FACTORS AFFECTING YEAR-CLASS STRENGTH AND GROWTH OF LAKE WHITEFISH (Coregonus clupeaformis) AND LAKE TROUT (Salvelinus namaycush) IN THE SMALLWOOD RESERVOIR, LABRADOR CANADA
A Thesis Submitted to the Committee on Graduate Studies in Partial Fulfilment of the Requirements for the Degree of Master of Science in the Faculty of Arts and Science
TRENT UNIVERSITY Peterborough, Ontario, Canada
© Copyright by Robert Perry 2009
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ABSTRACT
Factors Affecting Year-class Strength and Growth of Lake Whitefish {Coregonus clupeaformis and Lake Trout (Salvelinus namaycush) in the Smallwood Reservoir, Labrador Canada
Year class strengths and growth rates for lake trout Salvelinus namaycush and lake whitefish Coregonus clupeaformis inhabiting the Smallwood Reservoir, Labrador,
Canada, were influenced by facets of its creation and the temporal variability in water levels, associated with its operation.
Comparisons of 649 lake trout and 903 lake whitefish collected from two impounded and three unaltered water bodies located in the watershed revealed both species underwent short term episodic changes in growth and recruitment at the onset of impoundment. Filling of the reservoir was carried out between 1971 and 1974, and during this period there was a concurrent increase in whitefish recruitment above the long term average. Conversely, lake trout had weak recruitment for the 10 years following the initial impoundment (between 1972 and 1983). Year-class numbers from this period accounted for 18% of the sampled catch from the impounded water bodies. Whereas, in the natural water bodies the same number of water bodies contributed 47% to the sampled catch, 29% greater.
Long-term recruitment patterns for both species were influenced by winter draw down levels: If water levels during February were above average, recruitment was stronger and if levels were lower, recruitment was weaker. Incorporating the additional variable of February water level fluctuations into catch curve regression equations for II
lake whitefish and lake trout, an additional 10 % and 22% in recruitment variability
became apparent.
Using width measurements between annuli of otoliths it was determined that growth rates
for both species were also influenced by the initial years of impoundment. Lake whitefish
experienced the fastest growth rate increases during the initial period of flooding (1971 to
1975). Rates of growth during this time period exceeded the long-term average and were
faster than any other five year period between 1965 to 1995. Increases in growth rate were attributed to a zooplankton bloom which occurred concurrently at the time of reservoir creation. Following the period of exceptional growth, lake whitefish underwent a five year period (1976 to 1980) when growth rates were depressed.
Lake trout growth rates for the period between 1971 and 1980 were slower than the later period, between 1981 to 1985. Increases in growth rates subsequent to 1982 were attributed to the decreased recruitment that occurred between 1972 and 1981. It was hypothesized that the thinning in the lake trout population made available greater resources for those lake trout that remained. In addition, flushing of the reservoir probably returned water clarity to its original state, thereby enhancing food predation opportunities for lake trout.
I developed a technique based on the incremental calcium deposits of otoliths which can be used as an index to establish a biochronology of growth rates. The index can serve as a cost effective mechanism to assess the impacts that reservoir hydrological cycles have on impacted fish populations. Ill
THESIS STATEMENT
Impoundment of the Smallwood Reservoir impacted on both lake trout and lake whitefish by creating conditions that influenced their recruitment and growth. Whitefish recruitment and growth was enhanced during the initial years of impoundment. It was concluded this enhancement was caused by a zooplankton bloom that occurred concurrently. Lake trout recruitment weakened for approximately ten years subsequent to impoundment but eventually recovered. Growth rates for lake trout slowed during the first ten years subsequent to impoundment but strengthened as the population of lake trout thinned.
Reservoir draw-downs during the period when lake whitefish and lake trout incubate their eggs influenced recruitment strength. If February water levels were low then recruitment strength for both species was weakened; and if water levels were above average, recruitment was stronger. Exposure of spawning beds and scouring of ice, during low water periods, also contributed to the weakened recruitment. IV
ACKNOWLEDGEMENTS
In the course of writing this thesis I talked with many researchers, who without
exception gave of their time and knowledge with a generosity that can only be called
extraordinary. First and foremost, I gladly acknowledge my debt to Dr John Casselman
for his guidance and supervision when I was first fumbling with the original ideas for this
research. During the course of the work his uncompromising critique of both my
methodology and draft manuscripts demonstrated to me the importance of scientific
integrity and always pursuing a higher standard. I am grateful to Dr Nigel Lester for his
gentle, yet insightful guidance and critique of both my analysis and writing. His willingness to share ideas and resources served as a constant reminder that scientific inquiry works best as a communal endevour and that its pursuit is done on behalf of
everyone. I am also grateful to Dr Leon Carl who's earlier reviews and comments helped to guide this work. I want to extend my gratitude to my sister, Dr Elizabeth Perry whose support and editorial comments helped to ensure the thesis was completed.
I am deeply grateful for the support of my wife Tracy, and friends whose loving kindness always bolstered my spirits during those periods of uncertainty when I did not believe I would finish the work.
I would like to thank the Conservation officers Todd Kent, Chuck Porter, Mark Pritchett and Lindo Watkins of the Western Labrador detachment. The work was made a great deal easier because of their support. In particular, I would like to acknowledge the V considerable local and historical knowledge contributed by Officer Gary O'Brien whose friendship and patience will not be forgotten.
Finally, I would like to acknowledge my deep appreciation to Ken Curnew, who was
Chief of Inland Fisheries at the time this thesis was initiated. Without Mr Curnews patient support, editorial input and words of encouragement (generally known as
"harping") this thesis would not have been completed. VI
TABLE OF CONTENTS
Page
Abstract I
Thesis Statement III
Acknowledgements IV
Table of contents VI
List of Figures IX
List of Tables XII
List of Appendices XIV
1.0 Introduction 1
1.1 Study Area and Sites 3
1.2 Study Species 9
1.3 Study Variables and Rational 11
1.3.1 Recruitment 11
1.3.2 Growth rate 12
1.4 Objectives 14
Chapter 2 15
2.0 Lake whitefish background 16
2.1 Methods 18
2.1.1 Sampling 18
2.1.2 Age Assessment 22
Otolith Preparation 22 Age Validation and Precision 23
2.1.3 Variation in Year-class Strength 23
Year Class Frequency 23
2.1.4 Catch-curve Residuals 25
2.1.5 Analysis 28
2.1.6 Back-Calculations of Whitefish Growth 31 using Otoliths 2.2 Results 37
2.2.1 Age Validation and Precision 37
2.2.2 Calculations for Maceina's Catch-Curve 40 Residuals
2.2.3 Factors Influencing Year-Class Strength 47
2.2.4 Analysis of Year-class frequency 54
2.2.5 Otolith-Body Relationships, Growth and Residuals 57
2.2.6 Analysis of Impoundment and Growth 66
2.3 Discussion 79
Chapter 3 85
3.0 Lake Trout Background 86
3.1 Methods 88
3.1.1 Sampling and Data acquisition 8 8
3.1.2 Age Validation, Precision and Bias 91
3.1.3 Variation in Recruitment 91
3.1.4 Catch Curve Residuals 92
3.1.5 Back-Calculations of Lake Trout Growth Using 94 Otoliths 3.2 Results 96
3.2.1 Age Validation, Precision and Bias 96
3.2.2 Impoundment and Recruitment Strength 99
3.2.3 Impoundment and Growth: Relationship between 119 Otolith and Body Growth
3.2.4 Impoundment and Growth: Impoundment and Lake 122 Trout Growth Indices
3.3 Discussion 132
4.0 General Discussion and Conclusions 139
References 152
Appendices 163 LIST OF FIGURES
Page
Location of all sampled locations in the Churchill drainage basin, Labrador, Canada.
A schematic diagram demonstrating how otolith increment widths were converted into growth index measures. 34
An age-bias plot comparing age interpretations from whitefish otolith replicates. 38
Pooled whitefish year-class frequency distributions for the sample years 1997, 1998 and 1999. 41
Catch-curves and corresponding regressions (solid lines) for whitefish collected from locations (A) West Forebay, (B) Smallwood Reservoir and (C) Joseph. 43
The relationship between fish age and length for immature whitefish in locations Smallwood Reservoir, West Forebay and Joseph combined. 45
Linear regressions for the relationship between residuals derived from catch-curve regressions and coefficient of variation values for water-level at the Gabbro flood gates. 50
Trend through time between water-level fluctuation at the Gabbro flood gate (coefficient of variation) and whitefish year-class strength (student residuals). 52
Whitefish relative cumulative frequency distributions used in Kolmogorov-Smirnov paired sample tests. 55
Linear regressions describing the relationship between the natural logarithm of fish fork length and the natural logarithm of maximum otolith radius, by location. 59 Figure
2.10 Relationship between the loge transformed lake whitefish age (annuli number) and loge transformed increment 63 width.
2.11 A comparison among lakes of lake whitefish growth 72 years.
2.12 A comparison among lakes of whitefish growth periods
(five year intervals). 75
Chapter 3
3.1 An age bias plot comparing age interpretations made from lake trout otolith replicates. 97 3.2 Pooled lake trout year-class frequency data collected from the impounded water bodies. 102
3.3 Pooled lake trout year-class frequency data collected from the natural waterbodies. 104
3.4 A comparison of year-class composition between individual lakes within each water body type. 107
3.5 A comparison of year class composition between pooled data for the impounded and natural lakes. 109
3.6 (A) Catch-curve and corresponding regression (solid line) for lake trout pooled from West Forebay, and the Smallwood Reservoir. (B) Residual values derived from the catch curve regression. Ill
3.7 Pooled lake trout year-class frequencies from West Forebay, collected from anglers' catch during creel surveys in 1997, 1999 and 2002. 113
3.8 (A) Catch-curve and corresponding regression for lake trout sampled from West Forebay creel data (1997, 1999 and 2002). (B) Residual values derived from the catch curve regression. 115 Figure
3.9 (A) Catch-curve and corresponding regression for lake trout sampled from lakes Atikonak, Panchia and Joseph. (B) Residual values derived from the catch curve regression. 117
3.10 Error bars comparing lake trout growth years among individual lakes in each water body type (impounded and natural). 126
3.11 Error bars comparing lake trout growth periods among individual lakes in each water body type (impounded and natural). 128 LIST OF TABLES
Table Page
1.1 Species caught in each of the five locations. 6
2.1 The total effort, number of lake whitefish captured and catch per unit of effort (fish per hour) for each net in the standardized sampling. All sample years are included. 20
2.2 The combined number of lake whitefish used in both the year-class and otolith increment analysis. 21
2.3 Months included in each time period to calculate yearly mean seasonal temperatures. 30
2.4 February's Coefficient of variation values for water levels at the Gabbro flood gate and the lake whitefish year-class strength (Smallwood Reservoir and West Forebay) used to conduct the Pearson correlation tests. 49
2.5 Three equations, Linear, quadratic and cubic describing the relationship between natural logarithm of otolith radius and fork length. 58 2.6 Coefficients for linear regressions predicting loge transformed otolith increment widths based on the loge transformed fish age (annuli number). 62
2.7 The mean value of the whitefish residual growth during a specific year of formation for the two impounded lakes. 69
2.8 Mean value of whitefish residual growth during a specific year of formation for the three natural lakes. 70
2.9 Analysis of variance results for the comparison between growth (residuals) in the natural and impounded lakes. 78
3.1 The number of lake trout used for year-class and growth analysis. 90 3.2 The combined number of lake trout attributed to each time period. 106
3.3 Three equations ((1) Linear, (2) quadratic and (3) cubic) describing the relationship between natural logarithm of otolith radius and fork length for lake trout. 120
3.4 Relationship for the fitted linear regressions. Regressions describe the relationship between maximum otolith radius (loge transformed) and lake trout body length (loge transformed). 121
3.5 Coefficients (bo,bi) for the fitted linear regressions. Coefficients describe the relationship between individual measured otolith increment widths (loge transformed) and the corresponding annuli number (loge transformed). 125
3.6 A comparison of five lake trout growth periods using one-way analysis of variance (ANOVA's). 130
3.7 Pearson Correlation (p) results demonstrating the relationship between lake trout growth rate indices and yearly mean water levels for the period between 1972 and 1995. 131 XIV
LIST OF APPENDICES
Appendix Page
A Description of the relevant water bodies including; Order of flow, Surface area, Maximum Reservoir operating levels, Minimum operating levels, Maximum drawdown levels. 163
B The relationship between water level fluctuation and whitefish proportional year class strength. 164
C Tests for homoscedasticity and independence of error among otolith residual growth values for whitefish. 168
D Results from a Kolmogorov-Smirnov paired sample tests comparing year-class structure from an additional natural lake to the year-class structure of the impounded lakes 170
E The monthly hydrological pattern measured at the (A) Gabbro flood gates and (B) the Lobstick floodgates. 172
F The yearly mean height above sea level measured at the Gabbro and Lobstick flood Gates. 174
G The total effort, number of lake trout captured and catch per unit of effort for each net in the standardized sampling. 177
H The frequency of lake trout occurring in each year class. 178
I Linear regressions describing the relationship between the natural logarithm of fish fork length and the natural logarithm of maximum otolith radius, by lake. 180 J Relationship between the loge transformed fish age (annuli number) and loge transformed increment width. 1
K Tests for homoscedasticity and independence of error among otolith residual growth values for lake trout 1
L Pearson Correlation results demonstrating the relationship between lake trout growth rate indices and monthly mean water levels for the period between 1972 and 1996. 1 1
1.0 Introduction
Demand for electrical power in eastern Canada is most often satisfied by hydroelectric developments. These developments generally produce major changes in the lakes and rivers that are impounded to provide the necessary water storage. Typical reservoir creation involves impounding (flooding) and redirecting rivers and lakes to ensure a large volume of water for generating electrical power. This disruption often leads to changes in the abiotic factors affected by the impoundment.
The major abiotic changes associated with reservoir creation are shifts in water- flow rates and lake morphometries. Specifically, Wunderlich (1971) reported dramatic changes in water movements, which resulted in altered temperature and dissolved oxygen content after impoundment. Changes in lake depth can create changes in chemical composition and clarity of the water (Cuerrier 1954; Summerfelt 1971; Geen 1974;
Duthie and Ostrofsky 1975). These types of changes occur when leaching of flooded terrain creates a breakdown of terrestrial plant material.
Subsequently, these types of morphometric changes have been reported to affect various fish species. Since fish species have different oxygen tolerances, changes to oxygen levels may lead to habitat partitioning or differing fish assemblages (Engel and
Magnuson 1976; Tonn and Magnuson 1982). Cuerrier (1954) documented a decrease in average size of pelagic piscivorous lake trout Salvelinus namaycush when their habitat was impounded. The change was associated with a prey shift when immature lake trout became separated, due to the altered water depth, from the Rocky Mountain whitefish, their principal food source. Changes to Lake depth have also been reported to have 2
dramatic effects on recruitment. Spawning success of species in the Missouri River was related to fluctuating water levels (Patriaiche and Campbell 1958). Sammons and
Bettolli (2000) found that recruitment for largemouth bass Micropterus salmoides was directly related to reservoir hydrology. Heightened water levels expanded the littoral habitat, which increased growth and survival. Conversely, when water levels were low, loss of habitat resulted in reduced levels of recruitment.
Changes in water chemistry create trophic upsurges, which doubled primary productivity in the Smallwood Reservoir (Ostrovski and Duthie 1980). Runnstrom
(1955) reported an increase, then a subsequent decrease in the weight of charr Salvelinus alpinus after impoundment in Lake Tarron, Sweden. After the impoundment of Lake
Sharpe in South Dakota, several species grew faster in their first post-impoundment year than in both previous and subsequent years (Elrod and Hassler 1971).
Alteration of water thermal properties may also directly affect fish distribution, growth and reproduction. Fish are ectotherms: their internal temperature is controlled by the external medium, thus most fish thermo-regulate (Ferguson 1958; Coutant 1977).
Temperature optimums of different species indirectly create resource partitioning, thus potential predator-prey competitive interactions are minimized (Brandt et al.1980).
Water temperature directly affects growth by altering the rates of both food consumption and metabolism (Jobling 1995). At less than optimum temperatures, some species will fail to spawn (Pereira et al. 1995). Therefore, changes in water temperature normally associated with impoundment can directly affect fish distribution, growth and reproduction. 3
The purpose of this research is to determine the long-term effects of reservoir
creation on two fish populations: specifically lake whitefish Coregonus clupeaformis and
lake trout Salvelinus namaycush populations in the Smallwood Reservoir in the province
of Newfoundland and Labrador, Canada.
1.1 Study Area and Sites
The Smallwood Reservoir lies between 53° and 55°N and 63° and 66°W on the
Labrador Plateau, between 400 and 575 m above sea level. The reservoir is part of the
Upper Churchill River drainage basin. The bedrock for much of the plateau is
Precambrian and consists of metamorphic granitic gneiss intruded by gabbro-diorite and syenite. These rocks are slow to weather and are poor in nutrients. The plateau has been likened to a shallow saucer with notches in its rim through which it drains (Duthie and
Ostrofski, 1975). Drainage for this area is through the Lower Churchill River.
The natural drainage area of the Upper Churchill River was 56,000 km but by diversions has now been increased to 69,000 km2. The reservoir complex is composed of two interconnected reservoirs. The main Smallwood reservoir (formally Sandgirt,
Lobstick and Michikamau Lake) began flooding in 1971 and reached full pond in 1974 and the Ossokmanuan Reservoir (formally Gabbro and Ossokmanuan Lake) began flooding in 1961, reaching full pond in 1962. The Ossokmanuan Reservoir was created to supply power to the Twin Falls hydroelectric complex; however, this project was shut down with the advent of the Churchill Falls development. It has been estimated that 60% of the Smallwood Reservoir was formerly lake, while Ossokmanuan was originally 75% 4 lake (Duthie and Ostrofski 1975) (Appendix A). Total surface area of the combined reservoirs is 6,650 km2 with a storage volume of 34.4 billion cubic metres. Mean depth, calculated from area and volume, is 4.7 m (Bruce 1984). Of the ten lakes that made up the reservoir complex nine did not exceed a maximum depth of 12 m prior to flooding
(Duthie and Ostrofsky 1974). From upstream to downstream, the water flows from
Ossokmanuan Reservoir to the Smallwood Reservoir, through the Gabbro control structure, from the Smallwood Reservoir to the West Forebay through the Lobstick control structure and from the West Forebay to the East Forbay through the Whitefish
Falls control structure.
Species composition (Table 1.1) for the drainage basin does not differ among the areas that compose the reservoir complex and lakes that remain natural (non-impounded).
The climate is severe with long, cold winters and short, cool summers. Mean daily air temperature is -23°C for January and 13.6° C for July, with extremes of - 48° C and 30° C. Mean annual precipitation is 760 mm, approximately half of which falls as snow. The prevailing wind is westerly.
Vegetation is mainly peat bog. Forests are primarily softwoods, black spruce
Picea mariana and balsam fir Abies balsamea, with some white spruce Betula papyrifera.
Hardwood stands, consisting of trembling aspen Populus tremuloides and white birch
Betula papyrifera exist only in areas of good forest growth (Anderson 1985).
The five locations used in this study are the beginning of the West Forebay
(located just beneath the Lobstick control structure), The Smallwood Reservoir (located below the Gabbro control structure) and lakes Joseph, Atikonak and Panchia (Fig. 1.1).
There are two variables in the study: two areas that have undergone impoundment (West 5
Forebay and the Smallwood Reservoir) and three lakes that are natural (not impounded) and act as controls (Atikonak, Panchia and Joseph). All five locations are parts of the
Churchill drainage basin. 6
Table 1.1: Species caught in each of the five locations.
Family Scientific Names Common Names
Salmonidae Salvelinus fontinalis brook trout
Salvelinus namaycush lake trout
Salvelinus alpinus arctic charr
Salmo salar Atlantic salmon
Prosopium cylindraceum round whitefish
Coregonus clupeaformis lake whitefish
Esocidae Esox lucius northern pike
Cyprinidae Couesius plumbeus lake chub
Semotilus margarita pearl dace
Rhinichthys cataractae longnose dace
Catostomidae Catastomous catostomous longnose sucker
Catostomous commersoni white sucker
Gadidae Lota lota burbot
Gasterosteidae Gasterosteus aculeatus threespine stickleback
Cottidae Cottus bairdi mottled sculpin
Cottus cognatus slimy sculpin 7
Figure 1.1: Location of all sampled water bodies in the Churchill drainage basin,
Labrador, Canada. West Forebay and the Smallwood Reservoir locations were impounded between 1970 and 1974. Lakes Joseph, Panchia and Atikonak remain natural. K'OOVS ero-ow K'OffW K'WA' sro-o-iV 59" 05". i __J
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1.2 Study Species
The lake whitefish, hereafter also referred to as whitefish, is a freshwater species widely distributed in North America. In Labrador, whitefish are indigenous and are one of the major freshwater species. The greatest concentration of lake whitefish populations is in the north-western portion of Labrador, with populations being relatively sparse or even non-existent in south-eastern Labrador (Bruce 1974).
The lake whitefish is a schooling cool-water species with an optimal temperature preferendum for adults of 8 to 14° C with optimum growth at 11.9° C (Qadri 1968;
Coutant 1977). During summer whitefish inhabit the cooler waters of the hypolimnion in deep lakes. In Labrador, this is not possible because the reservoir lakes are shallow
(average 4.7 m).
