Chinook salmon and rainbow trout catch and temperature distributions in Lake Ontario

Journal of Great Lakes Research 35 (2009) 232–238

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Journal of Great Lakes Research j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / j g l r

Chinook salmon and rainbow trout catch and temperature distributions in Lake Ontario Thomas J. Stewart a,b,⁎, James N. Bowlby b,1 a b

Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, Ontario, Canada M5S 3B2 Ontario Ministry of Natural Resources, Lake Ontario Management Unit, RR # 4, Picton, Ontario, Canada K0K 2T0

a r t i c l e

i n f o

Article history: Received 7 July 2008 Accepted 26 November 2008 Communicated by Tim Johnson Index words: Chinook salmon Rrainbow trout Depth distribution Temperature

a b s t r a c t We determined the distributions of Chinook salmon and rainbow trout by describing seasonal mean vertical and bathymetric catch depths from 1997 to 2005 using angler creel surveys. We developed and applied a cross-validated model of Lake Ontario temperatures to determine the water temperatures associated with these distributions. During April, Chinook salmon and rainbow trout were found nearshore at a bathymetric depth of 20 m. However, rainbow trout were caught at shallower vertical depths (4 to 6 m) than Chinook salmon (8 to 10 m). Both species moved deeper and farther offshore during May, June, and July. Vertical catch depths were similar, but rainbow trout were found further offshore (40 to 65 m bathymetric depth) than Chinook salmon (35 to 50 m bathymetric depth) during June, July and August. During September, Chinook salmon moved closer to shore (25 to 35 m bathymetric depth) and to shallower depths (9 to 12 m), consistent with river mouth staging associated with spawning. Rainbow trout remained offshore (45 to 60 m bathymetric depth) in deeper water (11 to 16 m). The species occupied significantly different spatial habitats during April, August, and September. Mean catch temperatures of both species were similar and increased seasonally to 13 to 14 °C during August and September. Rainbow trout were caught at cooler temperatures than Chinook salmon during June and July. The estimated temperature distributions agree with independent field studies but are different then previously assumed in bioenergetic models. © 2009 Elsevier Inc. All rights reserved.

Introduction Chinook salmon (Oncorhynchus tshawytscha) and rainbow trout (O. mykiss) are the dominant offshore predators in Lake Ontario, Lake Michigan, and Lake Huron (Rand and Stewart, 1998a; Jonas et al., 2005; Mohr and Ebener 2005) and are common in Lake Superior and Lake Erie (Kocik and Jones, 1999). They are the primary species targeted by recreational anglers in offshore Lake Ontario (Stewart et al., 2004) and an important component of the Lake Michigan and Lake Huron recreational fisheries (Bence and Smith, 1999). Alewife (Alosa psuedoharengus) are the principal prey of both species (Rand and Stewart, 1998b; Lantry 2001; Mohr and Ebener 2005; Jonas et al., 2005). Growth and production of Great Lakes salmon and trout is limited by alewife production (Stewart and Ibarra, 1991; Rand and Stewart, 1998b; O'Gorman and Stewart, 1999), suggesting that Chinook salmon and rainbow trout compete for common alewife prey. In the Great Lakes, many studies have documented habitat partitioning among prey fish (Brandt, 1980; Brandt et al., 1980; Crowder et al., 1981; Urban and Brandt, 1993), but few studies have examined top

⁎ Corresponding author. Tel.: +1 613 532 5550. E-mail addresses: [email protected] (T.J. Stewart), [email protected] (J.N. Bowlby). 1 Tel.: +1 613 476 7842. 0380-1330/$ – see front matter © 2009 Elsevier Inc. All rights reserved. doi:10.1016/j.jglr.2008.11.012

