Characteristics and Spatial Segregation of Sympatric Saugers and Walleyes in the Ottawa River, Canada
Abstract
Few comparative studies exist on the sympatric populations of Saugers Sander canadensis and Walleyes S. vitreus, especially in northern rivers. Depth-stratified, standardized index netting surveys were conducted in the Ottawa River, Canada—a large, fragmented northern river. The objectives of this study were to (1) determine whether Saugers and Walleyes were spatially segregated within river reaches; (2) assess whether relative abundances of the two species were correlated within river reaches and ascertain factors affecting their relative abundances; (3) determine whether life history characteristics in this northern river were within the variation reported for each species; and (4) evaluate whether there was synchrony in year-class strength between the two species and identify the factors affecting year-class strength. Saugers and Walleyes were spatially segregated, specifically by depth. Probability of capture was greatest in the 20–35-m depth stratum for Saugers and in the 6–12-m depth stratum for Walleyes. Relative abundances of Saugers and Walleyes were correlated and were positively related to reach characteristics (i.e., mean and maximum depth, reach length, and river kilometers upstream from the confluence) but negatively related to thermal conditions (i.e., growing degree-days, water temperature at preferred depths, and human population in proximity to the river). Life history characteristics were within the literature-reported ranges except for Saugers, which were older with a smaller length at 50% maturity. Year-class strengths of Saugers and Walleyes were synchronous at the reach and river levels and were positively correlated with mean annual temperature but negatively correlated with mean annual river flow. Saugers and Walleyes can coexist within large water bodies. Recruitment synchrony suggests that spawning management efforts could benefit both species, which could be advantageous when managing the Sauger, a lesser studied species. However, life history differences advocate that harvest regulations (e.g., size limits) must be species specific in order to be effective.
Received June 13, 2013; accepted January 27, 2015
The Sauger Sander canadensis and Walleye S. vitreus are two closely related percid species with an apparent riverine ancestry (Collette et al. 1977; Kitchell et al. 1977) that have adapted to lentic environments (Bozek et al. 2011a). Both species are top predators and are highly valued by recreational, commercial, and subsistence fisheries (Johnston et al. 2012). Saugers and Walleyes are frequently sympatric, especially in large, turbid, or dark rivers and lakes (Dendy 1948; Scott and Crossman 1973; Fitz and Holbrook 1978; Lyons and Welke 1996; Bellgraph et al. 2008), and they have many similarities in various life history traits (Bozek et al. 2011a). For example, Saugers and Walleyes often exhibit similar seasonal movements, implying that they concurrently use the same areas within a river (Osterberg 1978; Bellgraph et al. 2008). Spawning areas are often communal, albeit they are generally not frequented simultaneously (Collette et al. 1977; Nelson and Walburg 1977; Rawson and Scholl 1978; Bellgraph et al. 2008). Recruitment can be highly variable for both Saugers and Walleyes (Lyons and Welke 1996), and they often display correlated year-class strengths (Swenson and Smith 1976; Osterberg 1978; Lyons and Welke 1996; Pitlo 2002), suggesting that the two species are affected by similar extrinsic factors. There can be extensive overlap in food selection (Scott and Crossman 1973; Swenson and Smith 1976; Collette et al. 1977; Osterberg 1978; Bellgraph et al. 2008) that potentially leads to interspecific competition, including preying on each other (Scott and Crossman 1973; Swenson and Smith 1976; Cohen et al. 1993). However, Saugers and Walleyes are also known to target different prey species (Rawson and Scholl 1978). Within reservoirs, the greatest difference in habitat selection between these two closely related species appears to be depth selection, with Saugers frequenting greater depths than Walleyes (Dendy 1948; Fitz and Holbrook 1978; Ickes et al. 1999). This difference possibly stems from differing temperature preferences (Dendy 1948) or adaptation to lower light conditions (Ali and Anctil 1977; Collette et al. 1977; Bozek et al. 2011b).
Given the similarities in life history characteristics between Saugers and Walleyes, there is the potential for interspecific competition between sympatric populations in the form of predation and competition for food. Therefore, sympatric populations of Saugers and Walleyes should be spatially segregated within a water body (Rawson and Scholl 1978; Bellgraph et al. 2008). In addition, if interspecific competition is a primary limiting factor (e.g., Swenson and Smith 1976), then the relative abundances of sympatric Saugers and Walleyes should be inversely related. In contrast, if Sauger and Walleye populations respond to environmental characteristics or variations in the same manner, then their relative abundances should be positively correlated. Walleyes have been intensely managed and studied, whereas Saugers have not benefited from the same amount of attention (Bozek et al. 2011a). There are even fewer comparative studies on sympatric populations of these species (Johnston et al. 2012). Moreover, existing studies on sympatric riverine populations pertain to areas in the middle to southern portions of the species’ natural range (e.g., Fitz and Holbrook 1978; Lyons and Welke 1996; Ickes et al. 1999; Bellgraph et al. 2008). Although large differences between northern and southern populations would not necessarily be expected, a study of sympatric populations in a northern river system is needed to capture some of the inherent variability in life history characteristics within the natural range of Saugers and Walleyes. Understanding the relationships between these two species may facilitate better management practices, especially for natural sympatric populations.
Saugers and Walleyes are indigenous to and sympatric in the Ottawa River, Canada—a large, fragmented northern river that provides an important recreational fishery. Standardized index netting surveys were conducted throughout the Ottawa River system to assess northern sympatric populations of these species. The main objective of this study was to ascertain whether the dynamics of northern sympatric populations of Saugers and Walleyes are consistent with previously published results on other sympatric populations throughout the species’ natural range. More specifically, the objectives were to (1) determine whether Saugers and Walleyes were spatially segregated within river reaches based on the probability of capture, relative abundance, and mean length of each species; (2) assess whether relative abundances of the two species were correlated within river reaches and ascertain factors affecting their relative abundances; (3) evaluate whether life history characteristics in this northern river were within the variation previously reported for each species; and (4) assess whether there was synchrony in the year-class strengths of Saugers and Walleyes and determine the factors affecting year-class strength.