Spawning occurs in fall, normally between early September and early October
(Inland Fish and Wildlife Division, Newfoundland and Labrador [IFWD]), unpublished data) in <8 m of water. It often takes place over a hard or stony bottom but sometimes over sand (Scott and Crossman 1973). Number of eggs produced relative to body length fluctuates greatly. For fish ranging between 370 mm and 462 mm, total egg counts ranged between 6,544 and 20,963 (Bruce and Parsons 1979). For successful development of lake whitefish eggs, temperature should range between 0.5 and 6.1° C, with the optimum being closer to 6.0° C (Price 1940; Tait 1973). Eggs hatch in early
April or May (Hart 1930), however hatch time may vary with location. Upon hatching, larvae shoal along the shoreline but move off to deeper water in early summer (Faber
1907 Reckahn 1970). 10
In Labrador the lake whitefish is a long-lived species, often exceeding 30 years of age (IFWD, unpublished data). Previous scale age interpretations suggest that whitefish reach sexual maturity at 5 years (Bruce 1984). More recent work based on otolith age interpretations shows Labrador whitefish reaching sexual maturity at 8 or 9 years (IFWD, unpublished data).
Lake trout, another species used in the study, is a cold-water species with a distribution extending across North America: from the Maritime Provinces and Labrador in the east to northern British Columbia in the west and the Northwest and Yukon
Territories in the north (Scott and Crossman 1973). In Labrador, lake trout are found in most of the larger lakes but are more prevalent in the north-western region (Bruce 1974).
Their presence seems to be limited by lakes large enough to provide a large volume of cool water during the summer months. Thermal preferendum for adult lake trout ranges between 8.0 and 12.0 °C (Ferguson 1958), mean = 11.5°C.
In lakes of the reservoir, lake trout can grow to 970 mm and are a long-lived species, reaching ages >35 years (IFWD, unpublished data). Lake trout are mainly piscivorous where forage fish are available. When large numbers of fish are not present, they usually eat plankton and aquatic insects (Ball 1988; Bruce 1974). Age and length at first maturity varies with lake size, available prey and lake elevation (Burr 1997). Lake trout may not spawn every year. Frequency may vary up to 4 years (Adams 1997). In
Labrador, spawning occurs in early fall between late August and mid-September.
Preferred spawning temperatures are between 10.6 and 13.9 °C (Scott and Wheaton
1954). However, photoperiod may be important in delineating spawning periods (Scott and Crossman 1974). There is evidence that lake trout have some site fidelity when 11
spawning, nevertheless they will re-establish themselves on other spawning beds if
traditional beds are not available (Martin 1960; Gunn and Sein 2000). Although lake trout
spawning occurs in early September, it may take 4 to 6 months for the eggs to hatch
(Martin 1957). Optimal incubation temperatures between 0.3° C and 1.0° C have been
recorded (Martin 1957). After hatching, young-of-the-year spend time in shallow water,
moving deeper as they grow (Adams 1997). The length of time they spend in shallow
varies with location and lake bathymetry (Scott and Crossman 1973).
1.3 Study Variables and Rationale
1.3.1 Recruitment
Detecting change in reservoir fish populations and monitoring density-dependent factors, typically involved collecting and analyzing long-term series of data. Collecting over long periods ensured that random short-term variations could be distinguished from overall long-term trends that may be important. Short-term changes may be of little consequence beyond that of normal random variation that would occur from year to year.
Collecting long-term data is both costly and time-consuming, and therefore a community may collapse before mitigating measures can be established. To quickly detect a population response to environmental degradation such as impoundment, more sensitive measures are required.
Recruitment is an indicator of the effects of impoundment on fish species.
Changes to exogenous factors such as water level, temperature and chemistry can result in changes to community structure of fish populations. Several authors have reported the 12
absence of year classes due to water drawdown. De Graff (1993) reported the presence of
weak lake whitefish recruitment in Aishihik and Sekulum lakes, in the Yukon, after
drawdowns that exposed whitefish breeding and rearing areas. Dewatering, in the form
of drawdowns, may increase mortality of newly emerging salmonid alevins (Becker et al.
1982). Drawdowns have also been associated with decreasing carp Cyprinus carpio populations and significant reductions in the number of centrarchids, particularly black
crappie Pomoxis nigromaculatus (Jester 1971). Beckman and Elrod (1971) recognized
altered fish assemblages, densities and growth patterns in young-of-the-year after impoundment of Lake Oahe. Species such as sturgeon Scaphirhynchus albus and paddlefish Polyodon spathula failed to produce young-of-the-year, while in other species distribution patterns changed and numbers increased.
To investigate whether impoundment has influenced recruitment variability for lake trout and lake whitefish I compared recruitment between impounded and natural lakes for lake trout and whitefish. I investigated whether or not there is a relationship between reservoir water levels and recruitment by correlating yearly recruitment values with reservoir mean monthly water levels. I also examined whether or not temperature affected year-class abundance.
1.3.2 Growth rate
Growth is a potential tool for examining the effects of impoundment on fish.
Growth offish depends directly upon the numbers of individuals present (Krebs 1985).
The effects of density-dependent factors such as food availability will be more noticeable in populations that undergo large fluctuations in number or in species composition (Elliot 13
1984, 1985a, b, c). Populations that have undergone little change remain relatively
constant in number and demonstrate fewer density-dependent effects (McFadden et al.
1967).
Fluctuations in individual growth patterns of fish in response to stressors may be one way to detect changes in density-dependent populations (Jobling 1995). These changes may be detected by examining the growth deposits found in calcified structures such as the otoliths (sagittae). The sagitta otoliths are acellular calcareous accretions (not bone) composed of calcium carbonate and are located in the inner ear canals of fish
(Campana and Casselman 1993). Otoliths function in sound reception, balance and orientation. The use of the otolith for investigating age and growth of fish has become commonplace. Researchers using growth deposits, known as annual marks (annuli) or year marks (Wilson et al. 1983), base age determination on the number of identifiable annual zones present. It should be noted, that the study of fish growth and recruitment relies on the accurate assessment of fish age. Highly refined otolith preparation techniques (Neilson and Geen 1981; Casselman 1987) have made the science of aging fish far more accurate than in the past and are replacing the less accurate age interpretation from scales (Aass 1972; Simard and Magnin 1972; Power 1978).
If growth rate of otolith annual increments can be affected by availability of food, a density-dependent factor, then any major change in fish abundance should affect annuli incremental width. Growth, represented by the width of these annual deposits, should fluctuate in relation to changes in temperature, fish density and available food. Further, growth patterns in fish are directly affected by lake productivity, size and thermal habitat 14 volume (Chen 1992). Thus, otolith growth patterns can differ in accordance with individual attributes of habitat (Casselman et al. 1981).
I determined whether otolith incremental growth can be used to determine the effects of impoundment. Incremental growth patterns of otoliths taken from fish in two impounded areas of a drainage basin were compared with those from three natural lakes in the same basin. I also examined the effects of air temperature on growth to separate those effects attributable to impoundment and those attributable to environmental temperature.
1.4 Objectives
(1) To determine whether recruitment of both lake whitefish and
lake trout was affected by impoundment.
(2) To determine whether growth of lake whitefish and lake trout was affected by impoundment. 15
Chapter 2 Effects of Impoundment On Lake Whitefish Year-Class Recruitment and Growth Rate 16
2.0 Lake Whitefish Background
The lake whitefish Coregonous clupeaformis is an abundant and valuable commercial resource in the lakes of Canada and has been extensively studied.
Nevertheless, the majority of work has been done on systems that have not been altered by anthropogenic influences. This study examines long-term effects of reservoir development on year-class strength and growth of lake whitefish in the Churchill Falls-
Smallwood Reservoir hydroelectric complex.
Flooding of the Smallwood Reservoir was begun in 1971 and completed in 1974.
During that period, considerable work was done to document the biological and chemical changes associated with reservoir creation (Duthie and Ostrofsky 1975; Ostrofsky and
Duthie 1980), as well as the fish community assemblages (Bruce 1974; Bruce and
Parsons 1979; Barnes 1981; Bruce 1984). However, with the exception of studies to quantify mercury levels, work was discontinued after the initial flooding. Consequently, little information exists to determine the long-term effects of impoundment on age structure, growth, and mortality of fish.
Research elsewhere has documented significant effects of impoundment on whitefish. Runnstrom (1955) reported that after regulation of Lake Vojmsjon (Sweden), the spawning period for whitefish was delayed due to increased volume and slower cooling of water in autumn.
Stone (1963) and Crowe (1969) noted that drawdowns reduced lake whitefish access to spawning grounds. Withler (1956) and Vernon (1958) found that whitefish had a preferred range of depth for spawning and that a change in lake level due to impoundment would place suitable spawning substrates beyond the preferred depths. 17
Fluctuating water levels may interfere with spawning and cause serious loss when eggs are deposited in shallow water. This is especially true for lake whitefish, which prefer to spawn in shallow water (<7.6 m Scott and Crossman 1973). Fluctuations in water level may lead to changes in incubation temperature or leave eggs exposed
(Machniak 1975; de Graff 1993). Increased siltation created from bank erosion and sedimentation during impoundment and water regulation may also suffocate eggs (Fudge andBodaly 1984; Stone 1963).
To determine whether impoundment of the Smallwood Reservoir affected lake whitefish recruitment and growth, I compared impounded lakes and natural lakes located within the Churchill River drainage basin. I examined whether the water regulation regime had influenced recruitment and whether there was a relationship between recruitment and air temperature. In addition, I compared whitefish growth, measured using incremental otolith growth, to determine whether it differed between impounded and natural water body types. 18
2.1 Methods
2.1.1 Sampling
Samples were collected during the last week of May and the first two weeks of
June in 1997, 1998 and 1999. Sampling periods were short to minimize individual
growth variations that may have occurred during the sampling period. In addition, to augment the sample size for growth analysis, archived otoliths were obtained through the
Department of Fisheries and Oceans and Hydro-Quebec for 1992, 1996 and 1999.
Standard nylon monofilament gill nets were used to collect fish. Eleven bar mesh gill net panels (15.24m by 2.43m), increasing in size from 1.27 to 12.7 cm by 1.27-cm increments, were attached in series from smallest to largest mesh sizes. These gangs were set perpendicular to shore. In each lake, averages of 3 net sets were placed per sample year and each set was left approximately 25 hours, whereupon nets were checked and emptied (Table 2.1).
For each sample site individual net catch per unit effort (fish per hour) was recorded. Total counts were recorded for all sampled species. Whole and eviscerated fish weight, fork length and sex were recorded for each fish, and otoliths were recovered. A total of 617 lake whitefish were sampled. Additional data were obtained from 286 archived samples (Table 2.2).
Daily water levels recorded at the Gabbro and Lobstick floodgates were recorded for the period between 1970 and 1998. The Gabbro flood gate controls water in a portion of the Smallwood Reservoir complex known as the Ossokuman Reservoir (formerly 19
Gabbro and Ossokuman Lake). This smaller reservoir is used as a holding facility and regulates water in the largest portion of the reservoir (formerly Lakes Sandgirt, Lobstick and Michikamou). The Lobstick flood gate regulates water flowing from Smallwood
Reservoir Lake into the West Forebay (Chapter 1, Fig. 1). The water then flows through
Jacobie Lake into the power generating facility near the town of Churchill Falls,
Labrador. These gates are the principal control structures for the main portion of the reservoir and provide the most reliable location to measure water level changes.
Daily air temperatures from 1960 to the present for the Smallwood Reservoir area were obtained from the Atlantic Climate Centre, Dartmouth Nova Scotia. 20
Table 2.1: The total effort, number of lake whitefish captured and catch per unit of effort (fish per hour) for each net in the standardized sampling. All sample years are included. Effort is in hours. Catch per unit of effort (CUE) has been converted to fish per hour. Average values for the sample year are in bold text.
Location Year Net set Effort Whitefish CUE (Hours) (Captures) (Fish/hour) Joseph 98/06/09 1 17.88 8 0.447 Joseph 98/06/09 2 22.52 85 3.776 Joseph 98/06/09 3 21.95 28 1.276 Joseph 98/06/10 4 16.32 16 0.981 19.66 1.620 Joseph 99/06/12 1 17.96 24 1.335 Joseph 99/06/12 2 17.93 12 0.669 Joseph 99/06/14 3 22.56 48 2.128 Joseph 99/06/17 4 6.66 0 0.000 14.78 1.033 Forebay 97/05/25 1 35.50 14 0.394 Forebay 97/05/26 2 24.00 26 1.083 29.75 0.738 Forebay 98/06/15 1 9.83 89 9.035 Forebay 98/06/17 2 23.18 77 3.320 Forebay 98/06/17 3 23.06 24 1.040 18.70 4.465 Forebay 99/05/28 1 19.50 8 0.410 Forebay 99/05/29 2 23.96 46 1.919 21.73 1.164 Smallwood 97/06/07 1 72.00 15 0.208 Smallwood 97/06/07 2 72.00 0 0.000 72.00 0.104 Smallwood 98/06/18 1 24.00 52 2.167 Smallwood 98/06/18 2 24.00 11 0.458 24.00 1.312 Smallwood 99/06/06 1 23.08 13 0.563 Smallwood 99/06/06 2 23.23 21 0.904 23.16 0.733 21
Table 2.2: The combined number of lake whitefish used in both the year- class and otolith increment analysis.
Water Body Location Year Number
Vest Forebay *1992 38
*1996 23
1997 40
1998 190
1999 54
Smallwood *1992 24
*1996 30
1997 15
1998 63
1999 34
Natural Atkonak *1999 57
Panchia *1999 114
Joseph 1998 137
1999 84
Total: 903
* indicates samples made available by the Department of Fisheries and Oceans and Hydro-Quebec. 22
2.1.2 Age Assessment
Otolith Preparation
The procedure for mounting and replicating otoliths is as described by Casselman
and Gunn (1992). Sagittal otoliths were embedded in a mixture of Araldite epoxy
(#GY502) and hardener (#HY956) at a ratio of 5:1 (Ciba-Geigy Canada Ltd, Dorval,
Quebec). Otoliths were sectioned transversely through the origin to a thickness of 400 to
450 um with a Low-speed Isomet saw (Buehler Canada Ltd., Toronto, Ontario). Epoxy
resin was used to mount the sections on glass slides. To polish the surface of the
sectioned otolith, fine grit lapping film of 600, 800 and 1200 were used. Polished
sections were acid etched using 2% HC1 (aq). The etched sections were replicated by
applying mild pressure to a piece of cellulose acetate. Acetate was softened by applying
a drop of acetone to the surface of the section for two minutes.
All acetate replicates were projected onto a digitizing tablet through a microscope
drawing tube at lOOx magnification. The location of each annulus was digitized using
Calcified Structure Age and Growth Data Extraction Software (Casselman and Scott
2000). Increments, located on the ventral surface of the otolith cross-section, were measured along a radius that extended from the nucleus (origin) to the proximal edge
(Fig. 2.1). 23
Age Validation and Precision
Since I was unable to use direct methods of validation, I used an indirect method of age validation in which I compared a subset of my interpretations of 76 otoliths to those of an experienced reader (Mr David Brown) who had extensive experience interpreting age of lake whitefish. Friedman Repeated Measures Analysis of Variance on
Ranks was used to determine whether significant differences existed between interpreters. Age bias plots were also created to identify where possible age bias may exist (Campana et al. 1995).
To quantify the level of precision, a subset of lake whitefish otoliths was aged three times. The subset consisted of 39 whitefish (>27 years of age). Older fish were chosen because repeatability in age determinations decreases with age. The use of older subsets ensured the greatest probability for variance. I again used a Freidman Repeated
Measures Analysis of Variance on Ranks to determine if whether there were any significant differences between aging trials.
2.1.3 Variation in Year-class Strength
Year Class Frequency
Comparisons of year-class frequency distributions among lakes were done only on data collected with standardized sampling gear. Data from three locations were used:
Joseph, the Smallwood Reservoir and the West Forebay; Lake Joseph was sampled for two years, while the Smallwood Reservoir and the West Forebay were sampled for three 24 years. To establish individual lake whitefish year-classes I subtracted the fishes assigned age from the year it was sampled.
Year-class frequency distributions were established for each lake by pooling data from 1997, 1998 and 1999 sample years. There were several assumptions made to allow pooling of the data:
(1) Due to the longevity of whitefish, the effect of mortality among sample years is minimal (M. J. Maceina, pers. comm.). (2) The time of year for sampling was standardized and therefore catchability did not significantly change between sampling years. (3) Differences in sampling effort among years did not influence the distribution.
To test whether this third assumption was correct I weighted each sample year equally by calculating the relative abundance (proportion) of each year-class in each year of sampling. I then summed the proportions for each year-class and compared the resulting distribution to the original pooled frequency distribution.
I compared the pooled year-class frequency distribution to that of the year-class distribution that had been corrected for effort using the Kolmogorov-Smimov's two- sample test. The test is sensitive to differences in location, dispersion and skews ( Sokal and Rohlf 2000) and therefore can be used to determine if a similar distribution exists for year-class frequency between the unadjusted pooled frequency distribution and the year- class distribution which had been adjusted for effort. For each of the three lakes there was not a significant difference between distributions (West Forebay: Da=.o5 = 0.1115 »
D = 0.0597; Smallwood Reservoir: Da=.05 = 0.1504 » D = 0.0889; Joseph: Da = 05 =
0.4263 » D =0.0242). Subsequently, as the influence of effort appeared negligible, I used the pooled frequency distributions for all further tests. 25
To test whether distributions were similar between the two impounded water bodies, I used the Kolmogorov-Smirnov's two-sample test. In this instance, acceptance of the null hypothesis would indicate a temporal concordance of lake whitefish recruitment between the two water bodies. I repeated the use of the test to determine whether lake whitefish year-class distributions for the impounded water bodies differed from those of the natural.
2.1.4 Catch-Curve Residuals
To derive an index of year-class strength, I used Maceina's application of catch- curve residuals (Maceina 1997; Maceina and Stimpert 1998). The most common method of estimating mortality rates involves linearizing a curve by plotting the natural logarithms of the number of fish surviving by year-class or age (Ricker 1975). Under the assumption of a constant rate of mortality, fish numbers surviving (Nt) will decline exponentially with year-class or age where;
Nt = N0e^
Nt = the numbers surviving fish of a particular year class
N0 = the initial number of individuals at time t = 0 Z = instantaneous annual mortality rate
The above equation can be rearranged to give the linear equation;
lnN yrds =lnN0-Zyrcls 26
The line of best fit through a catch-curve will have a slope equal to the instantaneous annual mortality rate (Z) and the estimate of survival (JVyreis) is equal to e'z.
Catch-curve analysis is restricted to the descending limb of the catch distribution because these data points represent the older, fully recruited, year-classes. The initial ascending data points are ignored because it is assumed that fish sampled within this portion are the younger age classes, which are not fully recruited to the fishery or are not yet vulnerable to the gear being used (Miranda and Bettoli 2007). This technique relies on the assumption that recruitment to the population is constant. In reality, this is not the case and Ricker (1975) mentioned that the data points around the fitted line tended to deviate due to fluctuations in recruitment.
Maceina (1997) exploited these deviations by subtracting the actual year-class frequencies from the fitted regression. He used these derived residual values in correlations with environmental variables to determine what influenced recruitment.
Maceina incorporated significantly correlated environmental variables back into the catch curve regressions to generate multiple linear regressions. The additional environmental variables improved the model by reducing the size of the error term (Maceina 1997). In order to compare among regression equations standardized student residuals (residual/ standard error of the residuals) were used. Student residuals were used because they display a t distribution where, for example, about 95% of student residuals will fall between -1.96 and +1.96 regardless of sample size or fit (r2) between the dependent and independent variables (Maceina 1997). Thus, comparison of individual data points can be made between regression equations. He also compared residual values between fish populations to determine whether recruitment patterns were similar between water 27 bodies, thus demonstrating that catch-curve residuals were indicative of year-class strength.
In this study I used residuals derived from catch curves of lake whitefish sampled from the Smallwood Reservoir and the West Forebay (1997, 1998 and 1999 pooled) and lake Joseph (1998 and 1999 pooled) to examine the effects of impoundment and temperature on recruitment strength.
To ensure that I achieved the line of best fit I first had to ensure that the regression was properly applied to the descending limb of the catch-curves. Because of possible impoundment effects, I could not ascertain the exact age for full recruitment to the sampling gear in the Smallwood Reservoir and the West Forebay. Since impoundment may have altered the normal trends in recruitment, it was difficult to fit the catch curves with the appropriate truncation.
Therefore, I established when fish were fully recruited to the sampling gear by using the catch curve of Lake Joseph (a control lake) as the standard. I identified the size at age when fish were fully recruited in the control lake and then used an ANCOVA test
(length as the dependent variable and age and location as the independent) to determine whether size at age was the same in the impounded lakes. The assumption was that if lake whitefish were fully recruited at a smaller size within the natural lakes, they would be fully recruited to the gear at the same size in the impounded lakes. Subsequent to the establishment of the appropriate truncation, I applied catch curve regressions for each of the three whitefish populations sampled. 28
2.1.5 Analyses
Flood records for two areas of the reservoir from Newfoundland and Labrador
Hydro were recorded at the Gabbro flood gate and the Lobstick flood gate. Both locations
recorded continuous daily water levels from 1972 to 1996. Daily water-level records
were used to calculate coefficient of variation values (CV) for each month. These values
were used as an index of monthly water-level fluctuation. In addition, mean monthly
values were calculated for the 24-year period between 1972 to 1996.
To determine whether lake whitefish year-class strength in each of the two
impounded water bodies was directly affected by changes in water level, I examined
correlations between the derived student residuals from the pooled catch-curves of the
West Forebay and the Smallwood Reservoir, respectively with mean monthly water level
and the CV index of water-level fluctuation. Trials were run using water level measures
taken at both the Gabbro and Lobstick flood gates. To ensure independence for the
repeated monthly trials, I chose a conservative critical value using the Dunn-Sidak
method (Sokal and Rohlf 2000). When significant correlations were observed between
the student residuals and an environmental variable I reran the catch-curve regression as
a multiple linear regression by incorporating the additional environmental variable.
