LAKE STURGEON GROWTH CHRONOLOGIES

A Thesis

Presented to

The Faculty of Graduate Studies

of

The University of Guelph

GREGORY TRENT OWEN LEBRETON

In partial ful filment of requirements

for the degree of

Doctor of Philosophy

December 1999

O G. LeBreton, 1999

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ABSTRACT

LAKE STURGEON GROWTH CHRONOLOGIES

GREGORY TRENT OWEN LEBRETON University of Guelp h, 1 999

Advisor: Professor F. W.H. Beamish

This thesis tested lake sturgeon (Aciporserfirlvescer~s) growth rings, contained in

cross sections of leading pectoral fin rays, against three criteria required of any structure

used in development of growth chronologies relevant to ecophysiological research. First,

widths of growth rings were related to overall somatic growth of the organism. Secondly,

synchrony of interannual growt h variations was quanti fi ed using growt h chronologies.

Finally, lake sturgeon growth rings and related chronologies were tested to determine if

these demonstrated the influence of large scale extrinsic factors. The results indicated

that radii of sturgeon fin ray cross sections do relate to variations in the somatic growth

and satisS, the first criterion. Secondly, individual chronologies from sturgeon sampled

in Lake St. Clair, Lake Temiskaming, Saskatchewan River, Lake Winnebago, and Lac St.

Louis demonstrated significant synchrony of interannual growth variations and satisfied

the second criterion. Finally, lake sturgeon ring widths and chronologies were related to

variations in air temperatures, an environmental factor previously associated with

sturgeon growth, thereby satisfying the third criterion and indicated that growth data

extracted from these natural archives was consistent with that already known regarding

sturgeon growth.

Having established the validity of lake sturgeon growth chronologies this

investigation explored the application of these as ecological tools Sturgeon

chronologies, from populations in which synchronous interannual growth variation was

detectable, were negatively correlated with neighbouring tree growth chronologies, a

relationship possibiy driven by growth response of fish and trees to annual temperature

variations. Importantly, these results dernonstrate the usefulness of growth chronologies

in cornparisons among diverse organisms. Fluctuations in synchrony of individual

chronologies from neighbouring fish and trees over tirne were also investigated. Based

on assumptions that strength of environmental factors increases growth synchrony within

a population, the results suggest that growth in nearby fish and trees responds sirnilarly to

environmental fluctuations yet these relations may differ between watersheds. Finally,

annual fluctuations in sturgeon growth, documented by chronologies, were successfully

modeled in two populations using past records of environmental and tree growth

variation. The same environmental factors explained growth variation in both

populations suggesting these factors are operating on sturgeon across large geographic

scales.

ACKNOWLEDGEMENTS

A great many people must be sincerely thanked for their kind support and

assistance throughout this endeavor. First my advisor, Dr. F.W.H. Beamish who, most

importantly, provided me the freedorn to explore and grow a project for which the end

was never assured, but who also gave much needed support and advice that can only

corne from experience. My committee, Dr. D. Noakes, Dr. D. Larson, Dr. J. Hubert, and

Dr. J. Casselman al1 of whom donated a great deal of time and criticism helping to mould

this work into what it has becorne. Al1 of those individuals from across Canada and the

United States who provided the calci £ied tissue samp les, t his basis for this research, and

much assistance in the field. My parents, who doubled as free field assistants, and my lab

mates who helped me maintain sanity throughout the years. Finally, the person who

provided the rnost support and encouragement through it all, even during times when al1

appeared lost, my dear wife, Julie. Without her, this could not have been.

TABLE OF CONTENTS

ACKNOWLEDGEMENTS

TABLE OF CONTENTS

LIST OF TABLES

LIST OF FIGURES

PREFACE

GENERAL INTRODUCTION

CHAPTER 1: Lake Sturgeon Growth Chronologies. Sync or Swim?

1.1 Abstract

1.2 Introduction

1.3 Materials and Methods

1.4 ResuIts

1.5 Discussion

CHAPTER 2: The influence of Environmental Factors on Lake

Sturgeon Growth.

Abstract

Introduction

Materials and Methods

Results

Discussion

CHAPTER 3 :

CHAPTER 4:

CHAPTER 5 :

The Influence of Temperature and Precipitation on

Tree Growth.

Abstract

Introduction

Materials and Methods

Results

Discussion

Interannual Growth Variations in Terrestrial and

Aquatic Ecosystems; A Cornparison using Fish

and Tree Rings

Abstract

introduction

Materials and Methods

Results

Discussion

Growth Synchrony in Neighbouring Aquatic

and Terrestrial Ecosysterns

Abstract

Introduction

Materials and Methods

Results

Discussion

CHAPTER 6 Modeling Lake Sturgeon Growth Using Past

Environmental and Tree Growth Data

6.1 Abstract

6.2 Introduction

6.3 MateriaIs and Methods

6.4 Results

6.5 Discussion

GENERAL DISCUSSION

REFERENCES

APPENDIX 1 Water and Air Temperature

APPENDIX II Lake Sturgeon Growth Chronologies

LIST OF TABLES

Table 2.1

Table 2.2

Table 2.3

Table 2.4

Table 2.5

Table 2.6a

Table 2.6b

Table 2.7a

Table 2.7b

Table 3.1

Table 3.2

Table 3.3a

Organizations and contact names frorn which lake sturgeon pectoral 39 fin ray samples were borrowed.

Populations and corresponding rneteorological stations from which 45 monthly air temperature and total precipitation were obtained.

Mean age, average fin ray radius, and total length at age 25 (L2 J) of 50 samples used from each population. (*) L25 were acquired directly fiom the research of Fortin et al. 1996. (**) LZ5 was estimated by averaging data from surrounding populations.

Interseries correlation coefficients for each population investigated, 53 years and numbers of growth chronologies correlated.

Calendar years spanned by lake sturgeon population growth 53 chronologies and year during which mean growth indices differed significantly from the mean as determined from the 95% confidence interval. (+) or (-) indicate whether relative growth was higher or lower, respective1 y for that particular year.

Correlation of sturgeon population growth chronologies with measures of mean air temperature during the current season of

56

growth. (*) p s 0.05, (**) p 5 0.0 1.

Correlation of population growth chronologies with measures of 56 mean air temperature during the previous season of growth. (*) p 0.05, (**) p a 0.0 1.

Correlation of population growth chronologies with measures of total 57 monthly precipitation during the current season of growth. (*) p c 0.05, (**) p < 0.0 1.

Correlation of population growth chronologies with measures of total 57 monthly precipitation during the previous season of growth. (*) p < 0.05, (**) p 5 0.0 1.

Regions, city and lat./iong. from which meteorological data were 76 obtained for the four sites from which tree rings were sampled.

Interseries correlation coefficients among consistently aged 79 individuals for four tree populations sampled.

Correlation of population growth chronologies with rneasures of 80 mean air temperature during the current season of growth. (*) p 5 0.05, (**) p 1 0.0 1.

Table 3.3 b

Table 3.4a

Table 3.4b

Table 4.1

Table 4.2

Table 4.3

Table 4.4

Table 5.1

Table 6.1

Correlation of population growth chronologies with measures of mean air temperature during the previous season of growth. (*) p 0.05, (**) p 5 0.01.

Correlation of population growth chronologies with measures of total monthly precipitation during the current season of growth. (*) p < 0.05, (**) p 5 0.01.

Correlation of population growth chronologies with measures of total monthly precipitation during the previous season of growth. (*) p < 0.05, (**) p 5 0.0 1.

Mean age of samples used in, time spans covered by, and number of samples average into population chronologies for fish and trees for seven locations of study.

Mean interseries correlation coefficients for each population investigated, years and numbers of growth chronologies correlated.

Significant correlation coefficients (Px0.05) calculated between fish and tree growth dunng either the current of previous season of growth.

Pearson correlation coefficients between fish and tree growth chronologies and past measures of air temperature during the season of growth. Trees lagged rows present correlation coefficients between tree chronologies and air temperatures from the previous growth season.

Time periods over which interseries correlation coeficients were calculated, resulting statistics and number of sarnples of lake sturgeon, Acipe>lserflrlvesceia, and white spmce, Picea glnttca, used fiom Lake Temiskaming and Saskatchewan River regions.

Mode1 variables as selected by stepwise multiple linear regression technique, coefficients and significance levels for coefficients. P6 = June total precipitation, SOI1 = Southem Oscillation Index from the previous growth season, SOI2 = Southem Oscillation Index from two years previous, SUN2 = sunspot numbers from two previous growth seasons, TREEl = white spmce growth ring indices from the previous season, TREEZ = white spmce growth ring indices from two years previous, T4 = mean April air temperature during the current season of erowth.

LIST OF FIGURES

Figure 1.1 Lake sturgeon pectoral fin ray cross-sections fi-om two individual 23 sampled in Lake St. Clair. (A) year class = 1973, age = 23 (B) year class = 1977, age = 19. Dotted lines indicate the radius along which ring widths were measured. Year notation indicates relative narrowing and widening of ring width during 1987 and 1990-199 1, respective1 y.

Figure 1.2 Lake sturgeon growth chronologies developed from samples 30 collected in (A) Lake St. Clair, (B) Saskatchewan River, (C) Mattagami River. Dotted lines indicate approxirnate 95% confidence intervals.

Figure 2.1 Sturgeon pectoral fin ray radius length as a function of total length 48 as measured from Lake St. Clair (open triangles), Saskatchewan River (open circles), lake Winnebago (black circles), and Mattagami River (biack triangles).

Figure 2.2 Von Bertalanfy curves constnicted from lake sturgeon populations 49 from Lake St. Clair (dashed), Saskatchewan (doaed), Lake Winnebago (dash-dot), and Mattagami River (solid).

Figure 4.1 Locations of fish and tree populations used to develop growth 9 1 chronologies in this investigation. Lake sturgeon populations; (1) Lake St. Clair, OntarioMichigan (420007N, 82"3OYW), (2) Lake Temiskaming, Ontario/Quebec (47"30'N, 79"3OYW), (3) Saskatchewan River, Saskatchewan, (53" 54'N, 102' 209W), (4) Lake Winnebago, Wisconsin (41"00'N, 88"20tW), (5) Lac St. Louis, Quebec (45"7OyN, 73'55'W), (6) Lac Parent, Quebec (48"25'N7 77' 1 SW), and (7) Mattagami River, northem Ontario (49" WN, 8 l 0 37'W), freshwater drum population, (9) Red Lakes in Minnesota (48O OO'N, 95' OO'W). Tree populations used; white spruce (Picea glcizïca) (2) Lake Temiskaming (3) Saskatchewan River, (7) Mattagarni River regions; white pine (Pimis strobiis) from near (5) Lac St. Louis. Previously published dendrochronologies, red pine (Pimcs resznosa) at (8) Hartwick Pines State Park, Michigan (44' 2SW, 84'27'W) and (9) Coddington Lake, Minnesota (47' 1 IN, 92" 12'W).

Figure 4.2a Lake sturgeon ring at age time series (solid line), 7-year running 98 average approximating general decrease with age (dotted line).

Figure 4.2b Lake sturgeon ring width at age following removal of long-term 98 trend approximated by 7-year running average.

Figure 4 . 2 ~

Figure 4.2d

Figure 4.3

Figure 4.4

Figure 4.5

Figure 4.6

Figure 4.7a

Figure 4.7b

Figure 4 . 7 ~

Figure 4.7d

Lake sturgeon absolute residual series (solid line). Decrease in variance with age approximated by 7-year ninning average (dotted line)

Individual lake sturgeon growth chronology following removai of long-term trends with 7-year ninning averages.

Mean age-related trend in al1 Lake Temiskaming sturgeon samples following application of 7-year mnning average curves.

Lake sturgeon population growth chronologies from sample assemblages display ing significant interseries correlation coefficients. (A) Lake St. Clair, (B) Lake Temiskaming, (C) Saskatchewan River, (D) Lake Winnebago, (E) Lac St. Louis.

Tree growth chronologies developed from terrestrial ecosystems near populations of lake sturgeon demonstrating significant interseries correlation. (A) white spmce population growth chronology from the Lake Terniskaming region, (B) white spmce population growth chronology from the Saskatchewan River region, (C) white pine population growth chronology from the Lac St. Louis region.

Growth chronologies developed with 7-year running averages demonstrating the influence of repeating the first and last data points to fit a mnning average through al1 growth data. Chronology assembled using first and last growth data points frorn individual chronologies (Solid line). Chronology assembled excluding first and last data points (Doned line).

Mean ring width at age for sturgeon sampled from Lake Temiskaming

An individual sample sturgeon's ring width at age tirne-series (solid line). Mean decrease in ring wiith with age as calculated from Lake Temiskaming (dotted line).

Residuals between an individual sturgeon's ring width at age series and mean ring width with age curve as calculated frorn Lake Temiskaming population data.

Absolute value of the residuals as calculated fkom an individual sturgeon sampled in Lake Temiskaming. Dotted line represents the population average curve of absolute residual values.

viii

Figure 4.7e Final individual chronology from Lake Temiskaming detrended using mean age related trends.

Figure 4.8 Lake Temiskaming population chronologies developed using (A) two 7-year running averages (B) population wide age related trends. Mean growth indices (solid lines), approximate 9 5% confidence intervals (dotted lines).

Figure 4.9 Red pine (Pims resitiosa) growth chronology fiom the Hartwick Pines State Park, Michigan (Koop and Garsino-Mayers 1994). (A) growth chronology as published, (B) growth chronology following the removal of long-term trends using multiple applications of 7- year running averages. Chronology (B) used as a terrestrial counterpan to growth chronologies from Lake St. Clair and Lake Winnebago.

Figure 5.1 Interseries correlation coefficients as a fùnction of mean annual temperature calculated over 1 O-year intervals for lake sturgeon (A) and white spruce (B), and Saskatchewan River sturgeon (C) and white spruce (D). dashed lines represent 95% confidence intervals, dotted represent prediction interval S.

Figure 5.2 Interseries correlation coefficients of white spruce from (A) Lake Temiskaming, and (B) Saskatchewan River as a hnction of sturgeon interseries correlation coeficients. Dashed lines represent 95% confidence intervals, dotted lines represent prediction intervals.

Figure 6.1 Observed (solid) and predicted (dotted) growth chronologies from Saskatchewan River (A) and Lake Terniskaming (B). Solid circles (A) indicate predicted values for 1987 and 1988. These data had been excluded Rom mode1 development.

Figure 6.2 Total length increment in lake sturgeon from the Saskatchewan River as a function of growth indices and age.

Figure Al. 1 Water temperature as a fùnction of air temperature from (A) Lake St. Clair, (B) Lake Temiskaming, (C) Saskatchewan River, @) Lac St. Louis, (E) Mattagarni River.

PREFACE

The reader will no doubt note some repetition throughout this thesis. As each

cbapters contained herein was designed first as a stand alone publication and secondly as

a thesis chapter, such redundancy was required when writing about separate, yet related,

issues throughout the four years it hns taken to complete this investigation. For this

reason, the author hopes the reader will excuse any recurrence of description or

met hodology .

GENERAL INTRODUCTION

Growh is a fundamental characteristic of living things (Ulanowicz 1986).

Defined as a change in magnitude, mass, energy or proximate cornponents (Weatherley

and Gill 1987), growth offers an easily observable and quantifiable metric which may be

compared throughout the living world. It is this cornparison of growth among widely

disparate organisms that is the focus of this work.

Fish and trees, the subjects of this investigation, are extremely dissimilar under

most biological classifications. Fish are vertebrate heterotrophs that move freely within

aquatic ecosystems while trees are stationary, vegetative autotrophs that generally inhabit

terrestrial environments. However, many fundamental processes, components, and

structures in both organisms are similar. Perhaps more striking is the similarity of growth

patterns found in fish and trees (Blackman 1905).

Fish grow in response to both intrinsic and extrinsic factors (Weisberg 1993).

lntrinsic factors incorporate such variables as age, sex, matunty and pathologicai

condition (Brett 1979). Extrinsic factors may be divided into abiotic components such as

temperature, oxygen, pH and light, and biotic components; food availability, competition,

etc. (Cuenco et al. 1985 a,b). Of these, temperature is extremely important (Lobon-

Cervia and Rincon 1998) as it operates to Pace or regulate the thermodynamics involved

in food processing and metabolism (Weatherley et al. 1991). When fish are fed to satiety,

growth increases with temperature to a maximum or optimal measure (Fry 1947). If

temperatures are further increased, fish growth declines as respiration and maintenance is

elevated beyond the limits t hat nutrient assimilation pathways can support (Brett 1979).

Furiher slight increases resuit in the upper lethal temperature being met by ce11 mortality

caused by coagulation of proteins and a resultant breakdown of metabolic hnction

(McCauley and Kilgour 1990, Blackman 1905).

As mentioned, other factors also influence the growth of fish. Oxygen, for

example, acts to impose limits on metabolic pathways curtailing growth (Stewart et al.

1967). Other environmental variables such as pH rnay operate to reduce growth by

requiring the individual to redirect energies towards homeost asis and away from somatic

tissue expansion (Brett 1979). Light and related photopenod generally act as directive

factors, cueing the intemal endocrine rhythms of fishes in response to alterations in

season (Al hossaini and Pitcher 1 988). Various abiotic factors have been indirectly

related to the growth of fishes. For example, increases in precipitation and the resulting

increase in discharge or volume of a waterway has been related to increased food supply

or oxygen levels (Guyette and Rabeni 1995) while temperature may influence availability

of food organisms (Gjasieter and Loeng 1987).

While these abiotic, extrinsic factors influence growth it should be noted that

growth is primarily dependent on the intake and assimilation of food substances that fuel

growth processes (Brett 1979). Therefore, the amount that an organism can grow is

directly related, not only to the quantity and quality, but also availability of food

resources. For this reason biotic factors such as intra- and interspecific cornpetition can

influence growth by altering food availability (Casselman 1990).

It is easy now to move from those factors influencing growth of fish to those

operating on growth of trees. Similar to fish, both intrinsic and extrinsic factors influence

tree growth (Teskey et al. 1995). Intrinsic factors incorporate such variables as age

(Cook and Peters 198 1), rnaturity and pathological condition (Cook et al. 1987, Reich et

al. 1987). Extrinsic factors may again be divided into components such as abiotic;

temperature, water, light, and nutrient availability (Teskey et al. 1995) and biotic;

cornpetit ion and parasitism.

Resource availability is the driving force behind the growth of trees as it is for

fish. For this reason any investigations into factors influencing tree growth must be

couched in the effects these have on net photosynthesis. For example, temperature

operates on processes related to both photosynthesis and respiration and may influence

each of these differently. Photosynthesis, as set of cheniical reactions, is govemed by the

laws of thermodynamics and increases with eievations in temperature (Fritts 1976). This

rise approaches a species-specific maximum following w hich any further increases in

temperature result in a decline in net photosynthesis (Downs and Hellmers 1975, Teskey

et al. 1995). This decline is the result of rapid respiration, also elevated, but reaching a

maximum at a higher temperature, outstripping the production of photosynthetic

pathways (Fritts 1976). Under these circurnstances, cell death can occur due to

starvation. As in fish, further, slight increases in temperature cause cell mortality from

protein coagulation and breakdown of metabolic function (Downs and Hellmers 1975).

In trees, the influence of water availability on growth is inextricably linked to

photosynthesis. For example, Conroy et al. (1 986) indicated that the flow of electrons

related to photosystem II was affected by low water availability. Furthermore, adverse

effects on photosynthesis may occur if water availability is low and evapotranspiration

exceeds the rate at which water can be restored to cells and tissues thereby causing

stomata to close (Slayter 1963). This closure, coupled with the reduced diffusion of

carbon dioxide into green cells of the plant, may result in lowered photosynthesis

(Gaastra 1963, Stenberg et al. 1995, Teskey et al. 1995). During periods when stomata

are closed, the reduced evaporation of water from the plant's surface also can elevate leaf

temperatures, raising respiration above the plant's photosynthetic abi l ities, reducing net

photosynthesis (Fritts 1976).

The common theme in the above discussion regarding the growth of fish and trees

has been the piasticity of growth to environmental variation. This growth characteristic,

similar among such distinct life forms, has been realized for some time. For example,

Blackman (1905) discussed the approximate doubling of growth rates with increases in

temperature by 10 O C in both lower vertebrates and plants at temperatures between 10 and

27 O C . It is this plasticity of growth and the characteristic of indeterminate body size

(Weatherley and Gill 1987) which are exploited in the current research.

In temperate zones, seasonal variation in growth rates, partially due to

temperature and food availability fluctuations, result in the development of rings, annuli

or growth increments in hard tissues of fish and trees (Fritts 1976, Casselman 1987). In

fish, whi le the exact physiological processes resulting in annuli deposition in calcified

tissues remains unknown, it is presumed that the layers of bone are foned on the surface

of the ray at the ray-dermis interface (Veinott and Evans 1999). It also is known that the

opaque zones of some calcified tissues, fomed dunng periods of summer growth, contain

relatively high proportions of protein material relative to the translucent, more

mineralized zones (Casselman 1974, Morales-Nin 1987). Changes ;n somatic growth of

the individual have been related to deposition in many calcified tissues including scales

(Newman and Weisberg l987), otoliths (Maceina and Betsill 1987), and cleithra

(Casselman 1990). In temperate species of trees, growth rings are annually developed

layers of xylem cells formed between early spring and late summer. Generally, the first

cells developed possess a large ce11 diameter and thin cell walls. As the growth season

ends, the cells become smaller and ce11 walls thicker. This increased tissue density forrns

the characteristic darkened section of the rings in a tree cross-section (Fritts 1976).

As discussed, the growth rates of both fish and trees are influenced by

environmental variation. Therefore, the widths of seasonal growth rings developed in the

hard tissues of these organisms may be used as records of past environmental influences

on growth. During periods favorable to growth, ring widths are relatively larger than

those formed during periods when growth is limited (Cyterski and Spangler 1996, Pereira

et al. 1995a,b, Grace and Norton 1990, Cook et al. 1987, Briffa et al. 1983). A series of

such ring widths, for which the influences of age have been removed, is defined as a

growth chronology. This data, when gleaned from a single individual, is an individual

growth chronology. An assemblage of individual chronologies from a group of

organisms, aligned by calendar year and averaged, is an estimate of a population

chronology. Population growth chronologies offer ecologists archives of past data

pertaining to population dynamics and environmental variation.

At this point the value offered by growth chronologies to ecology must be

discussed. As humans, our perception of the world is one that does not allow us the

ability to detect long-term change. In fact, Magnuson (1990) states that many ecological

processes occur in, what he temed, "the invisible present". We are unable to detect

variations in many ecosystern processes such as the accumulation of biornass,

reproduction, and movement of energy, water or organisrns as these rnust occur over

extended periods of time (Sinclair et al. 1993, Magnuson et al. 199 1). This makes the

development and testing of hypotheses in ecological studies difficult. Long-tem

ecological research, the branch of ecology that atternpts to address these issues, faces

certain dilemmas. Funding, resources and long-term commitrnents fiom individual

scientists are fiequently difficult to obtain for research that will not produce immediate

results. Other research, conducted using new scientific perspectives, may make a long-

term project uninteresting or obsolete. As equipment and personnel change through long-

term projects, calibrations and consistent methodologies can be lost (Risser 199 1). While

new technologies such as geographic information systems facilitate compilation and

manipulation of large databases, assembled over broad spatial scales, they do not solve

the temporal issue that data collection is required over time periods that may exceed the

lifespans of researchers.