METHODS
Study site.
The study was conducted in the Ottawa River, a large (maximum width = 3 km; maximum depth = 200 m), fragmented, brown-colored water body with a watershed of over 146,000 km2 and a mean annual flow of 1,968 m3/s (Telmer 1996). For 580 km, the Ottawa River forms the border between the provinces of Ontario and Quebec. The river is segmented into reaches by the presence of hydroelectric dams and rapids (Figure 1). This study was restricted to 10 Ottawa River reaches from Lake Temiscaming in the northwest to Lac Dollard des Ormeaux in the southeast (Figure 1; Table 1).
| Reach | Length (km) | Surface area (ha) | GDDs | Mean depth (m) | Secchi depth (m) |
|---|---|---|---|---|---|
| Lake Temiscaming | 111.5 | 32,227 | 1,701 | 35.7 | 1.8 |
| Lac la Cave | 49.0 | 3,028 | 1,762 | 19.7 | 1.6 |
| Holden Lake | 90.0 | 7,592 | 1,805 | 16.8 | 2.2 |
| Upper Allumette Lake | 76.9 | 13,212 | 1,955 | 10.1 | 2.5 |
| Lower Allumette Lake | 22.3 | 4,613 | 1,972 | 2.9 | 2.5 |
| Lac Coulonge | 18.1 | 2,888 | 1,983 | 3.8 | 2.5 |
| Lac du Rocher Fendu | 31.1 | 3,893 | 1,987 | 7.9 | 1.9 |
| Lac des Chats | 40.0 | 7,513 | 2,058 | 4.9 | 3.3 |
| Lac Deschênes | 52.8 | 10,900 | 2,163 | 5.2 | 1.8 |
| Lac Dollard des Ormeaux | 113.1 | 14,414 | 2,082 | 6.1 | 1.5 |
Map of the Ottawa River, Canada, including reaches where the present study was conducted and locations of hydroelectric generating stations (G.S.). Insert shows the location of the Ottawa River relative to Ontario and Quebec.
Saugers and Walleyes were obtained from two fish survey programs: the Fall Walleye Index Netting (FWIN) program, conducted from 1998 to 2003; and the Broad-Scale Monitoring (BSM) program, conducted from 2008 to 2010. Only fish attribute data were used from FWIN surveys, whereas BSM surveys were primarily used as a source of catch data for reasons detailed below.
The BSM program (Sandstrom et al. 2009) was initiated throughout Ontario, including 10 Ottawa River reaches. This protocol used the North American gill-net standard: 1.8-m-deep, 24.8-m-long, monofilament gill nets incorporating panels of various stretched mesh sizes (38, 51, 64, 76, 89, 102, 114, and 127 mm) sewn together in random order (Bonar et al. 2009). Two gangs were strapped together and randomly set overnight for a minimum of 16 h and a maximum of 22 h among different depth strata (1–3, 3–6, 6–12, 12–20, 20–35, 35–50, 50–75, and 75+ m), equally distributed throughout the water body (Sandstrom et al. 2009). The number of net sets was based on the surface area and maximum depth of the reach and a minimum requirement of two sets per depth stratum. Netting was conducted from late June through early September and was restricted to days when surface water temperatures were greater than 18°C (Sandstrom et al. 2009). Catch per unit effort (CPUE) was determined as the mean number of fish per gill-net gang (i.e., total number of target fish caught in the set divided by the number of gangs) and was area weighted by depth stratum.
The BSM program was designed to manage fisheries at a large scale rather than at the water body level (i.e., individual lakes). As such, obtaining attribute data from all fish caught was not deemed necessary. The general protocol was to collect aging structures from at least 20 individuals of each species within a water body and to measure lengths for all fish. Therefore, to supplement biological data, Sauger and Walleye samples taken during FWIN surveys on the Ottawa River were included. The FWIN protocol is a standardized assessment technique for Ontario (Morgan 2002) and was conducted at least once in all 10 Ottawa River reaches from 1998 through 2003. Sauger and Walleye attribute data were included to assist with constructing maturity schedules (gonadal differentiation was evident in fall samples; Purchase et al. 2006) and to provide samples for estimating von Bertalanffy growth parameters and age-class correlations. For FWIN, gill nets (1.8 m deep, 60.8 m long; stretched monofilament mesh sizes of 25, 38, 51, 64, 76, 102, 127, and 152 mm) were randomly set throughout the river reach in the fall when surface water temperatures were between 10°C and 15°C. Nets were set perpendicular to the shore in two depth strata (2–5 and 5–15 m), which were sampled equally (Morgan 2002). Nets were lifted after a 24-h period, all fish were extricated, and the nets were reset in another randomly selected location. Fish that were caught during BSM and FWIN surveys were identified, enumerated, and measured for TL (nearest mm) and weight (nearest 10 g); otoliths for use in age estimation were extracted from Saugers and Walleyes. Otolith ages were estimated by one experienced interpreter using the grind-and-polish technique at the Ontario Ministry of Natural Resources and Forestry (OMNRF) Northwest Aging Laboratory. Age estimates were not validated. Sex and stage of maturity (i.e., undeveloped, developing, or fully developed) were determined for each Sauger and Walleye by internally examining gonad condition and applying the key developed by Duffy et al. (2000). Sex and maturity stage were determined for Saugers and Walleyes caught during FWIN surveys but not for those caught during BSM surveys, as gonads are generally dormant during the temporal period in which BSM surveys were conducted (Duffy et al. 2000).
Temperature and dissolved oxygen profiles were taken during the summer BSM surveys by using a YSI meter (YSI, Inc., Yellow Springs, Ohio). Readings were obtained at 1–2-m intervals down to the bottom of the thermocline or down to a depth of 35 m and then were taken every 5–10 m thereafter.
Statistical analysis.