I also used mean daily air temperatures recorded at Churchill Falls to calculate
monthly means and variances (CV) for the period 1970 to 1996. Catch-curve residuals were used to investigate the effects of mean monthly air temperature and its variance on year-class strength. Comparisons and analysis were the same as those used for water
level. In total, three locations (Joseph, the Smallwood Reservoir and the West Forebay) 29
were examined for the effects of air temperature on year-class strength. Additional
correlations were also conducted on the recombined data averaged on a bimonthly and then seasonal basis (Table 2.3). 30
Table 2.3: Months included in each time period to calculate yearly mean seasonal temperatures. The four calculated means were correlated with mean growth index values on a year-by-year basis for 21 years (1974 to 1996).
Season Included Months
Fall August, September, October Winter November, December, January Spring February, March April Summer May, June, July 31
2.1.6 Back-Calculations of Lake Whitefish Growth Using Otoliths
The objective of this section is to describe growth of lake whitefish for the period between 1970 and 1996 in relation to the influences of reservoir hydrology and temperature. For this portion of the analysis I added data collected from Lakes Atikonak and Panchia. The challenge in describing growth for this period is that data was not collected for the majority of the time. To over-come this difficulty back-calculations of growth using the record traced in the incremental deposits of otoliths were used.
However, inferring changes in fish growth from annual otolith increments assumes that growth of the calcified structure is proportional to fish body growth (Busacker et al.
1990). If this is not the case, then we must be able to quantify the dissimilarities between body and calcified structure growth to identify the limitations of the comparison. For example, Casselman (1990) determined that the relation of fish body growth to otolith growth in northern pike is allometric, with otoliths growing at a faster rate during periods of slow somatic growth and at slower rates during periods of fast somatic growth. Thus he determined that otoliths are excellent for recording seasonal growth of slow-growing and old fish.
To determine the relationship between lake whitefish otolith size and body size, I used otolith samples taken from whitefish collected from lakes Atikonak (1999), Panchia
(1999), Joseph (1998, 1999) and the Smallwood Reservoir and the West Forebay locations (1992, 1996 - 1999). I used the natural logs of maximum otolith radius against body fork length to determine what relationship (linear, quadratic or cubic) best described the otolith-body relationship. 32
To determine the effects of impoundment on growth, I first had to separate variability in lake whitefish growth attributable to the influence of the environment from variability normally associated with age-dependent growth. Using CSAGES, I recorded measurements for the distance from the otoliths origin to an individual annulus.
Measurements were recorded, moving sequentially, out from the otoliths origin, along the radius used for age interpretation (Fig. 2.1 A and 2. IB). Each individual annulus distance was then subtracted from the annulus distance that preceded in the sequence to establish the increment width between annuli. To assign a growth-year to an individual increment width the associated fish's birth-year (year-class) was added to the assigned annulus number in the sequence. The derived increment width data were pooled by lake of origin and sex. Linear regressions were then fitted to the naturally log-transformed increment widths and annuli numbers (Fig. 2.1C). Where;
lnlw =-b1lnXa +b0
Iw = increment width
bQ = regression constant
bx = regression coefficient
Xa = fish age (annulus number)
To remove the age-dependant growth I calculated residuals by subtracting the observed individual data points from the values derived from the fitted regressions.
Residuals were tested to ensure that the age dependant effects had been removed (to achieve homoscedasticity) by plotting residuals against the predicted values and by 33 plotting a histogram of residuals to determine if the distribution was normal. Normal probability plots were also generated for each regression to determine if there was an independence of error. Subsequently, for the purposes of comparison, each resulting residual value (index value) was binned by its assigned growth year and the associated fish's lake of origin.
To determine if impoundment influenced whitefish growth I first tested for individual growth differences among impounded lakes and natural lakes. An absence of differences would indicate that lake whitefish were growing similarly and therefore could be pooled by water body type for further analysis. I ran separate two-way ANOVA's for each water body type, using the growth index values derived from the regressions as the dependent variable, with lake and growth year as the independent. Subsequent to this analysis I pooled the individual lakes by water body type, and reran the model, using growth year and water body type (impounded or natural) as the dependant variables. 34
Figure 2.1 A schematic diagram demonstrating how otolith increment widths were converted into growth index measures. (A) A photo of a lake whitefish otolith section
(lOOx magnification). The black box in the image represents the approximate location for the radius of interpretation. (B) The enlarged image contained in the black box from figure A. The radius of interpretation is superimposed on the image. The numbers 1 defines the approximate position of the first measured increment from origin to annulus
1. The number 2 demonstrates the second increment measured in the sequence. The number ten demonstrates the tenth increment measured in the sequence. (C) demonstrates the loge transformed increment widths regressed with the loge transformed annulus number for female whitefish in the West Forebay. The regression equation and significance level are also reported. Growth index values were derived by subtracting the actual increment width measures from those predicted by the regression equation 35
1.0 1.5 2.0 3.5 Loge annulus 36
Following the above analysis, to better determine if a short-term episodic event was occurring in growth I partitioned the yearly growth index values into six growth periods of 5-year intervals. The five year interval was established to coincide with the principle period of flooding, between 1970 and 1974.
To determine if the pattern of growth among periods remained congruous among impounded and natural lakes I ran two-way ANOVA's for each water body type using growth period and lake as the independent variables. Subsequent to this analysis I pooled individual lakes data by water body type and compared among periods within and between water body types. To test for specific differences between water body types I used one-way ANOVAs with the residuals as the dependent variable and period as the independent. 37
2.2 Results
2.2.1 Age Validation and Precision
The age-bias plot did not reveal any discemable deviation from the expected one- to-one relationship (Fig. 2.2). The data failed the normality test, and a Friedman Repeated
Measures Analysis of Variance on Ranks revealed no significant difference between my interpretations and those of the experienced reader (x ^ = 0.0667, P = 0.796).
During testing for precision, the data failed a normality test and therefore I used the Freidman Repeated Measures Analysis of Variance on Ranks. The test did not distinguish any significant differences between aging trials, and therefore my precision and interpretation offish age was consistent x = 1-143, P = 0.565). 38
Figure 2.2: An age-bias plot comparing age interpretations from lake whiteflsh otolith replicates (N = 152 interpretations). Reader 2 is the experienced interpreter. Bars represent the 95% confidence intervals around the mean estimate. 39
Age Interpretations of reader 2 40
2.2.2 Calculations for Maceina's Catch-Curve Residuals
For the Smallwood Reservoir and the West Forebay catch-curves were applied to the pooled data collected during 1997, 1998 and 1999. For Lake Joseph the catch-curve was applied to the pooled 1997 and 1998 data (Fig. 2.3). The catch-curve regressions used to derive student residuals for Maceina's applications were truncated beginning with the 1996 year-class (Fig. 2.4). In Lake Joseph, during the period of sampling full recruitment for whitefish appeared to begin with the 1996 year-class. The approximate length of a fish in the 1996 year-class ranged between 91 and 230 mm in length. The comparison of immature fish (0 to 8 years of age) from Lake Joseph to those from the
Smallwood Reservoir and the West Forebay revealed no significant differences in size at age (F= 0.757, P = 0.470) (Fig. 2.5). Therefore, it was assumed that fish in each lake became vulnerable to the gear at the same length interval and therefore the same year- class. 41
Figure 2.3: Pooled year-class frequency distributions for the sample years 1997, 1998 and
1999. Frequency distributions are separated by location. (A) Smallwood Reservoir and
(B) West Forebay represent the impounded water bodies, and (C) Lake Joseph represents the natural. (N) The number of samples collected for each lake. Black arrows indicate the period when the reservoir was filled. 42 (A) West Forebay 1997-1998 N = 284 25
* *
ol n ,w, 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Year class
30 i (B) Smallwood Reservoir 1997-1998 N=112 25
201
3. 151
101 I I
,n,m rrr 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Year class
(C) Lake Joseph 1998-1999 N = 221
ft 301
201
10
•n-nm 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 Year class 43
Figure 2.4: Catch-curves and corresponding regressions (solid lines) for whitefish collected from locations (A) West Forebay, (B) Smallwood Reservoir and (C) Joseph.
The dashed lines represent the 95% confidence intervals. Catch curves were created by pooling sample years for each lake. Using the natural logarithm for transformation, the regression equations were weighted proportionally for sample sizes at each age. The sample size (N), significance level (P) and the squared product moment correlation coefficient (r2) are also shown. 44
(A) West Forebay 4- Log, (freq) = -3.875 + (0.0709 * year class) "^ N= 29, r2= 0.496, P<.001 • •• 3-
2-
1- • • • "s^ ^\^
0- • v-<
A ' I I I I I I I I I I I I I 98 95 92 89 86 83 80 77 74 71 68 65 62 Year class (B) Smallwood Reservoir Log, (freq) = -4.457 + (0.066 * year class) N= 26, r2= 0.383, P<.001
2
1-
0- ••• •• •
-1 ~i i—i—i—i i i i i—i—1—r 98 95 92 89 86 83 80 77 74 71 68 65 62 Year class
4- . (C)Joseph X# Log, (freq) = -11.429 + (0.1530 * year class) \ x N= 21, r2= 0.868, P<.001 3-
2-
1- • \ \ \ 0-
X V 1 '\\ i ^ 1 1 1 i 1 1 1 M 1 1 1 98 95 92 89 86 83 80 77 74 71 68 65 62 Year class 45
Figure 2.5: The relationship between fish age and length for immature lake whitefish in locations Smallwood Reservoir, West Forebay and Joseph combined. (N) is the number of samples from individual lakes. (Hatched lines represent the 95% confidence intervals) 46
5001 Y(lengUl) = 34.293 X(age) +66.387 N = 278, F = 870.57, P < 0.001
400
300
200"
o Smallwood Reservoir 100" N = 43 o West Forebay N = 77 • Joseph N=158
10 Age in years 47
2.2.3 Factors Influencing Year-Class Strength
The coefficient of variation in water level as recorded at the Gabbro gate in
February demonstrated a significant correlation with year-class strength in both the
Smallwood Reservoir (pres,cv = 0.573, P = 0.005) and the West Forebay (pra>cv =0.583, P
= 0.004) (Dunn-Sidak method reconfigured alpha (a) to P = 0.005). (Table 2.4; Fig. 2.6 and 2.7 ). Therefore, year-class strength of lake whitefish was influenced by water-level fluctuation in the Ossokmanuan Reservoir. Significant negative correlations also existed at weaker alphas. There was a negative correlation between mean water levels in
Ossokmanuan Reservoir and year-class strengths in the Smallwood Reservoir for the month of March {pres,mean = -0.456, P = 0.038) and April (pres,mean = -0.505, P = 0.017); this negative correlation suggests a relationship whereby if water is held back in
Ossokmanuan Reservoir then recruitment downstream decreases. There is an inverse correlation between Ossokmanuan and the Smallwood Reservoirs for February mean water levels ( po-mean,s-mean = -0.529, P =0.016). There is also a negative correlation between February mean water levels in the Ossokmanuan and the West Forebay (p0- mean,wF-me Monthly, bi-monthly and seasonal means and temperature variances did not correlate with year-class strength. 48 I incorporated the additional term of water level fluctuation (CV values) into the original catch-curve regression models (Fig. 2.4) for the Smallwood Reservoir and the West Forebay to produce multiple linear regressions: Smallwood : (1) Loge (year class frequency) = - 4.457 + (0.066*year class) r2 = 0.383, P< 0.001 (2) Loge (year class frequency) = - 4.221 + (0.0563*year class) + (753.712* CV) R2 = 0.481, P< 0.001 West Forebay: (1) Loge (year class frequency) = - 3.875 + (0.0709*year class) r2 = 0.496, P< 0.001 (2) Loge (year class frequency) = - 3.402 + (0.0578*year class) + (708.045* CV) R2 = 0.514, P< 0.001 (3) Loge (year class frequency) = - 4.522 + (0.0695*year class) + (791.513* CV) R2 = 0.576, P< 0.001 (1) original regression (2) multiple linear regression (3) multiple linear regression (1971-1974 removed) In both instances, water level fluctuation was a significant term in the equations after accounting for the effects of year-class. For lake whitefish from the Smallwood water level fluctuations accounted for an additional 9.8 percent of the variance in year- class strength. While in West Forebay whitefish it accounted for 1.8 percent of the variance. For the West Forebay, if I removed the initial flooding period (1971 to 1974) from the model the variance attributable to water level fluctuation increased to 8 percent. The above results were also obtained when the analysis was rerun using residuals that had been derived from the equally weighted, proportional year-class data. (Appendix B). 49 Table 2.4: February's Coefficient of variation values (CV) for water levels at the Gabbro flood gate and the whitefish year-class strength (Smallwood Reservoir and West Forebay) used to conduct the Pearson correlation tests (pres,cv)- Whitefish index values were derived using Maceina's application of catch curve residuals (Fig. 2.6). Number of water-level records used to calculate CV is also reported. Year Number of Coefficient of West Forebay Smallwood water level variation residuals residuals records (CV) year-class year-class strength index strength index 1972 28 — 0.25 -0.56 1973 28 — 0.78 — 1974 28 0.0004 0.91 -0.76 1975 27 0.0002 -1.19 -0.86 1976 29 0.0001 -0.66 -0.95 1978 31 0.0009 -1.41 0.00 1979 31 0.0006 -1.52 0.94 1980 29 0.0014 0.63 0.73 1981 28 0.0017 1.09 0.63 1982 28 0.0015 0.72 -0.50 1983 28 0.0016 0.87 0.00 1984 29 0.0013 1.79 0.93 1985 28 0.0014 1.61 1.07 1986 28 0.0010 -0.04 -0.30 1987 28 0.0014 1.40 1.24 1988 29 0.0010 0.92 0.97 1989 28 0.0015 0.43 0.87 1990 28 0.0010 -0.33 1.82 1991 28 0.0008 -1.01 -0.04 1992 28 0.0002 -0.41 -2.60 1993 28 0.0001 -1.04 0.48 1994 28 0.0012 -1.38 0.17 1995 28 — -0.37 -1.89 1996 29 0.0011 -0.88 -1.37 50 Figure 2.6: Linear regressions for the relationship between residuals derived from catch- curve regressions and coefficient of variation values for water-level at the Gabbro flood gates: (A) West Forebay, (B) Smallwood Reservoir. Regression, (r2) Coefficient of determination, (P) significance level and (N) Number of data points contributing to the relationship are reported. 51 (A) West Forebay Residuals(Forebay)= = -1.031+( 106.89*CV values) 2.0 2 r = 0.339, P = 0.004, N = 22 „-' 8•4 8•5 1.5 87 0" • i* 81 ^* • ^0^ •f 74 88 1.0 8•3 • • 82 80 •^-^ 13 o.o - ^**tS*'« ^^ 90 95 g2 I • • ^ -.5 76 • 96 91 * -* ~ *^ a v —* -1.0 75 • ^ • 77 94 *•«* •f 78 • • ^ -1.5 • *•* ** **• -2.0 *£ 0.0000 .0005 .0010 .0015 .0020 Coefficient of variation (B) Smallwood Reservoir Residuals(Sm.„woo4)= -1.025+(1123.71*CV values) •—• r2 = 0.328, P = 0.005, N = 22 .^ <*- ,** 90 a*" CB° «** ctS^ ago 87 c^ 85 ® «* C== && 78 88 o O "• 89 M 1 ° 80 O "81 93 9 "3 m > 91 7? -* ^"^ • 83 15 0 ^^^^ 86 • • 82 ^***7A • 75 ^^-^^ # 76j^^^ ^. „ - ' 96 »^ •""" • •*• 95 ^" oo* .-* ^ " • *** 92 «*•* •^ •_ +* ^*•* ** ** 0.0000 .0005 .0010 .0015 .0020 Coefficient of variation 52 Figure 2.7: Trend through time between water-level fluctuation at the Gabbro flood gate (coefficient of variation) and lake whitefish year-class strength (student residuals) for (A) West Forebay and (B) Smallwood Reservoir for 1972 to 1996. The Gabbro flood gate feeds water into Smallwood Reservoir, which in turn feeds water into the West Forebay. Pearson Correlation Coefficient (pra>cv) and significance level (P) are reported. 53 0.0018 j (A) West Forebay J 3 0.0016 - Pres,cv= 0.583 JL P= 0.004 / ¥ \ n — 2 e 0.0014 ~~ 5 i o -• . V \\ i v 0.0012 / \\ i \ // 1 J3 \\ • 13 0.001 --^/ \ / li i\ 1 > \ • ' • ^ i \ 1 — 0.0008 \ ' ^ 1 * A ' * 1 o 3 \ A ' \ ' / c 0.0006 VJ \ / -1* o 0.0004 • /^* \ ' \ / & / \ / \ 1 l / -- 0.0002 i \ i m. j \l -2 o i -i -i \l 0 ~m,M . 1 r r , —1 l n— T 1 1 1 1-* T -3 72 73 74 75 76 77 78 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 Year 0.0018 -r (B) Smallwood Reservoir j 0.0016 - Pres,ci; = 0-573 / \ Jt. c P = 0.005 / w \ • -- o 0.0014 - 0.0012 •-/ +-\\ '/>*xV\ \ W -- 1 J2 / /•—• / \ \ // w \ \ «L '' • 13 > 0.001 - / x / ITI 1 3 • ' \ > \/ • 1 0.0008 - / ' V 1 0 ^ / ' V CZ1 a / ' \ 1 o 0.0006 - / ) \ 1 — 1 \1 / ) 0.0004 - m i 1 / ' o i \ / \ 1 \x\*j 0.0002 - 1 V ' U 1 \i 1 xi • Ni \i 0 -B-r-rf-r i i -#n 72 73 74 75 76 77 78 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 Year Residuals - Coefficient of variation 54 2.2.4 Analysis of Year-class frequency The comparison within impounded water bodies did not reveal any difference among frequency distributions (West Forebay vs. Smallwood, D = 0.1289 < D.05 = 0.1515) (Fig. 2.8). However, the comparison between each impounded water body to the natural waterbodies indicated a significant difference in year-class structure between sample populations (West Forebay vs. Joseph, D = 0.4608 > A05 = 0.1218; Smallwood vs. Joseph, D = 0.4506 >£>.05 = 0.1575) (Fig. 2.8). 55 Figure 2.8: Relative cumulative frequency distributions used in Kolmogorov-Smirnov paired sample tests. (A) Compares relative cumulative frequency distributions for the impounded water bodies, Smallwood and West Forebay. (B) Compares the relative cumulative frequency distributions for Joseph and Smallwood and (C) compares the relative cumulative frequency distributions of Joseph and the West Forebay. 56 1.2 (A) West Forebay vs Smallwood Reservoir (D = 0.1289 H West Forebay I I Smallwood Reservoir 64 66 1.2 (B) West Forebay vs Joseph (D = 0.4608 >D.0S= 0.1218) 1.0 • West Forebay 0.0 MUL • Joseph 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 1.2 (C) Smallwood Reservoir vs Joseph {D = 0.4506 >D.05= 0.1575) 1.0 •2 • Smallwood Reservoir 0.0 . -_F_.^JJrlr|Jr|fir • Joseph 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 Year Class 57 2.2.5 Otolith-Body Relationships, Growth and Residuals To examine the otolith body-length relationship, I first fit the three equations (linear, cubic and quadratic) to untransformed data. All relationships were significant. However, I improved the relationships by transforming both variables using natural logarithms the cubic and quadratic polynomial fitted to the transformed data during the exploratory analysis did not significantly improve the fit over the linear regression (Table 2.5). This suggests the rate of change in otolith radius is linearly related to the change in body length (Fig. 2.9). All regressions used to remove the age dependant effects and derive the residuals for the index of growth were significant (Table 2.6, Fig. 2.10). There was a linearity of relationship between the plot of residuals against fitted values and the histogram of residuals displayed a normal distribution therefore homoscedasticity was demonstrated (Appendix C). Each regression's normal probability plot displayed a linear relationship and therefore an independence of error was demonstrated. 58 Table 2.5: Three equations ((1) Linear, (2) quadratic and (3) cubic) describing the relationship between natural logarithm of otolith radius and fork length for lake whitefish. All R2 values were significant at P < 0.05. 2 Location Curve* R df F bo b, b2 b3 Atikonak 1 0.921 59 687.87 -4.892 0.866 - 2 0.921 58 340.00 -3.6775 0.417 0.041 3 0.921 58 340.00 -3.6775 0.417 0.041 Panchia 1 0.886 112 872.28 -5.085 0.895 2 0.916 111 603.12 5.545 -3.061 0.364 3 0.917 111 611.39 0.122 -0.208 0.035 Joseph 1 0.675 205 425.15 -3.283 0.578 2 0.715 204 255.79 -12.181 3.841 -0.297 3 0.719 204 261.19 -9.463 2.280 -0.019 Forebay 1 0.615 334 533.82 -4.106 0.733 2 0.621 333 273.19 1.864 -1.439 0.196 3 0.622 333 273.43 -0.706 -0.071 0.016 Smallwood 1 0.406 159 108.77 -3.729 0.6609 2 0.466 158 68.91 23.542 -9.012 0.855 3 0.466 158 68.80 14.392 -4.162 0.050 *i. y = b0 + bix 21 2. y = b0 + bix+ b2x 2 3 3. y = bo+bix+b2x +b3x 59 Figure 2.9: Linear regressions describing the relationship between the natural logarithm of fish fork length and the natural logarithm of maximum otolith radius, by location. (A) West Forebay and (B) Smallwood represent the impounded locations; (C) Atikonak, (D) Joseph and (E) Panchia represent the natural lakes. The solid line represents the regression, and the dashed lines represent the 95% confidence intervals. Sample size (N) and the correlation coefficient (r2) are also given. 60 1.0" „* (A) West Forebay .,--''' LogoY„,u,= 0.7331ogeXlmB,h-4.105 %«;'' S r2 = 0.615, N= 336 ,-iflik *^ s .5" .-' *1m» • Z^ -"*-*-* ^fi' 1.5. 4.0 4.5 5.0 5.5 6.0 6.5 1.0 (B) Smallwood Rservoir Loge Yndim= 0.66091ogs XlenBft -3.729 r2 = 0.406, N= 161 .5 60 c 0.0 1 T3 o o 60 _o 3 -1.0 -1.5 4.0 4.5 5.0 5.5 6.0 6.5 Natural log fork length (mm) 61 1.0 (C) Atikonak Log, Y^ = 0.86641o& X,eneth -4.892 r2 = 0.921, N = 57 0.0 -1.0 -1.5 4.0 4.5 5.0 5.5 6.0 6.5 1.0 " (D) Joseph log, Y„di„=0.5783 log, X^ -3.283 •,,..-""' r2 =0.675, N = 207 .-•''*'•* .5 ' .--''" • * ^ .-•• * *ft^<>— •.-* • *.+j&*' %*4± * • 0.0 ' «.• iA**/ • -.5 ' ^^^ * .-*'**' 1.0 • ,...--'"' <.="** 1.5 . 