Therefore, growth chronologies offer the ecologist an inexpensive and easily

obtainable archive of data docurnenting natural growth variation. Recently, such time-

series have been developed from xylem rings in trees (Larsen and MacDonald 1995),

shells of mollusks (Jones 1980), calcified tissues of fish (Pereira et al 1995a,b, Cytenki

and Spangler 1996), teeth of mammals (Boyd and Roberts 1993). and layers of corals

(Dmffel 1985) and ice (Sinclair et al. 1993) and used as archives for past environmental

quality. By developing analytical techniques for cornparison and construction of these

natural records we may enhance our understanding of the "invisible present" (Magnuson

1990).

It is important to reiterate here that growth chronologies offer ecologists the

ability to work across traditional fields of research. Few characteristics are as common

throughout the living worid as growth (Ulanowicz 1986) and variability (Magnuson et al.

199 1). Therefore growth chronologies, used to measure variabil ity in different li fe forms,

cm compare these metncs from realms as diverse as aquatic and terrestrial ecosystems.

Inter-ecosystem comparisons are required for biosphere research. Just as the study of

populations is accomplished by researching interactions between individuals or the study

of communities by researching populations that compose them, biosp here research must

be done by investigating interactions and dynamics between the composing ecosystems.

This investigation was divided into two main components. First, the lake

sturgeon, Aciperiser/rrfvescrris, and growth rings contained in the pectoral fin rays of this

species were assessed for their suitability in the development of growth chronologies

pertinent to ecophysiological researc h. To accomplish this, sturgeon growth rings were

tested against three criteria required of any structures used in growth chronologies. The

relation between fin ray radii and body size was established to ensure that ring widths

were measures of growth. Synchrony of interannual growth variations among individuals

within populations was tested to ensure that developed growth chronologies would

contain common growth patterns. Finally, lake sturgeon growth rings and related

chronologies were tested to determine if they demonstrated the influence of

environmental factors known to influence fish growth.

The second component of this investigation was to use these developed sturgeon

growth chronologies as ecological tools. First, these fish chronologies were correlated

with tree growth chronologies developed from nearby terrestnal systems to determine if

relations existed between interannual growth variations in neighbouring fish and trees.

Secondly, changes in growth synchrony with time in neighbouring fish and tree

populations were investigated. Based on the assumption that as the influence of

environmental factors on growth becomes more severe growth synchrony will increase,

this part of the research was directed towards determining if the environment operated

similarly on fish and tree growth. Finally, using the developed growth chronologies in

concert with past records of environmental and tree growth variation, fish growth was

modeled using mult i ple-linear regression techniques. These models demonstrated that

growth chronologies can enhance our understanding of environmental factors operating

on fish growth as well as providing us wit h sorne abilities to forecast growth variations

within a population.

Species

Fis h

The subject fish species for this investigation was the lake sturgeon, Acipet~ser

fitlvescem, a threatened organism possessing several characteristics that make it a

suitable candidate fiom which to research interannual growth variations. First, lake

sturgeon, though restricted to freshwater (LeBreton and Beamish 1998)- are widely

distributed in large lakes and rivers across North America fiom the salt water temination

of the St. Lawrence River to the Saskatchewan River West of Edmonton and from

Nebraska, Missouri and Alabama nonh to the Seal River on the West coast of James Bay

and Fort George on its east coast (Houston 1987, Scott and Crossman 1973). In all, the

geographic range of this species covers approximately 41' of longitude and 24" of

latitude (Power and McKinley 1997); therefore, lake sturgeon are present in various

climatic regimes which allows for comparative study of environmentally influenced

growth variation among populations. Secondly, lake sturgeon are widely noted for their

longevity. One individual captured in Lake of the Woods in 1953 was reported to be 154

years of age and weigh 94.6 kg (Scott and Crossman 1973). Relative to other species of

fish, this allows for an extensive growth database to be collected fiom each individual.

Thirdly, lake sturgeon exists in relatively low numbers due to intense over-fishing during

the early 1900's (Houston 1987, Birstein 1993), does not compete heavily with other

fishes (Sandilands 1987) and has few natural predators (Scott and Crossman 1973).

These facts combine to reduce the influences of intra- and inter-specific cornpetition on

sturgeon growth.

Most important to the use of lake sturgeon in growth chronology studies are the

annuaily resolvable growth rings in various calcified tissues (Rossiter et al. 1995). While

otoliths, opercules and fin rays have been studied for their documentation of sturgeon

age, the growth pattems in the cross-section of the pectoral fin ray are most commonly

used (Wilson 1987, Brennan and Cailliet 1989, Rien and Beamesderfer 1994). These

structures are easily sampled, prepared and read and can be non-lethally removed €rom

the individual (Rossiter et al. 1995). The widths of rings in these structures may record

interannuai variations in somatic growth, a mandatory requirement in the development of

growth chronologies and a point which must be validated before chronologies can be

constructed fiom an organism.

The rings observable in the cross-section of sturgeon fin rays have been used to

document annual growth variations in the past (Roussow 1957, Keenlyne and Jenkins

1993). These investigations interpreted ring patterns as records of reproductive cycles. It

was assumed that as an individual approaches maturity and energies are directed away

from somatic growth and into gonadogenesis, ring width would decrease. As the

sturgeon is a repetitive spawner, the cyclic deveiopment of the gonads over a period of

several years was believed to create the narrowing and widening of ring width pattems.

However, Guénette et al. (1992), who investigated these patterns in sturgeon From the St.

Lawrence River, concluded that widths of the last five rings in the pectoral fin ray was

not related to maturity of the individual. Therefore, one of the pnmary objectives of the

current study must be to determine that extrinsic factors influence the interannual

variation of lake sturgeon ring widths if they are to be used in the construction of growth

chronologies relevant to ecophysiological research.

Past investigations have significantly improved our understanding of

environmental influences on sturgeon growth. Fortin et al. (1 996) used covariate,

bivariate, and multivariate correlation and regression analysis to identify determinates of

variations in lake sturgeon body growth among 32 populations across North America.

This research concluded that mean annual air temperature, latitude, pH and conductivity

were suitable predictors of sturgeon growth but found that growth behaved somewhat

differently between the eastem and western parts of the distribution. In general, lake

sturgeon growth was found to decrease with latitude and mean annual temperature and

increase in more alkaline and conductive systerns. Growth in waterways with higher pH

and conductivity was suggested the result of these systems being more mineralized,

buffered and productive than more acidic, less productive systems (Bryant et al. 1998,

Fortin et al. 1996). In concert with these findings, Power and McKinley (1997)

concluded that the growth of lake sturgeon was significantly determined by the thermal

opportunity for growth as measured by the total degree days greater than 5°C.

Trees

The final three chapters of this thesis explore cornparisons between interannual

growth variations in sturgeon and neighbouring populations of trees. Each of the tree

species selected for this investigation has been extensively used in dendrochronological

investigations Vritts 1976). Annually developed growth rings, visible in the cross-

section of the trunk are easily sampled and measured. White spruce (Picea glarrca) was

sampled from the Lake Temiskaming, Saskatchewan River, and Mattagami River

locations. White pine (P ims strobrrs) was sampled near the Lac St. Louis location. A

previously published red pine (Pims resinoso) growth chronology was used for both the

Lake St. Clair and Lake Winnebago regions (Koop and Garsino-Mayers 1994).

White spnice is common throughout the northem or boreal forests and can be

found almost anywhere in Canada (Hosie 1969). In North America this species is found

from New England, West to Alberta and British Columbia and from Newfoundland

almost to the Bering Sea where it reaches the tree line (Rosendahl 1955). In Ontario the

geographic range ofthe white spruce extends to Hudson Bay (White 1973). Because of

their wide distribution, these trees exist under a wide range of soils and climate regimes.

Soi1 types preferred are well drained and silty (Pielou 1988). This species is noted to

suffer during dry fail and winter conditions in the more southern parts of its range

(Rosendahl 1955). The extensive geographic distribution of this species made it an ideal

terrestrial counterpart for the lake sturgeon in investigations into correlation of

interannual growth variations between these organisms. Interannual growth variations in

white spruce have been negatively related to air temperature during May and June of the

growth season. Growth in this species has also been positively related to June

precipitation during the growth year and June, July and August precipitation during the

previous year (Larsen and MacDonald 1 995).

White pine or eastern white pine is characteristic of the Great Lakes St. Lawrence

forest region (Hosie 1969) and grows on many different soi1 types From dry sandy and

rocky to water-saturated peat bogs but is noted to prefer clay or loam conditions

(Rosendahl 1955). This species is also noted to suffer during dry late summers and

autumns (Rosendahl 1955). Red pine are found t hroughout the southem Maritimes,

central Quebec and west to Manitoba (Rosendahl 1955). Preferring sandy sites, this

species generally avoids calcareous soils.

STUDY SITES

Lake St. Clair

Located at an approximate 42"OOW latitude and 82'30'W longitude, Lake St. Clair

is fed and drained by the St. Clair and Detroit Rivers, respectively. The smallest lake in

the Great Lakes system, the lake has a mean depth of approximately 3m (Bolsenga and

Herdendorf 1993). As this is such a shallow system the lake warrns and cools rapidly in

the spring and faIl respectively. Warming generally begins about mid March. Much of

the water that enters into Lake St. Clair cornes directly from Lake Huron and as a result

the thermal properties of the Iarger lake greatly influences Lake St. Clair. Temperatures

generally reach maximum values near 24°C during July and August on the east shore

(Bolsenga and Herdendorf 1993). Mean pH, total alkalinity and conductivity for this

system, between 1967-1982, were 8.3, 8 l.6mgl-', and 224 umhos cm", respectively

(Bolsenga and Herdendorf' 1993). Bedrock in this region is deeply buried by glacial till

deposits. The terrestrial systern surrounding the lake is extremely flat and intensively

fanned (Elolsenga and Herdendorf 1993). The Lake St. Clair drainage basin is located

within the central lowland province of the interior plains (Hunt 1974) which supports

American beech (Fagis amerkana) and sugar maple (Acer sacchanun) in deciduous

forest (Rowe 1972). For this reason, a suitable stand of coniferous trees could not be

located in this region. A red pine (Pinus resinoso) chronology from Michigan was used

as a surrogate.

Lake Temiskaming

Located at an approximate 47'30'N latitude and 79"30iW longitude, Lake

Temiskaming is a 105 km long, 0.5 to 17 km wide lake in a rift valley which forms the

head waters to the Ottawa River. The nonhern end of this lake and feeding river systems

are located on lacustrine clay deposits while the southern half of the lake lies on granitic

bedrock (Sallenave and Barton 1990). As the northem region of the lake is also in a

limestone outcropping, the water is relatively well buffered with a pH and conductivity

between 6.8-7.7 and 40-88psacm'1, respectively (Zettler and Carter 1986). To the south

of Lake Temiskaming the Laurentian highlands are relatively hilly while to the north the

Superior upland provinces of the Canadian shield immediately flatten out, display little

topography, and poor drainage (Hunt 1974). The boreal forest in this region i s also split

between the flat Haileybury clay to the north, supporting a small agricultural region, and

the rolling Timagami section which is commonly associared with white pine (Pitrics

strobtis) and exposed granitic bedrock (Rowe 1972). In both regions, white spmce is

found near lakes and rivers on well drained locations (Rowe 1972).

Saskatchewan River

The region of the Saskatchewan River fiom which sturgeon were sampled is

located near Cumberland Lake at an approximate 53'54N latitude and 102"20tW

longitude. Water is relatively warm in summer and slow moving throughout this system

(Wallace 199 1). The river at this location has an average discharge of 457m3 sec-' and

drains 289000km2 (Water Sunrey of Canada 1976). In this region the river divides into

multiple river channels thus approximately 900 km of watenvay are accessible to

sturgeon (Wallace 1991). Also known as the Cumberland Marshes, the area is a large

wetland region covering approximately 5000 km2 (Smith and Perez-Arlucea 1994).

Repetitive flooding of the low terrain has resulted in the development of substantial

levees. These are covered predominantly by low brush. In swamp zones, aquatic and

semi-aquatic flora, sedges and high grasses predominate (Cazanacli and Smith 1998).

This region is considered to be located in the central lowland of the interior plains and as

such is relatively flat (Hunt 1974). The boreal forests near the Saskatchewan River are

located in the Manitoba lowland region. In areas where the land is suitably drained,

patches of white spruce can be found along with black spruce (Picea mariana) and

tamarack (Larix laricina) (Rowe 1 972).

Lake Winnebago

Lake Winnebago and its interconnecting systems of lakes and rivers are located at

approxirnately 44a00N latitude and 8g020'W longitude. Two major rivers, the Fox and

the Wolf, and four lakes, Lake Poyan, Lake Winneconne, Lake Butte des Morts and Lake

Winnebago compose the 15366km2 watershed throughout which sturgeon are present

Fempinger 1988). Lake Winnebago is the largest of these lakes having a surface area

and mean depth of 558 km2 and 4.7- respectively (Choudhury et al. 1996). An ideal

habitat for sturgeon, this eutrophic lake possesses a flat soft mud bottom with grave1

shoals on the West shore (Pnegel and Winh 1975). Lake Winnebago is located within the

central lowlands of the interior plains (Hunt 1974).

Lac St. Louis

Lac St. Louis is located at approximately 45'20'N latitude, 73O55'W longitude and

is one of several fluvial lakes in this region of the St. Lawrence. Generally these lakes

are relatively wide (>5km) and shallow (mean depth6m) and as such water in Lac Si.

Louis has a residence time of less than 1 day (Carignan et al. 1993). The shorelines of

Lac St. Louis are heavily urbanized and have been severel y modified through ant hropic

activities such as dredging of channels and harbors and island creation for Expo '67. Lac

St. Louis has a length of approximately 23.3 km, a maximum width of 9.3 km resulting in

a surface area of 145 km2 (Carignan et al. 1993). Water enters Lac St. Louis From Lake

Ontario, Lac St. Francois and the Ottawa River. Flow through the lake is approximately

1-1.6 ms" in the shipping channels. The lake bottom is composed of fine silt and sand

overlying glacial deposits of marine clay (Hudon 1997). The terrestnal system

sunounding the lake is composed of rolling lowlands of deep calcareous soils (Hunt

1974). Forest composition is generally deciduous to mixed deciduous and conifer.

White pine are supported in this area on more acidic, shallow soils (Rowe 1972).

Lac Parent

Though published information describing Lac Parent is sparse some data fiom

other nearby bodies of water is documented. Located in Quebec at approxirnately 48'

25'N latitude and 77" 15'W longitude Lac Parent is fed and drained by the Mégiscane and

Bell Rivers, respectively. The watenvays have a pH and conductivity (pS) of 6.0,26 and

6.7,33, respectively (Fortin et al. 1996). Lac Parent is situated in the northern clay

section of the Boreal forest (Rowe 1972). Topography in this region is generally flat and

the area displays poor drainage as is evident from the many bogs and swamps. In regions

where drainage is improved, white spmce and black spnice (Picea mariana) are common

(Rowe 1972).

Mattagami River

Located at approximately 4g055'N latitude and 8 1'3 7'W longitude the northwards

flowing Mattagami River drains into James Bay. This system is highly influenced by

hydroelectric installations and as a result, experiences marked variations in water flow

(Payne 1987). The topography of the terrestrial region surrounding the Mattagami River

is generally flat to rolling plains of clay and till (Momson 1991). Exposed Precambrian

shield results in the river being broken by rapids. The area has been heavily logged and

cut to the approximate 120m buffer (Nowak and Jessop 1987). Mean total precipitation

in the region is approximately 858 mm per annum, with half of this falling during the

growth season. Potential evapotranspiration is estimated to be 495mm (Momson 1991).

The Mattagami River flows through the Superior upland region of the Canadian Shield.

The terrestrial system surrounding the river has irregular drainage and is dotted with

lakes (Hunt 1974). The boreal forest in this region grows over tills and lacustrine

deposits with the predorninant species being black spnice. In areas where drainage is

improved white spnice can be found (Rowe 1972).

CaAPTER 1

LAKE STURGEON GROWTH CHRONOLOGIES

SYNC OR SWrM?

Published:

Canadian Journal of Fisheries and Aquatic Sciences

1.1 ABSTRACT

Rings in the cross-section of pectoral fin rays in lake sturgeon (Acipemer

filvescens) were used to assess growth synchrony among individuals within populations.

Decision cnteria were based on correlation among individual chronologies developed

from samples collected in the Saskatchewan River, Saskatchewan and Lake St. Clair and

the Mattagami River, Ontario. Initially, using al1 measured samples, correlations among

chronologies were not significant within these three populations. However, as mean

aging error was reduced, correlations among chronologies increased to significant levels

in sarnples from the Saskatchewan River and Lake St. Clair. These correlations were

insignificant among consistently aged fish sampled from the Mattagami River. It was

concluded that interannual growt h variation in lake sturgeon is influenced by population

wide, extrinsic factors in some populations. The results of the curent investigation

suggest that both growth synchrony and aging error should be quantified during the

construction of growth chronologies for al1 organisms.

1.2 INTRODUCTION

Growth of exothermic organisms is influenced by extrinsic factors. If these

factors operate over large geographic scales, their influence will be exerted on ail

members of a population (Thompson and Page 1989). However, growth of exothermic

organisms is also infiuenced by intrinsic, physiological factors. These factors, such as

pathological condition and reproductive cycles, do not simi lady influence al1 members

of a population but operate independently on individuals. A population in which growth

is rnost influenced by large-scale, extri nsic factors, will display synchronous i nterannual

variations in growth among its members (Kreuz et al. 1982, Thompson and Page 1989).

However, if growth is most influenced by intrinsic factors, interannual growth variation

may be asynchronous throughout this population.

Growth chronologies are time series that display annual growth fluctuations in

individuals or populations over a series of calendar years. Constructed from rings or

annuli in hard tissues, growth chronologies usually have the effects of age on growth

mathematically removed (Ogle et al. 1994, Pereira et al. 1995a,b, Cyterski and Spangler

1996, Fritts 1976). These chronologies provide insight into the ecology of a species

(Pereira et al. 1995a,b), allow for the development of predictive models (Cyterski and

Spangler 1996) and may assist in the determination of factors influencing growth.

However, to develop a growth chronology for a population, al1 members of that

population must be responding to a similar set of growth influencing factors. For

example, if growth is most influenced by population wide, extrinsic factors, individual

growth chronologies will display synchronous variation and be highly correlated arnong

members of that population. Conversely, individual growth chronologies most

influenced by intrinsic factors rnay display asynchrony, show no correlation among

population members and therefore display no significant growth variations when

averaged into population chronologies.

The lake sturgeon, Acipem~fdvescer~s , is found from the estuarial waters of the

St. Lawrence River to the Saskatchewan River west of Edmonton and from Nebraska,

Missouri and Alabama north to the Seal River on the west coast of James Bay and Fort

George on its east coast (Houston 1987, Scott and Crossman 1973). Individuals may

exceed 100 years of age (Houston 1987). Growth rings in the pectoral fin rays of this

species are annually developed (Rossiter et al. 1995). These characteristics make this

species an exceptional candidate with which to investigate the synchrony of interannual

growth variations in different populations. Interestingly, there is debate regarding

whether extrinsic or intrinsic factors most influence growth in this species. Some

research indicates that lake sturgeon growth is controlled by intrinsic factors such as

gonadogenesis (Roussow 1957). However, this concept has not been substantiated by

recent investigations (Guénette et al. 1992). The objective of this investigation was to

develop growth chronologies from rings contained in the pectoral t h rays of lake

sturgeon and using the interseries correlation coefficient (Wigely et al. 1984) and Monte

Car10 simulations (Prager and Hoenig 1989, Edgington 1995) determine if synchronous

interannual growth variations were detectable among individuals wit hin three

populations.

1.3 MATERIALS AND METEIODS

Lake sturgeon pectoral fin rays were obtained from archived collections of fish

sampled fiom the lower Saskatchewan River, Saskatchewan (53" 54'N, 102'20'W)

between 1978 and 1982 (Wallace 199 l), from Lake St. Clair, Ontario (42" OO'N, 82'

30'W) between 1991 and 1996, and From the Mattagami River, Ontario (49'55'N,

8 1°37'W) during 1996 and

1997. Rays had been previously prepared, sectioned to approximately 250 Pm, and

mounted on giass microscope slides.

The widths of growth rings, the consecutive pairs of opaqüe and translucent zones

in each fin ray's cross-section, were used as a record of past growth for each individual

(Wilson 1987, Rossiter et al. 1995). Sections, which were too opaque or translucent to

read, were excluded frorn the analysis. Similarly, sections that displayed signs of

breakage were also excluded. This condition, described in detail by Wilson (1987), is

caused when fins become broken during upstream spawning migrations and is identified

by regions of discontinuous rings in the cross-section. Widths of growth rings were

measured using a compound microscope (40x) with a drawing tube situated over a

digitizing tablet. Ring width measurements were made along the most legible posterior

radius (Fig. 1.1). In total, 48, 58 and 108 sarnples were analyzed from the Saskatchewan

River, Lake St. Clair and the Mattagami River, respectively.

For each fin ray sample, ring widths were rneasured in three blind replicates.

From these replicates, the index of aging error, a measure of the inability to consistently

age a sample, was calculated (Beamish and Fournier 198 1). For al1 samples the last

complete

Figure 1 . 1 : Lake sturgeon pectoral fin ray cross-sections from two individual sampled in Lake St. Clair. (A) year class = 1973, age = 23 (B) year class = 1977, age = 19. Dotted lines indicate the radius along which ring widths were measured. Year notation indicates relative narrowing and widening of ring width during 1 987 and 1990- 1 99 1, respectively.

growth ring was assumed to have developed during the calendar year prior to capture and

al1 rings were dated with respect to this year.

To constmct chronologies, ring widths were adjusted for the decrease in relative

growth with age using a technique similar to that used in the analysis of tree rings by

dendrochronologists (Fritts 1976). An individual chronology is defined as a series of

growth data, collected fiom an individual fish, for which the decrease in relative growth

with age has been removed. A population chronology is a series of growth data

constnicted by averaging numerous individual chronologies, aligned by calendar year,

from a population.

Individual chronologies were constructed by calculating a 7-year running average

from the series of ring widths measured from a fin ray sample. This curve approximated

the decrease in ring width with age. The residuals were calculated as the difference

between a ring width and its corresponding ninning average value. Each residual

represented annual growth relative to that in the 3 years preceding and following it. The

variance of these residuals also decreased with age (Maceina 1992). Variance was

homogenized through each individual chronology as follows: residuals were converted to

absolute values and each series was tit with another 7-year running average. This curve

approxirnated the decrease in variance of the residuals with age. Each residual, with its

original sign (k), was then divided by its corresponding running average value.

Remaining age-related trends were detected by aligning and averaging chronologies from

each population by age. These age-related trends

average curves from each individual chronology.

were removed by subtracting the

The resulting individual chronologies

displayed annual growth fluctuations, measured in unitless growth indices, with a

homogeneous variance fluctuating about a mean of zero. Population chronologies were

constructed by averaging individual chronologies aligned by calendar year. From this,

average calendar year indices of growth and approximate 95% confidence intervals were

calculated.

