118 zooplankton, progressing to macroinvertebrates, and ultimately feeding on fishes (Liao et al. 2001; Uphoff et al. 2019). To ensure robust growth, condition, and survival, fish must have access to suitable-sized prey at all life stages.

121 In this study, we focus on diet shifts in adult gamefish that are likely due to prey preference or availability rather than anatomical mouth-gape limitations. Previous studies have largely focused on juveniles, showing that they are primarily affected by ZQM through suspension feeding, which reduces their planktonic food base (Nalepa 2010; Hansen et al. 2020; Hoyle et al. 2008). Here, we focus on adult size classes to help project their responses to changes in prey composition and availability following ZQM invasion, using baseline measures of diet and body condition in uninvaded prairie lakes. Using 15 fish-bearing lakes as model systems, we tested the hypotheses that:

129 1. Diet preferences will be species-specific, where "early switching piscivores," pike and walleye, will show marked feeding specialization. However, perch, "late switching piscivores", may demonstrate a more flexible feeding strategy.

132 2. Changes in diet composition for these gape-limited gamefish species will be evident as they grow.

134 3. Positive or negative trends in gamefish body conditions will be closely associated with their diet.

136 4. Physicochemical parameters (e.g., water temperature, dissolved oxygen, pH, and nutrient concentrations) will significantly influence the body condition of gamefishes.

139
## 2.0 METHODS

### 2.1 STUDY AREA

Initially, eighteen fish-bearing lakes in the Northern Great Plains (NGP) of Western Canada were sampled to provide important baselines on the ecological status and dynamics of gamefish populations across south-central SK (Figure 1). All study lakes were within the prairie and boreal transitional ecozones. Of the 18 lakes, twelve are in hydrologically closed basins (endorheic) that lack channelized surface inflow or outflow, and are mainly sustained through winter precipitation. The remaining six lakes are exorheic (open basins) draining into downstream systems (Table S1). Surface area of study lakes ranged from 0.35 to 33.0 km², with maximum depth ranging from 4.4 to 38.0 m (Table S1). Nine of the study lakes are part of a 21-lake Long-Term-Ecological-Research program conducted in the majority of drainage basins across southern SK since 2002 (Wissel et al. 2011; Starks et al. 2014; Nanayakkara et al. 2021). The Ministry of Environment, Government of Saskatchewan, uses the remaining nine lakes for fisheries assessment in the Saskatoon and Prince Albert areas (Saskatchewan Ministry of Environment 2017, 2018, 2019).

Five lakes lie within the southwestern semi-arid ecoregion, four within Canada's moist mixed grassland interior plains, eight in the Aspen Parkland, and one within the boreal transitional zone (Figure 1, Table S1). Fifteen lakes had high fish complexity (forage fishes, benthivores, and gamefishes), (Table S1). Of these, 10 were predominantly sustained by provincial stocking programs to replenish walleye populations stressed by angling pressure and low recruitment (Saskatchewan Ministry of Environment 2017, 2019). The remaining three lakes were excluded from further analyses due to their low
163 fish complexity. These lakes contained only forage fishes, which are potential prey for 164 gamefishes in lakes with higher complexity, along with other fishes commonly found in 165 low-complexity lakes (Essington et al. 2015) (Table S1). Of the three low-complexity 166 lakes, From and Grenfell were suspected to have experienced recent summer or 167 winterkills (Saskatchewan Fish Facts 2019), while Shannon Lake was anoxic at 168 sampling. The selected 15 lakes are exposed to large seasonal and inter-annual 169 variations (Sauchyn and Kulshreshtha 2008), and their small size makes them 170 particularly susceptible to the impacts of climate change and the potential invasion by 171 ZQM. Hence, they are excellent models for monitoring the possible effects of 172 environmental stressors, representing early-warning systems. 173

## 2.2 LAKE SAMPLING

### 2.2.1 Integrated water samples

175 The methods described by Cooper and Wissel (2012a, 2012b) and Starks et al. (2014) 176 were adopted for collecting water samples from May to August of 2019. Samples for 177 water chemistry were collected at the location of maximum depth. Lake depths were 178 recorded using an electronic depth meter, model 71624. Using a YSI model 556, vertical 179 profiles of temperature (°C), specific conductivity (µS cm⁻¹), total dissolved solids (mg 180 TDS L⁻¹), salinity (ppt), dissolved oxygen (mg O₂ L⁻¹, % saturation) and pH were 181 recorded at 0.5 m intervals for shallow lakes (< 10 m) and 1 m intervals for deep lakes (> 182 10 m). Water transparency was measured with a 20 cm diameter black and white Secchi 183 disk. 184

### 2.2.2 Gamefish samples
Gamefishes were captured overnight using sets of multi-panel gill nets (24.7 m long x 1.5 m deep; mesh sizes; 38.1, 50.8, 63.5, 76.2, 88.9, 101.6, 114.3, 127 mm) placed perpendicular to shore, between May and August of 2019. Multi-panel gill nets were used to obtain representative samples of the different sizes of gamefish species. Fishes were retrieved after 16-20 hours, identified to species (Scott and Crossman 1973) and their sexes determined. This was followed by in-situ total length (cm) and weight (g) measurements. Fish samples were then placed immediately on ice to reduce the decomposition of stomach content and stored at -15 °C (Zacharia and Abdurahiman 2004) for a few weeks.