Bayesian methods were predominately used for analyses in this study unless otherwise specified. A Bayesian approach was used because it (1) allowed for calculation of the probability of different values of a parameter given the data, (2) automatically incorporated uncertainty into the model, and (3) provided the probability of a relationship. Traditional methods determine significance of a test based on an arbitrary value. Bayesian analysis requires the explicit assignment of prior probabilities (Ellison 1996), which can be either informative (i.e., based on prior knowledge) or noninformative (i.e., not using prior knowledge). For these analyses, noninformative Gaussian priors were used (e.g., βi ∼ N[0, 1,000]) unless otherwise specified (e.g., calculation of von Bertalanffy growth parameters). A Markov chain–Monte Carlo simulation using two chains and 100,000 iterations with a 10,000-iteration burn-in period was employed to estimate posterior probability distributions for the model parameters.
To determine whether Saugers and Walleyes were using the same river reach but were segregating based on depth, the probability of catching a Sauger or a Walleye in a BSM net was calculated by using a binomial distribution, with the total number of nets set in each stratum as the trial and the number of nets with at least one target species as the success (i.e., present or absent in the net). The same approach was used to determine the probability of capturing both Saugers and Walleyes in the same net at different depth strata to elicit potential interspecific interactions.
Poisson regression was used to determine whether there was an effect of depth on the number of Saugers or Walleyes caught during BSM surveys. Numbers of each species caught in the net were regressed against depth stratum. The response variable was assumed to be distributed as a Poisson random variable because catch data were being used and because the assumptions of ANOVA could not be met by the traditional log10(CPUE + 1) transformation method. Categorical values were used for depth strata (e.g., 1–3-m stratum = 1; 3–6-m stratum = 2; etc.). The step function in WinBugs version 1.4 (www.mrc-bsu.cam.ac.uk/bugs/) was used to assess differences in CPUE among depth strata. The step function creates a Boolean variable based on a conditional relationship and counts the number of simulations in which the function was true. For example, to determine whether there is a significant difference in Sauger CPUE in the 1–3-m depth stratum versus the 3–6-m stratum (the probability that Sauger CPUE1–3 m is greater than Sauger CPUE3–6 m), the step function equals 1 when (CPUE1–3 m − CPUE3–6 m) ≥ 0, and the step function equals 0 if (CPUE1–3 m − CPUE3–6 m) < 0. The mean value after simulations represents the probability of that relationship (e.g., the Monte Carlo estimate of P[CPUE1–3 m − CPUE3–6 m]; Lunn et al. 2013).
A generalized linear model was used to determine whether there was an effect of depth on the size of Saugers or Walleyes captured during the BSM surveys. For this analysis, the TL of each species was the dependent variable, and depth stratum was the independent variable. Categorical values were used for depth strata as defined above. The step function in WinBugs was also used to ascertain differences in TL of both species among depth strata.
Principal components analysis (PCA) was conducted to examine the relationships between Sauger or Walleye relative abundances in BSM survey nets and reach characteristics. Variables used to describe reach characteristics included surface area; Secchi depth; maximum and mean depths; reach length; river kilometer (distance from the river confluence to the centroid of the reach); whether the reach was thermally stratified (dummy variable); whether the reach was regulated (dummy variable); water temperature at the surface, 6–12-m depth stratum, and 20–35-m stratum (minimum observed was used for the 20–35-m stratum); growing degree-days (GDDs); and human population within 50 km of the reach shoreline. Temperatures at the 6–12-m and 20–35-m depth strata (Table 2) were used because these depths were associated with the greatest probability of capturing a Walleye and a Sauger, respectively. Growing degree-days represented the heat units (>5°C) accumulated within a year and have been correlated with fish growth and maturity (Venturelli et al. 2010). The GDDs were determined for each river reach and were calculated by dividing the difference between the maximum and minimum daily air temperatures by 2 and subtracting 5 (minimum GDD value was constrained to 0). Cumulative GDDs were determined by summing the GDDs on an annual basis. Temperature data were obtained from weather stations in close proximity to each river reach (climate.weather.gc.ca). The variable describing the human population within 50 km of the reach was used as a surrogate for potential effort or development on the shoreline and was determined by using the 2011 Canadian Census information from Statistics Canada (www12.statcan.gc.ca/census-recensement/index-eng.cfm). Census data were incorporated with a municipal boundary layer around the Ottawa River in ArcMap version 9.3 (Environmental Systems Research Institute, Redlands, California), and the population within 50 km of the shoreline was determined by using the buffering tool.