Incorrectly aging a calcified tissue sample misaligns and disnipts patterns arnong

assembled individual chronologies thereby influencing the synchrony of growth variation

arnong members of a population. While the exact age for a sturgeon in this investigation

could not be determined, the uncertainty of the ages assigned to these samples could be

estimated using the index of aging error (Beamish and Fournier 198 1). The effect of

aging error on synchrony of growth variation among members oPa population was

determined by analyzing correlations among al1 individual chronologies and assemblages

of chronologies with mean indices of aging error equai to 0.005 and 0.000.

Correlations among chronologies were assessed using the interseries correlation

coefficient as outlined in equation 18 of Wigley et al. (1984). This statistic estimates the

mean correlation among al1 possible pairs of chronologies, excluding correlations with

self, and ranges from approximately zero, if growth fluctuations are cornpletely

asynchronous, to 1, if growth fluctuations are completely synchronous. The interseries

correlation coefficient was calculated for a set of individual chronologies using a two-

way analysis of variance applied to the growth data organized with chronologies in

columns, and calendar years in rows. Correlations were investigated for data From 1965

through to 1978, 1977 through to 1990, and 1982 through to 1995 from samples from the

Saskatchewan River, Lake St. Clair and Mattagarni River populations, respectively.

Samples not completely spanning these periods were excluded from the analysis.

The null distribution of the interseries correlation coefficient was estimated using

Monte Carlo simulations. To estirnate the distribution of coefficients, random sets of

chronologies which were similar in their mean, variance, length and number to those

extracted corn lake sturgeon samples were randomly generated. By generating 1000 sets

of these random chronologies and calculating the interseries correlation coefficient for

each set, a nul1 distribution was sampled for this statistic.

The level of significance (p value) for each correlation coefficient calculated from

sets of lake sturgeon growth chronologies was estirnated by setting x equal to the number

of sets of chronologies generated in the Monte Carlo simulation that were greater than the

calculated test statistic and setting y equal to the total number of sets generated. The

resulting significance level is calculated from (x+l)/(y+l). 1 is added to both the

numerator and denominator of this equation as the calculated test statistic is included in

the estimated nul1 distribution (Edgington 1995). For example, the interseries correlation

coefficient of the 23 sturgeon chronologies from the Saskatchewan River that displayed

no aging error and spanned 1965 to 1978 was 0.1 133. To calculate the null distribution

for this statistic, 1000 sets of 23 random chronologies, 14 years in length were generated

and the interseries correlation coefficients calculated for each set. Three of these

artificially generated coefficients were greater than 0.1 133. Therefore, the level of

significance (p) was calculated as (3+1)/(1000+1)= 0.0040. The critical value (p) of the 9

test statistics calculated in this investigation (3 interseries correlation coefficients for 3

populations), such that a=0.05, was determined to be 5 5.56 x 10" based on the

Bonferroni method of multiple comparisons (dk) (Judd and McClelland 1989).

1.4 RESULTS

The correlation coefficient among al1 58 chronologies from Lake St. Clair was

0.0407 (p=0.049). The mean age and index of aging error of these samples was 25.2

years and 0.02 1, respectively. When the mean index of aging error was reduced to 0.005

and then to 0.000 by removing sarnples from the analysis the corresponding correlation

coefficients increased to 0.0926 (n=37, rnean age=24.2, p=3.00x 1 o'~) and 0.1445 (n=22,

mean a g ~ 2 2 . 1 , p< 1.00~ IO"), respectively. The population chronology assernbled using

22 consistently aged samples spanned 196 1 through 1995 and displayed significant

growth variations in 1974, 1983, 1985-1988, 1990-199 1 and 1994 (Fig. 1.2a)'. The

confidence intervals throughout the early years of al1 chronologies are wide, as fewer data

points were available from older fish (Pereira et al. 1995b).

AH 48 lake sturgeon chronologies from the Saskatchewan River displayed a

correlation coefficient of 0.0581 (p0.023) and a mean age of 25.0 years. The mean index

of aging error among these samples was 0.0 12. When the mean index of aging error was

reduced to 0.005 and then to 0.000 the corresponding correlation coeficients increased to

values of O. 1044 (n=34, rnean age=26.0, p=3. 00x 1 05) and 0.1 13 3 (n=23, mean a g ~ 2 5 . 1 ,

p= 4.00~10")~ respectively. The population growth chronology assembled using the 23

consistently aged sampies spanned 1944 through 1981. Growth indices in this chronology

deviated significantly from the mean during 196 1, 1965, 1969, 197 1, 1972, 1974 and

1976 (Fig. 1.2b).

'~ppendix iI coniains the data for these chronologies. Note the Saskatchewan River data contained therein ha k e n extended in tirne, including growth data obtained during later research.

The correlation coefficient among 108 chronologies from the Mattagarni River

was 0.0075 (p=O.26 1). The mean age and index of aging error of these samples was 3 1.6

years and 0.0242, respectively. When the mean index of aging error was reduced to 0.005

and then to 0.000, the corresponding correlation coefficients were 0.0075 ( 1 1 4 3 , mean

age=3 1.4, p=0.340) and -0.003 7 (n=32, mean age=28.1, ~ 4 . 6 9 4 ) . respectively. The

population chronology developed fiom the 32 consistently aged samples spanned 1952 to

1996. Growth indices in this chronology deviated significantly from the mean during

1973, 1980, and 1989 (Fig. 1 3 ) .

1960 1970 1980 Year

Figure 1.2: Lake sturgeon growth chronologies developed from samples collected in (A) Lake St. Clair, (B) Saskatchewan River, (C) Mattagarni River. Dotted lines indicate approximate 95% confidence intervals.

1.5 DISCUSSION

Sturgeon chronologies, developed fkom consistently aged individuals, displayed

signi ficantly synchronous growth variations among fish from the Saskatchewan River

and Lake St. Clair populations. No significant correlations were detected among

chronologies from Mattagami River fish. Aging error was found to disrupt growth

patterns and significantly reduce correlation arnong chronologies. By removing

inconsistently aged individuals from the analysis, the strength of the common signal

contained in each population chronology was enhanced (Wigely et al. 1984). As

correlation among individual chronologies increased, numbers of years dunng which

growth differed signiticantly from the mean also increased.

From these results, it may be concluded that sturgeon growth is influenced by

population-wide extrinsic factors in the Saskatchewan River and Lake St. Clair. No such

evidence was found for fish fiom the Mattagami River. One extrinsic factor responsible

for controlling sturgeon growth throughout a population may be temperature. Air

temperature has been found to suitably predict average growth rates in a cornparison of

32 lake sturgeon populations (Fonin et al. 1996). Roussow ( 1957), investigating growth

patterns in lake sturgeon growth rings, indicated that growth was controlled by intrinsic,

physiological factors such as pathological condition and spawning periodicity. The

results of the current study indicate that intnnsic factors do not disrupt growth synchrony

in sturgeon from Lake St. Clair or the Saskatchewan River. Sturgeon, averaging 25 years

of age, displayed signiticantly synchronous growth fluctuations in these populations.

These results corroborate the findings of Guénette et al. (1992) which noted a lack of

correlation between state of maturity and the widths of the last five growth rings in lake

sturgeon from the St. Lawrence River. Their research also suggested that extrinsic factors

might influence interannual growth variation.

Relative to other exothermic organisms, lake sturgeon display relatively low

synchrony of interannual growth variations. Wigley et. al (1984) reported the interseries

correlation coefficients for several studies conducted on tree rings. Correlations for these

terrestrial populations of exotherms ranged from 0.1779 (n= 1 3, years= 100) to 0.5297

(n=18, years= 100). No levels of signiticance were reported with these statistics.

Recently growth chronologies have been constructed for several varied groups of

organisms from molluscs and mammals to fish and trees (Jones 1980, Boyd and Roberts

1993, Guyette and Rabeni 1995 ). These tirne series improve our understanding of

environmental factors influencing growth and assist prediction of population productivity

(Fritts 1976, Ogle et al. 1994, Guyette and Rabeni 1995, Pereira et al. 1995a,b, Cyterski

and Spangler 1996). It may be concluded fiom the current investigation that the

development of growth chronologies for any organism rnust be approached with caution

for several reasons. Chronologies constmcted from populations that fail to display

synchronous interannual growth variations, such as the Mattagarni River lake sturgeon,

may result in poorly developed chronologies and incorrect predictive models.

Furthemore, aging errors must be minimized to rnaxirnize the common signals among

individual chronologies and the reliability of population growth chronologies.

CHAPTER 2

THE INFLUENCE OF ENVIRONMENTAL FACTORS

ON LAKE STURGEON GROWTH

Submitted to: Transactions o f the AmeRcan Fisheries Society

33

2.1 ABSTRACT

The purpose of this investigation was to determine if ring widths in the cross-

sections of lake sturgeon pectoral fin rays satisfy three criteria required of structures used

in the development of growth chronologies. First, ring widths must be related to the

overall somatic growth of the organism. Second, ring widths must demonstrate

synchrony of interannual growth variation among individuals within a population.

Finally, fin ray rings and growth chronologies should be related to both interpopulation

and interannual variations of known environmental factors. This research demonstrated

that the widths of these rings document variations in somatic growt h by showing that

average radii of fin ray cross-sections, at age 25, were related to total length at the same

age using data from 7 populations sampled across North America. This investigation also

suggested that growth ring widths were influenced by large scale, population wide,

extrinsic factors in two ways. First, differences, between populations in fin ray cross-

sectional radii at age 25 were arongly correlated with mean annual, summer, and winter

air temperatures. Secondly, growth chronologies developed from populations that

demonstrate signi ficant synchrony of interannual growth variations among rnembers,

were consistently positively correlated with past air temperature records. This research

has provided strong evidence that growth rings contained in the cross-section of the lake

sturgeon pectoral fin ray can be used in the construction of growth chronologies and

investigations into ecosystem dynamics.

2.2 INTRODUCTION

Ecologists concemed with climate change and its impact must be able to detect,

interpret and predict varîability in ecosystems. However, ecosysterns display cornplex

patterns of natural variability on time scales that may exceed the life span of a researcher

(Risser 199 1, Lane et al. 1994). As a result, development and testing of hypotheses

surrounding climate and ecosystem interaction is dificult.

Retrospective investigations using growth chronologies allow researchers the

opportunity to study impacts of environmental variations on ecosystem dynamics over

extensive periods of tirne (Boyd and Robens 1993, Guyette and Rabeni 1995, Pereira et

al. 1995a,b, Cyterski and Spangler 1996). As growth of ectotherms is influenced by

environmental fluctuations, the widths of rings or annuli in the hard tissues of an

organism may offer a record of past environmental quality. Tirne-series assembled from

these data, or growth chronologies, display interannual growth variations in an individual

or a population. Dendrochronologies, growth chronologies compiled from tree ring data,

document past fluctuations of terrestrial ecosystems (Fritts 1976). Recently, using

calcified tissues such as scales, rays and otoliths in fish, several investigations have

developed similar time series for aquatic systems (Boehlert et al. 1989, Weisberg 1993,

Ogle et al. 1994, Pereira et al. 1995a,b, Cyterski and Spangler 1996, LeBreton et al.

1 999).

The lake sturgeon, Acipenserfirlvesceris, exhibits an extensive range throughout

North America (Scott and Crossman 1973), a notable longevity, and seasonal changes in

growth patterns of the pectoral fin ray cross-section (Houston 1987, Rossiter et al. 1995).

These characteristics, combined with our knowledge of the influence of air temperature,

pH, conductivity and latitude on sturgeon growth (Fortin et al. 1996, Power and

McKinley 1997) make this species an excellent candidate from which to develop growth

chronologies for use in ecophysiological research. However, the widths of fin ray rings

can only be used to constnict ecologically relevant growth chronologies if they are

related to the overall growth of the organism (Pereira et al. 1995b), demonstrate

synchrony of interannual variation among members of a population, and respond to

variations of large scale, population wide, extrinsic factors.

Air temperature and total precipitation are environmental variables that may

influence interannual variation in lake sturgeon growth chronologies. Air temperature,

through its effect on water temperature, controls the thermodynamics goveming growth

processes. The influence of air temperature on growth chronologies has been established

for other fish species (Guyette and Rabeni 1995, Pereira et al. 1995a,b). Total

precipitation influences discharge throughout aquatic systems and rnay alter thermal and

oxygen regimes thereby acting on fish growth (Guyette and Rabini 1995). Also, the

influence of these factors dunng the previous season may effect growth the following

year. For example, as s h o w in other ectotherms (Fritts 1 W6), elevated temperatures

during the late summer of the previous growth season can result in higher than normal

rates of respiration and utilization of stored energy reserves generally directed towards

growth early in the following year.

This study was conducted to detennine if the widths of lake sturgeon pectoral fin

ray rings were suitable for use in the development of growth chronologies. To assess

this, fin ray ring widths and related growth chronologies were tested to determine if they

satisfied three criteria. First, ring widths must be related to the overall somatic growth of

the organism. Secondly, ring widths must demonstrate synchrony of interannual growth

variation among individuals within a population. Finally, fin ray rings and growth

chronologies should be related to both interpopulation and interannual variations in

known environmental factors.

2.3 MATERIALS AND METHODS

Previously sectioned pectoral fin ray samples were obtained fiom archived

collections. Fin rays were used fiom Lake St. Clair, Ontario/Michigan (42"0OYN,

82*3OYW), Lake Temiskaming, Ontario/Quebec (47O3OYN, 79"3OYW), Saskatchewan

River, Saskatchewan, (53" 54'N, 102' 2O9W), Lake Winnebago, Wisconsin (44"00N,

88"201W), Lac St. Louis. Quebec (45'2OYN, 73"55'W). Lac Parent. Quebec (4g025'N, 77"

I 5' W), and Mattagami River, northem Ontario (49" S SN, 8 1" 3 7'W). Organizations and

contact persona1 from which these samples were borrowed are outlined in Table 2.1.

Sections too opaque or translucent to read were excluded from the analysis.

Sections that displayed signs of breakage were also excluded Born the analysis. This

condition, described in detail by Wilson (1 987)' is caused when fins break during

upstrearn spawning migrations and is identified by regions of discontinuous rings in the

cross-section. Ring widths, defined as one set of translucent and opaque rings, were

measured using a compound microscope (40x) with a drawing tube situated over a

digitizing tablet. Measurements were made along the rnost legible, posterior radius at

points of maximum acuteness on consecutive translucent rings (LeBreton et al. 1999).

For each fin ray sarnple, rings widths were measured in three blind replicates. Using

these replicated measures the index of aging error (Beamish and Fournier 198 1), a

measure of the inability to consistently age a sarnple, was calculated. To reduce the

influence of aging error, only those samples that were consistently aged throughout al1

three replicates were included in further analysis. For ail samples the last complete ring

was assumed to have developed during the calendar year prior to capture and al1 rings

were dated with respect to this year.

S turgeon Population Samples acquired frorn:

Lake S t. CI air

Lake Temiskaming and

Lac Parent

Saskatchewan River

Lake Winnebago

Lac St. Louis

Mattagarni River

Ontario Ministry of Natural Resources

Contact: Don MacLennan

Gouvernement du Quebec

Ministère de l'Environnement et de la

Faune

Contact: Daniel Nadeau

Saskatchewan

Environmental and Resource

Management

Contact: Rob Wallace

Wisconsin Department of Natural

Resources

Contact: Ron Brusch

Gouvernement du Quebec

Ministère de l'Environnement et de la

Faune

Contact: Pierre Dumont

Universities of Guelph and Waterloo

Contact: David Noakes and Scott

Mckinley

Table 2.1 : Organizations and contact names from which lake sturgeon pectoral fin ray samples were borrowed.

Fin ray rings must be related to the overall growth of the individual if they are to

be used in the development of growth chronologies and ecophysiological research. Two

procedures were used to validate this relation. First, a general linear mode1 was used to

determine if the relation between total length of the individual and radius length of the fin

ray cross-section were significantly related for those samples for which length data were

available, from Lake St. Clair, Saskatchewan River, Lake Winnebago, and Mattagami

River,. Fork length data from individuals from the Saskatchewan River were converted

to total length using a linear regression developed from Lake St. Clair and Mattagami

River samples as described by the equation:

Tl = 148.0 + 0.96FI (r2 = 0.99, n=164) (2.1)

where TI and FI are total length and fork length in rnillimeters, respectively.

A second methodology was required to demonstrate a similar relation between

body length and fin ray cross-sectional radii for those populations in Lake Temiskaming,

Lac St. Louis and Lac Parent as length data were not available for these samples.

However, total length at age 25 was reported for Lake Temiskaming and Lac St. Louis

populations in Fortin et al. (1996). The average total length at age 25 for Lac Parent fish

was caiculated fiom sturgeon in nearby Mégiscan E., Lac Guéguen, and the Bell River

for which length measurements were available (Fortin et al. 1996). Total length at age 25

for the Lake St. Clair, Saskatchewan River, Lake Winnebago, and Mattagami River

populations was obtained from calcu lated von Bertalanffy curves (Figure 2.2). Linear

regression was used to establish the relationship between total length at age 25 and the

average fin ray radius.

One criterion which must be met by fin ray rings if these measures are suitable for

use in the construction of ecophysiologically relevant growth chronologies is that

fluctuations in ring widths must be related to variations in large scale, extrinsic factors.

The cornpliance of fin ray rings to this criterion was investigated in two ways. First,

linear regression was used to descnbe relationships between fin ray radius length at age

25 and mean annual, sumrner, and winter air temperatures, latitude, pH, and conductivity

for each population (Fortin et al. 1996). Mean annual temperatures were defined as the

average air temperature in degrees Celsius from January to December. Mean summer

temperatures were defined as the average of air temperatures from the beginning of April

to the end of September. Mean winter temperatures were taken as the average air

temperature from the beginning of October, in the year prior to the season of growth, to

the end of Marc h.

Secondly, population growth chronologies were developed from sturgeon growth

rings and Pearson correlation coefficients were calculated between chronologies and

meteorological records for both the current and previous seasons of growth. Measures of

past mean summer month air temperatures and each summer month's total precipitation

were used. Any long-term trends in meteorological data, which growth chronologies do

not contain, were removed using the saine technique applied to sturgeon rings in the

development of growth chronologies as described below.

In the construction of growth chronologies, an individual chronology was

defined as a series of ring width data collected from a single fish for which the decrease

in relative growth with age has been mathematically removed. A population chronology

is a series of growth data constructed by averaging numerous individual chronologies,

aligned by calendar year, fiom a population. Ring width declines with age, a trend which

masks any correlations with past records of environmental quality. This trend was

removed fiom growth data with a technique similar to that used by dendrochronologists

(Fritts 1976). Each fin's growth data were fit with a curve, approxirnating the general

decrease of increment width with age, using a 7-year running average. The residuals, the

difference between an increment width and its corresponding 7-year average value, were

calculated. Therefore, each residual represented yearly ring width growth relative to that

in the 3 years preceding and following it. The variance of these residuals also decreased

with age (Maceina 1992) and was homogenized through each chronology to allow

cornparisons among samples of different ages. Residuals were convened to absolute

values and these data, for each sample, were fit with another 7-year running average.

This fitted curve approximated the long-tenn decrease in variance of the residuals with

age. Residuals, with their original signs (k), were then divided by the corresponding

fitted curve's values. Any remaining age-related trends were detected by aligning and

averaging chronologies from each population by age. These trends were removed by

subtracting the average growth index at age from each individual's chronology. (For a

more complete exploration of this methodology see Chapter 4.)

In total 24, 74, 84, 15, 9 1, 15, and 32 individual chronologies were assembled

into population growth chronoiogies fiorn consistently aged individuals from Lake St.

Clair, Lake Temiskaming, Saskatchewan River, Lake Winnebago, Lac St. Louis, Lac

Parent, and Mattagarni River, respectively. Mean growth indices in the developed

population chronologies had relatively wide confidence intervals and elevated variance

throughout early years, as fewer data points from older individuals are present. To ensure

a homogeneous variance throughout the population chronologies, data points pnor to the

first mean growth index that significantly differed from zero were removed from the

series. Resulting chronologies then spanned 1972-1 995, 196 1- 1992, 1959- 1993, 1969-

1993, and 1973- 1996, for populations sampled from Lake St. Clair, Lake Temiskaming,

Saskatchewan River, Lac St. Louis and Mattagarni River respectively. Lac Parent and

Lake Winnebago growth chronologies failed to display variation in growth that

significantly deviated from the mean of O until 1982 and 1985, respectively. Therefore,

data prior to 1949 and 1969, respectively in each of these chronologies, were arbitrarily

excluded, as these years were the first to display a relatively narrowed confidence

interval.

Meteorological data were used from sites as outlined in Table 2.2. Multiple data

sets were averaged for each location to reduce penods of missing data. Air temperatures

were used in this investigation as regularly collected water temperatures were not

available for al1 systems. Furtherrnore previous investigations have related somatic

growth in sturgeon to measures of air temperature (Fortin et al. 1996, Power and

McKinley 1997). The assumption that water temperatures, at least dunng periods of

growth, closely match variations in air temperatures is strongly supponed by water and

air temperature data available for the systems investigated (Appendix 1).

Finally, growth rings in the pectoral fin rays of lake sturgeon rnust demonstrate

some synchrony of interannual growth variation arnong members of a population if they

are to be used in growth chronology construction. If no synchrony of interannual growth

variation can be detected arnong members of a population, then a common environmental

signal cannot be extracted from these structures and they cannot be used in the

development of growth chronologies documenting the influence of environmental

variation on growth (LeBreton et al. 1999). To determine if fin ray growth rings satisfied

this criterion the synchrony of interannual growth variations among members of each

population was quantified using the interseries correlation coefficient as outlined in

equation 18 of Wigely et al. (1984). This measure approximates the average value of al1

pair-wise correlations among an assemblage of time series, excluding those correlations

with self Values for this statistic range from

Populations or Location Meteorological Station Lat ./Long.

Lake St. Clair Detroit city airport

Mount Clemens

Lake Temiskaming North Bay

Timmins

Val d'Or

Saskatchewan river Flin Flon

The Pas

Lake Winnebago Oshkosh

Fond du lac

Lac St. Louis Montreal

Lac Parent

Mattagarni River

Val d'Or

Kapuskasing

Table 2.2: Populations and corresponding meteorological stations from which rnonthly air temperature and total precipitation were obtained.

approximately O, if growth fluctuations are asynchronous, to 1, if growth fluctuations are

fully synchronous. The interseries correlation was calculated using a two-way analysis of

variance applied to growth data organized with individual chronologies in rows, and

calendar year in columns. Interseries correlation coefficients were calculated over the

petiods 1977-1990, 1978-1991, 1965-1978, 198 1-1994, 1980-1993, 19714984, and

1982-1995 for the Lake St. Clair, Lake Temiskaming, Saskatchewan River, Lake

Winnebago, Lac St. Louis, Lac Parent, and Mattagarni River, respectively.

The nul1 distribution of the inter-series correlation coefficient was estimated using

Monte Carlo simulations. To estimate the distribution of coefficients, random sets of

chronologies, similar in their mean, variance, length, and number to those extracted from

lake sturgeon sarnples were repeatedly generated. By generating 1000 sets of these

random chronologies and calculating the inter-series correlation coefficient for each set, a

nuil distribution for this statistic was created. The level of significance (p value) for each

calculated correlation coefficient was determined by setting the independent variable (x)

equal to the number of sets of chronologies generated in the Monte Carlo simulation

which were greater than the calculated test statistic, and setting the dependent variable (y)

equal to the total number of sets generated. The resulting significance level is calculated

from (x+l)/(y+l). The value 1 is added to both the numerator and denominator of this

equation as the calculated test statistic is included in the estimated nul1 distribution

(Edgington 1995).