5.5 (E) Panchia Log, Y„diu,=0.895Log, X„„81h -5.085 2 r = 0.886, N= 114 .-•- :x Natural log fork length (mm) 62 Table 2.6: Coefficients for linear regressions predicting loge transformed otolith increment widths based on the loge transformed fish age (annuli number). All regressions were run separated by lake and whitefish sex. (df) Degrees of freedom, (r2) squared product moment correlation coefficient, (P) significance level, and (bo,bi) regression coefficients are reported. Location Sex df r2 P bo bt Atikonak M 131 0.877 P< 0.001 3.4268 -0.7862 F 127 0.891 P< 0.001 3.4840 -0.7732 Joseph M 438 0.880 P< 0.001 3.5383 -0.8278 F 294 0.881 P< 0.001 3.4947 -0.7846 Panchia M 324 0.886 P< 0.001 3.4552 -0.7570 F 408 0.887 P< 0.001 3.3790 -0.7345 Forbay M 1679 0.849 P< 0.001 3.4480 -0.8001 F 1828 0.865 P< 0.001 3.4311 -0.7936 Smallwood M 719 0.848 P< 0.001 3.4380 -0.8067 F 688 0.874 P< 0.001 3.4485 -0.7974 63 Figure 2.10: Relationship between the loge transformed fish age (annuli number) and loge transformed increment width. All regressions were run separated by lake and whitefish sex. Otolith increment measurements were made from the surface of the otolith section along the radius of age interpretation. Derived residual values from these regressions were used as an index of fish growth in all subsequent tests, (df) Degrees of freedom, (r2) squared product moment correlation coefficient, (P) significance level. 64 Atikonak: Female log,(annuli width) = -0.773 log,(annuli) +3.484 4.5 Atikonak: Male 2 ^ _ ^ log,(annuli width) = -0.786 log,(atmuli) +3.426 K df=127,r =.891,P<0.001 4.0 " •> _ df = 131, r!= .877, PO.001 3.5 ^^S. "* •s. 0 ^\ ** 3.0 *** "* ^^^ ^ ~* v Q ^vj ° ^ ^ 2.5 "*• " Tl^ ° n"* 2.0 ^ ° 8 >** ° §a " N ** o ° a*^ °°o '"* 1.5 ^ N° " "^^NL^" ^ N 1.0 V ^"\^ s .5 »» 0.0 >» 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 ..5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.5 Joseph: Female 4.5 v Josep i: Male v "" - ' log,(annuli width) = -0.784 log,(annuli) +3.494 s . log,(annuli width) =-0.828 log,(annuli)+3.538 4.0 ! 4.0 s. 1U df=438,r2=.880,P<0.001 \ ~P vdf=294,r =.881,P<0.001 ^\l " \J v^ 3.5 3.5 *• HSX. ** v v ° ^v ; N *"* ^"""sJ 3.0 ! ^N 3.0 *»• v ^ N ^r \. n > .. \ t \0 v 2.5 \ 2.5 iN^o 0 •"> ^ j n ^^ 1 s, k5 8 **„„ ffNQ «„._>. •4 >v? ll^. 2.0 ° "*•* " nNs.B"s5"«. 2.0 ^pV K ° J0 N ftlTii»;>. 0 I °"sJ i °D „ .v ° ilc\ji'"'":\ N 2 ii\ "i- ° 4.5- 4.5 Panchia: Female Panchia: Male x ^ ^ 0 log.(annuli width) - -0.734 logXannuli) +3.379 N 4.0' ** ° lo&tannuli width) = -0.757 lo&fannuli) +3.455 3 4.0' "|k df=408,r =.887,P<0.001 . | v ^ df - 324, r - .886, /><0.001 3.5 3.5 ^ ^ > o >i ° V 1 N. 3.0 S V 3.0 r*s. § v x 2.5 S. ITsJlj ,-, 2.5 *a I ^""^St 0 * v. o 0 " J H ik. 9 »" -. "* * ^ T>i °O!VN 2.0 0 s 2.0 ^° - 8° ^ Hsa !J ifXau o §°a r° > . ? f J. "HJ°/^S 0 1.5' 1.5 0 ^ ° Q U oStO ofjo" *v S o \ N " o TPiissa^^oD ° " °^M o 0°o . N" 0 ° J^S^. °° ° ^•S&Wkfl* 1.0' N°° ° oXv 1.0 N 0 cg^ V .5 .5 0.0 0.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 Loge of the annulus number 65 4,5 '. West Forebay Female 4.5 West Forebay: Male vx lo&tannuli width) = -0.794 Io&(annuli) +3.431 log,(armuli width) = -0.8001 log,(annuli) +3.448 2 4.0' ^s df= 1828, r = 0.865, P<0.001 4.0 df = 1679, t= 0.849, PO.001 x ^s 3.5 3.5 •* ^ 3.0- !• . 3.0 N o N s. D "* 2.5' o *•» ^ A a 2.5 2.0' ^ • 2.0 N* ••. 1.5' 0 * J 1 MM 1.5 "J On nipJlfa V 1.0 - ^VS^*»* ^ 1.0 s. e a .5 o N .5 0.0 . 0.0 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 -.5 0.0 1.0 1.5 2.0 2.5 3.0 3.5 Smallwood: Female Smallwood: Male log,(annuli width) = -0.797 log,(annuli) +3.448 log,(annuli width) = -0.8067 log,(annuli) +3.438 df= 688, r!= 0.874, P<0.001 df= 719, r = 0.848, F<0.001 1.0 1.5 2.0 2.5 3.0 3.5 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 Loge of the annulus number 66 2.2.6 Analysis of Impoundment and Growth Overall growth between natural lakes did not differ (F = 0.001, P = 0.973) but growth among years did (F= 7.935, P <0.001). There was no significant interaction between impounded lakes and growth among years (F = 1.100, P = 0.331), and therefore both impounded lakes exhibited the same growth patterns among years (Table 2.7, Fig. 2.11). The comparison among natural lakes revealed no difference in growth among lakes (F = 0.236, P = 0.790). However, there was a significant difference in growth among the years (F = 3.092, P <0.001). There was no interaction between lakes and growth year (F = 1.100, P = 0.331) (Table 2.8 Fig. 2.11), indicating that both water bodies displayed the same growth patterns. Therefore, lakes of similar water body type (impounded or natural) were behaving similarly among growth years. As there was no evidence to suggest growth differed between lakes of similar water body type I pooled the index growth data by water body type (impounded or natural) and reran the two-way ANOVA using growth year and water body type as the two independent terms. The comparison between water body type indicated that overall growth between the two did not differ (F = 0.035, P = 0.851). Growth among individual years did differ significantly (F = 8.814, P < 0.001) and there was a significant interaction between water body type and growth year (F = 3.357, P < 0.001). Therefore, while growth was comparable between water body type, growth differences between types did exist in certain years and these differences were attributable to whether the lake whitefish came from impounded or natural lakes (Fig. 2.11). 67 To determine if a short-term episodic event was occurring in whitefish growth I partitioned the yearly growth index values into six growth periods of 5-year intervals. The five year interval was established to coincide with the principal period of flooding, between 1970 and 1974. To determine if sub-grouping the data by period had inadvertently altered the previous results I ran two way ANOVA's comparing among lakes in a water body type using period and lake as the independent terms. Regrouping growth years into five year periods did not influence the results. For the natural water bodies growth did not differ among lakes (F= 0.067, P = 0.935) but did differ among time periods (F = 5.849, P O.001). Scheffe's multiple comparison test indicated that only period 5 (1986 to 1990) differed from other periods in the natural lakes. The interaction term between lake and time period was not significant (F = 0.459, P = 0.885) (Fig. 2.12). For the impounded lakes growth between water bodies also did not differ (F = 0.10, P = 0.920) but did differ among periods (F = 13.009, P O.001). In the impounded water bodies growth differences existed among several periods. When compared to all other periods the interval between 1971 and 1975 displayed the strongest whitefish growth rates. This was followed by the weakest period for growth, 1967 to 1980. The interaction term between lake and time period was not significant (F= 1.564, P = 0.167) (Fig. 2.12). The subsequent comparison for lake whitefish growth between natural and impounded revealed that overall growth between water body types did not differ (F = 0.414, P = 0.520) but differences in growth existed among time periods (F = 15.802, P < 0.001). There was also a significant interaction between water body types and growth period (F= 10.939, P < 0.001) (Fig. 2.12). Thus, while overall growth was similar 68 between water bodies types. There were differences in growth that occurred in certain periods due to the influences of water body type. The one-way ANOVA tests comparing between water body types for each period indicate significant differences for all periods except periods 1 (1965 to 1969) and period 4 (1981 to 1985) (Table 2.9; Fig. 2.12). 69 Table 2.7: The mean value of the lake whitefish residual growth during a specific year of formation for the two impounded lakes. The number of measures that established the mean estimate (N) is included. Year of Forebay Smallwood Year of Forebay Smallwood Formation Mean(N) Mean(N) Formation Mean(N) Mean(N) 1965 -0.0917(1) 1981 -0.0690 (78) -0.0500 (30) 1966 0.0502 (1) — 1982 -0.0351 (96) -0.1144(36) 1967 -0.4824 (2) — 1983 -0.1035(111) -0.0518(41) 1968 0.4023(5) — 1984 0.0517(131) 0.0312 (44) 1969 0.0804 (6) — 1985 0.0281 (162) -0.0224 (56) 1970 -0.0539 (8) -0.0616 (1) 1986 -0.0121 (189) 0.0076 (68) 1971 0.1956(11) 0.2496 (1) 1987 -0.0565 (199) -0.0196(79) 1972 0.1697(20) 0.0217(2) 1988 0.0688 (226) 0.0551 (93) 1973 0.0837 (24) 0.1930(3) 1989 0.0291 (247) 0.0622 (107) 1974 0.2133 (34) 0.2100 (3) 1990 0.0309 (246) 0.0134(116) 1975 0.0961 (47) 0.0277 (4) 1991 0.0239 (274) 0.0138(133) 1976 =0.1239(49) 0.0969 (7) 1992 =0.0256(281) -0.0712 (139) 1977 -0.2123 (53) -0.0924 (9) 1993 -0.0904 (254) -0.0714(119) 1978 -0.2032 (58) -0.3361(11) 1994 0.0563 (262) 0.0817 (127) 1979 -0.1964(61) -0.0895 (20) 1995 0.0619 (268) 0.0355 (134) 1980 -0.0312(67) 0.0237 (25) 1996 0.3009 (22) 0.0739 (3) 70 Table 2.8: Mean value of lake whitefish residual growth during a specific year of formation for the three control (natural) lakes. The number of measures that established the mean estimate (N) is shown. Formation Atikonak Joseph Panchia Year Mean(N) Mean(N) Mean (N) 1967 — — 0.3114(2) 1968 — — 0.0750 (4) 1969 — — 0.1890(5) 1970 — — -0.2318(6) 1971 0.3521 (1) — -0.0695 (7) 1972 -0.2673 (1) — -0.2177 (8) 1973 -0.4699 (1) -0.2308 (1) 0.0395 (10) 1974 -0.0342 (1) 0.1639(1) -0.0723 (12) 1975 0.1103(3) -0.0411(2) -0.0177 (14) 1976 0.0422 (4) 0.0586 (3) 0.0077(15) 1977 -0.3721 (4) -0.1130(4) -0.0665 (15) 1978 -0.1955(4) 0.0036 (7) -0.1078(15) 1979 -0.1255(4) -0.1912(7) 0.0473 (18) 1980 -0.1220(5) -0.0694 (8) 0.0537(18) 71 Table 2.8(continued) Formation Atikonak Joseph Panchia Year Mean (N) Mean (N) Mean (N) 1981 -0.1097 (5) -0.0599 (8) 0.0343 (20) 1982 0.0859 (6) 0.0150(11) -0.0040 (20) 1983 -0.1031 (6) -0.0826(11) -0.0124 (22) 1984 -0.0492 (6) 0.0763 (14) 0.0231 (28) 1985 0.1289(8) 0.0291 (17) 0.0486 (29) 1986 -0.0167 (9) 0.0340 (21) -0.0216 (30) 1987 -0.0410(10) 0.1069 (26) 0.0366 (30) 1988 0.0508(11) 0.1327(33) 0.0898(31) 1989 0.1192(16) 0.0787 (50) 0.1092 (35) 1990 0.0828 (19) 0.0213 (62) 0.0419 (42) 1991 0.0260 (24) -0.0179 (70) 0.0211(48) 1992 -0.0822 (25) -0.0795 (78) -0.0030 (53) 1993 -0.0791 (25) -0.0731 (87) -0.1259(57) 1994 -0.0038 (26) -0.0015 (98) 0.0178 (65) 1995 0.0461 (37) 0.0267(117) -0.0345 (77) 72 Figure 2.11: A comparison among lakes of whitefish growth years. Graph (A) shows growth patterns for lakes in the natural water bodies; graph (B) shows growth patterns for lakes in the impounded water bodies. Growth patterns among lakes within each water body type are similar. (C) A comparison between impounded and natural water body types of lake whitefish growth rates among years. Values represent the mean of the residuals averaged by year. The bars represent the 95% confidence intervals around the mean estimates. Years between the black arrows indicate the principle period of reservoir flooding. 73 301 (A) Natural Lakes 69 71 73 75 77 79 81 83 85 87 89 91 93 95 Growth year 3.0 (B) Impounded Lakes o o 00 I on -5- West Forebay Pi -?- Smallwood Reservoir -3.0 69 71 73 75 77 79 81 83 85 87 89 91 93 95 Growth year 74 (C) Impounded vs Control (Natural) Natural lakes lakes 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 Growth year 75 Figure 2.12: A comparison among lakes of lake whitefish growth periods (five year intervals). Graph (A) shows growth patterns for lakes in the natural state; graph (B) shows growth patterns for lakes in the impounded state. Growth patterns among lakes within each water body type are similar. (C) A comparison between the impounded and natural water body types for whitefish growth periods (five year intervals). Values represent the mean of the residuals averaged for the five year period. The bars represent the 95% confidence intervals around the means. Flooding occurred during period two (71 - 75). Periods two (1971 to 1975), three (1976 to 1980), five (1986 to 1990) and six (1991 to 1995) significantly differed. (A) Natural Lakes -a a too o o jb .12 Pi 65 - 70 71 - 75 76 - 80 81 - 85 86 - 90 91 - 95 Period (five year intervals) .4 (B) Impounded Lakes .3- -.3 -.4 65 - 70 71 - 75 76 - 80 81 - 85 86 - 90 91 -95 Period (five year intervals) 77 (C) Impounded vs Control (Natural) " 5 - Impounded lakes -$- Natural lakes 65 - 70 71 - 75 76 - 80 81 - 85 86 - 90 91 -95 Period (five year intervals) 78 Table 2.9: Analysis of variance (ANOVA) results for the comparison between growth (residuals) in the natural (control) and impounded lakes. Comparisons were based on 5-year intervals. The mean for the growth index and 95% confidence intervals (CI) were calculated using the residual values averaged over 5-year time periods. (N) represents the number of incremental measurements compared. Period Impounded Natural Natural Impounded Statistic Significance (N) (N) Mean (CI) Mean (CI) 1 24 17 -0.0281 0.0414 F = 0.014 P = 0.907 1965-1970 (-0.1249,0.1810) (-0.1266,0.2095) 2 149 62 -0.0514 0.1405 F = 21.622 P< 0.001 1971-1975 (-0.1146,0.0118) (0.0948,0.1862) 3 360 131 -0.0376 -0.1339 F= 11.098 P= 0.001 1976-1980 (-0.0778, 0.0025) (-0.1650,-0.1029) 4 785 211 0.0117 -0.0210 F = 2.403 P= 0.121 1981-1985 (-0.0201,0.0436) (-0.0408, -0.0012) 5 1588 425 0.0598 0.0186 F = 8.672 P= 0.003 1986-1990 (0.0362, 0.0834) (0.0059,0.0314) 6 2016 887 -0.0228 0.0065 F = 7.979 P= 0.005 1991-1995 (-0.0396,0.0060) (-0.0048,0.0178) 79 2.3 Discussion Age interpretation must be both accurate and precise to evaluate the effects of impoundment on lake whitefish recruitment and growth. Age-bias plots indicated that my age interpretations were not significantly different from those of an experienced interpreter who consistently conducted accurate interpretations of lake whitefish growth. Also, there were no significant differences among the pair-wise comparison for my three aging trials, indicating that my interpretations were consistent and precise. When impounded lakes were compared, I found evidence for recruitment synchrony but this synchrony did not exist when year-classes from each impounded lake were compared to the natural (control) lake. This finding was supported with an additional comparison of a small sample of lake whitefish taken from Lake Atikonak (another natural lake, data supplied by Quebec and Labrador Hydro) with Lakes Joseph, Smallwood Reservoir and West Forebay whitefish (Appendix D). The comparison demonstrates that the Atikonak samples do not differ in recruitment from those of Lake Joseph but do differ in comparison from distributions of the Smallwood Reservoir and the West Forebay. During the first four years of impoundment (1971-1975), recruitment increased in the impounded lakes; however, the above-average recruitment was brief in duration and quickly reverted to long-term norms. This increase was noticeably absent from the natural water bodies. These findings are supported by a historical report created from data sampled during the flooding period (Barnes 1981). Between the periods 1971 through 1974 there was a 'sharp' increase in the percentage of immature lake whitefish 80 per sample. The most extreme case was documented for Smallwood where the average went from 8% in 1973 to 42% in 1974. Lindstrom (1962) also reported an increased abundance in whitefish young after impoundment of Lake Storavan. This phenomenon of above average recruitment during the initial reservoir flooding was also witnessed during an environmental monitoring program at the La Grande Hydroelectric Complex, in the province of Quebec. Researchers identified a strong but short pulse in lake whitefish recruitment strength immediately after the initial period of flooding (Deslandes et al. 1995; Therrien et al. 2004). The pulse in recruitment lasted only four or five years and then returned to levels indicative of the long-term average. Maceina's (1997) application of catch-curve residuals provided a valuable index for observing temporal trends in recruitment. Using the derived catch-curve residuals as an index of year-class strength, I was able to identify the critical month when reservoir hydrology had the greatest influence on recruitment. By comparing coefficient of variation values from water levels at the Gabbro flood gate with recruitment indices for both the West Forebay and the Samllwood Reservoir, I determined that variations in water levels for February influenced recruitment. Newfoundland and Labrador Hydro uses Ossakmanuan Reservoir as a holding facility (a sub-reservoir) to collect water from a vast area of the Churchill River drainage basin (Mike Duffeny, Superintendent of Operations, pers. comm.). After the annual autumn rains, Ossakmanuan reaches full capacity by mid-January, at which time the flood gates are opened and the water is allowed to drain from the Ossokmanuan Reservoir into Smallwood impoundment. To prevent the Ossakmanuan Reservoir from 81 overflowing the process of draining must be completed prior to spring runoff. Therefore, in February, the gates of the Gabbro control structure are opened wide and a massive volume of water (75,000 m3 per second) is allowed to flow through. This water is used to maintain the Smallwood Reservoir at a working level. Without the Ossakmanuan Reservoir the Smallwood would drain below working levels. After a short period, and prior to spring run-off, the water from Smallwood is released through the Lobstick flood gate whereupon the Smallwood Reservoir is lowered for hydro usage. During the four months after February, the reservoir is drawn down to its lowest levels (Appendix E). The magnitude of water level fluctuation directly depends on how wide the Gabbro flood gates are opened (Mike Duffeny, Superintendent of Operations, pers. comm.). Flood gate openings depend upon the overall water volume: if there is a great deal of water in the Gabbro reserve, the gates are opened wider for extended periods. This results in the downstream water bodies having higher water levels for longer periods. This draw-down process could influence recruitment in two ways. First, in winter when water is released from the Ossakmanuan Reservoir, it causes the ice to drop (Mike Duffeny, Superintendent of Operations, pers. comm.). This drop could cause a great deal of ice breakage and scouring. In addition, the increased flow also leads to the creation of frazil ice which occurs when turbulent water at 0.0°C undergoes heat loss to the atmosphere (Brown et al. 1993). Frazil ice is small, discoid or spicule shaped ice crystals that occur in the water column. While the water is super cooled frazil ice crystals will continue to grow and will form aggregates. These aggregates will stick to submerged objects that they come into contact with forming anchor ice. Frazil ice will be transported downstream depositing along shorelines, in shallows, and under surface ice 82 where flows are low (Brown et al. 1993). Incubating fish eggs of fall spawning species can be damaged or displaced by anchor ice when it freezes to the bottom, exposes eggs after detaching substrate material or dewaters redds (Brown et al. 1993). Second, decreased water levels expose shoals that are in shallow water (Mike Duffeny, Superintendent of Operations, pers. comm.). If the exposed shoals happen to be lake whitefish spawning shoals, this exposure may lead to desiccation and freezing of whitefish eggs. Frazil ice and the monthly hydrological cycles may not be an issue during years when overall water levels remain higher than average. However, when they are significantly lower than average, recruitment may be weakened. Lake whitefish recruitment decreased in the 1990's, possibly because of lower- than-average water levels. Overall temporal trends in yearly water levels (Appendix F) declined beginning in the late 1980s and continued into the 1990s. This overall decline is attributed to changing weather patterns that have decreased the amount of annual precipitation (Mike Duffeny, Superintendent of Operations, pers. comm.). These findings are supported by others who reported weakened recruitment due to water level draw-downs or "dewatering" (Grimas and Nilsson 1965, Machniak 1975, de Graff 1993). The dewatering exposed whitefish spawning beds, which led to the desiccation of eggs. This might be a probable explanation why weakened recruitment was witnessed beginning in the mid 1980's. During this time reservoir water levels were at their lowest. Otolith size and growth was a good indicator of body size and growth. In all instances, a plot of the log of the otolith radius against the natural log of the fish fork 83 length showed a significant linear relationship, indicating that the otolith could be used as an index for fish growth. In impounded water bodies, lake whitefish growth was faster between 1971 and 1975. From 1976 to 1980, growth rates declined significantly. The natural lakes did not display these patterns. The comparison between water body types revealed that growth rates in the impounded water bodies exceeded those in the natural lakes during the principle period of flooding (1971 -1975). The impounded water bodies displayed marked increases in growth rates. The comparison of the subsequent period (1976 to 1980) between impounded and natural water bodies indicated a significantly slower growth rate in the impounded water bodies. The witnessed initial increase in growth rates are corroborated by Barnes (1981) who reported a yearly increase in growth rates between 1973 and 1975. A brief increase in growth rates and condition factor was also evident at the Grand Hydroelectric Complex in Quebec (Therrien et al. 2004). For five years during the flooding, growth and condition factors exceeded the long term averages. Additionally, following the flooding of Lake Sharpe in South Dakota the first post-impoundment year classes for several species had the fastest growth rates of all years combined (Elrod and Hassler 1971). The increase in lake whitefish growth and recruitment between 1971 and 1975 could be an effect of increased eutrophication. Flooding tends to leach nutrients from the land into the water column (Barnov 1961; Ostrofsky and Duthie 1980; Schetange 1994). As nutrient levels rise, productivity increases and plankton blooms occur. Powell Consultants (1973), Duthie and Ostrofsky (1974) and Barnes (1981) found that plankton 84 blooms occurred concurrent with eutrophication in the reservoir. Whitefish (being planktivorous) no doubt benefited from this, as evidenced by increased growth rates and strong year-classes. These increases were short-lived: the subsequent 5-year period showed a marked decrease in growth rate and year-class strength. This finding supports other researchers who have concluded that whitefish undergo a pulse of good growth during the initial years of reservoir creation but quickly demonstrate a strong downward trend in growth subsequent to this period (Runnstrom 1964, Karlstrom 1971, cited by Machniak 1975). My findings support the hypothesis that lake whitefish year-class strength and growth were influenced by flooding associated with impoundment: however, these effects were transitory in nature about four years. Both were enhanced during the initial years of flooding however, the enhancement was short-lived, with the populations eventually reverting to pre-impoundment levels. My findings should help elucidate the impacts that the hydrological cycle has on the resident whitefish populations. 