predators (Olson et al., 1988). Sympatric fish species, feeding on the same prey, can reduce competition by separating in time or space (Ross, 1986). Both Chinook salmon and rainbow trout are caught by angling during daylight, suggesting diurnal feeding, and so temporal separation is likely limited. We hypothesize that Chinook salmon and rainbow trout reduce their potential competition for alewife by separating in space. A separation of these predators in space may result in occupation of different temperatures. Temperature is a critical variable in bioenergetic models predicting levels of fish consumption (Ney, 1993; Hanson et al., 1997). Bioenergetic-based estimates of consumption are important to quantify top-down influences on fish community structure and food webs (McQueen et al., 1989; Rand et al., 1995; Pauly et al., 2000). Moreover, these estimates of consumption have been used to assess levels of salmonine predator demand resulting in changes to stocking policy (Stewart and Ibarra, 1991; Jones et al., 1993; O'Gorman and Stewart, 1999). The temperature distribution of fish in the wild is presumably a complex interaction of their physiologically preferred temperature and their need to obtain food or avoid predation. With little information on Chinook salmon and rainbow trout distribution in the Great Lakes, bioenergetic studies have assumed occupied temperatures with very few supporting observations (Stewart and Ibarra, 1991; Rand et al., 1993; Rand and Stewart, 1998a). Bioenergetic models of Chinook salmon and rainbow trout consumption in the Great Lakes would be improved with deter-

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minations of catch temperatures as measures of potential occupied temperatures. Our objectives were to 1) describe the seasonal changes in the mean vertical and bathymetric catch depths of Chinook salmon and rainbow trout in Lake Ontario, 2) test the hypotheses that Chinook salmon and rainbow trout separate in space throughout the season, 3) determine and compare water temperatures associated with their catch distributions. Methods Catch rates We calculated catch rates of Chinook salmon and rainbow trout caught by anglers fishing in the Canadian waters of Lake Ontario from 1997 to 2005 based on survey data collected by the Ontario Ministry of Natural Resources, as detailed by Stewart et al. (2004). For each fishing boat trip, anglers were asked how many fish of each species were caught, the depth at which fish were caught, the bathymetric depth over which they were fishing, the time spent fishing and the species of fish being sought. The date and fishing port also were recorded for each fishing trip. Often, fishing was done offshore in areas with very few features to orient anglers. Anglers commonly use acoustic depth sounders to monitor the bathymetric depth and to locate specific fishing areas or bottom features. Two common fishing methods are to fish near the surface or to use downriggers which place the fishing bait or lure at a controlled depth. Use of the downrigger method means that vertical depths and bathymetric depths of fishing are monitored and usually known by anglers. We assumed that vertical and bathymetric depths reported by anglers were accurate and any errors due to mis-reporting or approximations were random. We only used fishing trips where anglers indicated they were fishing for salmon or trout and trips where at least one salmon or trout was caught regardless of the species being sought. Rare fishing trips with catch depths greater than 50 m or bathymetric depths greater than 115 m were deleted (113 observations comprising 0.7% of all records). Depths were assigned to 5 m categories with the depth estimated as the mid-point of the category. A total of 15,780 salmonid fishing trips were described from April to September, 1997 to 2005 over a broad range of vertical depths and bathymetric depths (Tables 1 and 2). The number of fishing trips recorded per month ranged from 53 to 792 among years with a mean of 303. We calculated the catchper-unit-of-fishing-effort (CPUE) (fish·angler-hour− 1) for both Chinook salmon and rainbow trout as the catch of fish divided by the time spent fishing. Catch distribution To estimate the vertical depth and bathymetric depth of the fish we assumed that fish density was proportional to CPUE and calculated a weighted mean vertical and bathymetric catch depth for each month and year using CPUE as weights. The use of a weighted mean ensures

Table 1 Number of fishing trips by vertical depth category and month between 1997 and 2005. Vertical depth (m)

April

May

June

July

August

September

2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5

576 692 427 273 228 93 53 9 19 4

242 266 252 153 53 18 15 4 3 2

280 233 217 173 153 58 40 3 6 3

1062 822 807 707 653 235 139 24 25 8

1191 956 1013 898 787 239 144 27 20 12

510 430 243 195 169 58 30 5 3 1

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Table 2 Number of fishing trips by bathymetric depth category and month between 1997 and 2005. Bathymetric depth (m)

April

May

June

July

August

September

2.5 7.5 12.5 17.5 22.5 27.5 32.5 37.5 42.5 47.5 52.5 57.5 62.5 67.5 72.5 77.5 82.5 87.5 92.5 97.5 102.5 107.5 112.5

