Catch and effort variation in the commercial gillnet fishery of Lake Winnipeg, Canada, in relation to environmental factors

Journal of Great Lakes Research 38 (2012) 26–34

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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 ev i e r. c o m / l o c a t e / j g l r

Catch and effort variation in the commercial gillnet fishery of Lake Winnipeg, Canada, in relation to environmental factors Jeffery Speers a, 1, Darren Gillis b,⁎ a b

Ontario Ministry of Natural Resources, 1450 Seventh Avenue East, Owen Sound, Ontario, Canada N4K 2Z1 Department of Zoology, University of Manitoba, Winnipeg, Manitoba, Canada R3T 2N2

a r t i c l e

i n f o

Article history: Received 3 April 2010 Accepted 5 May 2011 Available online 22 June 2011 Communicated by Thomas Hrabik Index words: Sander canadensis Sander vitreus Generalized linear models Fishery Lake Winnipeg Environmental effects

a b s t r a c t The impact of environmental factors on the fishing effort and catch rate of sauger (Sander canadensis) and walleye (Sander vitreus) was examined for the commercial fishery of Lake Winnipeg's south basin. Time was represented biologically as degree days. The influences of light intensity, wave height, barometric pressure, and the discharge rate of the Red River were examined using generalized linear models with the quasi-family of distributions. The percentage of the null deviance was found to be an appropriate selection criterion for choosing variables to include in the models. Effort declined throughout the early fishery and declined with increasing winds later in the year. Walleye catch rate declined through the early season. Sauger catch rates increased and then declined as the early season progressed and were associated with indicators of decreased light intensity. The relationship between catch rate and each statistically significant environmental variable was consistent with the habitat preferences of the species under examination. © 2012 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction Differences in water chemistry, light intensity, and water temperature often dictate the distribution and population fluctuations of fish and other aquatic organisms (Doan, 1942; Kling, 1998; Sundermeyer et al., 2005). The severity of these environmental influences is controlled by two factors: the range of conditions encountered and the strength of its association with the target species (Stoner, 2004). Due to this interaction with the environment, almost all fisheries data exhibits some spatial variation (Booth, 2000). The purpose of our study was to determine the prevalence and extent of environmentally mediated variation in the catch of the commercial fishery located in the south basin of Lake Winnipeg, Canada. The catch rates of sauger (Sander canadensis) and walleye (Sander vitreus) were selected because these two species are the most economically important fish in the lake with relatively detailed data available (Manitoba Conservation., 2003). Walleye and sauger are negatively phototaxic (Ali et al., 1977) with habitat suitability dependent upon water clarity and temperature (Nelson and Walburg, 1977; Lester et al., 2004). Sauger are adapted to lower light levels than walleye (Ali and Anctil, 1977) and both species

⁎ Corresponding author. Tel.: + 1 204 474 9683. E-mail addresses: [email protected] (J. Speers), [email protected] (D. Gillis). 1 Tel.: + 1 519 372 9837.

are poorly adapted to forage in clear water under high light levels. In experimental angling, Ryder (1977) found walleye catch rate to increase with declining light levels, as expected due to their crepuscular feeding habit. Both species prefer water temperatures of 18–22 °C (Hokanson, 1977; Wahl and Nielsen, 1985; Lester et al., 2004), a range which can be attained during the summer months within the south basin (Torigai et al., 2000). Based on this information, we hypothesized that increases in the catch rate of walleye and sauger in the south basin would increase as the season progressed, measured by degree days, and further increase with decreases in light intensity. Decreased light intensity was represented by increased cloud opacity, decreased atmospheric visibility, potentially increased sediment loading through increased Red River discharge rate, and possible increased sediment resuspension through increased wind action by affecting wave height. The effect of barometric pressure was examined due to its generally unknown impacts upon fish populations (Stoner, 2004). Data generated by commercial fisheries reflect the distribution and behavior of both fish and fishers (Hilborn, 1985). These fisheries are seldom performed as designed experiments, and thus their data often cannot provide the definitive hypotheses tests. However, the extent of the data available from commercial fisheries provides a quantity of information that is unattainable with the resources available for directed research. When analyzed while considering both fish and fisher behavior, it can provide insights into fishery systems that would not be revealed through classical fisheries analysis (Gillis, 2003; Branch et al., 2006).

0380-1330/$ – see front matter © 2012 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.jglr.2011.05.006

J. Speers, D. Gillis / Journal of Great Lakes Research 38 (2012) 26–34

Fig. 1. The south basin of Lake Winnipeg, located in southern Manitoba, Canada. Regional delivery sites are symbolized by a bullet (●) while the corresponding openwater centers are demarcated by an X. The division lines between regions are represented by dashed lines.

