Binkley et al. 2002), indicating that the time required to produce large trees suitable for

sawing into solid hardwood products may not be increased by high stocking rates.

The above evidence provides impetus for closer examination of the effect of stocking density

(competition) on eucalyptus plantation development, not only at the stand and tree level, but

also at the wood level. The broad objective of this study is to determine the effect of

competition on stand, tree, and wood growth and structure in fast-growing sub-tropical

Eucalyptus plantations. The intention is to take a holistic approach so it could be shown how

competitive interactions between trees shape the stand, and the extent to which stand and tree

development affect wood quality. Throughout the investigation a primary focus is on the

largest trees in the stand, as these trees are representative of the solid hardwood (sawlog) crop.

By structuring the investigation from the stand level through to the wood level, the pathways

through which competitive mechanisms affect stand, tree, and wood characteristics could be

investigated (Figure 1.1).


Figure 1.1: A model of tree growth (absolute size) and structure (relative size) in which current structural characteristics (bold arrows) shape the future growth and structure of the stand, tree, and wood. The sum of growth and structure of all trees in the stand determine stand growth and structure (a). The position of the individual tree within the stand structure (b) affects individual tree resource capture (competitive ability) (c) and growth partitioning (maximises future resource capture) (d). The structure of individual trees (e) affects growth partitioning (maintenance of structural requirements) (f) and wood structure (maintenance of structural, food storage and transpiration requirements) (g).

(a)

(b)

(c)

(d)

(e)

(g)

(f)


1.2 Structure of the Thesis

The thesis is broken into chapters, each of which focuses on aspects of the investigation of the

effect of competition on stand, tree, and wood growth and structure in sub-tropical eucalyptus

plantations;

• The Spacing Trial: provides the rationale for the location, silviculture and design of the

spacing trial, as well as establishment details and subsequent growth conditions.

• Stand Growth and Structure: focus on identifying competition intensity in the whole stand

and the competitive status of individual trees within the stand.

• Tree Growth and Structure: focus on comparison between dominant trees in different

planting densities, and between dominant and suppressed trees within planting densities.

• Wood Growth and Structure: focus on comparison between dominant trees in different

planting densities, and between dominant and suppressed trees within planting densities.

Patterns similar to those for tree growth and structure are considered as potential

indicators of functional changes in wood growth and structure.

• Conclusion: closes the study by providing a synopsis and discussing significant

discoveries, management applications, and future research requirements.

Chapter 2 The Spacing Trial Page 5

2. THE SPACING TRIAL

The spacing trial was established in March 1999 and managed by Greenfield Resource

Options P/L (GRO) on behalf of Southern Pacific Petroleum NL/Central Pacific Minerals NL

(SPP). The spacing trial is a small component of wide ranging reforestation trials designed by

GRO to determine the most appropriate species, establishment techniques and silvicultural

systems for eucalyptus plantations established to capture and sequester atmospheric carbon in

south-east Queensland (Turvey1 pers. comm. 2001).

2.1 Study Site

LOCATION

The spacing trial was established on the property ‘Lucy’, which is located approximately 20

km north of Gin Gin (latitude 24.91°S, longitude 151.85°E) in south-east Queensland,

Australia (Figure 2.1). It was located at a site on the property where the fastest possible

growth could occur in order to expedite the onset of competitive effects between trees.

Figure 2.1: Map showing the location of the ‘Lucy’ property in relation to the state of Queensland, Australia.

1 Dr N. Turvey, Managing Director, Greenfield Resource Options (Forestry Project Management & Consulting)


GEOLOGY AND LANDSCAPE

The property ‘Lucy’ is situated in the Burnett River catchment, which has a great diversity of

soils due to its geological complexity. The region is comprised mostly of hilly lands of

metamorphic and granite rocks, sediments and basalt (Hubble and Isbell 1983). The soils local

to ‘Lucy’ reflect the underlying granite geology.

The landscape local to ‘Lucy’ comprised of low, undulating hills with seasonal drainage. On

‘Lucy’ the spacing trial was located on a creek-flat bordered by two creeks in the midst of low

undulating hills, and much of the water moving through the surrounding landscape was

expected to filter through the site with the result that ground water would be readily available.

SOILS

Most soils on ‘Lucy’ were classified as brown or grey chromosols (Isbell 1996), which are

texture contrast soils of medium depth and fertility, with sandy loam surfaces overlying

medium brown or grey clays. The brown chromosols on ‘Lucy’ were characterised by low

erodibility and were widespread on the property, whereas the grey chromosols were of

moderate erodibility and were limited to steeper slopes.

In contrast, the creek-flat on which the spacing trial was situated had very deep (> 2 m),

alluvial fertile soil. The surface soil was sandy-loam textured grading to clay loams at depth

and was classified as a grey ferrosol (Isbell 1996). The deep soil profile was expected allow

trees unobstructed access to ground water.

CLIMATE

The area in which ‘Lucy’ is located is described by the Australian Bureau of Meteorology

(BOM) as having a hot, humid summer climate with a long term mean annual rainfall of 1050

mm (BOM 2005), and is characterised by wet, hot summers and dry, mild winters (Figure

2.2). Light frosts between 0 - 3°C occur annually from late May to early August, whereas

heavy frosts between -3 - 0°C occur from June to July but may only occur in 50 – 80% of

years.


0

50

100

150

200

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Month

Rain

fall (

mm

)

0

5

10

15

20

25

30

35

Tem

pera

ture

(°C)

Mean Monthly Rainfall Range

Mean Monthly Maximum Temperature Range

Mean Monthly Minimum Temperature Range

Figure 2.2: The mean monthly rainfall range and mean monthly minimum and maximum temperature range for the area in which ‘Lucy’ is located, based on standard year records (1961-1990) (BOM 2005).

2.2 Experimental Design

PLANTING DENSITY TREATMENT

The role of the spacing trial as established by GRO on behalf of SPP was to demonstrate the

effects of intra-specific competition on the accumulation and distribution of plant biomass and

carbon in stands and trees at competition intensities ranging from very low to very high levels

of competition (Turvey2 pers. comm. 2001). Planting density was an appropriate mechanism

to create different competition intensities between stands. The planting densities used were

250, 1,000, 5,000 and 10,000 stems per hectare (st/ha), which allowed a comparison between

very low competition in 250 st/ha and very high competition in 10,000 st/ha.

SPECIES SELECTION

A number of sub-tropical eucalypt species were under consideration by GRO for plantations

in south-east Queensland. Of these Eucalyptus grandis had exhibited excellent performance in

plantations both in Australia and overseas and had proven its potential to sequester carbon

quickly owing to its rapid early growth habit. E. grandis was therefore selected for the

spacing trial. In its native habitat mature E. grandis is a tall tree reaching 45-55 m in height

and exhibiting excellent form. Its main area of occurrence is coastal regions from Newcastle

2 Dr N. Turvey, Managing Director, Greenfield Resource Options (Forestry Project Management & Consulting)


to Bundaberg (latitude 25-33°S) on the east coast of Australia, and scattered populations

extend as far north as Townsville and Bloomfield (latitude 16-19°S) (Boland et al. 1992).

Whilst the research was targeted on fast-growing E. grandis, the pattern of results were

expected to apply to all fast-growing eucalyptus species exhibiting similar characteristics in

stand development, including similar height growth in dominant trees regardless of planting

density, the rapid differentiation of the stand into dominance cohorts due to asymmetric

competition, and a significant loss of photosynthetic capacity in shaded leaves.

DESIGN LAYOUT

The spacing trial contained four replicates of each planting density treatment, resulting in a

total of sixteen plots (four treatments multiplied by four replicates). The size and layout of the

trial was designed to maximise the use of the 1.6 ha area available. The spacing trial was

established in 24 x 24 m plots in a randomised block design (Figure 2.3). Within each plot,

trees were established in a square grid pattern, whereby the spacing within and between rows

was equal. This resulted in a square spacing of 6.20 m for 250 st/ha, 3.15 m for 1,000 st/ha,

1.41 m for 5,000 st/ha and 1.00 m for 10,000 st/ha. The outermost row of each plot was

excluded from measurements since it was considered to be a buffer row absorbing edge-

effects of each plot. The trial has a strong statistical design that allows differences in growth

between planting densities to be tested under the appropriate statistical assumptions.

Plot Layout Plot

Replicate

Treatment (st/ha) NORTH

1 2 5 6 7 8 9

1 1 2 2 2 2 3

10,000 5,000 250 10,000 5,000 1,000 250

4 3 14 13 12 11 10

1 1 4 4 3 3 3

1,000 250 1,000 250 5,000 1,000 10,000

15 16

4 4

10,000 5,000

Figure 2.3: The layout of the spacing trial.


2.3 Establishment

SEEDLING SOURCE

Seed of E. grandis was sourced from the Coffs Harbour Orchard, NSW, having been collected

by and purchased from the Australian Tree Seed Centre (ATSC) (Seedlot 18146). Trees

grown in the Coffs Harbour Orchard were originally sourced from native forest trees known

as ‘plus trees’ for their exceptional growth and form, and consequently the seed sourced from

the Coffs Harbour Orchard were biased towards competitive genotypes. This was of benefit in

the spacing trial as it provided a relatively even distribution of genotypes across the trial, with

the result that differences between individuals were more likely to be due to the process of

competition than the genetic ability of individuals to grow at different rates.

Seed was supplied to Minyon Forest Nursery, who was contracted to grow and deliver the

seedlings. Following germination seedlings were transferred to V-93 Hiko trays, which are

plastic trays consisting of 40 root plug ‘cavities’, each of 93 ml volume and with vertical root

training ribs. Seedlings were grown to a height of 25-30 cm and were required to have healthy

leaves and a well developed root system. Seedlings were hardened-off in the nursery prior to

delivery, and watered in the field prior to planting.

SITE PREPARATION

In order to provide uniform site preparation for all planting density treatments, the area of the

spacing trial was cultivated completely using a trailed 12 disc plough-harrow to a depth of 15

to 20 cm (Figure 2.4(a-b)).

Pre-plant weed control was used to ensure that seedlings did not experience significant

competition or competition induced mortality from weeds during their establishment. In order

to ensure that all weeds were eradicated, weeds were initially left to germinate and grow

following cultivation, and were treated only when weeds were more than 50 cm tall and

growing vigorously. Weeds were then treated using Roundup 360 (9 l/ha) together with LI700

(300 ml/100 l) as a penetrant and surfactant, and water (150 l/ha) to penetrate the dense grass

and thoroughly wet the vegetation cover. Weedicide was applied by an 8 m wide boom with

nozzles every 50 cm, at an operating pressure set at 2 Bar. The spray rig and storage tanks

were mounted on a 4WD Case 4230 80 HP tractor, and a standard operating speed of 8 km/hr

was used.


(a) (b)

(c) (d)

Figure 2.4: Images of the establishment of the spacing trial on the ‘Lucy’ property; (a) the creek-flat on which the spacing trial was established with the spacing trial cultivation visible in the lower half of the image; (b) broad-scale cultivation of the spacing trial; (c) the spacing trial at 3-months-old with a 250 st/ha plot in the foreground and a 10,000 st/ha plot in the background; (d) a 10,000 st/ha plot at 1 year old.

PLANTING

The spacing trial was planted by hand in March 1999. The exact planting location of each tree

was measured out and marked with non-toxic spray paint prior to planting to ensure the

correct spacing. A 20-25 cm deep hole was created using a spade and root plugs were placed

vertically in the hole, which was then in filled and firmed down by treading lightly on each

side of the seedling.


SILVICULTURE

Following planting, the spacing trial was fertilised with di-ammonium phosphate (Nitrogen

18%, Phosphorous 20%, Sulphur 1.7%) at a rate of 300 kg/ha, or 1,200 g/tree in 250 st/ha,

300 g/tree in 1,000 st/ha, 60 g/tree in 5,000 st/ha, and 30 g/tree in 10,000 st/ha. Care was

taken to distribute the fertiliser evenly around the root zone of the seedling so as not to bias

root growth in any direction. Post-planting weed control was carried out in June 1999,

approximately 2 months after planting. The weedicide consisted of a mixed of Lontrel at 0.9

l/ha, Verdict at 1.6 l/ha, Simazine flowable at 5 l/ha and LI-700 at 0.45 l/ha. The application

was broadcast by a tractor mounted rig with a computerised delivery system.

The establishment of the spacing trial was highly successful, with low mortality rates of 6.2%

for 250 st/ha, 6.6% for 1,000 st/ha, 6.2% for 5,000 st/ha and 3.1% for 10,000 st/ha in the first

year of growth. The eradication of weeds during early establishment was successful as

seedlings in the trial did not experience significant competition from weeds during early

establishment (Figure 2.4(c)). In later development grassy weeds did establish in the 250 and

1,000 st/ha planting densities, however the canopies had developed sufficiently that grass

growth did not cause any light competition. Canopy closure in the 5,000 and 10,000 st/ha

planting densities was very rapid, occurring between 6-12 months, and consequently grassy

weeds were unable to establish (Figure 2.4(d)).

RAINFALL

The year following planting had 1,077 mm annual rainfall, just exceeding the long term mean

annual rainfall of 1,050 mm (Figure 2.5), which aided the successful establishment of the

spacing trial. Annual rainfall then fell below the long term mean for the remainder of the

measurement period, and was lowest in the third year of growth which was a drought year.

0

200

400

600

800

1000

1200

1 2 3 4 5

Age (yrs)

An

nu

al

Ra

infa

ll (

mm

)

Mean Annual Rainfall Current Annual Rainfall

Figure 2.5: Mean annual rainfall and current annual rainfall recorded at Gin Gin, QLD (BOM 2005).


2.4 Sample Selection

Annual stem measurements were made on the whole spacing trial, excluding those trees in

buffer rows, and as a result 16 stems were measured annually in 250 st/ha plots, as compared

to 1,936 stems in 10,000 st/ha plots (Table 2.1). Detailed measurements were collected at ages

3 and 4 years.

Table 2.1: The number of stems in the spacing trial for each planting density; by plot and by treatment.

Planting Density No. Stems per Plot No. Stems per Treatment

250 st/ha 4 16

1,000 st/ha 30 120

5,000 st/ha 225 900

10,000 st/ha 484 1,936

2.4.1 3 Year Old Sample Selection

The primary purpose of non-destructive measurements at 3 years was to investigate branching

patterns. At 3 years, 2 trees per plot were selected using a systematic sequence of selection in

each plot, with the result that 8 trees per planting density were selected.

2.4.2 4 Year Old Sample Selection

The primary purpose of destructive measurements at 4 years was to investigate tree allometry

and wood quality. At 4 years, 2 trees per plot in 250 st/ha and 5 trees per plot in 1,000, 5,000

and 10,000 st/ha were selected using a stratified random sampling method. The selection was

made by dividing the range of tree sizes (as determined by stem diameter at breast height

(DBH)) into two equal size classes for 250 st/ha and five equal size classes for 1,000, 5,000

and 10,000 st/ha, and randomly selecting one tree per size class per plot. The stratified

random sampling method was necessary to ensure that the whole range of tree sizes in each

planting density was selected, since a true random sample was likely to result in a bias

towards the selection of smaller trees due to positive skewness in the range of tree sizes

(Figure 2.6).


0

15

30

Perc

en

tag

e F

req

uen

cy (

%)

0

15

30

0

15

30

0

15

30

0-1 2-3 4-5 6-7 8-9 10-11 12-13 14-15 16-17 18-19 20-21 22-23 24-25 26-27

Diameter Class (cm)

(a)

(b)

(c)

(d)

Figure 2.6: Histograms of the range in tree sizes of 4 year old E. grandis planted at; (a) 250 st/ha, (b) 1,000 st/ha, (c) 5,000 st/ha, and (d) 10,000 st/ha.

Chapter 3 Stand Growth and Structure Page 14

3. STAND GROWTH AND STRUCTURE

Stand growth and structural development is a dynamic process in which factors including the

genetic characteristics of the species present, the climate and resource availability, and the

rate of onset and intensity of competition, combine to influence the rate at which stand growth

occurs and stand structure changes. A comprehensive understanding of both stand growth and

stand structure is essential to understanding stand development, since each influences the

other.

3.1 State of Knowledge

3.1.1 Stand Growth

Stand size is typically measured at the stand level in terms of mean tree size or aggregate

stand size, and stand growth occurs when trees within a stand increase in mass and size.

During stand growth trees increasingly utilise gaps available in the stand until such time as the

entire site is accessed and occupied. The absolute limit of stand growth is determined by the

maximum biomass carrying-capacity of the site, and as stands approach this threshold stand

density (biomass per unit of space) increases. Stand density is historically used as a

management tool as it can provide an indication of the mean stem size and expected growth

rate of stands (Curtis 1970). Measures of stand density typically compare measures of stand

size such as stem number, stem diameter, stem volume and/or stem height, with the space

occupied by the stand (Bredenkamp and Burkhart 1990a). Some better known examples

include basal area, relative spacing (Hart 1928), the stand density index (Reineke 1933), the

S-curve (O'Connor 1935) and the -3/2 power law (Yoda et al. 1963).

During stand development, changes in the rate of stand growth occur. Changes in stand

growth rate are commonly measured in terms of the current annual increment (CAI) in mean

tree or stand size over one year of growth, and the mean annual increment (MAI) in mean tree

or stand size from initial establishment to a specified age (Brack and Wood 1998). The

general pattern of growth is well known (Gower et al. 1996); CAI increases at an increasing

rate in the early stages of stand establishment, following which it increases at a decreasing

rate until it peaks and begins a process of decline (Sprugel 1984; Ryan and Waring 1992).

MAI follows a similar (but delayed) sequence, and peaks at the point at which it crosses the

CAI schedule (Figure 3.1).


Figure 3.1: The general pattern of change in current annual increment (CAI) and mean annual increment (MAI) of forest stands over the course of development (Brack and Wood 1998).

The study of stand development in plantation eucalypts to date has emphasised stand growth

and neglected stand structure. A classic example of this is the correlated curve trend (CCT)

spacing trials established with E. grandis in South Africa (Laar and Bredenkamp 1979), in

which the response of stand development to different spacing and thinning treatments is

measured by mean stem diameter and stand basal area, thereby providing good information

about stand growth but generalised and incidental information about stand structure. The vast

majority of research on eucalypts is similar, with changes in stand growth and structure due to

treatments including planting density, thinning, fertilising, weeding, irrigation and pruning,

being reported in terms of mean tree size and/or aggregate stand size only.

Research in eucalyptus plantation stand growth has been extensive, and as a result the

productivity of eucalyptus plantations has been greatly enhanced, and modern models (both

empirical and process based) can be used to make reasonably accurate predictions of the

patterns of change in stand growth over a range of commercial plantation species and sites.

Yet whilst research allows more accurate definition of stand growth patterns, much of it does

little to provide an explanation as to why such stand growth patterns should occur. The reason

why specific stand growth patterns occur, and particularly why stand growth declines, has

emerged as a contentious issue since this information could aid further increases in plantation

productivity by enabling silviculturalists to delay the point at which stand growth rate

declines, and more universally it could aid the modelling of growth in tree species and forest

ecosystems that have not been intensively studied (such as is required for global carbon

budgeting).

Whilst the cause of declining stand growth is not known, there is a great deal of speculation

on what could be causing it. Possible explanations include one, or more likely a combination


of (Berger et al. 2004), the following; a change in the balance between photosynthesis and

respiration towards respiration as trees age (Kira and Shidei 1967), subsequently refuted in

several studies (Ryan and Waring 1992; Yoder et al. 1994; Ryan et al. 2004); a change in the

allocation of carbon from stem wood to other components such as leaves, branches, roots, and

reproductive components (Yoder et al. 1994; Ryan et al. 2004); a loss of carbon from the

stand ‘budget’ due to mortality (Ryan et al. 1997); an increase in the hydraulic constraints of

the canopy as trees grow larger causing a decrease in stomatal conductance and

photosynthetic capacity (Yoder et al. 1994; Gower et al. 1996; Hubbard et al. 2001),

subsequently refuted in several studies (Hubbard et al. 2002; McDowell et al. 2002; Barnard

and Ryan 2003; Ryan et al. 2004); a shortage in nutrient availability as the growing forest

captures the resources available (Binkley et al. 1995; Gower et al. 1996), subsequently

refuted in several studies (Ryan et al. 1997; Ryan et al. 2004); and a loss of resource use

efficiency in sub-dominant and suppressed stems (thereby reducing overall stand

productivity) (Binkley et al. 2002), previously refuted by studies on sub-alpine forests

(Kaufmann and Ryan 1986).

Changes in tree or stand structure are implicit in many of these explanations and a knowledge

of stand structure would be advantageous to prove or disprove the hypotheses, yet most

published research lacks information about stand structure. This is largely due to the emphasis

on stand growth and the subsequent amalgamation and/or summarization of raw data into

stand level values. Several authors have consequently called for more study into the changes

in forest structure that accompany stand growth, particularly declining stand growth (Gower

et al. 1996; Binkley et al. 2002; Berger et al. 2004).

3.1.2 Stand Structure

Stand structure describes the manner in which stand growth is distributed within the stand and

is typically described by a number of parameters as no one measure provides an adequate

description of stand structure. Parameters of stand structure include the stocking rate, the size

distribution of stems, the size variability of stems, the spatial distribution of stems, and the

phenology of and variability in tree morphology.

As a general rule, plantation stands are established with as uniform a structure as possible, and

if stand structure remained uniform then stand level growth measures like mean tree size and


aggregate stand size would provide an adequate description of stand structure. Plantation

stands, however, do not maintain structural uniformity, but rather structural diversity develops

as a range of tree sizes and shapes emerge over time, presumably due to a combination of

factors including genetic diversity, variation between micro-sites and competition between

individuals (Jacobs 1955; Harper 1967; Ford 1975; Opie et al. 1978; Evans 1982).

Competition in plant communities occurs when individuals use a resource or resources that

would otherwise have been used by their neighbour had they not been present (Donald 1963),

and the onset of competition is hastened by greater initial plant population densities and, in

the case of light competition, by greater stand growth rates due to better site quality (Weiner

1985). The development of structural variation within even-aged plant populations is

considered to be due to a ‘hierarchy of exploitation’ following the onset of intra-specific

competition (Harper 1967; Weiner 1986), whereby larger plants are able to capture a

relatively greater ratio of resources than smaller plants, resulting in greater relative growth

rates for larger plants (Ford 1975; Weiner 1986). Several authors suggest that the dominants

in tree stands are able to capture as many resources as they need, with the result that their

growth rate is largely unaffected by the level of competition in the stand (Bredenkamp and

Burkhart 1990b; Battaglia 2001; Franc 2001; Binkley et al. 2002).

Changes in relative growth rates between plants lead to changes in population structure, and

because these changes are the result of competition, the rate and extent of their occurrence are

considered indicative of the onset and intensity of competition in the population (Weiner

1986). Typical changes in stand structure as a result of increased competition are outlined in

the following paragraphs.

As competition increases, the stocking rate is reduced as the most suppressed stems die.

Mortality due to competition is termed density-dependant mortality or self-thinning, and has

been found to approximate a -3/2 gradient between mean plant mass and stocking density for

many plant species (particularly shade intolerant species) (Yoda et al. 1963) (Figure 3.2).

Whether this particular gradient applies to all species is a topic that has undergone

considerable debate (White and Harper 1970; Drew and Flewelling 1977; Westoby 1977;

Lonsdale and Watkinson 1982; Lonsdale 1983; Westoby 1984; Osawa and Sugita 1989;

Lonsdale 1990; Weller 1990; Zeide 1991; Bi 2001), however there is no doubt that a negative

relationship exists and for many species -3/2 is a reasonable approximation of its slope.


Figure 3.2: The -3/2 gradient between mean plant mass and tree density in natural single-species stands of Abies sachalinensis in Hokkaido, Japan. Source: Yoda et al. 1963.

Increased competition causes changes in the size frequency distribution of tree stands. Size

frequency distributions show the frequency of trees in consecutive size classes within the

stand, and are typically analysed by comparison to a normal (symmetric bell-shaped)

distribution. Single-species, even-aged tree stands are typically established with a normal size

distribution due to natural variation in seed size and germination rates. As competition

increases a positive skewness develops in the size distribution (Ford 1975; Kohyama and Hara

1989; Oliver and Larson 1996) due to asymmetric competition between large and small

individuals (Weiner and Thomas 1986), resulting in a small number of trees much greater

than the mean and a large number of trees slightly smaller than the mean. Furthermore, where

normal size distributions are uni-modal (single peaked), increased competition has been found

to cause bimodality (double peaks) in the size distribution of many plant populations (Ford

1975; Weiner 1986; Franc 2001).

Increased competition causes changes in the size variability within stands. Size variability is

the relative difference in size between individuals in the stand, and evidence shows that that

increased competition results in greater inequality in relative growth rates between large and

small individuals and therefore increased size variability (Weiner 1985; Weiner and Thomas

1986; Weiner et al. 1990b). Size variability can be measured using a standard statistical

measure of relative variation, the coefficient of variation (CV) (Weiner and Thomas 1986),

ltaylor

Typewritten Text

Figure removed due to copyright restrictions


and by using other tools including the Lorenze curve and the Gini coefficient (Weiner and

Thomas 1986; Taylor 1998), which were developed by economists to study the concept of

inequality. Lorenze curves are constructed by ranking size from smallest to largest and

plotting the cumulative increase in total size with the addition of consecutive individuals. The

plotted Lorenze curve is compared to the Lorenze curve for equality (assumes all individuals

are equal in size), and the extent to which the plotted Lorenze curve diverges from the

equality Lorenze curve provides an indication of the inequality. The gini-coefficient is a

numerical measure of the difference between the Lorenze curves, whereby the plotted

Lorenze curve is calculated as a percentage of the equality Lorenze curve. A reduction in the

gini-coefficient indicates greater inequality and therefore greater size variability.

Increased competition also causes changes in the spatial distribution of tree sizes. The spatial

distribution of large and small tree sizes is fairly random at the point of natural and plantation

stand establishment. Increased competition creates pressure for space with the result that trees

of the same size, particularly dominants, tend to develop at equal distances apart rather than in

clusters, the distance apart being reflective of their zone of influence (Ford 1975; Batista and

Maguire 1998). Finally increased competition has been shown to cause alterations in the

morphology of trees due to the plasticity of their responses to changing growing conditions

(Donald 1963; Harper 1967; Mohler et al. 1978; Marks et al. 1986; Weiner and Fishman

1994; Yokozawa and Hara 1995). Typical responses to increased competition include

increased crown lift, greater stem height to diameter ratios and greater aboveground to

belowground biomass ratios.

The information available on stand structure indicates that the patterns of change in stand

structure are well known. Where knowledge is lacking is in the ability to predict the actual

rate and extent of change in stand structure, and fundamental to this is the failure thus far to

fully investigate the effect of competition on both stand growth and stand structure

simultaneously, since competition is essentially the nature in which stand growth and stand

structure interact. Without such knowledge it is difficult to ensure that forest management

practices are optimised for maximum productivity and/or value. Furthermore, a better

knowledge of competition in stand development would enable more accurate modelling not

only of commercial forests, but also of natural forests and global carbon budgets, and might

provide evidence for the mechanism(s) behind declining stand productivity.


3.2 Experimental Rationale

The initial investigation of stand development in the spacing trial examines traditional

measures of stand growth and stand structure. This is an important first step providing

preliminary insight into the impact of the initial population (planting density) on biomass

accumulation and the onset and intensity of competition. It also provides a benchmark for

comparison between the spacing trial and other plantations.

In the early stages of stand development during which the spacing trial was measured it is

expected that increased planting density will lead to increased stand growth due to greater site

occupancy, and increased stand structural variation due to more rapid onset of and intensity in

competition (Table 3.1).

Table 3.1: Hypotheses of the effect of increased planting density on variables of stand growth and stand structure during early stages of stand development in sub-tropical E. grandis plantations.

Stand Variable Hypothesis

Stand Growth

Stand Total Stem Volume Increment

Increased planting density will result in increased stand total stem volume increment since the rate of increment in stand total stem volume is increased by greater site occupancy.

Stand Total Stem Volume

Increased planting density will result in increased stand total stem volume since stand total stem volume is the result of increment in stand total stem volume.

Stand Mean Stem Volume

Increased planting density will result in decreased stand mean stem volume since competition causes suppression in the size of a number stems within stands.

Stand Structure

Stand Mortality Increased planting density will result in increased stand mortality since competition causes density dependent mortality.

Stand Stem Volume Size

Distribution

Increased planting density will result in increased positive skewness and bimodality in the size distribution of stand stem volumes since competition causes suppression in the size of a number stems within stands.

Stand Stem Volume Size

Inequality

Increased planting density will result in increased stand stem volume size inequality since competition causes suppression in the size of a number stems within stands.

Stand Dominance Classes

Increased planting density will have no effect on the size of stems in the largest dominance class since competition does not cause suppression of all stems within stands.


3.3 Methodology

To test the hypotheses a number of data were collected from the spacing trial, and in some

cases collected data were used to calculate estimated values of additional tree and stand

variables. The information collected on stand growth and structure was investigated using

graphs and descriptive statistics.

3.3.1 Sample Age and Size

Some data were collected annually for every live tree in the whole trial, whereas other data

were collected at 3 and 4 years from a smaller sample size using various selection methods

(Table 3.2).

Table 3.2: The age and sample size of variables for which data was collected from the spacing trial.

Number of Trees Sampled Tree Variable

Age (yrs) 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha

Annual Measurements

1 15 111 843 1873 2 13 109 829 1706 3 13 109 80 80 4 8 20 20 20

Stem Height

5 n/a(a)

32 32 32

1 15 111 843 1873 Stem Base Diameter

2 13 109 829 1706

2 13 109 829 1706 3 13 109 792 1588 4 13 105 742 1491

Stem Diameter at Breast Height

5 n/a(a)

84 674 1372

3 Year Old Measurements(b)

Stem Diameter at 1 m Height Intervals 3 8 8 8 8

4 Year Old Measurements(b)

Stem Diameter at 1 m Height Intervals 4 8 20 20 20

(a) Due to whole tree destructive sampling at 4 years there were insufficient trees to adequately sample the 250 st/ha planting density treatment at 5 years.

(b) A full description of the sample selection methods for 3 and 4 year old measurements are provided in Chapter 2 – The Spacing Trial, sub-sections 2.4.1 and 2.4.2 respectively.

3.3.2 Data Collection and Calculation

Location – the spatial location of the tree within the trial. Tree locations were defined by plot

and tree number, which were identified on maps of the trial.


Stem Height – the vertical distance from the base of the stem at ground level to the apex of

the stem at the highest growing tip. Stem height was measured directly with a height stick at

1-2 years. At 3 and 5 years stem height was measured indirectly with a Lasertech

Hypsometer. At 4 years stem height was measured directly with a measuring tape. For stems

not measured, stem height was predicted using a stem height model (Table 4.4, p68).

Stem Base Diameter – the diameter of the cross-sectional area of the whole stem at 0.1 m

stem height. Stem diameter at base was measured directly with callipers at 1-2 years. Stem

diameter at base was calculated at 3-4 years using stem diameter at 1 and 2 m height in the

following formula (based on re-arranging the straight line formula y = mx + b):

stem diameter at base = ((y – b)

/m) + stem diameter at 2 m height

where y = 1 (1 m height)

b = 2 (2 m height)

m = (2 m height – 1 m height) / ( stem diameter at 2 m height – stem diameter at 2 m height)

Stem Base Basal Area – the cross-sectional area of the whole stem at 0.1 m height. Stem

basal area at base was calculated for every stem using stem diameter at base and assuming a

circular stem cross-section.

Stem Diameter at Breast Height (DBH) – the diameter of the cross-sectional area of the whole

stem at 1.3 m stem height. DBH was calculated for every stem at 1 year old using stem height

and stem diameter at base and assuming stems were conical. DBH was measured directly with

callipers at 2 years and with a diameter tape at 3-5 years.

Stem Basal Area at Breast Height – the cross-sectional area of the whole stem at 1.3 m

height. Stem basal area at breast height was calculated for every stem using stem diameter at

breast height and assuming a circular stem cross-section.

Stem Diameter at 1 m Stem Height Intervals – the diameter of the cross-sectional area of the

whole stem at 1 m stem height intervals. At 3 years access to the stem was gained using a

ladder and stem diameter at 1 m height intervals was measured directly with a measuring tape

to a height of 6 m. At 4 years access to the stem was gained by felling the stem and stem

diameter was measured directly with a measuring tape to the tip of the stem. Where branch

swellings obstructed measurements, they were moved up to the nearest clear stem section.


Stem Volume – the volume of the 3-dimensional shape of the whole stem, excluding the 0.1

m stump, at a given age. At 1-2 years stem volume was calculated using stem height, stem

basal area at base and stem basal area at breast height. The calculations assumed that the stem

section between the base and breast height is a frustum of a second degree paraboloid, and

that the stem section above breast height is a cone:

2nd

Degree Paraboloid Frustum = height* ((base basal area + top basal area) / 2)

Cone = height * base basal area * ⅓

At 3-4 years stem volume was calculated using stem diameter at 1 m stem height intervals and

stem height. Stem volume was calculated as the sum of the conical frustum volumes formed

between each stem diameter (with the top frustum forming a cone). The frustum volumes

were calculated using the formula:

Conical Frustum = ⅓ * π * (base radius2 + top radius

2 + (base radius*top radius)) * height

For stems at 3-4 years that were not measured for stem diameter at 1 m stem height intervals,

stem volume was predicted using stem volume models developed from stems that were

measured for stem diameter at 1 m stem height intervals (Appendix 1). At 5 years stem

volume was predicted using the 4 year old stem volume model (Appendix 1).

Stem Volume Increment – the increment in stem volume in the year up to the subscripted

age. Stem volume increment was calculated for each tree at each age by subtracting the

previous year’s stem volume from the current year’s stem volume.

Stand Total Stem Volume – the total stem volume per unit area in the stand. Stand total stem

volume was calculated at each age for each planting density as the sum of the stem volume

(m3) of all trees measured divided by the sum area (ha) occupied by all trees measured.

Stand Total Stem Volume Increment – the current annual increment (CAI) in stand total stem

volume per unit area over a year of growth and the mean annual increment (MAI) in stand

total stem volume per unit area from initial establishment to a specified age.

Stand Mean Stem Volume – the mean stem volume of each planting density. Stand mean

stem volume was calculated at each age for each planting density as stand total stem volume

divided by the number of trees in the trial.


Stand Mortality – the number of stems in the stand that have died. Stand mortality was

calculated from 1-4 years and for each planting density as the number of stems established in

the trial minus the current number of live stems in the trial. Percentage stand mortality was

calculated from 1-4 years and for each planting density as stand mortality divided by the

number of stems established in the trial.

Stand Stem Volume Size Distribution – the nature by which stem volumes range in size and

number in the stand. Size frequency histograms and skewness values of stand stem volumes

were generated at each age and for each planting density.

Stand Stem Volume Size Inequality – the inequality between stem sizes in the stand. Lorenze

curves, gini-coefficient values and coefficient of variation values of stand stem volumes were

generated at each age and for each planting density.

Stand Dominance Classes – dominance classes are groups of stems ranked according to their

relative performance in the stand, whereby greater stem volume indicated better performance.

In each planting density stem volumes were divided into 250 stem cohorts and 1,000 stem

cohorts: for example the 1,000 st/ha planting density had four groups in 250 stem cohorts

(1,000/250 = 4) and one group in 1,000 stem cohorts (1,000/1,000 = 1), and so on for other

planting densities. The dominance classes were constructed at each age and for each planting

density by ranking stems from largest to smallest and grouping stems in 250 stem cohorts or

1,000 stem cohorts in order of diminishing dominance (Table 3.3). The mean stem volume

was then calculated for each dominance class.

Table 3.3: The calculation of the number of stems in 250 stem cohorts and 1,000 stem cohorts in each planting density.

PLANTING

DENSITY

(ST/HA)

NO. STEMS

IN TRIAL STEM RATIO IN 250

STEM COHORTS

NO. STEMS IN

250 STEM

COHORTS (a)

STEM RATIO IN

1,000 STEM

COHORTS

NO. STEMS IN

1,000 STEM

COHORTS (a)

250 16 250

/250 = 1 16 * 1 = 16 n/a(b)

n/a(b)

1,000 120 250

/1,000 = 0.25 120 * 0.25 = 30 1,000

/1,000 = 1 120 * 1 = 120

5,000 900 250

/5,000 = 0.05 900 * 0.05 = 45 1,000

/5,000 = 0.2 900 * 0.2 = 180

10,000 1936 250

/10,000 = 0.025 1936 * 0.025 = 48 1,000

/10,000 = 0.1 1936 * 0.1 = 194

(a)Results were rounded to the nearest whole number.

(b)The 250 st/ha planting density did not allow a meaningful comparison of properties in the 1,000 stem

cohorts as there were not enough stems to fill the cohorts.


3.4 Results and Discussion

3.4.1 Stand Growth

STAND TOTAL STEM VOLUME

The results confirm the hypothesis that stand total stem volume will increase in response to

increased planting density, as at any given age stand total stem volume increased as planting

density increased (Figure 3.3). The result indicated that at the early stage of development in

which the trial was measured, any loss of biomass due to competition induced mortality in

higher planting densities was compensated by greater total growth in higher planting

densities, presumably due to greater site occupancy.

0

50

100

150

200

250

300

350

0 1 2 3 4 5

Age (yrs)

Sta

nd

To

tal S

tem

Vo

lum

e (

m3h

a-1

)

250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha

Figure 3.3: The stand total stem volume of E. grandis at planting density 250 st/ha from 1-4 years, and planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.

STAND TOTAL STEM VOLUME INCREMENT

The results confirm the hypothesis that stand total stem volume increment will increase in

response to increased planting density, as at any given age stand total stem volume mean

annual increment (MAI) increased as planting density increased (Figure 3.4). This result was

similar to the above result for stand total stem volume, which was expected since total stem

volume is the result of stem volume increment.


0

10

20

30

40

50

60

70

0 1 2 3 4 5

Age (yrs)

Sta

nd

To

tal

Ste

m V

olu

me

MA

I (m

3h

a-1

yr-1

)250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha

Figure 3.4: The mean annual increment (MAI) in stand total stem volume in E. grandis at planting density 250 st/ha from 1-4 years, and planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.

At 5 years stand total stem volume increment was still increasing in productivity (Figure 3.4),

which is typical of early stand development (Figures 3.1). The exception to increasing

productivity can be seen in the dip in volume increment between 3 and 4 years (Figure 3.4),

which was probably due to low rainfall from 2-4 years (Figure 2.5). Even with low rainfall,

the MAI in stand total stem volume of 27 m3/ha at 5 years for 1,000 st/ha was comparative to

plantation stands of similar stocking on good quality sites in Australia (Ipsen3 pers. comm.

2005). The MAI in stand total stem volume of 55 m3/ha for 5,000 st/ha and 68 m

3/ha for

10,000 st/ha at 5 years was very high compared to standard stocking rates around 1,000 st/ha.

STAND MEAN STEM VOLUME

The results confirm the hypothesis that stand mean stem volume will decrease in response to

increased planting density, as at any given age stand mean stem volume decreased as planting

density increased (Figure 3.5). It was not clear at this point whether reduced stand mean stem

volume in higher planting densities was the result of a reduction in the stem volume of all

trees, or the inclusion of a greater number of smaller trees, or a combination of the two.