2.4 RESULTS

The general linear model developed fiorn the four populations for which

individual length data were available displayed significant relationships between total fin

ray radius and total length of the individual in each population (p<0.0001) (Fig. 2.1).

Von Bertalanffy curves developed for samples from Lake St. Clair, Saskatchewan River,

Lake Winnebago, and Mattagarni River (Fig. 2.2) provide total lengths at age 25 for these

populations. Total lengths at age 25 are presented in (Table 2.3) as extracted from Fortin

et al. (1996). Average fin ray cross-section radii at age 25 for al1 7 sturgeon populations

are also outlined in Table 2.2. Total length (mm) at age 25 was significantly related to

average fin ray radius (mm) at age 25 and described by the Iinear relationship:

LZ5= 360.03 + 182.98(R) (~0.86, p=O.O14, n = 7) (2.2)

where LZJ is the total length at age 25 in mm and R is the fin ray radius at age 25 in mm.

In the investigation into the influence air temperature exerts on growth variations

arnong lake sturgeon populations, average radius of the fin ray at age 25 (R) was

significantly related to rnean annual (A), summer (S), and winter (W) air temperatures

and estimated by equations:

R = 4.21 +0.15 (A)

R = 3.24 + 0.13 ( S )

R = 5.45 + 0.12 (W)

Total Length (cm)

Figure 2.1 : Sturgeon pe~ofal fin ray radius length as a function of total length as measured from Lake St. Clair (open triangies), Saskatchewan River (open circles), Lake Winnebago (black circles), and Mattagarni River (black triangles).

Figure 2.2: Von Bertalanfy curves constructed from lake sturgeon populations from Lake St. Clair (dashed), Saskatchewan (dotted), Lake Winnebago (dash-dot), and Mattagarni River (solid).

Population Mean Mean Fin Radius at TL 23 (mm) Age age 25 (mm)

Lake St. Clair 22.1 5.85 1407 Lake Temiskaming 24.6 4.57 1072 * SaskatchewanRiver 25.3 4.72 1243

Lake Winnebago 28.1 5.6 1 1479 Lac St. Louis 22.5 4.80 1241*

Lac Parent 30.6 4.19 1060 ** Mattagarni River 28.1 3.74 1152

Table 2.3 : Mean age, average fin ray radius, and total length at age 25 ( L ~ J ) of samples used fiom each population. (*) L ~ J were acquired directly from the research of Fortin et al. 1996. (**) L25 was estimated by averaging data from surrounding populations.

The relations between average fin ray radius at age 25 and latitude, pH and

conductivity were estimated by the linear regressions:

R = 11.10 -0.13 (L,) (r=0.72, p=0.07, n=7) (2.6)

R = 1.67 + 0.41 (pH) (r=O. 3 5 , p=O .44 II=~) (2.7)

R = 4.30 + 0.003 (C) (r=O. 42, p=0.3 4, n=7) (2-8)

where L, pH, and C represent latitude, in degrees, pH and conductivity, in pS. The

relationship between latitude (L) and fin ray radius at age 25 (R), following the removal

of the Saskatchewan River data, is:

R = 16.97 - 0.26 (L) ( ~ 0 . 9 9 , p<O. O0 1, n=6) (2.9)

No other regressions were significantly irnproved by removal of the Saskatchewan River

data.

Growth chronologies (Appendix II) were developed to determine if synchronous

interannual variations in growth were det ectable among i ndividuals in a population. By

using the interseries correlation coefficient and Monte Carlo simulations, it was

deterrnined that interannual variations in sturgeon growth from Lake St. Clair, Lake

Temiskaming, Saskatchewan River, Lake Winnebago, and Lac St. Louis were

significantly synchronous (p<O.OS) among members of each population (Table 2.4). The

populations from Lac Parent and Mattagarni River failed to display signi ficant synchrony

(p>0.05) of interannual growt h variation (Table 2.4).

To assess what influence temperature and precipitation had on interannual

variations in the growth of lake sturgeon, population growth chronologies were

constructed. Calendar years spanned by the seven developed population growth

Population Interseries Years n P Correlation

Lake St. Clair O. 1445 1977-1990 22 <O.OO 1 Lake Temiskaming O. 1040 1978-1991 52 0.002 Saskatchewan River O. 1 133 1965-1978 23 0 .O04

Lake Winnebago 0.08 13 1981-1994 15 0.036 Lac St. Louis 0.0500 1983-1990 89 0.0 15 Lake Parent 0.0380 1971-1984 15 O. 15

Mattagarni River -0.003 7 1982-1995 32 0.698

Table 2.4: Intersenes correlation coefticients for each population investigated, years and numbers o f growth chronologies correiated.

Population Chronology Significant Growth Fluctuations from the Mean

Time span

-

Lake St. Clair

Lake Temiskaming

Saskatchewan

River

Lake Winnebago

Lac St. Louis

Lac Parent

Mattagarni River

Table 2.5: Calendar years spanned by lake sturgeon population growth chronologies and year during which mean growth indices differed significantly from the mean as determined from the 95% confidence interval. (+) or (-) indicate whether relative growth was higher or lower, respectively for that panicular year.

chronologies along with the years that mean growth indices significantly differed fiom

the average as determined by the 95% confidence intervals and the relative growth

deviation during those years (+) are outlined in Table 2.5.

The Lake St. Clair sturgeon growth chronology was positively correlated with

mean June temperatures (Table 2.6a) while the chronology for Lake Temiskaming was

positively correlated with mean annual, June, August and September air temperatures

(Table 2.6a). The growth chronology fiom Saskatchewan River sturgeon showed

significant, positive correlations with May, June and Septernber air temperatures (Table

2.6a). Correlation of the growth chronologies developed from Lake Winnebago and Lac

St. Louis samples were significant and positive with mean August, and June air

temperatures, respectively (Table 2.6a). The Lac Parent growth chronology failed to

display significant correlation with any of the measures of temperature (Table 2.6a) while

the Mattagami River growth chronology displayed positive and negative correlation with

mean August and September air temperatures, respectively (Table 2.6a).

Relatively few correlations were noted between lake sturgeon growth

chronologies and measures of air temperature frorn the previous season of growth. The

Lake St. Clair and Lake Temiskaming population chronologies were positively correlated

with mean August and May temperatures from the previous season of growth,

respectively (Table 2.6b). The growth chronologies from the Saskatchewan and

Mattagami Rivers were negatively correlated with mean September and Iuly air

temperatures from the previous season of growth, respectively (Table 2.6b). Lake

sturgeon growth chronologies from Lake Temiskaming and Saskatchewan River were

negatively correlated with June and May total precipitation from the current season of

growth, respectively (Table 2.7a). The Lake Winnebago growth chronology was

negatively correlated with April and July total precipitation but positively correlated with

the same variable from May (Table 2.7a). The Lac St. Louis sturgeon growth chronology

was positively related to April total precipitation and negatively related to total

precipitation in August (Table 2.7a). The chronologies developed from Lake St. Clair,

Lac Parent and the Mattagami River did not correlate with any measures of precipitation

dunng the current season of growth.

The Lake Temiskaming and Lac Parent growth chronologies were positively

correlated with September and August total precipitation, respectively, while the

Mattagami River chronology was negatively correlated with April precipitation, each

from the previous growth season (Table 2.7b). The Lake St. Clair, Saskatchewan River,

Lake Winnebago, and Lac St. Louis growth chronologies each failed to correlate with

measures of total precipitation from the previous growth season (Table 2.7b).

Population Correlation Coeficients

Aprii Lake St. Clair O, 07

Lake Temiskaming -0.05 Saskatchewan River O. 16

Lake Winnebago -0.26 Lac St. Louis -0.07

Lac Parent 0.09 Mattagarni River 0.30

June 0.42 * 0.46** 0.41 * -0.07 0.42" o. 12 0.13

Jul y 0.08 0.27 -0.18 0.09 0.28 0.03 0.08

Auyst 0.0 1

0.34" 0.05

OS2* * 0.35 O* 12

0.63 * *

September 0.01

O M * * 0.32* -0.06 0.06 O. 17 -0.37*

Table 2.6a: Correlation of sturgeon population growth chronologies with measures of mean air temperature during the current season of growth. (*) p 5 0.05, (**) p 5 0.01.

Population Correlat ion Coefficients

April Lake St. Clair -0.14

Lake Temiskaming -0.02 Saskatchewan River 0.20

Lake Winnebago -0.19 Lac St. Louis O. 13 Lac Parent -0.19

Mattaganii River -0.17

June -0.02 -0.07 -0.03 -0.12 0.02 o. I O O, 12

August 0.33 -0.15 -0.14 0.2 1 0.23 -0.15 -0.08

Sept en~ber -010 -0.20

-0.38* -0.07

-0.58** 0.2 1 0.3 1

Table 2.6b: Correlation of population growth chronologies with measures of mean air temperature during the previous season of growth. (*) p <; 0.05, (**) p 2 0.01.

2.5 DISCUSSION

This investigation provided evidence that lake sturgeon pectoral fin ray rings and

related growth chronologies met three criteria required of such structures if they are to be

used in the development of growth chronologies for ecophysiological research. First,

measured ring widths must be related to body growth of the organism (Pereira et al.

1995b). The results suggest that lake sturgeon fin ray rings satisfy this criterion. Fin ray

growth in individual fish from Lake St. Clair, Saskatchewan River, Lake Winnebago and

the Mattagarni River was directly related to total length. As well, using previous

published data, this investigation establis hed a linear relationship between total length

data at age 25 and mean fin ray radius at the same age for ail seven populations. These

results are similar to those found for calcified tissues in other species of fish (Alhossaini

and Pitcher 1988, Bradford and Geen 1992). [t should be noted that while actual

relations between the size of calci fied tissues and body length are l i kely curvilinear

(Casselman 1990), straight line regressions were applied to this data only to indicate that

a positive relation exists between fin ray radius widths and body length. It is not within

the scope of this research to investigate the exact relation between fin ray and body size

but only to establish that an increase in the size of one was related to an increase in the

other.

The second criterion required of structures used to develop growth chronologies

relevant in ecophysiological research States that sturgeon growth rings and related growth

chronologies must demonstrate significantly synchronous interannual growth variation

among members of a population. This research indicated that ring widths, sampled fiom

some lake sturgeon populations, do satisfy this criterion, as significant synchrony of

interannual growth variation was detected among sampied members of the Lake St. Clair,

Lake Temiskaming, Saskatchewan River, Lake Winnebago, and Lac St. Louis

populations (Table 2.4). However, simiiar synchrony could not be detected for the Lac

Parent or Mattagarni River populations.

The final criterion required of lake sturgeon fin ray rings was that the widths of

the these structures must be related to both interpopulation and interannual variations in

known environmental factors. The significant results of the regression and correlation

analyses indicates that lake sturgeon growth rings also satisfy this criterion. In concert

with the works of Fortin et al. (1996) and Power and Mckinley (1997), the present

investigation found that variations in the growth of lake sturgeon, as measured Frorn the

fin ray radius, were correlated to mean annual, summer and winter temperatures.

Correlations with sturgeon fin ray radii at age 25 were highest with mean summer

temperature and lowest with mean winter temperature. Correlation with winter

temperatures was lower due to data from Saskatchewan. This population experiences

colder winter temperatures, relative to summer temperatures, than other populations.

These results suggest that, at least in the Saskatchewan River population, summer

temperatures exert a greater influence than winter on sturgeon growth. Similarly, while

only marginal correlations were found between latitude and sturgeon growth from al1

populations, the removal of the Saskatchewan River data point significantly increased

this relationship. Sturgeon growth from the Saskatchewan River is higher than expected

based on latitude alone. This is likely due to the relatively warmer temperatures

experienced during the growth season at this western location relative to a similar

location in the east at the same latitude (McCauley and Kilgour 1990).

Conductivity and pH, factors generally associated with fish production (Bryant et

al, 1998), were not found to significantly predict lake sturgeon growth as measured From

the pectoral fin ray radii. While Fonin et al. (1996) did successfully use temperature,

conductivity and pH as determinants of sturgeon growth in an investigation among 32

North Arnencan populations, growth behaved differently along an east-west division.

While the present investigation has too few data points to be similarly partitioned by

longitude, the results obtained by rernoval of the Saskatchewan data point, the rnost

westerly population, from the latitude-growth cornparison, suggest a similar longitudinal

effect.

The current investigation used the Pearson correlation coefticient to detect the

influence of environmental variables on interannual growth variation as measured by

population growth chronologies. While multiple applications of correlative analysis may

result in spurious correlations, consistent results indicate a true reiationship. In this

investigation the consistent, positive correlations between growth chronologies, which

dernonstrated synchronous interannual growth variation (Table 2.4), and mean air

temperature dunng the current season of growth suggest a tme relationship. Sturgeon

populations in Lake St. Clair, Lake Temiskaming, Saskatchewan River, Lake Winnebago

and Lac St. Louis, which displayed high interseries correlation coefficients and are

therefore expected to contain strong environmental signals, were significantly and

positively correlated with measures of mean air temperature (Table 2.6a).

The growth chronology from Lac Parent failed to display significant interseries

correlation among members of that population and also fai led to correlate signi ficantly

with any measure of air temperature. The positive and negative correlation of the

Mattagarni sturgeon growth chronology with mean August and September air

temperatures, respectiveiy, is difficult to explain. Correlation of this chrondogy with any

measure of air temperature is not expected as these fish failed to display significant

interseries correlations arnong samples, and any environmental signal contained in this

chronology is expected to be weak. Based on the insignificant interseries correlations

among Mattagami River samples, and the large amount of noise present in the

corresponding chronology, it is unlikely that any environmental influences on growth are

resolvable from this time series. Therefore, the biological relevance of this growth

correlation with mean August and September air temperatures is suspect and Iikely

represents a spurious correlation.

Relative to correlations with air temperatures cunng the current season of growth,

few occurred between lake sturgeon growth chronologies and rnean temperatures from

the previous season (Table 2.6b). Also, t hose signi ficant correlations were inconsistent

among populations, half positive and the other half negative. Initially 1 predicted that

temperatures during the previous season rnay influence sturgeon growth during the

following year as is observed in other exothermic organisms. For example, elevated

ternperatures during the previous season can raise respiration and deplete energy reserves

required for winter survival and early spring growth in trees (Fritts 1976). These results

indicate that air temperatures from the previous season of growth do not exert a strong

influence on the interannual growth variation in lake sturgeon relative to those during the

cuirent season.

Lake sturgeon growth chronologies were related to measures of total

precipitation, however these relationships were inconsistent and explanation of them is

difficult (Table 2.7a). It was hypothesized that sturgeon growth may be positively

correlated with past measures of precipitation as increased discharge may improve

thermal and oxygen regimes in a watenvay (Guyette and Rabeni 1995). Also, decreased

discharge and reduced foraging habitat may lead to a decline in sturgeon growth

(McKinley et al. 1993). Total precipitation during the previous growth season displayed

significant correlation with only three of the seven population growth chronologies

developed and these relations were not consistant (Table 2.7b). The numerous significant

correlations between sturgeon growth and total precipitation during the current season

(Table 2.7a) suggest that precipitation does influence sturgeon growth. However, based

on the differing slopes (*) of these relations in different populations and throughout the

growth season, it is possible that these relations may fluctuate based on types of habitat,

watershed morphology or other ecosystem properties not quantitied in this investigation.

At this point, a potential weakness of this investigation should be discussed. The

techniques applied in this research to detrend fish growth data were borrowed from the

dendrochronological literature. However, sturgeon growth chronologies are much shorter

than the tree chronologies for which these techniques were developed (Fritts 1976).

Therefore, in this research there is an increased risk of making a Type 1 error and falsely

rejection of the nul1 hypothesis (Steel and Tome 1980, p. 88) when correlating sturgeon

chronologies with environmental data relative to when these technique are applied in

dendrochronology. Therefore, while the conclusions of the current investigation were

somewhat strengthened by the results from multiple analyses from different populations

the application of these techniques to a single population should be done with caution.

Aiso, care should be exercised when applying these techniques to growth data obtained

from organisms possessing shorter Iife spans (Boyd and Roberts 1993). If similar

research is to be undertaken on short lived life forms other techniques may be more

appropriate to extract environmental data from growth rings (Weisberg 1993) or to

compare interannual growth variation with environmental fluctuations (Prager and

Hoenig 1989, Pereira et al. 199Sa).

The results of the correlation between interannual variation in sturgeon growth

and measures of air temperature during the current season strongly supports the finding

of LeBreton et al. (1 999) t hat interannual variations in fin ray rings were related to

fluctuations in large scale, population wide, extnnsic factors. The current investigation

also supports the conclusion of Guénette et al. (1992) that the influence of

physiologically related, cyclic patterns do not dominate the widths of rings in lake

sturgeon pectoral fin rays. This, in concert with the results of the present study, argues

against the views that sturgeon pectoral fin ray rings can be used as records of

reproductive periodicity (Keenlyne and Jenkins 1993, Roussow 1957).

Growth chronologies displaying fluctuations in environmental quality cannot be

estimated from al1 populations. For example, samples from Lac Parent failed to display

significantly synchronous interannual growth variations among population members.

This may be the reason this growth chronology failed to correlate with any measures of

air temperature. It is proposed that the lack of interseries correlations among the

individual chronologies From Lac Parent and the Mattagarni River are related to their

older mean age and relatively slow growth (Table 2.3). Generally, as in these

populations, there is an increase in aging error in older, slower growing individuals

(Casselman 1987). Aging error, which rnisaligns chronologies in time, disrupts common

patterns among growth chronologies and decreases the interseries correlation coefficient.

This research provided evidence that growth increments in the pectoral fin ray of

lake sturgeon, fiom populations which display synchrony of interannual growth variation

among individuals, can be used in the construction of growth chronologies relevant to

ecophysiological research. Imponantly, such investigations can be applied without lethal

consequence, panicularly important for use with a threatened species, and used in

retrospective investigations designed to test environmental variation and ecosystem

dynamics.

CHAPTER 3

LNFLUENCE OF TEMPERATURE AND PRECIPLTATXON

ON T W E GROWTH

3.1 ABSTRACT

Three white spnice and one white pine tree ring chronologies were developed

from four Canadian locations. Interseries correlations were highly significant among

consistently aged individuals within each population. Tree growth, as measured by the

cross-sectional radii at age 25, was significantly correlated with mean annual air

temperatures. Generall y, interannual growth variations in white spmce, as measured by

chronologies developed fi-om ring widths, were negatively correiated with rnean air

temperatures during either the current or previous season of growth while the white pine

growth chronology was positively correlated with temperatures during the growth season.

Chronologies from both species demonstrated significant positive correlations with

precipitation during the current season of growth. Precipitation values from the previous

season demonstrated little correlation with the developed growth chronologies.

3.2 LNTRODUCTION

Dendroclimatology is the study of past climates from tree rings and has evolved

over several centuries from human cunosity in the seasonai growth patterns evident in the

trunks of fallen trees. In a footnote in Voyage of rhe Beagle, Charles Darwin (1839),

stated

" I have seen the trunk of an old tree in England, in which the

successive rings showed the tendency to periodically increase

and diminution of size; about every tenth ring being srnail...".

In 1737, Duhamel and Buffon, two naturalists fiom France, noted the effects of fiost

damage on the 29' ring from the bark of several newly cut trees (Fritts 1976).

In temperate tree species these rings are annually developed layers of xylem cells

formed between early spring to late surnmer. Generally, the first cells developed possess

a large ce11 diameter and thin ce11 walls. As the growth season ends, the cells become

smaller and the ce11 walls thicker (Fritts 1976). This increased tissue density forms the

characteristic darkened zone of the rings in a tree cross-section.

Plant growth is influenced by interna1 and extemal factors. Temperature and the

availability of water, light and soi1 minerals are extrinsic factors that influence tree

growth (Fritts 1976). As the first two of these are closely associated with meteorological

conditions, the widths of annually developed rings in cross-sections of tree stems have

been used as documentation of past ambient environments (Grace and Norton 1990,

Cook et al. 1987, Biffa et al. 1983). During growth seasons when environmental

conditions are favorable, water availability is not limiting and temperatures are neither

excessively above or below optimum for growth, the developed cells in the tree stem

experience rapid growth, forming wide rings. However, during seasons when

environmental factors are limiting, metabolic and physiological processes are slowed, ce11

development is lessened, and ring width is reduced. By assembling the width of tree

rings, measured between points of growth commencement and completion in the early

spnng and late summer, respectively, into senes of data, hereafier called tree growth

chronologies, and comparing these series to past records of environmental variation, we

may gain an understanding of the factors influencing annual tree growth (Cook and

Peters 1987, Fntts 1976). This information allows development of predictive models and

enhances our understanding of the influence of climatic variations on growth (Grace and

Norton L99O).

If tree rings are to be assembled into growth chronologies usehi in

ecophysiological research, two criteria should be established regarding their

documentation of growth. First, interannual growth variations must be synchronous

among individual trees within a population. Trees growing under ideal conditions may

not satisfy this criterion. In such individuals, the ambient environment is not sufficient

limiting to cause ring width variation frorn one year to the next. These individuals,

known as cornplacent trees, do not permit development of chronologies documenting the

influence of the environment on tree growth (Fritts 1976).

Secondly, to be of use in ecophysiological research, tree rings must demonstrate

some response to environmental variation. Failure to establish this lessens the usefulness

of a developed chronology in ecological studies. In the forests of North America, two

common factors influencing tree growth are temperature and precipitation (Cook et al.

1987, Larsen and MacDonald 1995). Tree growth has been both positively and

negatively related to variations in air temperature (Brand 1990, Larsen and MacDonald

1995, Pan and Raynal 1995). Low temperatures early in the growing season are noted to

restrict photosynthesis and respiration. On dryer, well drained sites, cool temperatures

during June and July have a positive effect on growth. This negative relation between

tree growth and temperature is the result of the elevated thermal environment increasing

water stress in these individuals, elevating respiration, and resulting in a reduced net

photosynthesis (Archambault and Bergeron. 1992).

Precipitation has also been positively correlated with annual tree growth (Guyette

and Rabeni 1995, Larsen and MacDonald 1995). Low precipitation and the resulting

decrease in soi1 moisture availability exert influence on growth in two ways. First,

reduced water availability causes stomata to close, reduces intercellular carbon dioxide,

and lowers photosynthetic activity, the products of which are required for growth

(Gaastra 1963, Stenberg et al. 1995, Teskey et al. 1995). Secondly, photosynthetic

pathways directly require water molecules for their function and a reduction in water

availability reduces operation (Conroy et al. 1986). Also, environmental conditions

during a particular season may exert an influence on growth dunng the following year

(Fritts i 976).

It was the purpose of this investigation to determine if tree rings sampled from

four Canadian locations satisfied these two critena. First, to ensure that interannual

growth variation among sampled trees was synchronous, the interseries correlation

coefficient was calculated for each of the four groups of trees sampled. Secondly, to

determine if the sampled tree rings demonstrate the influence of environmental variation,

1 investigated interpopulation and interannual growth differences as a response to

variations in air temperature and total precipitation from the current or previous growth

seasons.