21 196 256 251 357 180 375 149 128 137 50 25 73 24 29 36 7 8 30 4 4 9 2

18 68 162 226 205 73 97 33 37 22 17 5 19 12 3 3 2 3 6 2 2 6 0

14 29 36 87 162 108 219 95 84 85 46 26 57 11 13 31 6 5 32 3 4 8 3

13 24 44 129 475 427 913 489 451 504 215 185 271 63 41 84 26 9 52 9 10 13 18

5 39 118 366 765 450 810 436 422 403 194 187 331 128 96 142 53 30 109 34 33 62 33

20 157 163 202 271 157 184 82 65 60 28 19 48 20 27 28 16 9 27 11 7 10 5

that depths with higher catch rates, and presumably higher densities of fish, are given more weight. Taylor's power law plots of log10 mean and variance (Fry, 2003) by month across years indicate a normalizing transformation coefficient of 0.3–0.6. This suggests that CPUE is approximately distributed as a Poisson variable (Fry, 2003) so we applied a square root transformation to normalize the distribution and reduce the influence of extreme values. We describe seasonal changes in the mean vertical and bathymetric depth for each species by determining the among-year means and 95% confidence intervals for each month, assuming the estimates were independent normally distributed random variables. Our null hypothesis is that Chinook salmon and rainbow trout occupy the same location in space. The fish location is two dimensional, being defined as a bathymetric depth and a vertical depth. Therefore, single factor repeated-measures multivariate analysis of variance (MANOVA; Quinn and Keough, 2003) was used to test the hypothesis that Chinook salmon and rainbow trout occupied different locations (vertical depth and bathymetric depth) in space. For each month, we tested for the effect of species on the response variables of vertical depth and bathymetric depth. We used the multivariate test statistic Wilk's λ (α = 0.05) to evaluate the null hypothesis of overlap in locations. Catch temperature Temperature information collected during our study period (1997 to 2005) and region was insufficient to estimate the temperatureat-depth for individual fishing trip locations. From earlier studies (Stewart and Robertson, 1991), we postulated that it might be possible to get reasonably accurate and precise estimates of the mean Table 3 Results of multivariate analysis of variance for the effect of species on vertical depth and bathymetric depth. Month

Wilk's λ

Effect df

Error df

F

P

April May June July August September

0.27 0.97 0.88 0.81 0.51 0.29

2 2 2 2 2 2

11 13 15 15 15 15

14.79 0.17 1.00 1.73 7.19 18.61

b 0.01 0.84 0.39 0.21 b 0.01 b 0.01

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Fig. 1. Mean and 95% confidence interval of Chinook salmon (solid square ■) and rainbow trout (open circle ○) vertical depth (a) and bathymetric depth (b) by month in Lake Ontario during 1997–2005.

temperature-at-depth for broad regions using empirical models developed from temperature profile data and more readily available surface temperature information. We assembled 26,234 spatiallyreferenced observations of temperature-at-depth in Lake Ontario waters with bathymetric depths b116 m from April to September, 1967 to 2002, collected during Environment Canada, Canadian Department of Fisheries and Oceans, and the United States Environment Protection Agency surveillance cruises. Additional surface temperature data from 1995 to 2005 were obtained from the National Oceanic and Atmospheric Administration's CoastWatch Program and the National Data Buoy Centre (Station 45012). In Lake Ontario, nearshore run-off and tributary inputs cause the lake to warm beginning in the nearshore and gradually moving offshore as the season progresses (Rodgers, 1965). Stratification, caused by increased thermal resistance to mixing as surface waters warms, begins first in the nearshore and gradually expands to the offshore region. Annual variation in temperature also

