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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