3 Mr J. Ipsen, Forester, Integrated Tree Cropping (Hardwood Plantation Forestry Management)


0.00

0.05

0.10

0.15

0.20

0 1 2 3 4 5

Age (yrs)

Sta

nd

Me

an

Ste

m V

olu

me

(m

3)


Figure 3.5: The stand mean stem volume of E. grandis at planting density 250 st/ha from 1-4 years, and planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.

Overall, stand growth in the 250 and 1,000 st/ha planting densities compared well to

eucalyptus plantations of similar planting densities on good site quality, whereas stand growth

in the 5,000 and 10,000 st/ha planting densities was very high, showing that eucalypts have

the potential to capture carbon very quickly. The point at which MAI begins to decrease,

disregarding the drought between 3-4 years, had not yet occurred in the spacing trial.

Based on the above growth data, the best information available on stand structure from 0-5

years was that increased planting density resulted in increased total stem volume (Figure 3.3),

but decreased mean stem volume (Figure 3.5). This pattern of stand development is well

known and sawlog plantations are rarely established with high stocking densities since the

reduction in mean stem volume as stocking density increases is thought to be due in part to

slowed growth in the ‘final crop’ stems. The dismissal of higher stocking densities in sawlog

plantations is a sensible management outcome if stand growth patterns provide an accurate

description of slower growth in final crop trees, yet evidence suggests that this is not the case.

Several authors propose that dominant stems may continue to grow largely unrestricted

regardless of stocking density (Bredenkamp and Burkhart 1990b; Battaglia 2001; Franc 2001;

Binkley et al. 2002), with the result that the growth of final crop trees in highly stocked stands

might be unaffected by stocking density.


Interestingly, pulpwood plantations in Australia are not established at high planting densities,

despite that reduced mean stem size does not affect pulpwood value and that high planting

densities have the potential to reduce rotation length by increasing the rate of stand growth.

The major reasons cited are establishment and harvesting costs, which under current

technologies are significantly increased by increased planting density, and wood quality,

which is considered inferior below 10 years old due to low wood density, thereby rendering

the early thinning required in high density systems as non-commercial (Ipsen4 pers. comm.

2005). As such there is currently no economic benefit to be gained from increasing planting

density to shorten rotation lengths in pulpwood plantations.

3.4.2 Stand Structure

STAND MORTALITY

The results confirm the hypothesis that stand mortality will increase in response to increased

planting density, since at any given age absolute stand mortality increased as planting density

increased (Figure 3.6(a)). The trend was not as strong in percentage stand mortality since

mortality in 250 st/ha was higher than that for 5,000 st/ha and 1,000 st/ha (Figure 3.6(b)).

0

50

100

150

200

250

300

350

400

450

0 1 2 3 4Age (yrs)

Sta

nd

Mo

rta

lity

(N

o.

ste

ms

)


0

5

10

15

20

25

0 1 2 3 4Age (yrs)

Pere

cn

tag

e S

tan

d M

ort

ali

ty (

%)

(b)(a)

Figure 3.6: Stand mortality in E. grandis from 1-4 years for planting densities 250 st/ha, 1,000 st/ha, 5,000 st/ha and 10,000 st/ha by (a) absolute mortality (No. stems) and (b) percentage mortality (%).

4 Mr J. Ipsen, Forester, Integrated Tree Cropping (Hardwood Plantation Forestry Management)


Whilst the results generally align with the hypothesis, the difference of only 10% in

percentage stand mortality between planting densities 1,000 st/ha and 10,000 st/ha seemed

small given the large difference in population pressure. As such it could not be concluded that

the increased mortality in high planting densities was the result of increased competition since

it was possible that all planting densities had experienced approximately 18% incidental

mortality by 4 years. Further investigation of stand mortality was therefore required.

When the calculated values of stand mean stem volume and stand stocking (planting density

minus stand mortality) were plotted against each other in the fashion of Yoda (1963) for each

age and planting density (Figure 3.7), the results show little difference in gradient between

planting densities. In all planting densities but 250 st/ha the relationship between plant size

and population density is showing signs of approaching a negative ceiling like the -3/2 self-

thinning line defined by Yoda (1963) (Figure 3.2), and notably the 5,000 st/ha and 10,000

st/ha planting densities do not exhibit a steeper negative slope than 1,000 st/ha. Based on

these results the hypothesis that higher planting densities will exhibit greater competition

induced mortality during early stages of stand development must be refuted.

0.00

0.01

0.10

1.00

1 100 10,000

ln S

tan

d M

ea

n S

tem

Vo

lum

e (

m3)


ln Stocking (st/ha)

Figure 3.7: The natural logarithm of stand mean stem volume plotted against the natural logarithm of stocking of E. grandis for planting density 250 st/ha from 1-4 years, and for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha from 1-5 years.


The above results are surprising given the difference in population pressure between planting

densities; however it is possible that the results do not provide an accurate indication of stand

dynamics. If a ‘dead’ stem is defined as one that contributes no growth to the stand, then

‘mortality’ in the higher planting densities may be underestimated. Eucalypts are known for

the persistence of highly suppressed stems in the stand, which may live for several years

before dieing (Jacobs 1955). Such stems exhibit negligible growth, and if classed as ‘dead’ it

is likely that ‘mortality’ would increase by a greater amount in higher planting densities.

STAND STEM VOLUME SIZE DISTRIBUTION

The hypothesis for stand stem volume size distribution was that positive skewness and

bimodality in the size distribution of stand stem volumes would increase in response to

increased planting density. Size frequency histograms of stem volume were generated and

skewness calculated at each age for each planting density (Figure 3.8). These results should

not be compared directly due to the different sample number and size classes used between

ages and planting densities, however they do provide an indication of the pattern of change in

skewness.

Initial perusal of the frequency histograms and their skewness indicated that the stands

generally had a positive skewness regardless of their planting density and/or age, and that

skewness tended to increase during stand development (over time). Closer examination

showed that the skewness in the distribution of stem sizes in 250 st/ha was not significant at

any point (as indicated by skewness/standard deviation < 2), and that the skewness in the

distribution of stem sizes in 1,000 st/ha was significant at all ages, but was generally low and

even diminished at later ages. These results indicate that competition was not particularly

strong in planting densities 250 st/ha and 1,000 st/ha during the period of measurement.

In contrast, the skewness in the distribution of stem sizes in 5,000 st/ha and 10,000 st/ha was

significant at all ages, and showed a strong trend of increasing during stand development,

particularly for 10,000 st/ha. This indicates that strong competition occurred in planting

densities 5,000 st/ha and 10,000 st/ha during the period of measurement. Overall the results

confirmed the hypothesis that positive skewness in the size distribution of stand stem volumes

will increase in response to increased planting density.


(a) 250 st/ha (b) 1,000 st/ha (c) 5,000 st/ha (d) 10,000 st/ha

Age 1Age 1Age 1Age 1:::: Skew (-0.400/0.580) = -0.7 Age 1:Age 1:Age 1:Age 1: Skew (0.685/0.229) = 3.0 Age 1:Age 1:Age 1:Age 1: Skew (0.994/0.084) = 11.8 Age 1:Age 1:Age 1:Age 1: Skew (0.781/0.057) = 13.7

SV

.0087.0075.0062.0050.0037.0025.00120.0000

6

5

4

3

2

1

0

SV

.0080

.0075

.0070

.0065

.0060

.0055

.0050

.0045

.0040

.0035

.0030

.0025

.0020

.0015

.0010

.0005

0.0000

P: 1000 AGE: 1

20

10

0

Std. Dev = .00

Mean = .0027

N = 111.00

SV

.0125

.0112

.0100

.0087

.0075

.0062

.0050

.0037

.0025

.0012

0.0000

P: 5000 AGE: 1

140

120

100

80

60

40

20

0

Std. Dev = .00

Mean = .0031

N = 843.00

SV

.0095

.0090

.0085

.0080

.0075

.0070

.0065

.0060

.0055

.0050

.0045

.0040

.0035

.0030

.0025

.0020

.0015

.0010

.0005

0.0000

P: 10000 AGE: 1

400

300

200

100

0

Age Age Age Age 2222:::: Skew (0.486/0.616) = 0.8 Age Age Age Age 2222:::: Skew (0.816/0.231) = 3.5 Age Age Age Age 2222:::: Skew (0.949/0.085) = 11.2 Age Age Age Age 2222:::: Skew (1.493/0.059) = 25.3

SV

.063.056.050.044.038.031.025.019.013.006

P: 250 AGE: 2

5

4

3

2

1

0

Std. Dev = .02

Mean = .033

N = 13.00

SV

.0600

.0550

.0500

.0450

.0400

.0350

.0300

.0250

.0200

.0150

.0100

.0050

0.0000

P: 1000 AGE: 2

14

12

10

8

6

4

2

0

Std. Dev = .01

Mean = .0218

N = 109.00

SV

.0450

.0425

.0400

.0375

.0350

.0325

.0300

.0275

.0250

.0225

.0200

.0175

.0150

.0125

.0100

.0075

.0050

.0025

0.0000

P: 5000 AGE: 2

140

120

100

80

60

40

20

0

SV

.0475

.0450

.0425

.0400

.0375

.0350

.0325

.0300

.0275

.0250

.0225

.0200

.0175

.0150

.0125

.0100

.0075

.0050

.0025

0.0000

P: 10000 AGE: 2

500

400

300

200

100

0


SV

.250.225.200.175.150.125.100.075.050

3.5

3.0

2.5

2.0

1.5

1.0

.5

0.0

Std. Dev = .08

Mean = .125

N = 13.00

SV

.225

.213

.200

.188

.175

.163

.150

.138

.125

.113

.100

.088

.075

.063

.050

.038

.025

.013

P: 1000 AGE: 3

20

10

0

Std. Dev = .04

Mean = .078

N = 109.00

SV

.113

.106

.100

.094

.088

.081

.075

.069

.063

.056

.050

.044

.038

.031

.025

.019

.013

.006

0.000

P: 5000 AGE: 3

120

100

80

60

40

20

0

Std. Dev = .02

Mean = .033

N = 792.00

SV

.125.113

.100.088

.075.063

.050.038

.025.013

0.000

P: 10000 AGE: 3

500

400

300

200

100

0


SV

.38.31.25.19.13.06

P: 250 AGE: 4

6

5

4

3

2

1

0

Std. Dev = .11

Mean = .17

N = 13.00

SV

.288.263

.238.213

.188.163

.138.113

.088.063

.038.013

P: 1000 AGE: 4

20

10

0

Std. Dev = .06

Mean = .105

N = 105.00

SV

.188.175

.163.150

.138.125

.113.100

.088.075

.063.050

.038.025

.0130.000

P: 5000 AGE: 4

120

100

80

60

40

20

0

Std. Dev = .04

Mean = .044

N = 742.00

SV

.225

.213

.200

.188

.175

.163

.150

.138

.125

.113

.100

.088

.075

.063

.050

.038

.025

.013

0.000

P: 10000 AGE: 4

500

400

300

200

100

0

Age Age Age Age 5555:::: Skew (0.556/0.263) = 2.1 Age Age Age Age 5555:::: Skew (1.365/0.094) = 14.5 Age Age Age Age 5555:::: Skew (2.054/0.066) = 31.1

SV

.500

.475

.450

.425

.400

.375

.350

.325

.300

.275

.250

.225

.200

.175

.150

.125

.100

.075

.050

.025

P: 1000 AGE: 5

14

12

10

8

6

4

2

0

Std. Dev = .10

Mean = .192

N = 84.00

SV

.400.375

.350.325

.300.275

.250.225

.200.175

.150.125

.100.075

.050.025

0.000

P: 5000 AGE: 5

140

120

100

80

60

40

20

0

SV

.400.375

.350.325

.300.275

.250.225

.200.175

.150.125

.100.075

.050.025

0.000

P: 10000 AGE: 5

400

300

200

100

0

Figure 3.8: Size frequency histograms and skewness values for stand stem volumes of E. grandis from 1-4 years for planting density (a) 250 st/ha, and from 1-5 years for planting densities (b) 1,000 st/ha, (c) 5,000 st/ha, and (d) 10,000 st/ha. Skewness was significant where (skewness/standard deviation) > 2. Arrows indicate possible bimodality in the size distribution.

In bimodality there was some indication that a double peak in the size distribution was

beginning to occur at 4-5 years in 5,000 st/ha and at 5 years in 10,000 st/ha (Figure 3.8). The

evidence, however, was not strong, and therefore the hypothesis that bimodality in the size

distribution of stand stem volumes will increase in response to increased planting density

could not be confirmed.


STAND STEM VOLUME SIZE INEQUALITY

Lorenz curves provide a measure of inequality and were constructed by ranking stem volumes

from smallest to largest and then plotting the cumulative increase in percentage stand stem

volume against the cumulative increase in percentage stand stem count. If trees in the stand

were perfectly uniform the curve would follow the Lorenz curve for equality, all trees being

of equal size. The extent to which the stand curve digressed from the equality curve was an

indication of the inequality in stem size between stems in the stand. Lorenz curves of stand

stem volume were generated at each age for each planting density (Figure 3.9).

0.0

0.5

1.0

0.0 0.5 1.0Cu

mu

lati

ve P

rop

ort

ion

of

Sta

nd

Ste

m V

olu

me

Lorenz Curve for Equality Lorenz Curve for 250 st/ha Lorenz Curve for 1,000 st/ha Lorenz Curve for 5,000 st/ha Lorenz Curve for 10,000 st/ha

0.0 0.5 1.0

5 years old

0.0 0.5 1.0

3 years old

0.0 0.5 1.0

4 years old

0.0 0.5 1.0

2 years old1 year old

Cumulative Proportion of Stand Stem Count

Figure 3.9: Lorenz curves for stand stem volumes of E. grandis from 1-4 years for planting density 250 st/ha, and from 1-5 years for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha.

Increased departure from a uniform distribution is also indicated by a reduction in the gini-

coefficient (area under the stand Lorenze Curve/area under the equality Lorenze Curve) and

an increase in the coefficient of variation. The gini-coefficients and coefficients of variation

were calculated at each age and for each planting density (Table 3.4).

Table 3.4: Gini-coefficients and coefficients of variation for stand stem volumes of E. grandis from 1-4 years for planting density 250 st/ha, and from 1-5 years for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha.

PLANTING DENSITY

ST/HA AGE 1 AGE 2 AGE 3 AGE 4 AGE 5

250 0.733 0.728 0.714 0.705 n/a(a)

1,000 0.677 0.720 0.709 0.708 0.717

5,000 0.651 0.617 0.615 0.534 0.489

GINI

COEFFICIENT

10,000 0.686 0.566 0.532 0.446 0.408

250 53.9% 55.8% 60.6% 61.6% n/a(a)

1,000 59.5% 52.8% 54.4% 53.9% 51.5%

5,000 65.1% 70.9% 70.3% 85.8% 97.7%

COEFFICIENT

OF VARIATION

10,000 58.6% 83.9% 89.8% 109.7% 124.4%

(a) Due to whole tree destructive sampling at 4 years, there were insufficient trees to adequately sample planting density 250 st/ha at 5 years.


The results show that all stands had a degree of inequality (gini-coefficient > 0), and this was

no surprise since some variability in size within each population was anticipated given the

expectation of a normal distribution. A pattern similar to that of skewness (Figure 3.8) was

evident since size inequality tended to increase with age and planting density. Planting

densities 250 st/ha and 1,000 st/ha had similar levels of size inequality which did not change

much over the period of measurement. In contrast planting densities 5,000 st/ha and 10,000

st/ha started with similar inequality to the low planting densities, but then developed much

higher inequality over the period of measurement. Again this was indicative of little

competition in the low planting densities graduating to intense competition in the high

planting densities, and it confirmed the hypothesis that stand stem volume size inequality

would increase in response to increased planting density.

Overall the results for stand structure reveal evidence of stronger competition occurring in the

higher planting densities, as shown by increased skewness and increased size inequality. This

information, however, did not reveal how the most dominant stems compare across planting

densities or if the structural differences between planting densities was reflected in different

structures in terms of the number of dominance classes. Further investigation of stand

structure was required.

STAND DOMINANCE CLASSES

It was hypothesised that the size of stems in the primary dominance class (dominant stems)

would not change in response to competition intensity (planting density). This hypothesis was

investigated by ranking stem volumes and dividing them into 250 and 1,000 stem cohorts,

with the result that the 1,000 st/ha planting density had four groups in the 250 stem cohorts

(1,000/250 = 4) and one group in the 1,000 stem cohorts (1,000/1,000 = 1), and so on for

other planting densities. Mean stem volume for the whole stand and for the largest (dominant)

250 and 1,000 stem cohorts were plotted against age for each planting density (Figure 3.10).

The results show that mean stem volume of the largest (dominant) cohort increased as the

number of stems included in the cohort was reduced from 1,000 to 250 (Figure 3.10 (a-c)).

Mean stem volume in planting density 250 st/ha did not change between cohorts since each

included all stems in the 250 st/ha planting density. Similarly, mean stem volume in planting

density 1,000 st/ha did not change between the whole stand and the dominant 1,000 stem

cohort since both cohorts included all stems in the 1,000 st/ha planting density.


0.00

0.05

0.10

0.15

0.20

0.25

0.30

0 1 2 3 4 5

Me

an

Ste

m V

olu

me (

m3/h

a)


0 1 2 3 4 5Age (yrs)

(b) Dominant 1,000 Stem Cohort

0 1 2 3 4 5

(c) Dominant 250 Stem Cohort(a) Whole Stand Cohort

Figure 3.10: Mean stem volume of E. grandis from 1-4 years for planting density 250 st/ha, and from 1-5 years for planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha for (a) the whole stand cohort, (b) the dominant 1,000 stem cohort, and (c) the dominant 250 stem cohort.

It is of interest that there was little difference in the mean stem volume of the dominant 1,000

stem cohort between planting densities 1,000 st/ha and 10,000 st/ha (Figure 3.10(b)). If one

considers the dominant 1,000 stem cohort to be representative of dominant stems during the

period of measurement (which assumes that all stems in planting density 1,000 st/ha had a

dominant status), then the above shows that the primary dominance class does not change in

response to planting density. It is possible, however, that competition was starting to have a

restrictive effect on the higher planting densities since the dominant 1,000 stem cohort in

planting density 1,000 st/ha was beginning to increase in size compared to planting densities

5,000-10,000 st/ha. Nevertheless the difference in the mean stem size of dominants between

planting densities was remarkably small given the extreme levels of competition that were

present in the higher planting densities (Figures 3.8-3.9), providing strong evidence that

competition is asymmetric in that dominant stems tend to capture the amount of resources

required leaving intermediate and suppressed stems to cope with resource restrictions.

In comparison to the dominant 1,000 stem cohorts (Figure 3.10(b)), the dominant 250 stem

cohorts (Figure 3.10(c)) show that planting density 1,000 st/ha was of greater similarity to 250

st/ha than 5,000-10,000 st/ha. This was the more expected result since planting densities 250-

1,000 st/ha had similar measures of competition intensity; however it did raise the question as


to why this did not also occur in the dominant 1,000 stem cohorts (Figure 3.10(b)). The most

obvious answer was that size variability within the dominant 1,000 stem cohorts was greater

in planting density 1,000 st/ha than in 5,000 or 10,000 st/ha, so when the smallest 750 st/ha

were removed from the dominant 1,000 st/ha cohort to create the dominant 250 stem cohort,

there was a greater increase in mean stem size for 1,000 st/ha than for 5,000 st/ha or 10,000

st/ha (Figure 3.10(b,c)). This suggested that planting density 1,000 st/ha had multiple

dominance classes, despite relatively low measures of skewness and size inequality.

The above findings revealed that dominance classes changed between planting densities, and

their definition therefore required a physiologically meaningful division between classes

rather than the application of absolute numbers. A closer examination was consequently made

of the 250 stem cohorts to determine if stand structure could be better defined into dominance

classes. The examination was made on stem volume increment since rate of growth was

considered a flexible indicator of dominance and suppression over time (Figure 3.11).

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0 1 2 3 4 5

Me

an

Ste

m V

olu

me

MA

I (m

3y

r-1)

1,000 st/ha (1)1,000 st/ha (2)1,000 st/ha (3)1,000 st/ha (4)

0 1 2 3 4 5

Age (yrs)

5,000 st/ha (1)5,000 st/ha (2)5,000 st/ha (3)5,000 st/ha (4)5,000 st/ha (5)5,000 st/ha (6)5,000 st/ha (7)5,000 st/ha (8)5,000 st/ha (9)5,000 st/ha (10)5,000 st/ha (11)5,000 st/ha (12)5,000 st/ha (13)5,000 st/ha (14)5,000 st/ha (15)5,000 st/ha (16)5,000 st/ha (17)5,000 st/ha (18)5,000 st/ha (19)5,000 st/ha (20)

(b) 5,000 st/ha

37%

0 1 2 3 4 5

10,000 st/ha (1)10,000 st/ha (2)10,000 st/ha (3)10,000 st/ha (4)10,000 st/ha (5)10,000 st/ha (6)10,000 st/ha (7)10,000 st/ha (8)10,000 st/ha (9)10,000 st/ha (10)10,000 st/ha (11)10,000 st/ha (12)10,000 st/ha (13)10,000 st/ha (14)10,000 st/ha (15)10,000 st/ha (16)10,000 st/ha (17)10,000 st/ha (18)10,000 st/ha (19)10,000 st/ha (20)10,000 st/ha (21)10,000 st/ha (22)10,000 st/ha (23)10,000 st/ha (24)10,000 st/ha (25)10,000 st/ha (26)10,000 st/ha (27)10,000 st/ha (28)10,000 st/ha (29)10,000 st/ha (30)10,000 st/ha (31)10,000 st/ha (32)10,000 st/ha (33)10,000 st/ha (34)10,000 st/ha (35)10,000 st/ha (36)10,000 st/ha (37)10,000 st/ha (38)10,000 st/ha (39)10,000 st/ha (40)

(c) 10,000 st/ha

32%

(a) 1,000 st/ha

38%

Figure 3.11: The mean annual increment (MAI) in mean stem volume of 250 stem cohorts of E. grandis from 1-5 years for planting densities (a) 1,000 st/ha, (b) 5,000 st/ha, and (c) 10,000 st/ha. Red lines indicate 250 stem cohorts declining in mean stem volume MAI between 4-5 years (the end of the measurement period), noting that MAI decline between 3-4 years was ignored in this classification since this was a drought year. At 3 years (prior to the drought) arrows mark the stem cohort below which mean stem volume MAI has been shown to decline by 5 years, and the percentage growth rate compared to the dominant 250 stem cohort is indicated. Stars indicate 250 stem cohorts which cease to exist due to mortality, and the age by which they perish.


A striking aspect of the above results was that some cohorts in planting density 1,000 st/ha

were declining in productivity (Figure 3.11(a)), suggesting that these trees were experiencing

competitive effects despite previous indications that competition intensity was low in planting

density 1,000 st/ha. Also striking was the similarity between planting densities in the cohorts

which were declining in productivity by 5 years, as most of these began their decline in MAI

at 3 years (Figure 3.11) coinciding with the drought at 3 years (Figure 2.5). The benchmark

for declining MAI was similar for all planting densities in that any cohort with a growth rate

of approximately 35% or less of the largest cohort at 3 years would decline in MAI from 3

years onwards (Figure 3.11), suggesting a structural significance to the point at which stems

began to decline in productivity. This may explain why the slowest growing cohorts in

planting density 1,000 st/ha were declining in MAI by 4-5 years, despite lower evidence of

competition in that stand.

It was noteworthy that the few cohorts that declined in productivity during the drought

recovered increasing MAI in the following year. This provided evidence that stems find it

difficult to recover having lost their competitive edge, thereby corroborating the theory that

asymmetric competition generally causes trees to maintain or decrease in dominance status

rather than increase in dominance status.

The similarities between planting densities in the pattern of change in cohort productivity over

time provided evidence that every stand had multiple dominance classes, including planting

density 1,000 st/ha which had shown little previous evidence of competition. The meaningful

definition of these dominances classes, however, required relative rather than absolute

definition so that the number of cohorts in dominance classes could change between planting

densities and over time. In this case differences between cohorts in the rate of change in MAI,

as indicated by differences in the slope of the MAI line from one year to the next (Figure

3.11), were used to define cohorts into dominance classes. For example the dominant class

was defined as the dominant cohort, plus any cohort increasing in MAI within 90% of the

dominant cohort. The co-dominant class was defined as cohorts increasing in MAI at 50% to

90% of the rate of the dominant class, the suppressed class as cohorts increasing in MAI at

10% or below the rate of the dominance class, leaving an intermediate class defined as

increasing in MAI at 10% to 50% of the rate of the dominant class (Figure 3.12).


0.00

0.01

0.02

0.03

0.04

0.05

0.06

0 1 2 3 4 5

Me

an

Ste

m V

olu

me M

AI (m

3y

r-1)

0 1 2 3 4 5

Age (yrs)

(b) 5,000 st/ha

0 1 2 3 4 5

(c) 10,000 st/ha(a) 1,000 st/ha

Dominant Co-Dominant Intermediate Suppressed

Figure 3.12: The mean annual increment (MAI) in the mean stem volume of 250 stem cohorts of E. grandis from ages 1-5 years for planting densities (a) 1,000 st/ha, (b) 5,000 st/ha, and (c) 10,000 st/ha. The rate of change in mean stem volume MAI is indicated by the slope of the MAI line from one year to the next. Dominance classes are shown at 3 years and 5 years and consist of stems increasing in MAI at 100-90% (dominant), 90-50% (co-dominant), 50-10% (intermediate) and <10% (suppressed) of the rate of the largest 250 stem cohort during the previous year.

The above definition illustrates the traditional idea of dominance classes, and again showed

that cohorts tended to maintain or decline in dominance rather than rise (Figure 3.12). In

addition, higher planting densities had a propensity for more stems within each class,

including the higher dominance classes. It is possible that higher planting densities had

greater site occupancy, collected more resources and could therefore ‘afford’ to have a greater

number of larger stems, or alternatively trees in higher planting densities might have used

resources more efficiently, possibly triggered by higher competition intensity, allowing them

to grow larger. A third scenario is simply that higher planting densities had a greater number

of stems with ‘dominant’ genes since they had a greater initial population, thereby resulting in

a greater number of larger trees. A comparison of the difference in tree morphology between

planting densities might provide an indication of which is the case.

In addition to providing insight to stand dominance dynamics, the above results provide

evidence that declining stand productivity could be attributed to stand structural changes.

Clearly a large ratio of stems declining in MAI could cause total stand MAI to decline, even if

the dominant cohort were still increasing in MAI. It has been noted that the decline in the


productivity of smaller stems could be due to reduced resource use efficiency compared to

larger stems; however it may just be a case of reduced resource capture due to competition. A

comparison of the difference in tree morphology between dominant and suppressed stems

may shed light on the mechanism controlling declining productivity in trees.

Overall, the examination of productivity in the 250 stem cohorts illustrates the feedback

effects of stand growth and structure upon each other under the theory of asymmetric

competition. We start with stand structure; a certain number of stems in approximately normal

size distribution. Growth then occurs according to the capacity of the site and the level of site

occupancy, but is captured by stems dependent on their relative size due to asymmetric

competition, with the largest stems generally avoiding resource restrictions. This then changes

the structure towards positive skewness and increased inequality in the size distribution,

which increases the effects of asymmetric competition causing greater suppression in the

smaller stems and possibly declining mean growth in the whole stand due to slowed growth in

smaller stems. This effect may be exacerbated if the smallest stems are persistent (live but not

growing), as suggested by the results for stand mortality, as this has the effect of

mathematically reducing mean growth.


3.5 Summary

The examination of stand growth has shown that eucalypt plantations established at high

planting densities (5,000-10,000 st/ha) have the potential to capture carbon in their stems

twice as quickly as those planted at the standard 1,000 st/ha. Mean and total stand growth

measures, however, did not provide good information about stand structure.

Closer examination of stand structure showed evidence of strong competition occurring in the

higher planting densities as shown by increased skewness in size distribution and increased

size inequality. Dominant stems were shown to be remarkably similar in size across planting

densities despite a very large difference in the competition intensity apparent in the stands,

and higher density stands tended to have more stems in all dominance classes, not just the

smaller ones, suggesting either a greater capacity to ‘afford’ dominant stems through

increased site occupancy and/or increased resource use efficiency, or simply a greater number

of ‘dominant’ genes due to a greater initial population. A comparison of the difference in tree

growth and structure between dominant stems in different planting densities might provide an

indication as to which explanation is most likely.

Investigation of the growth rate of 250 stem cohorts revealed that a drought at 3 years

triggered declining growth rates in stem cohorts growing at less than 35% of the rate of the

dominant cohort, and that few stem cohorts recovered increased growth rates after the initial

drought-triggered decline. It had been noted that the decline in the productivity of smaller

stems could be due to reduced resource use efficiency compared to larger stems; however the

drought triggered decline suggests it may just be a case of reduced resource capture due to

competition. A comparison of the difference in tree growth and structure between dominant

and suppressed stems may shed light on the mechanism controlling declining productivity in

trees and stands.

The examination of stand growth and structure has shown that a more detailed investigation is

required of individual tree growth and structure, particularly in terms of comparing dominants

across planting densities and size inequality within planting densities, in order to shed light on

the mechanisms by which stand structural components affect stand growth and vice versa.

Chapter 4 Tree Growth and Structure Page 40

4. TREE GROWTH AND STRUCTURE

Stands comprise individual trees, and stand growth and structure is defined by the sum of

growth and structure of individual trees. Within the stand individual trees interact through

asymmetric competition, whereby larger trees capture a greater pool of resources relative to

their size and thereby grow at a greater relative growth rate compared to smaller trees.

Asymmetric competition causes stand growth and stand structure to change dynamically over

time since the relative growth rates of individual trees and the size difference between trees is

constantly changing (Watkinson et al. 1983; Specht 1985; Brand and Magnussen 1988;

Schwinning and Weiner 1998).

In addition to affecting relative growth rate, asymmetric competition affects tree structure.

Plants are thought to partition current growth amongst components to maximise future capture

of their scarcest resource (Weiner et al. 1990a), and trees experiencing a different balance of

resource capture will have different strategies for partitioning growth, and therefore a

different tree structure.

The comparison of tree growth and structure between planting densities will show how the

largest (dominant) trees differ due to different competitive stress between stands whereas the

comparison of tree growth and structure within planting densities will show how the largest

(dominant) trees differ to the smallest (suppressed) trees due to asymmetric competition.


4.1.1 Tree Growth

Tree growth is an increase in tree size, and is typically measured by increases in tree

dimensions, such as height, diameter and volume, and/or by increases in tree mass. Site

quality is one of the primary determinants of tree growth (productivity) since this determines

resource availability. On the vast majority of sites trees are subject to some sort of resource

deficiency resulting in reduced growth potential rather than any ill-health (Dell et al. 1995).

Site quality also affects tree productivity through soil quality (permeability for root expansion,

micro-organism presence for nutrient cycling, aeration for root respiratory gas exchange and

soil nutrient oxidation, and water retention for extending water availability) and climatic

conditions (affecting the rate of photosynthesis). With all else equal, improved site quality

will accelerate the rate of tree growth and therefore improve productivity.


Within the limits defined by site quality and genetic ability, tree growth follows a basic

process. The first step is primary growth, which is the lengthening of stem and branch shoots

as a result of cell division in specialised zones called meristems. Meristems are located at the

tips of all terminal shoots (apical meristems) and in the axil at which leaves join the stem

(axillary meristems) and consist of a rounded dome in which leaf primordia are located. As

the leaf primordia expand to form leaves, the dome expands upwards/outwards and

simultaneously forms new leaf primordia whilst lengthening the shoot (Wilson and White

1986; Salisbury and Ross 1992).

The most common measurement of primary growth is stem height. Many species exhibit slow

height growth in the preliminary establishment years (Oliver and Larson 1996), but eucalypts

are capable of very rapid height growth at an early age and the majority of plantation

eucalypts achieve their largest annual height increment before 5 years (Jacobs 1955; Opie et

al. 1978). The capacity of eucalypts to grow rapidly at an early age is due to their unique

naked bud system, which allows rapid crown expansion whenever environmental conditions

are suitable (Jacobs 1955; Florence 1996). Where other genera form resting buds at the end of

the growing season, in which a complete annual shoot is contained in embryonic form,

eucalypts form a naked bud which is capable of rapid development as soon as the parent leaf

unfolds and may form an indefinite number of leaves and shoots within any growth period. In

this way stem height growth may continue indefinitely whilst conditions remain favourable,

although in reality rapid expansion generally occurs in growth spurts (Jacobs 1955; Specht

1985; Florence 1996).

Shoot elongation from primary growth may also be measured by crown width, as measured by

the diameter of the widest part of the crown. As with stem height, crown width has the

capacity to expand rapidly due to the naked bud system, yet naked buds also have the effect of

restricting crown width due to a property known as crown shyness, whereby new shoots do

not form where crowns ‘brush’ together because naked buds are easily damaged by abrasion

(Jacobs 1955; Opie et al. 1978). Crown width in eucalypt species is therefore strongly

restricted by growing space (Opie et al. 1978; Laar and Bredenkamp 1979; Cameron et al.

1989; Zeide 1991).

Leaf formation from primary growth is usually measured by leaf mass (oven-dry) and leaf

area, which is the surface area of the upper side of leaves. Leaf mass provides information


about the amount of biomass dedicated to the photosynthate-producing component of the tree,

whereas leaf area provides information about the amount of light radiation being intercepted

for photosynthate production. The relationship between the two is positive but not constant

since leaves may vary in thickness in response to environmental conditions. Leaf area is

considered fundamental to understanding productivity since intercepted radiation is a primary

determinant of photosynthate production in individual trees and tree stands (Linder 1985;

Landsberg and Hingston 1996).

In contrast to primary growth, secondary growth is the lateral expansion (diameter growth) of

the shoots. Secondary growth occurs in the lateral meristem, which forms a thin and

uninterrupted layer between the wood and bark of stems. The lateral meristem consists of two

layers; the vascular cambium which is adjacent to the wood and the cork cambium which is

adjacent to the bark.

Wood growth initiates with the division of cambial initials in the vascular cambium. Cambial

initials adjacent to the wood core elongate and expand into wood cells, resulting in increased

diameter growth of the wood core, whilst cambial initials adjacent to the cork cambium

continue to divide, allowing the vascular cambium to ‘stretch’ around the wood growth

(Wilson and White 1986; Salisbury and Ross 1992). Bark growth follows a similar process in

the cork cambium, however, where new wood cells build upon previous wood layers, new

bark cells form under previous bark layers and must ‘stretch’ the outermost layers in order to

accommodate their own growth as well as the increased diameter of the wood core (Wilson

and White 1986; Salisbury and Ross 1992). Eucalypt species have distinct methods to

‘stretch’ the outermost bark layers, including fibrous bark that expands, bark that splits, and

bark that sheds to remove the outermost layer altogether (Jacobs 1955). The combined effects

of bark ‘stretch’ and weathering on bark ensure that whilst both wood and bark grow for the

life of the tree, the bark layer exhibits a relatively constant width compared to the growing

width of the wood core.

Stem diameter is the most common measure of secondary growth, and indeed the most

common of any measure made on trees. Stem diameter is usually measured as stem diameter

at breast height (DBH), which is the diameter of the whole stem at 1.3 m stem height, although

it is also common for the measurement to be made of the wood only.


Common measurements of the combined affects of primary and secondary growth (expansion

in length and width) include stem volume, stem mass and branch mass. Stem volume is

usually measured when the stem is green and is calculated from a number of stem diameter

measurements and stem height, since direct measurement of stem volume is difficult. Stem

volume has a positive relationship with both stem diameter and stem height; however the

relationship is not constant due to changes in stem shape. Mass is usually assessed by oven-

dry mass to calculate the amount of biomass (not including water) that is included in the

component under investigation. The mass of the stem is often partitioned into that of

stemwood and stembark, whereas branch mass is almost always measured as the combined

mass of branchwood and branchbark due to the mechanical difficulty of separating the two.

Stem mass shares a positive relationship with stem volume since larger stems are usually

heavier, however the relationship is not constant as wood density changes according to

species, age and growth rate.

EFFECT OF COMPETITION ON TREE GROWTH

Competition has the effect of limiting resource availability, and the productivity of a tree in a

competitive environment is dependent on its ability to maintain access to the resources

required to produce photosynthate. Resources like rainwater, nutrients, and carbon are

relatively evenly distributed between neighbouring trees, and the ability to capture these

resources is likely to be in some sort of ratio to the size of the organ collecting the resource. In

comparison, saturated light has an asymmetric distribution (radiating down from above) and

may be largely intercepted by a more dominant tree with greater crown height. Similarly,

water table moisture may be considered to have an asymmetric distribution (permeating up

from below) and may be largely intercepted by a more dominant tree with greater root depth.

For resources with asymmetric distributions, the position of individual trees in relation to

neighbouring trees is of vital importance since this affects relative resource capture and

subsequent photosynthate production. The effect of tree position on relative resource capture

(and relative growth rate) is amplified as competition for resources with asymmetric

distributions increases (Kuppers 1989; Oliver and Larson 1996) particularly in shade-

intolerant genera like Eucalyptus.

Studies of the effects of competition on primary growth show that mean stem height is

reduced by increased stocking density (Laar and Bredenkamp 1979; Bredenkamp 1987;

Cameron et al. 1989; Coetzee 1995; Coetzee et al. 1996; Bernardo et al. 1998; Coetzee and


Naicker 1998a; Coetzee and Naicker 1998b; Coetzee 1999), and the ratio of mean stem height

to mean stem diameter is increased by increased stocking density (Opie et al. 1978; Drew and

Flewelling 1979; Cameron et al. 1989; Schonau and Coetzee 1989; Coetzee 1995; Bi and

Turvey 1996; Pinkard and Neilsen 2003), as stems essentially become more elongated or less

tapered. Increased competition due to increased stand density also results in decreased mean

crown width (Opie et al. 1978; Laar and Bredenkamp 1979; Cameron et al. 1989; Zeide 1991)

and decreased mean leaf mass (Henskens et al. 2001; Pinkard and Neilsen 2003) due to crown

shyness in eucalypt trees. Correspondingly increased competition due to stocking density

results in reduced mean leaf area, as has been found in E. nitens (Medhurst and Beadle 2001;

Pinkard and Neilsen 2003), E. globulus (Henskens et al. 2001) and E. grandis (Leite et al.

1997).

A striking feature of stem height growth in even-aged eucalypt stands is that the height of

dominant trees will increase at approximately the same rate regardless of the level of

competition, provided the sites have similar resource availability (Pinkard and Neilsen 2003).

As a result of this relationship the stem height of the dominant trees in even-aged stands is

used as a reliable measure of site quality regardless of stocking density (Coetzee et al. 1996;

Oliver and Larson 1996; Coetzee and Naicker 1998a; Coetzee and Naicker 1998b), with the

restriction that stem height must be measured at a reasonably advanced age (typically 5 years)

to reduce errors attributable to short term climatic fluctuations affecting height growth, such

as drought or unseasonably cold temperatures. These findings indicate that the commonly

reported reduction in mean stem height with increased stocking density is probably due to a

greater number of shorter trees rather than a reduction in stem height growth in all trees.

