Needle and branch biomass turnover rates ofNorway spruce (Picea abies)

P. Muukkonen and A. Lehtonen

Abstract: Turnover rates of needle and branch biomass, number of needle cohorts, and needle-shed dynamics weremodelled for Norway spruce (Picea abies (L.) Karst.) in southern Finland. Biomass turnover rates, vertical distribution,and biomass of the branches were modelled simultaneously. The rate of needle turnover was determined from needle-sheddynamics. The potential litterfall of branches was modelled by combining the vertical distribution of branch biomassand the annual change in height of the crown base. The mean annual turnover rates for needle and branch biomass are0.10 and 0.0125, respectively. At the age of 5.5 years, 50% of the needles in the needle cohort have been shed. In ad-dition, at the age of 12 years, all needles of the needle cohort have been shed. Turnover of branch biomass was dependenton stand density and tree size. The modelled rates of biomass turnover agreed with measurements of needle and branchlitterfall. Many process- or inventory-based models use a single turnover rate for branch litterfall based on literature,and some of the models are fully ignoring the litterfall of branches. Species-specific turnover rates or dynamic litterfallmodels should be applied when carbon flows in forest stands are modelled.

Résumé : Le taux de renouvellement des aiguilles et de la biomasse des branches, le nombre de cohortes d’aiguilles etla dynamique de la perte des aiguilles ont été modélisés chez l’épicéa commun (Picea abies (L.) Karst.) dans le sud dela Finlande. Le taux de renouvellement de la biomasse, la distribution verticale et la biomasse des branches ont étémodélisés simultanément. Le taux de renouvellement des aiguilles a été déterminé à partir de la dynamique de la pertedes aiguilles. La litière de branches potentielle a été modélisée en combinant la distribution verticale de la biomassedes branches et la variation annuelle de la hauteur de la base de la cime. Les taux annuels moyens de renouvellementsont respectivement de 0,10 et 0,0125 pour la biomasse des aiguilles et celle des branches. À l’âge de 5,5 ans, 50 %des aiguilles de la cohorte d’aiguilles étaient disparues. De plus, à l’âge de 12 ans, toutes les aiguilles de la cohorted’aiguilles étaient disparues. Le renouvellement de la biomasse des branches dépendait de la densité du peuplement etde la dimension des arbres. Les taux de renouvellement de la biomasse obtenus par modélisation correspondaient auxmesures de litière d’aiguilles et de branches. Plusieurs modèles basés sur les processus ou sur des relevés utilisent untaux unique de renouvellement basé sur la littérature pour la litière de branches et quelques modèles ignorent complète-ment la litière de branches. Des taux de renouvellement propres à chaque espèce ou des modèles de la dynamique dela litière devraient être appliqués pour modéliser les flux de carbone dans les peuplements forestiers.

[Traduit par la Rédaction] Muukkonen and Lehtonen 2527

Introduction

Litterfall represents the most important source of elementflux to the forest floor. Fluxes of litterfall depend on severalecological factors, for example, species, climate, site quality,stand increment, stand age, stand density, and thinning (Pedersenand Bille-Hansen 1999).

Holstener-Jørgensen et al. (1979) made an extensive liter-ature review and concluded that in boreal conditions the to-tal amount of needle litter in Norway spruce stands variesfrom 1500 to 4500 kg·ha–1·year–1. Amounts of branch litterreported for Norway spruce in boreal forests vary from 100to 500 kg·ha–1·year–1 (Viro 1955; Nilsson and Wiklund 1992).In addition to large spatial variation in litterfall, the annualvariation is also large (Bille-Hansen and Hansen 2001).

The proportion of aboveground litter compartments ofNorway spruce (Picea abies (L.) Karst.) is nearly 73% forneedles, 13% for branches, 5% for cones, and 10% for othermixed litter (Viro 1955), which consists of seed, flowers,bud scales, epiphyte lichen, and small pieces of bark. Al-though the amount of branch litterfall is much lower thanthat of foliage litter, its contribution to the carbon stock ofthe soil is high, since it decomposes slowly; this should betaken into account when ecosystem models are built. Rela-tively few field measurements are available for large branchlitter of Norway spruce, while twigs are more often reportedwith needle litter. Therefore estimations of branch litterfluxes are based on measured conifer stands reported in eco-system study compilations like those of Reichle (1981) andCannel (1982).

The conifer crown is a population of shoots, each devel-oped from a branch of the main stem of the tree (Schoettleand Fahey 1994). The needles in internodes of the same ageform a needle cohort (Fig. 1), and a new needle cohort isproduced annually on the apices of the stem leader and theleader shoots of the branches (Jalkanen 1998).

The great variability in number of needles in the needlecohort indicates that the life-span of the needles is limited,

Can. J. For. Res. 34: 2517–2527 (2004) doi: 10.1139/X04-133 © 2004 NRC Canada

2517

Received 21 January 2004. Accepted 11 August 2004.Published on the NRC Research Press Web site athttp://cjfr.nrc.ca on 14 January 2005.

P. Muukkonen1 and A. Lehtonen. Finnish Forest ResearchInstitute, P.O. Box 18, FIN-01301 Vantaa, Finland.

1Corresponding author (e-mail: petteri.muukkonen@metla.fi).

that is, over time, the number of needles in the needle cohortdecreases because of aging of the needles (Ross et al. 1986).During the first 2 years, the needle mortality is negligible.Thereafter, the number of needles from a particular year de-creases drastically.