| Depth stratum (m) | ||||||||
|---|---|---|---|---|---|---|---|---|
| Reach | Variable | 0–3 | 3–6 | 6–12 | 12–20 | 20–35 | 35–50 | 50–75 |
| Lake Temiscaming | T | 19.8 | 19.8 | 19.8 | 19.8 | 7.9–19.8 | 4.9–7.9 | 4.9 |
| DO | 8.1–10.3 | 10.2–10.6 | 8.1–10.2 | 7.2–8.1 | 6.8–8.1 | 8.1–8.4 | 8.2 | |
| Lac la Cave | T | 19.7 | 19.7 | 19.7 | 17.7–19.7 | 7.4–17.7 | 5.9–7.4 | 5.8–5.9 |
| DO | 7.9 | 7.9 | 7.9 | 7.2–7.9 | 7.2–10 | 10.2–10.3 | 10.1–10.2 | |
| Holden Lake | T | 17.8 | 17.8 | 17.8 | 17.8 | 14.2–17 | 9.8 | |
| DO | 8.7–10.3 | 6.2–9.1 | 5.4–6.2 | 4.2–6.2 | 3.4–4.2 | 4.0 | ||
| Upper Allumette Lake | T | 20 | 20 | 20 | 20 | 9.5–19.9 | 8.8–9.5 | 8.8 |
| DO | 5.8–6.3 | 5.3 | 4.2–5.3 | 3.4–4.2 | 3.2–4.5 | 4.4 | 4.4 | |
| Lower Allumette Lake | T | 19.7 | 19.6 | 19.2 | ||||
| DO | 8.4 | 8.5 | 8.3–8.5 | |||||
| Lac Coulonge | T | 20.9–21.2 | 20.8 | 20.8 | 20.8 | 20.5–20.7 | ||
| DO | 7.9 | 7.8 | 7.6 | 7.2–7.8 | 7.3–8.0 | |||
| Lac du Rocher Fendu | T | 20.6 | 20.5 | 20.4 | 20.4 | 20.4 | 15.9 | |
| DO | 8.7–9.4 | 6.3–9.4 | 4.3–6.3 | 4.0–4.3 | 3.7–4.0 | 4.3 | ||
| Lac des Chats | T | 22.8–23 | 22.7 | 22.7 | 22.7 | |||
| DO | 8.1–10.0 | 7–9.2 | 3.9–6.3 | 2.1–3.6 | ||||
| Lac Deschênes | T | 21.8–22.3 | 21.6–21.8 | 21.6 | 21.5 | 16.6–21.4 | 16.1–16.3 | 16.1 |
| Lac Dollard des Ormeaux | T | 23.7–23.9 | 23.7 | 23.6 | 23.7 | 23.6 | ||
For the PCA, only area-weighted data on Sauger and Walleye CPUEs from the BSM surveys were used. The CPUE data were log10(CPUE + 1) transformed, whereas reach length, surface area, and human population within 50 km of the reach shoreline were log10 transformed. With the exception of categorical variables (e.g., thermal stratification or reach regulation), all variables used within the PCA were standardized. Principal components (PCs) were retained if they had an eigenvalue greater than 1.0. To assist with the interpretation of each PC (e.g., the first PC [PC1]), the correlation between each variable used in the PCA and that PC was determined by raising the product of the coefficient of the variable and the variance of the PC (i.e., eigenvalue) to the power of 0.5 (e.g., rij = aij[var Ci]0.5, where rij = correlation of the jth variable to the ith PC; a = coefficient of the jth variable to the ith PC; and Ci = the ith PC). Variables with r-values greater than 0.5 were considered to be strongly correlated with that PC (Afifi et al. 2004).
Mean size and age at 50% maturity for males and females were determined using logistic regression for each species. Fish were categorized as mature (1) or immature (0) and regressed with their respective TL or age. Von Bertalanffy growth parameters (Ricker 1975) were estimated for each sex and each species by fitting a nonlinear regression to the von Bertalanffy growth equation using noninformative uniform priors for the theoretical age at a length of zero (t0; −3 to 0), the Brody growth coefficient (K; 0–1), and the asymptotic length (L∞; 0–800 for Walleyes; 0–500 for Saugers). Total length and respective age were used for each species. Using FWIN attribute data only, the data for each species were pooled across river reaches in the aforementioned calculations.
Finally, synchrony of year-class strength between the two species was assessed at the reach level and river level (i.e., populations were collated) by correlating the residuals from a catch curve analysis (Maceina 1997). The loge(frequency at age) was regressed against age for Saugers and Walleyes caught during each FWIN survey (i.e., ages for all Saugers and Walleyes sampled were only available from FWIN data). To assess synchrony in year-class strength at the reach level, a regression was conducted on the residuals of the catch curve analyses for each reach to determine whether the relationships were significantly different from zero. Samples from Upper Allumette Lake, Lower Allumette Lake, and Lac Coulonge were pooled and treated as a single population for this analysis. The three river reaches are contiguous and are segmented by natural rapids; therefore, Saugers and Walleyes have the potential to move freely among these reaches, whereas movement from other reaches is impeded by the presence of hydroelectric generating stations at either end of the reach. For river reaches with a small sample size, a weighted regression was conducted using the predicted loge values from the aforementioned catch curve regression; this removed the contribution of older, rare fish in these small samples by assigning smaller weights and had the opposite effect for abundant cohorts (Maceina and Bettoli 1998). To assess the synchrony in year-class strength at the river level, the mean and SE of the residuals from the catch curve analyses for each cohort within each species were determined. The correlation of these mean residuals was then ascertained to assess cohort synchrony at the river level. Only data from the 1998 and 1999 FWIN surveys were used in the analysis of river-level synchrony. Regressions were conducted on the cohort (i.e., hatch year) instead of age so as to adjust and account for the different sample years. The residuals from the cohort regressions for Saugers and Walleyes were then used in a PCA to ascertain whether there was a relationship with environmental variables (cumulative annual GDDs, mean annual river discharge, and mean annual precipitation) for the period 1986–1998. These variables were standardized, and the PCA was conducted by using a covariance matrix. Principal components were retained if they had an eigenvalue greater than 1.0. Correlations between variables and the PC were determined to assist with interpretation, as detailed above.
Bayesian analyses were conducted using WinBugs version 1.4. All remaining analyses (PCAs and correlations) were conducted using STATA version 12.0 (StataCorp LP, College Station, Texas). Significance for all analyses was determined at an α of 0.05.
RESULTS
In total, 1,875 Saugers and 2,347 Walleyes were sampled during netting in the Ottawa River. There were 548 Saugers caught during BSM surveys for a CPUE of 0.345 fish/gang, and 1,327 Saugers were caught during FWIN surveys (Table 3). In contrast, 1,165 Walleyes were caught during BSM surveys for a CPUE of 0.59 fish/gang, and 2,182 Walleyes were caught during FWIN surveys. In the BSM surveys, TL ranges were 160–485 mm for Saugers and 173–699 mm for Walleyes; in the FWIN surveys, TL ranges were 134–446 mm for Saugers and 144–739 mm for Walleyes (Figure 2).