Study Sites

Lake Temiskaming

White spruce (Picea glairca) were sampled from the West shore of Lake

Temiskarning at approximately 47'30'N latitude, 7g030'W longitude, 8.5 km south of the

town of Haileybury in June 1996. Trees were sampled in relatively shallow soi1 over a

limestone outcropping. Topography of the region is relatively hilly and the sample sit

was located on a gentle slope near the lake. To the south the Laurentian highlands are

relatively hilly while to the north the Superior Upland provinces of the Canadian shield

immediately flattens out, dispiay little topography, and poor drainage (Hunt 1974). The

boreal forest in this region is also split between the flat Haileybury clay to the nonh,

which supports a srnall agricultural region, and the rolling Timagami section which is

commonly associated with towering white pine (Pimis strobiis) and exposed granitic

bedrock (Rowe 1972). In both locations white spmce are found near lakes and rivers on

well drained locations.

Saskatchewan River

White spruce were sampied from the Cumberland House region of Saskatchewan

in July L 996. The sample site was located at approximately 53'54'N latitude, 1 02"201W

longitude. Located in the Cumberland Marshes, this area composes a large wetland

covering approximately 5000km2 (Smith and Perez- Arlucea 1 994). Repetitive flooding

of the low terrain has resulted in the developrnent of substantial levees predominantly

covered by bnish. In swamp zones, aquatic and semi-aquatic flora, sedges and high

grasses are common (Cazanacli and Smith 1998). This region is Iocated in the central

lowland of the interior plains (Hunt 1974). The boreal forests near the Saskatchewan

River are located in the Manitoba Lowland region. In areas where the land is suitably

drained, patches of white spmce c m be found along with black spruce (Pzcea mariana)

and tamarack (Larix loricina) (Rowe 1972).

St . Lawrence River

White pine (Pinlcs strobics) were sampled from a region near Lac Deux

Montagnes, part of the Ottawa/St. Lawrence River system near Montreal. Located at

approximately 4S020'N latitude, 73'55'W longitude this site was located at the top of a

ridge of sandy soi1 which gently sloped down towards Lac Deux Montagnes,

approximately 2 km north-east of the town of Hudson, Quebec. Topography around the

site was composed of rolling lowlands of deep calcareous soils (Hunt 1974). Forest

composition is generally that of deciduous to mixed deciduous and conifer. White pine

are supported in this area on more acidic, shallow soils (Rowe 1972).

Mattagami River

Located at approximately 49'55'N latitude, 8 1°37W !ongitude this stand of white

spnice was located next to the Groundhog River, just south of its confluence with the

Mattagami River. Topography of the site sloped towards the watenvay and soi1 was

composed of a thin layer of till overtop of granitic bedrock. Surrounding the site, away

From the river location, the topography is generally flat to rolling plains of clay and till.

The area has been heavily logged and cut to within 120m of the water (Nowak and Jessop

1987). Mean total precipitation in the region is approximately 858 mm per annum, with

half this falling during the growth season. Potential evapotranspiration is estirnated to be

495mm (Momson 1991). The Mattagarni and Groundhog Rivers flow through the

Superior Upland region of the Canadian Shield and the terrestrial system surrounding the

river has irregular drainage and is dotted with lakes. (Hunt 1974). The boreal forest in

this region grows over till and lacustrine deposits. The predominant species is black

spruce but in areas where drainage is improved white spruce can be found (Rowe 1972).

3.3 MATERIALS AND METHODS

Approximately 100 trees were cored from each of the above locations. Tree rings

were sarnpled using an increment borer at approximately breast height. One core was

removed from each tree. Thirty of the 100 cores from each location were randomly

selected, dned, mounted, and sanded to improve ring clarity. Ring widths were digitized

using a compound microscope (10x) equipped with a drawing tube positioned over a

digitizing tablet. Ring widths from each tree were digitized in three blind replicates.

Using these replicated measures the index of aging error, a measure of the inability to

consistently age a sample, was calculated (Beamish and Fournier 198 1). To reduce the

influence of aging error only those samples that were assi~ned the same ages during the

three replicated measures were used throughout the analysis. For al1 samples the last

complete ring was assumed to have developed during the calendar year pnor to capture

and al1 annuli were dated with respect to this year.

Correlative analysis was applied to determine the influence air temperature and

precipitation had on growth differences among populations. Growth, as rneasured by

average tree radius at age 25, was ploned against mean annual air temperature and mean

annual total precipitation as liquid (mm). Age 25 was selected to allow the inclusion of

al1 trees sampled. As only 4 populations were investigated, the few degrees of Freedom

did not permit the influence of temperature and precipitation to be modeled using

multiple linear regression.

To remove long-term trends fiom the data and develop tree ring growth

chronologies, a methodology similar to that used on sturgeon growth data was applied

(LeBreton et al. 1999). Each sample's growth data was fit with a curve, approximating

the general decrease of ring width with age, using a 7-year running average. The

residuals, the difference between a ring width and its corresponding 7-year average value

were calculated. Each residual approximated annual ring growth relative to that in the 3

years preceding and following it. The variance through each chronology was

homogenized to allow correlation among samples as follows. Residuals were converted

to absolute values and these data, for each sample, were fit with another 7-year running

average. This fitted curve approximated the long-term decrease in variance of the

residuals with age. Residuals, with their original signs, were then divided by the

corresponding fitted curve's values.

An individual chronology is defined as a series of growth data collected frorn a

single tree for which the long-term trends have been removed. A population chronology

is a series of growth data constructed by averaging numerous individual chronologies,

aligned by calendar year, from a population. In total 25, 23, 24 and 24 tree core samples

were used to develop tree chronologies €rom Lake Temiskaming, Saskatchewan River,

Lac St. Louis, and Mattagarni River regions, respectively. To allow correlation of

population chronologies with temperature and precipitation records, data were excluded

prior to the tirst mean growth index that significantly differed from zero.

To determine if these chronologies displayed signi ficantl y synchronous growth

variation among members of each population, correlations among individuals were

assessed using the interseries correlation coefficient (Wigley et al. 1984). This statistic

assesses the mean correlation among al1 possible pairs of chronologies and ranges from

approximately zero, if growth fluctuations are completely asynchronous, to 1, if growth

fluctuations are completely synchronous. The interseries correlation coefficient was

calculated for a set of individual chronologies using a two-way analysis of variance

applied to growth data organized with chronologies in columns, and calendar year in

rows. Interseries correlation coefficients were calculated over 14-year periods, 1978-

1991, 1965- 1978, 1980-1993, and 1982-1995 for Lake Temiskaming, Saskatchewan

River, Lac St. Louis, and Mattagami River, respectively.

The relationship between annual variations in tree growth and air temperature and

precipitation was quantified by correlating growth chronologies with measures of air

temperature and total precipitation levels. Long-term trends, which are not present in the

tree growth chronologies, were removed fiom meteorological data using the same

technique as applied to tree growth data. The resulting monthly average temperature data

were correlated with growth chronologies during the current and previous seasons from

the months April through September. Total precipitation data were correlated with the

growth chronologies over the same months also during the current and previous seasons

of growth. Meteorological data were obtained from locations as outlined in Table 3.1

(Vose et al. 1992). Multiple data sets were averaged for each location to reduce periods

of rnissing data.

Reg ion Meteorological Sites Latitude and Longitude

Saskatchewan River

Lake Temiskaming Nonh Bay

Tirnrnins

Val d'Or

Flin Flon

The Pas

Lac St. Louis Mo nt real

Mont real

Mattagarni River Kapuskasing

Ka puskasing

Table 3.1: Regions, city and lat./Iong. fiom which meteorological data were obtained for the four sites From which tree rings were sampled.

3.4 RESULTS

Average trunk radii at age 25 were 47.5, 30.9, 62.6, and 30.5 mm for trees

sampled near Lake Temiskaming, Saskatchewan River, Lac St. Louis and Mattagami

River, respectively. In general, these measures of growth increased with mean annual air

temperature as estirnated by the regression:

RT= 3 1-59 + 5.27 T (r=0.99, p=0.01, n=4)

where RT is the mean tmnk radius for each stand of trees at age 25 and T is the mean

annual air temperature.

The relationship between tree growth and total precipitation was described by the

regression:

RI,= 3.69 + 0.05 P (r=0.80, p=0.19, n=4)

where P is the total annual precipitation.

Interannual growth variations were synchronous among members of al1 sampled

populations as reflected by the interseries correlation coefficient (Table 3.2). Each

population chronology spanned 1929- 1995, 1852- 1996, 1936- 1995, and 1865- 1995 for

the Lake Temiskaming, Saskatchewan River, Lac St. Louis and Mattagami River

populations, respectively. The Lake Terniskaming white spruce growth chronology

displayed significant, negative correlation with mean June air temperatures (Table 3.3a)

and positive correlation with total Juiy precipitation (Table 3.4a) during the current

season of growth. The Saskatchewan River white spruce growth chronology was

negatively correlated with mean Tune air temperatures during the current season of

growth (Table 3.3a) and mean June, July and August temperatures during the previous

growth season (Table 3 -3b). This chronology was positively correlated with total

precipitation during May, June and September of the current season of growth (Table

3.4a). The white pine chronology developed From the Lac St. Louis region was

positively correlated with mean A p d temperatures during the current season of growth

(Table 3.3a) and mean September air temperatures during the previous season of growth

(Table 3.3b). This growth chronology was positively correlated with total precipitation in

June of the current season of growth (Table 3.4a). The white spmce growth chronology

developed from the Mattagami River region displajjed positive correlation with mean

April air temperatures and negative correlation with mean June air temperatures during

the current season of growth (Table 3.3a) and negative correlation with mean July and

August air temperatures during the previous growth season (Table 3.3b). This

chronology was negatively correlated with total May and positively correlated with

August precipitat ion d u h g the previous season of growth (Table 3.4b).

Population Y ears n Interseries Correlation P value Lake Temiskaming 1978- 199 1 25 0.493 8 <O.OO 1 Saskatchewan River 1965- 1978 23 0.2860 <O.OO 1

Lac S t. Louis 1980- 1993 24 0.3279 cO.00 1 Mattagarni River 1982- 1995 24 0.2626 <O.OO 1

Table 3.2: Interseries correlation coefficients among consistently aged individuals for four tree populations sampled.

Population Correlation Coefficients

Apri 1 May June July August September Lake Temiskaming 0.07 -0.14 -0.32" 0.07 -0.18 O. 10 Saskatchewan River 0.13 -0.17 -0.41* * -0.07 O. 1 1 -0.10

Lac St. Louis 0.49" * -0.15 -0.1 O -0.0 1 -0.22 0.02 Mattagarni River 0.2 1 * 0.0 1 -0.24* -0.12 -0.08 -0.03

Table 3.3a: Correlation of population growth chronologies with measures of mean air temperature during the current season of growth. (*) p 5 0.05, (**) p 1 0.01.

Population Correlation Coefficients

Apri l May June July August September Lake Temiskaming 0.05 O. 02 -0.14 -0.17 O. 19 -0.06 Saskatchewan River -0.1 O 0.03 -0.30* * -0.38* * -0.54* * 0.0 1

Lac St. Louis O. 13 -0.06 -0.03 -0.04 -0.04 0.28" Mattagami River -0.05 0.07 -0.17 -0.25* -0.38* * 0.03

Table 3.3b: Correlation of population growth chronologies with measures of mean air temperature during the previous season of growth. (*) p 0.05, (**) p 5 0.01.

Population Correlation Coefficients

April May June July August September Lake Temiskaming O. 10 0.05 O. 10 0.23* -0.15 O. 15 Saskatchewan River -0.08 0.27** 0.21 * O. 17 -0.06 0.24 *

Lac St. Louis 0.09 0.34" * 0.08 0.20 0.04 O. 14 Mattagarni River -0.20 -0.1 1 0.05 0.08 -0.06 O. 16

Table 3.4a: Correlation of population growth chronologies wiih measures o f total monthly precipitation during the current season of growth. (*) p 5 0.05, (**) p 0.01.

Population Correlation Coefficients

April May June July August September Lake Temiskaming -0.03 0.05 0.1 1 0.02 O. 19 0.0 1 Saskatchewan River O. 14 0.0 1 0.03 0.03 O. 12 0.0 1

Lac St. Louis O, 13 -0.06 -0.03 -0.04 -0.04 0.28* Mattagarni River 0.0 1 -0.28" * O. 17 -0.19 0.20* * -0.07

Table 3.4b: Correlation of population growth chronologies with measures of total monthly precipitation during the previous season of g r o ~ h . (*) p 0.05, (**) p 5 0.01.

3.5 DISCUSSION

This investigation determined that growth variations between the populations of

tree sampled demonstrate a positive linear relation with mean annual air temperature.

Stands of trees in wamer regions possess a larger diarneter at age 25 than stands of trees

in colder areas. These results are similar to those demonstrated for other ectothermic Iife

forms (McCauley and Kilgour 1990, Power and McKinley 1997). A similar relationship

between interpopulation differences in tree growth and precipitation was not found.

These results are most interesting when compared with the negative correlations between

interannual tree growth variation and mean air temperature found in this investigation

(Table 3.3a). Similar results have been noted in other ectothermic life forms. While

growth in brown trout, Sulrno tmtta, was negatively correlated with average water

temperature during the years 1987-1995, growth rates in these warm locations were

higher then those reported From British trout population inhabiting cooler locations

(Lobon-Cervia and Rincon 1998). Local adaptations to the thermal regulation on growth

were cited as one possible reason for these growth differences between populations.

In general, interannual variations in white spnice growth were negatively

correlated with past measures of temperature during either the current or previous

seasons. These results concur with the findings of previous papers (eg. Archarnbault and

Bergeron 1992, Teskey et al. 1995). Negative correlations of growth variation with

temperatures dunng the current season are possibly the result of plant respiration being

increased with air temperature (Archarnbault and Bergeron 1992). Under these

conditions, respiration, which attains a maximum at a higher temperature than

photosynthesis, depletes the energy reserves of the plant, thereby reducing its capacity for

growth (Downs and Hellmers 1975, Teskey et al. 1995). Elevated temperatures during

the current season rnay also reduce growth by augmenting water stress on the plant,

causing stomatal closure and a decrease in carbon dioxide available for photosynthesis

(Fntts 1976). Furthermore, closed stomata decrease evaporative cooling on the needles

and rnay further elevate plant surface temperature causing respiration to increase (Fritts

1976).

The influence of temperatures on growth variation during the previous season,

observed in the white spmce studied in this investigation, rnay also be related to the use

of stored carbohydrates in the plant. In some species of spruce, maximum growth rates

rnay be attained prior to photosynthesis reaching its peak in midsummer (Luxrnoore et al.

1995). Therefore, growth in the spring requires the use of stored energies accumulated

late in the previous growth season. If temperatures were relatively high during late

surnmer of the previous year, eievated respiration may have consumed these storage

compounds (Jhxrnoore et al. 1995).

Interestingly, white pine displayed positive relations between mean April and

September air temperatures from the current and previous seasons of growth,

respectively. These results indicate that the relation between air temperature and growth

in the two species investigated rnay differ. In white pine, high rates of photosynthesis

have been shown to accompany rapid shoot growth in the spring (Maier and Testkey

1992). Dunng these periods higher temperatures rnay permit rapid growth and also

elevate photosynthetic activity to a level capable of supplying the materials required for

tissue expansion.

The relationships between interannual variations in tree growth and sumrner

month total precipitation during the current year were generally positive. This relation

was predicted as previous investigations have indicated a relation between soil moisture

availability and tree growth (Archambault and Bergeron 1992). Relatively few

correlations occurred between population growth chronologies and precipitation Born the

previous growth season. These results suggest that the influence of soil moisture on tree

growth is relatively imrnediate and not strongly influencing growth in the future.

In summary, populations of trees in warmer regions grow more rapidly than those

in colder areas. However, interannual growth variations demonstrated that white spnice

are negatively influenced by warmer temperatures during either the current or previous

growth season. This relationship did not hold for interannual growth variations in white

pine, which respond positively to temperatures. In both species investigated, trees

generally experienced greater growth dunng years of elevated precipitation.

CHAPTER 4

INTERANNUAL GROWTH VARIATIONS LN TERRESTRIAL AND AQUATIC

ECOSY STEMS;

A COMPARISON USING FISH AND TREE RiNGS

Submitted to: Canadian Journal of Fisheries and Aquatic Sciences

85

4.1 ABSTRACT

Interannual growth variations were compared among neighbouring populations of

lake sturgeon (Acipe~~serfifvesce,~~) and white spruce (Picea glziaca), white pine (Pimls

strobzcs), and red pine (Ph i s resh~osa). Measures of growth were obtained by removing

long-term trends From widths of rings in the hard tissues of both aquatic and terresrial

organisms and assembling these measures into growth chronologies. Interannual growth

variations were negatively correlated between sturgeon and nearby trees for those fish

populations that displayed strong interseries correlation. The three sturgeon populations

that displayed the lowest interseries co7rrelation coefficients failed to display signi ficant

correlation with tree growth. To detemine if temperature influenced relations between

fish and trees, the growth of these organisms were related to interannual variations in air

temperature. In general, fish displayed positive correlation with measures of air

temperature dunng the current season of growth while trees displayed negative

correlation with air temperatures from either the current or previous season of growth.

4.2 INTRODUCTION

Our current understanding of the similarities in natural variation in and among

neighbouring ecosystems is relatively limited. Modem ecological research tends to focus

on a particular species or type of organism and rarely crosses boundaries between plant-

animal or aquatic-terrestnal divisions. Furthermore variations among these systerns rnay

occur on time scales lasting moments to millennia, and fiequently exceed the life span of

researchers (Magnuson 1990, Magnuson et al. 199 1, Lane et al. 1994). For these reasons,

our understanding of variations in and among ecological systems and Our ability to

develop hypothesis surrounding them is greatly limited.

Solutions to this temporal problem may be found in the use of growth

chronologies. These tirne series, developed from the widths of rings contained in the

hard tissues of organisms, provide retrospective research with ecological data

documenting past growth variation (Fritts 1976, Gutierrez and Almirall 1989). Tree

growth chronologies, developed from xylern rings, have been extensively used to

document interannual growth variations in terrestrial systems (Fritts 1 976). Recently,

several investigations have developed similar time series from calcified tissues in fish,

thereby documenting environmental variation in aquatic systems (Weisberg 1993, Ogle et

al. 1994, Pereira et al. 1995ab, Cyterski and Spangler 1996, LeBreton et al. 1999).

The principle characteristic of growth in both fish and trees, which allows these

chronologies to be developed, is plasticity; the variation of growth with intemal and

extemal factors (Blackman LgOS, Weat herley and Gi 1 1 1 987). Therefore, the widths of

rings used to compile these chronologies are the net result of a set of factors, both

intnnsic and extrinsic, operating on the metabolic, behavioral and physiological processes

in the individual (Fritts 1976, Casselman 1987). Dunng periods of limiting factors, ring

widths are diminished relative to period when conditions are more conducive to growth.

This, in concert with the fact that both fish and trees demonstrate indeterminate growth

(Weatherley and Gill 1987), allows growth chronologies to be used as naturally recorded

archives of environmental quality.

As both fish and tree chronologies are assemblages of unitless measures of

relative growth, these data offer a rare opporiunity for the cornparisons of annual growth

variations, between diverse life forms, from neighbouring aquatic and terrestrial

ecosystems. The purpose of this investigation was to develop and correlate fish and tree

chronologies from neighbouring aquatic and terrestrial organisrns to determine if growth

variations in these systems were related and, if so, what influence air temperature had on

such growth patterns.

4.3 MATERIALS AND METHODS

Fish and tree chronologies were used to measure interannual variations in growth

of aquatic and terrestrial organisrns. Throughout this investigation a chronology refers to

a time series documenting annual growth variation in an individual or population for

which the influence of age has been removed. Individuai growth chronologies document

growth variations in a single organism, or sample, while population growth chronologies

record these average variations from an assemblage of individual chronologies. The

seven fish chronologies used in this investigation were developed from lake sturgeon,

Acipe,~serfi<lvesce,~s. Lake sturgeon exhibit an extensive range across North America

(Scott and Crossman 1973), extreme longevity (Houston 1987), and develop annual

growth rings in the pectoral fin ray (Rossiter et al 1995) which document interannual

growth variation (LeBreton and Beamish 1999, in review). These characteristics make

the lake sturgeon an excellent candidate from which to development chronologies for use

in investigations correlating annual growth variations among ecosysterns.

Previously sectioned sturgeon fin ray samples were borrowed from archived

collections (Fig.4.1). Sections too opaque or translucent to read were excluded from the

analysis. Sections that displayed signs of breakage were also excluded. This condition,

described in detail by Wilson (1987), is caused when fins break during upstream

spawning migrations and is identified by regions of discontinuous rings in the cross-

section. Ring widths, defined as the width of one set of translucent and opaque zones,

were rneasured using a compound microscope (40x) with a drawing tube situated over a

digitizing tablet. Measurements were made dong the most legible, posterior radius at

points of maximum acuteness on consecutive translucent zones (LeBreton et al. 1999).

For each fin ray sample, ring widths were measured in three blind replicates. By using

these replicated measures, the index of aging error, a measure of the inability to

consistently age a sample (Beamish and Fournier 1981), was calculated. To reduce the

influence of aging error samples not designated the same age during each replicate were

excluded from further analysis. For al1 samples the last complete growth incrernent was

assumed to have developed during the calendar year prior to capture and al1 increments

were dated with respect to this year.

Tree ring chronologies were used to quantify interannual variation in tree growth.

Four of the 5 tree growth chronologies used in this investigation were constmcted fi-om

sarnpled tree rings. Tree rings were sampled from terrestrial ecosystems near the

waterways from which fish were taken (Fig.4.1). Samples were taken at approximately

breast height with an increment borer. Individuals were not selected based on any

conscious criteria.

One core from each tree was mounted in wood and sanded to improve ring clarity.

Measurements of tree ring widths were conducted using the same equipment and

methodology applied to fish rings. Ring width series were measured in three blind

replicates. Again, samples, which were not designated the same age during each

replicate, were excluded from further analysis. No other exclusions occurred and cross

dating was not applied. The other tree ring chronology used in this investigation was

obtained from the International Tree Ring Data Bank. This chronology was developed

from red pine (Pims resiriosa) at the Hartwick Pines State Park, Michigan (Koop and

Garsino-Mayers 1994) and was used as the terrestrial counterpart to the sturgeon

chronologies from both Lake St. Clair and Lake Winnebago.

Figure 4.1 : Locations of fish and tree populations used to develop growth chronologies in this investigation. Lake sturgeon populations; (1) Lake St. Clair, Ontario/Michigan (42"00'N, 82'3 0' W), (2) Lake Temiskaming, Ontario/Quebec (47'3 O'N, 79'3 0' W), (3) Saskatchewan River, Saskatchewan, (53" 54'N, 102O 2OYW), (4) Lake Winnebago, Wisconsin (44'00W, 88"20fW), (5) Lac St. Louis, Quebec (45"207N, 73'55'W), (6) Lac Parent, Quebec (48" 2S7N, 77' 15'W), and (7) Mattagami River, northem Ontario (49' SSN, 8 1" 3 7'W), fieshwater drum population, (9) Red Lakes in Minnesota (48" OO'N, 95' OO'W). Tree populations used; white spnice (Picen gimca) (2) Lake Temiskaming (3) Saskatchewan River, (7) Mattagami River regions; white pine (Pimrs srrobzis) fiom near (5) Lac St. Louis. Previously published dendrochronologies, red pine (Pams resinosa) at (8) Hartwick Pines State Park, Michigan (44' 25N, 84'27'W) and (9) Coddington Lake, Minnesota (47" 1 IN, 92' 12'W).