It has been suggested that the retention of needlesthroughout the year is a mechanism for nutrient conservationand offers the potential for a gain in photosynthetic carbongain during favourable periods in fall, winter, or early springat times when deciduous species are leafless (Schoettle andFahey 1994). In coniferous trees, where as much as 70% ofthe canopy can consist of old (>1 year) needles, needle life-span is directly influenced by the growth environment andthe distribution of resources within the canopy (Balster andMarshall 2000). Therefore, changes in needle longevitycould affect the carbon balance, the rate of nutrient cycling,and ultimately, the net primary production of a forest eco-system.

Norway spruce does not shed needles one needle cohort ata time (Ross et al. 1986; Salemaa et al. 1993). This speciessheds needles from several of the oldest needle cohorts si-multaneously. However, the long-term mean amount of an-nual needle shed is about what it would be if one needlecohort were shed at a time, but in declining trees the annualneedle shed is clearly greater than the amount of one needlecohort. In Norway spruce, needle shed is not concentrated atthe end of the growing season; rather, the litterfall of Nor-way spruce needles seems to be distributed uniformly overthe year (Viro 1955; Salemaa et al. 1993). As much as 60%of the total annual needle shed of Norway spruce occurs dur-ing the growing season (Mork 1942; Viro 1955; Holstener-Jørgensen et al. 1979). Owing to differences in weather fromyear to year, needles of Norway spruce fall at different timesin different years (Viro 1955).

Norway spruce is a shade-tolerant species, which can beseen from the canopy shape and the branch longevity. Thelife-span of the branches is longer in this species than in pioneerconifers like Scots pine (Pinus sylvestris L.) (Kärkkäinen

2003). This faster turnover of branch biomass can also beestimated based on the published data like crown ratios,branch biomass models, and height development. Firstly, theaverage crown ratio (crown length per tree length) of Nor-way spruce trees is 0.76, while it is 0.62 for Scots pine inFinland (Hynynen et al. 2002). Seconly, the branch biomassof Norway spruce is almost twice as much as branch bio-mass of equal-sized Scots pine (Marklund 1988). Thirdly,the average height increment is lower with Norway spruce(Hynynen et al. 2002). And fourthly, it can be assumed thatin both of these species the crown base height follows equallythe tree height. By summarizing these assumptions, it can beassumed that the annual turnover rate of the branch biomassof Norway spruce is clearly lower than that for Scots pineand other pioneer conifers. Because the branch mortality andlitterfall of Norway spruce have not been studied as compre-hensively, more information on their biomass turnover isneeded.

Because of limited information about the litter flux ofspruce needles to the soil, and especially that of sprucebranches, an alternative method was developed for assessingneedle and branch litter flux. To estimate average turnoverrate of needle biomass and potential branch litterfall, thisstudy combined inventory variables and biomass measure-ments.

The objective of this study was to estimate the litterfall ofthe needles and branches of Norway spruce. The aims are toestimate the rate of needle turnover determined from needle-shed dynamics and to estimate the potential litterfall ofbranches modelled by combining the vertical distribution ofbranch biomass and the annual change in height of the crownbase. To understand carbon cycle and flows of forests, infor-mation on litter production by compartment is needed.

Material and methods

DataIn this study, two sources of data were used: (1) sample

trees and branches from the national tree research Valtakun-nallinen Puututkimus (VAPU) and (2) height to crown basedata from the National Forest Inventory permanent plots.VAPU data and National Forest Inventory data are independ-ent data sets.

The VAPU data used in this study consisted of measure-ments of sample trees on sample plots established by theFinnish Forest Research Institute in southern Finland (southof 62°4′N) during 1988–1990 (Korhonen and Maltamo 1990).Three to six sample trees (with dbh of more than 5 cm) fromthe dominant canopy layer closest to the plot centre were se-lected and felled (Fig. 2). From 94 sample plots, a total of196 and 80 Norway spruce trees were analysed for estima-tion of branch litterfall and needle litterfall, respectively.

Estimation of needle litterfall is based on needle cohortlongevity (VAPU database). For these data, first-order needlecohorts (Fig. 1) were estimated visually from two branchesin the 15th whorl from the top of the tree (Fig. 2a). The firstbranch pointed to the centre of the sample plot, and the sec-ond pointed in the opposite direction (Fig. 2b). Kendall’s co-efficient of concordance (Ranta et al. 1999) shows that thereare statistically significant similarities between the needlecohorts of the two measured directions. Therefore to avoid

© 2004 NRC Canada

2518 Can. J. For. Res. Vol. 34, 2004

Fig. 1. Needle cohorts. First-order needle cohorts are located onthe main stem of the branch.

measurements that are dependent on each other, it is reason-able to analyse the measurements of branches in only one di-rection. The percent survival of needles in each of the needlecohorts was estimated visually and classified into one of sixclasses: (1) 0%–5%, (2) 6%–25%, (3) 26%–50%, (4) 51%–75%, (5) 76%–95%, and (6) 96%–100%.

The diameter of every branch on the sample trees wasmeasured. The sample branches were selected randomly withprobability proportional to diameter. The sum of diameterswas divided by 10 (denote r), and then a random integer be-tween 1 and r was selected, and that integer indicated the lo-cation of the first sample branch according to the cumulativediameter sum starting from tree top. Sample branches werethen selected at an interval of r; and 10 branches per treewere chosen. Furthermore, the dry masses of the second,fifth, and eighth sample branches were determined in thelaboratory, while others were measured only for fresh mass.To have a more precise model for branch biomass, allbranches of the living crown that were less than 7.5 mm indiameter were excluded, and those branches constitute lessthan 1% of total branch biomass.