| BSM CPUE | |||
|---|---|---|---|
| Reach | Sauger | Walleye | Year-class synchrony |
| Lake Temiscaming | 0.79 (0.21) | 1.40 (0.52) | r = 0.70 (F1, 13 = 12.48, P = 0.004) |
| Lac la Cave | 0.59 (0.16) | 1.20 (0.23) | r = 0.68 (F1, 13 = 11.25, P = 0.005) |
| Holden Lake | 0.26 (0.11) | 0.63 (0.15) | r = 0.57 (F1, 13 = 6.17, P = 0.028) |
| Upper Allumette Lake | 0.41 (0.15) | 1.26 (0.40) | |
| Lower Allumette Lake | 0.23 (0.08) | 0.46 (0.16) | |
| Lac Coulonge | 0.13 (0.04) | 0.39 (0.24) | r = 0.67 (F1, 13 = 10.77, P = 0.006) |
| Lac du Rocher Fendu | 0.18 (0.12) | 0.00 | r = 0.17 (F1, 8 = 0.23, P = 0.642) |
| Lac des Chats | 0.24 (0.06) | 0.33 (0.14) | r = 0.13 (F1, 8 = 0.15, P = 0.71) |
| Lac Deschênes | 0.25 (0.08) | 0.08 (0.04) | r = 0.28 (F1, 8 = 0.69, P = 0.429) |
| Lac Dollard des Ormeaux | 0.47 (0.11) | 0.18 (0.07) | |
Length frequency distributions for (a) Saugers and (b) Walleyes sampled by two standard index netting techniques in the Ottawa River (black bars = catch from Fall Walleye Index Netting surveys; gray bars = catch from Broad-Scale Monitoring surveys).
The probability of capturing a Sauger was greatest at the 20–35-m depth stratum, whereas the probability of capturing a Walleye was greatest at the 6–12-m depth stratum (Figure 3). Likewise, Sauger abundance was significantly greater in the 20–35-m depth stratum than in other depth strata (Tables 4, 5), and Walleye abundance was significantly greater in the 6–12-m depth stratum than in other depth strata (Tables 4, 5). No Saugers or Walleyes were caught at water depths greater than 75 m. There was an effect of depth on the TL of Saugers and Walleyes captured (Figure 4). Saugers sampled from the 20–35-m depth stratum were significantly larger than those sampled from all other depth strata (Figure 4; Table 6), whereas Walleyes sampled from the 12–20-m depth stratum were significantly larger than those sampled from all other depth strata (Figure 4; Table 6).
| Depth stratum (m) | Sauger CPUE | Walleye CPUE |
|---|---|---|
| 1–3 | 0.3 (0.2, 0.5) | 1.3 (1.0, 1.7) |
| 3–6 | 1.2 (0.9, 1.4) | 3.1 (2.7, 3.5) |
| 6–12 | 1.7 (1.4, 2.1) | 2.4 (2.1, 2.8) |
| 12–20 | 1.4 (1.1, 1.8) | 0.7 (0.4, 0.9) |
| 20–35 | 2.0 (1.6, 2.4) | 0.05 (0.01, 0.13) |
| 35–50 | 0.4 (0.2, 0.6) | 0.04 (0.001, 0.15) |
| 50–75 | 0.08 (0.002, 0.31) | 0.002 (<0.001, 0.024) |
| Depth stratum (m) | |||||||
|---|---|---|---|---|---|---|---|
| Depth stratum (m) | 1–3 | 3–6 | 6–12 | 12–20 | 20–35 | 35–50 | 50–75 |
| 1–3 | — | 0.002 | <0.001 | 0.998 | 1.0 | 1.0 | 1.0 |
| 3–6 | <0.001 | — | 0.017 | 1.0 | 1.0 | 1.0 | 1.0 |
| 6–12 | <0.001 | 0.054 | — | 1.0 | 1.0 | 1.0 | 1.0 |
| 12–20 | <0.001 | 0.209 | 0.750 | — | 1.0 | 1.0 | 1.0 |
| 20–35 | <0.001 | <0.001 | 0.050 | 0.015 | — | 0.583 | 0.978 |
| 35–50 | 0.516 | 0.999 | 1.0 | 1.0 | 1.0 | — | 0.968 |
| 50–75 | 0.973 | 1.0 | 1.0 | 1.0 | 1.0 | 0.956 | — |
| Depth stratum (m) | |||||||
|---|---|---|---|---|---|---|---|
| Depth stratum (m) | 1–3 | 3–6 | 6–12 | 12–20 | 20–35 | 35–50 | 50–75 |
| 1–3 | — | 0.462 | 0.293 | 0.032 | 0.871 | 0.912 | 0.922 |
| 3–6 | 0.178 | — | 0.299 | 0.028 | 0.882 | 0.925 | 0.931 |
| 6–12 | 0.398 | 0.869 | — | 0.052 | 0.919 | 0.949 | 0.951 |
| 12–20 | 0.010 | 0.127 | <0.001 | — | 0.985 | 0.991 | 0.991 |
| 20–35 | <0.001 | <0.001 | <0.001 | 0.017 | — | 0.598 | 0.632 |
| 35–50 | 0.044 | 0.106 | 0.032 | 0.498 | 0.872 | — | 0.538 |
| 50–75 | 0.484 | 0.646 | 0.528 | 0.849 | 0.977 | 0.832 | — |
Probability of capturing a Sauger (solid line) or a Walleye (dashed line) at different depth strata during a standardized index netting program in the Ottawa River (dotted lines = 95% credible limits).
Mean total length of Saugers (dashed line) and Walleyes (solid line) sampled at different depth strata of the Ottawa River during Broad-Scale Monitoring surveys (dotted lines = 95% credible limits).
Both Saugers and Walleyes were caught in 17.7% of the BSM net sets and 48% of the FWIN net sets. The probability of capturing both Saugers and Walleyes in a net varied with depth: the probability was greatest at 6–12 m and lowest at depths greater than 20 m (Figure 5). The probability of capturing both Saugers and Walleyes in a net was significantly greater in Lake Temiscaming and Lac la Cave than in all other river reaches (Figure 6).
Probability of capturing both Saugers and Walleyes at different depth strata of the Ottawa River based on data from Broad-Scale Monitoring surveys (dotted lines = 95% confidence interval).
Probability of capturing both Saugers and Walleyes in each of 10 Ottawa River reaches based on data from Broad-Scale Monitoring surveys and Fall Walleye Index Netting surveys (dotted lines = 95% confidence interval).