## 2.3 STUDY SPECIES

Walleye, perch, and pike were observed across study lakes. Walleye occurred in all 15 high-complexity lakes, perch in 9, and pike in 6 (Table S2). Walleye is the focal species because it is the most preferred by anglers in study lakes (Nanayakkara and Wissel 2017). However, unlike native perch and pike, walleye populations lack genetic diversity (Starks et al. 2014), which is related to the stocking programs.

## 2.4 ETHICS STATEMENT

Animal sampling in this study was conducted under approved animal use protocols from the Animal Care Committee of the University of Regina, and in accordance with special collection research permits provided to B; Wissel by the Government of Saskatchewan, and the Ministry of Environment (SCP2019/AR08/FWLB).

## 2.4 LABORATORY ANALYSIS

### 2.4.1 Water chemistry
207 Depth-integrated water samples were pre-screened using a 80-µm mesh before
208 filtration through 0.45-µm sterile membrane filters. A subset of the filtered water was
209 frozen and later analyzed for nitrate (NO3), nitrite (NO2), ammonium (NH4+), total
210 nitrogen (TN), soluble reactive phosphorous (SRP), and total phosphorous (TP) (µg L-1)
211 following standard methods described by Pham et al. (2008). The remaining filtrate was
212 refrigerated and analyzed for alkalinity and Calcium (Ca) (mg L-1) following Pham et al.
213 (2008). Dissolved inorganic and organic carbon (DIC/DOC) were quantified using
214 refrigerated filtrate on an OI Analytical Aurora 1030W TOC analyzer. Algal biomass
215 (measured as chlorophyll a; chl a) was quantified by filtering 1 L through GF/C filters,
216 stored at -15 °C until extraction with acetone: methanol: water (80:15:5).
217 Concentrations were quantified using a spectrophotometer (Hewlett-Packard model
218 84452A) (Wetzel and Likens 1991; Cooper and Wissel 2012a). All laboratory analyses
219 were conducted at the Institute of Environmental Change and Society at the University
220 of Regina, Saskatchewan, Canada.

221 **2.4.2 Stomach content analysis**

222 Fish stomachs were removed and stored in 70% ethanol (Jobling et al. 2001) until
223 analysis. Two-hundred-and-eighty-three preserved gamefish stomachs comprising 188
224 walleye, 65 perch, and 30 pike were examined under a dissecting microscope (SZX12
225 Olympus model) to ascertain important prey contributing to their diets. Prey items were
226 identified to the lowest practical taxonomic resolution, mostly species-level for fishes
227 (Saskatchewan Watershed Authority 2010) and family-level for macroinvertebrates
228 (Thorp and Rogers 2011) and quantified by counting individuals of each taxon and
229 determining their volumes using the displacement method (Pinkas et al. 1970; Hyslop
1980; Manko 2016). We used the percent Index of Relative Importance (%IRI), a composite diet index integrating three individual diet measures (%N, %F, and %V or %M), to provide a robust estimate of diet composition (Chipps and Garvey 2007; Gelwick and McIntyre 2017). Although %IRI may be more time-consuming and require larger sample sizes for robust estimates (Ahlbeck et al. 2012), it offers a more representative measure of diet and enables unambiguous interpretation (Cortés 1997; Manko 2016). It also overcomes some of the inherent biases of single-component metrics. For example, %N and %F can overemphasize small prey, while bulk measures (%M/%V) may underestimate rarely consumed prey (but see Ahlbeck et al. 2012; Baker et al. 2014).

IRIᵢ of specific food category is given as:

$$ \mathit{IRI}_i = (\mathit{\%N}_i + \mathit{\%V}_i) \times \mathit{\%F} \quad (1) $$

Where %Nᵢ = Numerical percentage of specific food category, %Vᵢ = Volumetric percentage of specific food category and %Fᵢ = Frequency of occurrence.