The height to the crown base was measured on permanentsample plots established in 1985–1986 and remeasured in1995 by the National Forest Inventory. The subsample ofpermanent plots used in this study is located below 62°Nand consists of 267 plots, including a total of 782 measuredNorway spruces. In 1985 and 1995, all trees from the plotswere measured for dbh, height to crown base, and tree

height. The annual change in height to crown base was de-rived from the difference between measurements in 1985and 1995 (Fig. 3). The crown base was defined as the lowestwhorl with at least one living branch, separated from theother living whorls above by no more than one dead whorl.

Statistical analyses and modelling

NeedlesTo study needle-shed dynamics and to estimate the turn-

over rate of needle biomass, ordinal regression (Bender andBenner 2000) was used to model the relationship betweenage of the needle cohort and the survival class (Table 1). Thesurvival class (Y) is a categorical response variable with k +1 (6) ordered categories. The Y is a discretized variable of anunderlying latent continuous trait defined by cut-off points j.It is then natural to formulate a model by means of the cu-mulative probabilities γj. Let πj(x) = P(Y = j|X = x) be theprobability for realization of Y = j given X = x, j = 0, 1,…, k.The class of grouped continuous models is based upon thecumulative probabilities

[1] γj(x) = P(Y ≥ j|X = x) = πj (x), j = 1,…, k

The class of grouped continuous models is obtained bythe generalized linear model

[2] f [π (x)] = α + βx

in which the cumulative probabilities are used instead of π.

[3] f [γj(x)] = αj + βj x, j = 1,…, k

where f is an appropriate function, α is the intercept, and β isthe regression coefficient for X. The standard assumption in

© 2004 NRC Canada

Muukkonen and Lehtonen 2519

Fig. 2. Needle cohorts were estimated visually from the twobranches in the of 15th whorl from the tree top. The first branchpointed to the centre of the sample plot and the second onepointed in the opposite direction.

Fig. 3. Estimation of potential litterfall of branches (Bt/Btot),based on vertical distribution of branch biomass and annualchange in height of the crown base.

most applications is that the regression coefficient does notdepend on j; that means the model

[4] f [γj(x)] = αj + βx, j = 1,…, k

is considered. Thus, it is assumed that for the consideredlink function f the corresponding regression coefficients areequal for each cut-off point j. Since the logit link

[5] f (π) = ln[π/(1 – π)]

is used, the generalized linear model becomes

[6] ln( )

( )( )

( )

( )

γγ

α β γα β

α βj

jj j

x

x

x

xx x

e

e

j

j1 1−= + ⇔ =

+

+

+

From the model, we determined where the cumulativeprobabilities of ordinal regression reached its 50% limit.

Two approaches used to study survivorship are as follows:(1) the cohort approach and (2) the time-period approach(Fleming and Piene 1992a, 1992b). Fleming and Piene (1992b)concluded that, when survival rates are stationary, these ap-proaches produce similar estimates. The cohort approach in-volves following a group of individuals born simultaneouslythrough time (Fleming and Piene 1992b). The time-periodapproach uses data on the age structure of the population ata specific period of time to infer survival rates (Fleming andPiene 1992a). In this study, we applied the period approachto the data from national tree research (VAPU).

We characterized the decrease in needle survival over thecourse of time by percent survival according to the age ofthe needle cohort. The needle percent survival indicates theproportion of original needles present in a needle cohort at aparticular time. Since, the needle survival data are providedin classes, we converted those survival classes to percentsurvival using random numbers within the class boundaries.We made 1000 simulations.

The dry mass of living needle increases during the first4 years (Viro 1955). The mass of second-year needles, third-year needles, and needles older than that is 36%, 30%, and40%, respectively, higher than that of first-year needles.Norway spruce sheds needles from all needle cohorts, andmost of the needles will become yellow before they are shed(Salemaa et al. 1993). Upon yellowing, the spruce needlesbecome lighter, and the absolute amounts of nutrients inthem usually diminish, being transferred to the trunk (Viro1955). In other words, a substantial amount of the nutrientsrequired for construction of new needles each year can besupplied by the relocation of nutrients from aging needles(Schoettle and Fahey 1994). In this process spruce needleslose 13%–39% of their mass, depending on the age of theneedle cohort (Viro 1955).

The turnover rate of needle biomass (NL) in the time-period approach was calculated separately for each simula-tion and the model as follows

[7] NL =− × ×

×

+=

−

=

−

∑

∑

( )

( )

b b m d

b m

i i i ii

n

i ii

n

10

1

0

1

© 2004 NRC Canada

2520 Can. J. For. Res. Vol. 34, 2004

Age

ofne

edle

coho

rt(y

ears

)

Sur

viva

lcl

ass

Cla

ssbo

unda

ries

(%)

12

34

56

78

910

1112

Tot

al

596

–100

4947

2920

104

00

00

00

159

61%

59%

36%

25%

13%

5%0%

0%0%

0%0%

0%4

76–9

518

2012

929

3117

92

10

00

156

23%

25%

36%

36%

39%

21%

11%

3%1%

0%0%

0%3

51–7

54

512

1313

1816

124

00

098

5%6%

18%

18%

16%

23%

20%

15%

5%0%

0%0%

226

–50

41

08

815

2216

92

11

865%

1%9%

9%10

%19

%28

%20

%11

%3%

1%1%

16–

252

14

88

1010

1512

143

184

3%1%

5%5%

10%

13%

13%

19%

15%

18%

4%1%

00–

53

66

1010

1623

3554

6476

7837

74%

8%8%

8%13

%20

%29

%44

%68

%80

%95

%98

%T

otal

8080

8080

8080

8080

8080

8080

960

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

100%

Tab

le1.