Materials and methods Study area and its fishery This study examined the commercial fishery in the south basin of Lake Winnipeg (Fig. 1). This region has a surface area of 3 600 km 2, an average depth of 12 m (Torigai et al., 2000) and generally possesses a Secchi depth of less than 1.0 m (Brunskill et al., 1979). There are five commercial fishery licensing areas around the south basin, each associated with a single delivery location (Fig. 1). The fishery is comprised of small fishing vessels (less than 10 m in length) utilizing gillnets of regulated mesh size (Lysack, 1995). Gillnets are a passive fishing gear (Olin et al., 2004) that discriminate primarily based upon size and morphology rather than species. Thus, fishers can easily catch walleye, sauger, or both depending on the timing and placement of their nets relative to the habitat use of the two species. The data set Detailed commercial fishery data were available for the open-water fishery for the years of 1996–2004. Delivery records from a particular region were assumed to represent catch from within only that region. This assumption was considered reasonable because the fishing vessels were small with little means to preserve catch; thus the long trips required to travel to another delivery location were unlikely. Little discarding was expected because walleye and sauger are the most valuable fish species. The regional boundaries in this study were set by determining the approximate location from which the distance between one delivery location and its nearest neighbor were equal. Daily catch records as total round weight were obtained from the Freshwater Fish Marketing Corporation (FFMC, 1199 Plessis Road, Winnipeg, Manitoba, Canada R2C 3L4). These data were matched to daily delivery location data from the Interlake regional office of Manitoba Conservation (75 7th Avenue, Gimli, Manitoba, Canada R0C 1B0). Daily catch per unit effort (CPUE) values for each region were obtained in the form of round weight per delivery. CPUE was chosen as a measure of catch in order to eliminate any seasonal variation in effort. Ideally, effort

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would be measured as time and length of net fished. Unfortunately, this data is not routinely collected from the Lake Winnipeg fishery. Deliveries generally occurred on a daily basis due to small boat size and limited storage, so increasing numbers of recorded deliveries in the FFMC data were used as our best available index of fishing activity. Of the five regions shown in the south basin, only the Gimli region was examined in detail as it was responsible for 44% of the effort and 53% of the catch from the entire south basin over the years examined (Fig. 1). The entire south basin was also examined to obtain a basin-wide perspective on the relationship between environment and catch. Direct measurement of environmental variables was not available for the fishery. However, related measures were collected by Environment Canada (150–123 Main Street, Winnipeg, Manitoba, Canada R3C 4W2) and Manitoba Hydro (820 Taylor Avenue, Winnipeg, Manitoba, Canada R3M 3T1). These proxies form the basis of our environmental comparisons. Daily Red River discharge rates were obtained from Manitoba Hydro for the Red River near the Lockport Generating Station (Fig. 1). As the Red River introduces most of the sediment into Lake Winnipeg (Allan and Brunskill, 1977; Lake Winnipeg Research Consortium, 2001), it was used as a potential indicator of turbidity in the south basin. Hourly meteorological conditions were obtained from Environment Canada using the weather data from the Gimli weather station (World Meteorological Organization station index number 71856). The following six meteorological variables were included in the analyses: visibility, wind direction, wind speed, dry bulb air temperature, mean sea level pressure and cloud opacity. Daily variable values were obtained by averaging hourly values over each day. Only ‘daylight’ hours (06:00 to 18:00) were used for the calculation of the daily visibility and cloud opacity variables. A simple average of daily winds was not meaningful due to their directional nature. Because wind acts through wave action to interfere with gear, boat handling, and possible sediment resuspension we chose to combine the wind variables in an estimate of maximum significant wave height. This quantity is defined as the average height of the highest one-third of the waves encountered and is a standard measure for wave height impacts (Sorensen, 1997). Thus, in our analysis the maximum significant wave height was calculated from wind data to summarize its potential impact. Calculated wave height The theoretical maximum significant wave height (hereafter simply referred to as ‘calculated wave height’) was calculated in accordance with Sorensen (1997). This created an index related to wind that accounted for wind speed, direction, duration and area (as fetch). Fetch was measured in the four primary compass directions (north, east, south, and west). Fetch measurements were made from the open-water centers around each region to the shoreline (Fig. 1). On every occasion where wind shifted indirection more than 45° wave height was recalculated for the number of chronological hours the wind direction remained within the new direction. Daily wave height was calculated as the weighted average of all wave heights for a particular day. Weightings were assigned as the total number of chronological hours the wind came from a given direction. In this manner hourly wind direction and speed measurements were combined into a single predictor variable that incorporated direction, speed, and the distance over which wind could influence the water. Modeling Commercial fishing was prohibited during the month of August over the years examined (Manitoba Conservation., 2006). Consequently, the data were divided into an ‘early’ fishery period (May, June and July) and a ‘late’ fishery period (September and October). A strong relationship existed between temperature and day, driven by the seasonality of Lake Winnipeg. These two variables were combined as degree days using a base temperature of 6 °C, the approximate spring water temperature at which spawning begins for both species (Walburg, 1972; Jones et al.,