85 Chapter 3 Effects of Impoundment On Lake Trout Year-Class Recruitment and Growth Rate 86 3.0 Lake Trout Background Lake trout is highly prized as both a game and a commercial fish (Scott and Crossman 1973). Considerable research has been done on its life history, but few studies document changes resulting from water regulation or impoundment. Much of the work that does exist comes from occasional reports which usually inventory baseline data (Barnes 1981; Bruce 1974; Bruce and Parsons 1979). Water level fluctuations associated with impoundment have been documented to alter factors such as water chemistry, turbidity, flow rate and temperature. Daily water-level fluctuations normally associated with reservoirs may weaken year-class strength. Lake trout can hatch as early as mid-February or as late as the end of March (Casselman 1995). Therefore, eggs are vulnerable to desiccation for three to five months if spawning beds are exposed (Martin 1955, 1957; Wilton 1985). If water levels remain above spawning sites reproduction is not inhibited. Lake trout, being demersal spawners, usually broadcast their eggs over clean coarse substrate in shallow waters (< 10m deep) (Gunn and Sein 2000). Erosion and siltation, common during impoundment, may also detrimentally affect recruitment. Eggs and fry will remain in interstitial spaces of the substrate for seven months (October-April) and can be vulnerable to suffocation due to siltation (Sly and Evans 1996). Impoundment may change lake trout growth. Lake trout became stunted after the impoundment of Lake Minnewanka when they shifted from feeding on fish to feeding on chironomid larvae (Currier 1954). Growth rate and condition are known to increase as lake productivity increases (Johnson and Martinez 2000). 87 The thermal regime, could have been altered, interfering with spawning activities or directly affecting egg and larval development. If there was a delay in the rise of water temperatures, then the rate of embryo development might also have been delayed. Garside (1959) found that days till hatch for lake trout eggs were extended if temperatures were cool for long periods (50 days at 10.0°C, 67-85 days at 7.5°C , 108- 117 days at 5.0°C and 141 - 156 days at 2.5°C). Optimum temperature for age 0 lake trout falls between 10.0°C and 12.5°C (Edsall and Cleland 2000). Cooler temperatures could alter the survival rate of young-of-the-year. Water chemistry also can change with impoundment, weakening year-class strength. Oxygen and heavy metal levels can be altered, directly affecting survival of lake trout embryos. Carlson and Siefert (1974) reported that lake trout development was inhibited by reduced oxygen saturation (50% and lower). There is also evidence that embryonic and larval stages of fish are less tolerant of heavy metal pollution than adult forms and that younger fish can be under-represented in metal-polluted lakes (McKim 1977; Sherwood et al. 1999). In this study I describe the long-term effects of reservoir establishment on recruitment and growth rate of lake trout in the Churchill Falls-Smallwood Reservoir hydroelectric complex, located in Labrador, Canada. This reservoir was flooded between 1970 and 1974 and was only sampled during the initial years of Reservoir creation (Powell Consultants 1972, 1973; Bruce and Parsons 1979; Barnes 1981). Sampling was done to collect baseline data so that subsequent changes in fish-community composition and structure could be documented. However, sampling was discontinued after 1974, and 88 data do not exist to examine the long-term effects of impoundment on lake trout growth and recruitment. To determine whether Impoundment of the Smallwood Reservoir affected lake trout populations I used otolith age and growth to compare year-class structure and growth of lake trout from impounded and natural water bodies. Due to increases in siltation, water currents and changes in water chemistry, I hypothesized that impoundment would negatively impact lake trout recruitment and growth. 3.1 Methods 3.1.1 Sampling and Data Acquisition Sampling and data acquisition are as described in chapter 2. Lake trout samples were collected during the last week of May and the first two weeks of June, 1997, 1998 and 1999. Samples were collected in two impounded (the Smallwood Reservoir and the West Forebay) and one natural water body (Lake Joseph) (Fig. 1). Total counts were recorded for all sampled species by panel and net. Total counts were made for lake trout per water body and were divided by total effort to give individual lake trout CUE (Appendix G). Lake trout were measured for whole and eviscerated weights, fork length and sex. Otoliths were extracted for age and growth information. Otolith preparation and interpretation were identical to that used for lake whitefish otoliths as described in chapter 2. 89 To augment the data, two lake trout collections were obtained from Newfoundland and Labrador Hydro. Both collections were taken from lakes that are down river from Lake Joseph and were not impounded. These samples were collected from Lakes Atikonak and Panchia by a consultant group (LGL limited) on behalf of Hydro. They were sampled in August 1999. Prior to adding these collections I conferred with the consultants who collected the data to determine possible differences between sampling protocols. In both instances, the sampling protocols had only slight differences (LGL Limited, John Christian, pers. comm.). The consultants collected 118 lake trout from Lake Atikonak of which 76 were sampled. In Lake Panchia they caught 67 fish of which 63 were sampled (Table 3.1). Archived calcified structures were also obtained through the Department of Fisheries and Oceans and Hydro-Quebec for 1992 and 1996, and samples were also added from creel surveys conducted at West Forebay in 1996, 1998 and 2002 by the Inland Fish and Wildlife Division, Government of Newfoundland and Labrador. In total 649 collected and archived lake trout otoliths were interpreted for age and /or growth (Table 3.1). In total 598 lake trout were used for year-class analysis. From this collection a sub-sample of 507 (male and female lake trout combined) otoliths were interpreted for growth information. 90 Table 3.1: The number of lake trout used for year-class and growth analysis. The 2002 creel data were not used for growth analysis. (* indicates samples provided by either the Department of Fisheries and Oceans or Hydro-Quebec; 1992 and 1996 data was only used in growth analysis) Year Lake Number 1992 Smallwood 11* West Forebay 14* 1996 Smallwood 8* West Forebay 18* 1997 Smallwood 14 West Forebay 21 West Forebay (creel) 104 1998 Smallwood 8 West Forebay 18 Joseph 34 1999 Smallwood 23 West Forebay 26 West Forebay (creel) 37 Joseph 32 Atikonak 76* Panchia 63* 2002 West Forebay (creel) 142 Total: 649 91 3.1.2 Age Validation, Precision and Bias To give some indication of accuracy, I compared a subset of 75 of my age interpretations with those of an experienced interpreter who had been trained on known- age lake trout. To test for age bias, a Friedman Repeated Measures Analysis of Variance on Ranks was used to determine whether significant differences existed between interpreters (Sigmastat statistical software). Age bias plots were also created to identify where possible age bias may exist. To quantify the level of precision associated with my interpretations and determine whether I was interpreting age consistently, I conducted three trials on a subset of older fish (> 30 years; N = 57). I again used a Friedman Repeated Measures Analysis of Variance on Ranks to compare variance among trials. I chose the oldest fish to ensure the largest possible amount of variance. 3.1.3 Variation in Recruitment To determine whether a difference existed in year-class strengths between impounded and natural water bodies, I used a non-parametric, chi-square analysis (Sokal and Rolhf 2000). Unlike the lake whitefish, the lake trout year-class distributions in the impounded water bodies were bimodal, and therefore, discrete. Further, the Chi-square analysis compares point-by-point differences as you move across a distribution and, therefore, it is excellent in identifying any episodic temporal differences between year- class frequencies. Additionally, the analysis is done using sample proportions and therefore provides a weighting which compensates for any potential differences in 92 sampling effort between ponds. The test is also considered robust when working with small sample sizes. Due to small sample sizes it became necessary to group year-class frequencies into five, six year time periods. To ensure the sample was representative, only lake trout that were fully recruited from the sampling gear were examined, therefore lake trout younger than the 1989 year-class were excluded from the analysis. Even with these groupings, certain cells still did not achieve the minimum sample requirement of five data points per cell normally associated with a chi-square analysis (Yarnold 1970). After comparing individual water bodies I performed a final analysis that pooled all samples by water body type (impounded and natural), to increase the sample size. The two data sets consisting of five, six year groupings were compared using a chi-square. 3.1.4 Catch Curve Residuals To determine if there was a temporal relationship between reservoir water levels and lake trout recruitment patterns I used Maciena's application of catch curve residuals. The advantage in using this technique is, unlike the Chi-square analysis, it does not require arbitrary groupings of data and therefore allows for an accurate temporal examination of recruitment patterns. As water bodies within a type did not significantly differ in year-class distribution, I first grouped the data by type. Prior to regressing the data, to account for possible differences among years in sampling effort, year-class frequencies were also 93 weighted equally by calculating their relative abundance (proportion) for each water body and sample year. Proportions were summed by year-class. Proportions were then loge transformed and regressions were applied to the descending limb of the resulting distributions. Regressions were applied beginning with the year classes that were fully recruited to the gear which were the 1993 year-class for the impounded water bodies and 1991 year-class for the natural. For more detail on Maceina's application refer to chapter 2. In addition to the netting data, I performed an additional analysis on three years of creel data (1997, 1999 and 2002) sampled from anglers fishing the upper West Forebay. These data were examined separately because they were fisheries-dependent. This infers that data may be biased because anglers may choose to retain only fish of certain size classes and, therefore, age classes. Nevertheless, the sample was large and served as an independent source for comparison. Using the derived residuals as an index of abundance, I correlated the index values from both data sets with monthly mean water levels at the Gabbro and Lobstick flood gates. In addition to the correlations involving mean water levels, I also used the index of water-level fluctuation (CV) to determine whether they affected lake trout recruitment. Each month's CV value was calculated for a 22 year period (1972 to 1996). Due to the number of separate trials, the Dunn-Sidak methodology was applied to reduce the chance of type I errors (Sokal and Rohlf 2000). If any significant relationships were found, I reran the catch-curve regression as a multiple linear regression by incorporating the additional environmental variable. 94 3.1.5 Back-Calculations of Lake trout Growth Using Otoliths The objective of this section is to describe lake trout growth for the period between 1970 and 1996 in relation to the influences of reservoir hydrology. To collect growth information I repeated the methodology first defined for whitefish otoliths in Chapter 2. I used growth data from samples collected from Lakes Atikonak (1999), Panchia (1999), Joseph (1998, 1999) and locations in the Smallwood Reservoir and the West Forebay (1992, 1996 - 1999). To describe the lake trout otolith-body relationship three regression types (linear, quadratic, cubic) were fit to the loge transformed maximum otolith radius and loge transformed body fork length, separated by water body and sex. Subsequently, linear regressions applied to the loge transformed data were used to describe the relationship. To determine the effects of impoundment on growth I separated the average incremental growth, normally associated with a fish's age, from growth that was influenced by the environment as discussed in chapter 2. To determine if impoundment influenced lake trout growth, tests were first conducted to ensure water body types (impounded or natural) were similar. I ran separate two-way ANOVA's for each type, using the growth index values derived from the regressions as the dependent variable, with water body and growth year as the independent variables. Subsequent to this analysis I pooled the residual values by water body type. 95 To examine lake trout growth through time, I used the same procedures and statistical analysis first described for lake whitefish in chapter 2. I used Pearson correlations for each impounded location to determine whether there was a direct long- term relationship between monthly and fluctuating water levels (CV) and annual lake trout growth as in chapter 2. 96 3.2 Results 3.2.1 Age Validation, Precision and Bias There was no significant difference between my age interpretations and those of the experienced reader (t = 0.422, df = 74, P = 0.674). Therefore it is unlikely an age bias was present (Fig. 3.1). My interpretations were also consistent, because there were no significant differences between my three aging trials (x = 2.618, df = 2, P = 0.270). 97 Fig. 3.1: An age bias plot comparing age interpretations made from lake trout otolith replicates (N = 75 interpretations). Reader 2 had experience working with known age lake trout. Bars represent the 95% confidence limits. 98 •a « a © Iu 20h a Of 4> ex 10 20 30 40 Age Interpretation Reader 2 99 3.2.2 Impoundment and Recruitment Strength An initial examination of lake trout year-classes in the impounded water bodies revealed there was weak recruitment in both water-bodies between the periods of 1971 to 1982 (Fig. 3.2; Appendix H). This same deficiency was not present in the natural lakes (Fig. 3.3; Appendix H). The subsequent chi-square analysis comparing six year periods between the Smallwood Reservoir and the West Forebay revealed no significant differences among periods between locations (X2 = 3.992, df=4,p = 0.407) (Table 3.2, Fig. 3.4). This indicated a general similarity in year-class structure between the two water bodies. The natrual lakes also demonstrated similarities in year-class frequency among periods for the three water bodies, Joseph, Atikonak and Panchia (X 2 = 9.588, df = S,p = 0.295) (Table 3.2, Fig. 3.4). Yet, when comparing between the two pooled data sets, a significant difference was present (X 2 = 33.097, df = S,p <0.001) (Fig. 3.5). Examination of the percentages making up the sample composition in the impounded water bodies indicated that only 16% of the total samples collected fell between 1972 and 1983. For the natural lakes the same 12 year period accounted for approximately 47 % of the sample composition. Residuals derived with Maceina's application from proportional year-class strength also indicate the same trends as the Chi-square tests. Starting at 1972 proportional year-class strength fell below the average and remains below until approximately 1981 (Fig. 3.6).The same pattern in recruitment strength was also 100 observed from the creel data collections sampled from the upper West Forebay (Fig. 3.7, Fig. 3.8). Between 1971 and 1979 year-class strength dips below the average. These two observations are in contrast to the natrual lakes that, for the same period, consistently fall above or near the average (Fig. 3.9). The correlation of residuals derived from the netting year-class percentage data = with water level fluctuation (CV values) (pres,cv 0.457, P = 0.087) and mean water level {Pres.cv = 0.-.457, P - 0.087) at the Gabbro flood gates did not reveal any significant correlations (Dunn-Sidak reconfigured alpha; a of P = 0.005). However, the correlations using residuals derived from the creel data demonstrated the same pattern of significance first witnessed with the lake whitefish. Water level fluctuation at the Gabbro flood gates for the month of February approached significance (pres,cv = 0.606, P = 0.006). While water levels for the month of March significantly correlated with year-class strength (p,e,,mea„ = - 0.642, P = 0.003). Therefore, there is evidence to suggest that year-class strength of lake trout was also influenced by water-level in Gabbro whereby if water was held back in Gabbro, down-stream lake trout year-classes were weakened. Conversely, if water was released downstream to the West Forebay, lake trout recruitment was stronger. 101 I incorporated the additional term of water level fluctuation (CV) and Gabbro water depth into the original catch-curve regression derived for the West Forebay creel data to produce a multiple linear regression: (1) Loge (year class frequency) = -9.088 + (0.081* year class) r2 = 0.538, P< 0.001 (2a) Loge (year class frequency) = -11.575 + (0.102* year class) + (754.932* CV) R2 = 0.700, P < 0.001 (2b) Loge (year class frequency) = 248.630 + (0.093* year class) + (-0.543* depth) R2 = 0.690, P < 0.001 (1) original regression (2) multiple linear regressions (a) coefficient of variation for water level at Gabbro flood gate (CV) (b) depth at Gabbro flood gate (depth) Adding each term into the original catch-curve equation accounted for an additional 23.2 % (CV values) and 22.0% (water depth) of the variance in lake trout recruitment, respectively. 102 Figure 3.2: Pooled lake trout year-class frequency data collected from the impounded water bodies; (A) West Forebay and (B) Smallwood Reservoir. The data was collected in 1997, 1998 and 1999 using standardized sampling protocols. (C) Pooled lake trout year- class frequency data collected from the impounded water bodies combined. (N) Represents the total number of lake trout sampled. 103 (A) West Forebay: 1997-1999 N=65 oU 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 (B) Smallwood Reservoir: 1997-1999 N = 45 o 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 14 (C) Pooled data: West Forebay and Smallwood Reservoir N= 110 12 10 l\ 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 Year class Figure 3.3: Pooled lake trout year-class frequency data collected from the natural lakes (A) Lake Joseph, (B) Lake Atikonak and (C) Lake Panchia. (D) Pooled lake trout year- class frequency data collected from the natural lakes; Lakes Joseph, Atikonak and Panchia. (N) Represents the total number of lake trout sampled. 105 101 (A) Joseph: 1997,1998 N = 65 (C)Panchia:1999 N = 61 61 63 65 67 69 71 73 75 77 79 81 83 85 87 91 93 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 i (B)Atikonak: 1999 (D) Pooled data Atikonak, Joseph andPanchia N = 71 N=197 I3-X 61 63 65 67 69 71 73 75 77 79 81 83 85 87 91 93 61 63 65 67 69 71 73 75 77 79 81 83 85 87 91 93 Year class 106 Table 3.2: The combined number of lake trout attributed to each time period. The frequency data were used in the Chi-square analyses. The pooled data represents the totals sampled by water body type and were used to compare between the impounded and natural states. Water Body Location Yearly Time Intervals Type 60-65 66-71 72-77 78-83 84-89 Impounded Smallwood 2 4 0 5 21 West Forebay 6 13 2 12 28 Pooled 8 17 2 17 49 Natural Joseph 0 3 7 29 24 Atikonak 1 7 10 22 20 Panchia 3 3 10 18 24 Pooled 4 13 27 69 68 107 Figure 3.4: A comparison of year-class composition between individual lakes within each water body type. (A) Represents results for the comparison of impounded lakes. (B) Represents results for the comparison of natural lakes, (x2) Chi-square statistic, (df) Degrees of Freedom, (P) Significance level are reported. Results were considered significant at alpha (a) = 0.05. Numbers inside individual bars represent the sample percents. Sample percents were calculated from total numbers sampled. 108 50 (A) X2 = 3.992, df = 4, P = 0.407 47 43 40 30 20 20 18 10 11i, 9 [III! Smallwood . 4 3 I -l West Forebay 60- 65 66- 71 72 -77 78 •83 84-89 Time perio d 50- (B)X2 = 9.588, df = 8, P = 0.295 44 40 1 38 36 30 29 29 27 20 16 13 10 11 I I Joseph I I Atikonak I- 1 Panchia 60-65 66-71 72-77 78-83 84-89 Time period 109 Figure 3.5: A comparison of year class composition between pooled data for the impounded and natural lakes, (x2) Chi-square statistic, (df) Degrees of Freedom, (P) Significance level are reported. Results were considered significant at alpha (a) = 0.05. Numbers inside individual bars represent the sample percents. Sample percents were calculated from total numbers sampled. 110 2 50i X'Y == 33.097, df= 8,PO.001 4p 34 33 15 15 13 • I I Natural lakes 60-65 66-71 72-77 78-83 84-89 Time period Ill Figure 3.6: (A) Catch-curve and corresponding regression (solid line) for lake trout pooled from the Smallwood Reservoir and the West Forebay. The dashed lines represent the 95% confidence intervals. The catch curve was created by pooling sample year proportions for each water body. Using the natural logarithm for transformation, the regression equations were weighted proportionally for sample sizes at each age. The sample size (N), significance level (P) and the squared product moment correlation coefficient (r2) are also shown. (B) Residual values derived from the catch curve regression. A plot of the residuals serves as an index of abundance, illustrating the temporal pattern of recruitment for lake trout. 