Num

ber

and

prop

orti

on(%

)of

obse

rvat

ions

acco

rdin

gto

surv

ival

clas

san

dag

eof

need

leco

hort

.

where b is the percent survival of the needle cohort, m is amass factor indicating weighting of single needles over time,and d indicates loss of mass during yellowing of needles.The numerator indicates the total amount of needles re-moved annually, and the denominator indicates the totalamount of needles on a tree or single branch. In this study nis 12, which indicates the number of age classes.

BranchesThe potential branch litterfall was estimated according to

the method described by Lehtonen et al. (2004b). The drymass of each branch (excluding foliage) as a function ofbranch diameter was modelled with a mixed linear modelbased on the VAPU database. The mixed model approachwas justified by the correlated observations; in the sample,branches from the same tree are correlated with each other.The tree and branch levels were taken into account in therandom part of the model. The dry mass of branch i on treek (mki) was modelled as the following function of branch di-ameter (dki)

[8] ln mki(d) = ln A0 + A1[ln(dki)]0.12 + ln a0k +

a1k[ln(dki)]0.12 + ln εki

where A0 and A1 are fixed population parameters, while a0kand a1k are random tree parameters with zero expectations,which were estimated by the restricted maximum likelihoodmethod in a mixed procedure (SAS Institute Inc. 1999). Be-fore fitting the mixed model, branch diameter (dki) weretransformed to the power of 0.12. This was done after exam-ining residual figures of diameter–mass relationship of data.The transformation was done by nlin procedure (SAS Insti-tute Inc. 1999). The error term (lnεki) of the mixed modelwas assumed to be distributed normally.

The vertical distribution of branch biomass in tree crownswas modelled based on estimates of branch biomass. Thelive tree crowns were divided into 10 segments of equal rela-tive length from the base to the top of the crown (0%–10%,11%–20%, …, 91%–100%). Thereafter, the proportion of to-tal branch biomass for each segment was estimated and usedwhen the nonlinear model for vertical branch biomass distri-bution was fitted.

Then, the distribution of branch biomass (s) as a functionof relative height (h) and the crown ratio (cr) is

[9] s(h,cr) = (a0 + a1 × cr) × (h – 1)

+ (b0 + b1 × cr) × (h2 – 1) + (c0 + c1

× cr) × (h3 – 1) + ε

where a0, a1, b0, b1, c0, and c1 are parameters, and ε is theerror term. The relative height within the tree crown equalszero at the crown base and one at the top of a tree. The inte-gral in eq. 9 was not constrained to be equal to one, but be-ing close to 0.1 because of 10% segments. The parametersof the function were estimated by using the Gauss–Newtonmethod in the nlin procedure (SAS Institute Inc. 1999).

The height of the crown base was measured from sampletrees on permanent sample plots in 1985 and 1995. The an-nual change in the height of the crown base was estimated tobe one-tenth the change between 1985 and 1995. The branchbiomass gained by growth between crown base change mea-

© 2004 NRC Canada

Muukkonen and Lehtonen 2521

Sta

nd

Sta

ndag

ein

1998

(yea

rs)

Loc

atio

na

Beg

inni

ngof

litt

erco

llec

tion

No.

ofli

tter

trap

s

Bas

alar

ea(m

2 ·ha–1

)S

tock

ing

(no.

·ha–1

)

Mea

sure

men

tye

arof

stan

dva

riab

les

Est

imat

edne

edle

biom

ass

(kg·

ha–1

)b

Est

imat

edbr

anch

biom

ass

(kg·

ha–1

)b

Avg

.an

nual

need

leli

tter

fall

(kg·

ha–1

)

Avg

.an

nual

bran

chli

tter

(kg·

ha–1

)

Aul

anko

163

SF

1982

1040

235

1990

1568

027

720

2340

630

Hei

nola

145

SF

1961

1033

313

1991

1476

025

720

1750

380

Kit

tilä

200

NF

1961

626

722

1992

1495

027

180

900

170

Kuo

reve

si13

8S

F19

808

2660

019

9113

830

2271

015

7024

0R

ovan

iem

i15

5N

F19

608

2061

619

9111

730

1990

081

017

0S

iili

njär

vi99

SF

1961

622

332

1990

1105

018

920

1440

300

a SF,

sout

hern

;N

F,no

rthe

rnFi

nlan

d.b B

iom

ass

ofne

edle

san

dbr

anch

esw

ases

timat

edas

afu

nctio

nof

dbh

and

heig

htof

tree

sby

appl

ying

the

biom

ass

equa

tions

ofM

arkl

und

(198

8).

Tab

le2.

Des

crip

tion

ofli

tter

fall

coll

ecti

onst

ands

.

surements was assumed to be minor and was therefore ig-nored. The distribution of vertical branch biomass in 1985was estimated for each sample tree by applying the previousmodel. By using vertical biomass distribution, the amount ofbiomass lost because of annual increase in the crown height(h1) was calculated (Fig. 3). The amount of annually lostbiomass was proportioned to the total biomass distributionof the branches using

[10] B

s h h

s h h

h

r

cr)d

cr)d

=∫

∫

( ,

( ,

0

0

1

1

which gave an estimate of the proportion of potential branchlitter (Br) for each of the 782 trees on National Forest Inven-tory sample plots.

With measurements of the height of the crown base, anonlinear regression model was developed for the proportionof biomass lost annually as litter because of the rise of thecrown base in each tree. Tree size and stand density were in-cluded as independent variables. Other variables like fertil-ity, stand age, and basal area were tried to predict annualbiomass loss, but stand density was found to be superior.Thereafter the proportion of potential branch litter in thebranch biomass (Br) was modelled as a function of tree di-ameter (dbh) and stand density (n)

[11] B n a a n cr[(b b n) dhb ](dbh, ) e 0

2= + × × + ++ × ×( )0 ε

where a, a0, b, b0, and c are parameters, and ε is the errorterm. These parameters were estimated by using the Gauss–Newton method in the nlin procedure (SAS Institute Inc.1999).