can change the seasonal timing and amplitude of warming and stratification. This general pattern is modified by wind-induced upwellings, currents, and internal seiches and can result in persistent regional differences in thermal structure (Boyce et al., 1983; Simons and Schertzer, 1987; Stewart and Robertson,, 1991). We reasoned that seasonal changes in thermal structure would be related to distance from shore, vertical water depth, region, and annual variations in mean lake temperature. We used these variables to develop and crossvalidate predictive models of Lake Ontario temperature-at-depth. We calculated the average annual monthly surface temperature of the mid-lake region to describe the variation in lake temperature. The mid-lake region was defined as west of longitude 77.5 °W and east of longitude 78.5 °W and greater than 100 m bathymetric depth. An annual mid-lake temperature index (MID) was calculated as the mean surface temperature in this region each month. No mid-lake surface data were available for April and May, 2003. Instead, MID was estimated from Toronto, Ontario air temperature (Environment Canada Climate Archive) and MID regressions (May, N = 21, r2 = 0.58, P b 0.001, April N = 29, r2 = 0.49, P b 0.001) for these months. Other variables were depth category (D), bi-week designation (BW) where the first half of the month = 1 and the second half of the month = 2, sounding depth in metres (SD), and four regions (REG) based on north-south and east-west quadrants of the lake. The delineation between the north and south regions was at latitude 43.5°N and the delineation between the east and west regions was at longitude 77.8°W. The maximum depth category and depth interval (5 m or 10 m) differed each month to ensure there were enough observations within each depth category, but still capture sufficient variation in temperature-at-depth. Before stratification, temperature varied less with depth, so the depth categories were chosen to be broader and fewer, decreasing in breadth and increasing in number as the lake stratified. The depth categories used for each month were as follows: April −10 m categories with all depths greater than 29.9 m combined, and May, June, July, August and September − 5 m categories with all depths greater than 29.9 m, 34.9 m., 39.9 m, 49.9 m and 49.9 m combined, respectively. The temperature data described above were divided into three independent data sets. First a dense data set was chosen based on the criterion of six or more observations per depth category per year and month (April to September). This data set was further divided into two data sets, a larger model development data set, and a smaller random subset of 10% of the observations from the dense data set. A third sparse data set consisted of all the remaining data that had fewer than six observations in one or more depth categories per year. For the model development data set, a general linear model (GLM) was run with all variables and the interaction term D × SD using a stepwise forward-selection procedure (P to enter and P to remove = 0.05). The final model comprised only significant variables from the stepwise selection included. Based on an examination of residuals from an initial run, each of the April data sets (dense, dense random subset, and sparse) were further divided into two data sets and separate models developed and cross-validated, one with sounding depths less than or equal to 30 m and one greater than 30 m.

Table 4 Summary of parameters and statistics by month for the temperature model of Lake Ontario. Month

Parameters in the model

Depth cat. (m)

Model N (Yrs.)

Year range

Model N (Obs.)

R2

April (≤30 m) April (N 30 m) May June July August September

BW, REG, SD, MID BW, REG, SD, MID BW, REG, D, SD, MID, D × SD BW, REG, D, SD, MID, D × SD REG, D, SD, MID, D × SD BW, REG, D, SD, MID, D × SD BW, REG, D, MID, D × SD

10 to N29.9 10 to N29.9 5 to N29.9 5 to N34.9 5 to N39.9 5 to N49.9 5 to N49.9

13 9 5 7 9 13 10

1970–1984 1976–1984 1970–1992 1975–1982 1975–1987 1976–1992 1975–1987

1660 1910 1628 2008 2741 4457 3009

0.52 0.69 0.62 0.62 0.69 0.71 0.58

(BW = Bi-week, REG = Region, D = Depth, SD = Sounding Depth, MID = Mid-lake temperature index). R2 is the adjusted r2 for each model.

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Table 5 Parameter estimates for the monthly temperature models. Variable

Type

Intercept Sounding Depth (m) Mid-lake Surface Temperature (°C) BW = 1 BW = 2 NW Region NE Region SW Region SE Region 0–4.9 m 5–9.9 m 10–14.9 m 15–19.9 m 20–24.9 m 25–29.9 m 30–34.9 m 35–39.9 m 40–44.9 m 45–49.9 m N 49.9 m 0–4.9 m × Sounding Depth (m) 5–9.9 m × Sounding Depth (m) 10–14.9 m × Sounding Depth (m) 15–19.9 m × Sounding Depth (m) 20–24.9 m × Sounding Depth (m) 25–29.9 m × Sounding Depth (m) 30–34.9 m × Sounding Depth (m) 35–39.9 m × Sounding Depth (m) 40–44.9 m × Sounding Depth (m) 45–49.9 m × Sounding Depth (m) N 49.9 m × Sounding Depth (m)

Month

Constant Cont. Cont. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont. Cat. × Cont.