In the PCA for assessing potential influences on Sauger and Walleye CPUE among river reaches, three PCs were retained, explaining 87.1% of the variation in the data (Table 7). The first PC was strongly correlated with 12 of the original variables and accounted for 55.2% of the variation (Table 7). In general, river characteristics contrasted thermal conditions, suggesting that the relative abundances of Saugers and Walleyes would increase with increasing maximum and mean depths, distance upstream, and reach length and in thermally stratified reaches. In contrast, the relative abundances of Saugers and Walleyes would decrease with increasing GDDs; water temperatures at the surface, 6–12-m depth stratum, and 20–35-m depth stratum; and human population within 50 km of the reach shoreline. The second PC (PC2) was strongly correlated with seven of the original variables and accounted for 24.2% of the variation. This PC suggested that Sauger relative abundance was positively related to reach size, human population within 50 km of the reach, water temperatures at the surface and 6–12-m depth stratum, and reach regulation (Table 7). Relative abundance of Saugers or Walleyes was not a significant correlate to PC3, which accounted for 8.1% of the variation; therefore, PC3 was of little interest to this study (Table 7).
| Variable | PC1 | PC2 | PC3 |
|---|---|---|---|
| Sauger CPUE | 0.2710 | 0.2642 | 0.1580 |
| Walleye CPUE | 0.3053 | –0.0481 | 0.3933 |
| Surface area | 0.1907 | 0.3690 | 0.3649 |
| Maximum depth | 0.2840 | 0.2407 | –0.1883 |
| Mean depth | 0.3251 | 0.1159 | –0.0295 |
| Secchi depth | –0.1269 | –0.2276 | 0.5128 |
| Temperature at the surface | –0.2498 | 0.3288 | 0.1577 |
| Temperature at 6–12 m | –0.2383 | 0.3347 | 0.1865 |
| Temperature at 20–35 m | –0.3139 | 0.0453 | –0.1358 |
| Thermally stratified reach | 0.3309 | –0.0442 | 0.1026 |
| River kilometer | 0.3198 | –0.1353 | 0.0427 |
| Reach length | 0.2009 | 0.3660 | 0.0399 |
| Regulated reach | 0.0659 | 0.3393 | –0.5123 |
| GDDs | –0.3228 | 0.0714 | 0.1060 |
| Human population within 50 km of the reach shoreline | –0.1885 | 0.4146 | 0.1490 |
| Percentage of variation explained | 54.1 | 24.2 | 8.1 |
The oldest male and female Saugers sampled were 19 and 15 years of age, respectively; the largest male and female Saugers sampled were 441 and 430 mm TL, respectively. Mean size at 50% maturity was 215.0 mm (95% confidence interval [CI] = 205.2–223.0 mm) for male Saugers and 253.1 mm (95% CI = 248.5–257.4 mm) for female Saugers. Mean age at 50% maturity was 1.9 years (95% CI = 1.5–2.2 years) for male Saugers and 3.2 years (95% CI = 3.0–3.3 years) for females. (Note that these and subsequent ages represent fall ages. Fish would be considered 1 year older if they had been sampled during their spawning period in the subsequent spring.)
The oldest male and female Walleyes sampled were 23 and 22 years of age, respectively; the largest male and female sampled were 700 and 739 mm TL, respectively. Mean size at 50% maturity was 297 mm (95% CI = 292–302 mm) for male Walleyes and 434 mm (95% CI = 424–443 mm) for female Walleyes. Mean age at 50% maturity for male and female Walleyes was 2.3 years (95% CI = 2.1–2.4 years) and 5.2 years (95% CI = 4.9–5.4 years), respectively. For both Saugers and Walleyes, females were longer at age than male conspecifics. Male and female Walleyes were longer at age than Saugers of the same sex (Table 8).
| Species | Sex | L∞ | K | t0 |
|---|---|---|---|---|
| Sauger | ♂ | 356 (345, 368) | 0.20 (0.19, 0.22) | –2.90 (–3.0, −2.65) |
| ♀ | 382 (359, 405) | 0.20 (0.18, 0.26) | –2.45 (–2.97, −1.76) | |
| Walleye | ♂ | 443 (426, 460) | 0.28 (0.23, 0.33) | –1.93 (–2.37, −1.51) |
| ♀ | 778 (736, 799) | 0.10 (0.09, 0.11) | –2.66 (–2.94, −2.36) |
There was evidence of year-class synchrony between Saugers and Walleyes at the reach level (Table 3). Year-class strengths were significantly correlated in four of the seven reaches. Synchrony was most apparent in river reaches with higher Sauger and Walleye relative abundances (i.e., CPUEs). Ages were not available for Saugers from Lac Dollard des Ormeaux, and therefore this reach was excluded from analyses of synchrony. At the river level, there was synchrony in year-class strength between Saugers and Walleyes (r = 0.77, F1, 11 = 16.36, P = 0.002; Figure 7). Weak year-classes for both Saugers and Walleyes were produced in 1992 and 1996, whereas strong year-classes for both species were produced in 1995 (Figure 7). A strong year-class was also produced in 1991 for Walleyes but was not evident for Saugers (Figure 7). Based on PCA, there was a strong correlation between PC1 and four of the original variables, with PC1 explaining 48.2% of the variation (Table 9). Sauger and Walleye year-class strengths would therefore increase with mean annual GDDs, whereas they would decrease with mean annual flow. Sauger and Walleye year-class strengths were not strongly correlated with PC2 and thus did not show a relationship with mean annual precipitation (Table 9).
| Variable | PC1 | PC2 |
|---|---|---|
| Sauger cohort strength | 0.5217 | 0.4093 |
| Walleye cohort strength | 0.5451 | 0.2647 |
| GDDs | 0.5424 | −0.3502 |
| Mean annual flow | −0.3382 | 0.1511 |
| Mean annual precipitation | −0.1486 | 0.7854 |
| Percentage of variation explained | 48.2 | 25.8 |
Cohort strength of Saugers (open circles) and Walleyes (solid circles) in the Ottawa River, as represented by the residuals (±SE) of catch curve analyses.