Comparison with litterfall from litter-trap studiesThe models developed were compared with: (1) long-term

littertrap data of the Finnish Forest Research Institute(Metla) managed by Mr. T. Hokkanen (Table 2) and (2) thelitterfall reported in previous studies in Sweden, Norway,and Finland (Table 3).

Needle and branch litterfall was collected from six standsthroughout Finland (Table 2). The size of the litter traps was0.5 m2. Kouki and Hokkanen (1992) describe the collectionof litterfall in more detail. The proportion of the litter poten-tial made up of the total needle biomass and the total branchbiomass for the litter-collection stands was estimated by di-viding the estimated biomass for each litter-collection standby the average annual litterfall. The average annual needleand branch litterfall for these stands was assessed starting in1960 (Table 2). The needle biomass of these stands was esti-mated by applying biomass equations based on dbh, height,and crown length, and branch biomass was estimated by ap-plying biomass equations based on dbh and height to the lat-est tree-level data (Marklund 1988).

The reported litterfall figures were compared with esti-mates based on the present study. Reported biomasses weremultiplied by biomass turnover rate produced in this study.When biomass data were not available, foliage biomass andbranch biomass were subtracted from the stand volume (bybiomass expansion factor of Lehtonen et al. 2004a). Thesefunctions are based on biomass and volume equations thatwere applied on tree-wise data from permanent sample plotsof the Finnish national forest inventory. Functions can be ap-plied to forests less than 250 m3·ha–1. For stands with treevolumes greater than that, volumes of 250 m3·ha–1 were used.

Litter-trap estimates and model approach estimates of annuallitterfalls by compartment were analysed by a pairwise Stu-dent’s t test and by its nonparametric counterpart, a pairwiseWilcoxon test.

© 2004 NRC Canada

2522 Can. J. For. Res. Vol. 34, 2004

Reference LocationNo. ofsites

Age ofstand(years)

Needlelitter dataavailable

Branchlitter dataavailable

Biomassdataavailable

Standvolume dataavailable

Mork 1942 Norway 3 39–140 Y — — YViro 1955 Finland 3 68–75 Y Y — YBonnevie-Svedsen and Gjems 1957 Norway 3 35–55 Y — — YNilsson and Wiklund 1992 Sweden 6 — Y Y Y —

Note: Y, data available.

Table 3. Description of published litterfall studies.

Age of needle cohort (years)

Survival class Class boundaries (%) 1 2 3 4 5 6 7 8 9 10 11 12

5 96–100 0.71a 0.54a 0.36 0.22 0.12 0.1 0 0 0 0 0 04 76–95 0.93 0.89 0.75a 0.59a 0.41 0.26 0.14 0 0 0 0 03 51–75 0.97 0.94 0.88 0.79 0.64a 0.47 0.3 0.2 0 0 0 02 26–50 0.99 0.97 0.94 0.89 0.8 0.66a 0.48 0.3 0.2 0.1 0 01 6–25 0.99 0.99 0.97 0.94 0.89 0.8 0.66a 0.5 0.3 0.2 0.1 00 0–5 1 1 1 1 1 1 1 1 1 1 1 1

aDiagonal values indicate the 50% probability limit.

Table 4. Cumulative probabilities of needle cohorts according to survival class and age of the needle cohort modelled by ordinal regression.

Results and discussion

NeedlesIt was found that with a probability of 71%, the youngest

observed needle cohort (1 year old) belongs to class 5 (Ta-

ble 4 and Fig. 4), which indicates survival of 96%–100%. Ata probability of 90%, the 12-year-old needle cohort had shed95%–100% of all its needles.

When the survival class model of the ordinal regression isexpanded by random simulations, we construct a percent-survival model (Fig. 5). At the age of 5.5 years, 50% of theneedles of that needle cohort have been shed. In addition,when the needles are 12 years old, all needles of the needlecohort are shed. Needles are shed most rapidly when theyare 6–8 years old (Fig. 5). The results show that Norwayspruce does not shed needles one needle cohort at a time.Rather, it sheds needles simultaneously from several of theoldest needle cohorts, which confirms the conclusions ofSalemaa et al. (1993). The dynamics of needle shed found inthis study is similar to that reported by Niinemets andLukjanova (2003) for an Estonia stand (Fig. 5). The dynam-ics for northern Sweden reported by Flower-Ellis and Mao-Sheng (1987) differs markedly, because of the longer needlelongevity on northern sites.

The biomass turnover rates of the random simulationsvary from 0.07 to 0.13 when the weighting and yellowing ef-fects of needles are taken into account. Both the arithmeticmean and the median show that the turnover rate of needlebiomass for Norway spruces in southern Finland is 0.10.

BranchesBranch biomass was modelled according to branch diame-

ter, and estimates were calibrated for each sample tree by a

© 2004 NRC Canada

Muukkonen and Lehtonen 2523

Fig. 4. Probability and 95% confidence limits of the ordinal regression of the cohort approach. Survival classes on the y-axes representthe percent survival of needles in each of the needle cohorts. Survival classes were estimated visually and classified into six classes:(1) 0%–5%, (2) 6%–25%, (3) 26%–50%, (4) 51%–75%, (5) 76%–95% and (6) 96%–100%. The size of the bubble indicates the proba-bility that the needle cohort of curtain age belongs to the specific survival class.