April (≤30 m)

April (N 30 m)

May

June

July

August

September

3.171 − 0.064 0.683 − 1.000 1.000 0.075 − 0.009 − 0.414 0.184 – – – – – – – – – – – – – – – – – – – – – –

1.174 − 0.004 0.767 − 0.425 0.425 0.074 − 0.064 − − 0.010 – – – – – – – – – – – – – – – – – – – – – –

4.135 − 0.036 0.636 − 1.206 1.206 0.266 0.749 − 0.679 − 0.335 2.081 1.240 0.033 − 0.017 − 0.778 − 1.038 − 1.038 − 1.038 − 1.038 − 1.038 − 1.038 − 0.018 − 0.012 0.000 − 0.004 0.011 0.011 0.011 0.011 0.011 0.011 0.011

6.360 − 0.025 0.394 − 0.374 0.374 − 1.366 − 0.307 2.212 − 0.539 4.715 2.868 2.124 − 0.578 − 0.617 − 1.553 − 3.134 − 3.825 − 3.825 − 3.825 − 3.825 0.000 0.013 − 0.029 − 0.004 − 0.018 − 0.002 0.014 0.026 0.026 0.026 0.026

2.431 − 0.012 0.496 0.000 0.000 − 2.856 1.013 0.187 1.656 7.052 5.795 3.807 1.569 − 0.909 − 2.089 − 3.104 − 5.934 − 6.183 − 6.183 − 6.183 0.024 0.009 − 0.006 − 0.024 − 0.014 − 0.011 − 0.009 0.016 0.015 0.015 0.015

5.099 − 0.008 0.362 0.104 − 0.104 − 2.944 1.127 0.506 1.311 7.977 5.717 5.458 3.577 0.917 − 1.310 − 3.879 − 6.251 − 5.925 − 6.282 − 6.282 0.018 0.026 − 0.002 − 0.016 − 0.022 − 0.013 − 0.005 0.019 0.004 − 0.009 − 0.009

10.450 0.000 0.095 0.532 − 0.532 − 1.963 0.803 0.887 0.272 5.262 3.361 4.644 1.598 1.488 1.431 − 3.498 − 2.632 − 4.621 − 7.034 − 7.034 0.007 0.027 0.003 0.031 − 0.011 − 0.044 − 0.001 − 0.024 − 0.010 0.020 0.020

“Cont.” is a continuous variable and “Cat.” is a categorical variable (0 or 1). No data were available to estimate parameters for the SW region in April at depths N30 m.

Models were cross-validated by using the final model parameterizations to predict temperature of the sparse data set and the dense random subset. The comparison of observed and predicted temperatures for the dense random subset assessed the ability of the model to predict temperatures for years included in the model development. The same comparison for the sparse data set assessed the ability of the model to predict temperatures during years not included in the model. We evaluated the models for each month by regressing observed temperatures (Y) against predicted temperatures (X) using the zero intercept model Y = bX. Adequacy of the model was assessed by examining the slope (b) for a significant departure from 1.0 (α = 0.05). For our application, we were interested in the ability of the models to estimate the mean temperature-at-depth and so, we determined the mean and 95% confidence intervals of the differences between the individual predicted and observed temperatures (observed minus predicted) each month for both the sparse and dense random data sets. The temperature at the depth of capture of Chinook salmon and rainbow trout during individual fishing trips during 1997 to 2005 was estimated by using the empirical temperature models to estimate

temperature at capture depth from the annual mid-lake surface temperature index (MID), bi-week designation (BW) of the date of capture, sounding depth (SD) equivalent to depth of water over which fisherman reported fishing, region (REG) based on the port of entry, and the depth category (D) corresponding to the depth of capture. A weighted mean temperature was estimated for each month and year using the square root of CPUE as a weighting variable. We described seasonal changes in the mean catch temperature for each species by determining the among-year mean temperature and 95% confidence interval for each month. We calculated the difference between Chinook salmon and rainbow trout weighted mean catch temperatures for each month and year and evaluated whether the differences were significantly different from zero using a paired t-test (α = 0.05). Results Catch distribution Box-plots (not shown) of vertical depth and bathymetric depth indicated general symmetry in the distributions and homogeneity of

Table 6 Summary of model cross-validation statistics for the independent sparse data set by month. Month

Cross-validation N (Years)

Year range

Cross-validation N (Obs.)