DISCUSSION
Sympatric populations of Saugers and Walleyes were present in all 10 Ottawa River reaches, and their relative abundances were correlated among reaches. This study was able to quantify variation in relative abundances among various depth strata. The most apparent difference between the two species was depth selectivity, with Walleyes selecting shallower areas and Saugers selecting deeper areas, but there was extensive overlap. In the Ottawa River, variation in environmental conditions appears to be driving both Sauger and Walleye populations. These populations were positively related to reach characteristics but were negatively related to thermal conditions. Saugers and Walleyes exhibited synchrony in year-class strength, which was positively related to mean annual temperature and negatively related to mean annual flow.
Depth selection was the greatest difference observed in the spatial distribution of Saugers and Walleyes within the river. In this study, Walleyes were generally restricted to shallow areas, as has been observed in more-southerly populations (Swenson and Smith 1976; Fitz and Holbrook 1978; Ickes et al. 1999). Walleyes purportedly are more selective for the littoral zone (Fitz and Holbrook 1978), concentrating in these shallow areas during summer months when prey availability is greater (Swenson and Smith 1976) and preferring areas where water temperatures are approximately 25°C (Dendy 1948). In the present study, Saugers generally selected deeper areas, although extensive overlap (e.g., potential interaction) with Walleyes occurred in the 6–12-m depth stratum. Depth segregation of Saugers and Walleyes, as observed in this study, has been previously reported (Dendy 1948; Osterberg 1978; Rawson and Scholl 1978; Ickes et al. 1999). In a Tennessee reservoir, the overlap in selection was most prevalent at 6–9 m (Dendy 1948). Interspecific competition between Saugers and Walleyes could occur within this depth stratum, as food selection can be similar (Swenson and Smith 1976; Collette et al. 1977; Osterberg 1978; Bellgraph et al. 2008; Bozek et al. 2011b). However, differences in depth distribution appear to be associated with differences in feeding strategy. Walleyes are considered pelagic feeders, whereas Saugers are considered more demersally oriented (Swenson 1977; Rawson and Scholl 1978). An earlier study in the lower reach of the Ottawa River (Lac Dollard des Ormeaux) found that Saugers and Walleyes fed primarily on Emerald Shiners Notropis atherinoides. Both species migrated into shallow bays at night to feed. This behavior by Saugers (i.e., feeding at shallower depths than anticipated for a demersal species) was attributed to the lack of adequate prey within the sublittoral zone, purportedly due to the effects of impoundment and industrial effluent (Osterberg 1978). Walleyes can limit the natural abundance of Saugers through interspecific competition (Bellgraph et al. 2008), although this was not evident within the Ottawa River, corroborating that the two species can be sympatric, possibly enabled by depth segregation.
Saugers are better adapted to poor light conditions than are Walleyes (Ali and Anctil 1977; Bozek et al. 2011a), and Saugers select water temperatures of approximately 18°C (Dendy 1948), which may explain the differences in depth selectivity observed, although optimal temperatures of the two species are reportedly similar (Bozek et al. 2011a). These preferences by Saugers may have been adaptations to enable their segregation from Walleyes, thereby reducing intraspecific competition. Variation in Secchi depth among river reaches in this study was relatively small (1.8–2.5 m; with the exception of Lac des Chats, 3.3 m) but was well within the range considered optimal for Walleyes (Lester et al. 2004), as discussed below. In this study, Walleyes were observed at depths above the thermocline in the stratified river reaches, whereas Sauger abundance was greatest in the depth stratum that overlapped with the thermocline. Saugers and Walleyes were limited by depth, as neither species was located at depths greater than 75 m. Therefore, the existence of the thermocline may limit depth distribution even though percids are believed to undergo forays throughout the thermocline (Kitchell et al. 1977).
Both Saugers and Walleyes appeared to be further segregated conspecifically by depth (i.e., size-specific catch of each species varied with depth). Larger fish were sampled in the deepest depth at which the species was prevalent, and the smallest fish were located in the shallowest strata. Ontogenetic changes in diet and prey size (Galarowicz et al. 2006; Chipps and Graeb 2011) may help to explain depth segregation based on size. For example, young-of-the-year Walleyes were found to prefer water depths of 2–5 m, which were also highly correlated with prey fishes (Pratt and Fox 2001).
Sauger and Walleye abundances co-varied among river reaches, indicating that similar factors affected both species, as has been observed in large lakes (Johnston et al. 2012). In the Ottawa River, relative abundance was positively associated with reach characteristics but was negatively associated with thermal conditions (i.e., warm reaches had lower abundances of both species). Water body size (i.e., surface area) has previously been identified as a factor that is positively associated with Walleye abundance (Nate et al. 2000). Saugers and Walleyes are considered coolwater percids (Bozek et al. 2011a; Johnston et al. 2012). Although information is limiting for Saugers, Lester et al. (2004) reported that Walleye growth was related to Secchi depth and climatic conditions. Their study area, however, was throughout the province of Ontario, where the variation of Secchi depth and climate across water bodies was substantially greater than that in our study area. With the exception of one reach, Secchi depth within the Ottawa River was close to the optimal Secchi depth as identified by Lester et al. (2004) and therefore was probably minimized as an important covariate in the analyses.