The properties of primary growth are often reflected in secondary growth since the rate of

secondary growth in the stem and branches is dependent on the productivity and structural

requirements of the crown. This is the case for stem diameter, for which investigations of the

effect of competition on stem diameter show that increased stocking density results in

decreased mean stem diameter (Hart 1928; Reineke 1933; O'Connor 1935; Jacobs 1955; Yoda

et al. 1963; White and Harper 1970; Bowersox and Ward 1976; Belanger and Pepper 1978;

Opie et al. 1978; Laar and Bredenkamp 1979; Geyer 1981; Laar 1982; Baker and Attiwell

1984; Weiner 1985; Bredenkamp 1987; Brand and Magnussen 1988; Kohyama and Hara

1989; Bredenkamp and Burkhart 1990b; Ralph 1990; Coetzee 1995; Merriam et al. 1995; Bi

and Turvey 1996; Lee 1996; Bouvet 1997; Gerrand et al. 1997; Leite et al. 1997; Bernardo et


al. 1998; Pinkard and Neilsen 2003). Whilst universally reported, the knowledge that

increased competition decreases mean stem diameter is not always useful, as it is a gross

summary of stand structure that leaves many unanswered questions. For example, is the

reduction in mean stem diameter with increased competition caused by a reduction in stem

diameter in all trees or is it caused by a greater quantity of smaller trees or a combination of

the two? To what extent in self-thinning stands is mean stem diameter increment due to the

death of the smallest trees (thereby mathematically increasing mean stem diameter) and to

what extent is it due to growth in surviving stems? Such questions are pertinent to

understanding how trees and stands survive and mitigate the effects of competition, yet they

are rarely addressed.

Some studies begin to answer the above questions, showing that the diameter growth of the

largest (dominant) stems continues despite intense competition (Bredenkamp and Burkhart

1990b), and that where stem diameter growth of dominant trees is reduced by competition, the

effects of competition are asymmetric, in that dominant trees are less affected by competition

than suppressed trees (Weiner 1985; Brand and Magnussen 1988; Battaglia 2001). Further

information on how increased competition affects dominance classes, particularly the most

dominant class, is certainly pertinent to understanding the development of stand structure.

As with stem diameter, increased competition due to stocking density results in decreased

stand mean stem volume (Opie et al. 1978; Drew and Flewelling 1979; Cameron et al. 1989;

Schonau and Coetzee 1989; Coetzee 1995; Bi and Turvey 1996; Pinkard and Neilsen 2003).

One can also expect mean stem mass (Pinkard and Neilsen 2003) and mean live branch mass

(Henskens et al. 2001; Pinkard and Neilsen 2003) to decrease as competition increases due to

stocking density.

It can be concluded that the general effect of increased competition due to stocking density is

to reduce mean tree growth and therefore mean tree size. As previously emphasized, this

finding provides little information about size differences between dominants growing in

different levels of stand competition intensity, or about size differences between dominants

and suppressed trees growing in the same stand competition intensity.


4.1.2 Tree Structure

Like the vast majority of terrestrial plants, trees possess leaves, roots and stems. Unlike a

large proportion of plants, trees possess a woody stem and a mature height of at least 9

metres. Yet within these parameters trees have developed many different structures, the

variety of which forms the basis for taxonomic division.

Tree structure may be regarded as the general shape or form of the tree, and the size of

various tree components in relation to each other. Tree growth affords the opportunity for tree

structure to change, and tree productivity will determine the rate of change in tree structure.

Just as tree growth is capable of dynamic responses to growing conditions, tree structural

development is capable of plastic responses to growing conditions. The extent of structural

development in eucalypts will vary widely due to the physiological responses of trees to

changes in their environment, particularly to changes in competition due to stocking density

(Opie et al. 1978). Many of the physiological responses of trees to changes in their

environment are known as growth habits, and were first comprehensively described for

eucalypts by Jacobs (1955).

The tree component most affecting crown structure is the branches. Branch formation (the

number of branches formed per unit of shoot elongation) has been found to be under strong

genetic control (Pinkard and Neilsen 2003), suggesting that it is possible to select trees that

form fewer branches to reduce the negative effects of branches on wood quality, and/or

minimise the costs of removing branches. The potential danger of this approach, however, is

that trees with more branches might have more leaves and greater growth rates, and selection

for fewer branches might therefore result in a loss of productivity. Examination of branch

formation in trees with different growth rates would provide evidence of a relationship

between branch formation and productivity.

Crown structure subsequent to branch formation is principally measured by the vertical

distribution of branches, which is strongly affected by shade tolerance and branch shedding.

Shade tolerance is the ability of trees to survive and grow in low light. Eucalypts are

predominantly shade-intolerant compared to other genera (Opie et al. 1978; Montagu et al.

2003), as indicated by a large drop in the photosynthetic rate of eucalypt leaves when they

lose light saturation (Leuning et al. 1991b; Leuning et al. 1991a; Pereira et al. 1992; Sands

1996). Individuals will rapidly become suppressed when subjected to shade (Schonau and


Coetzee 1989), and when leaves are unable produce sufficient photosynthate for their own use

from the available light (the light compensation point) the leaves and ultimately the branch

dies (Givnish 1988).

Branch shedding is the process by which lower branches die and are ejected from the stem

(Jacobs 1955). Branch shed is prevalent in dense stands since it is driven by shade intolerance

(Opie et al. 1978; Givnish 1988), but it may also be accelerated by competition for other

resources (Pereira et al. 1989; Cromer et al. 1993). In this case limited nutrients and/or water

are preferentially allocated and/or reallocated to light saturated leaves in the upper crown

(Field and Mooney 1983; Pereira 1990; Sands 1995) in response to their higher

photosynthetic rates (Leuning et al. 1991b; Leuning et al. 1991a; Pereira et al. 1992; Sands

1996). Lower leaves may be above the light compensation point but there are insufficient

resources to allocate to the lower branches once the upper branches have been supplied, with

the result that lower branches are shed.

Vertical distribution in crown structure is most commonly measured by stem and crown

height. Stem height measures the height to which the crown apex has grown and crown

height, the distance between the stem base and the start of the live crown, measures the height

to which branches have shed. Crown depth is the length of the crown (stem height minus

crown height), and the crown depth ratio (ratio of crown depth to stem height) is indicative of

relative crown retention. Crown structure may also be measured by crown form, which is the

general geometric shape of the crown. Eucalypt crowns which increase rapidly in height are

generally conical, whereas eucalypt crowns which increase slowly in height have a more

rounded appearance (Jacobs 1955; Florence 1996).

Another common measure of crown structure is leaf specific area, which is the ratio between

leaf area and leaf mass, and is a measure of the ‘thinness’ of leaves. High leaf specific area is

indicative of ‘thin’ leaves and may be considered a more efficient allocation of biomass since

a greater leaf area is created for a given amount of biomass. Low leaf specific area is

indicative of ‘thick’ leaves and is the result of natural selection in species originating from dry

and/or harsh habitats that minimises desiccation and damage to leaves at the expense of

reduced efficiency in biomass allocation (Specht and Specht 1999; Sefton et al. 2002).


Leaf nutrient content, the concentration of nutrients within the leaf, provides an indication of

the amount of nutrients captured by the tree, and is of interest as the photosynthetic capacity

of leaves is improved by high nutrient concentrations (Leuning et al. 1991a). Studies of leaf

nutrient content generally focus on the macronutrients nitrogen (N), phosphorus (P) and

potassium (K) as these nutrients are most likely to be in deficit and therefore of interest in

terms of ensuring their adequate supply for optimal growth. A study pooling the results of

three South African experiments measuring leaf nutrient concentrations in E. grandis

concluded that the optimum foliar nutrient concentrations for maximised growth are 2.8% N,

0.15% P, and 0.75% K (Herbert 1992), the values of which fall between the adequate ranges

reported for plantation eucalypts in Australia (Dell et al. 1995).

Nutrient concentrations may vary seasonally and with location in the crown (Grove et al.

1996). In young E. grandis, N and P were most concentrated towards the top of the crown and

then towards the sides (Leuning et al. 1991b), and in 4 year old Eucalyptus deglupta N, P and

K were most concentrated in the outer crown (Lamb 1976). These data suggest that mobile

nutrients are preferentially allocated to light-saturated leaves in the outer crown, particularly

towards the top. In this way carbon assimilation is maximised since the enhanced

photosynthetic capacity of nutrient rich leaves coincides with light saturation, when

photosynthesis is most efficient (Leuning et al. 1991b; Sefton et al. 2002; Macfarlane et al.

2004).

Whilst improved leaf nutrient content leads to increased photosynthetic capacity (Kirschbaum

and Tompkins 1990; Leuning et al. 1991b; Sheriff and Nambiar 1991; Kirschbaum et al.

1992; Sands et al. 1992; Misra et al. 1998), it is important to note that this is not the only

strategy by which an improved nutrient status may be used to increase carbon assimilation.

An increased nutrient supply can also cause an increased ratio of biomass allocated to the

crown (Cromer et al. 1984; Cromer and Jarvis 1990; Leuning et al. 1991b; Sheriff and

Nambiar 1991; Herbert 1992; Kirschbaum et al. 1992; Sands et al. 1992; Misra et al. 1998)

and increased leaf specific area (Cromer and Jarvis 1990; Kirschbaum and Tompkins 1990;

Kirschbaum et al. 1992; Sands et al. 1992), thereby increasing carbon assimilation through

increasing leaf area rather than by increasing photosynthetic capacity.

Like crown structure, stem structure also changes as the tree grows. On a macro-level, stem

structure is fairly simple, the stem essentially consisting of an elongated cylindrical cone of


wood surrounded by a layer of bark. Fast growing eucalypts usually exhibit a single, straight

stem as strong apical dominance results in a single, dominant, growth tip (Jacobs 1955; Opie

et al. 1978), however disturbance to the dominant growth tip, such as fire, physical breakage,

herbivory, or disease, can cause another shoot or shoots to establish dominance, which may

affect stem structure by causing bends, sweep and/or double leaders.

Stem form provides an indication of the shape of the stem. Stem form may be measured under

or over the bark and it is determined by the manner in which stem diameter changes with

height up the stem, also known as stem taper (West 2004). Increased stem taper results in a

more conical stem form, whilst decreased stem taper results in a more cylindrical stem form.

Increased stem taper is thought to be a response to increased need for mechanical support

against the bending stresses caused by wind since greater wind exposure results in increased

stem taper (Jacobs 1955; Valinger 1992; Osler et al. 1996). For a given large-end diameter,

logs with decreased taper exhibit higher conversion efficiencies.

Another structural attribute of stems is the proportion of the stem comprised of bark (bark

ratio), either in terms of mass or volume. In general, the bark ratio diminishes as stem size

increases (Schonau and Boden 1982; Negi et al. 1984), and it is an important consideration

when making stem measurements as ignoring it can result in significant errors in estimation,

particularly if the whole stem is assumed to consist of wood. Stemwood structure is also an

important consideration in the investigation of stem structure given the commercial

importance of the wood component of the tree and potential for the cellular structure of wood

to impact on wood properties. Due to the complicated nature of wood structure this aspect of

tree structure is addressed separately in Chapter 5 – Wood Growth and Structure.

The information available about root growth is the most limited of all the tree components due

to the difficulty and expense of measuring roots intensively. The few studies done on

plantation eucalypts show that if resource availability is reduced by decreased site quality,

then the proportion of biomass allocated to root biomass rather than aboveground biomass

(root:shoot ratio) increases (Reis et al. 1985; Misra et al. 1998), which mirrors findings in

temperate forest species (Vogt et al. 1997). If resource availability is restricted by increased

stocking density, the proportion of biomass allocated to roots generally decreases (Eastham

and Rose 1990; Bargali et al. 1992; Fabiao et al. 1995; Bernardo et al. 1998; Leles et al.

2001; Saint-André et al. 2005) (Figure 4.1).


0.0

0.2

0.4

0.6

0.8

1.0

0 500 1000 1500 2000 2500

Planting Density (st/ha)

Ro

ot:

Sh

oo

t R

ati

oE. camaldulensis; 3.4 yrs; SE Brazil (Bernardo et al. 1998) E. camaldulensis; 4.3 yrs; Brazil (Leles et al. 2001)

E. pellita; 3.4 yrs; SE Brazil (Bernardo et al. 1998) E. pellita; 4.3 yrs; Brazil (Leles et al. 2001)

E. urophylla; 3.4 yrs; SE Brazil (Bernardo et al. 1998) E. grandis; 2.5 yrs; SE QLD Australia (Eastham et al. 1990)

Figure 4.1: The effect of planting density on the root:shoot ratio in young eucalyptus plantations.

Most of the structural properties outlined for tree structure are allometric relationships.

Allometric relationships define tree structure by comparing the growth of different

components within one tree or group of trees. This removes the effect of absolute size in the

comparison between different trees (Ryan et al. 1997). It is well known that the relative

amount of biomass allocated to crown, stem and roots changes with factors such as age, site

quality and competition (Pereira et al. 1997). Many studies of plantation eucalypt growth

involve some sort of examination of allometric relationships, the reason for which may range

from the need to apply biomass findings to other trees based on a convenient measure like

stem diameter (Attiwill 1979), to an examination of the effect of silvicultural treatments on

growth and biomass distribution (Birk and Turner 1992; Bennett et al. 1997; Bernardo et al.

1998; Reed and Tomé 1998), or to find some sort of diagnostic relationship (Turner 1986;

Bargali et al. 1992) such as the relationship between stand net primary productivity and

projected leaf area and site quality. In most studies, however, the primary focus is mean tree

growth and little attention is paid to how structure differs between trees.

Resource use efficiency, the efficiency with which resources are used to sequester a given unit

of carbon, is another measure which removes the effect of absolute size. It is typically

investigated at the leaf level in terms of individual resources such as water, nutrients and light.

Studies of resource use efficiency at the leaf level show that it tends to increase as resource


availability decreases. Water use efficiency (photosynthesis/stomatal conductance of H2O) is

increased as water availability decreases (Farquhar et al. 1982; Farquhar et al. 1989; Ares and

Fownes 1999, 2000; Binkley et al. 2004; Wildy et al. 2004), and light use efficiency

(photosynthesis/units of photosynthetically active radiation) is increased as light quality is

reduced (Hoad and Leakey 1994; Binkley et al. 2004). The effect on nutrient use efficiency as

nutrient availability decreases is less clear. Decreased nutrient availability results in decreased

total leaf area and increased leaf specific area (Cromer and Jarvis 1990; Kirschbaum and

Tompkins 1990; Kirschbaum et al. 1992; McDonald et al. 1992; Wendler et al. 1995; Dewar

1996; Harrington et al. 2001; Binkley et al. 2004) so that leaf nutrient concentrations remain

relatively constant on a weight basis, however it is unclear how this effects nutrient use

efficiency (photosynthesis/nutrient concentration) due to changes in leaf specific area.

Whilst there is evidence to support the theory that resource use efficiency is inversely

proportional to resource availability for individual resources, it is unclear what the effect of

improved resource use efficiency in one resource has on the sum of the resource use

efficiency of all resources (Sefton et al. 2002). Evidence shows that water scarcity will cause

improved water use efficiency by increasing stomatal closure, yet this action must have the

inverse effect of reducing light use efficiency since photosynthesis is restricted whilst light is

still available, thus ‘wasting’ photosynthetically active radiation. At the leaf level it is

therefore difficult to determine whether the benefits of improved resource use efficiency in

one resource will be ‘cancelled’ by reduced resource use efficiency in a different resource.

Tree growth efficiency is the total resource use efficiency at the tree level, rather than

individual resource use efficiency at the leaf level. Tree growth efficiency is a measure of tree

vigour whereby the total amount of resources captured by the tree are compared to the total

amount of tree growth. Leaf area is generally used as the measure of resource capture as it is

indicative of light interception, water transpiration and nutrient availability (Stoneman and

Whitford 1995). The few studies that have specifically analysed tree growth efficiency

indicate that compared to trees with lesser resource capture, trees with greater resource

capture have either greater relative growth, and therefore better tree growth efficiency

(Stoneman and Whitford 1995; Binkley et al. 2002) or similar relative growth and therefore

similar tree growth efficiency (Kaufmann and Ryan 1986). These results suggest that the sum

of resource use efficiency tends to be reduced by restrictions in the supply of individual

resources.


EFFECT OF COMPETITION ON TREE STRUCTURE

Branch formation (the number of branches formed per unit of shoot elongation) is unaffected

by competition, as shown in a study of E. nitens in which increased competition due to

planting density had no effect on the number of branches per unit of crown length (Pinkard

and Neilsen 2003). This finding is noteworthy as it shows that trees start off with a base

crown structure dependent on genetic properties, and that subsequent differences in crown

structure are due to branch growth and branch shed rather than branch formation.

Increased competition for light generally results in greater branch shed and therefore higher

mean crown height in eucalypts (Cameron et al. 1989; Henskens et al. 2001; Pinkard and

Neilsen 2003). There is some suggestion that crown height in dominant trees may be less

affected by competition than crown height in sub-dominant trees. Studies of 15 to 25 year old

E. delegatensis and E. regnans established in spacings of 200 to 2000 stems ha-1

(Hastings

and Opie 1974), and 11.5 year old E. pilularis established from 121 to 1250 stems ha-1

(Opie

et al. 1978), indicate that the crown height (log length) of dominants only increases up to

planting density of 400-500 st/ha, whereas mean crown height continues to increase

indefinitely with competition intensity. These findings imply that competition for resources,

rather than the light compensation point, is driving branch shed, since crown height would be

equal for all trees based on ambient light conditions in the canopy if the light compensation

point were driving branch shed.

Increased competition due to stocking density causes a decrease in the mean crown depth

ratio, as shown in E. grandis (Laar and Bredenkamp 1979), E. nitens (Medhurst and Beadle

2001), E. globulus (Pinkard and Neilsen 2003) and E. marginata (Jarrah) (Stoneman and

Whitford 1995). As mean crown depth ratio is considered a strong indicator of the

competitive stress experienced by stands, it has been suggested to use it as an indicator for

scheduling thinning operations (Hughes 2000). Within a given competition intensity,

dominant trees exhibit a greater crown depth ratio than suppressed trees (Stoneman and

Whitford 1995; Medhurst et al. 1999).

Increased competition skews mean foliage distribution upwards on the stem, as found in

investigations of E. nitens (Medhurst and Beadle 2001; Pinkard and Neilsen 2003), indicating

a less conical shape. The extent of this skewness is greater for suppressed individuals than


dominant individuals (Pinkard and Neilsen 2003), which is consistent with the general

consensus that crowns growing slower in height are less conical.

For leaf specific area increased competition due to planting density was found to have no

effect on leaves developing in the upper crown in 6 year old E. globulus (Macfarlane and

Adams 1998) and 7 year old E. nitens (Pinkard and Neilsen 2003), which is the expected

result for leaves of the same species forming in the same evaporative climate. A study of E.

nitens plantations found that leaves formed in the upper crown zone exhibited a lower leaf

specific area than leaves formed in the lower crown zone (Medhurst et al. 1999), and similar

results were found in separate studies including eucalypt species (Poorter and Evans 1998;

Evans and Poorter 2001), indicating that leaf specific area is reduced by increased exposure at

the time of leaf formation. In consequence, increased competition is expected to result in

increased leaf specific area in the mid and lower canopy due to increased ‘crowding’ and

shading of leaves in the canopy.

There are few studies specifically investigating the effect of competition on leaf nutrient

content in eucalypts, although some idea can be drawn from existing knowledge. In general,

increased stocking density will lead to greater competition for site resources and therefore

reduced average nutrient availability per stem. Previous findings have shown that tree

canopies respond to reduced nutrient availability in one of three ways: (i) decreased leaf

nutrient concentration, (ii) decreased total leaf biomass and/or (iii) decreased leaf specific

area. It is not always clear which is strongest or has the greatest affect on plant growth. One

study of nitrogen in E. grandis seedlings concluded that changes in total leaf biomass and leaf

specific area, or crown structure, correlated more strongly with plant growth than changes in

leaf nutrient concentration and photosynthetic capacity (Cromer and Jarvis 1990).

Mean stem taper has been found to decrease with increased competition due to stocking (Opie

et al. 1978; Cameron et al. 1989; Schonau and Coetzee 1989; Coetzee 1995; Bi and Turvey

1996; Coetzee and Naicker 1998b). This is probably a consequence of increased competition

resulting in reduced mean crown size (less crown for wind to catch) and more shelter from

neighbours, and therefore less requirement for mechanical support against bending stresses

caused by wind. This finding suggests that crown size relative to stem height has some affect

on stem taper. Studies of the effects of pruning (essentially reducing the crown size relative to

stem height) on stem taper partially support this idea, in that pruning was found to reduce


stem taper in conifers by causing an upward shift in relative stem growth (Pinkard and Beadle

2000). Pruning 50% of the live crown in E. nitens, however, had little effect on stem taper

(Pinkard et al. 1998).

There are no studies specifically investigating the effect of competition on bark ratio in

eucalypts, however studies of American sycamore (Platanus occidentalis L.) show that bark

ratio (for a given stem size) was unaffected by increased competition intensity due to planting

density (Saucier et al. 1972; Wittwer et al. 1978), implying a strong relationship between

stem size and bark ratio. It might therefore be expected that increased competition due to

planting density will result in a greater mean bark ratio (due to the presence of a greater

number of smaller stems), but no difference in bark ratio between dominant stems of a similar

stem size.

In summary, the existing understanding of tree structure shows that some components of tree

structure change considerably in response to competition, and others do not appear to respond

greatly to competition. The degree of confidence in these findings also varies according to the

amount of information available and how conflicting it is. Competition appears to have no

affect on branch formation, leaf specific area or leaf nutrient content in exposed leaves,

however increased competition results in decreased branch growth, increased branch shed,

decreased crown depth ratio, a more cylindrical crown form, increased mean leaf specific area

in the mid and lower canopy and possibly decreased leaf nutrient concentrations in the mid

and lower canopy. Competition has no effect on the proportion of wood or bark for a given

stem size, however increased competition does increase the stand mean bark ratio due to

increasing the ratio of smaller trees, and it encourages straight stem form and more cylindrical

stem form. Overall increased competition causes tree mass to be skewed away from the crown

(and probably the roots), and towards the stem. Whilst this pattern of change is generally well

known, the exact manner in which individual tree structure, coupled with relative size and

position in the stand, affects individual tree growth, is not clear.



The effect of competition on tree growth and tree structure provides insight into interactions

between growth and structure and how the development of individual trees might collectively

affect stand development. Planting density is used to approximate the general level of

competition in the stand, and stem diameter is used to approximate the level of competitive

pressure experienced by individuals in the stand.

For a given stem diameter, increased planting density is expected to result in increased stem

growth and decreased crown growth, and therefore a skewing of tree structure towards the

stem rather than the crown (Table 4.1). The relative effect of planting density is expected to

decrease as stem diameter increases.

Table 4.1: Hypotheses of the effect of increased planting density on variables of tree growth and tree structure during early stages of stand development in sub-tropical E. grandis plantations.

Tree Variable Hypothesis

Tree Growth

Stem Increased planting density will result in increased stem growth (stem height, stem volume, stem mass) for a given stem diameter.

Crown Increased planting density will result in decreased crown growth (crown width, crown leaf area, crown mass) for a given stem diameter.

Tree Structure

Branch Number Increased planting density will have no effect on branch number.

Branch Shed Increased planting density will result in increased branch shed (increased crown height and decreased crown depth ratio).

Tree Form Increased planting density will result in decreased conicity of tree form (stem form and crown form).

Tree Mass Ratios Increased planting density will result in tree mass ratios skewed towards the stem.

Leaf Specific Area Increased planting density will have no effect on the leaf specific area of dominant trees, but will result in increased leaf specific area in suppressed trees.

Leaf Nutrient Content Increased planting density will result in decreased leaf nutrient content.


4.3 Methodology

To test the hypotheses a number of data were collected from the spacing trial, and in some

cases collected data were used to calculate estimated values of additional tree and stand

variables. The hypotheses for tree growth and structure were tested by statistical analysis of

collected and calculated data.

4.3.1 Sample Age and Size

In addition to data that were collected annually for every live tree in the whole trial (Table

3.2), data were collected from the spacing trial at 3 and 4 years from smaller samples (Table

4.2).

Table 4.2: The age and sample size of variables for which tree data were collected from the spacing trial.

Number of Trees Sampled Tree Variable

Age (yr) 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha

3 Year Old Measurements(a)

Stem Diameter at 1 m Height IntervalsBranch Formation

Crown Height3 8 8 8 8


Stem Diameter at 1 m Height IntervalsCrown Height

Crown DiameterTree Green Mass

Tree Sample Green MassStem Sample Diameter

Tree Sample Oven-Dry Mass Leaf Specific Area

4 8 20 20 20

Leaf Nutrient Content 4 8 8 8 8 (a)

A full description of the sample selection methods for 3 and 4 year old measurements are provided in Chapter 2 – The Spacing Trial, sub-sections 2.4.1 and 2.4.2 respectively.


The method of collection or calculation for each tree variable outlined in Table 4.2 is

explained in the following paragraphs.

Stem Diameter at 1 m Stem Height Intervals – the diameter of the cross-sectional area of the

whole stem at 1 m stem height intervals. At 3 years access to the stem was gained using a

ladder and stem diameter at 1 m height intervals was measured directly with a measuring tape


to a height of 6 m. At 4 years access to the stem was gained by felling the stem and stem

diameter was measured directly with a measuring tape to the tip of the stem. Where branch

swellings obstructed measurements, they were moved up to the nearest clear stem section.

Branch Formation - the number of branches that have left evidence of having formed on the

stem, where evidence of the formation of a branch is shown either by the presence of a branch

or by a branch scar in the bark. Branch number was measured directly by counting branches

and scars at 3 years (stem accessed by ladder), and the height from the base of the stem to

each branch formed was measured directly with a measuring tape.

Crown Height – the height from the base of the stem to the lowest live branch of the crown.

Crown height was measured directly with a measuring tape at 3 years (crown accessed by

ladder) and at 4 years (crown accessed by felling trees for destructive sampling).

Crown Depth Ratio – the ratio of the vertical space occupied by the live crown (crown depth)

to stem height. Crown depth ratio was calculated as:

(stem height – crown height) / stem height

Crown Diameter – the diameter of the cross-section of the crown at a given height from the

base of the stem. Crown diameter was measured directly with a measuring tape at 1 m height

intervals at 4 years (crown accessed by felling trees for destructive sampling). The single

measurement of crown diameter at each interval (rather than two or more diameter

measurements) assumes that the crown cross-section is circular, which is a reasonable

assumption for young healthy trees as they tend to have a strong central stem and conoid

shape (Philip 1994). These measurements of crown diameter are likely to be underestimates,

since the branches were not subject to gravity pulling them down and out as measurements

were made on the felled tree.

Crown Width – the crown diameter at the widest point of the crown. Crown width was

determined as the largest crown diameter.

Crown Volume – the volume of the crown 3-dimensional shape. Crown volume is usually

modelled as the volume of a cone (Philip 1994), however since crown diameter was measured

at 1 metre intervals, crown volume was more accurately calculated by summing the volumes


of the conical frustums formed between each crown diameter measurement (with the top

frustum forming a cone).

Crown Form Factor – the ratio of the crown volume to that of a cylinder with the same width

and depth as the crown (Philip 1994). Given form factors suggest the following general

shapes: 0.25 neiloid; 0.33 conoid; 0.50 quadratic paraboloid; 0.60 cubic paraboloid; 1.00

cylinder.

The above data were compiled from measurements made on whole trees (either standing or

having been felled). The following data were compiled from measurements which required

that felled trees be broken down into various components.

Tree Green Mass – the fresh mass of the aboveground tree. Following dimensional

measurements of trees felled for whole tree destructive sampling at 4 years, the trees were

broken down into their stem, leaf and branch components. For each tree the stem was pruned

of all branches and weighed with 50 kg scales to the nearest gram. Leaves were stripped by

hand from the pruned branches over a tarpaulin, placed in garbage bins and weighed with 50

kg scales to the nearest gram. Stripped branches were divided into live and dead branches,

stacked into garbage bins and weighed with 50 kg scales to the nearest gram. The whole

process was conducted as quickly as possible and in the shade to minimise evaporation,

particularly from the leaves. The sum of the green mass of tree components (excluding dead

branches) then provided tree green mass.

Tree Sample Green Mass – the fresh mass of samples taken from the stem, leaf and branch

components of destructively sampled trees. The mass of every sample was weighed on

scientific scales to the nearest milligram as soon as possible after collection to minimise

evaporation from the samples, and all samples were then stored in labelled paper bags. Stem

samples consisted of 50 mm thick disks that were taken at breast height (1.3 m) and at 25%,

50% and 75% of stem height. Stem samples were weighed whole, following which the

stembark was removed and kept as stembark samples, and the stemwood was weighed and

kept as stemwood samples. Leaf samples consisted of two samples of several leaves from

each tree; one of fully formed new leaves from the top of the canopy and one of mature leaves

from the middle of the canopy. Branch samples consisted of several 50 mm branch sections of

live and dead branches from random positions in the canopy.


Stem Sample Diameter – the diameter of the cross-section of stem samples. Stem samples

consisted of 50 mm thick disks that were taken at breast height (1.3 m) and at 25%, 50% and

75% of stem height. The diameter of each sample was measured twice (on perpendicular

angles) using callipers, and the two diameter measurements were averaged to provide a single

diameter measurement. The process was repeated on each stem sample for the stemwood

diameter, which excludes bark from diameter measurement.

A number of whole stem calculations required the use of data from the stem. For this purpose

the stem was divided into sections relating to the closest stem sample by placing the ‘break’

between stem sections midway between the stem sample locations (Figure 4.2).

Figure 4.2: Diagram showing the division of the stem into four sections based on the location of the stem samples.

Stem Volume – the volume of the stem, the stemwood and the stembark. The volume of each

stem section (Figure 4.2) was calculated using the formula for a conical frustum:

Conical Frustum = ⅓ * π * (base radius2 + top radius

2 + (base radius*top radius)) * height

The radius at the base and apex of each stem section frustum was determined as the average

radius of the two closest stem samples, since the division between each stem section was

equi-distant from the stem sample on either side. The volumes of the stem section frustums

were then summed to determine stem volume. The same method was used to calculate

stemwood volume using stemwood diameter. Stembark volume was determined as the

difference between stem volume and stemwood volume.

Stemwood Green Mass– the fresh mass of the stemwood. Stemwood green mass was not

measured directly due to the difficulty of debarking whole stems, therefore a number of


calculations were required to provide an estimate of stemwood green mass. The ideal method

to estimate stemwood green mass is to multiply the green mass of each stem section by the

proportion of stemwood green mass in each stem sample, with the sum of the stem sections

providing stemwood green mass. This method was not possible, however, since the green

mass of the stem was weighed whole rather than in the stem sections.

The alternative was then to divide stem green mass into stem sections equivalent to those

determined for stem volume. The proportion of stem volume in each stem section was

calculated as the stem section volume divided by stem volume. Stem green mass was then

divided into the same proportions and multiplied by the proportion of stemwood green mass

in the equivalent stem sample, the sum of which determined stemwood green mass. This

method makes the assumption that the ratio of stem volume to stem mass (stem density) does

not change within the tree: however it was considered the best method to use with the

available data. Stembark green mass was then determined as the difference between stem

green mass and stemwood green mass.

Bark Volume Ratio – the volume of stembark relative to stem volume. Stembark ratio was

calculated by dividing stembark volume by stem volume.

Stem Form Factor – the ratio of stem volume to that of a cylinder with the same diameter and

height as the stem (Philip 1994), whereby the diameter of the stem cylinder is taken as stem

diameter at breast height. Given form factors suggest the following general shapes: 0.25

neiloid; 0.33 conoid; 0.50 quadratic paraboloid; 0.60 cubic paraboloid; 1.00 cylinder.

Tree Sample Oven-Dry Mass – the mass of samples taken from the stem, leaf and branch

components of each tree having had liquid water removed from cellular cavities. The wet

samples were placed in a scientific oven and dried at 80°C until their mass had stabilised for

one week (indicating that no further water would evaporate from the sample). The oven-dry

mass of samples was weighed using scientific scales to the nearest milligram.

Tree Oven-Dry Mass – the mass of the aboveground tree having had liquid water removed

from cellular cavities. The oven-dry mass of the aboveground tree was calculated as the sum

of the oven-dry mass of the tree components (excluding dead branches). The oven-dry mass

of the stem components were calculated for each stem section by multiplying the green mass


of the stemwood or stembark by the proportion of oven-dry mass in the equivalent stemwood

or stembark sample. The sum of the stem sections then provided stemwood or stembark oven-

dry mass. The oven-dry mass of the leaf component was calculated by multiplying the green

mass of the leaves by the average proportion of oven-dry mass in the two leaf samples. The

oven-dry mass of the branch component was calculated by multiplying the green mass of the

branches by the proportion of oven-dry mass in the branch samples.

Leaf Specific Area – the upper leaf surface area per unit of leaf oven-dry mass, the inverse of

which is leaf specific weight (Specht and Specht 1999; West 2004). A 10 mm hole punch was

used to punch 2 discs from 5 leaves in every leaf sample (care was taken to avoid the leaf

mid-rib), resulting in 10 leaf disks per leaf sample. The leaf disks were dried as per previous

tree samples and the oven-dry mass of the leaf disks were weighed using scientific scales to

the nearest milligram. Leaf specific area was then determined by dividing the surface area of

the leaf disks by the oven-dry mass of the leaf disks.

Crown Leaf Area – the total upper leaf surface area of leaves in the tree crown. Crown leaf

area was calculated as:

crown leaf oven-dry mass * leaf specific area

Leaf Nutrient Content – the percentage concentration of nitrogen, phosphorus and potassium

in the leaves (%). Of the 68 destructively sampled trees, a sub-sample of 32 trees (the largest

and smallest tree from each plot) were selected for leaf nutrient analysis. In preparation for

analysis, leaf samples from the top and middle of each tree canopy were oven-dried and

ground to a fine powder. The leaf nitrogen content was determined by analysis with a LECO

carbon/nitrogen/sulphur analyser (CNS 2000). Ground leaf samples were prepared for further

analysis by creating a microwave digest solution. The leaf phosphorus and potassium solute

content (mg/L) were then determined by analysis with a Perkin Elmer ELAN 6000

Inductively Coupled Plasma - Mass Spectrometer (ICP-MS).

Tree Mass Ratio – the oven-dry mass of tree components relative to tree oven-dry mass. Tree

mass ratios were calculated for the stem, live branch and leaf components of the tree by

dividing the oven-dry mass of the component by tree oven-dry mass.


Growth Efficiency Ratios – the amount of growth produced in the tree and tree components

relative to the amount of resources used, whereby the amount of resources used were

approximated by crown leaf area. Growth efficiency ratios were calculated for tree mass, stem

mass and stem volume by dividing the mass or volume of the component by crown leaf area.

4.3.3 Data Analysis

Raw data were entered into a Microsoft Excel spreadsheet and copied to MLwiN and SPSS to

facilitate an examination for recording errors (unusually high or low values and missing

values) using histograms and residual distributions. Where unusual values were identified the

original field data sheets were cross-checked for recording errors and mistakes were

corrected. Where unusual values were also present in the original field data sheets they were

included in the data set, with the exception of those which were impossible.

Due to the positive skewness in the size distribution of trees in the spacing trial (Figure 2.6)

the data set analysed consisted of a stratified random sample of the population. The data are

not therefore a true random sample of the population and the confidence intervals determined

for relationships between dependent variables and the factors affecting them are not

applicable to the population. In fact the confidence intervals determined in the analyses are

likely to be greater than those that would have been determined from a random sample,

particularly at the lower end of the DBH range.

Since these data formed a hierarchical structure with a minimum two levels of plot and tree, it

was appropriate to use multilevel modelling to analyse the data5 (Snijders and Bosker 1999).

As the most extensive multilevel package (Snijders and Bosker 1999), the MLwiN software

(Rasbash et al. 2003) was used for this purpose. The dependent variables modelled were

generally scale numbers greater than or equal to zero, with the assumption that these variables

had continuous distributions above zero, and that their residuals had normal distributions at all

levels. In these cases a hierarchical linear model identifying random variation around the

intercept coefficient (random intercept model) was used to analyse data and define the model

which reduced random variation by the greatest significant amount (Snijders and Bosker

1999; Rasbash et al. 2003). An hierarchical linear model is capable of identifying random

variation around slope coefficients (random slope model). Random variation around slope

5 Dr L. Brooks, Student Advisor, Research Methodology Unit, Southern Cross University.


coefficients was identified only if doing so reduced total random variation by a significant

amount.

In several cases it was necessary to transform the dependent variable raw data in order to

build a more realistic model. In the case that the raw data of the dependent variable displayed

a heteroscedastic distribution (fanning out) against predictive factors, then a natural logarithm

transformation was employed on both the dependent variable and predictive variable data in

order to reduce heteroscedasticity during analysis. Where the raw data of the dependent

variable approached zero at the lower end of its range, then a natural logarithm transformation

was employed on the dependent variable data so that the dependent variable could approach

zero but not become negative in the model. Where the dependent variable was a scale number

with a finite distribution between zero and one (such as a ratio) and approached its range

limits, then a sine transformation was employed so that the dependent variable could approach

its limits but not cross them.

Some dependant variables had discrete rather than continuous distributions, and residuals did

not exhibit normal distributions. In these cases specialised models assuming a non-normal

residual distribution (i.e. Poisson, binomial, multinomial) were required (Snijders and Bosker

1999; Rasbash et al. 2003).

At this stage of the thesis the main objective was to investigate the effects of competition on

the tree properties measured. As such the primary factors tested are planting density (P) as a

measure of the general level of competition in the stand, and stem diameter at breast height

(DBH) as a measure of the level of competition pressure experienced by individuals in the

stand. Given the potential for spatial variation in tree properties, sample position was

occasionally tested as a factor in order to detect any patterns in spatial variation. Age was also

included as a factor where applicable, since it was likely that relationships between tree

properties, P and DBH would change over time.



Immediately prior to examining the results, it is useful to examine the properties of the two

factors used to explore the effect of competition; planting density and stem diameter at breast

height. Planting density is a measure of population size and is used to approximate the general

level of competition in the stand. The planting densities used had a mean growth space per

tree of 38.44 m2 for 250 st/ha, 9.92 m

2 for 1,000 st/ha, 1.99 m

2 for 5,000 st/ha and 1.00 m

2 for

10,000 st/ha. It is noteworthy that mean growth space per tree becomes more equitable as

planting density increases, with the result that 5,000 st/ha and 10,000 st/ha have the least

difference in mean growth space, despite having a difference of 5,000 st/ha between them.

Stem diameter at breast height (DBH) is a measure of tree size indicating the competitive status

of trees in the stand. The trees selected for analysis of tree growth and structure included trees

from the range of dominance classes identified in each planting density, ensuring that trees of

all levels of competitive status were included in the analyses (Figure 4.3).

0.00

0.04

0.08

0.12

0.16

0.20

0.24

0.28

Ste

m D

iam

ete

r a

t B

reas

t H

eig

ht

(m)

(b) 1,000 st/ha(a) 250 st/ha (d) 10,000 st/ha(c) 5,000 st/ha

Selected TreesDBH Range

Dominant Co-Dominant

Intermediate Suppressed

Dominance Status

250 st/ha 1,000 st/ha

5,000 st/ha 10,000 st/ha

Figure 4.3: The stem diameter at breast height (DBH) of 4 year old E. grandis trees selected for analysis of tree growth and structure in planting densities (a) 250 st/ha, (b)1,000 st/ha, (c) 5,000 st/ha, and (d) 10,000 st/ha. The DBH of select trees (off-set points) in each planting density are shown in comparison to the DBH range and the dominance classes identified in each planting density. The division of each planting density into dominance classes is based on stem volume mean annual increment (MAI) in 250 stem cohorts (Figure 3.12), whereby 250 stem cohorts increasing in stem volume MAI at 100 to 90% of the largest 250 stem cohort are defined as dominant, 90 to 50% are co-dominant, 50 to 10% are intermediate and <10% are suppressed.