Coefficient of Obs. vs. Pred.

R2

Mean of difference (°C)

Lower 95% bound

Upper 95% bound

April (≤30 m) April (N 30 m) May June July August September

14 21 12 14 12 18 13

1968–2001 1968–2002 1968–1993 1967–1993 1967–1993 1967–2002 1967–1992

813 1480 1055 1370 1266 2690 1634

1.09⁎ 1.00 1.02 0.96⁎ 0.99 0.99 1.02

0.19 0.44 0.53 0.62 0.76 0.78 0.59

0.31 0.02 0.32 − 0.36 − 0.10 − 0.32 0.10

0.22 − 0.01 0.16 − 0.48 − 0.25 − 0.45 − 0.08

0.41 0.05 0.48 − 0.23 − 0.04 − 0.20 0.28

An asterisk indicates a coefficient significantly different than one (P b 0.05).

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Table 7 Summary of model cross-validation statistics for the independent random data set by month. Month

Cross-validation N (Years)

Year range

Cross-validation N (Obs.)

Coefficient of Obs. vs. Pred.

R2

Mean of difference (°C)

Lower 95% bound

Upper 95% bound

April ≤30 m) April (N 30 m) May June July August September

13 9 5 7 9 13 10

1970–1984 1976–1984 1970–1992 1975–1982 1975–1987 1976–1992 1975–1987

187 212 183 225 305 498 334

1.02 1.02 0.97 1.00 1.01 1.00 0.99

0.55 0.63 0.60 0.67 0.72 0.70 0.62

0.00 0.07 − 0.09 − 0.02 0.21 0.04 − 0.14

− 0.18 0.00 − 0.31 − 0.34 − 0.15 − 0.25 − 0.47

0.17 0.13 0.13 0.31 0.57 0.33 0.19

None of the coefficients was significantly different than one (P b 0.05).

variances between species within a month. Non-symmetrical distributions, indicating potential deviation from normality, were evident for bathymetric depths during May for both species and for rainbow trout vertical depths during September, but deviations from normality were not significant (Kolmogorov–Smirnov test, P = 0.20). No outliers were detected. Chinook salmon and rainbow trout occupied significantly different locations during April, August and September (Table 3). During April, Chinook salmon were caught 8 to 10 m below the surface while rainbow trout were shallower at 4 to 6 m depth (Fig. 1). Both species were found nearshore during April in waters with a mean bathymetric depth of about 20 m (Fig. 1). During May and June, both species moved deeper and farther offshore (greater bathymetric depth). Most Chinook salmon and rainbow trout were caught 11 to 17 m below the surface during June, July and August. Both species moved further offshore during these months, to bathymetric depths of 35 to 65 m. During September, Chinook salmon moved closer to shore (25 to 35 m bathymetric depth) while rainbow trout remained offshore (45 to 60 m bathymetric depth). At this time, Chinook were caught in shallow water (9 to 12 m), while rainbow trout were caught in deeper waters (11 to 16 m). Catch temperature For most months, all variables initially included in the temperature model were retained in the GLM stepwise forward selection (Table 4). Depth (D) was not significant for April and bi-week designation was not significant for July. Region (REG) was significant in all models. Parameter estimates for the models are shown in Table 5. Crossvalidation of the models indicates that they were able to predict temperature-at-depth very well using both the sparse and random

Fig. 2. Mean and 95% confidence interval of Chinook salmon (solid square ■) and rainbow trout (open circle ○) catch temperatures by month in Lake Ontario during 1997–2005.