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2003). This represented time in a more biologically relevant manner (see Neuheimer and Taggart, 2007 for an overview). ‘Year’ was also added in the form of a qualitative factor in order to address any underlying interannual variation in fish availability. Multicollinearity between all environmental variables was examined using variance inflation factors. The model was generated by using the delta approach and fitting generalized linear models (GLMs) to each period of the data. The delta approach was used in order to deal with the instances where CPUE was zero (Pennington, 1983; Maunder and Punt, 2004). This meant dividing the data into binary (catch or no catch) and continuous (nonzero CPUE) portions. The delta approach was chosen because the residuals produced from simpler models that included zeros deviated greatly from the homoscedastic and normal distribution assumptions implicit in those models. GLMs were used for both the binary and continuous portions of the CPUE analysis (McCullagh and Nelder, 1989). GLMs represent systematic trends and variation around these trends separately, providing greater flexibility than the classical transformations used with linear models. The variation can take a variety of forms, but in fisheries the gamma distribution is often employed to address the typically skewed catch distributions. The systematic component, representing the trend in expected values (μ), is fitted using a link function (g(μ)) that presents the relationship in a linear form: gðμ Þ = β0 + β1 X1 + β2 X2 …

ð1Þ

where the Xi are the predictor variables. This allows predictors to be specified in a manner similar to classical linear models. However, it is important to note that the link function is being applied to the expected values (μ) and it is not being used to transform the original observations. An expression for the observed values (Y) can be written as: −1

Y e Distributionðμ; θÞ; μ = g

ðβ0 + β1 X1 + β2 X2 …Þ

ð2Þ

where βi and Xi are as in Eq. 1. Distribution() is the pattern of deviations around the expected values, θ represents the parameters of this distribution, and g−1() is the inverse of the link function. The ~ relationship can be read “is modeled by” and represents a draw from a random distribution rather than a strict equality. This distribution forms the basis of the maximum likelihood methods that are used to estimate parameters in light of specific observations (Y). A more detailed explanation of the delta-gamma model applied to catch data in fisheries research can be found in Stefánsson (1996). Parameter estimates for the GLMs were performed in the R statistical programming environment using R's standard statistical libraries, specifically the glm() function (R Development Core Team, 2010). The binary portion of the data was modeled using a quasibinomial distribution (to address overdispersion) with a complimentary log– log link function. The small number of CPUE = 0 records suggested an asymmetrical relationship with the variables under examination and thus required the use of the asymmetrical complimentary log–log link (Kutner et al., 2005). This choice of link function was also supported by the Hosmer–Lemeshow test for goodness-of-fit, a common test for binary response variables (Agresti, 1996). The resulting model for the trend in expected values (μ) can be written as: logð−logð1−μ ÞÞ = β0 + β1 X1 + β2 X2 …

ð3Þ

with Xi and βi defined as in the previous equations. The continuous portion of the data was modeled using a gamma distribution with an inverse link function. The systematic portion of this model is: 1 = μ = β0 + β1 X1 + β2 X2 …

ð4Þ

with Xi and βi defined as in the previous equations. The gamma distribution was chosen because the mean appeared to vary with the

standard deviation (McCullagh and Nelder, 1989). The inverse link function was selected over alternatives such as the log link based upon the distribution of residuals. The resultant residual variance did not differ significantly (p b 0.05) from one (McCullagh and Nelder, 1989) based on a chi-squared distribution with n-1 degrees of freedom (Bhattacharyya and Johnson, 1977). Formal model suitability was determined by calculating the resultant standardized studentized residuals (referred to as ‘residuals’) in accordance with McCullagh and Nelder (1989) and testing them for homoscedasticity and normality using the Breusch–Pagan and Lilliefors tests, respectively. Variables were added and removed as dictated by a bi-directional stepwise variable selection procedure (Fox, 2002). Polynomial terms for the predictor variables other than year were examined to allow for simple deviations from linear relationships. Polynomials were limited to the third order. To reduce multicollinearity among polynomial terms all original variables were centered prior to analysis (Kutner et al., 2005, p. 135). The ‘year’ variable was always included as the first variable in the model (regardless of statistical significance) in order to address unknown interannual variation in abundance (Maunder and Punt, 2004). A variable was considered statistically significant if it explained at least 2% of the null deviance when using the quasibinomial family (Maunder and Punt, 2004). Akaike's and Bayesian Information Criteria (AIC and BIC, respectively) usually used for this purpose require a maximum likelihood estimation of the dispersion parameter which cannot be calculated for this family (McCullagh and Nelder, 1989). The null deviance criterion is not well established in the fisheries literature. Thus, we chose to test its performance against AIC and BIC using the continuous portion of the CPUE data. This addressed the relative severity of the 2% deviance threshold in comparison to the more common AIC and BIC methods. A more formal application of QAICc (Burnham and Anderson, 2002) was also examined as a criterion for variable selection in models based upon quasi-likelihood. To focus our discussion on stronger relationships we chose the most conservative method. The analysis of fishing effort was conducted similar to our CPUE modeling, but with an error distribution family and link function appropriate to the nature of the data: counts of the number of deliveries. GLMs are becoming customary for count data (Manly, 2001). Classically these errors would be expected to follow the Poisson distribution. However in many cases, including ours, this is not appropriate due to overdispersion in the observed data. The quasipoisson distribution provides an alternative that estimated the value of the dispersion parameter (Fox, 2002) and was used with the log link function for our effort analyses. The negative binomial provides another possible error function for overdispersed count data. However, its underlying assumption of a variance quadratic in the mean results in GLM parameter estimates that place less weight on larger values (Ver Hoef and Boveng, 2007) in contrast to our interest in conditions associated with periods of high catch rates and effort. The complexity of link functions containing polynomial terms makes simple interpretation of model parameters more difficult than in the case of classical linear models. This is especially complicated by centering the original values to avoid multicollinearity prior to parameter estimation. To simplify the interpretation of the final models, plots of the predicted responses on the original predictor variables were constructed. These allow a visual examination of the trends represented in the fitted relationships presented in the original scales of measurement. Results Overview of the fishery The periodic nature of the south basin fishery is clearly evident in the time series of effort and catch rates throughout the years studied (Fig. 2). Generally, walleye catch rates decline in the early season and