Age related trends in growth ring data must be removed to allow growth

chronologies to be developed frorn both fish and trees. This process allows growth

variations in organisms of different ages to be compared and environmental effects to be

extracted From these chronologies. While rnany techniques are available to remove age

related trends fiom growth rings in various organisms (Cook and Peters 1987, Boyd and

Roberts 1993, Pereira et al. 1995ab) this investigation requires a single technique

applicable to both fish and trees. Fish chronologies have been constructed using

exponential curves to remove age-related trends from otolith growth data (Pereira et al.

1995ab). While this technique may be applicable to sturgeon growth data, visual

inspection of the collected tree ring data indicated that this methodology could not be

satisfactorily used to construct tree growth chronologies. As tree growth chronologies

have been extensively developed in the past, a dendrochronological technique, using

mnning averages to estimate long-tenn trends in the growth data, was applied to detrend

both sturgeon and tree growth data (Fritts 1976). A second technique, using mean growth

trend curves, calculated for each population, was also applied to the sturgeon growth data

fiom samples collected in Lake Temiskaming to insure that the first methodology was

suitable for construction of growth chronologies.

The first detrending technique applied to individual fish and tree growth data

removed long-term trends using a 7-year ninning average (Fig.4.2A). This nmning

average was fitted to the original data and the residuals between the fitted curve and the

growth data were calculated. The result of this operation is demonstrated in Fig.4.2B.

This plot displays the residuals of the described operation having a mean of O and a

heterogeneous variance decreasing with age. It is important to note that the ninning

average was not used to smooth the data but to approximate a smooth trend through the

data. The effects of using running averages of different Iengths (5 or 9 years) were not

fully explored.

In order to hornogenize the variance throughout each individual's growth

chronology another technique was borrowed from dendrochronology. The absolute

values of the residuals from the last operation were calculated (Fig. 4.2.C) and another 7-

year running average was used to approxirnate the general decrease in variance with time.

To homogenize or standardize this variance, the residuals, with their original signs were

divided by the values of this second mnning average. The end result of this operation is

an individual growth chronology composed of growth indices (Fig.4.2D). These

measures are unitless and have a homogeneous variance about O as a result of the final

division operation. Again it is important to note that the running average was not used to

smooth the data but to approximate a smooth long-term trend in the data. Any further

age related trends in fish growth data were detected by aligning and averaging al1

individual chronologies by age (Fig. 4.3). This average curve, related to the inability of

the running average to fit early growth data, was then subtracted frorn all individual

sturgeon chronologies in each population. This tinal step was not required in tree growth

data manipulations due to the length of these tirne series resulting in an improved fit of

the mnning averages.

Individual chronologies were assembled into population growth chronologies

fiom consistently aged sturgeon (Fig. 4.4, Appendix II) and trees (Fig. 4.5). Mean

growth indices dunng early years of these chronologies had relatively wide confidence

intervals and higher variance, as fewer data points from older individuals were present.

To ensure that the variance throughout each population chronology was relatively

homogeneous, thereby allow correlations between growth chronologies and

environmental time series, data points prior to the first mean growth index that differed

significantly from zero were excluded from further analysis.

When fitting a 7-year running average to a series of data the first and last three

data points cannot be fit by the curve as these points fail to have sufticient data

surrounding them. As sturgeon growth series data are relatively short this loss of six data

points from each curve results in a substantial loss. Some spreadsheet software

circumvent this issue by repeating the first and last data points three tirnes thereby

allowing a 7-year mnning average to be calculated for the beginning and end of the

series. To determine the influence this repetition of data would have on chronology

development, Lake Temiskaming sturgeon population growth chronologies were

developed using two techniques. First, growth chronologies were developed by fitting 7-

year mnning averages to the data in its original state. This required the omission of the

first and last three data points in the final construction of the growth chronologies.

Secondly, growth chronologies were developed by fitting 7-year running averages to the

growth data for which the first and last data points had been repeated three times to allow

the running average to be computed throughout the entire series length. These two

population chronologies were then compared (Fig.4.6)

To determine if the chronology development technique utilizing 7-year mnning

averages was suitable for removal of age related trends from Iake sturgeon growth data

another methodology was applied to the Lake Temiskaming population to develop a

comparable population chronology. This technique used the average trend of decreasing

ring width with age as calculated fiom al1 sampled fish from Lake Temiskaming (Fig.

4.7A) to remove age-related trends from each individual's growth data. Each individual

fish's growth data was fit using this mean ring width at age curve (Fig. 4.7B). The

residuals were calculated between the average curve and the ring width data from each

individual fish (Fig. 4.7C). These residuals have a heterogeneous variance fluctuating

about a mean of O. To standardize this variance, the absolute values of al1 residuals for

each sampled sturgeon fiom Lake Temiskaming were averaged by age. This mean curve

represented the average decrease in variance of the residuals throughout the population

(Fig.4.7D). The residuals from each individual, with their original signs (î), were then

divided by this curve. The resulting individual growth chronology was then composed of

unitless growth indices which had a mean of zero and a hornogeneous variance

(Fig.4.7E). A Lake Temiskaming sturgeon population growth chronology was

construaed by averaging al\ individual growth chronologies detrended using this

technique. The two Lake Temiskaming sturgeon chronologies, one developed using 7-

year mnning averages and one developed using mean age related trends were then

compared (Fig.4.8).

Tree growth chronologies were developed using the multiple applications of 7-

year ninning averages to detrend the growth data as outline in Figure 4.2. The first and

last data points in each time series were repeated three times to ailow the running average

to be constructed throughout each series. To ensure the tree chronology obtained from

the International Tree Ring Data Bank dispiayed interannual variations fluctuating on

similar fiequencies as the fish and tree chronologies developed in the present

investigation, any long-term trends were removed using multiple applications of 7-year

running averages as applied to lake sturgeon and tree growth data (Fig. 4.9).

The degree of synchrony of interannual growth variation among individuals in

each population was quantified in both the aquatic and terrestrial ecosystems using the

interseries correlation coefficient (Wigley et al. 1984). This statistic approximates the

mean value of correlations among al1 possible pair-wise comparisons in an assemblage of

series, excluding comparisons with self. equal to zero. This statistic must be calculated

over a specific span of calendar years. Therefore, any data from a sample not within this

tirne span is excluded from the analysis. Similarly, if a sample fails to fully cover the

selected time span it must be excluded from the analysis. Populations, which display

significant interseries correlations among individuals, contain the signals of large-scale

extrinsic factors in their growth chronologies. Populations, which fail to display

signi ficant ly synchronous interannual variations in growth, are not responding to large-

scale extrinsic factors and growth chronologies developed from these populations will

possess a large noise component. The length of time over which the interseries

correlation coefficients were calculated was the same for each population (14 years).

These calendar years were selected such that the greatest number of individual

chronologies were included in the analysis. This statistic was calculated over the same

periods of time for trees sampled from Lake Temiskaming, Saskatchewan River, Lac St.

Louis, and Mattagami River.

To determine if a relationship between interannual growth variation in

neighbouring aquatic and terrestrial organisms existed, the 7 sturgeon chronologies were

correlateci with their counterpart tree chronologies. Correlations were calcuiated between

fish and tree chronologies during the calendar year of growth. Fish growth was also

correlated with tree growth h m the following season as trees may show strong

correlation with air temperatures from the previous year (Archambault and Bergeron

1992, Larsen and MacDonald 1995).

To detennine if air temperature influenced interannual variations in growth of

terrestrial and aquatic organisms, Pearson correlation coefficients between fish and tree

chronologies and past records of mean April, May, lune, luly, August, and September air

temperatures were calculated. Statistics were calculated in both fish and trees using only

those calendar years covered by fish chronologies. Long-term trends in the temperature

time series, which are not contained in the developed growth chronologies, were removed

using the same method as applied to develop growth chronologies. This data

manipulation results in the same trend removal as demonstrated in Figure 4.9. Air

temperature data were obtained from stations located at Detroit city airport (42"42N, 83'

02'W) and Mount Clemens (42" 62'N, 82*83'W), Michigan for the Lake St. Clair location;

Nonh Bay (46' 3SN, '79" 43'W), Timmins (48" 57N, 8 1" 37'W), Ontario, and Val d'Or

(48O07'N, 77"78'W), Quebec for the Lake Temiskaming location; Flin Flon (54"77W,

10 1' 85'W) and The Pas (53' 97N, 10 1% lO'W), Manitoba for the Saskatchewan River

location; Oshkosh (44O03N, 88'55'W) and Fond du Lac (43" 80N, 8804StW), Wisconsin

for the Lake Winnebago location; two stations in Montreal, Quebec (45"50W 73'58'W

and 45" 47N 73" 75'W) for the Lac St. Louis location; Val d'Or (48"07'N, 77'78'W),

Quebec for the Lac Parent location; and two sites in Kapuskasing (49" 40N, 82' 43' W),

Ontario for the Mattagami River location (Vose et al. 1992).

Figure 4.2A: Lake sturgeon ring width at age time series (solid line), 7 year running average approximating general decrease with age (dotted line).

Figure 4.2B: Lake sturgeon ring width at age following removal of long terni trend approximated by 7 year running average.

Figure 4.2C: Lake sturgeon absolute residual senes (solid line) Decreased variance with age approxirnated by 7 year mnning average (doned line)

I

O 10 20 30

Age Figure 4.2D: Individual lake shirgeon growth chronology - -

following removal of long term trends with 7 year running averages.

*ge Figure 4.3: Mean age-related trend in dl Lake Temiskaming shlrgeon samples following aa~plication of 7 vear mnning averaee curves.

Year

Y ear

Figure 4.4: Lake sturgeon population growth chronologies from sample assemblages displaying significant interseries correlation coefficients. (A) Lake St. Clair, (B) Lake Temiskaming, (C) Saskatchewan River, (D) Lake Winnebago, (E) Lac St. Louis. Dotted lines indicate 95% confidence intervals.

Year

Figure 4.5 : Tree growth chronologies developed from terrestrial ecosystems near populations of Iake sturgeon demonstrating significant interseries correlation. (A) white spmce population growth chronology from the Lake Temiskaming region, (B) white spruce population growth chronology from the Saskatchewan River region, (C) white pine population growth chronology from the Lac St. Louis region. Dotted lines represent 95% conficence intervals.

Y ear

Figure 4.6: Growth chronologies developed with 7 year mnning averages demonstrating the influence repeating the first and last data points to fit a mnning average to al1 growth data. Chronology assembled using first and last growth data points from individual growth chronologies (Solid line). Chronology assembled excluding first and iast data points @otted line).

Figure 4.7A: Mean ring width at age for sturgeon sampled from Lake Terniskarning. Dotted line indicatis 9506 CI.

Figure 4.7B: An individual sample sturgeon's ring width at age time senes (solid line). Mean decrease in ring width with age as calculated from samples from Lake Terni skami ng (dotted line).

Figure 4.7C: Residuals between an individual sturgeon's ring width at age series and mean ring width wi th age curve as calculated from Lake Temiskaming population data.

O 10 20 30 Age

Figure 4.7D: Absolute value of the residuals as calculated from an individual shirgeon sampled in Lake Temiskaming. Dotted line represents the population average curve of absolute residuals values.

Figure 4.7E: Final i ndividual growth chronology from Lake Temiskaming detrended using mean age related trends.

Year

Figure 4.8 : Lake Temiskarning population chronologies developed using (A) two 7 year running averages (B) population wide age related trends. Mean growth indices (solid lines), approximate 95% confidence intervals rdoned lines)

I 1 1 1 1

1940 1950 1960 1970 1980

Year

Figure 4.9: Red pine (Pims resimsa) growth chronology from the Hartwick Pines State Park, Michigan (Koop and Garsino-Mayers 1994). (A) growth chronology as pu bl is hed, (B) growth chronology following the removal of long-term trends using multiple application of 7-year mnning averages. Chronoiogy (B) used as a terrestrial counterpart to growth chronologies from Lake St. Clair and Lake Winnebago.

4.4 RESULTS

The plots of two population growth chronologies from Lake Temiskaming

developed by fitting 7-year running averages both with and without the first and last data

points repeated three times are displayed in Figure 4.6. These chronologies exhibit

similarity such that there was extensive data overlap. Two other Lake Temiskaming

population growth chronologies developed using either (A) 7-year running averages or

(B) mean age related trends to detrend the growth data are displayed in Figure 4.8. The

mean growth indices in these chronologies (Fig. 4.8) significantly deviate from the mean

of O dunng (A) 14 and (B) 7 years, respectively between 1961 and 1992 based on the

calculated 95% confidence intervals. Also these chronologies (Fig. 4.8) were associated

with interseries correlation coefficients of (A) 0.1040 (p=0.002) and (B) 0.0509 (p=0.02),

respectively as calculated between 1978 and 199 1. The borrowed red pine growth

chronology (Koop and Garsino-Mayers 1994) displayed long-term trends in the original

data (Fig.4.9A). This chronology demonstrated a mean of zero and a homogeneous

variance throughout the time series, yet short-terrn growth variations remained in the

data, following the application of the 7-year running average detrending technique (Fig.

4.9B).

In general, individual trees were oider and developed tree growth chronologies

were longer relative to sturgeon (Table 4.1). Interseries correlation coefficients among

assemblages of individual tree growth chronologies were always higher than among their

fish counterparts (Table 4.2). The 4 sturgeon populations that demonstrated the highest

interseries correlation coefficients also had population growth chronologies which

significantly correlated with nearby trees chronologies during either the current or

previous seasons of growth. These significant correlations were ail negative (Table 4.3).

Significant correlations between the four sturgeon growth chronologies which

displayed the highest interseries correlation coefficients and past measures of temperature

were all, and only positive (Table 4.4). Conversely, those sturgeon chronologies,

developed from assemblages of time series which failed to display significant interseries

correlation coefficients, either did not correlate (Lac Parent) or demonstrated inconsistent

correlations (Mattagami River) with any measure of past temperature (Table 4.4).

Only negative correlations were significant between white spruce growth

chronologies from Lake Temiskaming, Saskatchewan River and Mattagami River areas

and past measures of temperature during either the current or previous season of growth

(Table 4.4). The red pine chronology from Michigan (Koop and Garsino-Mayers 1994)

displayed both positive and negative significant correlations with past temperatures from

the Lake St. Clair and Lake Winnebago areas (Table 4.4). The white pine chronology

from the Lac St. Louis region also displayed significant positive correlation with past

measures of temperature (Table 4.4).

Population Fish Trees

Mean FinaI N Mean Final N

Age Chronology Age Chronology

Time Span Time Span

Lake St. Clair

Lake Temiskaming

Saskatchewan River

Lake Winnebago

Lac St. Louis

Lac Parent

Mattagami River

Table 4.1: Mean age of samples used in, time spans covered by, and number of samples average into population chronologies for fish and trees for seven locations of study.

Years Sturgeon Interseries

P Tree n Interseries

Correlation Correlation

Lake St. Clair 1977- 1990 O. 1445 22 <0.001 -- -- -- Lake Temiskaming 1978- 199 1 O. 1040 52 O. 002 O -493 8 25 <0.001

Saskatchewan River 1965- 1978 O. 1133 23 O. 004 0.2860 23 <0.001

Lake Winnebago 1981-1994 0.08 13 15 0.036 -- -- Lac St. Louis 1980- 1993 0.0500 89 0.0 15 0.3279 24 cO.00 1

Lake Parent 1971-1984 0.0380 15 0.150 -- -- -- Mattagarni River 1982- 1995 -0.0037 32 0.698 0.2626 24 <0.001

Table 4.2: Mean interseries correlation coefficients for each population investigated, years and numbers of growth chronologies correlated.

Reg ion Correlat ion P CorreIation P

Fis h-Trees Fish-Trees

current year Trees lagged one year

Lake St. Clair

Lake Temiskaming

Saskatchewan River

Lake Winnebago

Lac St. Louis

Lake Parent

-- Mattagarni

River

Table 4.3: Significant correlation coefficients (P<0.05) calculated between fish and tree growth during either the current of previous season of growth.

-- - -

Region Years April May June July Aug. Sept.

Lake St. Clair Fish 1972-1995 0.07 0.04 0.42* 0.08 0.01 0.0 1

Trees 1972-1987 0.39 0.28 -0.09 -0.24 -0.19 0.22

Trees 1972-1986 0.54* 0.13 -0.12 -0.30 -0.14* 0.05 Lagged

Lake Fish 1961-1989 -0.05 -0.22 0,16** 0.27 0.34* 0.43**

Temiskaming Trees 1961-1989 0.05 0.13 -û.35* 0.07 -0.01 0.11

Trees 196 1-1989 0.09 0.03 -0.10 -0.21 O. 15 -0.18 La~ged

Saskatchewan Fish 1959-1990 0.16 0.38* 0.41" 4.18 0.05 0.32* River Tnes 1959-1990 0.19 -0.19 43Q* 0.04 O. 15 -0 .O6

Trees 1959-1990 4.17 0.10 -0.28 -0.19 -O.GO** -0.07 Lagged

Lake Fish 1369-1995 -0.26 -0.07 -0.07 0.09 0.52** -0.06

WiMcbago Trces 1969-1987 0.49* 0.25 4.07 0.14 -0.19 0.07

Trees 1969-1986 0.29 0.06 -0.15 -0.38 -0.10 4 - 0 1 Lagged

Lac St. Louis Fish 1969-1989 -0.07 -0.35 0.42* 0.28 0.35 0.06

Trees 1969-1989 0.64"" 4.17 -0.10 -0.14 -0.15 -0.08

Trecs 1969-1989 -0.04 0.03 4 .25 4.34 -0.06 -0.02

Lake Parent Fish 1950-1984 0.09 -0.05 0.12 0.03 O. 12 0.17

Trees 1950-1984 0.06 -O. 11 -0.41** -0.05 -0.08 0.07

Trees 1950- 1984 0.08 O. 10 -0.04 4.08 0.16 -0.02 Lagged

Mattagarni Fish 1973-1995 0.30 4.25 O. 13 0.08 0.63** -0.37* River Trees 1973-1995 0.23 O. 14 -0.13 -0.11 4.06 4.18

Trees 1973-1994 4.01 -0.04 4.03 -0.42* -0.21 0.05

Table 4.4: Pearson correlation coefficients between fish and tree growth chronologies and past measures of air temperature during the season of growth. Trees lagged rows present correlation coefficients between tree chronologies and air temperatures kom the previous growth season. * indicates p<O.OS, * * indicates pCO.0 1.

4.5 DISCUSSION

Comparisons of the V ~ ~ O U S detrending techniques used in this investigation

indicated that the methodology which applied 7-year running averages to remove long-

term trends from the data and homogenize or standardire variance (Fig. 4.2) was a

suitable technique for the development of chronologies. The calculation of these 7-year

running averages in growth data requires that the first and last three data points be

excluded From use in chronology development. To circumvent such loss of data in this

investigation the first and last data points of each individual's ring width time series were

repeated three times and the ninning averages fitted to these lengthened series. By

developing population chronologies with and without the first and last three data points

from each individual the influence of this data repetition can be seen in Figure (4.6). As

these chronologies were so similar that extensive data overlap occurred it was determined

that the repetition of the first and last data points in individual chronology development

had a negligible effect and could be applied to eliminate data loss dunng chronology

construction.

Further support for the 7-year ninning average technique was provided by the

Lake Temiskaming population chronology constructed from growth data detrended using

rnean age trend curves calculated from al1 sampled data (Fig. 4.7). Comparisons of both

population chronologies developed using either 7-year running averages or mean age

related trends (Fig. 4.8) indicated that the chronology constructed by multiple

applications of a 7-year ninning average (Figure 4.8A) had twice as many years for

which the mean growth indices significantly differed from zero. Funhermore this

chronology was associated with an interseries correlation coefficient approximately twice

as large as the chronology developed using mean age related trends. These results

indicate that the multiple application of 7-year mnning averages extracts growth

variations resulting from environmental fluctuations better than the technique using mean

age related trends. Also, as similar growth fluctuations are present in both these

chronologies (Fig. 4.8), each developed usi ng very di fferent techniques, these results

indicate that chronologies constructed using the 7-year mnning average methodology are

representative of annual population growth fluctuations without the influence of age.

Based on these results, al1 other growth chronologies developed from fish or tree growth

data were constructed using multiple applications of 7-year mnning averages.

The application of mnning averages to detrend lake sturgeon growth data is useful

in this investigation as demonstrated by comparisons of the above techniques. However,

similar rigorous comparisons should be conducted when applying these techniques to

other species. As the calculation of a mnning average requires special data manipulation

at either end of a time series, this technique may not be applicable to growth data from

shorter lived life forms. Detrending of shorter time series may require the use of other

techniques such as the application of linear models to remove age related trends (Boyd

and Roberts 1993). Certainly, for shorter lived fish species present in large population

sizes, such that many age and year classes may be sampled the techniques outlined by

Weisberg 1993 may be useful.

The salient result of this investigation was the significant, negative correlation

between interannual growth variations in neighbouring aquatic and nearby terrestrial

organisms. Lake sturgeon growth chronologies were negatively correlated with nearby

tree chronologies fiom the Lake St. Clair, Lake Temiskaming, Saskatchewan River, and

Lake Winnebago regions dunng either the calendar year of growth or with tree

chronologies lagged by one year (Table 4.3). It is important to note here that correlations

between fish and tree growth chronologies only involved those fish population which

demonstrated the highest interseries correlation coefficients (Tables 4.2 and 4.3). The

three fish populations which displayed the lowest interseries correlation coefficients

failed to correlate with tree growth chronologies (Tables 4.2 and 4.3). These results

support the assumption that growth chronologies developed from populations

demonstrating low interseries correlation coefficients fail to contain valid population

growth variation data.

Correlative analysis between fish and tree growth chronologies and past measures

of air temperature may indicate one environmental factor partially responsible for the

noted negative relations between fish and tree growth. Sturgeon chronologies which

correlated with tree growth, those fiom Lake St. Clair, Lake Temiskaming, Saskatchewan

River, and Lake Winnebago were al1 positively correlated with interannual variations in

temperature (Table 4.4). No negative correlations were significant. Interannual growth

variation in trees from regions near Lake St. Clair, Lake Temiskaming, Saskatchewan

River were negatively correlated with measures of past temperatures. It must be noted

however that the Michigan red pine chronology demonstrated positive correlation with

some measures of air temperature fiom the Lake Winnebago and Lake St. Clair region.

Therefore, while temperature may be partially responsible for the negative relationship

between fish and tree growth, other environmental factors may also operate on these

relations. This may also explain, in part, why the correlation was not significant between

the Lac St. Louis sturgeon and white pine growth chronologies. White pine in the Lac St.

Louis region were positively correlated with mean April temperature from the current

season of growth indicating that the relation between growth in this species and

temperature di fers from that in other investigated tree species. Therefore the lack of

correlation between the Lac St. Louis sturgeon growth chronology and neighbouring

white pine may be the result of both the low interseries correlation expressed by the fish

chronologies as well as the growth response to temperature of the investigated species of

trees.