As is common in other sympatric populations, Walleyes in the Ottawa River attained a larger size and greater age than Saugers (Nelson and Walburg 1977; Johnston et al. 2012). Specifically, female Walleyes had the greatest growth potential within the river (Nelson and Walburg 1977). However, Saugers matured earlier and at a smaller size than Walleyes, consistent with other studies (Johnston et al. 2012). Bozek et al. (2011a) conducted a comprehensive review of Sauger and Walleye life history characteristics across their natural range. Walleye demographics within the Ottawa River (e.g., maximum age, age and size at 50% maturity, L∞, and K) were within the variation previously observed in other populations across the species’ range (Bozek et al. 2011a). Saugers in the Ottawa River were similar, as many life history traits (e.g., age at 50% maturity, L∞, and K) were within the variation reported (Bozek et al. 2011a). However, the maximum age documented for Saugers in the present study (19 years) was greater than the previously reported value (13 years), and the mean size at 50% maturity was smaller than reported (Bozek et al. 2011a). Percid life history characteristics are known to be plastic, and longevity varies over their geographical range. The relatively high maximum age of Saugers in the present study may reflect this effect, whereas the small mean size at 50% maturity could be a function of growth, as the mean age at 50% maturity for Saugers was within the variation observed previously (Bozek et al. 2011a). Slower growth in the Ottawa River population of Saugers may contribute to their longevity.
Year-class synchrony was evident between Saugers and Walleyes in the Ottawa River. This was most pronounced in the upper river reaches, where the relative abundances of the two populations were the greatest and—probably more influential—where the sample sizes included in the analyses were the greatest. A weak relationship in the lower reaches may have been a function of small sample sizes, despite an attempt to correct for this by using a weighted regression (Maceina and Bettoli 1998). The present study corroborates other studies that have reported year-class synchrony between Saugers and Walleyes (Swenson and Smith 1976; Lyons and Welke 1996; Pitlo 2002), indicating that the two species are influenced by similar environmental factors (Nelson and Walburg 1977; Colby et al. 1979; Pitlo 2002; Graeb et al. 2010). In this study, year-class strengths for both species were positively related to GDDs but negatively related to river flow, consistent with other studies’ observations of strong year-classes (Pitlo 2002; Graeb et al. 2010). Mean annual GDDs were used as a surrogate for water temperatures in this study. Water temperature is considered to be the most important abiotic factor influencing Walleye survival and is associated with year-class strength (Bozek et al. 2011a). Effects of flow seem to depend on timing: high flows during the spawning period appear to enhance recruitment (Colby et al. 1979; Johnston et al. 1995), whereas high flows during the hatching and larval drift periods appear to reduce recruitment (Mion et al. 1998). Increases in suspended sediment during high-flow years were believed to negatively affect survival of Walleye larvae, thereby influencing year-class strength (Mion et al. 1998). In contrast, turbidity has been shown as a factor in Sauger year-class strength—specifically, high turbidity is correlated with recruitment (Doan 1941). Alas, turbidity measurements for assessing potential correlations in the present study were not available.
Synchrony in year-class strengths was evident throughout the river. The strong year-classes were 1991 and 1995, whereas the weak year-classes were 1992 and 1996. These year-classes were identical to the strong and weak year-classes identified for Walleye populations in northern Wisconsin lakes (Beard et al. 2003), supporting the idea that drivers of percid recruitment are larger than the river reach scale. However, these year-class strengths did not match those in other studies (Pitlo 2002; Zhao et al. 2009), suggesting that climatic effects strongly influence percid reproductive success at a regional scale (Lyons and Welke 1996; Beard et al. 2003) but that other intrinsic factors (e.g., water levels in highly managed systems) have an overriding effect (Nelson and Walburg 1977).
A limitation of this study was that the results are based on gillnetting conducted over a small period of time during the season. Gill nets were set overnight, which may have maximized capture efficiency, as Walleyes and presumably Saugers are more mobile during twilight periods and therefore are more vulnerable to capture at night (Carlander and Cleary 1949; Osterberg 1978; Bozek et al. 2011b). Thus, the capture of Saugers and Walleyes in the same net does not necessarily imply an interaction between the species but instead may suggest that they frequented the same area during the period in which the net was set. The BSM surveys were conducted throughout the different reaches during late June–early September, when water temperatures were greater than 18°C, and could be considered representative of the greater portion of the open-water (i.e., ice-free) season with the exception of the fall. Walleyes have been reported to concentrate in shallow water during July–September, increasing their depth over that time period (Swenson and Smith 1976). Variation in depth selection by both species therefore should have been captured by the study period and should have emerged in the results.
Management Implications
Saugers and Walleyes coexisted in the Ottawa River, corroborating that both species can be supported within a water body (Dendy 1948; Fitz and Holbrook 1978; Lyons and Welke 1996). Interspecific competition that would cause the decline of one species over another (Bellgraph et al. 2008) was not evident. The coexistence of the two species can probably be attributed to the characteristics of the Ottawa River, a large, deep system that enabled the species to segregate at least by depth. This implies that other water bodies with similar characteristics could be managed to support sympatric populations of Saugers and Walleyes.
Similarities observed between Saugers and Walleyes suggest that management efforts employed for one species could benefit the other. For example, synchrony in recruitment suggests that efforts to manage spawning habitat (e.g., spawn shoal rehabilitation, flow manipulation, etc.) should benefit both species; this would be helpful to fisheries managers given the paucity of information on spawning enhancement efforts for Saugers relative to Walleyes (Bozek et al. 2011b). However, life history differences between Walleyes and Saugers advocate that harvest regulations (e.g., size limits) must be species specific in order to be effective.
Finally, assessment techniques targeting a given species during temporal periods similar to those used here could, through a stratified sampling approach, target the depths identified as having the greatest probability of capture for that species. Moreover, depth-restricted sampling strategies that have been established for Walleyes (e.g., FWIN: nets set at depths < 15 m; Morgan 2002) may not be suitable for adequately assessing sympatric Sauger populations, despite the adequate numbers sampled in the nets. Given the conspecific depth segregation detected in this study, larger Saugers within the population may be missed by depth-restricted sampling protocols.
ACKNOWLEDGMENTS
I thank the BSM crews from the OMNRF Pembroke and Kemptville district offices; the Laurentian Freshwater Cooperative Unit for conducting a portion the FWIN surveys; and Henri Fournier (Quebec Ministry of Natural Resources) for sharing FWIN data. Nigel Lester (OMNRF) provided insightful comments on the manuscript; Tal Dunkley (OMNRF) assisted with GIS analyses. Funding for this project was provided by the OMNRF Science and Information Division.