112 (A) Log^roportion^J = -7.55 + (0.060*year class) N = 25, r2 = 0.413, P< 0.001 a o o oa, t-i a. ai S3 2 H +-» o 00 o l -i "3 0 T 95 90 85 80 75 70 65 60 Year class "55 O o o o r — _> 'C 1) 73 "55 Figure 3.7: Pooled lake trout year-class frequencies from the upper West Forebay, collected from anglers' catch during creel surveys in 1997, 1999 and 2002. (N) Represents the total number of lake trout sampled from anglers catch. 114 30 1 Pooled creel data 1997, 1999, 2002 N = 283 25 20 o c a & 15 CD U-t 10 0. on ~ i ~ ~ i i n 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 Year class 115 Figure 3.8: Catch-curve and corresponding regression (solid line) for lake trout sampled from West Forebay creel data (1997, 1999 and 2002). Each samples year-class frequencies were converted to proportions prior to pooling. The dashed lines represent the 95% confidence intervals. Using the natural logarithm for transformation, the regression equations were weighted proportionally for sample sizes at each age. The sample size (N), significance level (P) and the squared product moment correlation coefficient (r ) are also shown. (B) Residual values derived from the catch curve regression. A plot of the residuals serves as an index of abundance, illustrating the temporal pattern of recruitment for lake trout. 116 4 H (A) Loge(proportion) = -4.483 + (0.081*year-class) a o 3^ o 1/3 cS 4o? 2"i o l-\ w> o -1 80 75 Year class a o m S-I O o 1o5 1) 55 65 70 75 80 90 95 Year class 117 Figure 3.9: Catch-curve and corresponding regression (solid line) for lake trout sampled from lakes Atikonak, Panchia and Joseph. Each samples year-class frequencies were converted to proportions prior to pooling. The dashed lines represent the 95% confidence intervals. Using the natural logarithm for transformation, the regression equation was weighted proportionally for sample sizes at each age. The sample size (N), significance level (P) and the squared product moment correlation coefficient (r2) are also shown. (B) Residual values derived from the catch curve regression. A plot of the residuals serves as an index of abundance, illustrating the temporal pattern of recruitment for lake trout. 118 (A) Loge(proportion) = -4.866 + (0.089*year-class) 4 H N = 30, r2= 0.645 ,P< 0.001 2 "I 0 Year class 119 3.2.3 Impoundment and Growth: Relationship between Otolith and Body Length All fitted models applied to the loge transformed data of otolith radius and fish length demonstrated significance. However, neither the cubic nor quadratic models had significantly better fits then the linear regression (Table 3.3). Therefore, linear regressions were used to describe the relationship between maximum otolith radius and fish body length (Table 3.4; Appendix I). 120 Table 3.3: Three equations ((1) Linear, (2) quadratic and (3) cubic) describing the relationship between natural logarithm of otolith radius and fork length for lake trout. All R2 values were significant at P < 0.05. 2 Location Curve* R df F bo bi b2 b3 Atikonak 1 0.617 74 119.01 -3.5478 0.6330 2 0.673 73 74.95 10.8631 -4.2489 0.4114 3 0.673 73 75.22 2.6345 -0.3168 0.0414 Panchia 1 0.576 61 82.98 -4.0785 0.7221 2 0.576 60 40.82 -3.4561 0.5227 0.0160 3 0.576 60 40.82 -3.4561 0.5227 0.0160 Joseph 1 0.494 63 61.44 -2.5507 0.4798 2 0.504 62 31.48 -11.6470 3.3253 -0.2223 3 0.504 62 31.48 -11.6470 3.3253 -0.2223 Forebay 1 0.344 241 126.34 -4.3995 0.7310 2 0.344 240 62.95 -1.4528 -0.1748 0.0696 3 0.344 240 62.95 -1.4528 -0.1748 0.0696 Smallwood 1 0.477 61 55.60 -3.5982 0.6132 2 0.580 60 41.46 18.0590 -6.4449 0.5739 3 0.582 60 41.85 5.2823 -0.5054 0.0600 — '1- y(otolith radius) bo + biX(fork length) = 2. V(otolith radius) bo + biX(fork length)+ b2X (fork length) = 3. V(otolith radius) bo + blX(fork length)+ b2X (fork length)+ b3X (fork length) 121 Table 3.4: Relationship for the fitted linear regressions. Regressions describe the relationship between maximum otolith radius (loge transformed) and lake trout body length (loge transformed). All regressions were run allowing for water body and sex. All regressions were significant at P < 0.001. Regression coefficients and constants are reported (bo,bi). 2 Location Sex r df F bo bi Atikonak M 0.469 36 31.76 -3.8350 0.6772 F 0.738 36 101.32 -3.4024 0.6117 Panchia M 0.666 30 59.83 -5.4851 0.9467 F 0.544 29 34.56 -3.3323 0.6030 Joseph M 0.400 26 17.37 -2.2508 0.4329 F 0.554 35 43.49 -2.7379 0.5089 Forebay M 0.315 88 40.49 -4.4298 0.7375 F 0.262 104 36.91 -3.4937 0.5933 Smallwood M 0.433 28 21.39 -3.0274 0.5186 F 0.521 31 33.73 -4.3046 0.7279 122 3.2.4 Impoundment and Growth: Flood Levels and Lake Trout Growth Indices In all instances the regression equations fit to the loge transformed otolith increment width measures and annuli number were significant (Appendix J). All subsequent comparisons or tests describing lake trout growth rates used the residual values derived from these equations as indices. There was a linearity of relationship between the plot of residuals against fitted values and the histogram of residuals displayed a normal distribution therefore homoscedasticity was demonstrated (Appendix K). Each regression's normal probability plot displayed a linear relationship and therefore an independence of error was demonstrated. Two-way ANOVA results indicated that overall growth between West Forebay and the Smallwood Reservoir did not differ (F = 0.257, P = 0.612). However there was a growth difference among years (F = 3.740, P < 0.001). The interaction term between locations and year was not significant (F = 1.186, P =0.242) indicating that both water bodies exhibited the same patterns of growth among years (Fig. 3.10A). The comparison of growth among the natural (control) lakes indicated that overall growth differences did not exist between lakes Panchia, Atikonak and Joseph (F = 0.229, P = 0.796). There was a significant overall difference among years (F= 2.457, P O.001). However, the interaction term was not significant, which is an indication that all three lakes displayed the same pattern of growth (F = 0.773, P = 0.871) (Fig. 3.10B). As there was no evidence to suggest growth differed between water bodies of similar type I pooled the index growth data by type (impounded or natural) and reran the 123 two-way ANOVA using growth year and water body type as the two independent terms. The comparison between types indicated that overall growth between the two did not differ (F = 0.813, P = 0.367). Growth among individual years did differ significantly (F = 5.031, P < 0.001). However, the interaction term was not significant and therefore growth among years was similar between water body types (F = 1.392, P < 0.096) (Fig. 3.10). To determine if a longer episodic event was occurring in lake trout growth, I partitioned the yearly growth index values into six growth periods of 5-year intervals. The five year interval was established to coincide with the principle period of flooding, between 1970 and 1974. To determine if sub-grouping the data by period had inadvertently altered the previous results I ran two way ANOVA's comparing among water bodies in a type using period and water body as the independent terms. Regrouping growth years into five year periods did not influence the results. For the natural water bodies growth did not differ among lakes (F= 0.464, P = 0.629) but did differ among time periods (F = 4.174, P = 0.002). The interaction term between lake and time period was not significant (F = 0.586, P = 0.790) (Fig. 3.11). For the impounded locations growth between water bodies also did not differ (F = 0.037, P = 0.847) but did differ among periods (F = 11.404, P O.001). The interaction term between water body and time period was not significant (F= 1.252, P = 0.282) (Fig. 3.11). The impounded water bodies differed for the two periods that fell between 1971 and 1980 when compared with the three periods between 1981 to 1995. The latter three periods displayed significantly stronger growth rates then the earlier two (Fig. 3.11). The natural lakes growth rates only differed for one period (1986-1990) when compared with two other periods (1981-1985; 1991-1995). 124 The subsequent comparison for lake trout growth between natural and impounded water bodies revealed that overall growth between types did not differ (F = 0.594, P = 0.441) but differences in growth existed among time periods (F = 14.875, P < 0.001). There was also a significant interaction between water body type and growth period ( F = 10.939, P < 0.001) (Fig. 3.11). Therefore, while overall growth was similar between types. There were differences in growth that occurred in certain periods due to the influences of water body type. The one-way ANOVA tests comparing growth rates between types indicate significant differences between natural and impounded for 1976 - 1980 and 1986 - 1990 (Table 3.6). The series of Pearson correlations used to examine the relationship between monthly reservoir water levels and lake trout growth indicated significant correlations for most months (a = 0.005) (Appendix L). In all instances the correlations were negative suggesting an inverse relationship between water depth and lake trout growth rates. Because there were so many significant correlations, it did not appear that water levels from one particular month were influencing lake trout growth. Therefore, I conducted an additional comparison using yearly mean water depth and lake trout growth (Table 3.7). The results indicate a general relationship between yearly mean water depth and lake trout growth, whereby in the initial years of flooding growth rates slowed. 125 Table 3.5: Coefficients (bo,bi) for the fitted linear regressions. Coefficients describe the relationship between individual measured increment widths (loge transformed) and the corresponding annuli number (loge transformed). All regressions were run allowing for water body and sex. All regressions were significant at P < 0.001. Location Sex r2 df F bo b! Atikonak M 0.877 432 3085.170 3.465 -0.727 F 0.863 536 3366.800 3.433 -0.724 Panchia M 0.865 427 2726.640 3.518 -0.737 F 0.903 424 3948.520 3.575 -0.766 Joseph M 0.878 385 2765.620 3.580 -0.779 F 0.852 479 2765.390 3.621 -0.800 Forebay M 0.785 1436 5251.580 3.337 -0.711 F 0.827 1622 7780.680 3.367 -0.716 Smallwood M 0.826 283 1343.06 3.489 =0.771 F 0.814 414 1809.75 3.418 =0.771 126 Figure 3.10: Error bars comparing lake trout growth years among individual water bodies in each type (impounded and natural). (A) Represents water bodies in the impounded type. (B) Represents growth rates for the three natural lakes (Joseph, Panchia and Atikonak.). Bars represent the 95% confidence intervals around the mean estimates. (C) Compares growth between impounded and natural water bodies, all lakes combined. (A) Impounded waterbodies 127 —J— WestForebay -J- Smallwood Reservoir 71 73 75 77 79 81 83 85 87 89 91 93 95 Year (B) Natural Lakes •o o o 00 3 'So U 71 73 75 77 79 81 83 85 87 89 91 93 95 Impounded waterbodies Natural lakes(control) 71 73 75 77 79 81 83 85 87 89 91 93 95 Year 128 Figure 3.11: Error bars comparing lake trout growth periods among individual lakes in each type (impounded and natural). (A) Represents period growth (five year intervals) from water bodies in the impounded type. (B) Represents period growth rates for the three natural lakes (Joseph, Panchia and Atikonak.). (C) Compares lake trout period growth, pooled by type (impounded and natural). Bars represent the 95% confidence intervals around the mean estimates. 129 [ (A) Impounded waterbodies —I West Forebay -r- Smallwood Reservoir 71-75 76-80 81-85 86-90 91-95 (B) Natural Lakes ~a in 60 o Atikonak 71 - 75 76 - 80 81 - 85 86 - 90 91 - 95 (C) Combined -A- Impounded waterbodies r _JL_ Natural lakes (control) 71-75 76-80 81-85 86-90 91-95 Period (five year intervals) 130 Table 3.6: A comparison of five growth periods using one-way ANOVA's. Residual values used in the comparison were derived from exponential power functions separated by water body and sex (N indicates the number of residual values within each water body type). Period Impounded Natural F Significance (Years) N N P 1971-1975 338 111 1.733 0.189 1976-1980 433 235 6.016 0.014 1981-1985 716 517 2.043 0.153 1986-1990 1234 802 6.298 0.012 1991-1995 1459 979 1.166 0.280 131 Table 3.7: Pearson Correlation (p) results demonstrating the relationship between lake trout growth rate indices and yearly mean water levels for the period between 1972 and 1995. Water levels were measured daily at the lobstick and Gabbro flood gates. Yearly Growth indices were established using residuals generated from loge transformed incremental otolith measures fit with linear functions. (N) Represents the number of years included in the comparison. Significance values that fell near or bellow alpha (a = 0.05) are in bold text Flood Gate Statistic West Smallwood Combined Location Forebay Resevoir Indices Indices Indices Lobstick Pearson Correlation -0.570 -0.505 -0.618 Significance 0.004 0.012 0.001 (N) 24 24 24 Gabbro Pearson Correlation -0.406 -0.292 -0.413 Significance 0.055 0.177 0.050 (N) 23 23 23 132 3.3 Discussion The comparison of my age interpretations with those of an interpreter who was practiced in known-age lake trout did not differ significantly. Nor did the age bias plot reveal any significant deviations from a one-to-one relationship. Precision also did not appear to be an issue as the pair-wise comparisons for the three aging trials did not reveal any significant differences in ages between trials. I found evidence for a direct effect whereby impoundment altered recruitment for lake trout populations in the Smallwood Reservoir and the West Forebay by weakening recruitment between 1972 and 1983. The chi-square test indicated that year-class structures within the Smallwood Reservoir and the West Forebay were similar. Comparison of year-class structures among the three natural lakes also indicated a similarity in structures. Closer examination of the frequency data in the impounded water bodies determined that 17% of the sampled population was accounted for between the period 1972 and 1983. This significantly differed from the three natural lakes, as evidenced by 47% of the sampled population being accounted for during the same period. This equates to a relative difference of 30%. I have provided a table that specifies the amount of effort used to acquire the standardized netting data (Appendix G). Given that the sampling was standardized and the amount of effort per net to acquire the samples from the Smallwood Reservoir and the West Forebay was not less than the effort employed for the natural pond, it is unlikely that the lack of representation between the period 1972 and 1983 is due to inadequate 133 effort. On the contrary, with particular reference to the lake trout sampled from the Smallwood impoundment, a great deal of effort was necessary for the few samples that were acquired. I would argue that the increased effort infers additional importance on those year-classes that were present in the sampled population. Maceina's application of catch-curve residuals provided valuable indices for observing temporal trends in recruitment without manipulating the year-class structure with arbitrary groupings. In both instances, the residual values generated from the creel and standardized netting data demonstrate weakened recruitment for the same period. Yet, in the natural lakes, the residuals generated from the pooled year-class data did not. Similar to the lake whitefish, I found some evidence of a long-term relationship between weakened recruitment and water levels. Recruitment strength for the lake trout appeared to be influenced by February water levels in Gabbro whereby, if water was held back in Gabbro, lowering water levels downstream at the West Forebay, then lake trout recruitment was weakened. Conversely, if water was released in larger volumes, lake trout recruitment downstream was stronger. Mean seasonal water levels among the three reservoirs was syncopated (Ossokomanuan, Smallwood and the West Forebay)(Appendix F). Nevertheless, it should be noted, that lake trout and whitefish year-class indices for The Smallwood and West Forebay populations did not correlate with water levels at the Lobstick flood gates (Smallwood Reservoir water levels) and Jacobie flood gates (West Forbay water levels). Two possible explanations may account for this discrepancy. The daily activity pattern of these two control structures varies considerably, making water measurements from these two gates quite variable (Ken Tobin, Superintendent Operations pers. Comm.). Both 134 these control structures control the flow of water into the hydro facility at the Churchill Falls hydro electrical plant. If the hydro electricity demand goes down then both gates will close. Conversly, if demand for electricity increases then both gates open. This opening and closing occurs throughout the year and on a daily basis. Conversely, the Gabbro structure tends to open in February and remains open until the Ossokomanuan Reservoir is drained by April or May. Therefore, measurements at Gabbro tend to be less variable and more indicative of the seasonal trends. Furthermore, the Ossokomanuan Reservoir holds a storage volume 10 X smaller than the Smallwood Reservoir. This difference means that as the water is drained from Ossokmanuan Reservoir changes to the water level are more pronounced then in the Smallwood (Ken Tobin, Superintendent Operations pers. Comm.). Therefore to achieve good statistical power for tests involving Ossokomanuan reservoir requires a smaller sample size of sampled fish then the Smallwood Reservoir. The data set was robust in regards to statistical test for Ossokomanuan water levels but inadequate for the Smallwood Reservoir water levels. Given that major recruitment failure occurred between 1972 and 1983, when water levels were at their highest, it is unlikely that this disruption can solely be attributable to any long-term temporal patterns in hydrology from the Gabbro flood gates. Given the severity of the initial disruption, it is likely that environmental changes associated with the initial years of flooding most likely influenced recruitment. During the initial stages of reservoir evolution, several documented changes in the reservoir may have led to recruitment failure. In preparation for the creation of the reservoir, no attempt was made to remove trees from the area to be flooded. Therefore, it is likely that siltation and debris fields were created after the vegetation died. This may have led to traditional 135 spawning sites being smothered and a resulting failure of lake trout recruitment. Lake trout may have moved to deeper spawning sites, less suitable for egg development, and it might have taken several years before new suitable spawning sites were located. Indirect evidence for this mass siltation can be found in earlier reports by researchers which describe eutrophication and decreased water clarity (Duthie and Ostrofsky 1975; Ostrofsky and Duthie 1980). This report of recruitment failure is not the first; other studies have demonstrated this relationship between recruitment failure and impoundment creation. Subsequent to the creation of the La Grande Hydro Electric Complex in Quebec, Canada, lake trout failed to adequately reproduce and overall abundance is on the decline (Belzile et al. 2000). In Lake Cayuga, located in New York State, naturally occurring lake trout populations could not be sustained due to the smothering effect of siltation on lake trout spawning beds (Youngs and Oglesby 1972). Doyon (1997) attributed lake trout recruitment failures at the Caniapiscau Reservoir in Northern Quebec to a loss of spawning sites and an inability of spawners to find new sites. He suggests that reservoir draw-downs further compound the problem by increasing mortality of lake trout eggs. Another possible explanation for the recruitment failure could be that after reservoir creation, both chemistry and water currents changed (Duthie and Ostrofsky 1975; Ostrofsky and Duthie 1980). These changes alone or in combination with the debris may have temporarily disrupted recruitment success. Over time, recruitment in the Smallwood Reservoir and the West Forebay did become stronger. It is unlikely conditions on the spawning beds returned to normal, as the 136 old spawning beds would have existed in deeper water. Another possibility is that the lake trout adjusted to the changed environment by selecting alternative spawning sites as their principle sites were lost (Gunn and Sein 2000). My findings support the hypothesis that lake trout growth in the Smallwood Reservoir and the West Forebay may have been indirectly influenced by impoundment. The examination of growth rates among the five time intervals in the impounded water bodies indicates that lake trout growth improved subsequent to 1981. Before that time, growth was significantly weaker. The first two periods (between 1971 and 1980) demonstrated weaker growth than the latter three periods (1991 tol995). When comparing lake trout relative growth rates between impounded and natural lakes, there was a detectable difference. During the period between 1976 to 1980 growth was significantly weaker for lake trout in the impounded water bodies. As time progressed growth rates began to improve and between 1986 to 1990 growth rates in the impounded water bodies exceeded growth rates for all periods of lake trout in the natural lakes. By comparing growth rate with mean monthly water levels, I found evidence of an inverse temporal pattern between lake trout growth rates and reservoir water levels. Lake trout growth rate increased concurrently with decreased water depth. There did not appear to be any single month where water levels were particularly important. Water levels measured at the Lobstick flood gates significantly correlated to growth indices measured from West Forebay lake trout for each of the 12 months. This same inverse pattern for growth rate was also evident in the Smallwood impoundment lake trout however, no significant correlations were observed. It is likely that correlations involving the lake trout growth indices from the Smallwood did not achieve 137 significance because of small sample sizes. Nevertheless, the Smallwood lake trout did display the same negative pattern in 10 months of the year. One possible explanation for this long term relationship is related to the concentration of prey. During periods of high water level(larger volumes) the reservoir surface area is much larger and therefore the concentration of prey could be diminished as habitat availability increases. Conversely during times when the reservoir is low the prey concentration could go up as suitable habitat diminishes. Comparisons of lake trout growth indices and water level measurements taken at the Gabbro flood gate indicated only four significant relationships (May, April, July and August). As first discussed in the whitefish chapter, the pattern of filling and draining at the Gabbro flood gate is different from other areas in the reservoir. Ossokmanuan acts as a sub-reservoir, storing water during the fall for latter release in the spring when peak hydro demand occurs. It is likely that the correlations that occurred at the Gabbro flood gate happened during the spring and summer months because the Gabbro flood gates are open during that period and water levels are more indicative of the overall reservoir levels (Mike Duffeny, Reservoir Superintendent of Operations, pers. comm.). As significant relationships were found across all months, it is unlikely that any single month influenced lake trout growth more than any other. It is more probable that the phenomenon is related to the overall variation in yearly mean water levels, in particular those water levels associated with the initial years of flooding, between 1971 and 1980. Significant correlations from the additional comparison of lake trout growth with yearly mean water levels supports this hypothesis. 