data sets. Slopes of equations relating observed to predicted temperature were all close to 1.0 and significantly different from one in only two cases (Tables 6 and 7). The 95% confidence intervals of the mean differences between the predicted and observed temperatures were within +0.5 °C (one exception) for both the sparse data set (Table 6) and the dense random data set (Table 7). The ability of our models to predict the mean temperature-at-depth with reasonable accuracy and precision over a broad range of years (1967 to 2002) gave us confidence in applying them to the fishing trip information to estimate mean catch temperatures during 1997 to 2005. During April, Chinook salmon were caught in waters from 3 to 5 °C and then increasingly warmer temperature through August (Fig. 2). They occupied a narrow band of temperatures from 13 to 14 °C during August and September. Rainbow trout showed a similar seasonal pattern but at lower temperatures until August and September. Mean catch temperatures of Chinook salmon and rainbow trout were statistically different only during June and July (Fig. 3). Discussion Chinook salmon and rainbow occupied different spatial habitats in April, August, and September. We detected no such difference during May, June, and July. In Lake Ontario, most alewife are offshore during May and June, occupying temperatures of less than 4.0 °C during May and less than 7.0 °C during June (O'Gorman et al., 2000), and likely move to warm water to spawn in late June or early July (Hook et al., 2007). During May and June, most alewife occupy cooler temperatures than the mean catch temperatures we estimated for the predators. However, alewives were the primary food item in stomachs of anglercaught Chinook salmon and rainbow trout in May and June (Lantry,

Fig. 3. Mean and 95% confidence interval of the difference between Chinook salmon and rainbow trout weighted mean catch temperature (Chinook salmon minus rainbow trout) by month in Lake Ontario during 1997–2005. The P-value resulting from a paired t-test is shown above each value.

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2001). Our study, the diet study, and alewife distributional studies occurred in different regions of Lake Ontario and may be unrelated. Alternatively, predators may be choosing to seek food in shallower warmer water, and encounter some alewife, albeit at low density. Great Lakes salmon and trout diets show highest prey diversity during the spring, although diets are still dominated by alewife (Brandt, 1986; Jude et al., 1987; Rand and Stewart, 1998b; Lantry, 2001). The inclusion of alternative prey in the predators' diet in spring is consistent with lower alewife availability and this could result in increased competition for alewife among predators. Differences in the locations of Chinook salmon and rainbow trout during April and September may be explained by the seasonal spawning migrations of these species to and from Lake Ontario tributaries. Most Lake Ontario rainbow trout spawn in tributaries during April and return to the lake, and this may explain the shallower nearshore catch distribution of rainbow trout during April. Similarly, spawning Chinook salmon stage at river mouths and begin moving into tributaries during September and this may explain their shallower, nearshore catch distribution during September. During June and July, the analysis of catch distributions and temperatures were contradictory. Although Chinook salmon and rainbow trout apparently occupied different temperatures, we detected no significant differences in location between the species. This apparent contradiction may have resulted from the higher statistical power of the paired t-test of temperature differences, and suggests that these species were actually caught in different locations but this was not detected by the MANOVA. During August and September, the species separated in space but catch temperatures were not statistically different. During these months, the differences in nearshore and offshore temperatures are less pronounced as thermal fronts that reduce mixing of nearshore and offshore are less prevalent (Ullman et al., 1998). Under these conditions, our observation of a small difference in catch depths within the epilimnion and a large separation in the distance from shore would not be associated with differences in temperature. Our observations offer new insights and corroborate past studies of Chinook salmon and rainbow trout distribution. This study and Olson et al. (1988) found Chinook salmon at similar depths in Lake Ontario during spring and late summer. However, they observed Chinook salmon deeper (16 to 22 m) during August 1981 and 1982 in southern Lake Ontario than the depth ranges we observed, consistent with the observation of deeper thermoclines during August in the south (Stewart and Robertson, 1991). Haynes and Keleher (1986) had intermittent radio tracking success of Chinook salmon during spring and summer in Lake Ontario offshore of 5 km during 1984. Our data suggests their intermittent signals were more likely due to depth attenuation of the radio signals than movement offshore, as during April we observed Chinook salmon from 8 to 11 m, close to the depth of complete attenuation of radio signals (12 m). However, some tracking maps presented in Haynes and Keleher (1986) show a crosslake movement of Chinook salmon consistent with the general offshore movement we observed. In this study, the offshore movement of rainbow trout was consistent with the timing of thermal bar formation in Lake Ontario (Rodgers 1965), which is consistent with other studies of rainbow trout movement and distribution associated with temperature (Haynes et al., 1986; Bowlby and Daniels, 2003; Hook et al., 2004). In Lake Ontario, Chinook salmon were caught deeper than rainbow trout during April to mid-June (Aultman and Haynes, 1993), consistent with our April estimates, but different than our May and June estimates, where catch depths were similar. Previous bioenergetic models assumed that these species occupied the warmest temperature available, and with Chinook salmon not exceeding 11 °C and rainbow trout not exceeding 15 °C (Stewart and Ibarra, 1991; Rand et al., 1993). These assumptions are different than our determinations of catch temperatures, especially during summer months, when Chinook salmon were in warmer water and rainbow