Fig. 5. Predicted survival percentages with 95% confidence lim-its of needle cohorts in the cohort approach, according to age ofthe needles. The model of the present study is compared withthe models of Flower-Ellis and Mao-Sheng (1987) and Niinemetsand Lukjanova (2003).

mixed model. Thereafter, estimates of branch biomass wereused in modelling the vertical biomass distribution of thebranches. Most of the variation in biomass distribution ofthe vertical branches was explained by relative height on thetree and by the crown ratio (Table 6).

The combination of change in the crown base and the ver-tical distribution of biomass provided an estimate for poten-tial branch litter. This relative potential branch litterfall wasshown to be dependent on tree dimensions and stand density.Therefore, a nonlinear model was constructed in which rela-tive potential branch litterfall is modelled as a function ofdbh and stand density (stems per hectare) (Table 7; Fig. 6).A similar effect of density to height at the crown base hasalso been noted in studies of timber quality, where the heightof the lowest living branches has been studied with differentstockings (Madgwick et al. 1986; Johansson 1992).

The average potential branch litterfall was 1.25% of thetotal branch biomass of Norway spruce. It was found thatthe relative potential branch litterfall is less than half of the

potential litterfall for pines, which for Scots pines was 2.7%(Lehtonen et al. 2004b). Therefore, when carbon fluxes ofspruce forests in southern Finland are estimated, the model(eq. 11) with dbh and stocking density or a constant turnoverrate of 0.0125 for branches should be applied (Table 7).These estimates are based on average change in height of thecrown base during 10 years and because of high interannualvariation in litterfall are therefore not comparable to littermeasurements from a single year.

If silvicultural practises change and density regimes areaffected, the average turnover rate could be biased for newconditions, while presented model (eq. 11) takes these changeswith stocking density into account.

Our model estimates only the branch death in the lowercrown but does not address small branch and twig deathfrom the interior areas of the crown. This source of branchlitter plays only a minor role in the total branch litterfall andis often reported with litter-trap studies. Also our modellingapproach is up-scaling tree-wise measurements of canopydynamics to stand level; the up-scaling is based on represen-tative sampling of branch biomass and crown base height de-velopment for southern Finland. Therefore, we consider ourresults to be applicable within the region of southern Fin-land, while the models and idea could be applied after re-parameterization for any other forest region.

The litterfall of large branches is very seldom measured.Several ecosystem- and soil-carbon models (Wang et al. 2001;Yarie and Billings 2002; Komarov et al. 2003; Masera et al.

© 2004 NRC Canada

2524 Can. J. For. Res. Vol. 34, 2004

Modelled

Southern Finland Northern Sweden Estimated on the basis of measurements

Needles 0.10 (0.0126) 0.14a 0.05b (0.0036) 0.08a,b 0.11c

Branches 0.0125 — 0.0115d

aWithout weighting and yellowing effects.bCalculated from needle-shed dynamics reported by Flower-Ellis and Mao-Sheng (1987).cEstimated turnover rate was determined from the amounts of litter and biomass (Mork 1942; Viro 1955; Bonnevie-

Svedsen and Gjems 1957; Nilsson and Wiklund 1992; Finnish measurements).dEstimated turnover rate was determined from the amounts of litter and biomass (Viro 1955; Nilsson and Wiklund

1992; Finnish measurements).

Table 5. Needle and branch biomass turnover rates of Norway spruce (standard deviation in parentheses).

Parameter Estimate Approximated SE

a0 0.0472 0.2946a1 -0.0661 0.3658b0 0.6627 0.6338b1 -0.6510 0.7864c0 -0.7977 0.3765c1 0.6702 0.4671

Note: No. observations = 1858; SSerror = 7.7521; SStotal =30.6667.

Table 6. Parameter estimates with approximatedstandard errors (SE) for the model of branch bio-mass distribution (eq. 9).

Fig. 6. Models for potential turnover rate of branch litter as afunction of diameter, when stocking varies from 500 to 2500trees·ha–1.

Parameter Estimate Approximated SE

a –0.005 13 0.006 11a0 0.000 012 0.000 001 929b 0.000 007 32 0.000 757b0 –0.000 000 764 0.000 000 225 2c 0.004 67 0.004 55

Note: No. observations = 782; SSerror = 0.0908; SStotal =0.2381.

Table 7. Parameter estimates with approximatedstandard errors (SE) for potential branch litter model(eq. 11).

2003; Paul et al. 2003) are estimating branch litter as a con-stant ratio, meanwhile some studies are fully ignoring branchlitterfall because of lack of data. According a literature re-view, values for branch litterfall for Norway spruce in Scan-dinavia varied between 100 and 600 kg·ha–1 (Fig. 7). Beingsuch a significant litter source, it should not be ignored, andsome effort should be made to catch the correct level and thedynamics of the branch litterfall. Thereafter, the combina-tion of models is justified when there is a lack of suitable lit-ter-trap studies. The combination of methods, like branchmortality dating (Maguire 1994), biomass models of livingand dead branches (Marklund 1988), and litter-trap measure-ments (Kouki and Hokkanen 1992) could be used for branchturnover rate assessments.

Comparison with measured litterfallOur results show that the rates of biomass turnover calcu-

lated in this study provide estimates of litterfall that are sim-ilar to measured amounts of litterfall (Fig. 7).

Both the Student’s t test (t = –1.512, p = 0.146) and theWilcoxon test (Z = –1.408, p = 0.159) for paired comparisonsshow that there is no statistically significant difference betweenmeasurements and estimations of needle litterfall (Fig. 7a).Nor does branch litterfall differ statistically significantly be-tween measurements and estimations (t = 0.315, p = 0.758;Z = –1.022, p = 0.307) (Fig. 7b). These results indicate that,when there is a need to estimate the amount of branch andneedle litterfall, the rates of biomass turnover found in thisstudy are relevant and useful estimators.