138 Growth also significantly differed 10 years after impoundment (between 1986 and 1990). The strong growth may be attributable to weaker recruitment in the preceding 10- year period. After inundation of the impoundment, lake trout recruitment weakened. This lack of recruitment may have resulted in a population decrease, diminishing competition for resources (McDonald and Hershey 1989). Those lake trout that remained may have benefited by increased growth rates. Increases in lake trout growth rates subsequent to population thinning have been documented by others. Following increased exploitation an overall decline in abundance led to improved lake trout body condition in Toolik Lake, Alaska (McDonald and Hershey 1989). This gradual improvement in lake trout body condition coincident with a decrease in lake trout numbers was also seen at La Grande Hydroelectric Complex in Quebec (Therrien et al. 2004). 139 4.0 General Discussion and Conclusions Reservoir creation for generating electrical power involves flooding, redirecting rivers and water bodies to ensure a steady source of water in large volumes. Such alteration in natural discharge often leads to habitat changes that can detrimentally affect fish populations. These potentially detrimental effects make it important to understand how resident fish populations respond to reservoir hydrology so that deleterious impacts can be mitigated. The primary purpose of the research was to determine the affect of impoundment at the Smallwood Reservoir on recruitment and growth of whitefish and lake trout. Determination of this was based on a sample collected over a three-year period from five water bodies (two impounded and three natural). Studies on impoundment have shown changes in water flow, depth, temperature and water chemistry (Cuerrier 1954; Wunderlich 1971; Summerfelt 1971). In response, fish change behaviourally and physiologically, and changes in fish assemblages occur (Cuerrier 1954; Engel and Magnuson 1976; Tonn and Magnuson 1982). Responses can be either positive or negative, depending upon the species and its ecology and environmental requirements. Lake trout in the Smallwood Reservoir complex were negatively affected by impoundment (1971 to 1974). Year-class strength for lake trout decreased markedly in both the Smallwood Reservoir and the West Forebay between 1972 and 1980, when impoundment first occurred (Fig. 3.4 and Fig. 3.6). Natural lakes in the area that were not impounded showed no similar decreases in recruitment (Fig. 3.4 and Fig. 3.9). Recruitment failures and population declines were also documented for lake trout after 140 impoundment of the eastern sector of La Grande Hydro Electric Complex in Quebec (Belzile et al. 2000). Although recruitment in the Smallwood Reservoir recovered, this did not occur in the case of La Grande impoundment (Belzile et al. 2000). Impoundment of La Grande Hydro Electric Complex in Quebec occurred roughly 10 to 15 years after the Impoundment of the Churchill Reservoir. Therefore, in the Belzile et al. (2000) study, it may have been too soon for recovery to have occurred. One possible explanation for the loss in lake trout recruitment may be the alteration of spawning habitat caused by the initial period of inundation (1970 to 1974). Approximately 1,600 km2 of taiga (low bog land and spruce forest) was flooded. The resulting decomposition would have led to increased production of detritus in the impounded water bodies. This sedimentation, which could have occurred over several years, would have created increased siltation. Siltation may have smothered emerging larva (Casselman 1995). Smothering may also have created localized oxygen depletion in the interstitial spaces of the spawning bed substrate. Oxygen depletion has been reported to weaken lake trout recruitment (Garside 1959; Carlson and Siefert 1974; Casselman 1995). Some evidence for the occurrence of this siltation exists: earlier reports describing water quality in the reservoir describe increased eutrophication and decreased water transparency (Duthie and Ostrofsky 1975; Ostrofsky and Duthie 1980). For example, nitrate load recorded at sampling stations pre and post flooding increased three fold and it was recorded that Secchi disc visibility recorded at the same stations reduced from 13m (1970) to 6m (1973) (Duthie and Ostrofsky 1975). Colour hazen units, a standardized measure of colour in water, increased from 0 units (1970) to 10 units (1973) (Duthie and Ostrofsky 1975). 141 Machniak (1975) in a summary report describing the general effects of reservoir creation on lake trout reported negative effects of siltation following creation. It has been demonstrated that sedimentation can increase mortality of salmonid eggs and alevins (Cordone and Kelley 1961; Younge and Oglesby 1972; Sly and Evans 1996). Siltation may also have reduced reproductive success by removing important chemosensory cues which cause adult lake trout to aggregate at traditional spawning sites. Foster (1985) reported that adult lake trout will use the scent from young-of-the-year by-products, such as egg cases and fecal matter, to identify traditional spawning beds. Therefore, sedimentation may act to smoother important chemoatractants, thereby disrupting spawning. Smothering of eggs and covering of spawning beds may not have been the only factor that led to the declines in recruitment. Between 1970 and 1975 lake whitefish recruitment dramatically increased and it has been documented that Corigonids eat small fish, larva and eggs of other species (Tohtz 1993; Doyon et al. 1998; Pothoven 2005). Therefore, the strong whitefish cohorts which occurred at the onset of impoundment may have influenced lake trout recruitment through increased predation as well as competition (Carl 2008). Additionally, juvenile lake trout and whitefish share the same food source. Both rely on zooplankton and other invertebrates in their first years of development (Scott and Crossman 1973; Carl 2008). Therefore, given the large numbers of lake whitefish, competition for resources may also have played a roll, with lake whitefish juveniles out-competing lake trout for available resources. The Impoundment and its associated diversions also caused variable flow rates and altered chemistry in the Smallwood Reservoir and the West Forebay (Duthie and 142 Ostrofsky 1975; Ostrofsky and Duthie 1980). It was reported that alkalinity, phosphates and nitrates all increased (Duthie and Ostrofsky 1975; Ostrofsky and Duthie 1980). Increases in nitrogen and phosphorous levels associated with eutrophication can reduce lake trout recruitment by creating anoxic conditions in areas where over-wintering lake trout embryos reside (Lienesch et al. 2005). Therefore, these types of changes, combined with increased siltation, and increased lake whitefish recruitment may have led to decreased lake trout recruitment. One probable explanation for the increase in lake trout recruitment after approximately a decade is that lake trout, might have forgone their tendency towards spawning-site fidelity, and adjusted to their new environment by seeking more favourable spawning locations (Gunn and Sein 2000). Lake whitefish responded differently to impoundment, showing an increase in recruitment concurrent with the initial period of impoundment (1971-1974). This increase in recruitment was short-lived, and since 1975 recruitment appears to have returned to a level typical of the long-term mean (Fig. 2.3). In contrast, lake whitefish recruitment in the natural lakes did not display this spike but remained near the long-term. Recruitment data came from large, older fish and are confirmed by observations by Barnes (1981), who reported a sharp increase in juvenile lake whitefish from 1971 to 1974 in the West Forebay and Smallwood impoundments. For example, in Small wood impoundment between 1973 and 1974, immature whitefish increased from 8% to 42% (Barnes 1981). It is probable that this increase in whitefish year-class strength occurred because of a concurrent increase in zooplankton (Duthie and Ostrofsky 1975). A major food source for young of the year lake whitefish (Raisanen and Behmer 1982; Tohtz 1993). 143 For example, in 1970 the zooplankter Diaptomus minutus had a mean abundance of 550 per m ; by 1973 its abundance had increased to 3013 per m . This dramatic increase typifies what occurred with the vast majority of zooplankton (Duthie and Ostrofsky 1975). Additionally, debris and sedimentation probably did not interrupt spawning activities as it did for lake trout. Lake whitefish, do not exhibit the same tendency towards spawning site fidelity as lake trout, instead they are opportunistic when selecting sites (Begout Anras et al. 1999). In La Grande hydroelectric complex, lake whitefish also responded to the initial flooding of the reservoir with a dramatic increase in recruitment (Deslandes et al. 1995; Therrien et al. 2004). There, the increase lasted for approximately four years but then decreased to pre-impoundment recruitment levels. They also attributed this increase in recruitment to the nutrient enrichment that followed flooding, creating large phytoplankton and zooplankton blooms. There is some evidence of a long-term relationship between water level and whitefish recruitment; recruitment was positively correlated with February water levels (Smallwood, preSiCV = 0.573, P = 0.005; West Forebay pres,cv =.583, P = 0.004) (Fig. 2.6). If water levels were higher in the reservoir then recruitment was stonger and when water levels were lower than recruitment was weaker. This differs from the findings of Des Landes et al. (1995), who reported no trend between whitefish recruitment strength and water levels. Yet it supports others who reported weakened recruitment for whitefish due to low water levels (Grimas and Nilsson 1965; Gaboury and Patalas 1984; de Graff 1993; Begout Anras et al. 1999). These reports indicate that weak recruitment occurred as a result of drawdown, or dewatering during winter months. In the extreme, dewatering 144 exposed whitefish spawning beds, leading to desiccation of eggs (de Graff 1993; Begout Anras et al. 1999; Tapio et al. 2002). During spawning, lake whitefish do not exhibit tendencies towards site fidelity, however, they do exhibit strong preference for physical characteristics associated with shallow near-shore sites (Begout Anras et al. 1999). Therefore, while they may change spawning sites they are likely to choose sites that are susceptible to the effects of water draw down. Further, overall lake geomorphology may play a role in the vulnerability of whitefish eggs to desiccation. Des Landes et al. (1995) suggested that whitefish in the La Grande complex could access deeper spawning sites because of reservoir depth. In the Smallwood Reservoir this option is likely unavailable because the maximum depth seldom exceeds 12 m. Therefore the probability that lake trout and whitefish select spawning shoals affected by draw-down is greater. For example, in the Smallwood Reservoir, whitefish recruitment was weak beginning in the early 1990s. Reservoir officials report lower than average water levels for this period. The overall temporal trend in yearly water levels (Appendix F) shows a general decline in water level beginning in the mid 1980s, continuing into the 1990s. Reservoir authorities attribute this overall decline to increasing hydro demand from the United States and to less water availability which forces them to maintain the reservoir at lower levels (Mike Duffeny, Superintendent of Operations, pers. comm.). From the 1980s through the 1990s there was a general change in the climate whereby mean annual precipitation declined in both the Churchill Falls and Wabush areas (Atlantic Climate Data Centre). I found evidence that lake trout year-class strength, similar to that of whitefish, was influenced by water levels in February. This is not surprising, given that lake trout tend 145 to select similar spawning conditions and undergo a similar incubation period as lake whitefish (Martin 1957; Scott and Crossman 1973; Gunn and Sein 2000). Therefore, during periods when water levels were lower than average, lake trout recruitment was weakened; conversely when water levels were higher lake trout recruitment strengthened ( Pres.cv — 0.606, P = 0.006). This relationship was seen only in creel data from the West Forebay area. The West Forebay creel data was a much larger sample (N = 283) than the gill netting data (N = 110) and therefore better statistical resolution could be achieved. The recruitment decline, associated with low water levels, indicates that reservoir operators need to take into account the reproductive cycles of both whitefish and lake trout if populations are to remain viable in impoundments. The incubation period for whitefish and lake trout generally runs between October and April (Martin 1957; Scott and Crossman 1973; Freeberg et al. 1990) During this period, water levels should not be drawn down to levels that will allow exposure of spawning beds during critical periods for egg incubation. Between 1974 and 1986 draw-downs averaged approximately 1.25m from peak levels. It would appear that this level of draw-down minimized the negative impact on recruitment strength. Therefore, consideration should be given to reducing flow rates whereby water levels remain near peak levels during the critical incubation period. The potential threat of frazil ice must also be considered when reservoir operators open flood gates during winter months. Frazil ice forms in waters that are turbulent and super cooled (Lawson and Brockett 1990). When cold, turbulent water (0°C) undergoes further heat loss nucleation of small (0.1-1.0mm) discoid or spicule ice crystals form (Lawson and Brockett 1990). While the water is super cooled the frazil ice continues to 146 form and stick to one another forming aggregates. Frazil ice and aggregates will stick to any submerged object it comes into contact with, forming anchor ice (Lawson and Brockett 1990). Incubating fish eggs of whitefish and lake trout can be damaged when it freezes to the bottom in areas of low current, exposes eggs after detaching substrate material or dewaters redds (Brown et al. 1993). During February, the Gabbro control structures are opened wide enough to allow 75, 000 m per second of water to pass downstream to the Smallwood Reservoir. This turbulent water could lead to the creation of large amounts of frazil and anchor ice. Given my strong correlations of year class strength with water fluctuations at the Gabbro flood gate during February, frazil ice in combination with low water levels, could have a strong influence on recruitment. As previously stated, the initial short-term increase in lake whitefish recruitment in the Smallwood Reservoir and the West Forebay probably occurred because of increased zooplankton production during the filling of the reservoir (Powell Consultants 1973; Duthie and Ostrofsky 1974; Barnes 1981). Given that plankton is the principal food source for young=of-the-year lake whitefish (Raisanen and Behmer 1982; Tohtz 1993), it is possible that increased plankton abundance accelerated growth and increased survival. During the four years that lake whitefish demonstrated strong recruitment, adult lake whitefish growth rate also increased. Growth rates between 1970 and 1975 were stronger than during any other time during the period studied (Fig. 2.13). A similar finding describing the interrelationship between zooplankton production and increased whitefish growth and recruitment was reported for coregonids in Lake Brienz, Switzerland (Miiller et al. 2007). 147 The increased growth rate exhibited by whitefish during impoundment was short- term and was followed by a period of decreased growth rate (1976-1980). Increased zooplankton production created by eutrophication may have been short in duration and, therefore, growth and recruitment could not be sustained with the increased biomass. Others have documented similar relationships between lake whitefish growth, recruitment and impoundment. Body condition of whitefish increased dramatically during the initial years of flooding at the La Grande complex Reservoir but returned to normal levels when zooplankton abundance declined (DesLandes 1995; Therrien et al. 2004). The first whitefish year-class after impoundment of Lake Sharpe in South Dakota had a faster growth rate than all previous and subsequent year classes (Elrod and Hassler 1971). Lake trout growth rates also slowed between 1976 and 1980 (Fig. 3.11). Given the strong recruitment of lake whitefish between 1970 and 1975 competition for resources may have also contributed to the decrease in growth rates for both species (Carl 2008). An alternative, perhaps complementary hypothesis may be the concentration- redistribution phenomena (Deslandes et al. 1995). The impoundment created a vast area of new habitat. The increase in habitat led to a redistribution of fish whereby traditional habitat was abandoned for newly available space. The redistribution weakened concentrations of lake trout prey species. As the availability of prey became diluted lake trout growth rates declined (Richard Verdon Pers. Comm.). It is probable that the slowing in growth rates for both species can, in part, be attributed to the change in water clarity that occurred during this period (Duthie and Ostrofsky 1975). For both zooplanktivores and piscivores, the decreased light penetration and increased turbidity would have greatly reduced the reaction distances for 148 visual foraging (Henderson and Northcote 1985; Miner and Stein 1986; Vogel and Beauchamp 1999). Therefore, the probability of encountering prey and subsequently feeding opportunities would also decline. Increased lake trout growth rates between 1982 and 1991 may be associated with changes in population density following a period of weak recruitment between 1972 and 1980. Between 1982 and 1991 growth of lake trout in the Smallwood Reservoir and the West Forebay increased more than during any other time. During the 1980s, as natural mortality (without replacement) occurred, competition for prey probably diminished and those lake trout that remained may have benefited from a relative increase in prey abundance. This relationship between abundance and condition was also seen in lake trout at La Grande Hydroelectric Complex, where body condition of lake trout increased after a decrease in overall lake trout abundance (Therrien et al. 2004). Additionally, between 1982 and 1991 the reservoir was being maintained at minimum operation levels (Appendix F). Operating the impoundment in this manner removed vast areas of fish habitat. It is likely that lake trout prey species were force into smaller areas and at higher concentrations. Lake whitefish recruitment also returned to values more indicative of the long term average and therefore competition from this species may have also been lessened. Additionally, it is probable that flushing of the reservoir occurred and water clarity values moved closer to pre-impoundment conditions. Therefore, it is likely that visual foraging distances increased, enhancing feeding opportunities. Most studies of the effects of impoundment on fish come from long-term sampling of catch per unit of effort (CUE), fish length at age and body condition 149 (Deslandes et al 1995; Therrien et al. 2004) For example, Deslandes et al. (1995) when reporting on the effects of the La Grande Complex impoundment used data collected between the period of 1997 and 1992. As stated earlier, using data collected over long periods ensures that random short-term variations can be distinguished from overall long- term trends that may be important. In this study, data were assembled quite differently, from age and growth archived in otoliths acquired from only a few years (three) of intensive sampling. Otolith-derived indices can serve as an efficient and timely alternative for establishing biochronologies of whitefish and lake trout growth without the expense of a major sampling program. Using Maceina's technique for catch curve residuals I was able to establish 26-year recruitment history for both species (Maceina 1997; Maceina and Stimpert 1998). This differs from lengthy studies, which rely on single point measures sampled over many years. For example, my short-term sampling corroborated results from two separate 20-year sampling programs (Belzile et al. 2000; Therrien et al. 2004). Given the appropriate sample size, managers can derive, from a single sampling, enough data to estimate recruitment and growth for multiple generations of long-lived species such as lake trout and whitefish. One of the additional strengths of the otolith technique is that it does not require a large sample size to achieve good statistical resolution. Each otolith contains growth information for multiple years and therefore fewer fish are required (Secor et al. 1995). Individual otolith increments can be stratified based on the year that the incremental growth occurred. As such, all increments can be used as replicate observations. This is in contrast to length at age regression comparisons which suffer from several fundamental 150 difficulties. Length at age analysis relies on the comparison among sample years of fish length binned by age. This binning can confound an individual fish's growth history making it impossible to allow for statistical modeling of growth. It does not allow for modeling, whereby growth can be partitioned into portions due to environmental changes, age group effects and year class effects (Weisberg and Frie 1990). Only through multiple years of sampling can this difficulty be overcome. Additionally, indices based on fish length to weight ratios can vary seasonally due to the influences of the fish's reproductive cycle and water temperatures, therefore derived values can vary depending on the time of year when samples were obtained (Pastor 1983; Pope and Kruse 2007). Condition indices can also vary in accordance with fish sex, genetic strain, location and species interactions (Pastor 1983; Cada 1987; Cone 1989). This variability can lead to difficulties when comparing index values among years and between fish populations. Therefore, to ensure that a body condition index is comparable among years, field sampling must adhere to strict protocols, that include large sample sizes. These protocols can limit the utility of the technique. In contrast, the analysis of growth using residuals derived from incremental otolith growth removes variability associated with age effects, location and sex and therefore gives the technique greater utility (Periera et al. 1995). This study provides important management insights into impoundment and fish and fisheries. Increases in lake whitefish and decreases in lake trout recruitment can be linked to environmental changes caused by impoundment. These changes should be taken into consideration when establishing harvest levels and regulations on newly created reservoirs. Lake trout should be given special consideration when regulations for the 151 fishery are being established. Since lake trout recruitment virtually ceases after impoundment, harvest should be reduced to protect the spawning stock. This precautionary approach would ensure that reproductively viable individuals remain in the population past the critical period of reduced lake trout recruitment (a decade in this period). In contrast, lake whitefish exploitation could be increased after the initial years of impoundment to take advantage of the increased productivity and recruitment that occurs. 