An examination of the select trees in each planting density indicates that the largest select

trees in low planting densities are larger than the largest select trees in high planting densities,

partly due to being larger in general and partly due to individuals being selected, by chance,

from the upper end of the dominant range (the shaded in blue in Figure 4.3). Despite the

difference in size between the largest select trees, however, the average size of the top five

select trees (dominant trees) is remarkably similar given the large difference in the

competition intensity between planting densities. It should be noted that within each planting

density the largest trees selected are defined as dominant trees and the smallest trees selected

are defined as suppressed trees, in order to allow comparison of dominant tree properties

across planting densities, and dominant and suppressed tree properties within planting

densities.

The following results are the product of testing the hypotheses of the effects of increased

competition on tree growth and structure (Table 4.1). For each variable the results are

presented in tables and figures. The tables show the statistically significant regression

coefficients of the model developed for each variable. Where an interaction between two

predictive factors is significant at 95% significance (p < 0.05), the singular effect of both

factors must also be included in the model regardless of their significance due to the

significance of the interaction. The figures show the differences in each variable along the

selected DBH range of each planting density, and overlapping of the 95% confidence intervals

indicates no significant difference between planting densities and/or stem diameters.

4.4.1 Tree Growth

STEM DIAMETER

Mean stem diameter at breast height (DBH) decreased as planting density increased, for the

whole stand (Table 4.3(a)), and for the largest (dominant) 250 stem cohort (Table 4.3(b))

(Figure 4.4). The results show that whilst the largest 250 stem cohort mean DBH was reduced

by increased planting density (Figure 4.4(b)), the absolute difference in the mean DBH of the

largest 250 stem cohort between planting densities equated to about 1 years growth by 5 years

old. This was small given the severe competition that was evident in high planting densities,

and shows that to some extent dominant stems were able to capture resources regardless of

competition.


Table 4.3: The fixed-effect regression coefficients of the random intercept model of the effects of planting density (P) (st/ha) and age (A) (yrs) on (a) stand mean diameter at breast height (m) and (b) the largest 250 stem cohort mean diameter at breast height (m).

VARIABLE COEFFICIENT S.E. P - VALUE INTERACTION COEFFICIENT S.E. P - VALUE

INTERCEPT 0.0216 0.0176 p = 0.220 lnP*A -0.0082 0.0006 p < 0.001

lnP 0.0011 0.0020 p = 0.582

A 0.0832 0.0057 p < 0.001


INTERCEPT 0.0281 0.0256 p = 0.272 lnP*A -0.0037 0.0009 p < 0.001

lnP 0.0014 0.0029 p = 0.629

A 0.0578 0.0081 p < 0.001

0.00

0.05

0.10

0.15

0.20

0.25

0 1 2 3 4 5

Age (yrs)

Me

an

Ste

m D

iam

ete

r a

t B

reas

t H

eig

ht

(m)

(a) Stand

0 1 2 3 4 5

Age (yrs)

(b) Largest 250 Stem Cohort


250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI

Figure 4.4: The relationship between the dependent variables stand mean diameter at breast height (DBH) (m) and largest 250 stem cohort mean DBH (m) and the factors planting density (st/ha) and age (yrs). The predicted values of (a) stand mean DBH and (b) largest 250 stem cohort mean DBH (with 95% confidence intervals) are plotted against age and identified by planting density.

STEM HEIGHT

For a given stem diameter, stem height increased as planting density increased. Within

planting densities, stem height had a positive relationship with stem diameter (Table 4.4)

(Figure 4.5). The results for stem height indicate that increased competitive pressure (planting

density) stimulated faster growth in stem height for a given stem diameter, though increased

shelter from close neighbours may also have aided faster height growth. The positive

(a)

(b)


correlation between stem height and stem diameter was expected as trees with greater stem

height tend also to have greater diameter.

Table 4.4: The fixed-effect regression coefficients in the random slope model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on stem height (m).


INTERCEPT -4.916 2.327 p = 0.035 √DBH*lnP -3.778 0.421 p < 0.001

√DBH 9.544 3.370 p = 0.005 lnP*√A -1.111 0.153 p < 0.001

lnP 1.710 0.229 p < 0.001 √DBH*lnP*√A 5.440 0.187 p < 0.001

√A 1.881 1.490 p = 0.207

0

10

20

30

DBH (m)

Ste

m H

eig

ht

(m)

(a) 1 year

(c) 3 years

0

10

20

30

0.0 0.1 0.2 0.3

(d) 4 years

0.0 0.1 0.2 0.3

(e) 5 years

0

10

20

30(b) 2 years

0102030

250 st/ha 1,000 st/ha

5,000 st/ha 10,000 st/ha

250 95% C.I. 1,000 95% C.I.

5,000 95% C.I. 10,000 95% C.I.

DBH (m)

Figure 4.5: The relationship between the dependent variable stem height (m) and the factors DBH (m), planting density (st/ha), and age (yrs). The predicted value of stem height (with 95% confidence intervals) is plotted against DBH and identified by planting density for ages (a) 1 year, (b) 2 years, (c) 3 years, (d) 4 years, and (e) 5 years.

Consistent with other studies of stem height in even-aged eucalypt monocultures (Pinkard and

Neilsen 2003), increased planting density had no effect on the stem height of dominant trees

from 3 to 5 years (Figure 4.5). A difference in dominant stem height between planting

densities was apparent from 1 to 2 years since the largest trees in high planting densities had

significantly greater stem height than those in low planting densities. The greater early height

growth of dominant trees in high planting densities was likely due to stimulus by earlier onset

of competition and/or greater shelter provided by closer proximity of neighbours. It begs the

question, however, how did dominants in low planting densities then catch up in height


growth, rather than lag behind indefinitely? A possible explanation is that trees reached a

height at around 2 years at which shading, particularly of slanted light in the morning and

evening, has stimulated dominant trees in low planting densities to catch up in height growth.

STEM VOLUME

For a given stem diameter, stem volume and stemwood volume increased as planting density

increased. Within planting densities, stem volume and stemwood volume had a positive

relationship with stem diameter (Table 4.5) (Figure 4.6).

Table 4.5: The fixed-effect regression coefficients of the random slope model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on (a) the natural logarithm of stem volume (m

3) and (b) the natural logarithm of stemwood volume (m

3)(a)

.

(a) VARIABLE COEFFICIENT S.E. P - VALUE (b) VARIABLE COEFFICIENT S.E. P - VALUE

INTERCEPT 1.082 0.094 p < 0.001 INTERCEPT 1.4330 0.1070 p < 0.001

lnDBH 2.265 0.031 p < 0.001 lnDBH 2.3080 0.0450 p < 0.001

lnP 0.076 0.009 p < 0.001 lnP 0.1050 0.0140 p < 0.001

A 0.167 0.019 p < 0.001

(a)Stemwood volume was only measured at one age (4 years) hence age had no effect.

0.0

0.1

0.2

0.3

0.4

0.0 0.1 0.2 0.3DBH (m)

Ste

m V

olu

me

(m

3)

0.0 0.1 0.2 0.3DBH (m)

(b) Stem (4 years)(a) Stem (3 years)

0.0 0.1 0.2 0.3DBH (m)

(c) Stemwood (4 years)

Raw Data 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha

250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI

Figure 4.6: The relationship between the dependent variables stem and stemwood volume (m

3) and

the factors DBH (m), planting density (st/ha) and age (yrs). The predicted values of stem volume (m3) at

ages (a) 3 years and (b) 4 years, and stemwood volume (m3) at age (c) 4 years (with 95% confidence

intervals) is plotted against DBH and identified by planting density for each tree measured. Windows enlarge the 95% confidence intervals.


The results for stem volume show that increased competitive pressure (planting density)

resulted in increased stem growth for a given stem diameter, which was likely due to

increased stem height for a given stem diameter. The relatively small difference in volume

between planting densities 5,000 and 10,000 st/ha compared to other planting densities was

expected as they had the least difference in mean growth space per tree. Overlapping 95%

confidence intervals between 5,000 st/ha and 10,000 st/ha (Figure 4.6) show that there was no

significant difference in stem volume between the two high planting densities.

The results for stemwood volume (Figure 4.6(c)) followed the same pattern as stem volume

(Figure 4.6(b)), the difference between the two being indicative of stembark volume. The

results indicate that competitive pressure had a large affect on stemwood volume growth, with

dominant trees in 5,000 and 10,000 st/ha only three fifths the size of dominant trees in 1,000

st/ha, and less than half the size of relatively free-growing trees in 250 st/ha. Clearly, the

relatively small difference in stem diameter that was apparent between dominant trees (Figure

4.4) has been compounded in the estimates of stem volume.

STEM MASS

For a given stem diameter, stem and stemwood oven-dry mass increased as planting density

increased. Within planting densities, stem and stemwood oven-dry mass had a positive


Table 4.6: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of (a) stem oven-dry mass (kg) and (b) stemwood oven-dry mass (kg).



lnDBH 2.240 0.045 p < 0.001 lnDBH 2.268 0.046 p < 0.001

lnP 0.035 0.014 p = 0.012 lnP 0.054 0.015 p < 0.001

Whilst the results for stem mass show a similar pattern to those for stem height and volume in

that increased competitive pressure (planting density) resulted in increased stem growth for a

given stem diameter, the effect of planting density on stem mass does not appear to be as

great as it was on stem height and volume. At 4 years old there was a significant increase in

stem height (Figure 4.5(d)), stem volume (Figure 4.6(b)) and stemwood volume (Figure

4.6(c)) for a given stem diameter as planting density increased from low to high planting

densities (1,000 st/ha to 5,000 st/ha). In contrast, the increase in stem and stemwood mass


from low to high planting densities (1,000 st/ha to 5,000 st/ha) was not significant, as

indicated by overlapping 95% confidence intervals between 1,000 st/ha and 5,000 st/ha

(Figure 4.7).

0

40

80

120

160

200

0.0 0.1 0.2 0.3DBH (m)

Ste

m O

ven

-Dry

Mas

s (

kg

)

(a) Stem

0.0 0.1 0.2 0.3DBH (m)

(b) Stemwood


250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI

Raw Data

Figure 4.7: The relationships between the dependent variables stem and stemwood oven-dry mass (kg) and the factors DBH (m) and planting density (st/ha). The predicted values of (a) stem and (b) stemwood oven-dry mass (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured. Windows enlarge the 95% confidence intervals.

The weaker effect of planting density on stem mass compared to stem volume suggests two

possibilities. The first possibility is that there was greater variation in the stem mass data than

the stem volume data. The second possibility is that increased planting density had a negative

effect on stem density; mass is the product of volume and density, and the positive effect of

planting density on stem volume would be reduced in stem mass if planting density had a

negative effect on stem density.

CROWN WIDTH

For a given stem diameter, crown width decreased as planting density increased. Within

planting densities, crown width had a positive relationship with stem diameter (Table 4.7)

(Figure 4.8).


Table 4.7: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of crown width (m).

VARIABLE COEFFICIENT S.E. P - VALUE

INTERCEPT -0.263 0.215 p = 0.221

√DBH 5.328 0.319 p < 0.001

lnP -0.086 0.018 p < 0.001

0

1

2

3

4

5

6

7

8

0.0 0.1 0.2 0.3DBH (m)

Cro

wn

Wid

th (

m)


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

10,000 st/ha

250 st/ha

1,000 st/ha

5,000 st/ha

Me

an

Gro

wth

Sp

ace

Be

tween

Tre

es

(m)

Figure 4.8: The relationship between the dependent variable crown width (m) and the factors DBH (m) and planting density (st/ha). The predicted value of crown width (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured. The mean growth space between trees is shown as a horizontal line for each planting density.

The results for crown width showed no significant difference between planting densities 250

to 1,000 st/ha or 5,000 to 10,000 st/ha. This was unexpected for 250 to 1,000 st/ha since trees

in 250 st/ha had twice the mean growth space compared to trees in 1,000 st/ha (Figure 4.8),

yet all trees in planting density 250 st/ha used less space than the mean growth space available

such that canopy closure had not occurred. This suggested that branch shed would be poor in

250 st/ha as more light would filter into the lower canopy, and it also showed that crown

width in 1,000 st/ha was not significantly restricted compared to relatively free-growing trees.

In high planting densities horizontal crown expansion was probably restricted by increased

competitive pressure. The overlapping of crowns in the high planting densities, shown by the

width of most crowns in 5,000 to 10,000 st/ha exceeding the mean growth space (Figure 4.8),

indicates that crowns were crowded. Crown overlapping in eucalypts is usually due to crowns


growing directly under or over neighbouring crowns rather than intertwining, although some

intertwining is not uncommon in young, dense stands (Jacobs 1955; Florence 1996).

CROWN LEAF AREA

For a given stem diameter, the leaf area of the crown decreased as planting density increased.

Within planting densities, crown leaf area had a positive relationship with stem diameter

(Table 4.8) (Figure 4.9).

Table 4.8: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of crown leaf area (m

2).


INTERCEPT 4.016 0.359 p < 0.001

lnDBH 1.844 0.133 p < 0.001

lnP -0.247 0.042 p < 0.001

0.0

0.5

1.0

1.5

0.0 0.1 0.2 0.3DBH (m)

Cro

wn

Le

af

Are

a (

m2)


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

Figure 4.9: The relationship between the dependent variable crown leaf area (m

2) and the factors DBH

(m) and planting density (st/ha). The predicted value of crown leaf area (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured.

The results for crown leaf area were similar to crown width, except that the slope of the

relationship between stem diameter and crown size was more convex (exhibits greater

upwards curvature) for crown leaf area than for crown width. This suggests that increased

crown width provided a fairly constant increase in growth potential, whereas increased crown


leaf area provided a diminishing increase in growth potential. The result probably reflects

changes in saturated light capture, whereby increased crown width was more likely to

increase saturated light capture than increased crown leaf area.

CROWN MASS

For a given stem diameter, crown leaf and branch oven-dry mass per tree decreased as

planting density increased. Within planting densities, crown leaf and branch oven-dry mass

had a positive relationship with stem diameter (Table 4.9) (Figure 4.10).

Table 4.9: The fixed-effect regression coefficients in the random slope model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on the natural logarithm of (a) crown leaf oven-dry mass (kg) and (b) crown branch oven-dry mass (kg).



lnDBH 1.457 0.568 p = 0.010 lnDBH 2.789 0.130 p < 0.001

lnP 0.182 0.141 p = 0.197 lnP -0.339 0.054 p < 0.001

INTERACTION COEFFICIENT S.E. P - VALUE

lnDBH*lnP 0.166 0.072 p = 0.021

0

10

20

30

40

0.0 0.1 0.2 0.3DBH (m)

Cro

wn

Ov

en

-Dry

Ma

ss

(k

g)


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

0

20

40

60

80

100

120

0.0 0.1 0.2 0.3DBH (m)

Cro

wn

Ove

n-D

ry M

ass

(kg

)

(a) Crown Leaf Oven-Dry Mass (b) Crown Branch Oven-Dry Mass

Figure 4.10: The relationship between the dependent variables crown leaf and branch oven-dry mass (kg) and the factors DBH (m) and planting density (st/ha). The predicted values of (a) crown leaf oven-dry mass and (b) crown branch oven-dry mass (with 95% confidence intervals) are plotted against DBH and identified by planting density for each tree measured.


A comparison between crown leaf and branch oven-dry mass (Figure 4.10) shows that as

planting density increased for a given stem diameter, the relative decrease in branch mass was

greater than the relative decrease in leaf mass, so that increased planting density resulted in

less branch mass per unit of leaf mass for a given stem diameter. This was probably due to

decreased crown width (shorter branches) in higher planting densities (Figure 4.8). The

convex shape of the positive slope between stem diameter and crown mass (Figure 4.10)

again suggested diminishing stem growth returns to increased crown size.

In summary, the tree growth data showed that in all cases stem diameter had a positive

relationship with tree growth variables, and this was expected since trees with greater

competitive status (stem diameter) generally have greater growth in all tree components.

Increased planting density appeared to alter the relationship between crown size and stem

growth, so that trees in high planting densities appeared to exhibit greater stem growth returns

by producing more stem biomass per unit of crown biomass.

4.4.2 Tree Structure

TREE FORM

Stem and crown form were estimated using form factor, where decreased form factor was

indicative of increased conicity. For a given stem diameter, stem form factor was unaffected

by increased planting density and crown form factor increased as planting density increased.

Within planting densities, stem form factor had a negative relationship with stem diameter

and crown form factor was unaffected by stem diameter (Table 4.10) (Figure 4.11).

Table 4.10: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) stem form factor and (b) the arcsine of crown form factor.


INTERCEPT 0.702 0.024 p < 0.001 INTERCEPT 0.261 0.086 p = 0.002

√DBH -0.703 0.062 p < 0.001 lnP 0.032 0.011 p = 0.003

Past evidence has shown that tree stems requiring increased mechanical support (due to

increased tree size or reduced shelter from wind sway) achieve support through developing a

conical stem shape (Jacobs 1955; Valinger 1992; Osler et al. 1996). These results confirmed

the positive relationship between increased stem size and a more conical stem shape (reduced

form factor) (Figure 4.11(a)). In contrast, increased planting density (increased shelter) had no


effect on stem form (p = 0.95), suggesting that either (i) there was no difference in shelter

between planting densities (high planting density plots may have provided shelter to

neighbouring low planting density plots), (ii) ambient wind levels were low, so shelter had

little effect on stem form, and/or (iii) wind sway did not affect stem form in young E. grandis.

0.2

0.3

0.4

0.5

0.6

0.7

0.0 0.1 0.2 0.3DBH (m)

Fo

rm F

ac

tor

(a) Stem

Conoid

Quadratic

Paraboloid

Cubic

Paraboloid

0.0 0.1 0.2 0.3DBH (m)

(b) Crown

Raw Data All Planting Densities

All Planting Densities 95% CI


250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI

Neiloid

Figure 4.11: The relationships between the dependent variables stem form factor and crown form factor and the factors DBH (m) and planting density (st/ha). The predicted values of (a) stem form factor and (b) crown form factor (with 95% confidence intervals) are plotted against DBH and identified by planting density (where applicable) for each tree measured.

Crowns in fast-growing young eucalypts are generally conical (Jacobs 1955; Florence 1996),

but increased competition can result in the foliage distribution being skewed upwards

(Medhurst and Beadle 2001; Pinkard and Neilsen 2003) and the extent of upward skew is

greater for suppressed individuals than dominant individuals (Pinkard and Neilsen 2003). The

above results confirm that increased competition resulted in foliage being skewed upwards

(less conical crown shape) (Figure 4.11(b)), however the extent of upward skew was not

greater for suppressed individuals since DBH did not effect crown form factor (p = 0.51). The

crown form factors of all planting densities were above the form factor representing a conoid

shape, even for crowns in 250 st/ha which are essentially ‘free-growing’ trees experiencing

very little competition. This result shows that the assumption of a conoid shape in young

eucalyptus crowns is not correct for E. grandis.


BARK RATIO

Bark ratio is the proportion of the stem comprised of bark. For a given stem diameter, bark

ratio by volume and by oven-dry mass decreased as planting density increased. Within

planting densities, bark ratio by volume and by oven-dry mass had a negative relationship

with stem diameter (Table 4.11) (Figure 4.12).

Table 4.11: The fixed-effect regression coefficients of the random slope model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) bark ratio by volume (m

3 m

-3) and (b) bark

ratio by oven-dry mass (kg kg-1

).



lnP -0.0160 0.0030 p < 0.001 lnP -0.0170 0.0020 p < 0.001

lnDBH -0.0290 0.0120 p = 0.016 DBH -0.2290 0.0550 p < 0.001

0.0

0.1

0.2

0.3

0.0 0.1 0.2 0.3DBH (m)

Bark

Rati

o

(a) By Volume

0.0 0.1 0.2 0.3DBH (m)

(b) By Oven-Dry Mass


250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI

Raw Data

Figure 4.12: The relationship between the dependent variables bark ratio by volume and bark ratio by oven-dry mass and the factors DBH (m) and planting density (st/ha). The predicted value of (a) bark ratio by volume and (b) bark ratio by oven-dry mass (with 95% confidence intervals) are plotted against DBH and identified by planting density for each tree measured.

The negative effect of planting density on bark ratio was similar to the negative effect of

planting density on crown leaf area and mass (Figures 4.9, 4.10). This suggests a positive

relationship between crown size and bark ratio, which is possible given that the crown

produces photosynthate and bark is the avenue by which photosynthate is translocated down


the stem. The negative correlation between stem diameter and bark ratio was expected since

larger stems are known to have a lower proportion of bark due to bark shed (Schonau and

Boden 1982; Negi et al. 1984).

BRANCH FORMATION

Branch formation is the number of branches originally formed on the stem. For a given stem

diameter, branch formation (between 0 and 6 m stem height) was unaffected by increased

planting density (p = 0.062). Within planting densities, branch formation had a positive


Table 4.12: The fixed-effect regression coefficients in the Poisson model of the effect of stem diameter (DBH) (m) on the natural logarithm of branch formation (between 0 and 6 m stem height) (count).


INTERCEPT 4.296 0.020 p < 0.001

DBH2 2.357 0.615 p < 0.001

60

70

80

90

100

0.00 0.05 0.10 0.15 0.20

DBH (m)

Branch Number from 0-6 m Stem Height (count)

Raw Data All Planting Densities All Planting Densities 95% C.I.

Figure 4.13: The relationship between the dependent variable branch formation (Branch Number from 0-6 m Stem Height) (count) and the factor DBH (m). The predicted value of branch formation (with 95% confidence intervals) is plotted against DBH for each tree measured.

For practical purposes the positive correlation between stem diameter and branch formation

was small as there was only a 7% increase in branch formation from the smallest to the largest

tree measured compared to a 375% increase in stem diameter. Even so, the relationship is


feasible since a genetic trait for a greater number of branches could result in a larger crown

and greater growth. Alternatively, fewer branches in smaller trees could be due to

suppression, in which case all trees would have the same branch formation at the base of the

stem, correlating to a period when tree size was relatively equal, but suppressed trees would

have fewer branches at higher points up the stem, correlating to the onset of competition.

An investigation was therefore made of how branch formation changes with stem height. The

0 to 6 m stem section was divided into twelve 0.5 m stem sections, and a comparison was

made of the number of branches formed in each stem section (Figure 4.14). Examination of

the raw data showed little evidence that small trees distributed their branches differently from

large trees; therefore the number of branches formed does not appear to be affected by

competition. Given the strong positive relationship between stem diameter and crown size

(Figure 4.8, 4.9, 4.10) and evidence that increased stem diameter resulted in only a small

increase in branch formation (Figure 4.13), it is apparent that crown size is primarily

dependent on what happens to branches subsequent to formation (such as the extent of size

growth and/or branch shed) rather than the number of branches formed.

0

5

10

15

20

25

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

0.5 m Stem Sections

Branch Number per 0.5 m Stem Section (count)

250 st/ha DBH: 0.191 m 1,000 st/ha DBH: 0.168 m 5,000 st/ha DBH: 0.120 m 10,000 st/ha DBH: 0.130 m

250 st/ha DBH: 0.103 m 1,000 st/ha DBH: 0.071 m 5,000 st/ha DBH: 0.077 m 10,000 st/ha DBH: 0.038 m

DBH

DBH

DBH

DBH

DBHDBH

DBHDBH

Figure 4.14: The measured branch formation (Branch Number per 0.5 m Stem Section (count) from 0 to 6 m stem height of the largest and smallest select tree from each planting density. Each tree is identified by planting density, and the stem diameter (DBH) is shown in legend. The data of each tree are slightly offset so that data points are less obscured by overlapping.


BRANCH SHED

For a given stem diameter, crown height (the height to which branches have shed) increased

as planting density increased at 3 and 4 years old. Within planting densities, crown height had

a positive relationship with stem diameter (Table 4.13) (Figure 4.15).

Table 4.13: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on the natural logarithm of crown height (m).


INTERCEPT -11.418 1.605 p < 0.001 lnP*A -0.350 0.082 p < 0.001

DBH 2.478 0.652 p < 0.001

lnP 1.396 0.209 p < 0.001

A 3.517 0.628 p < 0.001

0

5

10

15

20

0.0 0.1 0.2 0.3

DBH (m)

Cro

wn

He

igh

t (m

)

(a) 3 years

0.0 0.1 0.2 0.3

DBH (m)

(b) 4 years


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

Figure 4.15: The relationship between the dependent variable crown height (m) and the factors DBH (m), planting density (st/ha) and age (yrs). The predicted value of crown height (with 95% confidence intervals) at ages (a) 3 years and (b) 4 years is plotted against DBH and identified by planting density for each tree measured.

Branch shed is usually thought to be driven by light competition, whereby trees shed branches

below the light compensation point resulting in a uniform crown height (branch shed) within

the stand since the stand is subject to relatively uniform ambient light conditions in the lower

canopy. Branch shed may be hastened, however, by competition for water and/or nutrients,


whereby trees have insufficient resources to increase the upper crown (essential for continued

access to saturated light) and also maintain the lower crown, and therefore the lowest

branches are shed in order to re-allocate resources to higher branches. In the latter case it is

possible that dominant and suppressed trees will develop different branch height due to

different relative resource capture.

The results for crown height show that increased competition resulted in increased branch

shed. The large change in crown height between planting densities for a given stem diameter

(similar resource capture) (Figure 4.15) suggests that light competition was the factor most

affecting branch shed. There was evidence, however, that branch shed was in part hastened by

competition for water and/or nutrients since increased dominance status (stem diameter)

resulted in increased branch shed, and the positive relationship between dominance status and

branch shed became stronger as stand competition (planting density) increased (Figure 4.15).

Age had a positive relationship with branch shed (Table 4.13) (Figure 4.15). This was

expected as branch shed is an ongoing process, and the increase in crown height over time is

indicative of the responsiveness of the crown to growing conditions.

A second parameter relevant to branch shed is the crown depth ratio, which is the ratio of

crown depth (vertical space occupied by the crown) to stem height. It is often used as a

measure of relative crown retention and tree vigour (Hughes 2000) and it is indicative of

dominance status within the stand (Stoneman and Whitford 1995; Medhurst et al. 1999). For a

given stem diameter, crown depth ratio was found to decrease as planting density increased.

Within planting densities, crown depth ratio had a positive relationship with stem diameter

(Table 4.14) (Figure 4.16).

Table 4.14: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and age (A) (yrs) on the arcsine of crown depth ratio.


INTERCEPT 4.481 0.544 p < 0.001 lnP*A 0.079 0.027 p = 0.003

lnDBH 0.158 0.030 p < 0.001

lnP -0.362 0.070 p < 0.001

A -0.907 0.210 p < 0.001


0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3

DBH (m)

Cro

wn

Dep

th R

ati

o

(a) 3 years

0.0 0.1 0.2 0.3

DBH (m)

(b) 4 years


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

Figure 4.16: The relationship between the dependent variable crown depth ratio and the factors DBH (m), planting density (st/ha), and age (yrs). The predicted value of crown depth ratio (with 95% confidence intervals) at ages (a) 3 years and (b) 4 years is plotted against DBH and identified by planting density for each tree measured.

The results for crown depth ratio show that increased competition resulted in decreased crown

depth ratio. It appears, however, that crown depth ratio was not necessarily a good indication

of tree vigour, since for a given stem diameter the higher planting densities had a lower crown

depth ratio (less crown retention) yet greater stem volume and mass (Figures 4.6, 4.7). Within

planting densities, crown depth ratio was a good indicator of vigour and dominance status,

since it shares a positive relationship with stem diameter (Figure 4.16). This finding

corroborates evidence that dominant trees exhibit a greater crown depth ratio than suppressed

trees (Stoneman and Whitford 1995; Medhurst et al. 1999).

Age was shown to have a negative relationship with crown depth ratio (Table 4.14) (Figure

4.16), indicating that increases in branch shed were greater than increases in stem height

between 3 and 4 years, since there was a decrease in relative crown retention over that time.

This cannot happen indefinitely since it would result in trees with no crown, however it is

likely to happen in times of stress, and the incidence of this occurring from 3 to 4 years was

likely to be related to the drought that occurred over this period of time (Figure 2.5).


LEAF SPECIFIC AREA

Leaf specific area is the ratio of leaf area to leaf oven-dry mass, and is a measure of leaf

‘thinness’. Reduced leaf specific area indicates ‘thicker’ leaves; a growth response of leaves

forming under greater exposure to desiccating factors like extreme temperatures and low

humidity (Specht 1985; Salisbury and Ross 1992; Specht and Specht 1999). For a given stem

diameter, leaf specific area decreased as planting density increased. Within planting densities,

leaf specific area had a positive relationship with stem diameter (Table 4.15) (Figure 4.17).

Table 4.15: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and crown location (CL) on leaf specific area (cm

2g

-1).


INTERCEPT -0.065 0.024 p = 0.006 lnDBH*CL 0.019 0.006 p = 0.001

lnDBH -0.078 0.010 p < 0.001

lnP -0.006 0.002 p = 0.003

CL 0.038 0.012 p = 0.001

0.0

0.4

0.8

1.2

0.0 0.1 0.2 0.3

Le

af

Sp

ec

ific

Are

a (

cm

2 g

-1)

(a) Crown Location - Upper

0.0 0.1 0.2 0.3DBH (m)

(b) Crown Location - Middle


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

DBH (m)

Figure 4.17: The relationship between the dependent variable leaf specific area (cm2 g

-1) and the

factors DBH (m), planting density (st/ha) and crown location. The predicted value of leaf specific area (with 95% confidence intervals) at crown locations (a) upper and (b) middle is plotted against DBH and identified by planting density for each tree measured.


Within species, the greatest influence on leaf specific area is the exposure of leaves to

desiccating elements during formation. As leaves mostly form in the upper crown (noting that

leaves in the middle crown formed in a past upper crown), the location of the upper crown

within the whole canopy determines the exposure of developing leaves. Increased stem

diameter was therefore expected to correlate with decreased leaf specific area due to increased

exposure of the upper crown as a result of increased stem height (Figure 4.5). The results for

leaf specific area confirmed that this was the case since stem diameter was shown to share a

strong negative relationship with leaf specific area that was significant within all planting

densities, as there was no overlap of the 95% confidence intervals between the largest and

smallest DBH within each planting density (Figure 4.17). Clearly the level of exposure of

leaves during formation did have a strong negative influence on leaf specific area.

In comparing between planting densities it was apparent that there was no significant

difference in leaf specific area between the largest trees in each planting density, as indicated

by overlap of the 95% confidence intervals (Figure 4.17). Given that there was also no

significant difference in stem height between the largest trees in each planting density (Figure

4.5), then the above result was expected since the leaves of the largest trees in each planting

density all formed at the highest level of exposure in the upper canopy. Also apparent was that

for a given stem diameter increased planting density resulted in decreased leaf specific area.

This was unexpected since greater shading (less exposure) in high planting densities was

thought to result in higher leaf specific area, however if stem height is again considered

(Figure 4.5) it is apparent that higher planting densities had taller stems for a given stem

diameter, and may therefore be expected to develop lower leaf specific area for a given stem

diameter due to greater exposure for a given stem diameter.

The results for leaf specific area reflect the history of canopy development. We can see that

the leaf specific area of the largest trees was lowest, indicating that their upper crowns formed

in the highest exposure in the upper canopy, and that the leaf specific area of the largest trees

remained constant from the middle to the upper crown, indicating the constant presence of

their upper crowns in the upper canopy. In comparison the leaf specific area of the smallest

trees was highest, indicating that their upper crowns formed in lower exposure within the

canopy, and the leaf specific area of the smallest trees increased from the middle to the upper

crown, indicating that their upper crowns have been subject to progressively less exposure

(more shading) as crowns developed and the canopy became more crowded.


LEAF NUTRIENT CONTENT

Leaf nutrient content was measured as a percentage of leaf dry mass. For a given stem

diameter, the leaf nitrogen content decreased as planting density increased, whereas leaf

phosphorus content and leaf potassium content were unaffected by planting density. All leaf

nutrient contents had a negative relationship with stem diameter (Table 4.16) (Figure 4.18).

Table 4.16: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and crown location (CL) on (a) leaf nitrogen content (%), (b) leaf phosphorus content (mg kg

-1) and (c) leaf potassium content (mg kg

-1).



√DBH -3.047 0.674 p < 0.001 √DBH -3548.535 1108.661 p = 0.001

P -0.000033 0.000014 p = 0.018

CL -0.265 0.076 p < 0.001

(c) VARIABLE COEFFICIENT S.E. P - VALUE

INTERCEPT 5557.280 560.232 p < 0.001

√DBH 176.587 47.465 p < 0.001

1.0

1.5

2.0

2.5

3.0

0.0 0.1 0.2 0.3

Leaf

Nu

trie

nt

Co

nte

nt

(%)

(a) Nitrogen

Crown Location - Upper

1.0

1.5

2.0

2.5

3.0

0.0 0.1 0.2 0.3

DBH (m)

(b) Nitrogen

Crown Location - Middle


250 95% CI 1,000 95% CI 5,000 95% CI 10,000 95% CI

Raw Data

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.1 0.2 0.3

DBH (m)

(d) Potassium

Crown Location - Whole

0.0

0.2

0.4

0.6

0.8

1.0

1.2

0.0 0.1 0.2 0.3

DBH (m)

(c) Phosphorus

Crown Location - Whole

All Planting Densities

All Planting Densities 95% CI

DBH (m)

Figure 4.18: The relationship between the dependent variables leaf nitrogen, phosphorus and potassium content (% dry mass) and the factors DBH (m), planting density (st/ha) and crown location. The predicted values of (a) leaf nitrogen content in the upper crown, (b) leaf nitrogen content in the middle crown, (c) leaf phosphorus content in the whole crown, and (d) leaf potassium content in the whole crown (with 95% confidence intervals) are plotted against DBH and identified by planting density where applicable.


Neither planting density nor stem diameter were expected to affect leaf nutrient content since

it was thought that trees would accommodate nutrient shortages by reducing total leaf mass or

leaf specific area. The results for leaf nutrient content disprove this hypothesis since both

planting density and stem diameter were found to affect leaf nutrient content.

The results show that increased planting density correlated with decreased leaf nitrogen

content, but apparently not phosphorus or potassium content. Possibly nitrogen was the

nutrient in greatest demand (shortest supply) relative to the available nutrient supply, and

therefore limitations in nitrogen supply became apparent before limitations in other nutrients,

and were most apparent in the higher density stands that were likely to use more nutrients.

Leaf nitrogen content in the spacing trial was within previously reported ranges (Table 4.17),

but generally below the optimum level of 2.8%. Leaf phosphorus and potassium content were

also within previously reported ranges, but approximated the optimum levels of 0.15% and

0.75% respectively, rather than below. This comparison confirms that nitrogen was the

nutrient in greatest relative demand (shortest relative supply) since leaf nitrogen content was

generally low, especially in higher planting densities. Furthermore, leaf nitrogen content was

greater in the upper crown than the middle crown (Figure 4.18), indicating that nitrogen was

being mobilised from the middle to the upper crown due to excess demand for nitrogen.

Table 4.17: Published leaf nutrient contents arising from studies on E. grandis plantations compared to results for the current study.

Leaf Nutrient Content (%) Study Details

Nitrogen Phosphorus Potassium

Typical range in young E. grandis plantations (Judd et al. 1996).

1.25 - 2.75 0.075 - 0.200 0.60 - 1.15

Mean content in 9.25 year old E. grandis in NSW, Australia (Birk and Turner 1992)

1.56 0.084 0.60

Encountered range in E. grandis plantations in South Africa (Herbert 1992).

1.25 - 3.35 0.100 - 0.350 0.36 - 1.19

Optimum content in E. grandis plantations in South Africa (Herbert 1992).

2.80 0.150 0.75

Current Study 1.4 - 2.4 0.12 – 0.20 0.65 - 0.95

The negative relationship between stem diameter and leaf nutrient content (Figure 4.18) was

similar to the negative relationship between stem diameter and leaf specific area (Figure

4.17), indicating that shaded leaves had increased leaf specific area and increased leaf nutrient

content. This correlation has been observed previously (Cromer and Jarvis 1990; Stewart et

al. 1990; Kirschbaum et al. 1992), and it is typically attributed to more photosynthetically-


active components per unit of leaf area (Salisbury and Ross 1992; Kriedemann and Cromer

1996). In shaded leaves this maximises light use-efficiency by allowing use of all the limited

light incidentally hitting the leaf (Salisbury and Ross 1992), however it probably results in a

reduction in nutrient use-efficiency due to high nutrient requirements. In consequence,

smaller, more shaded trees appear to maximise light use-efficiency at the expense of reduced

nutrient use-efficiency, and conversely it is possible that larger, less shaded trees maximise

water and/or nutrient use-efficiency at the expense of reduced light use-efficiency.

TREE MASS RATIOS

Tree mass ratios are the oven-dry mass of the stem, branch and leaf components of the tree

relative to tree oven-dry mass. For a given stem diameter, stem mass ratio was found to

increase as planting density increased, whereas branch and leaf mass ratios were found to

decrease as planting density increased. Within planting densities, stem mass ratio had a

negative relationship with stem diameter, whereas branch and leaf mass ratios had a positive


Table 4.18: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) stem to tree mass ratio, (b) branch to tree mass ratio and (c) leaf to tree mass ratio.


INTERCEPT 0.1350 0.0710 p = 0.057 INTERCEPT 0.6106 0.0553 p < 0.001

lnDBH -0.0640 0.0160 p < 0.001 lnDBH 0.0303 0.0150 p = 0.043

lnP 0.0640 0.0090 p < 0.001 lnP -0.0521 0.0073 p < 0.001


INTERCEPT 0.2429 0.0220 p < 0.001

lnDBH 0.0327 0.0061 p < 0.001

lnP -0.0110 0.0029 p < 0.001

The results for tree mass ratios indicate that increased planting density resulted in tree mass

ratios skewed towards the stem rather than the crown (Figure 4.19). The results further

indicate that skewness of tree structure towards the stem was greater for smaller trees, since

stem diameter had a negative relationship with stem mass ratio (Figure 4.19(a)) and a positive

relationship with branch and leaf mass ratio (Figure 4.19(b-c)). Overall, the greater the level

of competition pressure experienced by individual stems, at the general stand level (increased

planting density) and within the stand (decreased stem diameter), the greater the ratio of tree

mass was contained in the stem rather than in the branches or leaves.


0.5

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3

DBH (m)

Mas

s R

ati

o (

kg

kg

-1)

(a) Stem to Tree

0.0

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3

DBH (m)

(b) Branch to Tree

0.0 0.1 0.2 0.3

DBH (m)

(c) Leaf to Tree


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

Mas

s R

ati

o (

kg

kg

-1)

Figure 4.19: The relationship between the dependent variables stem, branch and leaf mass ratio and the factors DBH (m) and planting density (st/ha). The predicted value of the ratio of (a) stem, (b) branch and (c) leaf mass to tree mass (with 95% confidence intervals) is plotted against DBH and identified by planting density for each tree measured.

The above findings raise the question of growth efficiency; the structural comparison of the

total amount of resources captured, as approximated by leaf area, to the amount of growth

produced. In terms of stem growth efficiency (the efficiency of producing a marketable

product), it appears that trees experiencing greater competitive pressure (high planting

density/low stem diameter) were more efficient since they produced the greatest relative stem

mass (Figure 4.19(a)) from the smallest relative crown size (Figure 4.19(b-c)). This logic,

however, is not in concert with previous findings that stem cohorts experiencing greater

competitive pressure (high planting density/low stem volume increment) were decreasing in

productivity (Figure 3.11). Closer examination of growth efficiency will provide insight into

whether trees under the greatest competitive pressure (small, shaded trees) with declining

productivity, were in fact most growth efficient.