J. Speers, D. Gillis / Journal of Great Lakes Research 38 (2012) 26–34

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Table 1 Models generated with different variable selection techniques (AIC, BIC, and deviance threshold) using the continuous walleye catch per unit effort data from the south basin region (the Gimli region possessed a similar trend). Variable

Variable selection method AIC

BIC

Deviance threshold

p-value

p-value

p-value

% Null dev

b 0.01 – b0.01 b0.01 b0.01

b0.01 – b0.01 b0.01

0.000 12.855 34.331 2.872

b0.01 – b0.01 b0.01

b0.01 – b0.01 b0.01

0.000 11.244 31.269 2.247

Early period (sample size = 400) Intercept b0.01 – Yeara Degree_Day b0.01 Degree_Day2 b0.01 Degree_Day3 b0.01 Late Period (sample size = 544) Intercept b0.01 Yeara – Degree_Day b0.01 Degree_Day2 b0.01 Visibility3 0.087

a Year p-values are not shown because year was always included in the model as the first variable by default, regardless of statistical significance.

Fig. 2. Effort (as daily deliveries) and catch rate (kg/delivery) for walleye and sauger from 1996 to 2004 in the south basin of Lake Winnipeg.

varied in the late season through all of the years examined. Sauger patterns are not as clear, but generally they appear highest in the early season and vary at lower levels in the late season. Effort, as represented by deliveries, tends to decline through the early season. In the late season deliveries tend to initially be high and then decline. There is a strong similarity to these patterns among years, suggesting seasonal factors dominate much of the variation in effort and catch rates in this fishery. However, the examination of factors related to variation in catch rates and effort required more detailed modeling. Model selection criterion: deviance threshold The variables selected as relevant by AIC, BIC, QAICc and the 2% null deviance threshold variable selection methods all produced similar models. The 2% null deviance threshold applied a greater penalty to the inclusion of a variable in the model than the AIC selection method. AIC included some variables which had p-values larger than 0.05. BIC and the deviance threshold method produced the most similar models in terms of the type and number of variables included (Table 1). However, the 2% criterion was consistently more conservative. Thus, the 2% null deviance criterion was used in all models. CPUE modeling All regions exhibited a significant relationship between CPUE and at least one of the environmental variables. Multicollinearity among variables always yielded a variance inflation factor less than 2. The probability of non-zero catch remained above 0.90 for the entire numerical range of each environmental variable except at their extremities, where data are sparse. Consequently, the binary models were judged to explain the data poorly. Zeros catches for a particular day were uncommon (b3%) in the data because deliveries tended to contain at least some of either of the high valued species examined. Unfortunately, these zeros could not be incorporated into our continuous model as the gamma error distribution is not defined for zero. Other possible adjustments were considered unsatisfactory due to their impact on the residuals of the resulting models. Ultimately, zeros were not