152 References Aass, P. 1972. 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Journal of the Fisheries Research Board of Canada 29:787-794. 163 Appendix A Description of the relevant water bodies including; Order of flow (upstream to down stream), Areas flooded, Surface area, Maximum Reservoir operating level (above sea level), Minimum operating level (above sea level), Maximum drawdown level. Impoundment Impounded Flow Surface Maximum Minimum Maximum Lakes Area Water Water Drawdown (km2) Level Level (m) (m) Ossokmanuan Gabbro 834.00 497.15 475.03 4.12 Ossokmanuan Smallwood Sandgirt, 5750.00 472.74 464.06 8.68 Reservoir Lobstick Michikamau West Forebay Lobstick 452.93 Flour Lake Jacobie East Forebay 116.5 448.5 164 Appendix B Factors Influencing Proportional Year-Class Strength The same relationship remained between water level fluctuations and year- class strength when I used abundance data corrected for differences in yearly sampling effort. A significant relationship between the coefficient of variation in water level recorded at the Gabbro gate and year-class proportion for the Smallwood existed (pres,cv = 0.591, P = 0.004) (Fig. Al). The relationship for the West Forebay was somewhat weakened but it still approached significance (preSiCV = 0.400, P = 0.065) (Fig Al). Therefore, I incorporated the additional term of water level fluctuation (CV values) into revised catch-curve regression models that used proportion of year-class, rather than direct frequencies, to produce multiple linear regressions: Smallwood Reservoir: (1) Loge (year class proportion) = - 3.319 + (0.065*year class) r2 = 0.216, P = 0.025 (2) Loge (year class proportion) = - 89.285 + (0.046*year class) + (883.832* CV) R2 = 0.436, P = 0.004 West Forebay: (1) Loge (year class proportion) = - 3.894 + (0.071 *year class) !* = 0.311, P =0.005 (2) Loge (year class frequency) = - 132.817 + (0.068*year class) + (584.607* CV) R2 = 0.368, P =0.013 (3) Loge (year class frequency) = - 164.195 + (0.083*year class) + (686.943* CV) R2 = 0.436, P = 0.005 (4) original regression (5) multiple linear regression (6) multiple linear regression (1971-1974 removed) 165 In both instances, adjusting the data for differences in effort did not influence significance however, the magnitude of results were altered. Water level fluctuation remained a significant term in the multiple regressions. For whitefish from the Smallwood location water level fluctuations accounted for an additional 50 percent of the variance in proportional year-class strength. While in the West Forebay lake whitefish it accounted for 15 percent of the variance. For the West Forebay, if I removed the initial flooding period (1971 to 1974) from the model the variance attributable to water level fluctuation increased to 29 percent. 166 Appendix B (continued) Figure Al: Linear regressions for the relationship between residuals derived from data corrected for effort (sum of year-class proportions) and coefficient of variation values for water-level at the Gabbro flood gates: (A) West Forebay, (B) Smallwood Reservoir. Regression, (r2) Coefficient of determination, (P) significance level and (N) Number of data points contributing to the relationship are reported. 3.0 (A) Smallwood Reservoir Residuals(Smallwood)= -0.794 +(887.539-"CV values) ,..--" r2 = 0.349, P = 0.004, N = 22 _.----'" 2.0 84 78- ---""" • 10 87 8 % • 89 i\^—~-~~~^~^ 13 86 —— 93 82 > a • — 94 77 » • .-—-—•""'i'l 83 76 ^^-^f^ m • 96 m • i-H • • 95 - • 75 - .1.0 • __..-----""" _„--""" 92 .---"' -3.0 0.0000 .0005 .0010 .0015 .0020 Coefficient of variation 3.0" (B) West Forebay Residuals(Forebay)= -0.523 +(592.170*CV values) r2 = 0.160, P = 0.065, N = 22 _____.-—"""" 2.0" "" 8435 74 ••SO en a • 81 1.0" 88 87 s 82 . 13 e —— " > 95 ^____ ' o ^e ^____ o.o- @ ^—-— 93 92 ^______— 78 a 89 __i-*— " • 96 94 • — 90 a a «L> • 75 -1.0 91 77 • • -2.0 _..---"" -3.0 0.0000 .0005 .0010 .0015 .0020 Coefficient of variation 168 Appendix C Tests for homoscedasticity and independence of error among otolith residual growth values for lake whitefish. (A) A plot of all lake whitefish residual growth values (loge transformed) set against annulus number. Residuals were derived from regressions that were fit allowing for water body and sex. (B) Histogram of all residuals displaying a normal distribution. (C) A plot of observed otolith incremental width values against the predicted values. 169 (A) c o 0 Q ° 1 ] 11 n ° n n 1 c lillliili rL i IIIUP i C ik->*' J y y u u y y L = y s „ ° > £ -1 a ° 8 •§ 10 20 30 40 Annuli 1400 (B) Std. Dev = .27 1200 Mean = 0.00 N= 6655.00 J 1000 «D J— *o 800 o^ , c = 600 400 200 H -2.88 -2.63-2.38-2.13-1.88 -1.63-1.38-1.13 -.98 -.S3 -.38 -.13 .13 .38 .63 Residual values (log, transformed) 8 2 3 Fitted incremental values 170 Appendix D Relative cumulative frequency distributions used in Kolmogorov-Smirnov paired sample tests. (A) Represents the comparison of relative cumulative frequency distributions for Lakes Atikonak and Joseph.(B) Compares the relative cumulative frequency distributions for Atikonak and the West Forebay and (C) compares the relative cumulative frequency distributions of Atikonak and the Smallwood Reservoir. The comparisons of the impounded water bodies with the natural Lake Atikonak significantly differed. 171 1.21 (A) Atikonak vs Joseph (£> = 0.1152 •2 • Atikonak 0.0 ...IMHMW I I Joseph 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 1.2 (B) Atikonak vs West Forebay (D = 0.4091>D.05= 0.1971) 1.0 .6 H Atikonak I I West Forebay 0.0 —i 1—^ r ^0M$i 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98 (C) Atikonak vs Smallwood (£> = 0.4278 > D.05= 0.2209) 1.0 i •21 i HI Atikonak 0.0 J .^MfflM i I I Smallwood Reservoir 64 66 68 70 72 74 76 78 80 82 84 86 92 94 96 98 Year Class 172 Appendix E The monthly hydrological pattern measured at the (A) Ossokmanuan Reservoir (Gabbro flood gates) and (B) the Smallwood Reservoir (Lobstick flood gates) The Gabbro flood gates release water into the Smallwood Reservoir which in turn feeds water, through the Lobstick flood gates, into the West Forebay. Between February and June water levels at both flood gates are lowest. Water levels are measured according to the height above sea level (in meters (m)). The months are symbolized by the number sequence. (January (1), February (2) ...... etc.). Bars represent the 95% confidence interval around the mean estimate. 481 (A) Ossokmanuan Reservoir (Gabbro flood gate) 173 480 \ \ \ ---•4 479- \ "• \ / V —4— / \ & 478 \ -ir CJ \ \ / 4- 474 N= 684 652 699 693 727 715 710 708 709 702 702 700 1 2 3 4 5 6 7 8 9 10 11 12 Month 472 (B) Smallwood Reservoir (Lobstick flood gate) 47' j^rrri^-it-^ r X % 470" se a i \ \ \ \ ti t abov e 469 \ 1 \ 1 i 'N 1 Hei g \ 1 1 1 -^ f 1 468 1 1^. ^ J 467 -I 1 1 1- -I 1- -I 1- N = 711 671 713 738 772 771 773 771 739 780 758 716 1 2 3 4 5 6 7 8 9 10 11 12 Month 174 Appendix F The yearly mean height above sea level measured at the Gabbro and Lobstick flood Gates. Overall yearly mean water levels were calculated using daily measurements recorded over each year. (A) Represents the yearly averages for the Ossokmanuan Rservoir (Gabbro flood gates) and (B) the Smallwood Reservoir ( Lobstick flood gates).. The Gabbro flood gates release water into Smallwood Reservoir which in turn feeds water, through the Lobstick flood gates, into the upper West Forebay. Water levels are measured according to the height above sea level (in meters (m)). Subsequent to 1984 overall water levels have dropped below the long term average. Bars represent the 95% confidence interval around the mean estimate. 480 (A) Ossokmanuan Reservoir (Gabbro flood gate) 479 i i i I 478 " \ \ > i \ S3 0> \ K en > 477 " \ > O iMih1 .i •S 476 " h f \/ —i—\ \ n i 475 474 -1—i 1 1 1 1 1 1 1 1 1 1 1— 72 75 77 80 82 84 86 89 91 93 96 98 Year 475 (B) Smallwood Reservoir (Lobstick flood gate) 0 472 / \ > cd 466 -i 1 1 1 1- -i 1 1 1- -i 1 1- 71 73 75 77 80 82 84 86 88 90 92 94 96 98 Year 176 Appendix F (continued) Seasonal correlations for the three reservoir water levels. Monthly groupings reflect the seasonal pattern of water storage and drainage involved in the operation of the respective reservoirs. Mean water levels were recorded at the Gabbro (Ossokomanuan water level), Lobstick (Smallwood water levels), and Jacobie (West Forebay water levels) flood gates. Monthly means were compared between 1972 and 1996. Pearson correlation coefficient (Pearson), significance (P) and number of sample months used in the correlations (N) are also reported. Significant correlations are in bold. Grouped Reservoir activity Flood gate Gabbro Jacobie months correlations Jan. to May Emptying of the Ossokomanuan Lobstick Pearson =0.116 Pearson =0.265 Reservoir beginning in late P = 0.235 P = 0.005 January. Gabbro gate opens. N=106 N=112 Lobstick and Jacobie remain open in accordance with hydro Jacobie Pearson =0.291 demand. P = 0.002 N=107 June to Sept. Ossokomanuan is emptied by Lobstick Pearson = 0.777 Pearson = 0.496 May. Reservoirs begin to refill P < 0.001 P < 0.001 with spring runoff. In May all N = 89 N = 92 Reservoirs are at their lowest. Jacobie Pearson =0.380 P < 0.001 N = 89 Oct. to Dec. Ossokomanuan is approaching Lobstick Pearson =-0.069 Pearson =0.496 filled. Gabbro gate is closed. P= 0.581 P = 0.279 Lobstick and Jacobie open and N = 67 N = 69 close based on hydroelectiric demand. Jacobie Pearson = 0.380 P = 0.267 N = 67 177 Appendix G The total effort, number of lake trout captured and catch per unit of effort (fish per hour) for each net in the standardized sampling. All sample years are included. Effort is in hours. Catch per unit of effort (CUE) has been converted to fish per hour. Location Year Net set Effort Lake Trout CUE (Hours) (Captures) (Fish/hour) Joseph 98/06/9 1 17.88 5 .28 98/06/9 2 22.51 10 .44 98/06/9 3 21.95 3 .14 98/06/10 4 16.31 17 1.04 Joseph 99/06/12 1 17.96 4 .22 99/06/12 2 17.93 9 .50 99/06/14 3 22.56 20 .89 99/06/17 4 6.66 0 .00 Forebay 97/05/25 1 35.50 15 .42 97/05/26 2 24.00 7 .29 Forebay 98/06/15 1 9.83 6 .61 98/06/17 2 23.18 10 .43 98/06/17 3 23.06 7 .30 Forebay 99/05/28 1 19.50 16 .82 99/05/29 2 23.96 12 .50 Smallwood 97/06/7 1 72.00 7 .10 97/06/7 2 72.00 7 .10 Smallwood 98/06/18 1 24.00 5 .17 98/06/18 2 24.00 3 .13 Smallwood 99/06/6 1 23.08 7 .30 99/06/6 2 23.23 19 .82 178 Appendix H The frequency of lake trout occurring in each year class. Excluding the collections from creel data, Lake Atikonak and Panchia, all samples were collected in late May and early June using standardized mesh and sampling protocols. Impounded Natural Year-class Forebay Forebay Smallwood Joseph Atikonak Panchia (Creel) 59 0 2 0 0 0 0 60 0 0 0 0 0 0 61 1 1 2 0 0 1 62 1 3 0 0 0 0 63 1 1 0 0 0 1 64 0 0 0 0 1 0 65 3 8 0 0 0 1 66 0 2 0 1 2 1 67 1 5 0 1 0 0 68 1 3 0 0 2 0 69 7 4 1 1 1 1 70 2 0 1 0 1 0 71 2 4 2 0 1 1 72 1 1 0 1 1 1 73 0 1 0 1 3 3 74 0 4 0 0 3 2 75 0 1 0 1 2 1 76 1 2 0 2 1 3 77 0 3 0 2 0 0 179 Appendix H (continued) Impounded Natural Year-class Forebay Forebay Smallwood Joseph Atikonak Panchia (Creel) 78 2 6 0 3 3 1 79 1 4 1 6 1 4 80 1 9 1 7 9 2 81 5 77 1 5 2 8 82 1 12 0 4 5 3 83 2 23 2 4 2 0 84 5 18 2 2 2 5 85 7 18 1 7 5 0 86 6 22 3 8 6 6 87 4 16 5 3 2 5 88 1 19 4 1 3 1 89 5 14 6 3 2 7 90 3 17 2 0 4 1 91 1 17 5 2 7 2 92 0 15 0 1 0 0 93 0 11 6 0 0 0 94 0 2 0 0 1 1 95 0 1 0 0 3 0 96 0 0 0 0 0 1 97 0 0 0 0 1 0 Total 65 283 45 66 76 63 180 Appendix I Linear regressions describing the relationship between the natural logarithm of fish fork length and the natural logarithm of maximum otolith radius, by location. (A) West Forebay and (B) Smallwood represent the impounded water bodies; (C) Panchia, (D) Atikdnak and (E) Joseph represent the natural lakes. The solid line represents the regression, and the dashed lines represent the 95% confidence intervals. Sample size (N) and the correlation coefficient (r2) are also given. 181 1-0T (A) West Forebay 1.0 (C) Panchia Log, Y-ta= 0.73 Hog. X,^- 4.339 Lo&Ymd„=0.7221ogeXlongth- 4.079 2 r = 0.344, N = 243 x = 0.576, N = 63 •2 0.01 0.0 -.41 5.1 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 1.0i (B) Smallwood Reservoir 1.0 (D) Atikonak Log, ¥„,,„= 0.6131OgeXtaglh-3.598 Log. YI-to=0.6331og>Xk^-3.548 i = 0.477, N = 63 r2 = 0.617, N = 76 ° > o.o 0.01 5.2 5.4 5.6 5.8 6.0 6.2 6.4 6.( >.8 7.0 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6.8 7.0 Natural log fork length (mm) 1°1 (E) Joseph Log. Y.^ 0.3211og. Xlcng,- 1.537 ...--"."" .8 r = 0.268, N = 66 0.0 5.4 5.6 5.8 6.0 6.2 6.4 6.6 6. 8 7.0 Natural log fork length (mm) 182 Appendix J Relationship between the loge transformed fish age (annuli number) and loge transformed increment width. All regressions were run separated by water body and lake trout sex. Otolith increment measurements were made from the surface of the otolith section along the radius of age interpretation. Derived residual values from these regressions were used as an index of fish growth in all subsequent tests, (df) Degrees of freedom, (r) squared product moment correlation coefficient, (P) significance level. 183 5.0 Atikonak Female log (annuli width) = -0.724 log„(annuli) +3.433 e 5.0 Atikonak: Male 4.5- 2 df = 536, r = .863, P<0.001 log^annuli width) = -0.727 log,(annuli) +3.465 4.b df=432,r2=.877,P<0.001 4.0 ^ 4.0 3.5 . ^v Nv>, ^ ^\P ^v 3.0 •- - JI—^ \Tj r - 3.5 IV. "S- °"^ i \ ^ .5 1.0 • V° ° ^>w 0.0 ** 5 -.5 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 5.0- ;ph: Female ] Jos( Joseph: Male logiannuli width) = -0.8002 log,(annuli) +3.621 5.0- log,(annuli width) = -0.7793 logt(annuli) +3.580 4.5 v df=479,r2=.852,P<0.001 4.5 .. df = 385, r2= .878, PO.001 4.0 \1 4.0 \^ **• V 3.5" 3-5 -v [i\ "v. \| "O 3.0" "v. ^\ fi V. 3.0 2.5 s» r . -•> irvo r- [Mo ? s- - 2.5 0 "*• 2.0 a ;\ "V U U | •JUii^S. 2.0 M h 1.5 TOfe - 1.5 3 V 1.0" o v. %„ >\ V 1 0 N. ^"S. .5 *fc 5 V ^» 0.0 **• 0.0. "" -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 5.0 5.01 Panchia: Female Panchia: Male logiannuli width) = -0.7662 log,(atrnuli) +3.575 log,(annuli width) = -0.7374 log,(annuli) +3.518 2 4.5- „ df = 424, r = .903, P<0.001 4.5 s df = 427, r2= .865, P<0.001 *. v» 4.0 v 4.0 ^*v. n ^ v» ^*Vr ^ •v. 3.5 ^^ a v^ «. Sv V. 3.5 ^o v Q ^s. o v 3.0 »L "\f v. l |T\*J V o 3.0" ^v ^§v^ 0 v. 2.5 " v. O^J D 1 v. \ Hfv. v o ITss S n v " 2.5- ^ ° isJ " N. 2.0 x4 r>vD 0 x bfSj °^ (1 ° O V 0 5pM: aS v. 2.0" ^vS 0 1.5 0 " -°8"oo^e "v. ^ V 1.5' * 1.0 o •> v°§ °°^^° "v 0 >k° o"N"Sw V °° oO3^^. ° v» •x N. •> o „ ag * ?v. v. •v ^"s^ 1.0 .5 V,°o S0 ^. •N. v0 ° ^w 0 v, ^ 0.0 "** .5 D -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Loge of the annulus number 184 5.0 West Forebay: Female 5.0 West Forebay: Male log^annuli width) = -0.716 log,(armuli) +3.367 log,(annuli width) = -0.7115 loge(annuli) +3.337 2 4.5 df= 1622, r =.827, PO.001 4.5 df= 1436, r2=. 785, PO.001 4.0 4.0 •** 3.5 3.5 ^ " „ 3.0 3.0 N. •s. 2.5 2.5 n"^ o **» 2.0 2.0 J OpK„- ^ 1 1.5 1.5 1.0 1.0 o "* °o 08 ^N. .5 .5 ^v N. 0.0 0.0 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 5.0 Smallwood: Female b.O Smallwood: Male log,(annuli width) = -0.771 lo&Cannuli) +3.418 2 log^annuli width) = -0.7705 loge(annuli) +3.489 4.5 •> df=414,r =.814,P<0.001 4.6 df=283,r2=.826,P<0.001 V o 4.0 n ^ 4.0 *** 3.b 1 "^ 3.b s. o ^s. *>* IK 3.0 x. 0 ^s^ ] ° o 3.0 0 oK ° •^ ° °K 0 D -So 2.5' UK 2.5" K °\ j n K -J 0 P ° = v >• u u^SLfi ° ° °^ ^ o 8 v 2.0- 0 iM S ° o ^ 2.0 0 1.5" o 1.5" o K^ 0^!o»% ^ 1.0- 0 " oK. oo°8 8%£ % ^ 1.0" 0 0 ^ °°o^\ .5" = *u CV>K .5 X II) N, 00 -.5 0.0 .5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 0.0 1.0 1.5 2.0 2.5 3.0 3.5 4.0 Loge of the annulus number 185 Appendix K Tests for homoscedasticity and independence of error among otolith residual growth values for lake trout .(A) A plot of all lake trout residual growth values (loge transformed) set against annulus number. Residuals were derived from regressions that were fit allowing for water body and sex. (B) Histogram of all residuals displaying a normal distribution. (C) A plot of observed otolith incremental width values against the predicted values. 186 3000 (A) V3 13 ^2000 •: of 1000 t-i Std. Dev = .29 Mean = .00 ^ 4 N = 7109.00 Residual values (log, transformed) 6 / N -a es -3 3 T3 -4 c« u -5 0) 13 > a o T3 > SH O Fitted incremental values 187 Appendix L Pearson Correlation (p) results demonstrating the relationship between lake trout growth rate indices and monthly mean water levels for the period between 1972 and 1996. Water levels were measured daily at the Lobstick and Gabbro flood gates. Yearly growth indices were established averaging residuals generated from incremental otolith measures fit with power functions. (N) Represents the number of years included in the comparison for each month. Significance values that fell near or below alpha ( Month Flood Gate Statistic Forebay Smallwood Combined Location Indices Indices Indices January Lobstick Pearson Correlation -0.414 -0.404 -0.462 Significance 0.044 0.050 0.023 (N) 24 24 24 Gabbro Pearson Correlation 0.101 0.274 0.172 Significance 0.661 0.230 0.456 (N) 21 21 21 February Lobstick Pearson Correlation -0.449 -0.415 -0.497 Significance -0.032 0.049 0.016 (N) 23 23 23 Gabbro Pearson Correlation 0.255 0.177 0.264 Significance 0.253 0.430 0.236 (N) 22 22 22 March Lobstick Pearson Correlation -0.530 -0.482 -0.575 Significance 0.009 0.020 0.004 (N) 23 23 23 Gabbro Pearson Correlation -0.331 =0.154 -0.319 Significance 0.143 0.504 0.158 (N) 21 21 21 April Lobstick Pearson Correlation -0.585 -0.500 -0.628 Significance 0.003 0.013 0.001 (N) 24 24 24 Gabbro Pearson Correlation -0.448 -0.302 -0.452 Significance 0.037 0.172 0.035 (N) 22 22 22 188 Appendix L (continued) Month Flood Gate Statistic Forebay Smallwood Combined Location Indices Indices indices May Lobstick Pearson Correlation -0.524 - 0.434 -0.560 Significance 0.009 0.034 0.004 (N) 24 24 24 Gabbro Pearson Correlation -0.276 -0.149 -0.265 Significance 0.214 0.508 0.233 (N) 22 22 22 June Lobstick Pearson Correlation -0.537 -0.450 -0.576 Significance 0.007 0.027 0.003 (N) 24 24 24 Gabbro Pearson Correlation -0.349 -0.493 -0.419 Significance 0.111 0.020 0.052 (N) 22 22 22 July Lobstick Pearson Correlation -0.515 -0.518 -0.572 Significance 0.010 0.009 0.004 (N) 24 24 24 Gabbro Pearson Correlation -0.558 -0.625 -0.640 Significance 0.007 0.002 0.001 (N) 22 22 22 August Lobstick Pearson Correlation -0.543 -0.518 -0.596 Significance 0.006 0.010 0.002 (N) 24 24 24 Gabbro Pearson Correlation =0.467 =0.550 =0.543 Significance 0.029 0.008 0.009 (N) 22 22 22 189 Appendix L (continued) Month Flood Gate Statistic Forebay Smallwood Combined Location Indices Indices Indices September Lobstick Pearson Correlation -0.560 -0.523 -0.612 Significance 0.004 0.009 0.001 (N) 24 24 24 Gabbro Pearson Correlation -0.297 -0.460 -0.373 Significance 0.169 0.027 0.080 (N) 23 23 23 October Lobstick Pearson Correlation -0.528 -0.445 -0.566 Significance 0.008 0.029 0.004 (N) 24 24 24 Gabbro Pearson Correlation -0.212 -0.234 -0.258 Significance 0.344 0.294 0.246 (N) 22 22 22 November Lobstick Pearson Correlation -0.632 -0.521 -0.676 Significance 0.001 0.009 0.001 (N) 24 24 24 Gabbro Pearson Correlation -0.022 0.195 0.026 Significance 0.919 0.373 0.905 (N) 23 23 23 December Lobstick Pearson Correlation -0.668 -0.531 -0.708 Significance 0.001 0.008 0.001 (N) 24 24 24 Gabbro Pearson Correlation 0.355 0.110 0.338 Significance 0.105 0.625 0.124 (N) 22 22 22 sSLs -r n > ° 1.5 2.0 ^K 8 u -so o UTIvlSo o 0- 0|l!i > 1.0 -2 oft, <=3^\ 1.5 «8 2 ^sMS v