GROWTH EFFICIENCY

Growth efficiency compares the total amount of resource capture to the amount of growth

produced. Sophisticated methods for estimating light capture and water transpiration were not

at the disposal of the current study, and crown leaf area was therefore used as rough measure

of the potential light capture and water transpiration of individual trees. The growth


efficiencies of tree mass, stem mass and stem volume were calculated as the ratio of each

variable to crown leaf area. For a given stem diameter, growth efficiency was found to

increase as planting density increased. Within planting densities, growth efficiency had a

positive relationship with stem diameter (Table 4.19) (Figure 4.20).

Table 4.19: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m) and planting density (P) (st/ha) on (a) tree mass growth efficiency (kg m

-2), (b)

stem mass growth efficiency (kg m-2

) and (c) stem volume growth efficiency (m3 m

-2).


INTERCEPT 202.886 106.325 p = 0.056 INTERCEPT 22.295 93.672 p = 0.813

lnDBH 157.756 39.932 p < 0.001 lnDBH 110.178 33.894 p < 0.001

lnP 53.284 11.040 p < 0.001 lnP 57.028 9.738 p < 0.001


INTERCEPT -0.353 0.190 p = 0.062

1/DBH 0.022 0.009 p = 0.015

lnP 0.142 0.025 p < 0.001

0

200

400

600

0.0 0.1 0.2 0.3DBH (m)

(a) Tree Mass to Leaf Area

0.0

0.3

0.6

0.9

1.2

0.0 0.1 0.2 0.3DBH (m)

(c) Stem Volume to Leaf Area

0.0 0.1 0.2 0.3DBH (m)

(b) Stem Mass to Leaf Area


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw DataV

olu

me

pe

r U

nit

Le

af

Are

a (

m3 m

-2)

Mas

s p

er

Un

it L

eaf

Are

a (

kg

m-2

)

Figure 4.20: The relationship between the growth efficiency (mass or volume per unit leaf area) of the dependent variables tree mass, stem mass and stem volume, and the factors DBH (m) and planting density (st/ha). The predicted value of the growth efficiency of (a) tree mass, (b) stem mass and (c) stem volume (with 95% confidence intervals) are plotted against DBH and identified by planting density for each tree measured.


The results for tree growth efficiency indicate that planting density had no significant affect

on the tree growth efficiency of dominant trees since the 95% confidence intervals overlap

between the largest trees in all planting densities (Figure 4.20(a)). Within planting densities

increased dominance status (stem diameter) resulted in increased tree growth efficiency

(Figure 4.20(a)). It is noteworthy, however, that tree growth efficiency measurements do not

include the roots, and it is therefore expedient to consider whether the inclusion of root mass

would affect the above results.

Evidence from previous studies shows that the proportion of biomass allocated to roots

generally increases as stocking density decreases (Eastham and Rose 1990; Bargali et al.

1992; Fabiao et al. 1995; Bernardo et al. 1998; Leles et al. 2001; Saint-André et al. 2005)

(Figure 4.1). In consequence, the underestimation of tree mass caused by not including root

mass was likely to be greater for trees in low planting densities, and the difference in tree

mass growth efficiency between dominants in low and high planting densities would have

been reduced if root mass were included in the tree mass measurement. The finding that

planting density did not affect tree growth efficiency in dominant trees is therefore

corroborated by considering the inclusion of root mass in tree mass.

Within planting densities it is likely that competition for ground water is similar to

competition for light in that it is asymmetric; deeper roots having access to the most saturated

soil, and shallower roots having to make do with what remaining water is able to seep past

deeper roots. A previous study shows that roots extend to a greater soil depth in higher

planting densities (given no physical obstructions) (Eastham and Rose 1990), presumably

stimulated by increased water deficits closer to the surface as a result of greater competition

for water. Just as stem height extends higher for dominant trees, it is likely that root depth

extends lower, with the result that relative root mass is similar between dominant and

suppressed trees and tree growth efficiency would not be significantly affected by including

root mass in the tree mass measurement. The finding that increased dominance status (stem

diameter) resulted in increased tree growth efficiency is therefore corroborated by considering

the inclusion of root mass in tree mass.

The results for stem growth efficiency followed the same pattern to those for tree growth

efficiency; however significant differences occurred between dominant (largest) trees in high

and low planting densities (Figure 4.20(b)). Dominant trees in high planting densities had


greater stem growth efficiency since they allocated a higher proportion of tree mass to the

stem per unit of leaf area. This result was implied in the previous tree mass ratio results

(Figure 4.19) since dominant trees in all planting densities had similar relative leaf mass, yet

increased planting density resulted in a reduced proportion of mass allocated to branches and

an increased proportion of mass allocated to the stem in dominant trees. Overall it appears

that regardless of planting density, dominant trees had similar photosynthate production per

unit leaf area, as evidenced by similarities in leaf specific area (Figure 4.17) and leaf nutrient

content (Figure 4.18), and similar tree growth efficiency. Stem growth efficiency, however,

increased in dominant trees as planting density increased due to a change in growth portioning

strategy away from the branches and towards the stem.

The finding that dominant trees had greater stem growth efficiency than suppressed trees was

not in concert with tree mass ratio results since on a mass basis, suppressed trees had the

greatest stem mass per unit leaf mass (Figure 4.19). Given that growth efficiency is calculated

using leaf area, the likely explanation for this disparity is leaf morphology. Suppressed trees

were shown to have greater leaf specific area than dominant trees within planting densities

(Figure 4.17), indicating that leaf area has been maximised by ‘spreading’ a given leaf mass

over a larger area. So despite suppressed trees having the greatest stem mass per unit leaf

mass (Figure 4.19), the corresponding increase in leaf specific area is sufficient to cause

suppressed trees to exhibit reduced stem growth efficiency (stem mass per unit leaf area)

compared to dominant trees.

The results for stem volume growth efficiency (Figure 4.20(c)) follow a similar pattern to the

previous growth efficiency measurements; however there is a greater magnitude of difference

between dominant trees in different planting densities, suggesting that dominant trees in low

planting densities have greater wood density (i.e. less volume for a given mass). The issue of

wood density is pertinent to wood quality and is addressed in greater detail in Chapter 5 –

Wood Growth and Structure.

4.4.3 Implications for Stand Growth and Structure

Examination of stand structure in the previous chapter showed that the mean size of the top

(dominant) 250 and 1,000 stem cohorts were remarkably similar in size, despite very strong

competition occurring in the high planting densities. The results for tree growth and structure


showed that whilst dominant trees in high planting densities had greater stem growth

efficiency than dominant trees in low planting densities due to different carbon partitioning

strategies, they were in fact significantly smaller than dominant trees in low planting densities

despite similar tree growth efficiencies. The similarity in size of the top 250 and 1,000 stem

cohorts between planting densities was therefore not due to a similarity in the size of the very

largest trees, but rather due to high planting densities having modest size and greater size

uniformity in the top cohort (i.e. no smaller trees in the top cohort dragging the average size

of the cohort down).

Examination of the 250 stem cohorts in the previous chapter indicated that high planting

densities had a greater representation of dominant and co-dominant trees in the top four 250

stem cohorts (Figure 3.12). A closer examination of the top four 250 stem cohorts (top 1,000

stem cohort) shows that planting densities 5,000-10,000 st/ha had less size inequality in the

top 1,000 stem cohort than 1,000 st/ha (Figure 4.21), despite having greater size inequality in

the whole stand (Figure 3.9). This indicates that trees in the top 1,000 stem cohort in high

planting densities were of similar high dominance in the stand, whereas trees in the top 1,000

stem cohort in 1,000 st/ha included dominant, intermediate and suppressed trees. As a result

the mean stem volume in the dominant 1,000 stem cohort is similar between planting

densities, despite the most dominant trees in high planting densities being smaller than the

most dominant trees in low planting densities.

The above discussion shows that high planting densities exhibited more dominant and co-

dominant trees per unit area than low planting densities. It is possible that high planting

densities had greater site occupancy, and were therefore able to ‘fit’ more dominant and co-

dominant trees into the stand matrix. Alternatively high density stands may force trees to

perform to their maximum potential in order to avoid suppression and death. The fierce

competition for dominance from the seedling stage renders it unlikely that any trees would

‘dawdle’ in growth, hence there was greater uniformity amongst the top ranked stems.

Finally, it may simply have been the case that all planting densities had a similar proportion of

dominant genotypes present in the stand, and consequently high density stands had a greater

absolute number of trees with dominant genotypes present in the stand by virtue of having a

greater population size.


0.0

0.5

1.0

0.0 0.5 1.0

Cu

mu

lati

ve P

rop

ort

ion

of

Ste

m V

olu

me

in t

he T

op

1,0

00 s

t/h

a S

ize C

lass

Equality

1,000 st/ha

5,000 st/ha

10,000 st/ha

1,000 st/ha

GC = 0.707

CV = 53.9%

MSV = 0.105 m3

Mortality = 12.5%

TSV = 11.00 m3

Cumulative Proportion of Stem Count

in the Top 1,000 st/ha Size Class

5,000 st/ha

GC = 0.857

CV = 26.7%

MSV = 0.099 m3

Mortality = 0.0%

TSV = 17.88 m3

10,000 st/ha

GC = 0.859

CV = 27.1%

MSV = 0.093 m3

Mortality = 0.0%

TSV = 17.95 m3

Lorenze Curves

Figure 4.21: Lorenz curves and the gini-coefficient (GC), coefficient of variation (CV), mean stem volume (MSV), mortality and total stem volume (TSV) values for stem volume in the dominant 1,000 stem cohort of E. grandis at age 4 years for planting densities 1,000 st/ha, 5,000 st/ha, and 10,000 st/ha.

A separate finding in the previous examination of stand structure was that several of the

bottom 250 stem cohorts in planting densities 1,000-10,000 st/ha were declining in

productivity (Figure 3.11) despite that total and mean stand productivity were increasing

(Figures 3.4, 3.5). A separate study suggested that declining stand productivity could be due

to reduced growth efficiency in sub-dominant and suppressed trees (Binkley et al. 2002). The

results for tree growth and structure support this argument since they indicate that suppressed

trees had lower growth efficiency than dominant trees, possibly due to leaf morphology and

reduced nutrient use-efficiency during photosynthesis in more shaded leaves.

The evidence that increased dominance status resulted in increased growth efficiency is of

relevance to total resource use-efficiency and the debate on declining stand productivity. One

could argue that suppressed trees should have greater total resource use-efficiency given that

studies at the leaf level show that resource use-efficiency increases in response to resource

restrictions, and suppressed trees clearly experience resource restrictions. This theory,

however, implies that suppressed trees have the ability to resist decreasing growth rates by

being more efficient, whereas the analysis of stand growth implied that trees tend to move

down dominance classes and were unable to resist decreased growth rates once suppressed. It


therefore seems probable that dominant trees have superior total resource use-efficiency

leading to greater tree growth efficiency, and that declining stand productivity is in part

caused by reduced resource use-efficiency in suppressed trees rather than dominant trees. The

issue of total resource use-efficiency, which is essentially the balance between trading off

improved resource use-efficiency in one resource against reduced resource use-efficiency in

other resources, therefore appears to be of importance in understanding the mechanisms

causing suppression in trees and declining stand productivity.

If one regards the top 1,000 st/ha as the final solid wood crop, keeping in mind that it would

probably be harvested in one or two commercial thinning operations in addition to the final

harvest operation, then the results for tree growth and structure show that high planting

density could be a practical management tool. High planting density had the effect of

increasing size uniformity in the top 1,000 st/ha at the expense of a small reduction in mean

stem diameter. Furthermore high planting density provides an ‘insurance policy’ against

mortality, such that total stem volume in the top 1,000 st/ha is almost certainly increased due

to zero mortality. High planting densities also have the potential to provide a substantial

biomass harvest and financial return early in the life of the plantation, and possibly multiple

times if biomass harvest stems coppice. That there was no significant difference between

5,000 st/ha and 10,000 st/ha in every result indicates that at most a 5,000 st/ha planting

density would be dense enough to achieve the above results.

The potential gains of using high planting density as a management tool are worth pursuing

only if such management is not detrimental to the final product, which is usually solid wood

or wood fibre. Therefore it is appropriate to examine the effect of competition on wood

growth and structure before further considering high planting density as a management tool.


4.5 Summary

In comparing dominant trees between planting densities (Table 4.20), dominant trees in high

planting densities were significantly smaller than dominant trees in low planting densities in

all aspects of tree growth other than stem height. Tree structure variables indicate that

compared to dominant trees in low planting densities, dominant trees in high planting

densities had a larger proportion of biomass allocated to the stem rather than the crown but

similar rates of photosynthesis per unit leaf area, and consequently dominant trees in high

planting densities had better stem growth efficiency than dominant trees in low planting

densities. Dominant trees in high planting densities had similar tree growth efficiency to

dominant trees in low planting densities.

Table 4.20: A summary of the results for tree growth and structure comparing dominant trees in low (250-1,000 st/ha) and high (5,000-10,000 st/ha) planting densities.

Planting Density Result Location Stand Variable

Low High Figure Page

Tree Growth

Stem Diameter high low Figure 4.4 66

Stem Height similar similar Figure 4.5 67

Stem Volume high low Figure 4.6 68

Stem Mass high low Figure 4.7 70

Crown Width high low Figure 4.7 71

Crown Leaf Area high low Figure 4.9 72

Crown Mass - Leaf high low Figure 4.10 (a) 73

Crown Mass - Branch high low Figure 4.10 (b) 73

Tree Structure

Tree Form (cylindrical : conical) - Stem similar similar Figure 4.11 (a) 75

Tree Form (cylindrical : conical) - Crown low high Figure 4.11 (b) 75

Bark Ratio high low Figure 4.12 76

Branch Formation similar similar Figure 4.13 77

Branch Shed - Crown Height low high Figure 4.15 79

Branch Shed - Crown Depth Ratio high low Figure 4.16 81

Leaf Specific Area similar similar Figure 4.17 82

Leaf Nutrient Content similar similar Figure 4.18 84

Tree Mass Ratio - Stem : Tree low high Figure 4.19 (a) 87

Tree Mass Ratio - Branch : Tree high low Figure 4.19 (b) 87

Tree Mass Ratio - Leaf : Tree high low Figure 4.19 (c) 87

Growth Efficiency - Tree low high Figure 4.20 (a) 88

Growth Efficiency - Stem similar similar Figure 4.20 (b-c) 88

In comparing suppressed and dominant trees within planting densities (Table 4.35),

suppressed trees were significantly smaller than dominant trees in all aspects of tree growth.

Where differences between suppressed and dominant trees occurred in tree structure,


suppressed trees exhibited greater skewness of the crown towards the crown apex and mass

towards the stem, and these trends were usually stronger in high planting densities. Despite

greater stem mass per unit leaf mass, suppressed trees exhibited lower stem growth efficiency

than dominant trees, probably due to greater leaf specific area. Suppressed trees exhibited

lower tree growth efficiency than dominant trees, possibly due to the shaded leaves of

suppressed trees having lower total resource use-efficiency.

Table 4.21: A summary of the results for tree growth and structure comparing suppressed and dominant trees (as determined by small and large stem diameter) within planting densities. Planting densities are marked with a � when the trend between suppressed and dominant trees was significant (95% confidence intervals overlap), and a � when the trend between suppressed and dominant trees was insignificant (95% confidence intervals do not overlap).

Dominance Status Significance within

Planting Density (st/ha) Stand Variable

Suppressed Dominant 250 1,000 5,000 10,000

Tree Growth

Stem Height less more � � � �

Stem Volume less more � � � �

Stem Mass less more � � � �

Crown Width less more � � � �

Crown Leaf Area less more � � � �

Crown Mass - Leaf less more � � � �

Crown Mass - Branch less more � � � �

Tree Structure Tree Form (cylindrical : conical) - Stem more less � � � �

Tree Form (cylindrical : conical) - Crown same same � � � �

Bark Ratio - By Volume more less � � � �

Bark Ratio - By Mass more less � � � �

Branch Formation less more � � � �

Branch Shed - Crown Height less more � � � �

Branch Shed - Crown Depth Ratio less more � � � �

Leaf Specific Area more less � � � �

Leaf Nutrient Content more less � � � �

Tree Mass Ratio - Stem : Tree more less � � � �

Tree Mass Ratio - Branch : Tree less more � � � �

Tree Mass Ratio - Leaf : Tree less more � � � �

Growth Efficiency - Tree less more � � � �

Growth Efficiency - Stem less more � � � �

Overall the results for tree growth and structure show that increased competition had the

effect of reducing tree size and skewing mass distribution towards the stem. The major

strategy for mitigating the effects of competition appeared to be to shed the least efficient

mass, which in the crown was the lower leaves and branches, and in the stem was butt-swell

(thickening at the base of the stem causing a more conical stem shape), and in the roots could


possibly be auxiliary tap roots. Growth could then be concentrated into maximising the

chance of improving resource capture by increasing stem height and probably increasing tap

root depth. It is likely that in young, competitive stands stem diameter only increases insofar

as to provide the minimum support requirement for the crown.

In terms of stand structure, the similarity in mean stem diameter in the top (dominant) 250 and

1,000 stem cohorts between planting densities was due to a combination of better stem growth

efficiency in dominant trees in high planting densities, and a greater number of dominant

and/or co-dominant trees in the dominant 1,000 stem cohort in high density stands. Within the

dominant 1,000 stem cohort, high planting densities exhibited low size inequality and

therefore similar dominance between trees, whereas low planting densities exhibited greater

size inequality and therefore variable dominance between trees, providing evidence that high

planting densities had more dominant and co-dominant trees per unit area than low planting

densities. This resulted in comparable stand mean stem volume in the top (dominant) 250 and

1,000 stem cohorts between planting densities, and greater total stem volume in the dominant

1,000 stem cohort in high planting densities due to zero mortality compared to low planting

densities.

The results for tree growth and structure support the argument that declining stand

productivity could be due to reduced resource use efficiency in co-dominant and suppressed

trees. The lower tree growth efficiency exhibited by suppressed trees may be due to a reduced

resource use-efficiency balance during photosynthesis in shaded leaves.

The results for tree growth and structure indicated that high planting density could be a

practical management tool since in the top 1,000 st/ha it increased size uniformity, eliminated

mortality and increased total stem volume, despite a reduction in mean stem diameter. The

use of high planting density as a management tool is worth pursuing only if not detrimental to

the final wood product. Therefore it is appropriate to examine the effect of competition on

wood growth and structure before considering the use of high planting density as a

management tool.

Chapter 5 Wood Growth and Structure Page 97

5. WOOD GROWTH AND STRUCTURE

Wood growth and structure is an important aspect of plantation management as wood

products are the primary produce currently emerging from hardwood plantations in Australia

(Turner et al. 2004). The vast majority of hardwood plantations in Australia are fast-growing

eucalyptus plantations that are managed for pulpwood production (Turner et al. 2004), and the

majority of research on eucalyptus plantations has historically focused on improving growth

in order to maximise pulpwood production, with less effort expended in research on the

potential of hardwood eucalyptus plantations to produce solid clearwood products.

In concert with the development of the pulpwood industry, there has been building pressure to

increase plantation production of solid hardwood products given that Australia has a trade

deficit in sawnwood and is decreasing production of native hardwood sawnwood due to

increased conservation of native forests (Turner et al. 2004). The development of a solid

hardwood plantation industry, however, is hampered by a lack of knowledge and experience

in growing eucalypts for sawlogs or veneer logs. The potential for current eucalyptus

pulpwood plantations to produce sawnwood products has been recognised, but is generally

discounted since highly stocked hardwood pulpwood plantations are considered unlikely to

produce quality sawlogs (Turner et al. 2004). Within this environment there is impetus for

increased research into the effect of plantation growth on wood structure, and whether high

stocking rates are detrimental to wood quality.


5.1.1 Wood Growth

In all tree species, wood is a heterogenous substance composed of cells originating from a

thin outer layer called the vascular cambium, which forms an uninterrupted cone around the

stemwood surface (Jane 1970; Wilson and White 1986; Zobel and Buitjtenen 1989). The

vascular cambium consists of fusiform initials and ray initials, collectively known as cambial

initials (Jane 1970; Wilson and White 1986).

Cambial initials have thin primary walls, and are joined to adjacent cells by a thin layer called

the middle lamella (Jane 1970; Wilson and White 1986). Wood growth begins with the

division of cambial initials to form two ‘daughter’ cells (Figure 5.1(a)), whereby the

outermost cell remains a cambial initial and the innermost cell differentiates to form one of


the many cell types found in the wood (Jane 1970; Wilson and White 1986). The mechanism

determining what cell type each initial forms is unknown, however it is thought that the

proximity of other cell types may have some influence. It is known that plant hormones like

auxins trigger the process of differentiation, however it is not known whether they cause the

initial division of cambial initials, or whether cambial initials divide regardless and hormones

cause the differentiation of daughter cells (McCann 1997). Once differentiation has been

initiated hormones are not thought to be required to maintain the process (Stacey et al. 1995).

Figure 5.1: The process of wood growth including (a) the division of cambial initials, (b) cell elongation and expansion, (c) deposition of the secondary wall and (d) lignin impregnation.

The process of differentiation from cambial initial to wood cell has three stages (Wardrop

1965). The first stage is a change in cell shape and an increase in cell size from the cambial

initial (Figure 5.1(b)) (Wardrop 1965; Jane 1970). These changes are possible as the primary

wall and middle lamella of cambial initials are extensible, allowing the cell to grow (Jane

1970). The growth of cells during differentiation is thought to be driven by turgor pressure

within the tree; whereby filaments in primary walls are stretched like a spring by turgor

pressure, and additional materials are laid down to fill in the gaps. In this way cells are able to

grow faster when there is greater water availability due to a greater turgor pressure (amongst

other factors) (Salisbury and Ross 1992). The growth of cells causes them to push against

other cells, and with no room to move inwards due to existing wood, growing cells expand

outwards creating diameter growth in the stem (Wilson and White 1986).

The second stage of differentiation is the deposition of a secondary wall onto the primary

wall, which may begin in the middle of the cell before the primary wall has finished (Jane

1970) expanding at the tips (Figure 5.1(c)) (Wardrop 1965; Jane 1970; Panshin and De Zeeuw

(a) (b) (c) (d)


1980; Wilson and White 1986). The secondary wall consists of three layers, which together

are much thicker than the primary wall (Jane 1970; Wilson and White 1986). Unlike the

primary wall, the secondary wall is inflexible and therefore prevents any further expansion of

the growing cell (Salisbury and Ross 1992).

The final stage of differentiation is lignin impregnation between the filaments constructing the

cell walls (Figure 5.1(d)) (Jane 1970; Panshin and De Zeeuw 1980; Wilson and White 1986).

Lignification of the middle lamella and primary wall usually begins during the deposition of

the secondary wall, whilst lignification of the secondary wall begins once the secondary wall

is complete (Wardrop 1964, 1965; Panshin and De Zeeuw 1980; Downes and Ward 1993;

Donaldson 2001). Lignin deposition usually begins first in the cell wall corners, from where it

spreads across the remaining cell wall (Donaldson 2001), and in normal wood most lignin is

located in the middle lamella and primary wall (Wardrop 1964, 1965). Following

differentiation the now mature wood cell dies, and its organelles may be dissolved or

deposited on the inner face of the cell wall before the cell opens up for fluid translocation

(Panshin and De Zeeuw 1980).

5.1.2 Wood Structure

The ultimate structure of wood is the structure of the material wood is made from, including

the nature of the composite materials and their arrangement (Jane 1970; Wilson and White

1986). The wood of all trees generally contains 65-80% holocellulose, 20-35% lignin, and 1%

extraneous materials (Jane 1970; Panshin and De Zeeuw 1980; Wilson and White 1986).

Holocellulose consists of linear polymers, including pure cellulose, which are crystalline in

nature and chain together to form long filaments called microfibrils (Wardrop 1964; Panshin

and De Zeeuw 1980; Wilson and White 1986). In comparison, lignin consists of three-

dimensional and amorphous polymers (Wardrop 1964; Jane 1970; Panshin and De Zeeuw

1980; Wilson and White 1986; Donaldson 2001). Extraneous materials are mineral and

organic deposits, and their 1% ratio increases up to around 20% in heartwood due to their

deposition into wood cells during conversion from sapwood to heartwood (Panshin and De

Zeeuw 1980; Wilson and White 1986). Extractives are thought to form ‘in-situ’, or very close

to, the cells they are deposited in (Hillis 1971).


Wood cell walls are composite structures including the middle lamella, the primary wall and

the secondary wall (Figure 5.2) (Jane 1970; Panshin and De Zeeuw 1980; Wilson and White

1986). The middle lamella consists mainly of extraneous pectic substances which have been

heavily lignified, and contains no cellulose (Jane 1970; Wilson and White 1986). The primary

wall is thin and consists of microfibrils (cellulose) embedded in lignin. The microfibrils are

scattered in flat helices around the wall, with some vertically orientated at the corners of the

cell (Wardrop 1964; Jane 1970; Panshin and De Zeeuw 1980; Wilson and White 1986).

Evidence suggests microfibrils in the primary wall have been pulled apart, creating a ‘latticed’

effect, which is thought to have facilitated cell wall expansion during cell formation (Boyd

and Foster 1975).

Figure 5.2: The ultimate structure of a typical wood cell (adapted from Jane 1970).

The secondary wall has a higher ratio of cellulose than in the primary wall, and consists of

three layers called the S1, S2 and S3 layers (Wardrop 1964; Jane 1970; Panshin and De Zeeuw

1980; Wilson and White 1986). Microfibril arrangement varies between the three S layers

(Figure 5.2) due to changes in the number of lamellae (sheets of microfibrils) (Wardrop 1964;

Panshin and De Zeeuw 1980; Wilson and White 1986), the orientation of microfibrils within

each lamellae (left to right helix (S) or right to left helix (Z)) (Wardrop 1964; Panshin and De

Zeeuw 1980; Wilson and White 1986), and the average angle of each lamella from the

vertical (Wardrop 1964; Panshin and De Zeeuw 1980; Wilson and White 1986). Of the S

layers the S2 layer is the thickest, and therefore the most important in determining many

physical properties of wood (Wilkins 1986). Again there is evidence to suggest that ‘latticing’

S3 microfibrils form 0-12 lamellae, with either S

or Z helices, with angles between 50-70°.

S2 microfibrils form 30-40 lamellae, all with Z

helices and with an angle between 10-30°.

S1 microfibrils form 4-6 lamellae, with either S or

Z helices, with angles between 50-70°.

Primary Wall

Middle Lamella


occurs in order to facilitate cell wall expansion, although the extent of this in the secondary

wall is less than in the primary wall (Boyd and Foster 1975).

The basic ultrastructure of wood cells has been established for many years, yet there is still a

great deal that is unknown. The chemical composition of lignin is only partly described, as is

that of the many extractive compounds found in wood, and the description of these substances

is complicated by the variety of lignins and extractives found in different species (Hillis 1971;

Wilson and White 1986), and different wood cell types (Donaldson 2001). The cell organelles

involved in synthesising and moving cell wall components have generally been isolated,

however the mechanisms by which holocellulose (microfibrils) and lignin are created,

deposited, and oriented are in doubt (Muhlethaler 1965; Panshin and De Zeeuw 1980;

McCann 1997; Donaldson 2001).

Together wood cells form a solid cellular structure, which in hardwood genera such as

Eucalyptus, is relatively complex. The structure is comprised of five cell types, these being

fibres, parenchyma, vessel elements, tracheids and fibre-tracheids (Figure 5.3). All five

elements form elongated structures, which are arranged axially (parallel) to the stem, branch

or root they occur in, with the exception of parenchyma which may also be arranged radially

(Wardrop 1964; Panshin and De Zeeuw 1980; Wilson and White 1986). An additional feature

of the wood anatomy of eucalypts is the openings, or pits, that occur between cells and allow

fluids to move from cell to cell (Wardrop 1964; Panshin and De Zeeuw 1980; Wilson and

White 1986).

Figure 5.3: 3D image of hardwood cellular structure featuring (a) fibres, (b) ray parenchyma and (c) vessel elements (Meylan and Butterfield 1972).

ltaylor

Typewritten Text

Figure removed due to copyright restrictions


Fibres – Compared to cambial initials, fibres are 4-6 times longer (Wardrop 1965), although

in plantation grown E. globulus fibres were found to be only twice the size of cambial initials

(Ridoutt and Sands 1993, 1994). Fibres may be mature in the middle whilst the tips are still

juvenile and elongating (Wardrop 1965). In consequence, fibres are long narrow cells, often

with forked or serrated tips where the expanding tip has divided around some obstruction,

such as a ray (Wardrop 1965). Fibres typically have thick cell walls that become heavily

lignified, and their simple pits are small with an elongated, slit-like shape, resembling a

flattened funnel in the thick cell wall (Panshin and De Zeeuw 1980). Following cell wall

development, most fibres are left senescent after their protoplasts are dissolved and replaced

by sap.

Fibres primarily function for mechanical strength and occur in large groups within the wood

structure (Panshin and De Zeeuw 1980), constituting up to 70% of wood in eucalypts

(Wardrop 1964). Due to their intrusive growth, resulting in high surface area to volume ratios,

fibres have high intercellular cohesion. It is this intercellular cohesion, in addition to thick cell

walls, which produce the generally superior mechanical properties of hardwoods. Fibres may

also undergo food storage capacities in specialised septate fibres, which form a longitudinal

column of cells used for food storage. Septate fibres are prominent in tropical species and

species in which longitudinal parenchyma (specialised food storage cells) are not abundant.

They may be a flexible response mechanism for storing short-term oversupplies of food

production.

Parenchyma - Parenchyma develop from a single cambial initial into a ‘ribbon’ of multiple

parenchyma cells, which is typically 1-3 cells wide and multiple cells high. Parenchyma may

occur on the axial and radial axis, and radial parenchyma are called rays because they form

radial bands through the wood in the direction of pith to bark. Rays are generally straight, but

may become deflected by the developmental expansion of adjacent cells. The rays of

hardwoods are diverse, varying in length, height and width (Panshin and De Zeeuw 1980).

Parenchyma primarily function for food storage within the tree, and contain metabolic

products like starch grains, or other specialised substances like oils and salt crystals (Panshin

and De Zeeuw 1980). Parenchyma remain alive and functional for the duration of the

sapwood, after which their contents are mobilised and used and they are converted to

heartwood (Panshin and De Zeeuw 1980).


Vessel Elements - Vessel elements are cells that align in a vertical column to form a ‘tube’,

and they are characteristic of hardwoods rather than softwoods, although they are not found in

all hardwood species (Panshin and De Zeeuw 1980; Wilson and White 1986). Each vessel

element is derived from a cambial initial, and during differentiation the cell walls become

thickened with highly pitted secondary walls, except at the ends where adjacent vessel

elements align to form a column (Panshin and De Zeeuw 1980; Wilson and White 1986).

Rather than thicken these end-points dissolve, leading to the formation of an open tube

(Panshin and De Zeeuw 1980; Wilson and White 1986). The mature vessel is then left as an

inert transpiration pipe filled with sap (Wilson and White 1986).

Vessels primarily function for water conduction (Panshin and De Zeeuw 1980; Wilson and

White 1986), and in terms of mechanical strength, they are comparatively weak (Wilson and

White 1986). Evidence suggests that in comparison to tree height, the majority of vessels are

short at up to 20cm in length (Wilson and White 1986). They form a continuous transpiration

pathway by sharing pits (openings) with other vessels and cell types (Panshin and De Zeeuw

1980; Wilson and White 1986).

Vasicentric-Tracheids - Tracheids are not present in all hardwoods, however they are present

in Eucalyptus species in the form of vasicentric-tracheids, which are characteristically found

adjacent to vessels (Panshin and De Zeeuw 1980). Compared to fibres, vasicentric-tracheids

have rounded tips, thin walls, and are not greatly elongated (Panshin and De Zeeuw 1980).

They are usually distorted in shape due to the expansion of the adjacent vessel elements.

Vasicentric-tracheids have round pits, called bordered pits (Panshin and De Zeeuw 1980),

many of which join to vessel element pits. Like vessels, vasicentric-tracheids are primarily

water conducting structures that are mechanically weak.

Fibre-Tracheids - Fibre-tracheids are cells that largely resemble fibres, except that they have

bordered pits similar to tracheids (Panshin and De Zeeuw 1980). In consequence they are

defined fibre-tracheids, however for all intensive purposes they have the same behaviour and

function as fibres, and are usually referred to as fibres (Panshin and De Zeeuw 1980).


5.1.3 Wood Types

The description of wood to this point has described normal wood, which is also known as

mature sapwood. There are, however, a number of other wood types found in tree stems that

are important sources of variability in wood (Zobel and Buitjtenen 1989).

Juvenile and Mature Wood - As trees develop, their wood cells change between consecutive

growth layers. These changes are rapid during early development but become more gradual

over time, and they affect wood properties, principally because fibres become longer and

wider, with thicker cell walls. Juvenile and mature wood refer to the rate of change in wood

cells between consecutive growth layers. Juvenile wood is characterised by rapid changes in

wood cells and is restricted to the stem core, whereas mature wood is characterised by gradual

changes in wood cells and encases the juvenile core (Wilson and White 1986; Zobel and

Buitjtenen 1989). In practice there is no sharp distinction between juvenile and mature wood

(Wilson and White 1986), the change between the two occurs as the tree reaches maturity and

the rate of change in wood cells between consecutive growth layers diminishes (Zobel and

Buitjtenen 1989). In young trees all the wood is juvenile, and this changes as mature wood

begins to form at around 10-20 years in eucalypts (Wilson and White 1986). From this point

onwards the juvenile/mature wood ratio declines as the ratio of mature wood increases.

The ratio of juvenile to mature wood is an important consideration for plantation managers

because the properties of these wood types have significant implications for the quality of

product produced. In eucalypts, producers of reconstituted wood products generally prefer a

high juvenile/mature wood ratio because of superior pulping and gluing qualities of juvenile

wood (Zobel and Buitjtenen 1989), whereas producers of solid wood and bio-fuel products

prefer a low juvenile/mature wood ratio due to the greater strength, stability, durability and

energy content of mature wood (Bootle 1983; Groves and Chivuya 1989). This raises the

question of whether the juvenile/mature wood ratio can be manipulated other than by waiting

for the mature wood to grow.

It is difficult to answer the above question when little is known about the triggers for causing

changes in wood cells or the function of changes in wood cells. One line of thought is that the

age of the vascular cambium triggers the characteristics of wood cells formed, and the rate of

change from juvenile to mature wood is genetically predetermined (Wilkes 1988). Since the

change to mature wood results in wood that provides more structural support, it is thought that


the function of the change in wood cells is to provide greater structural support for the larger

tree. On reflection, however, the hypothesis that the age of the vascular cambium triggers the

characteristics of wood cells formed is inconsistent with the hypothesis that the function of

the change in wood cells is to provide greater structural support for the larger tree, since the

correlation between age and size is by no means constant but depends on the growing

conditions. If the function of the change in wood cells is to provide adequate structural

support for the tree, this then suggests that tree size rather than age should trigger changes in

wood cells.

A separate hypothesis suggests that changes in wood cells are triggered by a reduced

concentration of crown hormones, whereby the onset of mature wood formation occurs as

crown height lifts and the influence of the crown decreases (Zobel and Buitjtenen 1989). This

hypothesis has merit given that crown hormones move slowly and require metabolic energy to

travel, and are therefore less likely to exert influence on stemwood if the distance between the

two is greater.

Dependent on which hypothesis is true, then what might be the effect of increased

competition (planting density) on the juvenile/mature wood ratio? In terms of growth pattern,

increased planting density is likely to result in similar size growth prior to onset of

competition but restricted size growth subsequent to the onset of competition, and it is also

likely to result in an increased rate of crown lift due to more rapid onset of and intensity in

competition. If age and/or tree size is the trigger for mature wood production, then increased

planting density would result in an increased juvenile/mature wood ratio due to similar early

growth but restricted later growth. On the other hand, if crown influence is the trigger for

mature wood production, then increased planting density would result in a decreased

juvenile/mature wood ratio due to an increased rate of crown lift. These deliberations, whilst

simplistic, illustrate the potential to manipulate wood properties by managing stand growing

conditions.

Sapwood and Heartwood - When wood is first formed it is physiologically active in both

water transpiration and food storage, and is referred to as sapwood since it is the wood in

which sap-flow occurs. After some time the food content in the sapwood is mobilised and

used, and extractives are deposited into the wood cell cavities, essentially blocking them up

(Rudman 1966; Bamber 1985). This conversion results in the loss of the physiological


functions that characterize sapwood. The resulting inert wood is called heartwood since it

resides in the centre of the stem.

The extractives found in heartwood are thought to form adjacent to the cells they are

deposited in (Hillis 1971), an hypothesis that is supported by evidence that there is a

substantial increase in the respiration rate of sapwood in the zone adjacent to the heartwood

(Bamber 1976). During the process of conversion, extractive content increases from around

1% in sapwood to around 20% in heartwood (Panshin and De Zeeuw 1980; Wilson and White

1986), and the increased extractive content contributes to increased wood density (Groves and

Chivuya 1989).

Once initiated, heartwood continues to expand throughout the life of the tree, whilst a band of

sapwood is maintained around the heartwood due to continuing wood growth (Panshin and De

Zeeuw 1980). In eucalypts heartwood formation may begin at the relatively early age of 4 to 5

years (Bamber 1985), although one study indicates that E. grandis begins earlier than this

(Bhat et al. 1988). The radial width of the heartwood then expands with age, whereas the

width of the sapwood remains relatively constant, with the result that the proportion of

stemwood comprised of heartwood gradually increases with age (Bamber 1985). In E. grandis

the proportion of heartwood in the stemwood has been found to increase from 36.8% at 3

years to 66.4% at 9 years. The proportion of heartwood decreases with increasing height

within the stem, indicating that heartwood formation either begins at a later stage and/or

proceeds at a slower rate, as height within the stem increases (Bhat et al. 1988; Taylor et al.

2002).

The trigger for heartwood formation is unclear. It may be a way of mitigating the negative

effects of damage in sapwood caused by natural aging, air embolisms, and/or pathogens

(Panshin and De Zeeuw 1980; Bamber 1985). Alternatively, heartwood formation may be an

active physiological process, the function of which is to optimise the sapwood conducting

area based on the physiological requirements of the tree crown (Rudman 1966; Bamber

1976). Whilst the first theory may apply in isolated incidences of damage, in practice it is

likely that the latter theory is prevalent given the strong relationship between conducting

sapwood area and leaf area. The latter theory is supported by the finding that the ratio of

heartwood decreases with increased height within the stem, since this is the logical result of a

constant sapwood conducting area but decreasing stem diameter.


Reaction Wood - Reaction wood is a specialised wood that forms in tree stems when they

grow on a lean or in response to other factors like wind sway or fast growth. In hardwoods

reaction wood forms on the upper side of leaning trees and tends to pull the tree up. It is also

referred to as tension wood given that it is under tension. In contrast, the reaction wood of

softwoods forms under compression on the lower side of leaning trees and pushes trees up,

and is known as compression wood (Zobel and Buitjtenen 1989). The properties of reaction

wood differ considerably from normal wood, and it is undesirable for most end products

(Zobel and Buitjtenen 1989). Reaction wood is minimised by using silvicultural techniques to

reduce wind sway and grow trees as straight and as vertical as possible (Zobel and Buitjtenen

1989).