Further evidence supporting these results are provided by a fish chronology

developed by Pereira et al. (1995a) from freshwater drum, Aplodi,iotzrs grirruziens,

sampled in the Red Lakes in Minnesota (48" OO'N, 95' OO'W). Significant, negative

correlations occur between this chronology and a tree ring chronology developed from

nearby red pine sampled in Coddington Lake, Minnesota (47' 1 I 'N, 92' 12'W)

(Graumlich 1994). Conelations between these chronologies were significant when tree

growth was lagged by one calendar year (F-0.27, p=0.03) using data as published and

data following the removal of long-term trends with 7-year running averages (r=-0.3 1,

p=0.02) similar to that displayed in Figure 4.9. Again, the possible influence of

temperature on this relationship is evident if one looks at correlations between these

chronologies and air temperature. The dnirn growth chronology was significantly

correlated with mean June (r=0.5 5, pC0.0 l), July (r=O.S?, pCO.0 1), August (r=0.42,

p<0.0 1) and September ( ~ 0 . 3 6 , pX0.0 1) air temperatures during the growth season. Red

pine at Coddington Lake, Minnesota negatively correlated with mean Septernber (F-

0.35, p<0.01) air temperatures during the year pnor to growth.

While multiple applications of correlative analysis rnay result in spurious

correlations, consistent results indicate that a relationship does exist. In this

investigation, three key results indicate consistencies. First, the four sturgeon populations

that displayed the highest interseries correlations among members, indicative of a

common environmental signal being recorded in the growth chronology, were all, and

only, positively correlated with measures of past air temperature and negatively

correlated with nearby tree growth. Secondly, sturgeon from Lac St. Louis, Lac Parent

and Mattagami River, which displayed the lowest interseries correlation coeficients, and

therefore contained a low environmental signal, did not correlate with tree growth.

Finally, this negative relationship between interannual growth variations in fish and trees

is also detectable among chronologies from previous, separate investigations.

Admittedly, some significant correlations recorded in this investigation express a

low percentage of the variation as indicated by the correlation coefficients (r) and related

coefficients of determination (8). However, the purpose of this investigation was only to

atternpt to detect significant relations between fish and tree growth and ascertain if

temperature could influence these relations in seven ecosystems from across Nonh

America. It is probable that these correlations may be improved if more biologically

relevant measures of temperature, such as degree-days or consistently recorded measures

of water temperature are applied to these analyses. These measure were however not

available to this investigation for al1 ecosystems investigated.

The findings of the current investigation compliment those of several previous

works directed at correlating variations between aquatic and terrest rial systems. Research

by Guyette and Rabeni (1 995) concluded that positive correlations exist between

interannual variations in the growth of rock bass (Ambfoplites mpestris) and trees in the

Ozark region of the United States. Similarly, distribution of albacor tuna ( T h ~ ~ t ~ t ~ z i s

alalrrnga) and ring widths of conifers in western North America were Iinked by large-

scale atmospheric patterns, a major component of which was related to temperature

(Clark et al. 1975). Also, Ottestad (1 960) remarked on positive correlations between

annual fisheries catches of cod (Gudrrs callarias) and ring widths of Scots pine (Pims

silvestris) from a region in northem Nonvay. Each of these previous investigations, in

concert with the present study, suggest that correlated variations in neighbouring

terrestrial and aquatic systems are in response to the influence on growth of similar

environmental variables.

In the current investigation, interseries correlation coefficients were consistently

greater among tree than fish growth chronologies in al1 populations (Table 4.1). Several

factors may decrease the correlation among fish relative to trees including migration

through environmental gradients (Scott and Crossman 1973), behavioral modification of

their environment (Houston 1987) and intnnsic, physiological factors (Keenlyne and

Jenkins 1993; Roussow 1957).

This investigation presents evidence t hat fish and trees from sorne neighbouring

aquatic and terrestrial ecosystems of Nonh America display interannual growth variations

that are controlled by similar environmental variables. When presented with similar

results from previous investigations it becomes clear that these relationships are not

unique among freshwater and terrestrial systems but are also evident between marine and

terrestrial ecosystems in both Nonh Arnerica and Europe. It is possible that these

relationships play a role in stability of the biosphere. Future research should be directed

towards determining the strength, universality and variation throughout time of these

aquatic-terrestrial relations to assist Our understanding of their ecological role and how

these might be influenced by future climate change.

CHAPTER 5

GROWTH SYNCHRONY

IN NEIGEBOWUNG AQUATIC AND TERRESTRIAL ORGANISMS

5.1 ABSTRACT

Growth synchrony, as measured by the interseries correlation coefficient, was

detennined among samples of lake sturgeon, Aciper~serfitIvesce>~s, and white spmce,

Picea glauca, f'rom the Lake Temiskaming and Saskatchewan River regions. This

research was based on the assumption that synchrony of annual growth variations among

members of a population will increase as the influence of environmental factors becomes

more severe. 1 investigated the temporal variations of growth synchrony in neighbouring

populations of fish and trees to determine if similar sets of environmental factors rnay

influence growth in these organisms. This investigation also compared temporal

variation in growth synchrony in fish and tree population with variations in air

temperature. The results of this investigation indicated that similar sets of environmental

factors influence growth in neighbouring populations of fish and trees. However, this

research also suggested that, while temperature may demonstrate similar relations with

fish and tree growth in neighbouring populations, the way temperature operates on

growth of these organisms may differ between ecosysterns.

5.2 INTRODUCTION

Fish and trees possess certain characteristics that make them excellent candidates

for retrospective investigations into growth dynamics. Growth plasticity in response to

environmental factors (Blackrnan lgO5), indeterminate growth (Weatherley and Gi I l

1987), and annually developed rings in calcified and xylem tissues of fish and trees,

respectively, result in naturally recorded archives of environmental quality contained in

the widths of these growth rings (Fritts 1976, Pereira et al. 1995a,b, LeBreton and

Bearnish 1999, in review).

Interannual growth variations between lake sturgeon, Acipe~~serfttlvescer~s, and

white spmce, Picea glutica. sampled from several populations across North America

have been shown to negatively correlate (LeBreton and Beamish 1999, ijt review). This

previous investigation indicated that the positive and negative influence air temperature

has on growth in fish and trees, respectively, rnay be responsible for negative relations

between growth in these organisms.

However, the influence temperature exens on growth in specific populations of

fish and trees is still unclear. Temperature rnay operate on growth processes in a myriad

of fashions. For example, low summer temperatures rnay reduce the rate of growth in

fish yet increase growth in trees. Within such a system, while warmer temperatures will

alter growth in both organisms, the influence of warmer temperatures rnay not be as

severe, relative to colder periods. Therefore, during warmer periods, growth in fish and

trees rnay be Iimited by other factors. In such an ecosystem it rnay be hypothesized that

growth synchrony among individuals would be increased during periods of cold, as the

severity of this factor results in most member of a population being similarly influenced.

During warmer periods, as various other factors begin to operate on growth, growth

synchrony rnay begin to decrease. It rnay be fùnher hypothesized that this pattem would

be seen in colder climates where temperatures do not approach the species-specific

optimum for growth.

Conversely, in another ecosystem, warmer periods rnay increase growth in fish

yet reduce growth in trees as evapotranspirative stress and maintenance respiration is

increased (ûowns and Hellmers 1975, Teskey et al. 1995). In this system, while colder

summers will alter growth, the influence of lower temperatures is not as severe and,

during colder periods, other factors rnay become limiting throughout the populations. In

this system it rnay be hypothesized that growth synchrony among members of a

population would increase during warmer periods and decrease during colder. It rnay be

brther hypothesized that such a growth pattern would be experienced in relatively warm

regions.

During penods when temperatures approach either the minimum, optimum, or

greatly exceed the optimum for growth we rnay expect the influence of temperature on

growth to be strong. During other penods the influence of temperature rnay be expected

to be less severe (Gjesæter and Loeng 1987). If we assume that synchrony of interannual

growth variation among members of a population will increase during periods when

environmental factors become most influential, then this synchrony can be used to

determine what influence temperature has on growth by comparing fluctuation in growth

synchrony with variations in air temperature. Furthermore, if periods of growth

synchrony can be compared between neighbouring populations of fish and trees this will

fùrther our understanding of how similar, or dissimilar, sets of environmental factors

operating on growth in these organisms are. The main objective of this research was to

compare measures of growth synchrony in fish and trees with variations in air

temperature. The secondary objective was to compare growth synchrony in populations

of fish and trees to determine if periods of elevated synchrony CO-occurred in these

organisms.

Study Sites

Lake Temiskaming

White spruce (Picea glauca) were sampled from the West shore of Lake

Temiskaming at approximately 47'30W latitude, 79'30'W longitude, 8.5 km south of the

town of Haileybury in June 1996. Trees sampled were in relatively shallow soi1 over a

limestone outcropping. Topography of the region is relatively hilly and trees were

sampled on a gentle dope near the lake. To the south the Laurentian highlands are

relatively hilly while to the nonh the Supenor Upland provinces of the Canadian shield

imrnediately flattens out, display little topography, and poor drainage (Hunt 1974). The

boreal forest in this region is also split between the flat Haileybury clay to the north,

which suppons a small agncultural region, and the rolling Timagami section which is

commonly associated with towering white pine (Pitius strobirs) and exposed granitic

bedrock (Rowe 1972). In both locations white spnice are found near lakes and rivers on

well drained locations.

Saskatchewan River

White spmce were sampled fiom the Cumberland House region of Saskatchewan

in July 1996. The sample site was located at approximately 53'54'N latitude, 102~20'W

longitude. This area composes the Cumberland Marshes, a large wetland region covering

approximately 5000km2 (Smith and Perez-Arlucea 1994). Repetitive flooding of the low

terrain has resulted in the development of substantial levees predominantly covered by

bmsh. In swamp zones, aquatic and semi-aquatic flora, sedges and high grasses are

common (Cazanacli and Smith. 1998). This region is located in the central lowland of

the intenor plains (Hunt 1974). The boreal forests near the Saskatchewan River are

located in the Manitoba Lowland region. In areas where the land is suitably drained,

patches of white spruce can be found along with black spnice (Picea mariana) and

tamarack (Larix larickz) (Rowe 1 972).

5.3 MATERIALS AND METHODS

Growth chronologies were developed fiom lake sturgeon, Acipenserfilvescens,

sampled in Lake Temiskaming and Saskatchewan River. White spruce, Picea g!a~tca,

growth chronologies were developed fkom samples collected fiom neighbouring

terrestrial systems. These sites were selected as interannual growth variations have been

correlated between sturgeon and white spruce from these locations (LeBreton and

Beamish 1999 in review).

Fish and tree growth chronologies were developed using the techniques as

outlined in LeBreton et al. (1999). Individual growth chronologies, defined as a series of

growth increment data collected from a single fish for which the decrease in relative

growth with age has been removed. The interseries correlation coefficient is a statistical

tool applied to assemblages of growth chronologies to determine synchrony among

members of a population. The higher the interseries correlation coeficient the more

synchronous the interannual growth variations among individuals (Wigiey et al. 1984). If

this metric is applied to an assemblage of individual chronologies, over various temporal

periods, it can be used to determine how growth synchrony varies throughout time in a

population. To detemine how correlations among individuals varied through time,

interseries correlation coefficients were calculated for assemblages of individual

chronologies from each population over 10-year intervals with a shifl of 2 years between

each.

The nul1 distribution of the interseries correlation coefficient was calculated using

Monte Car10 simulations. To estimate the distribution of coefficients, random sets of

chronologies, similar in mean, variance, length and number as those extracted from lake

sturgeon and tree samples were generated. By generating 3000 sets of these random

chronologies and calculating the interseries correlation coefficient for each set, a nul1

distribution was estimated for this statistic. The level of significance (p value) for each

correlation coefficient was determined by setting the independent variable (x) equal to the

number of sets of chronologies generated in the Monte Carlo simulation which were

greater than the calculated test statistic, and setting the dependent variable (y) equal to the

total number of sets generated. The resulting significance level is calculated From

(x+ l)/(y+ 1). The value 1 is added to both the numerator and denominator of this

expression as the calculated test statistic is included in the estimated nul1 distribution

(Edgington 1995). Al1 interseries correlation coefficients for assemblages of fish

chronologies were ploned against those calculated fiom sets of nearby tree ring

chronologies. Linear regression was used to quantify the relationship between synchrony

of fish and tree growth in each ecosystem.

To detemine the influence air temperature had on periods ofgrowth synchrony a

linear regression was calculated between mean annual air temperatures ( O C ) and

interseries correlation coefficients calculated from fish and tree data during each IO-year

interval. Meteoroiogical data fiom North Bay (46" 35W, 79" 43'W), Timrnins, Ontario

(48" 57N, 81' 37'W), and Val d'Or, Quebec (48"07'N, 77'78'W) was used to calculate

mean annual air temperature for the Lake Temiskaming location, while temperatures

from Flin Flon (54'77'N, 101" 85'W) and The Pas, Manitoba (53" 97N, 101% IO'W)

were used for the Saskatchewan River location (Vose et al. 1992). While, other measures

of monthly air temperature (maximum, minimum) were investigated for their relation

with interseries correlation coefficients, the results were similar to those with average

annual temperature. For this reason, only the results of the latter are presented.

5.4 WSULTS

The interseries correlation coeficients were consistently higher among tree

growth chronologies than fish. Al1 inteneries correlation coeficients calculated from

trees were significant during tirne periods investigated (Table 5.1). Interseries correlation

coefficients were calculated over 12 ten-year penods for fish in Lake Temiskaming.

Interseries correlation coefficients for population are recorded in Table 5.1. All of these

12 statistics calculated were significant at the p<O. 1 level while 8 were significant at the

p<0.05 level (Table 5.1). Intersenes correlation coefficients were calculated over 1 I ten-

year periods for fish from the Saskatchewan River. Eight of the 1 1 statistics calculated

were significant at pC0.1 while 3 of these statistics were significant at the pC0.05 level

(Table 5.1).

Regressions between mean annual temperature and fish and tree interseries

correlation coeficients from the Lake Temiskaming location (Fig. 5.1) were quantified

by the equations:

IF= -0.22 + 0.218T ($ = 0.63, p=0.004, n=12) (1)

IT = -2.03 + 3.120T (if = 0.52, p=0.0 12, n 4 2 ) (2)

where IF and IT are interseries correlation coefficients from fish and trees, respectively

and T is mean annual air temperature.

Regressions between mean annual temperature and fish and tree interseries

correlation coefficients h m the Saskatchewan River location (Fig. 5.1) were quantified

by the equations:

IF= 0.03 - 0.0 18T (? = 0.45, p=0.024, n=l 1) (3)

IT = 0.26 - 0.140T (?=0.7?,p<O.O01,n=ll) (4)

Regressions calculated between interseries correlation coefficients in fish and

trees were significant from the Lake Temiskaming (5) and Saskatchewan River (6)

ecosystems and expressed by the equations:

TT = 0.20 + 3.24 FT (?=O. 59; ~ ~ 0 . 0 1) ( 5 )

Ts = 0.1 1 + 4.50 Fs (?=0.56; pCO.0 1) (6)

where TT and Ts, and FT and Fs refer to interseries correlation coeficients from

trees and fish in Lake Temiskaming and Saskatchewan River, respectively (Fig. 5.2).

Lake Temiskaming Saskatchewan River

Interseries Correlation Coefficients

Years Fish N Trees N Fish N Trees N

Table 5 .1 : Time periods over which interseries correlation coeficients were calculated, resulting statistics and number of samples of lake sturgeon, Acipenserfiivesceris, and white spruce, Picea glairca, used fiom Lake Temiskaming and Saskatchewan River regions. (**) p<0.5, (*) p<0.01.

Mean Annual Temperature

Figure 5.1 : Interseries correlation coefficients as a function of mean annual temperature calculated over ten year intervals for Lake Temiskaming lake sturgeon (A) and white spmce (B), and Saskatchewan River sturgeon (C) and white spmce @). Dashed lines represent 95% confidence intervals, dotted represent prediction intervals.

Sturgeon Intersenes Correlation Coefficients

Figure 5.2: Interseries correlation coefficients of white spruce from (A) Lake Temiskaming, and (B) Saskatchewan River as a function of sturgeon interseries correlation coefficients. Dashed l i nes represent 95% confidence intervals, dotted lines represent prediction interval S.

5.5 DISCUSSION

The current investigation demonstrated that significant straight-line relationships

exist between interseries correlation coefficients in both fis h and trees and mean annual

temperature. These results, in concert with the findings of LeBreton and Beamish (1999,

in review), further strengthen the proposai that temperature is a factor responsible for

establishing the negative relations between fish and tree growth.

Interestingly, the relations between interseries correlation and temperature differ

between geographic locations. For example, in the Lake Temiskaming region, growth

synchrony in both fish and trees increases dunng periods of relatively higher temperature

(Fig. 5. la) while in the Saskatchewan River region growth synchrony increased among

both fish and trees during colder periods (Fig. 5.1 b). The mean annual temperatures for

the Lake Terniskaming and Saskatchewan River regions are 3.Z0C and 0.3'C, respectively

(Fortin et al. 1996). These results appear to support the preliminary hypothesis indicating

that warmer temperatures are relatively more influential in warmer regions and that

colder temperatures are relatively more influential in colder regions, at least among the

two systems invest igated.

As sturgeon and tree growth display positive and negative correlation,

respectively with interannual variations in air temperature in both the Terniskaming and

Saskatchewan regions (LeBreton and Beamish 1999 in review) some conclusions can

now be drawn regarding the influence of temperature on fish and tree growth in these

populations. In the Lake Temiskaming populations warm temperatures appear more

influential on growth in both organisms than cold. However, in the Saskatchewan River

regions, these relations are reversed. Some research postdates that mean air

temperatures may increase as a result of suggested future climate warming (Hansen et al.

1981). As temperature has been shown to influence the growth and synchrony of growth

in fish and trees, generalizations regarding impacts of these possible climatic changes

may be inferred from the results of this investigation. If we assume that future mean

annual temperatures will similarly increase in both the Lake Temiskaming and

Saskatchewan River regions, the results predict that global warming would more strongly

influence growth in the Lake Temiskaming location; increasing growth synchrony within

fish and tree populations by elevating growth in fish and decreasing growth in trees. In

the Saskatchewan River area, resulting warmer temperatures rnay be expected to lower

growth synchrony among both fish and trees. The ecological implications of increased or

decreased growth synchrony and the effects this has on ecosystem stability or resilience

are unknown.

The results of the cunent investigation also indicate that growth synchrony within

a population of either fish or trees fluctuates over time. No previousl y published

investigations addressing this issue are known though it has important implications for

chronology analysis. These results suggest that the quality of growth data, fiom any

organism, contained in chronologies and used for documentation of environmental

variation will also fluctuate over time. Therefore, growth chronologies that extend into

the distant past may contain periods of data that correlate poorly with measures of past

environmental factors and periods where correlations are high. As these chronologies are

frequently used to reconstnict past climate variation, reconstruction will be more accurate

during periods in a chronology when samples demonstrated higher interseries correlation.

Interestingly, linear regression demonstrated that periods of increased synchrony

in tree rings are accompanied by similar increases in nearby fish in both the Lake

Temiskaming and Saskatchewan River regions (Fig.5.2a.b). These results lend fùrther

support to the conclusions of LeBreton and Beamish (1999, bi review) suggesting that

significant, negative correlation between interannual growth variation in fish and trees

may be the result of similar environmental factors, of which air temperature is a major

component, operating on growih in both organisms.

This investigation suggests that the influence of air temperature on fish and trees,

while similar between neighbouring populations, may Vary between ecosystems. The

reasons for this is not understood. This research has further supported the premise that

air temperature variation exerts an intluence on the growth of fish and trees and rnay be

the driving factor responsibie for the negative relation between growth in aquatic and

terrestrial organisms.

MODELING LAKE STLRGEON GROWTH

USING PAST ENVIRONMENTAL

AND TREE GROWTH DATA

6.1 ABSTRACT

This investigation developed a multiple linear regression model describing annual

variations in the growth of lake sturgeon from the Saskatchewan River region as a

function of past environmental variables and nearby tree growth. Annual sturgeon

growth variations were quantified using a growth chronology developed fiom the ring

widths contained in cross-sections of the pectoral fin ray. Environmental variables

included in the model were mean April air temperatures and total June precipitation, both

during the current season of growth, the mean Southem Oscillation Index From the

previous and two years previous growth seasons and sun spot numbers from two years

previous. Tree ring growth chronology data was also included in this analysis from both

the previous and two years previous. The developed regression explained 68.8 percent of

the variation in the Saskatchewan River lake sturgeon growth chronology (p<0.001).

When the variables from this model were applied to data from the Lake Temiskaming

sturgeon growth chronology the resulting model explained 48.5 percent of the variation

and was associated with a p value K0.0 1. This investigation also developed a regression

modeling annual total length increment in Saskatchewan River lake sturgeon as a

function of age and growth indices. Using the developed models, in combination with

previously published equations documenting relations between mass and total length,

annual biomass increments may be estimaied using easily collected environmental and

tree growth data.

6.2 INTRODUCTION

Lake sturgeon, Acipenserfrtlvescens, have suffered an extensive depletion in

numbers during the last century (Houston 1987). Habitat destruction, pollution, and

over-fishing, in concert with late maturation and slow growth, have combined to threaten

the persistence of this economically important organism (Brousseau 1987, Dumont et al.

1987, Noakes et al. 1999) yet knowledge of lake sturgeon growth dynamics remains

sparse (Nilo et al. 1997, Veinott and Evans 1999). Recent investigations (LeBreton and

Beamish 1999 in review) have developed techniques to extract relatively long time series

of lake sturgeon growth data fiorn their pectoral fin ray ring widths similar to those

applied in tree growth (Fritts 1976). These investigations have also demonstrated that

sturgeon and nearby tree growth chronologies fluctuate in response to similar

environmental variables. The objective of the present study was to deveiop a mode1

relating sturgeon growth to environmental quality and past tree growth and use this to

identib specific factors as early predictors of subsequent productivity.

6.3 MATERIALS AND METHODS

The influence of environmental factors on fish growth has been described by Brett

(1979) and Weatherley and Gill(1987) and, along with the availability of the data,

provided the basis for model development. As outlined in these reviews, temperature is

an important determinant of fish growth. Generally growth is optimum at a species-

specific temperature and decreases if the thermal regime is above or below that point. As

fish inhabit water, it is the temperature of this medium that controls growth. However, as

consistently collected water temperatures were not available from the study sites, air

temperatures were used as a surrogate (Appendix 1). Sturgeon activities are reduced

dunng cold temperatures (Shelukhin et al. 1990) and it is believed that growth is also

minimal during the winter months (Wilson 1987). Therefore, mean air temperatures

during April, May, and June were included as possible variables during model

development. Precipitation is an environmental factor influencing fish growth and is

thought to operate indirectly on growth processes by improving thermal and oxygen

regimes in water systems (Guyette and Rabeni 1995, Duchesne and Magnan 1997). As

consistently recorded past measures of total precipitation were recorded for these study

sites, the measures during April, May and June were included as possible variables

during model development.