considered in the subsequent analysis. Unless otherwise specified, all of the following discussion refers to only the continuous CPUE data. The continuous CPUE data exhibited a statistically significant relationship with a number of the variables examined. The percentage of the deviance explained, the p-value for each of the statistically significant polynomial orders of the variables, and the order of variable addition for each model are listed in Table 2. The relationships between the observed and expected continuous CPUE data for both species displayed a similar form in each region and period (Fig. 3). The mean percentage of the deviance explained in all regions for the walleye data was 42.6 for the early period and 37.8 for the late period while for the sauger data it was 19.4 for the early period and 49.2 for the late period. Analysis of deviance did not reveal any statistical significance with barometric pressure, calculated wave height, or atmospheric visibility. The form of the relationships between the CPUE data and the variables found to be statistically significant is depicted in Fig. 4. All regions for which the variable was statistically significant exhibited a similar form. The response value was dictated by the variable coefficients generated by the modeling process and shows only the changes in CPUE magnitude in relation to the particular environmental variable. All statistically significant orders of a variable are combined in the graph for that variable. Our model represented the decline in walleye CPUE in the south basin (measured as degree days) in the early period (monotonic decrease) but suggested that it generally remained constant through time for most of the late period (Fig. 4). The Gimli region displayed a similar response. It is important to note that the fluctuation in the degree day response of walleye CPUE in the late period is forced by two extreme CPUE values. In their absence the functional form is effectively a horizontal line. Sauger showed a more complex relationship to the predictor variables. High sauger CPUE over the entire south basin for the early period was related to high cloud opacity (monotonic increase) and mid-range Red River discharge rates (concave down) (Fig. 4). In contrast to the walleye pattern, sauger CPUE in the Gimli region in the early period peaked around 500 degree days and then declined (concave down). Although using a delta approach incorporating GLMs did increase the number of relationships meeting the model assumptions compared to alternative methods such as multiple regression, the residual requirements of a normal distribution and an equal variance were not always met. In our GLMs residual homoscedasticity was achieved in 4 of the 8 of cases, a normal distribution was achieved in 2, and an ideal variance was achieved in only 2 of the models. However,

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J. Speers, D. Gillis / Journal of Great Lakes Research 38 (2012) 26–34

Table 2 Continuous catch per unit effort models by period and region for walleye and sauger. Generalized linear models used the gamma distribution and inverse link function. CPUE was reported as kg/delivery. Early period – Walleye and Sauger Variable

Late period – Walleye and Sauger p-value

% Null dev

Variable

Walleye, Gimli Region (Sample Size = 373) Intercept 1.22 × 10−2 1.10 × 10−3 Yeara – – Degree_Day 2.60 × 10−5 1.97 × 10−6 Degree_Day2 −6.23 × 10−8 1.15 × 10−8

b0.001 – b 0.001 b0.001

0.000 5.666 24.821 4.784

Walleye, South Basin Region (Sample Size = 399) Intercept 1.75 × 10−2 1.34 × 10−3 Yeara – – Degree_Day 3.98 × 10−5 2.60 × 10−6 2 −8 Degree_Day −7.60 × 10 1.73 × 10−8

b0.001 – b0.001 b0.001

0.000 12.855 34.331 2.872

Sauger, Gimli Region (Sample Size = 343) Intercept 1.36 × 100 2.29 × 10−2 Yeara – – Degree_Day −4.20 × 10−4 5.70 × 10−5 2 −6 Degree_Day 1.07 × 10 2.31 × 10−7

b0.001 – b0.001 b0.001

0.000 9.731 12.102 6.282

Sauger, South Basin Region (Sample Size = 388) 6.90 × 10−3 Intercept 3.79 × 10−2 Yeara – – Discharge2 1.50 × 10−7 2.83 × 10−8 −5 Discharge −4.62 × 10 1.07 × 10−5 Opacity −2.19 × 10−4 6.55 × 10−5

b0.001 – b0.001 b0.001 b0.001

0.000 1.589 3.535 3.559 2.152

a

Coefficient estimate

Standard error

Coefficient estimate

Standard error

p-value

% Null dev

Walleye, Gimli Region (Sample Size = 530) Intercept 7.57 × 10−3 4.60 × 10−4 Yeara – – Degree_Day −3.00 × 10−5 2.65 × 10−6 Degree_Day2 −2.96 × 10−8 6.73 × 10−9

b0.001 – b0.001 b0.001

0.000 8.397 19.900 2.627

Walleye, South Basin Region (Sample Size = 544) Intercept 8.65 × 10−3 4.43 × 10−4 Yeara – – Degree_Day −3.72 × 10−5 2.30 × 10−6 2 −8 Degree_Day −3.00 × 10 6.43 × 10−9

b0.001 – b0.001 b0.001

0.000 11.244 31.269 2.247

Sauger, Gimli Region (Sample Size = 516) Intercept 2.25 × 10−2 2.03 × 10−3 Yeara – –

b0.001 –

0.000 41.623

Sauger, South Basin Region (Sample Size = 538) Intercept 2.84 × 10−2 1.94 × 10−3 Yeara – –

b0.001 –

0.000 56.852

Year p-values are not shown because year was always included in the model as the first variable by default, regardless of statistical significance.

in all cases the residuals appeared to approximate these assumptions, as illustrated in Fig. 5.