5.1.4 Wood Properties

High wood quality has the potential to increase the value of plantations by improving

conversion efficiencies, increasing potential product ranges, and producing greater

percentages of high grade/high value products (Malan et al. 1997). The improvement of

plantation wood quality is commonly addressed through selection and breeding programs

since the use of silviculture to influence wood quality is generally regarded as ineffective

(Zobel and Buitjtenen 1989). Wood quality is rarely considered in economic analyses of

silvicultural management options (Downes and Raymond 1997), yet the potential of wood

quality to effect plantation value behoves managers to improve knowledge on links between

silviculture and wood quality.

Wood growth in trees varies in response to genetic control, tree development and

environmental changes (Zobel and Buitjtenen 1989), of which the latter two may be affected

by silviculture. Wood quality is almost always improved by reduced variability in wood

(Zobel and Buitjtenen 1989; Malan et al. 1997), and the potential for growing conditions to

effect wood variability is an important silvicultural consideration. Examination of wood

properties will facilitate investigation of variability in wood (Zobel and Buitjtenen 1989), and

should indicate whether silvicultural techniques are beneficial for both wood volume and

wood quality, or otherwise allow trade-offs between improving either wood volume or wood

quality to be accounted for.


Wood Anatomy – The most commonly measured anatomical features in eucalypts are the

wood cells; fibres, vessels and parenchyma. Changes in anatomical diversity are generally

measured from the stem centre (pith) to the stem surface (bark), and from the stem base (base)

to the stem tip (apex).

Most eucalypt species exhibit similar patterns of anatomical diversity within the stem. From

pith to bark the length, diameter and wall thickness of fibres tend to increase (Bamber and

Curtin 1974; Taylor 1984; Bamber 1985; McKimm and Ilic 1987; Wilkes 1988; Bhat et al.

1990). The proportion of fibrewall material in fibres generally increases from pith to bark

(Wilkes 1988), although this is not always the case, particularly in the first few years of

growth (McKimm and Ilic 1987). Vessel diameter increases and vessel frequency decreases

from pith to bark (Bamber and Curtin 1974; Bamber 1985; McKimm and Ilic 1987; Wilkes

1988). The proportion of space occupied by each cell type generally remains constant at

>60% for fibres, (Wilkes 1988)10-20% for vessels and 20-30% for rays (Wilkes 1988),

however rapid changes in growth rate can result in fluctuations (Bamber 1985).

From base to apex, the reports of changes in wood anatomy are conflicting. In E. grandis fibre

length has been found to gradually decrease with height (Bhat et al. 1990), whereas for

eucalypts in general fibre length increases to a point well up the bole, then decreases at higher

levels (Wilkes 1988). Decreases in fibre length at higher levels in the stem may be the result

of more rapid division and differentiation of cambial initials due to greater proximity to the

crown (Wilkes 1988), whereby cambial initials and fibres do not have time to reach full

length before division and differentiation occur. The greater expression of this phenomenon in

E. grandis (Bhat et al. 1990) may be due to a greater apical influence over the tree in general.

There is little information pertaining to changes in other cell properties on the vertical axis

and this would appear to be a substantial knowledge gap.

Wood Density - Density is a measure of mass per unit of volume, and wood density is the

amount of wood substance present in a harvested (green) volume (Zobel and Buitjtenen

1989). Wood density is most commonly measured as basic density, the mass of oven-dry

wood per unit of green volume (g/cm3 or kg/m

3), and specific gravity, the ratio of the oven-

dry mass of a given volume of wood to the mass of an equal volume of water at 4°C (Zobel

and Buitjtenen 1989). Other wood density measures include green density, air-dry density and


oven-dry density, for which both the mass and volume parameters are measured at the said

moisture content (Groves and Chivuya 1989).

Wood density is related to many wood properties and is widely considered to be the most

important property of wood affecting utilisation and conversion (Hillis and Brown 1984;

Malan 1989; Zobel and Buitjtenen 1989; Haslett and Young 1990). In solid wood products

high wood density increases strength and toughness properties (Bootle 1983; Yang and

Waugh 1996b) and in fuel the energy yield of wood is improved by higher wood density

(Gough et al. 1989; Groves and Chivuya 1989). In reconstituted wood products like paper,

high wood density reduces burst strength, tensile strength and folding endurance, but

increases tear strength, whereas in chipboard and medium density fibreboard, high wood

density reduces flake and fibre bonding areas and board strength (Zobel and Buitjtenen 1989).

Given the universal significance of wood density, plantation producers increasingly include

wood density as a priority consideration in selection and breeding programs (Dickinson et al.

2001).

Changes in wood density are caused by changes in the frequency, dimension and chemistry of

wood cells (Malan 1989; Zobel and Buitjtenen 1989; Ilic et al. 2000). In hardwoods increased

fibrewall thickness has the greatest positive influence on wood density (Malan and Gerischer

1987), however reductions in fibre and vessel diameter and vessel frequency may also

increase wood density (Zobel and Buitjtenen 1989). Wood density is therefore an average

measurement that does not fully reveal the distribution of wood cell types (Zobel and

Buitjtenen 1989). In practice wood density is relatively simple to measure, however the

changes in wood anatomy that create the changes in wood density are more difficult to

observe, and generally require microscopy (Downes and Raymond 1997).

The basic wood density of most commercial eucalypts ranges between 450 - 900 kg/m3

(Bootle 1983), whilst the basic wood density of E. grandis ranges between 400 - 670 kg/m3

(Bootle 1983; Downes and Raymond 1997; Ilic et al. 2000). Within annual growth layers

eucalypts are characterised by low variation in wood density on the horizontal axis (Zobel and

Buitjtenen 1989), with the result that visible annual growth rings (caused by wood density

increasing from summer to winter) are absent from species like E. grandis and E. globulus

(Downes and Raymond 1997). Between successive annual growth layers the wood density of

eucalypts generally increases from pith to bark (Downes and Raymond 1997; Ilic et al. 2000).


This pattern has been confirmed for several plantation eucalypts including E. grandis

(Bamber and Humphreys 1963; Bamber et al. 1969; Hans et al. 1972; Taylor 1973a, b;

Schonau 1974; Hans 1976; Bamber et al. 1982; Taylor 1984; Malan and Gerischer 1987;

Malan 1988; Wilkins 1990; Malan 1991; Wilkins and Horne 1991; Malan and Hoon 1992), E.

nitens (Nicholls and Pederick 1979; McKimm 1985; Yang and Waugh 1996a), E. globulus

(Yang and Waugh 1996b), E. regnans (Nicholls and Griffin 1978; Frederick et al. 1982; Yang

and Waugh 1996a), E. obliqua (Nicholls and Griffin 1978) and E. pilularis (Bamber and

Curtin 1974). A notable deviation from the above pattern is occasionally found in E. grandis,

for which wood density may exhibit an initial decrease between growth layers before

increasing, a trend that becomes stronger with height and appears to be related to slower

growth (Taylor 1973a; Wilkins 1989, 1990; Wilkins and Horne 1991). Initial decreases in

wood density from pith to bark have also been found in a study of fast-grown E. nitens

(McKimm and Ilic 1987).

From base to apex, wood density within annual growth layers in E. grandis has been found to

remain constant with height (Malan 1988) and decrease with height (Bamber et al. 1969;

Wilkins and Horne 1991), whereas the average wood density of all growth layers generally

increases with increased height within the stem (Downes and Raymond 1997; Ilic et al. 2000),

as has been found for E. grandis (Taylor 1973a; Malan 1988; Coetzee et al. 1996), E.

globulus (Downes and Raymond 1997), E. nitens (Purnell 1988; Yang and Waugh 1996a)

and E. regnans (Dargavel 1968; Chafe 1981; Frederick et al. 1982; Yang and Waugh 1996a).

In deviation to the above results, a number of studies have found that average wood density of

plantation eucalypts may initially decrease with increased height within the stem before

increasing (Downes and Raymond 1997), as was found in E. grandis (Bamber et al. 1969;

Taylor 1973b; Vital and Della Lucia 1987; Wilkes 1988; Bhat et al. 1990; Wilkins 1990;

Wilkins and Horne 1991), E. globulus (Beadle et al. 1996; Raymond and MacDonald 1998),

E. nitens (Purnell 1988; Lausberg et al. 1995; Beadle et al. 1996; Raymond and MacDonald

1998), and E. regnans (Frederick et al. 1982). Two studies found that average wood density

remained constant with height in E. grandis (Hans 1976) and E. globulus (Yang and Waugh

1996b). In essence it is apparent that different patterns of changes in wood density within and

between growth layers may result in average wood density increasing, decreasing or

remaining constant with increased height within the stem (Figure 5.4).


Average wood density of the whole stem increases with successive growth layers, and the

average wood density of young plantation timber is comparable to native forest timber if

compared at similar ages (Haslett et al. 1990; Yang and Waugh 1996a, b). Increases in

average density over time may be affected by many factors, however the dominant controlling

factor appears to be age (Bamber et al. 1969; Wilkes 1988), hence the understanding that

wood property comparisons between plantations must take age into account (Zobel and

Buitjtenen 1989). Increases in average stem wood density are most rapid during the juvenile

stage of growth, slowing down as the cambium reaches maturity at 10-20 years (Wilkes

1988). Trees with high wood density at an early age are generally found to produce wood with

high wood density in subsequent growth (Taylor 1984), which has positive implications for

selecting for wood density at an early age.

Figure 5.4: Stylised depictions of changes in wood density within the stem (average wood density shown by vertical bars). The base of each stem is the same, with wood density increasing in successive growth layers. Within growth layers wood density may (a) remain constant with increased height, or (b,c) decrease with increased height. As a result of the above patterns, average wood density may (a) increase with increased height, (b) decrease and then increase with increased height, or (c) remain constant with increased height.

Knot Content - knots consist of the woody base and/or scar tissue of branches within the stem

(Wilson and White 1986). The primary effect of knots is to reduce wood strength as a result

of the deflection of normal wood tissue around the knot (Bootle 1983; Hillis 1984). Both the

number and size of knots are important considerations since a given increase in knot content

results in a comparatively greater loss of mechanical strength in wood (Yang and Waugh

1996b). Other serious defects associated with knots include kino veins (Jacobs 1955; Bootle

1983; Gerrand et al. 1997), and decay entry (Hillis 1984; Glass and McKenzie 1989). The

occurrence of knots is not common in wood sourced from native forests (Bootle 1983),

however a high incidence of knots in plantation-grown tropical timbers is common (Haslett et

(a) (b) (c)


al. 1990; Montagu et al. 2003), and several studies of plantation grown eucalypt species have

shown that knots are the leading cause of defect and downgrade in solid wood produced in

plantations (Waugh and Rozsa 1991; Borough and Humphreys 1996; Yang and Waugh

1996a, b). The opportunity to minimise knot content (maximise clear wood) clearly represents

an effective mechanism by which to improve the value of eucalypt plantations (Montagu et al.

2003).

Branching habits, including the number and size of branches, the angle branches make with

the stem, the rate of branch shed, and the persistence of dead branches, all affect the number

and size of knots (Hillis and Brown 1984). Of the branching habits, branch number appears to

be inherited (Hillis and Brown 1984), whereas branch size, branch angle and the rate of

branch shed are strongly influenced by growing conditions in the stand (Florence 1996).

Silvicultural techniques that stimulate growth, such as thinning and fertilising, have the

potential to increase knot content by stimulating branch growth and persistence (Hillis 1984).

Silvicultural techniques that encourage branch shed, such as close spacing, have the potential

to decrease knot content.

Genera and species differ widely in the efficiency of branch shed, but most eucalypts shed

branches quickly when grown in stands (Florence 1996). The branch ejection mechanism is

primarily responsible for efficient branch shed in eucalypts since dead branches are

effectively removed from the stem, whereas in other species dead branches may persist for

long periods. The process of branch shed in eucalypts starts as the crown grows upwards and

the lower branches become moribund and die. A layer of tannins, latex or resins usually

develops between the branch base and the stemwood during the moribund period, essentially

separating the branch from the stemwood. At this point a number of scenarios may occur, and

in the best case scenario the whole branch is ejected from the wood and bark of the stem. This

is a favourable growth habit of eucalypts since it removes the branch down to the solid wood,

minimising the knotty core and allowing rapid occlusion of the branch scar.

In many cases, however, the dead branches become brittle and break, leaving a stub in the

bark. As with whole branches, stubs may be ejected from the wood and bark of the stem

(Figure 5.5(a)), and this is said to occur for nearly all branches up to 2 cm in diameter (Jacobs

1955). Where stubs have not ejected they may in fact be ejected from the wood but become

caught in the bark (Figure 5.5(b)). The bark then drags the stub through the wood layers as the


stem grows outwards, creating a kino vein behind the stub. Kino is a gum-like substance that

hardens to a brittle mass on exposure to air, causing a structural hole in the wood. Stubs that

are not ejected from the wood remain stuck, and are eventually occluded by the diameter

growth of the tree with the result that the dead stub is enclosed in the stem (Figure 5.5(c)).

Figure 5.5: The nature in which branch shed affects the development of the knotty core, the defect core and clearwood. (a) An ejected stub causing a small knotty core, a small defect core and moderate clearwood growth, (b) an ejected stub held in the bark causing a small knotty core, a large defect core and no clearwood growth, and (c) an un-ejected stub causing a moderate knotty core, a moderate defect core and small clearwood growth.

In each scenario openings in the stem bark caused by dead branches or stubs are potential

sites for decay entry. Some protection from infection is obtained by the layer of tannins, latex

or resins between the branch/stub and stemwood; however this protective layer can fail. A

longer occlusion period caused by larger scars or persistent stubs will increase the chance of

decay entry. When infection does occur it generally travels from the point of entry to the pith

of the stem, with the result that decay is generally contained in the defect core (Glass and

McKenzie 1989).

Due to the problems associated with poor branch shed, including reduced clearwood

production and decay entry, plantation growers usually elect to minimise the knotty core by

pruning branches. In Australia pruning methods were developed in pine plantations, where

dead branches tend to persist for long periods before finally decaying to the stage when they

snap off. In order to hasten occlusion and maximise clear wood production, dead branches

were pruned off. The effectiveness of pruning depends on branch diameter, the method of

cutting branches and the length of remaining stubs. Due to its high cost pruning is generally

KNOTTY CORE DEFECT CORE CLEARWOOD

(a) (c) (b)


restricted to the lowest sawlog (butt log) of the final stocking trees only (Hillis and Brown

1984).

Pruning dead branches has been known to be problematic when applied to eucalypt

plantations. It has been found in E. nitens that pruning dead branches can interrupt the

sequence of branch shed, resulting in the stub getting caught in the bark (Figure 5.5(b)) and

causing a kino vein through what was intended to be clearwood (Gerrand et al. 1997). It was

concluded that branches should be pruned when they are live to avoid this kind of defect;

however pruning live branches introduces further problems, one of which is infection. Live

branches are more susceptible to decay entry as they have not yet developed a protective layer

between branch stubs and stemwood. In pruned E. regnans infection incidence increased as

pruned live branch stub diameter increased (Glass and McKenzie 1989), presumably due to an

increased occlusion period. Decay from infection was found to extend inwards (including

upwards and downwards) from the initial entry point, but the extent of decay was not related

to branch diameter. Decay was not detected in wood laid down after infection occurred, and

was therefore thought to be restricted to the defect core. Despite the finding that decay

appeared to be limited to a part of the stem that is otherwise useless, the authors supported the

guideline to prune branches before reaching 2.5 cm branch diameter in order to minimise

decay entry.

The extent and timing of pruning is also of concern because live branches are part of the

productive crown and it is uncertain how much of the crown can be removed before growth

rate is negatively impacted. Studies show that eucalypts are quite flexible, as up to 50% of the

crown can be removed with no significant loss in growth rate (Pinkard and Beadle 1998a;

Pinkard and Beadle 1998b), however if branches need to be pruned before reaching 2.5 cm

diameter (Glass and McKenzie 1989), then pruning may be required on a number of occasions

in order to remove branches before they get to big without removing too much of the crown.

Given that each pruning event adds to operational costs, it is debatable that the expense could

be justified when eucalypts tend to effectively shed branches to a diameter of 2 cm (Jacobs

1955). This may be the case since a study of E. grandis showed that pruning caused only a

small increase in the yield of clearwood at 25 years age (Bredenkamp et al. 1980).


THE EFFECT OF COMPETITION ON WOOD PROPERTIES

The general consensus remains that age and genotype appear to be the critical factors

governing the rate of anatomical change within stems, whereas environmental changes have

only minimal impact (Bamber 1985; Wilkes 1988). Despite this, rate of growth is known to

affect several wood properties, suggesting that competition-induced changes in growing

conditions will also affect wood properties.

Wood Anatomy – Findings on the effect of growth rate and competition on wood anatomy are

conflicting. Studies of E. grandis found that increased growth rate due to dominance within

the stand had no effect on average fibre length (Bamber et al. 1982; Wilkes and Abbot 1983;

Taylor 1984), fibre diameter (Bamber et al. 1982; Wilkes and Abbot 1983), or fibre wall

thickness (Bamber et al. 1982). Similarly, increased growth rate due to silvicultural treatments

like initial spacing, fertilising, and thinning had no effect on fibre length (Wilkins and

Kitahara 1991; Malan and Hoon 1992).

In contrast, studies of 8.5 yr old E. grandis progenies (Malan 1991) and E. grandis grown in

different parts of South Africa (Taylor 1984) revealed a correlation between an increased rate

of height growth and decreased average fibre length, consistent with the hypothesis that faster

growth causes more rapid division of cambial initials resulting in decreased fibre length. A

study of 27 to 34 year old regrowth E. regnans (Higgs and Rudman 1973), found that

increased growth rate due to fertilisation resulted in decreased average fibre length, whereas

increased growth rate due to thinning increased the average fibre length. It was thought that

fertilising alleviated nutrient shortages only, hence favouring sporadic and rapid height

growth increases when water was available, whereas thinning alleviated light, water and

nutrient stresses and space restrictions, hence favouring sustained growth increases both in

height and diameter.

In 40 year old eucalypt dry open-forest species, increased growth rate did not affect fibre

diameter but it did result in greater average fibre wall thickness (Wilkes and Abbot 1983).

The same finding was implied in E. grandis thinning trials (Malan and Hoon 1992) due to the

correlation between wood density and fibre wall thickness. Growth rate does not appear to

affect the ratio of fibres present (Wilkes and Abbot 1983; Malan and Hoon 1992).


Trees with fast growth rates exhibit gradual changes in fibre properties from pith to bark,

whilst suppressed trees exhibit rapid changes in fibre properties from pith to bark. When

expressed in terms of relative distance from pith, however, growth rate has little effect on the

rate of change in fibre properties (Malan and Hoon 1992). This suggests that increased growth

rate has no effect on fibre dimensions at the time of differentiation, but it does reduce within-

tree variability by spreading the same amount of change in fibre dimensions over a greater

area.

In response to increased growth rate, vessel frequency has been found to decrease in eucalypts

(Wilkes and Abbot 1983), particularly E. grandis (Bamber et al. 1982; Malan and Gerischer

1987). Vessel diameter has been found to increase (Wilkes and Abbot 1983; Malan 1991) and

decrease (Bamber et al. 1982) with increased growth rate, and the proportion of stemwood

comprised of vessels has been found to increase (Wilkes and Abbot 1983), decrease (Bamber

et al. 1982), and remain constant (Malan 1991; Malan and Hoon 1992) with increased growth

rate.

In response to increased growth rate, the proportion of stemwood comprised of parenchyma

(rays) has been shown to increase (Bamber et al. 1982; Malan and Gerischer 1987), and

remain constant (Wilkes and Abbot 1983; Malan and Hoon 1992). It is possible that the

volume of parenchyma in stemwood may increase in response to increased growth rate due to

a greater requirement for lateral conduction (through ray parenchyma) and/or excess food

storage in fast grown trees (Bamber et al. 1982).

Anatomical variation between trees due to different environments appears to be minimal, and

often of no practical significance, particularly in relation to pulp and bio-fuel products (Malan

and Gerischer 1987; Malan 1991; Malan and Hoon 1992). In contrast, anatomical variation

between trees in similar environments is often high (McKimm and Ilic 1987), suggesting that

wood anatomy is strongly controlled by genotype (Wilkes 1988). Wood cells most affected by

faster growth are the physiologically active vessels and parenchyma (Bamber et al. 1982),

however any conclusion as to the physiological function of these changes is complicated by

conflicting results, particularly for vessels. Further research is required to improve knowledge

of the physiology of wood formation in eucalypts (Wilkes 1988). In addition, the effect of

growth rate on within-tree variability, and the associated consequences for solid wood

products, is deserving of further research.


Wood Density - Investigation of the effect of competition on wood density is complicated by

two issues. The first is that wood density is known to have high genetic variation (De Villiers

1968; Wang et al. 1984; McKimm 1985; Malan and Gerischer 1987; Malan 1988), and this

has to be accounted for before variation due to other factors can be identified. Some authors

specifically note that possible relationships between wood density and growth rate may have

been masked by large variation (Wilkins and Horne 1991), however more often one reads that

a trend was apparent, however it was not significant. The second issue is the interpretation of

the effect of growth rate on wood density, which is complicated by the investigation of

average wood density with little focus on the changes in wood density within the stem. For

example, take two identical stands of the same age which have grown at the same rate up until

100mm radius (Figure 5.6).

0.3

0.4

0.5

0.6

0.7

0.8

0 100 200 300

Distance from the pith (mm)

Air

-dry

wo

od

de

ns

ity

(g

/cm

3)

Stand A Stand B

Mean Density B = 0.591

Mean Density A = 0.677

Figure 5.6: A stylised example of the effect of increased competition (reduced growth rate) on wood density where wood density is determined by age and continues to increase at the same rate over time. Two stands of the same age, A and B, have the same growth rate up to a 100 mm radius. At this point Stand A is thinned and the remaining stems continue to grow at a similar rate, whereas Stand B is not thinned, resulting in the development of competition and a reduced mean growth rate (adapted from Malan and Hoon 1992).

At the point of 100mm radius, this point Stand A is thinned and the remaining stems continue

to grow at a similar rate, whereas Stand B is not thinned, resulting in the development of

competition and a reduced mean growth rate. In both stands wood density is determined by

age and continues to increase at the same rate over time. As the result of increased

competitive pressure in stand B there is an increase in the gradient of change in wood density,

which is caused by the reduction in growth rate rather than an increase in the rate of change in

wood density per se. Furthermore, mean wood density is reduced in stand B compared to

stand A, which must be the case if wood density increases at the same rate over time, but


growth is slowed down at some point (Bamber and Humphreys 1963). This analysis shows

that if age is the primary determinant of wood density, then faster growth at a later age must

result in increased mean wood density. Any deviation from this trend raises the hypothesis

that some factor other than age is affecting the level of wood density at the time of formation.

Several eucalypt studies therefore support the hypothesis that wood density at the time of

formation is unaffected by growth rate, but that increased average growth rates generally

result in increased average wood density. This was found to be the case where increased

average growth rate was due to intensive silviculture such as fertilising and weeding (Bamber

et al. 1982; Wilkins 1989, 1990; Wilkins and Horne 1991; Beadle et al. 1996; Cromer et al.

1998), reduced stocking (Higgs and Rudman 1973; Wilkins 1989; Malan and Hoon 1992;

Coetzee et al. 1996; Coetzee and Naicker 1998b; De Bell et al. 2001), and greater dominance

(Bamber and Humphreys 1963; Bamber et al. 1969; Wilkes 1984).

In contrast, other studies indicate that increased growth rate in eucalypts results in unchanged

or decreased average wood density. This was found where increased growth rate was due to

intensive silviculture (Higgs and Rudman 1973; Raymond and Muneri 2000), better site

quality (Muneri and Raymond 2000), decreased stocking (Schonau 1974; Vital and Della

Lucia 1987), and greater dominance (Taylor 1984; Malan 1991). These results suggest that

factors other than age and genotype affect wood density, and more specifically they imply that

increased growth rate results in decreased wood density at the time of formation.

Whatever the effect of growth rate on wood density at the time of formation, growth rate does

affect the rate of density variation from pith to bark. An increased growth rate corresponds to

an increase in the size of successive growth layers, with the result that a similar increase in

wood density over time is spread through a larger volume of wood, and the gradient of change

from pith to bark is reduced (Malan and Hoon 1992; De Bell et al. 2001). That increased

growth rate may result in reduced variability in wood has implications for plantation

management. Fast grown logs are commonly regarded as exhibiting poor conversion

efficiency, with problems such as end-splitting appearing more pronounced. Malan and Hoon

(1992), however, found that when the degree of end-splitting in E. grandis was adjusted for

log-size there was little difference in end-splitting between logs of different growth rates. This

indicates that the inherently greater stress in larger logs was compensated for by lower within-


stem variation, and suggests no loss in conversion efficiency from managing plantations for

fast growth.

In general the effect of growth rate on wood density is difficult to interpret, given the many

causes of differences in growth rate, the various methods of measuring wood density, and the

nature of the comparison between trees. Further investigation needs to determine the age of

wood at the time of formation in order to clarify the issue, and wood density should be

considered in comparison to other parameters of the tree. Of particular interest is the positive

relationship between wood density and tree height (Bamber and Humphreys 1963; Bhat and

Bhat 1984; Coetzee et al. 1996), as this suggests tree structure affects wood density.

Knot Content - Competition is well recognised as a tool by which to manipulate branching

habits and knot content. As competition increases knots become smaller since the additional

shade created by the denser upper canopy accelerates natural pruning and restricts branch

growth to smaller diameters (Hillis and Brown 1984). The level of stocking required,

however, to create the desired branch shed without sacrificing growth is uncertain. Studies

comparing plantation grown E. globulus (Yang and Waugh 1996b), E. nitens (McKimm 1985;

Yang and Waugh 1996a) and E. regnans (Yang and Waugh 1996a) with their native forest

counterparts have shown plantation grown timber had a higher incidence of knots and larger

knots. The evidence shows that compared to the ‘wheat-field’ regeneration exhibited by

native forests, the stocking rates in the plantations were not high enough to stimulate similar

branch shed.



The effect of competition on wood variables is examined to determine how tree development

impacts wood quality. Planting density is used to approximate the general level of competitive

pressure in the stand, and stem diameter is used to approximate the level of competition

pressure experienced by individuals in the stand. Increased competition, due to increased

planting density and decreased stem diameter, is expected to effect wood variables in different

ways (Table 5.1).

Table 5.1: Hypotheses of the effects of increased planting density and decreased stem diameter on wood variables during early stages of stand development in sub-tropical E. grandis plantations.

Wood Variable Hypothesis

Sapwood

Sapwood Basal Area Sapwood basal area will decrease as a result of increased planting density and decreased stem diameter.

Stemwood Sapwood Ratio

Stemwood sapwood ratio will decrease as a result of increased planting density and decreased stem diameter.

Wood Anatomy

Stemwood Ray Ratio Stemwood ray ratio will decrease as a result of increased planting density and decreased stem diameter.

Ray Height Ray height will decrease as a result of increased planting density and decreased stem diameter.

Stemwood Vessel Ratio Stemwood vessel ratio will not be affected by increased planting density or decreased stem diameter.

Vessel Diameter Vessel diameter will not be affected by increased planting density or decreased stem diameter.

Stemwood Fibre Ratio Stemwood fibre ratio will increase as a result of increased planting density and decreased stem diameter.

Fibre Diameter Fibre diameter will not be affected by increased planting density or decreased stem diameter.

Fibrelumen Diameter Fibrelumen diameter will increase as a result of increased planting density and decreased stem diameter.

Fibrewall Ratio Fibrewall ratio will decrease as a result of increased planting density and decreased stem diameter.

Stemwood Fibrewall Ratio

Stemwood fibrewall ratio will decrease as a result of increased planting density and will not be affected by decreased stem diameter.

Wood Density

Stemwood Basic Density

Stemwood basic density will decrease as a result of increased planting density and will not be affected by decreased stem diameter.

Table Continued...


Wood Variable Hypothesis

Branching Habits

Branch Diameter Branch diameter will decrease as a result of increased planting density and decreased stem diameter.

Branch Diameters > 2cm

Branch diameters > 2 cm will decrease as a result of increased planting density and decreased stem diameter.

Branch Angle Branch angle will decrease as a result of increased planting density and decreased stem diameter.

Branch Mortality Branch mortality will increase as a result of increased planting density and decreased stem diameter.

Branch Form Branch form will skew towards scars and stubs rather than branches as a result of increased planting density and decreased stem diameter.


5.3 Methodology

The spacing trial was sampled for wood variable measurements on two occasions, at which

time data were collected or otherwise calculated using collected data. The hypotheses for

wood growth and structure were tested by statistical analysis of collected and calculated data.

5.3.1 Sample Age, Size and Preparation

Trees were sampled for wood variable measurements when the trees were 3 and 4 years old

(Table 5.2). At 3 years old wood variables were measured in situ, whereas at 4 years old trees

were destroyed and wood variables were measured on extracted samples. Using a chainsaw,

wood disks were extracted from heights of 1.3 m stem height and 25%, 50% and 75% stem

height, totalling 272 stemwood disk samples. Using a bandsaw, small wood blocks were taken

from the stemwood disk samples at radii of 25%, 50%, 75% and 100% of stem radius,

totalling 1,088 stemwood block sub-samples (Figure 5.7).

Table 5.2: The age and sample size of wood variables for which data were collected from the spacing trial.

Number of Trees Sampled Wood Variable



Branching Habits 8 8 8 8


Sapwood Wood Anatomy

Wood Density 8 20 20 20

(a) A full description of the sample selection methods for 3 and 4 year old measurements are provided in Chapter 2 – The Spacing Trial, sub-sections 2.4.1 and 2.4.2 respectively.

Figure 5.7: The location of stemwood disk samples and block sub-samples taken from tree stems.

75% stem height

50% stem height

25% stem height

1.3 m height

Stemwood

Disk Samples

Stemwood

Block Sub-Samples

50% stem radius

25% stem radius

100% stem radius

75% stem radius


Most wood variables could be observed and measured with the naked eye, with the exception

of wood anatomy variables which required microscopy. Of the microscopic methods available

for observing wood anatomy, scanning electron microscopy (SEM) imaging is excellent as it

provides the ability to observe wood anatomy in high resolution detail. SEM imaging,

however, requires the investment of substantial time into the preparation and measurement of

specimens, and the scope of this study did not provide sufficient time and financial resources

to include all the 1088 wood blocks in SEM analysis. In order to reduce the number of

samples, yet capture spatial variation in wood anatomy, the wood blocks were reduced to

those sourced from 1.3m and 50% stem height, and 50% and 100% stem diameter, resulting

in a total of 272 wood blocks for wood anatomy analysis. Both the horizontal surface (HS)

and tangential longitudinal surface (TLS) were prepared for SEM analysis, as these planes

enabled optimal observation of the features under study.

Several steps were required to convert wood blocks into SEM specimens (Heady 2000), and

these included (i) size reduction, (ii) softening, (iii) cutting, (iv) dehydration, (v) mounting,

and (vi) coating. Each step is explained in the following paragraphs.

(i) Size Reduction The wood blocks were too large to be used directly in SEM, so smaller

sized pieces were sawn from the outer edge of the wood blocks using a bandsaw. These pieces

resembled short match sticks measuring approximately 3 mm by 3 mm at the ends and 15 mm

in length. To simplify subsequent preparation, three longitudinally orientated specimens and

three horizontal-radially orientated specimens were cut from each wood block. Specimens

were placed in labelled specimen jars (one per wood block).

(ii) Softening Specimens were soaked in distilled water in labelled 5 mL glass vials until they

became saturated and sank to the bottom, a process which took up to three days. Three drops

of ethanol were added to each vial to minimise microbial colonisation of the specimens and

covers were placed over the vials to avoid contamination by dust and fungal spores. Once

saturated, specimens became soft enough to obtain a non-frayed cut of the specimen surface.

(iii) Cutting Each specimen was clamped firmly in a small vice, with the 3 mm2 end

uppermost and horizontal to the bench. The vice was stuck to the base of a stereo-microscope

(through which the cutting action was observed) with heavy duty double sided tape. Cutting

was carried out using a hand-held, hard-backed single-edged microtome blade (Kucera 1986).


Initial cuts to expose ends were made with used blades. The final end surface was prepared by

slicing a thin section off the exposed surface using a new blade. A thin section was preferred

as it minimised distortion to the underlying surface from compression caused by the blade,

resulting in a flat and true plane. Hand-held cutting was preferred to using a microtome since

the microtome available could not take thin enough slices, which resulted in chattering and

jags across the cut surface. The lower portion of each specimen was then removed, creating a

3 mm3 specimen block with one face prepared. Longitudinally orientated specimens produced

a horizontal surface (HS) on the prepared face, while horizontal-radially orientated specimens

produced a tangential longitudinal surface (TLS) on the prepared face.

(iv) Dehydration The strong vacuum of the SEM necessitated drying the specimen blocks

beforehand, as rapid and violent evaporation of moisture from specimens into the vacuum

could cause damage to the prepared surface and distortion of the specimen image. Freshly cut

3 mm3 specimen blocks were placed with the prepared surface uppermost in 5 mL Petri dishes

within a desiccator containing silica gel, and allowed to dry at atmospheric pressure and room

temperature. Specimen blocks were dried for at least two days before mounting.

(v) Mounting The dehydrated specimen block was glued to a 12 mm aluminium SEM

specimen stub, with its prepared surface uppermost, using finger-nail varnish as adhesive.

There was space for up to four specimen blocks on each SEM stub, therefore two HS and two

TLS specimen blocks from each wood block were placed on each stub. Specimen blocks from

different wood blocks were never placed on the same stub in order to avoid incorrect

identification. Specimen stubs were labelled on top and bottom with indelible pen.

(vi) Coating Wood is a poor conductor of electricity, and therefore specimen blocks had to be

rendered fully conductive by coating them with a thin film of metal in order to prevent

‘charging’ distortions of the SEM image. A 10 nm coating of pure gold was applied to all

specimen stubs in an argon gas sputter coating unit using a 20 mA current for three minutes.


Data were collected for sapwood, wood anatomy, wood density and branching habit variables.

The method of collection or calculation of each variable is explained in the following

paragraphs.


SAPWOOD

Stem Heartwood Diameter – heartwood was identified by spraying the cross-sectional surface

of the main stem, adjacent to the points from which disk samples had been removed, with a

dye solution containing 5% iodine. Within a few minutes dye in contact with sapwood was

drawn into the stem with the sap, whereas dye in contact with heartwood remained on the

surface, delineating the heartwood as a darker stained area. Heartwood was measured on

orthogonal diameters using callipers, and stem heartwood diameter was calculated as the

average of the two orthogonal measurements.

Stem Sapwood Basal Area – stem sapwood basal area was calculated as stem basal area

minus stem heartwood basal area. Stem basal area and stem heartwood basal area were

determined using the stem diameter and stem heartwood diameter of disk samples and

assuming a circular shape.

Stem Sapwood Ratio – the stem sapwood basal area divided by stem basal area.

WOOD ANATOMY

Certain procedures were adopted for the collection of wood anatomy data by SEM. For the

study of a given anatomical feature, imaging distortions were held constant by maintaining

the same working distance (the physical distance between the final lens and the specimen) and

electron beam current settings. These procedures ensured that imaging distortions were

uniform across all measurements of a given feature, thereby allowing accurate comparison

between specimens. The settings for each anatomical feature were determined during

preliminary exploratory viewing based on the order of magnification required. Anatomical

features were measured using a point to point measuring facility built into the SEM. In

addition, SEM images were converted into Tagged Image File Format (TIFF) files, which

were then analysed using the ImageJ V1.30 program (Rasband 2003).

Wood Cell Ratios – the ratio of the cross-sectional area of ray parenchyma (rays), vessels and

fibres to stemwood cross-sectional area. The cross-sections of rays were observed on the

tangential longitudinal surface (TLS) using the SEM. This was the best surface to make a


measure of ray area as it provided an image of the height and the width of the rays6. The

cross-sections of vessels were observed on the horizontal surface (HS) using the SEM. This

was the best surface to make a measure of vessel area because it provided an image of the

radial and tangential width of the vessels7. TIFF images of rays and vessels were recorded for

each wood sample and analysed using ‘ImageJ V1.30’ (Rasband 2003). Image analysis

involved highlighting rays (Figure 5.8(a)) and vessels (Figure 5.8(b)) in red as regions of

interest (ROI’s). Once all rays or vessels in an image were identified as ROI’s, a pixel count

was done of the whole image and then of the ROI’s, essentially providing a measure of the

area of each, and stemwood ray and vessel ratios were calculated for each wood sample as the

pixel count of the ROI divided by the pixel count of the whole image.

(a) (b) (c)

(d) (e) (f)

Figure 5.8: Images of measurements of the wood cell anatomy of E. grandis. (a) A tangential longitudinal surface view showing one ray highlighted as a region of interest. (b) A horizontal surface view showing one vessel highlighted as a region of interest. (c) A tangential longitudinal surface view showing the height of one ray being measured as the distance between the two crosshairs. (d) A horizontal surface view showing the radial diameter of one vessel being measured as the distance between the two crosshairs. (e) A horizontal surface view showing the radial diameter of one fibre being measured as the distance between the two crosshairs. (f) A horizontal surface view showing the radial diameter of one fibrelumen being measured as the distance between the two crosshairs.

6 Ray area on the TLS is a good measure of ray volume since rays extend for a considerable distance in the radial

direction, as shown by the narrow double line extending through SEM images (Figures 5.8(b,d))

7 Vessel area on the HS is good measure of vessel volume since vessels extend for a considerable distance in the

longitudinal direction.


Fibre ratio was then calculated as the proportion of stemwood not occupied by rays or vessels

under the assumption that anything not constituting a ray or a vessel could be designated as a

fibre.

Ray Height – the cross-sectional distance from the top to the bottom of the ray. A line

transect was placed on the SEM image, and from left to right every ray crossing the line

transect was measured until 20 rays had been measured. Ray height was measured directly in

nanometres using a point to point measuring facility built into the SEM (Figure 5.8(c)).

Vessel Diameter – the cross-sectional distance from edge to edge of the vessel. It was

necessary to measure vessel diameter in the radial and tangential directions since vessels were

usually elliptical in the radial direction (Figure 5.8(d)), and a single measurement in either the

radial or tangential direction would result in an overestimate or underestimate of vessel

diameter. For every whole vessel visible in the SEM image the radial and tangential diameter

was measured, resulting in the measurement of 6-10 vessels. Vessel diameter was measured

directly in nanometres using a point to point measuring facility built into the SEM (Figure

5.8(d)).

Fibre Diameter – the cross-sectional distance from edge to edge of the fibre. A line transect

was placed on the SEM image and working from left to right every fibre crossing the line

transect was measured until 20 fibres had been measured. Fibres were measured alternatively

in the radial and tangential direction to reduce bias due to any elliptical orientation of the

fibres in either direction. Fibre diameter was measured directly in nanometers using a point to

point measuring facility built into the SEM (Figure 5.8(e).

Fibrelumen Diameter – the cross-sectional distance from edge to edge of the cavity inside the

fibre. Fibrelumen diameter was measured on every fibre for which fibre diameter had been

measured, in the same direction as fibre diameter was measured, and in the same fashion fibre

diameter had been measured (Figure 5.8(f)).