In Swedish data on litterfall measurement, Norway spruceneedles have a significantly higher rate of biomass turnover,and therefore our estimations give lower values than measuredlitterfall does (t = 3.058, p = 0.022; Z = –1.99, p = 0.046). InNorway (t = 1.327, p = 0.233; Z = –0.734, p = 0.463) andFinland (t = 0.001, p = 0.999; Z = –0.059, p = 0.953), thereare no statistically significant differences between predic-tions and measurements. When litterfall of branches is com-pared for Swedish data, our model estimates significantlyoverestimate branch litterfall (t = 6.137, p = 0.001; Z = –2.201,p = 0.028). In Finland, there is no significant difference be-tween estimated and measured branch litterfall (t = 0.51287,p = 0.620; Z = –0.178, p = 0.859). Litterfall collections andmeasurements represent situations on selected forest sites.

In many cases, stand level branch litterfall measurementsconsist of only smaller branches and twigs. The litter mea-surements are normally made by litter collectors, which donot collect larger branches.

We assumed that comparisons using Finnish litterfall mea-surements and the reported values of Nilsson and Wiklund(1992) would be more reliable since, in Finnish data, thebiomass equations are used to tree-wise data, while Nilssonand Wiklund (1992) give pre-estimated biomass values. Forother sources (Mork 1942; Viro 1955; Bonnevie-Svedsenand Gjems 1957) the biomass values were calculated fromstand volume, which in comparisons might give distorted re-sults. When modelled and measured litterfall were comparedfor the whole of Scandinavia, we succeeded in predictinglitterfall satisfactorily. Both, the litter measurements and Finn-ish data used for modelling needle biomass turnover rate,consist of only few study sites. Therefore, broader discus-sion about the causes of differences is not desirable.

Applicability of resultsSpecies-specific estimation of litter production by com-

partment is essential for understanding the carbon cycle andflows of forests. In studies concerning the carbon balance offorests, rates of biomass turnover are usually estimated fromlitterfall measurements, or inverse number of the maximumnumber of needle cohorts are used. In some cases, rates ofbiomass turnover for other tree species are applied to Nor-way spruce. For example, if the rates of biomass turnover forScots pine are applied to Norway spruce, we overestimatethe input of needle and branch litter to the soil system.

The results of this study can be used in modelling the carbonbalance of boreal Norway spruce forests. Biomass turnover

© 2004 NRC Canada

Muukkonen and Lehtonen 2525

Fig. 7. Comparison of modelled and measured (a) needlelitterfall and (b) branch litterfall.

rates derived in this study are applicable as average valuesfor large areas. Our modelled rates of biomass turnover forneedles and branches are suitable for conditions correspondingwith those in southern Finland. Turnover rates for northernSweden calculated from needle-shed dynamics reported byFlower-Ellis and Mao-Sheng (1987) are suitable for condi-tions corresponding with those in northern Finland.

Acknowledgements

The authors thank the Academy of Finland for financing“Integrated method to estimate carbon budgets of forests”(project No. 52768), which is part of the Research Programmeon Sustainable Use of Natural Resources (SUNARE). Weare also grateful to the National Forest Inventory for providingdata for the national tree research (VAPU) and to Mr. TatuHokkanen for providing data on litterfall measurement. Theauthors also thank Dr. Raisa Mäkipää and Mr. MikkoPeltoniemi for their comments on the manuscript, Dr. Joannvon Weissenberg for checking the English language of thearticle, and Kati Liukkonen for drawing Figs. 1, 2, and 3.

References

Balster, N.J., and Marshall, J.D. 2000. Decreased needle longevityof fertilized Douglas-fir and grand fir in the Northern Rockies.Tree Physiol. 20: 1191–1197.

Bender, R., and Benner, A. 2000. Calculating ordinal regressionmodels in SAS and S-Plus. Biom. J. 42: 677–699.

Bille-Hansen, J., and Hansen, K. 2001. Relation between defoliationand litterfall in some Danish Picea abies and Fagus sylvaticastands. Scand. J. For. Res. 16: 127–137.

Bonnevie-Svedsen, C., and Gjems, O. 1957. Amount and chemicalcomposition of litter from larch, beech, Norway spruce and Scotspine stands and its effect on the soil. Medd. Nor. Inst. Skogforsk.14: 111–174.

Cannel, M.G.R. 1982. World forest biomass and primary produc-tion data. Academic Press Inc., London. p. 391.

Fleming, R.A., and Piene, H. 1992a. Spruce budworm defoliationand growth loss in young balsam fir: period models of needlesurvivorship for spaced trees. For. Sci. 38: 287–304.

Fleming, R.A., and Piene, H. 1992b. Spruce budworm defoliationand growth loss in young balsam fir: cohort models of needlefallschedules for spaced trees. For. Sci. 38: 678–694.

Flower-Ellis, J.G.K., and Mao-Sheng, Y. 1987. Vertical and cohortlife-tables for needles of Norway spruce from northern Sweden.Sver. Lantbruksuniv. Inst. Skogsekol. Skoglig Marklara Rapp.57: 1–24.

Holstener-Jørgensen, H., Veracion, V.P., and Yao, C.E. 1979.Litterfall studies in an irrigation trial in Norway spruce. Forstl.Forsogsvaes. Dan. 36: 413–432.

Hynynen, J., Ojansuu, R., Hökkä, H., Siipilehto, J., Salminen, H.,and Haapala, P. 2002. Models for predicting stand developmentin MELA System. Finn. For. Res. Inst. Res. Pap. 835.