Effort modeling Both the early and late fishery periods of both regions exhibited a significant relationship between effort and one or more of the variables examined (proportion of the deviance explained by the variable(s) was greater than or equal to 2%). The percentage of the deviance explained and the p-value for each of the statistically significant variables in the order that they were added to the model is listed in Table 3. The mean

percentage of the deviance explained in both regions was 60.6 for the early period and 9.4 for the late period. While these relationships were statistically significant, much of the variation remained unaccounted for (as evident by the low percentage of the null deviance explained). The relationship between the observed and expected effort for both periods displayed a similar form in each region (Fig. 6). Degree days were statistically significant in the early period only, while calculated wave height was only statistically significant in the late period (Table 3). Year was always statistically significant with the exception of the late period in the entire south basin region. All other environmental variables were never statistically significant. The form of the relationships between effort and the variables found to be statistically significant are depicted in Fig. 7. All regions and periods for which the variable was statistically significant exhibited a similar form. The effort response was calculated using all of the polynomial coefficients for a variable that were generated by the modeling process. High effort in the south basin during the early period was related to low degree day values (monotonic decrease), while during the late period it was related to low calculated wave height values (monotonic decrease) (Table 3). Data drawn from the Gimli region alone displayed a similar form. Models constructed in both regions did not exhibit an improved fit (based on the percentage of the null deviance explained) when the observed buoy wave height was included in place of calculated wave height. The suitability of the models to the data was indicated by the residual variance, which differed from the ideal value of one (McCullagh and Nelder, 1989) in all 4 of the models generated. Similarly, the residuals were not normally distributed in all models, while residual homoscedasticity was achieved in 2 of the 4 models (residual histogram and scatterplot are shown in Fig. 8, all regions and periods exhibited a similar form).

Discussion Fig. 3. The relationship between continuous catch per unit effort (CPUE) and environment from the early period for the entire south basin region (all years) based on a generalized linear model using the inverse link function and a gamma distribution. Sample sizes for walleye and sauger are 399 and 388, respectively. The 95% estimation interval is represented by the dashed lines, while the 95% prediction interval is depicted by the dotted lines.

These analyses illustrate the impact of environment on both the deployment of fishing effort and the subsequent catch rates in the Lake Winnipeg gillnet fishery. Catch rate was influenced by time of year, measured in degree days, differently for sauger and walleye, but sauger catches were also influenced by light levels as reflected by

J. Speers, D. Gillis / Journal of Great Lakes Research 38 (2012) 26–34

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Fig. 4. The relationship between continuous catch per unit effort (CPUE) and environment as determined by the regression coefficients of each model (the region and period are specified on each graph).

cloud opacity and Red River discharge. Effort, common for both species, declined throughout the early period, but was influenced by wind action in the late period. The GLMs used to construct the relationships generated residuals which often deviated from ideal residual patterns. This should be considered when interpreting these results as the precision of the variable coefficients may be overestimated (Kutner et al., 2005). However, these violations of the model assumptions did not produce unreasonable residual distributions (Ortiz and Arocha, 2004) and were thus judged an appropriate descriptor of the effect of environment on catch.

Additionally, the 95% estimation interval crossed the 95% prediction interval at high CPUE for all of the CPUE models. This crossing could be interpreted as the point at which insufficient data are present to give any degree of confidence in the calculated trend. Consequently, the models generated are unlikely to be useful in identifying environmental conditions during which high CPUE can be expected. Wide prediction intervals are also symptomatic of relationships which explain a small amount of the observed variation (Kutner et al., 2005). This limits the use of the models as strong predictive tools, though they remain useful in identifying factors that influence catch rate. The use of landed quantities as catch measures is also not ideal. Neither is the use of meteorological variables as proxies for conditions in the water. However, the data available in many fisheries will fall short of that available from directed surveys and experimental studies. Detailed logbook programs, dockside monitoring, and observers are aspects of fisheries monitoring that have not yet been introduced to Lake Winnipeg. With limited information and resources we must examine historical information in fisheries before more focused studies are proposed or undertaken.

Variable characteristics and deviance threshold criterion

Fig. 5. The residuals of the continuous catch per unit effort (CPUE) model for sauger from the Gimli region during the early period (sample size = 343). (a) Histogram of the residuals with the theoretical normal distribution indicated. (b) Relationship between the residuals and the predicted effort value.

The variables examined were appropriate for making inferences about the relationship between environment and catch. The analysis of multicollinearity between variables always produced a variance inflation factor (VIF) less than 2, where problematic multicollinearity is said to occur if VIF is 10 or larger by convention (Kutner et al., 2005, p. 409). The use of air temperature rather than water temperature was reasonable as both measures are highly correlated (Doan, 1942). However, meteorological data supplied by a single weather station was unlikely to be representative of environmental conditions over the entire south basin. This is a cause for concern if attempting to attribute specific environmental variable values to catch, but not for examining environmental correlates which may be due to unmeasured intermediate factors. It will also be reasonable when environmental conditions are similar across a limited geographical area such as the south basin.