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trout were in cooler water than previously assumed. Do our estimated catch temperatures provide better estimates of occupied temperature applicable to bioenergetic models? Chinook salmon (Brett, 1971) and lake trout (Martin, 1952; Morbey et al., 2006) have been observed, presumably feeding, at higher temperatures before retreating to cooler water. Also, Pacific salmon in their native range have shown considerable diurnal and short-term variation in their occupied temperatures attributed to foraging (Walker et al., 2000). Chinook salmon and rainbow trout in Lake Ontario could be taking feeding forays into less preferred warmer waters but maintaining non-feeding positions at cooler temperatures. However, we think this is unlikely. Our results are consistent with independent Lake Ontario studies based on gillnets which observed that Chinook salmon selected 14.4 °C (Olson et al., 1988) and 13.2 °C (Stewart and Robertson, 1991) in summer. In contrast, Wurster et al. (2005) measured stable isotopes of oxygen of Chinook salmon otoliths from Lake Ontario, and applied an aragonite temperature–fractionation relation scaled to mean lakewide surface temperatures to estimate occupied temperatures. They concluded that Lake Ontario Chinook salmon occupy much warmer temperatures, as high as 19 to 20 °C during summer, and often approach their incipient lethal limit of 21 to 22 °C. We cannot readily account for the difference between these much warmer estimates and all other observations and estimates. However, the weight of evidence suggests that Chinook salmon occupy temperatures warmer than previously assumed in bioenergetic models. Less information is available for occupied temperatures of rainbow trout. In Lake Michigan, in proximity to a thermal discharge, rainbow trout (≥1 kg) preferred 15 °C (Spigarelli and Thommes, 1979). In other studies, stocked rainbow trout in freshwater reservoirs occupied temperatures of 16 to 21 °C (Horack and Tanner, 1964), 10 to 17 °C (Jones, 1982), 11 to 22 °C with a mean catch temperature of 18 °C (Stables and Thomas, 1992), and 8 to 13 °C with a mean of 10 °C (Barwick et al., 2004). Without, direct observations to the contrary, we think it is reasonable to assume that our estimates of rainbow trout catch temperature represents occupied temperatures in Lake Ontario. We recommend using our estimates of catch temperatures as measures of occupied temperatures to reassess the level of Chinook salmon and rainbow trout predator demand in Lake Ontario. Angling statistics are primarily collected to monitor trends in harvest and fishing effort and our study demonstrates their additional value to ecological studies. However, we must be cautious in using angler CPUE to infer fish density. Fishing effort was not random, but we benefited from large sample sizes that included fishing trips over the complete range of vertical depths and bathymetric depths assessed in the study. This broad based sampling means that, as a group, anglers sampled most of the habitat within our sampling frame. It is very unlikely that there were large aggregations of fish missed by anglers. However, we were dependent on fish taking a lure or bait in order to be detected, and non-feeding or satiated fish may have alternative distributions. Also, catch rates could be influenced by non-linear changes in catchability with density (Peterman and Steer, 1981), angling skill or the feeding behaviour of the fish. The ability of our empirically derived temperature models to reliably estimate the temperature-at-depth in Lake Ontario may have other applications in Lake Ontario such as describing the thermal distribution of other fish species, estimating spatially explicit and whole-lake zooplankton production from temperature (Shuter and Ing, 1997) and estimating thermal habitat (Christie and Regier, 1988). The approach we used of reconstructing three dimensional thermal structure scaled to indices of surface temperature may also have application in predicting temperature-at-depth in the other lakes. Acknowledgments The authors thank Norine Dobiesz and Susan Doka for providing additional temperature data. Funding for the project was provided by

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