Fibrewall Ratio – the ratio of fibrewall basal area to fibre basal area. Fibre basal area and

fibrelumen basal area were determined using fibre diameter and fibrelumen diameter and

assuming a circular shape. Fibrewall ratio was then calculated as fibre basal area minus

fibrelumen basal area.


Stemwood Fibrewall Ratio – the ratio of total fibrewall basal area to total stemwood basal

area. Stemwood fibrewall ratio was calculated for each wood sample using the formula:

Fibrewall Ratio **** Fibre Ratio

WOOD DENSITY

Stemwood Disk Sample Volume – stemwood disk sample volume was measured in the field

by water displacement. Green disks were submerged in a known volume of water in a

container on a level surface, and the volume of the combined disk and water was measured

directly to the nearest 5 mL. Stemwood disk sample volume was then calculated as the

combined volume minus the known water volume.

Stemwood Disk Sample Oven-Dry Mass – the stemwood disk samples were placed in a

scientific oven and dried at 80°C for one week. When their mass had stabilised the oven-dry

mass of the samples was measured to the nearest milligram.

Stemwood Disk Sample Basic Density – the oven-dry mass per unit of green volume, usually

measured in kg m-3

, for each stemwood disk sample. In order to convert the data to kg m-3

, the

following formula was used on the sample data that had been recorded in grams and

millilitres:

(stem disk sample oven-dry mass / 1,000) //// (stem disk sample volume /1,000,000)

BRANCHING HABITS

Branch Height – the distance from the base of the stem at ground level to the base of the

branch at stem intercept. Access to the branches was obtained by leaning a 6 m ladder against

the stem and using ropes to tie the ladder off to a minimum of three anchor trees around the

select tree. It was necessary to use anchor trees since in many cases the stem of the tree under

investigation was not large enough to support the mass of the ladder and person leaning

against it. Branches were located either by their presence or by a scar in the bark. Branch

height was measured directly with a measuring tape for every located branch.

Branch Aspect – the compass direction at which the branch has formed on the stem. Branch

aspect was estimated based on the known layout of the trial, and rounded to the nearest


multiple of 45° (i.e. north, northeast, east, southeast, south, southwest, west and northwest)

for every located branch.

Branch Diameter – the diameter of the base of the branch in a cross-section perpendicular to

the branch axis. Branch diameter was measured directly with callipers for every located

branch.

Branch Angle – the angle from which the branch diverges from ‘pointing’ to the ground.

Branch angle was measured directly with a protractor, and rounded to the nearest multiple of

5° for every located branch.

Branch Status – the life stage of the branch (summarised into the categories alive and dead).

Branch status was judged as alive if there were green leaves on the branch and dead if there

were brown desiccated leaves or no leaves on the branch for every located branch.

Branch Form – the stage of branch shed (summarised into the categories un-shed, part-shed

and full-shed). Branch form was judged as un-shed if the branch remained fully intact on the

stem, part-shed if the stub or base of the branch remained intact on the stem and full-shed if

only the scar of a branch remained on the stem for every located branch.

5.3.3 Data Analysis

The methodology of data analysis described for tree growth and structure was applied to the

analysis of wood growth and structure, with the exception of wood anatomy. Wood

anatomical properties are known to exhibit a high degree of genetic variability, a

characteristic that can reduce the chance of identifying variability caused by environmental

conditions. In order to allow a higher degree of ‘noise’ into the analysis whilst still identifying

trends in variability due to environmental conditions, the threshold of significance was

reduced from 95% to 90% confidence for the analysis of wood anatomical properties. It will

be noted that despite the reduction in significance to 90% confidence, the p value of most

variables and their interactions remained below 0.05, indicating that these variables would be

accepted at the 95% confidence level.


The main objective of the data analysis of wood growth and structure was to investigate the

effects of planting density (P) and stem diameter (DBH) on wood variables. Since wood is

known to be affected by position in the stem, the effects of sample positions (where

applicable) on wood variables were also investigated. Sample positions included sample

height at breast height (SHBH) and at 25%, 50% and 75% of stem height (SH%); sample

diameter at 50% and 100% of stem diameter (SD%); branch height (BH); and branch aspect

(BAS

X).



5.4.1 Sapwood

In addition to testing the effects of planting density and stem diameter on sapwood, the effect

of sample height (SHX) was also tested. The sample heights tested were breast height (1.3 m)

and 25%, 50% and 75% of stem height.

SAPWOOD BASAL AREA

Increased competition had positive and negative effects on sapwood basal area (Table 5.3).

Sapwood basal area increased as planting density increased, although the effect was not

significant at sample heights SHBH and SH25%, whereas it decreased as stem diameter

decreased (Figure 5.9).

Table 5.3: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and sample height (SH%) on sapwood basal area (m

2).


INTERCEPT -0.0027 0.0010 p = 0.007 SH%*DBH2 -1.0092 0.0470 p < 0.001

SH% 0.0047 0.0011 p < 0.001 SH%*lnP -0.0006 0.0001 p < 0.001

DBH2 0.6421 0.0404 p < 0.001 DBH

2*lnP -0.0209 0.0059 p < 0.001

lnP 0.0004 0.0001 P = 0.003 SH%*DBH2*lnP 0.0610 0.0069 p < 0.001

0.00

0.01

0.02

0.03

0.0 0.1 0.2 0.3DBH (m)

Sa

pw

oo

d B

as

al

Are

a (

m2)

0.0 0.1 0.2 0.3

DBH (m)

(d) SH75%

0.0 0.1 0.2 0.3DBH (m)

(c) SH50%

0.0 0.1 0.2 0.3DBH (m)

(b) SH25%


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

(a) SHBH

Figure 5.9: The relationship between the dependent variable sapwood basal area (m

2) and the factors

DBH (m), P (st/ha) and SH (%) at sample positions (a) SHBH, (b) SH25%, (c) SH50% and (d) SH75%. The predicted values of sapwood basal area (with 95% confidence intervals) are plotted against DBH and identified by P.


Sample height had a negative effect on sapwood basal area, since for a given planting density

and stem diameter sapwood basal area decreased as stem height increased (Figure 5.9(a-d)).

This result was expected since stems are known to taper as stem height increases. Overall the

results show that larger stems have a greater area of sapwood, however it was unclear whether

the relative amount of sapwood in stems changed due to competition.

SAPWOOD RATIO

Increased competition had no effect on sapwood ratio (Table 5.4); although there was a trend

for high planting densities to exhibit greater sapwood ratio as sample height increased (Figure

5.10).

Table 5.4: The fixed-effect regression coefficients in the random intercept model of the effects of planting density (P) (st/ha) and sample height (SH%) on sapwood ratio.


INTERCEPT 0.6697 0.0214 p < 0.001 SH%*lnP 0.0000124 0.0000028 p < 0.001

SH% 0.3263 0.0168 p < 0.001

lnP -0.0000023 0.0000036 p = 0.523

0.4

0.6

0.8

1.0

0.0 0.1 0.2 0.3DBH (m)

Sap

wo

od

Rati

o (

%)

0.0 0.1 0.2 0.3

DBH (m)

(d) SH75%

0.0 0.1 0.2 0.3DBH (m)

(c) SH50%

0.0 0.1 0.2 0.3DBH (m)

(b) SH25%


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

(a) SHBH

Figure 5.10: The relationship between the dependent variable sapwood ratio and the factors P (st/ha) and SH% at sample positions (a) SHBH, (b) SH25%, (c) SH50% and (d) SH75%. The predicted values of sapwood ratio (with 95% confidence intervals) are plotted against DBH and identified by P.


The factor most affecting sapwood ratio is sample height, since the sapwood ratio decreases

(heartwood ratio increases) as sample height decreases (Figure 5.10(a-d); which is to be

expected given that heartwood formation starts at the base of the stem.

Given that the sapwood ratio is similar between all trees and that the ratio of stem size (mass

and volume) to leaf area is greater in high planting densities (Figure 4.20(b,e)), the above

findings indicate that increased competition intensity results in increased stemwood water-

flow capacity per unit crown size. This relationship could be advantageous to trees by

allowing water stressed trees in high planting densities to take rapid advantage of water when

it does become available by increasing water-flow capacity. Alternatively increased water-

flow capacity could be due to an inability of trees in high planting densities to form

heartwood. Evidence shows that heartwood formation requires substantial energy input

(Panshin and De Zeeuw 1980; Wilson and White 1986), and decreased heartwood ratio

(increased sapwood ratio) relative to crown size in trees in high planting densities could be

due to a shortage of energy for forming heartwood. In this case increased stemwood water-

flow capacity per unit crown size may be disadvantageous to the tree, possibly by requiring a

greater proportion of captured water to remain in the stem (to maintain turgor pressure) rather

than transfer to the crown.

5.4.2 Wood Anatomy

In addition to testing the effects of planting density and stem diameter on wood anatomy, the

effects of sample height (SHX) and sample diameter (SDX) were also tested. The sample

heights tested were breast height (1.3 m) and 50% of stem height and the sample diameters

tested were 50% and 100% of stem diameter.

STEMWOOD RAY RATIO

Rays are physiologically active wood cells with the primary role of food storage (Panshin and

De Zeeuw 1980), and it is likely that the ratio of rays in stemwood is to some degree

indicative of the capacity of the tree to produce food requiring storage. Stemwood ray ratio

was found to decrease as competition increases (Table 5.5), as shown by the decrease in

stemwood ray ratio as planting density increases at SD50% and SD100% (Figure 5.11(a,b)), and

as shown by the decrease in stemwood ray ratio as stem diameter decreases at SD100% (Figure

5.11(b)). Stemwood ray ratio was unaffected by stem height sample position.


Table 5.5: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and sample diameter (SD%) on stemwood ray ratio.


INTERCEPT 0.246 0.028 p < 0.001 SD%*DBH 0.638 0.171 p < 0.001

lnP -0.013 0.002 p < 0.001

SD% -0.090 0.021 p < 0.001

DBH -0.356 0.143 p = 0.012

0.00

0.05

0.10

0.15

0.20

0.0 0.1 0.2 0.3DBH (m)

Ste

mw

oo

d R

ay R

ati

o

0.0 0.1 0.2 0.3DBH (m)

(b) SD100% (a) SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.11: The relationship between the dependent variable stemwood ray ratio and the factors P (st/ha), DBH (m) and SD% at sample positions (a) SD50% and (b) SD100%. The predicted values of stemwood ray ratio (with 90% confidence intervals) are plotted against DBH and identified by P.

In the smallest trees (low end of DBH range) stemwood ray ratio decreased from SD50% to

SD100% in planting densities 1,000-10,000 st/ha (Figure 5.11), whereas in the largest trees

(high end of DBH range) stemwood ray ratio increased from SD50% to SD100% in planting

densities 250-1,000 st/ha (Figure 5.11). In both cases it is feasible that this correlates to

relative resource availability and excess food storage in the trees. In the case of the smallest

(suppressed) trees from 1,000-10,000 st/ha, it is likely that when the inner stemwood formed

at an earlier point in time, competition and relative resource capture were more uniform

between trees. As competition intensified, however, the relative resource capture would have

fallen by a greater amount for suppressed trees than for dominant trees, with the result that

recently formed stemwood in suppressed trees has a reduced stemwood ray ratio due to a

reduced stimulus for excess food storage. In the case of the largest (dominant) trees from 250-


1,000 st/ha, it is possible that these stands had not achieved full site occupancy at the point in

time at which the inner stemwood formed. In consequence there was opportunity for

dominant trees to increase relative resource capture, resulting in increased stemwood ray ratio

in recently formed wood due to an increased stimulus for excess food storage.

Between the two heights sampled stemwood ray ratio was unaffected by stem height,

suggesting that the stimulus affecting stemwood ray ratio is equal along the length of the

stem. This finding strengthens the above hypothesis that ray formation is stimulated by

relative resource capture and excess food production, since sugar concentrations in the sap are

likely to be reasonably uniform throughout the stem.

The results for stemwood ray ratio provide evidence that ray formation is a physiologically

responsive characteristic (Bamber et al. 1982). Since rays serve the purpose of storing excess

food it is probable that trees with a greater stemwood ray ratio have greater relative resource

capture and a greater propensity to ‘insure’ against resource loss by producing and storing

excess food. The results indicate that suppressed trees are most likely to die during periods of

resource shortage since they have a reduced food storage capacity. In terms of the physiology

of wood formation, it is of interest to determine whether stemwood ray ratio changed due to a

change in ray size and/or ray frequency.

RAY HEIGHT

Exploratory observations revealed that rays in E. grandis exhibit greater variability in height

than in width since they are almost universally uniserate (one cell wide), but ranged in height

from a few cells high to over 20 cells high. Ray height was therefore considered the best

measure of ray size, and any change in stemwood ray ratio not correlating with ray height

could then be attributed to a change in ray frequency.

Ray height was found to decrease as competition increased (Table 5.6), as shown by the

decrease in ray height as stem diameter decreases at all sample positions (Figure 5.12(a-d)).

Ray height showed a trend to decrease as planting density increased; however this was

significant only in interaction with radial and vertical variation in ray height, whereby the

trend for ray height to decrease as planting density increased became stronger with increased

sample height and increased sample diameter (Figure 5.12(a-d)). The results for ray height

indicate that ray size is positively correlated with the rate of stem growth.


Table 5.6: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on mean ray height (µm).


INTERCEPT 151.44 27.40 p < 0.001 lnP* SD%* SH% -7.49 3.17 p = 0.018

DBH 255.74 69.17 p < 0.001

lnP -1.52 2.50 p = 0.543

SD% 13.05 9.43 p = 0.166

SH% 44.00 20.08 p = 0.028

100

150

200

250

300

0.0 0.1 0.2 0.3DBH (m)

Me

an

Ray

Heig

ht

(µm

)

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

100

150

200

250

300

Me

an

Ra

y H

eig

ht

(µm

) (a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.12: The relationship between the dependent variable mean ray height (µm) and the factors P (st/ha), DBH (m), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of mean ray height (with 90% confidence intervals) are plotted against DBH and identified by P.

The decrease in stemwood ray ratio due to increased planting density (Figure 5.11) was

probably the result of decreased ray frequency rather than decreased ray height, since ray

height has been shown to be largely unaffected by planting density (Figure 5.12). Altogether,

the examination of ray morphology suggests that ray frequency is negatively correlated with

planting density whereas ray size is positively correlated with stem growth rate, with the

result that stemwood ray ratio decreases as competitive pressure (increased planting density

and decreased stem diameter) increases.


STEMWOOD VESSEL RATIO

Vessels are physiologically active wood cells with the primary role of water translocation

(Panshin and De Zeeuw 1980; Wilson and White 1986), and the ratio of vessels in stemwood

may therefore be indicative of the water translocation capacity of the tree. Stemwood vessel

ratio was found to be unaffected by increased competition, except in the most suppressed

stems (Table 5.7, Figure 5.13).

Table 5.7: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), sample height (SH%) and sample diameter (SD%) on stemwood vessel ratio.


INTERCEPT 0.2084 0.0138 p < 0.001 DBH-1*SH%

2 -0.0169 0.0065 p = 0.009

DBH-1 -0.0013 0.0009 p = 0.149 DBH

-1* SD%*SH%

2 0.0237 0.0064 p < 0.001

SD% -0.0419 0.0124 p = 0.001

SH%2 0.0060 0.0475 p = 0.899

0.05

0.10

0.15

0.20

0.25

0.0 0.1 0.2 0.3DBH (m)

Ste

mw

oo

d V

es

sel

Ra

tio

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

0.05

0.10

0.15

0.20

0.25

Ste

mw

oo

d V

esse

l R

ati

o (a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%

Raw Data Predicted Value Predicted Value 90%C.I.

Figure 5.13: The relationship between the dependent variable stemwood vessel ratio and the factors DBH (m), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of stemwood vessel ratio are plotted against DBH (with 90% confidence intervals).

Stemwood vessel ratio was unaffected by increased planting density and was largely

unaffected by stem diameter, except in the smallest stems in which it was marginally

decreased (Figure 5.13(a-d)). In practical terms average stemwood vessel ratio was constant at

16-18% between the points sampled, corresponding to previous findings that vessel ratio is


constant at between 10-20% of wood volume (Wilkes 1988). In relation to water translocation

through the sapwood, the above results indicate that the average water translocation capacity

(vessel content) of the sapwood is similar between planting densities and between dominance

classes, except for the most suppressed trees in which the average water translocation capacity

is reduced.

VESSEL DIAMETER

Exploratory observations revealed that the horizontal cross-section of vessels was elliptical,

and that the longest cross-sectional diameter was in the radial direction, suggesting that

vessels were ‘squashed’ in the radial direction during formation. It was therefore thought

appropriate to analyse vessel tangential diameter and vessel radial diameter separately, rather

than analyse vessel diameter as the average of the two.

Increased competition had positive and negative effects on vessel tangential and radial

diameter (Table 5.8(a,b)). Vessel tangential diameter increased in response to increased

planting density, and decreased in the most suppressed (smallest) stems (Figure 5.14). Vessel

radial diameter followed a similar pattern, however the effect of planting density was reduced

(planting density had no significant effect at individual sample positions), whereas the effect

of stem diameter was increased (there was a more pronounced slope between stem diameter

and vessel radial diameter) (Figure 5.15).

Table 5.8: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on (a) vessel tangential diameter (µm) and (b) vessel radial diameter (µm).


INTERCEPT 59.71 9.21 p < 0.001 lnP*SH%2 -16.32 7.88 p = 0.038

SD% 28.75 4.50 p < 0.001 SD%*lnP*SH%2 23.23 6.00 p < 0.001

DBH-1 -1.89 0.30 p < 0.001 DBH

-1*lnP*SH%

2 0.75 0.39 p = 0.034

lnP 3.46 1.18 p = 0.003 SD%*DBH-1*lnP*SH%

2 -1.37 0.46 p = 0.003

SH%2 1.52 43.92 p = 0.972


INTERCEPT 126.04 15.52 p < 0.001 DBH-1*SH%

2 -57.41 25.29 p = 0.023

SD% 21.94 7.01 p = 0.002 SH%2*lnP -54.52 20.02 p = 0.006

DBH-1 -3.58 0.51 p < 0.001 SD%*DBH

-1*SH%

2 56.44 23.33 p = 0.016

lnP 3.63 2.02 p = 0.072 SD%*SH%2*lnP 43.25 10.00 p < 0.001

SH%2 159.73 151.60 p = 0.292 DBH

-1*SH%

2*lnP 8.25 2.84 p = 0.004

SD%*DBH-1*SH%

2*lnP -8.86 2.51 p < 0.001

(a)

(b)


0

50

100

150

200

0.0 0.1 0.2 0.3DBH (m)

Me

an

Ve

ss

el

Ta

ng

en

tial

Dia

me

ter

(µm

)

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

0

50

100

150

200(a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.14: The relationship between the dependent variable mean vessel tangential diameter (µm) and the factors DBH (m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of mean vessel tangential diameter (with 90% confidence intervals) are plotted against DBH and identified by P.

0

50

100

150

200

0.0 0.1 0.2 0.3DBH (m)

Me

an

Ve

ss

el

Ra

dia

l D

iam

ete

r (µ

m)

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

0

50

100

150

200(a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.15: The relationship between the dependent variable mean vessel radial diameter (µm) and the factors DBH (m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of mean vessel radial diameter (with 90% confidence intervals) are plotted against DBH and identified by P.


Vessel tangential and radial diameter increased as sample diameter position increased (from

SD50% to SD100%), however vessel tangential and radial diameter were little affected by

increased sample height. Initial observations that vessel radial diameter exceeds vessel

tangential diameter are visible in a comparison of the results (Figures 5.14, 5.15), and it is

apparent that the difference between the two increases as stem diameter increases.

The hypothesis that vessels are ‘squashed’ in the radial direction during formation suggests

that vessel radial diameter is more affected by growth rate than is vessel tangential diameter.

The above results support this hypothesis since vessel radial diameter was more affected by

stem diameter (growth rate) than was vessel tangential diameter. It is likely that in growing

vessels the least resistance to expansion is towards the bark, hence their elliptical shape in the

radial direction, particularly in rapidly expanding vessels in faster growing trees. The pressure

to ‘find’ space would apply to all expanding wood cells, yet vessels in particular were

observed to have an elliptical shape. Their more plastic response could be due to having

relatively thin, and therefore more elastic, cell walls.

The relationship between vessel diameter (radial and tangential), stem diameter and planting

density indicates that there was little difference in vessel diameter between the dominant

(largest) trees in each planting density (Figures 5.14, 5.15). This is similar to the finding that

there was little difference in stem height between the dominant trees in each planting density

(Figure 4.3), suggesting that vessel diameter is controlled either by stem height or the same

stimuli as stem height. A comparison between stem height and stem diameter as the primary

factor explaining variation in vessel diameter (Table 5.9) showed that stem height explained

more variation in vessel diameter than stem diameter, particularly for vessel tangential

diameter.

Table 5.9: A comparison between stem diameter and stem height as the primary predictive variable for (a) vessel tangential diameter (µm) and (b) vessel radial diameter (µm). The comparison is made through the deviance reduction (maximum likelihood) whereby the greater the reduction in deviance, the better the fit between the dependent and predictive variables.

(a) MODEL DEVIANCE DEVIANCE REDUCTION (b) MODEL DEVIANCE DEVIANCE REDUCTION

INTERCEPT 15194.07 Empty Model INTERCEPT 16464.26 Empty Model

INTERCEPT + DBH 15168.55 15194.07 – 15168.55 = 25.52 INTERCEPT + DBH 16426.66 16464.26 – 16426.66 = 37.60

INTERCEPT + SH 15151.92 15194.07 – 15151.92 = 42.15 INTERCEPT + SH 16424.72 16464.26 – 16424.72 = 39.54

Overall the results indicate that the best predictor of vessel diameter was stem height,

however stem diameter was also an important predictor of vessel diameter as vessel radial


diameter was positively affected by stem radial growth rate. Since stemwood vessel ratio was

constant regardless of competition or sample position, vessel frequency may be assumed to

follow the opposite pattern to vessel diameter, so that stemwood vessel ratio remains constant.

STEMWOOD FIBRE RATIO

Fibres are physiologically inactive wood cells with the primary role of structural support.

Fibre properties affect wood quality since increased fibre content results in stronger, more

durable wood and decreased spatial variation in fibre properties results in more stable wood

(Zobel and Buitjtenen 1989; Malan et al. 1997). Stemwood fibre ratio was found to increase

as competition increased (Table 5.10), as shown by the increase in stemwood fibre ratio as

planting density increased, particularly at SD50% (Figure 5.16(a,c)), and by the increase in

stemwood fibre ratio as stem diameter decreased, particularly at SD100% (Figure 5.16(b,d)).

Table 5.10: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on stemwood fibre ratio.


INTERCEPT 0.4949 0.0951 p < 0.001 DBH*SD% -0.6774 0.3176 p = 0.033

DBH 0.2215 0.2541 p = 0.383 SD%*lnP -0.0182 0.0110 p = 0.098

SD% 0.2780 0.1137 p = 0.014 SD%*SH%2 -0.2252 0.0932 p = 0.016

lnP 0.0237 0.0095 p = 0.013

SH%2 0.1451 0.0736 p = 0.049

The results show that the stemwood fibre ratio of dominant (large) trees in each planting

density remained relatively constant regardless of sample position. Given that fibres occupy

the space in wood that is unoccupied by physiologically active cells, the above finding

indicates that dominant trees have maintained their ratio of physiologically active wood cells

over the measurement period. In contrast, at SHBH the stemwood fibre ratio of suppressed

(small) trees increased from earlier to later formed wood (from SD50% to SD100%) (Figure

5.16), indicating a reduction in the ratio of physiologically active wood cells over time. These

results show that the ratio of physiologically active wood cells decreases as relative resource

capture decreases.

Given that fibres provide the primary structural support for the stem and tree, the finding that

larger trees had a smaller stemwood fibre ratio suggests that faster grown trees will be less

dense than slower growing trees. Previous studies of wood properties, however, do not

consistently report such a correlation, especially in relation to eucalyptus wood density. It is


therefore appropriate to investigate fibre morphology to examine whether stems may

compensate for a reduced stemwood fibre ratio by increasing individual fibre strength.

0.6

0.7

0.8

0.9

1.0

0.0 0.1 0.2 0.3DBH (m)

Ste

mw

oo

d F

ibre

Rati

o

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

0.6

0.7

0.8

0.9

1.0

Ste

mw

oo

d F

ibre

Rati

o (a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.16: The relationship between the dependent variable stemwood fibre ratio and the factors DBH

(m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of stemwood fibre ratio (with 90% confidence intervals) are plotted against DBH and identified by P.

FIBRE AND FIBRELUMEN DIAMETER

Fibre and fibrelumen diameter were found to increase as competition increased (Table

5.11(a,b)), as shown by the increase in mean fibre diameter (Figure 5.17(a,b)) and the increase

in mean fibrelumen diameter (Figure 5.17(c,d)) as planting density increased. The effect of

increased competition on fibre and fibrelumen diameter was reduced compared to the effect of

increased competition on other cell types, since mean fibre diameter and mean fibrelumen

diameter were unaffected by decreased stem diameter.

Table 5.11: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on (a) fibre diameter (µm) and (b) fibrelumen diameter (µm).


INTERCEPT 8.0350 0.9130 p < 0.001 SH%-1*SD% -0.2070 0.1160 p = 0.074

SH%-1 0.0670 0.0440 p = 0.128 SH%

-1*SD%*lnP 0.0300 0.0130 p = 0.021

SD% 1.2790 0.4860 p = 0.008

lnP 0.2670 0.1050 p = 0.011

(a)



INTERCEPT 5.0540 1.0420 p < 0.001 SH%-1*SD% -0.3630 0.1320 p = 0.006

SH%-1

0.0650 0.0500 p = 0.194 SH%-1*SD%*lnP 0.0460 0.0150 p = 0.002

SD% 1.2290 0.5570 p = 0.027

lnP

0.3350 0.1190 p = 0.005

6

9

12

15

Me

an

Dia

mete

r (µ

m)

6

9

12

15

0 10 20 30 40 50 60SH%

Me

an

Dia

mete

r (µ

m) (c) Fibrelumen SD50%

(b) Fibre SD100% (a) Fibre SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

0 10 20 30 40 50 60SH%

(d) Fibrelumen SD100%

Figure 5.17: The relationship between the dependent variables mean fibre diameter (µm) and mean fibrelumen diameter (µm) and the factors P (st/ha), SH% and SD%. The predicted values of mean fibre diameter (with 90% confidence intervals) are plotted against SH% and identified by P at sample positions (a) SD50% and (b) SD100%. The predicted values of mean fibrelumen diameter (with 90% confidence intervals) are plotted against SH% and identified by P at sample positions (c) SD50% and (d) SD100%. The 90% confidence intervals at SH50% are offset so as not to obscure each other.

That mean fibre and fibrelumen diameter were unaffected by growth rate (stem diameter)

provides evidence for the general consensus that fibre dimensions do not exhibit a plastic

response to environmental conditions. The finding, however, that increased planting density

resulted in increased mean fibre and fibrelumen diameter shows that environmental conditions

can affect fibre dimensions. The results for fibre and fibrelumen diameter are similar to those

of crown height (Figure 4.13) as both were increased by increased planting density, but

unaffected by stem diameter. This indicates a positive correlation between crown height and

fibre and fibrelumen diameter, suggesting that increased crown height (increased mean

distance of the crown from stemwood formation) may have an effect on mean fibre and

fibrelumen diameter.

(b)


The pattern of change in mean fibre and fibrelumen diameter with sample height supports the

above hypothesis, since lower stem sample height (SH%), or increased distance from the

crown, resulted in increased mean fibre and fibrelumen diameter (Figure 5.17). Furthermore,

the above trend was strongest in high planting densities which have greater crown height

(Figure 4.13) and for which the reduction in sample position from SH50% to SHBH could result

in a greater increase in the relative distance from the crown. These findings are similar to a

trend found in fibre length (Wilkes 1988) whereby fibre length increases as crown proximity

decreases. In that case it was suggested that increased fibre size at lower levels in the stem

may be due to reduced apical influence and less rapid wood formation, with the result that

fibres have more time to reach full size before the secondary wall is laid down.

The results corroborate previous findings that fibre diameter increases with radial growth of

the stem, since mean fibre diameter increases as sample diameter increases, especially in high

planting densities. The results also show that suppressed stems tend to have a greater change

in wood properties with radial growth of the stem, since the mean fibre and fibrelumen

diameters of high planting densities change by a greater degree from SD50% to SD100% than the

mean fibre and fibrelumen diameters of low planting densities.

Overall the results for mean fibre and fibrelumen diameter generally confirm previous

findings, with the exception that increased planting density had a positive effect on mean fibre

and fibrelumen diameter, possibly by reducing crown influence on wood at the time of

formation. In combination, mean fibre diameter and mean fibrelumen diameter follow similar

patterns and it is difficult to determine whether fibrewall thickness is changed. It is therefore

appropriate to examine fibrewalls directly.

FIBREWALL RATIO

Fibrewall ratio was found to decrease as competition increased (Table 5.12), as shown by the

decrease in fibrewall ratio as planting density increased, particularly at SHBH (Figure

5.18(c,d)), and by the decrease in fibrewall ratio as stem diameter decreased, particularly at

SH50% (Figure 5.18(a,b)).


Table 5.12: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on fibrewall ratio.


INTERCEPT 0.6146 0.1051 p < 0.001 lnP* SD% -0.0263 0.0157 p = 0.094

lnP -0.0256 0.0125 p = 0.041 SH%2* SD% -1.3581 0.3594 p < 0.001

DBH2 -0.3242 0.7955 p = 0.684 lnP* DBH

2* SH%

2 1.2050 0.5745 p = 0.036

SH%2 -0.0523 0.1373 p = 0.703 lnP* SH%

2* SD% 0.1547 0.0412 p < 0.001

SD% 0.2194 0.1267 p = 0.083

0.2

0.4

0.6

0.8

0.0 0.1 0.2 0.3DBH (m)

Me

an

Fib

rew

all R

ati

o

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

0.2

0.4

0.6

0.8

Mea

n F

ibre

wall

Rati

o

(a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.18: The relationship between the dependent variable mean fibrewall ratio and the factors DBH (m), P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of fibrewall ratio (with 90% confidence intervals) are plotted against DBH and identified by P.

Comparison between the results for stemwood fibre ratio (Figure 5.16) and fibrewall ratio

(Figure 5.18) reveal that the pattern of the results are generally opposing. Where stemwood

fibre ratio had a positive correlation with planting density, fibrewall ratio had a negative

correlation with planting density, and where stemwood fibre ratio had a negative correlation

with stem diameter, fibrewall ratio had a positive correlation with stem diameter, both of

which indicate that stems compensate for a smaller ratio of fibres by increasing relative

fibrewall thickness; thereby maintaining adequate mechanical support. Overall it was unclear

whether total fibrewall material in the stemwood was increased or decreased by increased

competition due to the compensation effect between stemwood fibre ratio and fibrewall ratio.


STEMWOOD FIBREWALL RATIO

Stemwood fibrewall ratio is the ratio of stemwood containing fibrewall material (rather than

the ratio of fibre containing fibrewall material). Stemwood fibrewall ratio was found to

decrease as competition increased (Table 5.13), as shown by the decrease in stemwood

fibrewall ratio as planting density increased (Figure 5.19). In practical terms, however, the

effect of competition was not strong as the difference in stemwood fibrewall ratio due to

planting density was small at an approximate 6% mean difference between 250 st/ha and

10,000 st/ha, and stemwood fibrewall ratio was unaffected by stem diameter.

Table 5.13: The fixed-effect regression coefficients in the 90% confidence interval random intercept model of the effects of planting density (P) (st/ha), sample height (SH%) and sample diameter (SD%) on stemwood fibrewall ratio.


INTERCEPT 0.2073 0.1082 p = 0.055 lnP* SH%2 -0.1692 0.0757 p = 0.025

lnP 0.0100 0.0135 p = 0.459 lnP* SD% -0.0456 0.0169 p = 0.007

SH%2 1.4652 0.6052 p = 0.015 SH%

2* SD% -2.5830 0.7655 p = 0.001

SD% 0.3916 0.1354 p = 0.004 lnP* SH%2* SD% 0.3016 0.0958 p = 0.002

0.1

0.2

0.3

0.4

0.5

0.0 0.1 0.2 0.3DBH (m)

Ste

mw

oo

d F

ibre

wa

ll R

ati

o

0.0 0.1 0.2 0.3DBH (m)

(d) SHBH SD100%

0.1

0.2

0.3

0.4

0.5

Ste

mw

oo

d F

ibre

wa

ll R

ati

o

(a) SH50% SD50% (b) SH50% SD100%

(c) SHBH SD50%


250 90% C.I. 1,000 90% C.I. 5,000 90% C.I. 10,000 90% C.I.

Raw Data

Figure 5.19: The relationship between the dependent variable stemwood fibrewall ratio and the factors P (st/ha), SH% and SD% at sample positions (a) SH50% SD50%, (b) SH50% SD100%, (c) SHBH SD50% and (d) SHBH SD100%. The predicted values of stemwood fibrewall ratio (with 90% confidence intervals) are plotted against DBH and identified by P.

The results show that stemwood fibrewall ratio in planting densities 5,000-10,000 st/ha has

low spatial variability as it is unaffected by stem position. Stemwood fibrewall ratio is also


unaffected by sample position in planting densities 250-1,000 st/ha, except at sample height

SHBH and sample diameter SD100% (Figure 5.19(d)), where stemwood fibrewall ratio is

significantly greater than at most other sample positions. This finding coincides with high

fibrewall ratio in planting densities 250-1,000 st/ha at the same sample position (Figure

5.18(d)), indicating that the increase in stemwood fibrewall ratio in planting densities 250-

1,000 st/ha was due to increased fibrewall ratio.

Overall the results for stemwood fibre ratio and stemwood fibrewall ratio indicate that despite

trees in high planting densities exhibiting a higher stemwood fibre ratio, they actually

distribute less biomass to fibre production within a given volume of stemwood than if they

were at low planting densities. It was hypothesised that trees in high planting densities have

lower concentrations of photosynthate in the tree than trees in low planting densities, since

they exhibited a lower stemwood ray ratio (Figure 5.11) indicating a lower tendency to

produce excess food requiring storage. The results for stemwood fibre ratio provide evidence

for this hypothesis since a lower concentration of photosynthate in trees in higher planting

densities could trigger a more frugal use of photosynthate during fibre differentiation, leading

to relatively thin fibrewalls and less fibrewall material in the stemwood. Alternatively, trees in

high planting densities may have a reduced stemwood fibrewall ratio due to having smaller

crowns and more shelter from neighbouring trees and therefore less requirements for support,

however if this were the case one might expect no difference in the proportion of

physiologically active cells in the stemwood.

The consequence of the results for stemwood fibrewall ratio is that trees grown in high

planting densities are likely to have reduced variation in wood density, but lower wood

density overall than trees in lower planting densities. It is of interest to investigate wood

density to determine if results for wood anatomy correlate with and/or provide a physiological

explanation for changes in wood density.

5.4.3 Stemwood Basic Density

In addition to testing the effects of planting density and stem diameter on stemwood basic

density, the effect of sample height (SHX) was also tested. The sample heights tested were

breast height (1.3 m) and 25%, 50% and 75% of stem height.


Increased competition had positive and negative effects on stemwood basic density (Table

5.14). Stemwood basic density was found to decrease as planting density increased,

particularly at lower sample heights (Figure 5.20(a-b)), though the effect was not significant

at individual sample heights since the 95% confidence intervals overlapped between all

planting densities. In contrast, stemwood basic density increased in response to decreased

stem diameter, particularly in the most suppressed (smallest) trees (Figure 5.20). In addition,

stemwood basic density increased as sample height increased, though the effect was not

significant for the largest stems in 250 st/ha.

Table 5.14: The fixed-effect regression coefficients in the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and sample height (SH%) on stemwood basic density (kg m

-3).


INTERCEPT 490.951 51.013 p < 0.001 SH%2*DBH

-1 85.569 19.418 p < 0.001

SH%2 -558.986 153.174 p < 0.001 SH%

2*lnP 85.255 18.635 p < 0.001

DBH-1 4.507 2.041 p = 0.027 SH%

2*DBH

-1*lnP -9.004 2.257 p < 0.001

lnP -16.519 7.180 p = 0.021

300

400

500

600

700

0.0 0.1 0.2 0.3DBH (m)

Ste

mw

oo

d B

asic

Den

sit

y (

kg

m-3

)

0.0 0.1 0.2 0.3DBH (m)

(d) SH75%

0.0 0.1 0.2 0.3DBH (m)

(c) SH50%

0.0 0.1 0.2 0.3DBH (m)

(b) SH25%


250 95% C.I. 1,000 95% C.I. 5,000 95% C.I. 10,000 95% C.I.

Raw Data

(a) SHBH

Figure 5.20: The relationship between the dependent variable stemwood basic density (kg m

-3) and

the factors DBH (m), P (st/ha) and SH% at sample positions (a) SHBH, (b) SH25%, (c) SH50% and (d) SH75%. The predicted values of stemwood basic density (with 95% confidence intervals) are plotted against DBH and identified by P. Sample positions for which corresponding wood anatomy measurements have been analysed are highlighted with bold black borders.


In the previous examination of stemwood fibrewall ratio (Figure 5.19) it was hypothesised

that wood density would follow the same pattern as stemwood fibrewall ratio, whereby trees

in high planting densities would have reduced variation in wood density and lower overall

wood density than trees in low planting densities. In comparing results between the two,

however, it is important to note that different sampling techniques were used. Stemwood

fibrewall ratio was sampled at two points along the disk radius, whereas stemwood basic

density made measurement of the entire disk. Consequently the stemwood fibrewall ratio

results may not fully coincide with the stemwood basic density measurements. It is worth

noting that the stemwood fibrewall ratio measurements at SD100% are more likely to be

representative of stemwood basic density since the measurement taken at the outer

circumference of the disk is representative of a greater volume of the disk.

The results for stemwood basic density confirm that for a given stem diameter trees in high

planting densities tend to have lower wood density, but indicate that there is no significant

difference in wood density between the largest (dominant) trees in different planting densities

(Figure 5.20). As discussed previously, the results for stemwood basic density tend to

correlate more closely with the stemwood fibrewall ratio results at SD100% than those at

SD50%. This is apparent in the similar pattern of change between dominant trees in different

planting densities in both stemwood fibrewall ratio and stemwood basic density at sample

height SHBH (Figures 5.19(d), 5.20(a)) and sample height SH50% (Figures 5.19(b), 5.20(a)).

In contrast, the hypothesis that trees in high planting densities would have reduced variation

in wood density is refuted, since higher planting densities tended to show greater variation in

wood density as stem height increases, particularly in suppressed trees. It is noteworthy that

whatever the planting density, dominant trees vary little in basic density, particularly in the

lowest 0-6 m stem section (Figure 5.21). This indicates that dominant trees have similar wood

quality in the primary sawlog section of the stem regardless of planting density.


300

400

500

600

700

0 2 4 6 8 10 12 14 16

Sample Height (m)

Bas

ic D

en

sit

y (

kg

m-3

)250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha


Most Dominant Tree:

Most Suppressed Tree:

Figure 5.21: Variation in predicted stemwood basic density with increased stem height of the most dominant and most suppressed tree measured in each planting density.

The finding that high planting densities exhibited less spatial variation in stemwood fibrewall

ratio, but more spatial variation in stemwood basic density, compared to lower planting

densities, suggests that factors other than the proportion of stemwood comprised of fibrewall

material affect wood density. One such factor may be the density of the fibrewall material.