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Table 3 Effort models by period and region. Generalized linear models were developed with a quasipoisson distribution and an inverse link function. Effort was represented as number of deliveries. Early Period Variable

Late Period Coefficient estimate

Standard error

p-value

% Null deviance

Variable

1.08 × 10−1 – 2.31 × 10−4

b0.001 – b0.001

0.000 35.362 29.359

Gimli region (sample size = 530) Intercept 2.82 × 100 Yeara – Wave_Height −5.76 × 10−1

South basin region (sample size = 400) 9.13 × 10−2 Intercept 3.14 × 100 Yeara – – Degree_Day −3.32 × 10−3 2.25 × 10−4 2 −6 Degree_Day −7.09 × 10 1.52 × 10−6

b0.001 – b0.001 b0.001

0.000 26.304 28.217 2.131

Gimli region (sample size = 375) Intercept 2.16 × 100 Yeara – Degree_Day −3.95 × 10−3

a

Coefficient estimate

Standard error

p-value

% Null deviance

6.07 × 10−2 – 1.17 × 10−1

b0.001 – b0.001

0.000 6.122 3.852

South basin region (sample size = 544) Intercept 3.59 × 100 5.59 × 10−2 Yeara – – Wave_Height −6.61 × 10−1 9.60 × 10−2

b0.001 – b0.001

0.000 1.923 7.085

Year p-values are not shown because year was always included in the model as the first variable by default, regardless of statistical significance.

Our comparative analyses showed that the 2% deviance criterion for the inclusion of a variable was an appropriate threshold. This variable selection technique applied a comparable penalty to the inclusion of a variable when compared to QAICc or the more common BIC method. In contrast, the AIC selection method included variables with p-values larger than 0.05 and was thus considered too liberal for these analyses. Influences on CPUE Some variables were found to predict CPUE in all periods for both the Gimli region, and the entire basin, with the exception of sauger CPUE in the late period. However, the models created using the binary CPUE data generally predicted a constant, low probability of a CPUE= 0 record under any combination of environmental variable values. The failure to generate a useful binary model of fishing success is not surprising given the available data. Unsuccessful fishing will not result in landed fish, so the zeros that we see represent days where only one of the two species was delivered. Alternatively, a paucity of zeros could also result from effective fishing occurring over the entire range of the meteorological conditions observed. Regardless of the exact cause, the binary models were judged inadequate for identifying related environmental factors for either species due to poor contrast. Improved data collection from the commercial fishery that explicitly records unsuccessful days fishing could provide the data necessary to fit both portions of delta-gamma

Fig. 6. The relationship between effort and environment for the entire south basin region (all years) based on a generalized linear model with the log link function and a quasipoisson distribution. Sample sizes for the early and late period are 400 and 544, respectively. The 95% estimation interval is represented by the dashed lines, while the 95% prediction interval is depicted by the dotted lines.

model of catch rate. This would allow a more representative examination of factors influencing the commercial fishery than was possible with the data at hand. Time, as measured by degree days, was usually related to catch rate for both species in the early period, but not strongly related to catch rate in the late period. In the late period the significance of degree days in the model of walleye catch rate is likely an artifact produced by two extreme observations at the end of the season. Generally speaking, for both species seasonal activities that may influence catch rate do not vary much throughout the late period. However, this is unlikely to be true during the early fishery that coincides with spawning activities. In the early period the management goal is to open the fishery after 80% of the walleye spawning has occurred. Thus, spawning aggregations that increase gillnet efficiency will have already formed when the data series begins in each year. Under this management regime a decline in walleye catch rate is to be expected as walleye disperse after spawning. The increase and subsequent decline of sauger catch rates near Gimli suggest that their later spawning aggregations are being effectively exploited. It is interesting to note that there was no corresponding increase in fishing effort detected during the time that sauger CPUE increased. It appears that those who were fishing caught sauger more efficiently, but few actually returned to the water when sauger was more available. The lower value of sauger during the years studied and the common quota shared with walleye favored walleye as a target species that was actively pursued, which is consistent with

Fig. 7. Effort responses to variables for both periods from the entire south basin region as determined by the regression coefficients of each model.