Jalkanen, R. 1998. Fluctuation in the number of needle sets andneedle shed in Pinus sylvestris. Scand. J. For. Res. 13: 284–291.

Johansson, K. 1992. Effects of initial spacing on the stem andbranch properties and graded quality of Picea abies (L.) Karst.Scand. J. For. Res. 7: 503–514.

Kärkkäinen, M. 2003. Puutieteen perusteet. Metsälehti Kustannus,Hämeenlinna, Finland. [In Finnish.]

Komarov, A., Chertov, O., Zudin, S., Nadporozhskaya, M.,Mikhailov, A., Bykhovets, S., Zudina, E., and Zoubkova, E.

2003. EFIMOD 2 — a model of growth and cycling of elementsin boreal forest ecosystems. Ecol. Model. 170: 373–392.

Korhonen, K.T., and Maltamo, M. 1990. Männyn maanpäällistenosien kuivamassat Etelä-Suomessa. Finn. For. Res. Inst. Res.Pap. 371: 1–29. [In Finnish.]

Kouki, J., and Hokkanen, T. 1992. Long-term needle litterfall of aScots pine Pinus sylvestris stand: relation to temperature factors.Oecologia, 89: 176–181.

Lehtonen, A., Mäkipää, R., Heikkinen, J., Sievänen, R., and Liski,J. 2004a. New approach to formulate biomass expansion factors(BEF) by stand age for Scots pine, Norway spruce and birch.For. Ecol. Manage. 188: 211–224.

Lehtonen, A., Sievänen, R., Mäkelä, A., Mäkipää, R., Korhonen,K.T., and Hokkanen, T. 2004b. Potential litterfall of Scots pinebranches in southern Finland. Ecol. Model. 180: 305–315.

Madgwick, H.A.I., Tamm, C.O., and Fu, M.Y. 1986. Crown devel-opment in young Picea abies stands. Scand. J. For. Res. 1: 195–204.

Maguire, D.A. 1994. Branch mortality and potential litterfall fromDouglas-fir trees in stands of varying density. For. Ecol. Man-age. 70: 41–53.

Marklund, L.G. 1988. Biomassafunktioner för tall, gran och björk iSverige. Sverig. Lantbruksuniver. Inst. Skogstaxering Rapp. 45:1–73. [In Swedish.]

Masera, O.R., Garza-Caligaris, J.F., Kanninen, M., Karjalainen, T.,Liski, J., Nabuurs, G.J., Pussinen, A., de Jong, B.H.J., and Mohren,G.M.J. 2003. Modelling carbon sequestration in afforestation,agroforestry and forest management projects: the CO2FIX V.2approach. Ecol. Model. 164: 177–199.

Mork, E. 1942. Om strøfallet i våre skoger. Medd. Nor. Inst.Skogforsk. 29: 297–365. [In Norwegian.]

Niinemets, Ü., and Lukjanova, A. 2003. Needle longevity, shootgrowth and branching frequency in relation to site fertility andwithin-canopy light conditions in Pinus sylvestris. Ann. For. Sci.60: 195–208.

Nilsson, L.-O., and Wiklund, K. 1992. Influence of nutrient andwater stress on Norway spruce production in south Sweden —the role of air pollutants. Plant Soil, 147: 251–265.

Paul, K.I., Polglase, P.J., and Richards, G.P. 2003. Predicted changein soil carbon following afforestation or reforestation, and anal-ysis of controlling factors by linking a C accounting model(CAMFor) to models of forest growth (3PG), litter decomposi-tion (GENDEC) and soil C turnover (RothC). For. Ecol. Man-age. 177: 485–501.

Pedersen, L.B., and Bille-Hansen, J. 1999. A comparison of litterfalland element fluxes in even aged Norway spruce, sitka spruceand beech stands in Denmark. For. Ecol. Manage. 114: 55–70.

Ranta, E., Rita, H., and Kouki, J. 1999. Biometria. Helsinki Uni-versity Press, Helsinki. [In Finnish.]

Reichle, D.E. (Editor). 1981. Dynamic properties of forest ecosys-tems. Cambridge University Press, Cambridge.

Ross, J., Kellomäki, S., Oker-Blom, P., Ross, V., and Vilikainen, L.1986. Architecture of Scots pine crown: phytometrical charac-teristics of needles and shoots. Silva Fenn. 20: 91–105.

Salemaa, M., Jukola-Sulonen, E.-L., Nieminen, T., and Nöjd, P.1993. Latvatunnukset ja puun kasvu elinvoimaisuuden ilmentäjinä.Finn. For. Res. Inst. Res. Pap. 446: 77–92. [In Finnish.]

SAS Institute Inc. 1999. SAS system [computer program]. SAS In-stitute Inc., Cary, N.C.

Schoettle, A.W., and Fahey, T.J. 1994. Foliage and fine root lon-gevity of pines. Ecol. Bull. 43: 136–153.

Viro, P.J. 1955. Investigations on forest litter. Comm. Inst. For.Fenn. 45: 1–65.

© 2004 NRC Canada

2526 Can. J. For. Res. Vol. 34, 2004

Wang, S., Grant, R.F., Verseghy, D.L., and Black, T.A. 2001.Modelling plant carbon and nitrogen dynamics of a boreal aspenforest in CLASS — the Canadian Land Surface. Ecol. Model.142: 135–154.

Yarie, J., and Billings, S. 2002. Carbon balance of the taiga forestwithin Alaska: present and future. Can. J. For. Res. 32: 757–767.

© 2004 NRC Canada

Muukkonen and Lehtonen 2527