The secondary wall in fibre cells is the primary determinant of wood density (Wilkins 1986).

The secondary wall is comprised of linear, crystalline polymer strands called microfibrils,

which are laid down in latticed layers to form the cell wall (Figure 5.2). Following cell wall

formation lignin, a three-dimensional and amorphous polymer, is impregnated into spaces

between the microfibrils, essentially cementing them into place and strengthening the cell

wall structure. Theoretically the density of the fibrewall material could be increased with

improved lignin impregnation, since this would result in fewer ‘gaps’ left between

microfibrils. It is possible that patterns of lignin impregnation vary with planting density,

leading to greater variation in stemwood basic density in high planting densities than

suggested by the variation in stemwood fibrewall ratio.

Overall the results for stemwood basic density indicate no significant difference in the wood

quality of the primary sawlog section of the stem between dominant trees in different planting

densities. Suppressed trees have been shown to exhibit higher wood density, which could


prove advantageous for biomass quality by increasing the carbon content of wood; and higher

variability in wood density, which would not affect biomass quality.

5.4.4 Branching Habits

Branching habits are included in the analysis of wood growth and structure as they are

indicative of knot content in the wood structure. Conditions that stimulate persistent, live and

growing branches result in wood with increased knot content, which is detrimental to wood

quality for most end-uses.

The effects of branch height (BH) and branch aspect (BAS) on branching habits were tested in

addition to the effects of planting density and stem diameter. Specialised models assuming a

non-normal residual distribution (binomial and multinomial models) were used to analyse

branching habit data that had discrete rather than continuos distributions (i.e. data for branch

mortality were either dead (1) or alive (0) rather than a scale number) (Snijders and Bosker

1999; Rasbash et al. 2003). These models return the probability that branches will have the

characteristic under analysis (i.e. the probability that a branch will be dead), rather than a

predicted value.

BRANCH DIAMETER

Branch diameter decreased as competition increased (Table 5.15), as shown by the decrease in

branch diameter as planting density increased and stem diameter decreased (Figure 5.22(a)).

Table 5.15: The fixed-effect regression coefficients of the random intercept model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA

S)

(º) on branch diameter (m).


INTERCEPT 0.01826 0.00211 p < 0.001 lnBH* DBH2 0.10926 0.02011 p < 0.001

lnBH 0.00038 0.00032 p = 0.230 sin(BAS*

PI/180)*DBH

2 0.29903 0.07544 p < 0.001

sin(BAS*

PI/180) 0.00096 0.00048 p = 0.045 sin(BA

S*

PI/180)*lnP*DBH

2 -0.03679 0.01313 p = 0.005

lnP -0.00149 0.00023 p < 0.001

DBH2 0.11237 0.04386 p = 0.009


0.00

0.01

0.02

0.03

0.04

0.05

0.0 0.1 0.2

DBH (m)

Branch Diameter (m)

Raw Data 250 st/ha 1,000 st/ha 5,000 st/ha 10,000 st/ha

0 1 2 3 4 5 6

Branch Height (m)

(b)

0 90 180 270 360

Branch Aspect (°)

(c)(a)

Figure 5.22: The relationship between the dependent variable branch diameter (m) and the factors DBH (m), BH (m), P (st/ha) and BA

S (°). The predicted values of branch diameter are plotted against (a)

DBH, (b) BH and (c) BAS and identified by P. Branch diameter of 2 cm is highlighted by a bold black

line.

The results for branch diameter also show that increased branch height and an easterly aspect

(45-135°) resulted in increased branch diameter (Figure 5.22(b-c)), indicating that branches

receiving more incidental and/or morning light grow to a greater diameter. Light may not be

considered a limiting resource in eucalypt forests given their relatively open crown structure,

however the photosynthetic capacity of eucalyptus leaves is greatly enhanced by saturated

light with the result that limited nutrient and/or water resources are preferentially allocated to

leaves receiving more saturated light at the optimum time of day for photosynthesis

(mornings). Canopy light dynamics have a significant affect on branch diameter since branch

diameter growth is stimulated by the ability of its leaves to capture light for photosynthesis,

which is in turn affected by branch position. The results for branch diameter provide strong

evidence that light patterns and aboveground competition for light impact on eucalyptus

crown structure and influence growth dynamics and stand structure in eucalyptus plantations.

Observation of eucalyptus branch shed has found that branches up to 2 cm diameter shed well

(Jacobs 1955), whereas branches over 2 cm diameter may require pruning to reduce wood

knot content (Montagu et al. 2003). The model for branch diameter predicts that few branches

exceed 2 cm diameter (predicted values in Figure 5.22), whereas the raw values show that

many branches exceed 2 cm diameter. This underestimation of branch diameter by the model


is of concern as the model implies that pruning is not required to reduce knot content, whereas

the raw values suggest that pruning is required to reduce knot content. It is therefore

appropriate to investigate the occurrence of branches greater than (>) 2 cm diameter to

provide a better indication of whether pruning is required to minimise knot content.

BRANCH DIAMETERS > 2 CM

The occurrence of branch diameters > 2 cm was found to decrease as competition increased

(Table 5.16), as shown by the decrease in the probability of branch diameters > 2 cm as

planting density increases and stem diameter decreases (Figure 5.23(a)).

Table 5.16: The fixed-effect regression coefficients in the binomial model of the effects of stem diameter (DBH) (m), planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA

S) (º) on

the probability of branch diameter > 2 cm (BDx2

) where; BD>2

= 1, BD<2

= 0,

BD>2

model output = ln(#BD>2

/#BD<2)

BD>2

= exp(#BD>2

/#BD<2) / (1 + exp(#BD>2

/#BD<2))

BD<2

= 1 – BD>2


INTERCEPT -1.765 4.957 p = 0.722 lnP* sin(BAS*

PI/180) 0.535 0.236 p < 0.023

lnP 0.105 0.821 p = 0.898 lnP*DBH-1 -0.222 0.094 p < 0.018

lnBH -2.197 1.379 p = 0.111 lnBH* sin(BAS*

PI/180) -0.478 0.233 p = 0.040

sin(BAS*

PI/180) 1.398 0.318 p < 0.001

DBH-1 1.130 0.579 p = 0.051

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2

DBH (m)

Probability of Branch Diameter > 2 cm


0 1 2 3 4 5 6

Branch Height (m)

(b)

0 90 180 270 360

Branch Aspect (°)

(c)(a)

Figure 5.23: The relationship between the probability of branch diameter > 2 cm (BD>2

X) and the factors DBH (m), BH (m), P (st/ha) and BA

S (°). The predicted values of the probability of branch

diameter > 2 cm are plotted against (a) DBH, (b) BH and (c) BAS and identified by P.


The results for branch diameters > 2 cm show that increased branch height and an easterly

aspect (45-135°) result in an increased probability of branch diameters > 2 cm (Figure 5.23(b-

c)), confirming that branches in more light saturated situations are likely to grow larger. The

concern that the model of branch diameter underestimated branch diameters > 2 cm was

legitimate, since all planting densities are shown to have a chance of branch diameters > 2 cm

whereas the branch diameter model predicted that only the 250 st/ha planting density would

have branches > 2 cm (Figure 5.22). Overall the results suggest that high planting density

stands self-prune effectively, whereas low density stands would probably benefit from

pruning to reduce knot content. In consequence, the additional costs of establishing higher

density stands might be offset by a reduced requirement for pruning.

BRANCH ANGLE

Branch angle provides an indication of the progress of branch shed, with branches exhibiting

a low branch angle (angled towards the ground rather than the sky) being likely to shed more

rapidly. Branch angle was found to decrease as competition increased (Table 5.17(a)), since

branch angle decreased as planting density increased and stem diameter decreased. The effect

of increased competition, however, was clouded by interactions with branch height and

branch aspect, and the predicted values of branch angle do not provide a clear indication that

increased planting density and decreased stem diameter result in decreased branch angle

(Figure 5.24(a)). The effects of branch height and branch aspect on branch angle were

somewhat clearer; the results indicating that increased branch height and an easterly aspect

(45-135°) generally result in increased branch angle (Figure 5.24(b-c)).

Table 5.17: The fixed-effect regression coefficients in the random slope model of the effects of (a) stem diameter (DBH) (m), planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA

S)

(º) on branch angle (°); and (b) branch diameter (BD) (m) on branch angle (°).


INTERCEPT 107.074 6.662 p < 0.001 BH* sin(BAS*

PI/180) 1.888 0.438 p < 0.001

BH -8.269 3.433 p = 0.016 BH*DBH-1 1.632 0.388 p < 0.001

sin(BAS*

PI/180) 4.559 0.661 p < 0.001 BH*lnP*DBH

-1 -0.195 0.045 p < 0.001

lnP -3.530 1.017 p = 0.001

DBH-1 1.160 0.367 p = 0.002


INTERCEPT 62.145 2.440 p < 0.001

√BD 529.926 16.688 p < 0.001

(b)

(a)


40

80

120

160

200

0.0 0.1 0.2

DBH (m)

Branch Angle (°)


0 1 2 3 4 5 6

Branch Height (m)

(b)

0 90 180 270 360Branch Aspect (°)

(c)

0.00 0.03 0.06

Branch Diameter (m)

Raw Data Predicted Values

(d)

Deviance Reduction:

14408.3 - 13631.6 = 776.7

Deviance Reduction:

14408.3 - 13725.6 = 682.7

(a)

Figure 5.24: The relationship between the dependent variable branch angle (m) and the factors DBH (m), BH (m), P (st/ha) and BA

S (°), with the predicted values plotted against (a) DBH, (b) BH and (c)

BAS and identified by P; and the relationship between the dependent variable branch angle (m) and

the factor BD (m), with the predicted values plotted against (d) BD (with 95% confidence intervals) and identified by P. A comparison between the two relationships is supplied by the deviance reduction (maximum likelihood) whereby the greater the reduction in deviance, the better the fit between the dependent variable and the factors.

Given the large spread in the predicted values of branch angle in relation to planting density

and stem diameter, branch angle might be better fitted directly to another branch characteristic

that is affected by competition. This possibility was investigated with branch diameter (Table

5.17(b)), and the results indicate that the fit between branch angle and branch diameter was

better than the fit between branch angle and the factors (planting density, stem diameter,

branch height and branch aspect), as evidenced by a greater reduction in the (maximum

likelihood) deviance of the model (Figure 5.24). The strong positive relationship between

branch angle and branch diameter (Figure 5.24(d)) indicates that increased competition

(increased planting density and decreased stem diameter) will have similar effects on both

characteristics. In consequence it is confirmed that branch angle decreases as competition

increases, providing evidence that increased competition will result in more advanced branch

shed and therefore decreased knot content.


BRANCH MORTALITY

Branch mortality was found to increase as competition increased (Table 5.18), as shown by

the increase in the probability of branch mortality as planting density increased (Figure 5.25).

Branch mortality was unaffected by stem diameter.

Table 5.18: The fixed-effect regression coefficients in the binomial model of the effects of planting density (P) (st/ha), branch height (BH) (m) and branch aspect (BA

S) (º) on the probability of branch

mortality from 0-6 m stem height (BMX) where; dead = 1, live = 0,

dead model output = ln(#dead

/#live),

BMDEAD = exp(#dead

/#live) / (1 + exp(#dead

/#live))

BMLIVE = 1 – BMDEAD.


INTERCEPT -5.862 1.152 p < 0.001 lnBH*sin(BAS*

PI/180) 0.537 0.198 p = 0.007

lnP 1.403 0.168 p < 0.001

lnBH -1.149 0.144 p < 0.001

sin(BAS*

PI/180) -1.265 0.266 p < 0.001

0.0

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5 6Branch Height (m)

Probability of Branch Mortality


0 45 90 135 180 225 270 315 360Branch Aspect (°)

(b)(a)

Figure 5.25: The relationship between the dependent variable branch mortality (BMX) and the factors P (st/ha), BH (m) and BA

S (°). The predicted values of the probability of branch mortality are plotted

against (a) branch height and (b) branch aspect and identified by P.

The finding that increased planting density resulted in a greater probability of branch

mortality was expected since increased planting density leads to greater shading, thereby

stimulating an increased rate of branch senescence. In consequence, branches in planting

densities 5,000 st/ha and 10,000 st/ha had almost a 100% chance of mortality from 0-6 m


stem height (Figure 5.25). Branches in 1,000 st/ha may approach a similar probability of

mortality as branch senescence and crown lift continues, however branches in 250 st/ha may

never reach the same probability of mortality due to lack of shading in these stands. That stem

diameter had no effect on the probability of branch mortality indicates that branch mortality is

unaffected by growth rate; an hypothesis which is corroborated by the result that stem

diameter had no effect on crown height (Figure 4.13). These results provide further evidence

that stems in high planting densities have a reduced knot content compared to stems in low

planting densities since the process of branch shed is more advanced in high planting densities

due to the higher probability of branch mortality.

Further results for branch mortality show that the probability of mortality decreased as branch

height increased (Figure 5.25(a)). This is logical since high branches have a lower instance of

shading from overhead branches than low branches, and therefore a reduced chance of

mortality. Branch mortality was also affected by aspect, with branches occurring on easterly

aspects (45-135°) having a reduced probability of mortality (Figure 5.25(b)), suggesting that

morning (easterly) light is of greater importance to photosynthate production than afternoon

(westerly) light since branches with an easterly aspect are more persistent. This result seems

probable given that the photosynthetic capacity of leaves has been found to be highest in the

mornings, particularly on cloudless days (Kuppers et al. 1986; Whitehead and Beadle 2004).

This provides further evidence that light conditions are an important factor affecting crown

dynamics, even when light is apparently abundant as is the case for the 250 st/ha planting

density.

BRANCH FORM

Branch form is the stage of branch shed of individual branches, and the possible stages consist

of scars, stubs and branches (full-shed, part-shed and un-shed branches). Branch form skewed

towards scars and stubs rather than branches as competition increased (Table 5.19(a,b)), as

shown by the increased probability of scars and stubs (Figure 5.26(a-b)) and the decreased

probability of branches (Figure 5.26(c)) as planting density increased and stem diameter

decreased.


Table 5.19: The fixed-effect regression coefficients in the multinomial model(a)

of the effects of stem diameter (DBH) (m), planting density (P) (st/ha) and branch height (BH) (m) on the probability of branch form (BFX) where; scar = 0, stub = 1, branch = 2

scar model output = ln(#sc

/#br)

stub model output = ln(#st

/#br)

BFSCAR = exp(#sc

/#bn) / (1 + exp (#sc

/#br) + exp(#st

/#br))

BFSTUB = exp(#st

/#br) / (1 + exp(#sb

/#br) + exp(#sc

/#br))

BFBRANCH = 1 - (BFSCAR + BFSTUB)


INTERCEPT 1.381 0.545 p = 0.011 √DBH*P 0.000021 0.000011 p = 0.056(a)

√DBH -6.770 1.567 p < 0.001 BH-1*P -0.000383 0.000240 p = 0.110

(a)

BH-1 -0.031 0.062 p = 0.617

(a)

P 0.000166 0.000076 p = 0.029


INTERCEPT 1.037 0.668 p = 0.121 √DBH*P 0.000074 0.000023 p = 0.001

√DBH -5.611 1.897 p = 0.003 BH-1*P -0.001055 0.000317 p = 0.001

BH-1 -0.690 0.166 p < 0.001

P 0.000220 0.000097 p = 0.023

(a) Each table represents either the scar section or the stub section of a single multinomial model of branch form,

and consequently variables that are insignificant in one section must be included both sections if they are found significant in the other section, and vice versa.

0.0

0.2

0.4

0.6

0.8

1.0

0.0 0.1 0.2DBH (m)

Probability


Branch Form Scar

0.0 0.1 0.2

DBH (m)

(b)

0.0 0.1 0.2

DBH (m)

(c)

Branch Form Stub Branch Form Branch

0 1 2 3 4 5 6

Branch Height (m)

(f)

0 1 2 3 4 5 6

Branch Height (m)

(e)

0.0

0.2

0.4

0.6

0.8

1.0

0 1 2 3 4 5 6

Branch Height (m)

Probability

(d)

(a)

Figure 5.26: The relationship between the dependent variable branch form (BFX) and the factors DBH (m), BH (m) and P (st/ha). The predicted values of (a,d) the probability of scars (BFSc), (b,e) the probability of stubs (BFSt) and (c,f) the probability of branches (BFBr) are plotted against DBH and BH and identified by P.

(b)

(a)


The findings show that increased competition resulted in more advanced branch shed since

increased planting density and decreased stem diameter (Figure 5.26(a-c)) correlated with a

greater probability of advanced branch shed (scars and stubs), and a greater probability of

full-shed branches (scars) compared to part-shed branches (stubs). As with previous results,

these results further suggest that increased competition will result in reduced knot content in

the stemwood by stimulating more advanced branch shed.

The results for branch form also show that the higher the branch the greater the probability of

more advanced branch shed (Figure 5.26(d-f)). This result is surprising as lower branches

have a greater probability of branch mortality (Figure 5.25), and might therefore be expected

to exhibit more advanced branch shed. Observations in the field do show that very small

branches at the base of the stem can persist for a remarkably long time. It is possible that these

branches die at such a tiny size that when they are ejected from the wood layer they are not

heavy enough to apply the downward pressure required to break free of the bark layer, and

therefore remain persistent as tiny branches embedded in the bark.


5.5 Summary

Knowledge of wood growth and structure is an important aspect of plantation management

since wood is the primary product of plantations in Australia. The development of a solid

hardwood plantation industry is hampered by a lack of knowledge and experience in growing

eucalypts in plantations for sawlogs or veneer logs. The potential for current eucalyptus

pulpwood plantations to produce solid hardwood products is discounted since highly stocked

pulpwood plantations are considered unlikely to produce quality sawlogs (Turner et al. 2004).

This provides impetus to determine whether high stocking rates are detrimental to the wood

quality of final sawlog crop trees (dominant trees) in eucalyptus plantations.

In this study planting density (competition) had little effect on sapwood area or sapwood ratio

for a given stem diameter. Sapwood area increased with increased stem diameter, but

sapwood ratio did not change. Sapwood area decreased with height up the stem, and sapwood

ratio increased with height up the stem. Given that the sapwood ratio was similar between all

trees and that the ratio of stem mass and volume to leaf area was greater in high planting

densities, the findings indicate that increased competition resulted in increased stemwood

water-flow capacity per unit crown size. It was unclear whether an increased water-flow

capacity per unit crown size would be of advantage or disadvantage to the tree.

Dominant trees in high planting densities had no change in stemwood ray ratio or ray height

within the stem, and therefore no change in ray frequency. In contrast dominant trees in low

planting densities increased in stemwood ray ratio radially within the stem, and this was due

to increased ray frequency since ray height did not change within the stem. Dominant trees in

high planting densities exhibited a 5-10% lower stemwood ray ratio within the stem compared

to dominant trees in low planting densities, and this was in part due to having a shorter ray

height. The results suggest that dominant stems in high planting densities were not able to

produce as much excess food for storage as dominant trees in low planting densities, and may

therefore be less resilient to stressful events such as drought or defoliation.

Stemwood vessel ratio was unaffected by planting density, and in dominant trees the

stemwood vessel ratio was constant at approximately 17% within the stem. In contrast, vessel

diameter increased as planting density increased; however there was no significant difference

in vessel diameter between dominant trees in different planting densities. The effect of


planting density on vessel diameter was similar to the effect of planting density on stem

height, and stem height may therefore influence vessel diameter.

Stemwood fibre ratio increased with increased planting density, particularly in the inner stem,

and dominant trees in high planting densities had stemwood fibre ratios in the order of 6%

greater than dominant trees in low planting densities. Stemwood fibre ratio increased with

decreased stem diameter, particularly in the outer stem. Stemwood fibre ratio followed the

opposite pattern to stemwood ray ratio, indicating that higher fibre ratios were balanced by

lower ray ratios, and vice versa. In consequence, the relative constancy of stemwood fibre

ratio within the stem in dominant trees showed that the ratio of physiologically active wood

cells in dominant trees was maintained over time, possibly due to no change in relative

resource capture. In contrast, the stemwood fibre ratio in suppressed trees increased in more

recently formed wood, suggesting a reduction in the ratio of physiologically active wood cells

over time, possibly due to reduction in relative resource capture.

Both fibre and fibrelumen diameter increased as planting density increased, and as they were

unaffected by stem diameter, dominant trees in high planting densities exhibited greater fibre

and fibrelumen diameter than dominant trees in low planting densities. Increased crown

height, or decreased crown proximity to wood during wood formation, may stimulate

increased fibre and fibrelumen diameter, since crown height shares similar relationships with

planting density and stem diameter as do fibre and fibrelumen diameter. The effect of height

within the stem on fibre and fibrelumen diameter supports this hypothesis since decreased

height within the stem (decreased proximity to the crown) resulted in increased fibre and

fibrelumen diameter. Furthermore, the above trend was strongest in high planting densities

which had greater crown height and for which increased height within the stem could result in

a greater change in the relative proximity of the crown. In combination, the results did not

provide clear indication whether decreased stemwood fibre ratios were compensated by

increased fibrewall ratios as it was difficult to determine whether fibrewall thickness was

changed.

Direct investigation of fibrewall ratio showed that the fibrewall ratio decreased in response to

increased planting density and decreased stem diameter. The pattern of the results for

stemwood fibre ratio and fibrewall ratio were opposing, indicating that stems compensate for

a smaller ratio of fibres by increasing relative fibrewall thickness. It was unclear whether total


fibrewall material in the stemwood was increased or decreased by increased competition due

to the compensation effect between stemwood fibre ratio and fibrewall ratio.

Stemwood fibrewall ratio decreased as planting density increased, however the effect was not

significant throughout the whole stem. Stemwood fibrewall ratio did not change within the

stem in planting densities 5,000-10,000 st/ha, whereas in planting densities 250-1,000 st/ha

the stemwood fibrewall ratio increased in the lower, outer part of the stem, which coincided

with a high fibrewall ratio in planting densities 250-1,000 st/ha in the same stem section.

Stemwood fibrewall ratio was unaffected by stem diameter. The results indicate that trees in

high planting densities distribute less biomass to fibre production within a given volume of

stemwood than low planting densities. This could be due to several factors including low

concentrations of photosynthate could trigger a more frugal use of photosynthate during fibre

differentiation, resulting in a relatively thin fibrewall. Overall the results indicate that trees

grown in high planting densities are likely to have less variation in wood density, but lower

wood density overall than trees in low planting densities.

Increased competition had conflicting effects on stemwood basic density. Stemwood basic

density was found to decrease as planting density increased, though the effect was only

significant between 250 st/ha and 10,000 st/ha. In contrast, stemwood basic density was found

to increase as stem diameter decreased, particularly in suppressed trees. The wood anatomy

data did not indicate a plausible link between this relationship and the wood anatomy

variables measured. It is possible that anatomical variables other than those measured, such as

fibrewall density, also effect wood density. Height within the stem had a positive effect on

wood density, and trees in high planting densities exhibited greater increases in wood density

with increased height within the stem than trees in low planting densities, particularly in

suppressed trees. Overall there was little difference in wood density variation in the lower

stems (0-6 m) between dominant trees in high and low planting densities, indicating little

difference in the wood quality of the primary sawlog section of the stem between dominant

trees in different planting densities. Suppressed trees were shown to exhibit higher wood

density, which could prove advantageous for biomass quality by increasing the carbon content

of wood, and higher variability in wood density, which would not affect biomass quality.

Branching habits indicate that trees in high planting densities are likely to have lower knot

content than tree in low planting densities as they exhibited smaller branch diameters, lower


branch angles, a greater probability of branch mortality and more advanced branch shed. The

effects of branch height and branch aspect on most branch characteristics provide clear

evidence that aboveground competition for light has significant implications for growth

dynamics and stand structure in eucalyptus plantations. It was significant that high planting

densities had a much reduced probability of the presence of branches exceeding 2 cm

diameter, as this implies that high planting density stands self-prune effectively, whereas low

density stands would probably require pruning to reduce knot content. In consequence, the

additional costs of establishing higher density stands might be offset by a reduced requirement

for pruning.

Chapter 6 Conclusion Page 164

6. CONCLUSION

6.1 Synopsis

The majority of eucalyptus plantations in Australia are managed for pulpwood production, the

silviculture for which includes establishment with relatively high planting densities (over

1,000 st/ha). Due to diminishing supply from native forests, there is increasing pressure to

establish plantations for solid hardwood products. This process could be fast-tracked by

converting current eucalyptus pulpwood plantations to solid hardwood plantations. This

option, however, is generally discounted since higher stocked pulpwood plantations are

thought to restrict growth of the final sawlog crop and are considered unlikely to produce

solid hardwood with low wood variability (to prevent splitting and warping of sawn wood)

and high wood density (for strength and durability).

The above perception was investigated in this study by examining the effect of planting

density on stand, tree, and wood structure, particularly of the largest trees which represent the

likeliest source of solid hardwood products. This allowed a detailed comparison of tree

growth and structure between dominant trees from low and high planting densities, and

between dominant and suppressed trees within low and high planting densities. A wide range

of planting densities were used (250-10,000 st/ha) and in that a large number of trees were

destructively sampled from every dominance class in all planting densities. The investigation

of properties from the stand and tree levels through to the wood level facilitated a unique

insight into whole tree growth and the pathways by which trees respond to interaction due to

competition (Figure 1.1).

The examination of stand growth and structure in E. grandis at 4 years of age revealed that

eucalypt plantations established at high planting densities (5,000-10,000 st/ha) had the

potential to capture carbon in their stems twice as quickly as those planted at the more

common density of 1,000 st/ha or less. Stand structure showed evidence of strong competition

occurring in the high planting densities, and the largest trees in high planting densities were

smaller than the largest trees in low planting densities. Yet high density stands had a greater

number of co-dominant and intermediate trees compared to low density stands, with the result

that the combined stem volume of the largest 1,000 stem cohort in each planting density was

similar from planting densities 1,000-10,000 st/ha (Figure 6.1).


99 93

83127

92

0

50

100

150

200

250

1,000 5,000 10,000

Planting Density (st/ha)

Ste

m V

olu

me

(m

3h

a-1

)

Largest 1,000 st/ha (Solid Hardwood Crop)

Remaining Stems (Biomass Crop)

Figure 6.1: The stem volume of the largest 1,000 st/ha and the stem volume of the remaining stems in 4 year old E. grandis in planting densities 1,000 st/ha, 5,000 st/ha and 10,000 st/ha.

Investigation of the growth rate of 250 stem cohorts revealed that a drought in year 3

triggered declining growth rates in stem cohorts growing at less than 35% of the rate of the

dominant 250 stem cohort, and few stem cohorts recovered increased growth rates once

growth rate had started to decline It was unclear whether the decline in the productivity of

smaller stem cohorts was due to reduced relative resource capture or due to reduced growth

efficiency, nevertheless the results show that declining stand productivity could be due to

reduced productivity in smaller trees rather than larger trees.

The examination of tree growth and structure showed that dominant trees in high planting

densities were significantly smaller than dominant trees in low planting densities in all aspects

of tree growth other than stem height. Dominant trees in high planting densities had similar

tree oven-dry mass accumulation per unit leaf area to dominant trees in low planting densities,

but had a larger proportion of tree oven-dry mass allocated to the stem as opposed to the

crown. In consequence dominant trees in high planting densities had similar tree growth

efficiency but better stem growth efficiency than dominant trees in low planting densities.

Increased competition was therefore shown to restrict the growth of dominant trees by

restricting resource capture rather than by reducing the efficiency of growth, since the tree

growth efficiency of dominant trees was not significantly affected by competition. Dominant


trees in high planting densities compensated to some degree for restricted growth rates by

partitioning a greater proportion of tree growth to the stem.

Compared to dominant trees, suppressed trees had lower tree growth efficiency and lower

stem growth efficiency, and the hypothesis that declining stand productivity is due to reduced

tree growth efficiency in suppressed and intermediate trees was therefore supported. An

hypothesis was proposed that trees under greater shading (suppressed trees) had reduced tree

growth efficiency due to an unfavourable resource use-efficiency balance, however it was not

possible to further test this hypothesis in the course of the current study.

The examination of wood growth and structure showed that all trees had a similar sapwood

ratio regardless of dominance or planting density, however higher planting densities had

greater stemwood water-flow capacity per unit crown size since the ratio of stem volume to

leaf area was greater in higher planting densities. It was unclear whether this would be an

advantage to the tree.

In wood anatomy, dominant trees in high planting densities had stemwood ray ratios of

approximately 7.5%, which was 5-10% less than the ray ratio of dominant trees in low

planting densities. Since rays have the physiological function of storing excess food, the result

suggests that dominant trees in high planting densities did not produce as much excess food

for storage as dominant trees in low planting densities, and in comparison may be less

resilient to stressful events such as drought or defoliation. Dominant trees in high planting

densities had no significant change in ray ratio or ray height between sample positions,

suggesting constant relative resource capture, whereas dominant trees in low planting

densities had increased ray ratio as sample diameter increased (due to increased ray frequency

since ray height did not change), suggesting increased relative resource capture.

Dominant trees in high and low planting densities had similar stemwood vessel ratios of

approximately 17%. For all dominant trees the vessel ratio was constant between sample

positions, whereas vessel diameter increased as sample diameter increased, suggesting that

vessel frequency decreased as sample diameter increased since there was no significant

change in vessel ratio between sample positions. The effect of planting density on vessel

diameter followed a similar pattern to the effect of planting density on stem height, in that

there was no difference in vessel diameter between dominant trees in each planting density,


however for a given stem diameter vessel diameter increased as planting density increased

and vessel diameter decreased as stem diameter decreased. Stem height may therefore

influence vessel diameter.

Dominant trees in high planting densities had a stemwood fibre ratio of approximately 75%,

which was 5-10% greater than the fibre ratio of dominant trees in low planting densities. For

all dominant trees the fibre ratio was constant between sample positions, whereas the fibre

ratio of suppressed trees tended to increase as sample diameter increased, suggesting a

reduction in the ratio of physiologically active wood cells over time, possibly due to a

reduction in relative resource capture. Dominant trees in high planting densities had increased

fibre and fibrelumen diameter compared to dominant trees in low planting densities. For

dominant trees in low planting densities the fibre and fibrelumen diameters were constant

between sample positions, whereas for dominant trees in high planting densities the fibre and

fibrelumen diameters increased as sample diameter increased and decreased as sample height

increased, resulting in the hypothesis that decreased crown proximity to wood during wood

formation stimulates increased fibre and fibrelumen diameter. Dominant trees in high planting

densities had reduced relative fibrewall thickness compared to dominant trees in low planting

densities, and there was no significant difference in relative fibrewall thickness between

sample positions for all dominant trees. The results indicate that trees compensate for a

reduced stemwood fibre ratio by increasing relative fibrewall thickness, and due to this

compensatory effect it was unclear whether there was a change in the total fibrewall material

in the stemwood of dominant trees.

Direct investigation of stemwood fibrewall ratio showed that dominant stems in high planting

densities had decreased stemwood fibrewall ratio compared to dominant trees in low planting

densities, however at most sample positions there was no significant difference between

planting densities 1,000-5,000 st/ha. For dominant trees in high planting densities the

stemwood fibrewall ratio was constant at approximately 30% between sample positions,

whereas the stemwood fibrewall ratio of dominant stems in low planting densities tended to

increase as sample diameter increased. The results indicate that dominant trees in high

planting densities partition 5-10% less biomass to fibre production within a given volume of

stemwood compared to dominant trees in low planting densities, suggesting a more frugal use

of photosynthate during fibre differentiation, possibly due to lower concentrations of

photosynthate. In consequence dominant trees in high planting densities are likely to have


lower wood density than trees in low planting densities, yet they may also have lower

variation in wood density.

Investigation of wood density showed that whilst dominant trees in high planting densities

showed a tendency for lower wood density and greater wood density variation, there was no

significant difference in wood density or wood density variation in the primary sawlog section

of the stem between dominant trees from all planting densities. Suppressed trees generally

exhibited increased wood density, which could prove advantageous for biomass quality by

increasing the energy content of wood. It is probable that variables other than those measured,

such as intercellular spaces, also effect wood density since the results for wood density did

not entirely correlate with those for wood anatomy.

Investigation of branching characteristics showed that dominant trees in high planting

densities were likely to have lower knot content than dominant trees in low planting densities

as they exhibited a greater probability of branch mortality, more advanced branch shed,

decreased branch diameter and stub length, and lower branch angle. The much reduced

probability of the presence of branches exceeding 2 cm diameter in high planting densities

implies high planting density stands self-prune effectively, whereas low density stands would

probably require pruning to produce knot-free timber.

In summary it is concluded that the perception that high planting density plantations are

incapable of producing an equivalent volume of sawlogs of similar quality compared to low

planting density plantations is refuted by the evidence found in this study.

6.2 Major Discovery

The major discovery to be gained from this study is the utmost importance of accounting for

stand structure in competition research and forest modelling. Recent research on competition

has tended to focus on mean stand growth, whereas the results from this study indicate this is

an oversimplification which underestimates the productivity of the largest trees in higher

density stands and does not adequately account for the structural benefits of high planting

densities (i.e. increased size uniformity in the top 1,000 st/ha, reduced branching). This

clearly has the potential to result in management decisions which do not achieve the optimum


desired result since they do not account for all the factors which may affect productivity and

quality.

The strong relationship of most tree growth and structural attributes to general population

pressure (planting density) and competitive status in the stand (stem diameter) indicates the

importance of placing tree growth and structure into the context of the stand in which the tree

is growing. Clearly trees do not grow as individuals, but are highly sensitive in many ways to

stand growing conditions and their position within it (Figure 1.1). This is strongly illustrated

by the exceptional consistency of the pattern of results in this study, including the constant

lack of significant difference between 5,000 st/ha and 10,000 st/ha (which were most similar

in mean space per tree), from the stand level right down to the wood level.

Placing tree growth and structure into the context of the stand should take a ‘top down’

approach due to asymmetric competition (whereby larger trees capture relatively more

resources). The first step is to assess the extent to which general population pressure (stand

density) affects dominant trees. In this case population pressure does not affect height growth;

however it does suppress diameter growth of the crown and encourage branch shed, which in

turn reduces total tree growth. Once the dominant cohort has been assessed and defined, the

remaining trees may be defined relative to the dominant cohort. The relative size of individual

trees compared to the dominant cohort determines the strength of asymmetric competition and

the extent of growth suppression experienced by individual trees. The process of asymmetric

competition is so reliable that, all remaining undisturbed, non-dominant trees are virtually

certain of diminishing in dominance over time (Figure 3.12).

6.3 Management Applications

The salient management application of this study was that high planting density had no

detrimental effect on the sawlog quality of dominant trees compared to low planting density.

Rather, the sawlog quality of dominant trees in high planting density plantations may be

improved compared to dominant trees in low planting densities due to lower knot content.

This research has immediate management applications since it shows that eucalyptus

pulpwood plantations may be converted to solid wood plantations without any loss to solid

wood quality as a result of establishment with relatively high planting densities.


High planting densities have shown potential to improve plantation productivity compared to

the ‘standard’ planting density of 1,000 st/ha by producing a biomass crop (or crops) in

addition to a more uniform sawlog crop (Figure 6.1). In current markets this may not improve

profitability since biomass is not a valuable commodity; however increasing fuel prices are

likely to improve the future profitability of biomass crops by stimulating the development of

bio-fuel markets. In consequence, high planting densities could prove more profitable than the

‘standard’ 1,000 st/ha in the future, especially considering that biomass crops would provide

an early return cash crop on the plantation investment. A further useful finding was that there

was no practical difference between the results for 5,000-10,000 st/ha, so the benefits of high

density plantations can be achieved at planting densities of 5,000 st/ha, or possibly less.

The conclusions reached in this study were based on the study of E. grandis, however the

qualitative result pattern is likely to apply to all fast-growing eucalyptus species exhibiting

similar characteristics in stand development to E. grandis, including similar height growth in

dominant trees regardless of planting density, the rapid differentiation of the stand into

dominance due to asymmetric competition, and a significant loss of photosynthetic capacity in

the shaded leaves.

6.4 Further Research Requirements

A major research requirement emerging from this study is the need to apply the

growth/structure relationships defined to a process based model and test their validity as the

stand matures. The above investigation should also focus on the extent to which declining

stand productivity is due to structural changes in the stand and its trees.

There remain practical management issues to overcome before high planting density

plantations could be implemented on a commercial basis. Given the overall potential for high

planting density to improve plantation productivity and profitability compared to ‘standard’

planting densities, further research on practical management issues may be considered a

constructive investment in the plantation industry.

A major issue is that of high harvest costs per stem under modern harvesting technologies,

which are unlikely to be cost efficient for biomass crops due to the low value of individual

stems. The harvest of early biomass crops is more likely to be cost efficient if it involved the


harvest of complete rows by a continuous pass machine, similar to cane or grain harvesters,

such as those used in Europe for woody biomass crops. This option, however, suggests that

rows need to be alternatively stocked with sawlog trees and biomass trees so that sawlog trees

are not harvested in biomass crops. A number of planting configurations and silvicultural

techniques could be used to achieve this outcome; however research is required to identify the

combination that will maximise the value of high planting density plantations.

The removal of multiple biomass crops in addition to the final sawlog crop increases the

potential for harvesting to reduce site nutrient balances and increase soil compaction in the

plantation. Research is required on technologies that will ensure that site nutrients are

maintained and compaction minimised, examples of which include returning biomass ash

from bio-energy plants back to the plantation (as is done in Sweden), and developing biomass

harvest machinery that deposits nutrient rich tree components (leaves and branches) on site in

the process of harvesting and spreads its weight over a larger ground contact area. The

removal of biomass crops also increases the potential for epicormic shooting in retained

sawlog trees, which is detrimental to wood quality, and research is required to ensure that the

likelihood of epicormic shooting is minimised.

References Page 172

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Appendices Page 185

APPENDICES

Appendix 1: Stem Volume Models

3 YEAR OLD STEM VOLUME MODEL

An investigation was made of the effects of diameter at breast height (DBH), stem height (SH)

and planting density (P) on stem volume at 3 years (Table A1.1). Note that interactions

including DBH and SH are significant and therefore DBH and SH must be included in the

model despite not being significant individually.

Table A1.1: The fixed-effect regression coefficients of the random intercept model of the effects of DBH (m), SH (m) and P (st/ha) on stem volume (m

3) at 3 years.


INTERCEPT 0.0267 0.0105 p = 0.011 DBH2*SH

2 0.0319 0.0076 p < 0.001

DBH2 -3.4788 1.9607 p = 0.076 DBH

2*lnP 0.8233 0.2854 p = 0.004

SH2 0.000323 0.000027 p = 0.237 DBH

2*SH

2*lnP -0.0032 0.0011 p = 0.004

lnP -0.0032 0.0012 p = 0.009

4 YEAR OLD STEM VOLUME MODEL

An investigation was made of the effects of diameter at breast height (DBH), stem height (SH)

and planting density (P) on stem volume at 4 years (Table A1.2).

Table A1.2: The fixed-effect regression coefficients of the random intercept model of the effects of DBH (m) and SH (m) on stem volume (m

3) at 4 years.


INTERCEPT -0.0115 0.0038 p = 0.003 DBH2*SH

2 0.0058 0.0009 p < 0.001

DBH2 3.2608 0.3036 p < 0.001

SH2 0.00010 0.00002 p < 0.001

Documents

The effect of competition on stand, tree, and wood growth and structure in subtropical Eucalyptus grandis plantations