J. Speers, D. Gillis / Journal of Great Lakes Research 38 (2012) 26–34

Fig. 8. The residuals from the model of the early period in the Gimli region (sample size = 375). (a) Histogram of the residuals with the theoretical normal distribution indicated.(b) Relationship between the residuals and the predicted effort value.

the effort pattern observed. The lack of a general South Basin relationship between degree days and catch rates for sauger indicates that other factors dominated catch rates away from Gimli in the years examined. Cloud opacity and the discharge rate of the Red River influenced sauger catch rates over the south basin as a whole. As hypothesized, low light intensity in the form of high cloud opacity and possible increases in turbidity due to elevated Red River discharge could contribute to high sauger CPUE. The relative lack of responsiveness of walleye to indicators of light intensity was unexpected given that both walleye and sauger are known to be negatively phototaxic (Ali et al., 1977), and that light intensity is the primary abiotic factor controlling the distribution of walleye (Lester et al., 2004). However, sauger's eyes are more adapted to low light conditions (Ali and Anctil, 1977) which may have made them more responsive to this factor. Also, given the high turbidity of the south basin (Brunskill et al., 1979), it was possible that high levels of atmospheric light intensity did not produce the higher light intensity levels below the water's surface which would have been avoided by walleye. This reasoning could also explain why indicators of light intensity were statistically significant in only 2 of the 8 sauger CPUE models; the south basin may have been simply too turbid for variations in atmospheric light intensity to have had a measurable effect. Similarly, wind as represented by calculated wave height was never associated with variation in catch rates. However, this study represented wind action indirectly and may not have provided an accurate representation of wave-induced sediment resuspension. It was also likely that the amount of sediment introduced by the Red River (Allan and Brunskill, 1977; Lake Winnipeg Research Consortium, 2001) masked any increases in turbidity due to wave-induced sediment resuspension events. In addition, the general high turbidity of the south basin may have caused gillnet efficiency to remain unaffected by the range of wave heights encountered, regardless of net location or period deployed. Despite the fact that barometric pressure has known effects on various freshwater fish species (Peterson, 1972; Jeffrey and Edds, 1999; Stoner, 2004), no relationship between barometric pressure and CPUE was found in this study. It was possible that within the south basin other factors, such as annual fluctuation in fish abundance and the discharge rate of the Red River, may mask weaker effects of barometric pressure. Alternatively, the influence of barometric pressure may act through the other environmental variables described above and thus not appear as a separate factor in our analysis. Some fish species such as salmonids have been shown to regularly avoid

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highly turbid areas such as river plumes (Heege and Appenzeller, 1998). However, our study observed Red River discharge to be related to CPUE in only one instance. The biological mechanism behind this result was unclear, but it may have been the only occasion where turbidity as dictated by the discharge of the Red River was within the range of biological response. As sauger prefer more turbid conditions than walleye (Nelson and Walburg, 1977) it was possible that sauger favored the turbidities generated at mid-range discharges. The drop in sauger CPUE at high discharges could have represented turbidities too high for this species which may have moved elsewhere or became inactive under these conditions. The lack of statistical significance with any variables other than year in the late period of the sauger CPUE data indicated that the environmental conditions examined did not affect sauger CPUE during this time period. The statistical significance of the year effects for both species in general was accredited to annual fluctuations in fish abundance according to standard practice (Maunder and Punt, 2004). Unfortunately, other annual variations, such as age structure and its possible interactions with other factors, cannot be statistically distinguished from abundance in this analysis and may have been missed. Finally, local habitat differences and fluctuations in environmental conditions that differ from the source locations of our South Basin indices could have added variation to the observed relationships between CPUE and the explanatory variables. Behavioral processes of both fish and fishers in the south basin may have increased the variation in the observed CPUE values examined and made it more difficult to discern environmental effects. Gillnet catch may not indicate changes in local fish abundance. For example, abundance has been significantly underestimated by gillnet catch under high fish numbers (Olin et al., 2004). This has been attributed to increased avoidance of the net by the detection of fish already caught, although this property may have been reduced under the conditions of high turbidity found in the south basin. Finally, the re-allocation of fishing effort in response to spatially and temporally variable fish densities has the potential to bias CPUE-meteorological associations (Campbell, 2004). This bias would arise if fishers systematically fish one area under one range of environmental conditions and fish another area under a different range of environmental conditions. Unfortunately, the spatial resolution of the fisheries data does not allow us to investigate this further.

Conclusions The patterns between timing and environmental variables observed, fishing activities, and fishing success were consistent with the underlying biology of the exploited species and the nature of a gillnet fishery. However, these patterns differed between species, areas examined, and time of the year. The complexity of this system and the nature of the data prevents firm conclusions about the underlying processes, but it is clear that different processes are dominating at different times and areas. We should be cautious when interpreting our models as their assumptions were often strictly violated. However, the methods employed here were superior to simple linear models, whose assumptions were violated to a greater degree in all cases. More complete knowledge of the environment is required to strengthen the relationship between CPUE and the environment. More detailed knowledge regarding the spatial and temporal variation in gillnet placement would allow changes in CPUE due to changes in net location over time to be addressed. The incorporation of more direct measures of environmental conditions such as percent cloud cover, water temperature, and local wave height may increase our ability to quantify the role of environmental variables in the persecution and success of the Lake Winnipeg fishery.

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