Growth in fish and other animals is also influenced by the effects of large scale

atmospheric patterns on climate (Pereira et al. 1995a, Glynn 1988). For example the

Southem Oscillation Index (SOI) (Glynn 1988) and North Atlantic Oscillation (NAO)

(Post and Stenseth 1998), measures of see-saw differences in atmospheric pressure

between Tahiti and Darwin, Australia and Akuryeri Iceland and the Azores, respectively,

have both been recorded to influence the climate of continental North America. As

ecological processes frequently occur over extended penods of time (Magnuson et al.

199 1, Post and Stenseth 1998, Stenseth et al. 1999), SOI and NA01 data from the

previous and two years previous was included as potential variables for the sturgeon

growth model. The final environmental factor included for consideration in the equation

modeling sturgeon growth was annual sunspot activity. Research indicates that solar

activity may indirectly influence growth through its operation on total irradiance,

ultraviolet irradiance, zir temperatures and precipitation (LaMarch and Fritts 1972,

Sinclair et al. 1993 and references t herein). Therefore annual solar act ivity represented

by sun spot numben dunng the previous and two years previous seasons were included

as a possible variables for the developed model. Tree ring indices sampled frorn nearby

terrestrial systems fiom the previous two growth seasons were also made available during

model construction. Tree growth is postulated to assist modeling fish growth as it may

estimate recent past primary production and thereby be related to food avaiiability.

Saskatchewan River and Lake Temiskaming lake sturgeon growth chronologies

were developed fiom cross-sections of pectoral fin ray ring widths using methodologies

as outlined in LeBreton and Beamish (1999, in review). White spmce were sampled fiom

terrestrial systems located near the watenvays in which sturgeon were caught.

Comprehensive site descriptions are documented in LeBreton and Beamish (1999, in

review). White spmce growth chronologies were developed from tree rings using the

same methodology as used to develop lake sturgeon chronologies.

The two-way interactions were calculated among al1 potential variables. Data

fiom the Saskatchewan River lake sturgeon growth chronology (1959-1988) was used to

develop the initial model. As the Saskatchewan River time series was relatively short,

only 29 years in length, only the last two data points (19874988) were excluded from the

original model development to allow some measure of the ability of the model to predict

growth dunng the last two years. Variables included in the regression were selected via a

stepwise multiple linear regression technique (Campana 1984, Duchesne and Magnan

1997). The probability of the F value associated with entry of each variable into the

model was 0.07 and the probability of removal was 0.10. Two models were developed

using these selected variables, one incorporating only the variables selected and the other

also including the individual components of the interaction terms selected. For the first

model developed the Durban-Watson statistic and variance inflation factors were

calculated for the model and included variables respectively. These regressions were

then used to model growth in another population of sturgeon to determine if the variables

selected were generall y related to sturgeon growth. If the variables included in the model

developed for the Saskatchewan River location are truly influential on lake sturgeon

growth, then we would expect these variables to satisfactonly model growth in another

system. To determine the ability of the variables contained in the Saskatchewan

regressions to model growth in other populations of sturgeon the same two sets of

variables were applied to interannual growth variations in the Lake Temiskaming

population chronology between 1961 and 1989.

To increase the applicability of the developed growth models, a regression

converting the unitless growth index to a total length increment, was developed from total

length data collected from Saskatchewan River sturgeon. Fin ray cross-section ring

widths were converted to total length increments based on the assumptions that the length

of the fin ray radius was relative to the total body length, and that the individual ring

widths were equated to corresponding total length increments. The variation in total

length increment between ages 5 and 20 was modeled as a hnction of the growth index

and age of the individual. This age span was selected so as to minimize the influence of

sema1 maturity on growth while still maintaining a reasonable shed database (Fortin et

al. 1996).

6.4 RESULTS

In total 137 variables, representing both independent and interaction ternis, were

available for inclusion in the regression modeis. The first multiple linear regression

equation developed fiom Saskatchewan River data, excluding the last two data points

(1 987 and l988), included six variables, al1 except the constant, representing interaction

terms (Table 6.1). This model described 68.8% of the variance associated with the

Saskatchewan River lake sturgeon growth chronology and was significant (F=9.7,

p<O.OOl) (Fig. 6. LA). The Durban-Watson statistic and variance inflation factors were

1.59 and below 2.4 for each included variable, respectively. When this model predicted

the final two data points the described variation increased to 69.6% (Fig. 6.1 A). The

second rnodel developed for this growth chronology included ail independent

components of the interaction terms from the first model. This model was composed of

13 variables including the constant, described 77.7% of the variance and was also

significant (P4.3, p = 0.004).

When the original 6 variables frorn the fint Saskatchewan River regression

equation were used to model Lake Temiskaming sturgeon growth using environmental

and tree growth data fiom that region, 48.5% of the variation was explained by the model

(Table 6.1) and it was significant (F=4.138, p = 0.008) (Figure 6.1B). When the thirteen

variables fiom the second model developed for the Saskatchewan systems were applied

to Lake Temiskaming data this mode1 explained 64.4% of the variation and was

marginally significant (F=2.2, p=0.069)

In order that the unitless growth index be converted to a more tangible metric,

useful in fisheries management applications, a regression describing total length

increment as a function of growth indices and age was estimated by:

T = 153.163 + 27.93 GI - 14.46 A + 0.413 A' - 1.03 GI A (?=0.87, p<0.001) (6.1)

where T is the annual total length increment in millimeters, A is the age of the individual

dunng the current season of growth, and GI is the growth index of the individual at that

age (Figure 6.2).

Saskatchewan River Lake Temiskaming

Variables Coefficient P Coefficient P

Constant -5.7E-2 0.087 1.5E-2 0.748

P6SOI2 2.OE-3 O. 004 - 1.9E-4 0.790

SOI 1 TREE 1 -0.2 1 0.008 0.32 0.009

SUN2TREE2 3.5E-3 cO.00 1 -2.9E-3 0.055

P6TREE2 -4.2E-3 0.00 1 2.9E-3 0.0 13

T4TREE 1 6.OE-2 0.007 - 1.9E-2 0.58 1

Table 6.1: Mode1 variables as selected by stepwise multiple linear regression technique, coefficients and significance levels for coefficients. P6 = June total precipitation, SOI1 = Southern Oscillation Index from the previous growth season, SOI2 = Southern Oscillation Index h m two yean previous, SUN1 = sunspot numbers fiom two previous growth seasons, TREE 1 = white spruce growth ring indices from the previous season, TREE2 = white spmce growth ring indices from two years previous, T4 = mean April air temperature during the current season of growth.

Year Figure 6.1 : Observed (solid) and predicted (dotted) growth chronologies from Saskatchewan River (A) and Lake Temiskarning (B). Soiid circles (A) indicate predicted values for 1987 and 1988. These data had been excluded from mode1 development.

Figure 6.2: Total length increment in lake sturgeon from the Saskatchewan River as a function of growth indices and age.

6.5 DISCUSSION

This research has developed a regression model for the mean population growth

indices in lake sturgeon fiom the Saskatchewan River using easily obtained or collected

environmental and tree growth data from the current, previous and two-years previous

growth seasons. A model was also developed for converting growth indices in fish of a

particular age to total length increments. Using these equations, in combination with

previously published data relating mass and total length in the subject population, annual

biomass accumulation for an individual can now be easily determined. For example, an

average 15-year old fish in the Saskatchewan River was approximately 1143 mm in

length just pnor to the 1976 growth season (Wallace 199 1). From the developed growth

chronology, the Saskatchewan River lake sturgeon exhibited a mean growth index of 0.38

during 1976. Therefore, using Equation 6.1 the average total length increment for an

individual of that age during that year would be approximately 33.9 mm, equivalent to a

change in mass fiom 8.0 to 8.9 kg using sturgeon body metric relations as published in

WaIlace (1 99 1).

Two results fiom this investigation indicate that the variables composing the

developed models are operating on sturgeon growth. First, the exclusion of the last two

data points (1987-1988) from the development of the original Saskatchewan River

growth model allowed some measure of the predictive abil ities of this model to be

investigated. When both of these points were predicted by this model the percent

variance described increased slightly fiom 68.8% to 69.6% (Fig. 6.1A) indicating that

this model does dernonstrate some ability to forecast sturgeon growth. Secondly, when

the variables modeling Saskatchewan River growth data were appiied to model Lake

Temiskaming sturgeon growth (Table 6.1) the regression equations were significant and

explained either 48.5 or 64.4 % of the variation depending on the variables included in

the model. From these results it is apparent that the environmental variables composing

the regression models developed are operating over large geographic distances and

influencing sturgeon growth in various populations. It should also be noted that

multicollinearity was not a problern with the initial variables selected for the

Saskatchewan River growth model as indicated by the variance inflation factors.

Autocorrelation in this model was also not likely influential as detennined by the

Durban-Watson statistic.

Of great interest to this research is the fact that tree growth From the previous

(TREE1) and two years previous (TREE2) growth seasons were a component of the

model developed for the Saskatchewan River population. Furt hermore, the t e n s

containing these variables significantly contributed to the growth model for the Lake

Temiskaming population (Table 6.1). These results indicate that the use of growth data

collected from easily sampled neighbouring trees can assist modeling of sturgeon growth.

In conclusion, this investigation developed several regressions to show how

growth chronologies from neighbouring trees, along with past measures of environmental

variation, can be used to model lake sturgeon growth. It is proposed that this technique

could assist research or management by reducing the resources and time required for

annual data collection from sturgeon populations. Archived calcified tissue samples and

easily collected environmental and tree growth data can be used to quickly develop these

models. Following model development only a few individual sturgeon need by sampled

every year to ensure that model calibration is maintained.

GEWRAL DISCUSSION

This investigation explored the ability of lake sturgeon growth rings to satis&

three criteria required of any stmcture used in the development ofgrowth chronologies

relevant to ecophysiological research. First, fluctuations in growth ring widths must be

related to some measure of somatic growth. If these structures do not satisfy this

criterion, they simply cannot be used as growth records. Secondly, individual growth

chronologies must display some degree of detectable synchrony of interannual growth

variations among members of a population. Failure to do so results in population

chronologies composed of noise and of M e use in ecophysiological research. Finally,

the widths of growth nngs and the related population growth chronologies must

demonstrate some relation to environmental variations; preferably with environmental

factors known to influence growth in that organism. Failure to do so greatly reduces the

use of these structures in ecological research and may indicate that the chronologies have

been incorrectly developed. It should be mentioned that one other criteria which growth

rings must satisfy if they are to be developed into growth chronologies is that they rnust

be developed in the organism over a known tirne interval (daily, annually, etc.). The

work by Rossiter et al. (1995) established that sturgeon growth rings could be used to

assign yearly ages to fish thereby indicating their annual development.

The relation between widths of lake sturgeon growth nngs and variations in body

size was established in Chapter 2. Total fin ray radii length was related to body length in

4 populations of sturgeon and fin ray radii at age 25 was related to body length at age 25

for al1 sturgeon populations investigated. These results indicated that the lake sturgeon

pectoral fin ray rings could be used as records of annual growth variation.

The synchrony of interannual growth variations in lake sturgeon rings, and the

cornpliance of these stmctures to the second criteria, was also investigated. The

synchrony of ring width variations in these structures from Lake St. Clair and the

Saskatchewan River was established in Chapter 1. It was important to establish that

synchrony among individual growth chronologies was detectable early in this research as

failure to do so would have ended this investigation. Prior to these finding some critics

noted that previous investigations found sturgeon ring widths to be dominated by intrinsic

physiological cycles related to reproduction (Roussow 1957, Keenlyne and Jenkins

1993). If reproductive cycles had been strong determinants of lake sturgeon growth,

asynchronous interannual growth variation among individuals would have occurred as

synchronous gonadal development is not noted in this species (Nowak and Jessop 1987).

The conclusions of this research do not, of course, indicate that intrinsic factors have no

influence on the growth of lake sturgeon or their growth rings but only indicate that these

factors do not completely dismpt the signal from extrinsic factors and further indicate

that these structures may be used as past records of environmental quality. Interestingly,

growth synchrony could not be detected among individuals From the Lac Parent or the

Mattagarni River populations. As the samples fiom these populations were collected from

relatively older individuals it is possible that the associated aging errors were increased

(Casselman 1990) thereby misaligning chronologies in time and dismpting common

growth signals. The work conducted in Chapter 1 is applicable, not only to sturgeon

research, but also to ail chronology development. This chapter indicated that aging error

must be quantified and minimized regardless of the species or life form in question. The

influence of aging error has been show to dismpt the common signals among

individuals and reduce growth synchrony within a population. Furthermore, growth

synchrony among sarnpled individuals must be quantified to ensure that a growth

chronology, containing the signals from large-scale population wide extrinsic factors, can

be developed.

The compliance of sturgeon to the final criterion, requiring that growth rings and

related population chronologies be related to variations in known large scale extrinsic

factors was established in Chapter 2. M i l e synchrony of interannual growth variation

among lake sturgeon in Lake St. Clair and Saskatchewan River, detected in Chapter 1,

did indicate that sturgeon growth rings were dominated by the influence of population

wide extrinsic factors, this did not confirrn that these factors were related to climatic or

environmental variation. As outlined by Weisberg (1993). the signals from extrinsic

factors contained in growth chronologies may be influenced by fisheries management or

data collection practices. Widths of lake sturgeon growth rings and related population

chronologies were positively related to both interpopulation and interannual variations in

air temperatures. These results indicated, not only compliance wit h the final criteria

tested, but also that the data extracted from fin rays agreed with what is known about

environmental influences on sturgeon growth as determined by previous investigations

(Fortin et al. 1996, Power and McKinley 1997).

The synchrony of growth variations in tree rings was also established in Chapter

3. These results indicated that synchrony of interannual growth variations can be

established among tree ring chronologies without the use of cross-dating techniques so

long as aging errors are quanti fied and minimized. Furthermore, these findings indicated

that the trees sampled for this investigation were suitable for use in chronology

development .

The results of C hapters 1 and 2 established that lake sturgeon population growth

chronologies could be developed fi-om the growth rings contained in the leading pectoral

fin ray. Chapters 4, 5, and 6 explored the use of these chronologies as ecological tools.

The application of these chronologies to detect correlation of interannual growth

variations among diverse organisms was investigated in Chapter 4. The 4 lake sturgeon

chronologies frorn populations demonstrating the highest interseries correlation

coefficients were negatively correlated with nearby tree chronologies. The results of

correlations between the chronologies from both sturgeon and trees and past records of

temperature suggest that temperature variations are partially responsible for the negative

relation between fish and tree growth. Support for these results were found in other fish

and tree chronologies indicating that these relations were not restricted to the fish species

in question. Previous investigation have indicated that relations also exist between

aquatic and terrestnal organisrns in Nonh America at lower latitudes (Guyette and Rabeni

1999, at the west Coast of North America (Clark et al. 1979, and in Northem Europe

(Ottestad 1960). While the relations between fish and tree growth in the current

investigation, which were negative, differ from relations described in these previous

works, environmental influences have been consistently cited in this and previous

research as the driving forces behind these fish-tree growth correlat ions.

Relations between the environment and fish and tree growth were funher

investigated using the interseries correlation coefficient as an indicator of the strength of

environmental influences on growth in Chapter 5 . This work revolved around the

assumption that as the influence of environmental factors increases so to will the

intersenes correlation among members of a population. Interseries correlation was

calculated over ten-year intervals, each interval being shifled two years in time with

respect to others. The interseries correlations among neighbouring fish were compared

with those among nearby trees in the Saskatchewan River and Lake Temiskaming regions

and positive relations between these were estab lished. These results furthered the

proposal that fish and tree growth responds to similar sets of environmental variables.

Interestingly, when the fish and tree interseries correlations were related to mean annual

air temperatures dunng 1 O-year intervals, significant and similar relations were found

among neighbouring fish and trees. However, these relations were not similar when the

resuits were compared among Saskatchewan River and Lake Temiskaming populations.

While fish and trees in the Saskatchewan River appear most influenced by colder

temperatures, these same species in the Lake Temiskaming region grow most

synchronously during warmer periods. While the ecological significance of these

relations is poorly understood, they rnay enhance our understanding of how climatic

fluctuations may influence various ecosystems.

The applicable nature of this research was developed in Chapter 6 by developing

multiple linear regressions models for lake sturgeon growth as a fùnction of easily

collected environmental and tree growth data. This work also provided a mode1 for

estimation of annual total length incrernents from age and growth indices data. It was

shown that variables selected for this mode1 of sturgeon growth from the Saskatchewan

River were also applicable to sturgeon growth data collected from Lake Temiskaming.

These results indicate that the variables composing these rnodels are influencing sturgeon

growth over large geographic scales.

Overall, this work has indicated that much ecological information can be gleaned

fi-om growth rings in the pectoral fin rays of lake sturgeon. These structures can be

exploited without lethal consequences to the target organisrn and may be used to Save

time and funds, as repeated, annual sampling of body measurements is not required.

Several interesting questions have arisen during this investigation. For example, how

common are these negative relations between neighbouring aquatic and terrestrial

organisms? How does interannual synchrony of growth variations within a population

alter depending on habitat type, size of water body, or location throughout a geographical

range? These are questions which have arisen because of the current investigation and

briefly suggest that much more research is yet to be conducted on the development and

comparison of growth chronologies from widely diverse organisms.

As humans we can only experience the present. For this reason Our ability to

comprehend, much less form hypotheses surrounding, ecological processes which occur

over extended period of tirne (Magnuson 1991) is greatly limited. The growth rings

investigated herein have been demonstrated to contain ecological data which, when

assembled into growth chronologies, can be used to greatly enhance our understanding of

ecosystem dynamics. Recent research has been conducted on similar growth data fiom

shells of molluscs (Jones 1980), caicified tissues of fish (Cytenki and Spangler 1996,

Pereira et al 1995b), teeth of mammals (Boyd and Roberts 1993), and Iayers of corals,

pollen and ice (Sinclair et ai. 1993). These data offer ecology the rare opportunity to

compare similar metrics among widely different life forms. This work brings together

knowledge from multiple backgrounds and firthers our attempt to move From a

reductionist concept of ecology towards a holistic perspective. This thesis has not

answered al1 the questions that it uncovered; however, it is my hope that this work will

become part of ongoing research, utilizing naturally occumng archives of growth data

found in hard tissues of many diverse organisms and directed towards enhancing Our

understanding of ecosystem dynamics.

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APPENDIX 1

WATER AND AIR TEMPERATURE

Temperature is an environmental factor known to strongly influence growth in

fish and trees (Weatherley 1990, Spiecker 1995). However, while tree growth may be

determined by air temperature, fish growth is controlled by the ambient temperature of

the water in which they live. While air temperature has been recorded for an extensive

pet-iod of time across Nonh America, consistently recorded water temperature time series

are not available. Therefore, this investigation used air temperature as a surrogate for

water temperature. However, t his requires the assump t ion t hat these two measures are

closely related. To ensure that this assumption is valid, inconsistently recorded rneasures

of water temperature were regressed against air temperatures recorded from the nearest

rneteorological location for the Lake St. Clair, Lake Temiskaming, Saskatchewan River,

Lac St. Louis and Mattagami River locations. Water ternperatures could not be obtained

fiom the Lake Winnebago or Lac Parent locations. Water ternperatures were recorded

within 24 hours of the air temperature recordings. Water temperatures were not recorded

when air temperatures were below zero for al1 data sets. Therefore, those water

temperatures that were recorded when air temperatures were below zero were excluded

fiom fûrther analysis to ensure that the cornparisons made between air and water

temperatures were similar for al1 locations. Linear regressions between air and water

temperature (Fig. Al . 1) were significant in the Lake St. Clair, Lake Temiskaming,

Saskatchewan River, Lac St. Louis, and the Mattagami River regions as described by the

equations:

Wt = 7.66 + 0.64 At (? = 0.64, p<O.000 1) Lake St. Clair

Wt=5.81 +0,60 At (? = 0.48, p<0.0001) Lake Temiskaming

Wt = 5.34 + 0.82 At (? = 0.70, p<0.000 1) Saskatchewan River

Wt = 1.12 + 0.78 At ($ = 0.65, p<0.000 1) Lac St. Louis

Wt = 5.14 + 0.81 At (r2 = 0.60, p<0.000 1) Mattagarni River

Where Wt is water temperature and At is air temperature.

As the relations between air and water temperatures for each location were al1

highly significant (p<0.000 1 ) these results suggested that air ternperatures, above o°C,

were suitable for use as a surrogate for water temperatures throughout this investigation.

Water Temperature (OC) Water Temperature (OC)

Water Temperature (OC)

M W W O 0 i O V i O V i O

APPENDIX KI LAKE STURGEON GROWTH CHRONOLOGIES

Lakc St. Clair Saskatchewan River Year Growth 95% C.I. Year Growtb 95% C.L Ycar Growth 95% C.L

Index Indes Index 1961 0.21 - 1944 0.01 - 1979 0.01 0.25

Lake Temiskaming Year Growth 95% C.L

Index 1949 -0.03 - 1950 -0.10 0.6 1 1951 -0.84 2.16 1952 0.71 0.66 1953 0.36 0.80 1954 4.90 0.92 1955 0.12 0.95 1956 0.75 0.83 1957 -0.1 1 1.19 1958 4.05 0.7 1 1959 0.02 0.5 1 1960 -0.09 0.38 1961 0.55 0.36 1962 -0.01 0.38 1963 4.27 0.53 1964 -0.45 0.34 1965 0.10 0.42 1966 0.31 0.37 1967 0.02 0.38 1968 0.17 0.38 1969 -0.18 0.29 1970 -0.27 0.25 1971 4.02 0.22 1972 -0.28 0.22 1973 0.32 0.27 1971 -0.1 1 0.25 1975 0.45 0.26 1976 O. 10 0.28 1977 -0.05 0.23 1978 -0.27 0.2 1 1979 -0.07 0.22 1980 -0.28 0.23 1981 0.18 0.25 1982 -0.06 0.26 1983 0.49 0.25 1984 0.10 0.3 1 1985 -0.17 0.32 1986 4.63 0.27 1987 0.44 0.40 1988 0.43 0.24 1989 0.09 0.3 1 1990 -0.56 0.32 1991 0.26 0.34 1992 -0.57 0.23

Lake Winnebago Year Growth 95%C.L

ln dex 1957 0.76 - 1958 0.08 13.20 1959 0.40 33.48 1960 -1.65 0.10 1961 0.66 5.39 1962 0.76 4.43 1963 -0.93 1.68 1964 -0.53 1.3 7 1965 -0.22 1.5 1 1966 0.64 1.6 1 1967 -0.59 0.73 1968 -0.29 0.63 1969 0.03 0.4 1 1970 0.67 1.15 1971 0.01 0.88 1972 -0.34 0.6 1 1973 -0.06 0.6 1 1974 -0.11 0.60 1975 0.51 0.73 1976 -0.15 0.89 1977 -0.08 0.55 1978 0.19 0.58 1979 0.11 0.41 1980 0.18 0.50 1981 -0.37 0.63 1982 0.05 0.60 1983 0.09 0.53 1984 0.36 0.50 1985 -0.72 0.49 1986 -0.71 0.60 1987 0.09 0.62 1988 0.57 0.65 1989 0.60 0.44 1990 -0.03 0.55 1991 0.08 0.53 1992 -0.22 0.54 1993 0.48 0.79 1991 0.12 0.48 1995 4-75 0.28

Lac St. Louis Year Growth 95% C.L

Lac Parent Mattagarni River Ycar Growth 95% C.L Ycar Growth 95% C.L Year Growth 95% C.L

Index Index Index