The single index for extrapolating IRIᵢ was calculated as follows:

$$ \%N = N_i/N_t \times 100 \quad (2) $$

$$ \%V = V_i/V_t \times 100 \quad (3) $$

$$ \%F = F_i/F_t \times 100 \quad (4) $$

Where Nᵢ = the number of food categoryᵢ and N_t = the total number of individuals in all food categories, Vᵢ = the volume of food categoryᵢ and V_t = the total volume for all food
categories in all individuals, and $F_i$ = number of fish containing food category$_i$ and $F_t$ = the number of fish with food in their stomachs.

IRI values were then expressed in percentages for possible comparison among food items based on Cortés (1997) and Desmond et al. (2002) summing total IRI values across all prey items:

$$ \%IRI_i = 100 \times \frac{IRI_i}{\sum_{i=1}^{n} IRI_i} \quad (5) $$

Where %IRI$_i$ is the percent index of relative importance of food item$_i$, IRI$_i$ is the index of relative importance of food items$_i$, and n is the total number of food categories calculated at a given taxonomic level.

To calculate %IRI, we grouped the three gamefish species into two size categories based on total lengths: walleye (≤ 40cm and > 40cm), pike (≤ 60cm and > 60cm), and perch (≤ 22.5cm and > 22.5cm). We then compared %IRI between the size groups for each species. We focused on adult sizes to assess the extent to which the game species realized a switch to piscivory once their mouth gapes were sufficiently large, as existing literature largely focuses on early life stages (Yang 2022). For all three species, the size cut-offs represented age-classes of 4-5 years. All captured fish were sufficiently large for piscivory, though its likelihood increases with size. Prey were grouped by dominant taxa: fish, amphipods, chironomids, other invertebrates, plant matter, and unidentified components.

### 2.4.3 Fish condition

Relative weight (Wr) condition indices were computed for 514 gamefishes comprising 317 walleye, 41 pike, and 156 perch (Table 1) and reported as a whole number using
equations described by Willis (1989), Willis et al. (1991), Murphy et al. (1991) and Blackwell et al. (2000).

Wr of gamefish populations was estimated using the following equation:

$$Wr = (W/Ws) \times 100 \quad (6)$$

Where Wr = relative weight, W = actual weight, and Ws = standard weight for fish of the same length. Ws values were calculated for each fish exceeding the required minimum total length (walleye > 150 mm, pike > 100 mm, perch > 100 mm). Parameters for Ws were derived from the regression equations of Blackwell et al. (2000). Values of Wr = 95-105 indicate good condition, near 80 suggest poor health due to unfavorable environmental conditions, and extremely high scores (> 115) indicate excess energy reserves (Murphy et al. 1991; Blackwell et al. 2000).

## 2.5 STATISTICAL ANALYSIS

### 2.5.1 Relative importance of prey items to gamefishes

We used Multivariate analysis of variance (MANOVA) based on redundancy analysis (RDA) to test significant differences in diet composition between small and large groups of three gamefish species. RDA models for gamefish diet composition were fitted using the 'rda' function (vegan package, Oksanen et al. 2013) with 'size group' as the main factor and 'lake' as a covariate. Diet data were square root transformed for normality. Prior to analyses, the PERMDISP2 test (Anderson 2006) was used to assess the assumption of homogeneous multivariate dispersion between size groups via the betadisper function (vegan package, Oksanen et al. 2013). This step was necessary
because multivariate analyses, such as PERMANOVA, assume similar group dispersions
(variances). Differences in dispersion can confound PERMANOVA results, making it
unclear whether significant effects arise from differences in group centroids (true
differences in composition) or differences in within-group variability. Therefore, we
validated this assumption ensuring that subsequent tests of diet composition differences
reflect genuine ecological patterns rather than artifacts of unequal variability.

RDA based on MANOVA provides a robust alternative to traditional MANOVA, with less
stringent assumptions. Unlike classical MANOVA, RDA does not require multivariate
normality and is well-suited for our non-normally distributed diet data. Besides it
supports permutation-based testing and offers triplot representation of results (Borcard
et al. 2011). To complement this analysis, we used Wilcoxon rank-sum tests to assess
whether the contributions of individual diet items differed between fish size groups.

### 2.5.2 Effects of total length on fish condition

Linear mixed-effects (lme) models, fitted by restricted maximum likelihood, were used
to assess the impact of total length on gamefish body condition while controlling for lake
/site) effects. lme analysis was performed using the lmer function from the R package
lme4 (Bates et al. 2015) with total length set as a fixed effect and site (lake) as a random
effect. Three models per gamefish species were evaluated (Table S3). To facilitate model
convergence, we used the 'bobyqa' optimizer (optimix R version 2020-4.2). We assumed
a Gaussian distribution, checked model residuals for normality, and conducted standard
diagnostic tests to confirm regression assumptions.

Model selection was established by the most parsimonious model, informed by the
Akaike information criterion corrected for small sample sizes (AICc). The significance of
the model coefficient (total length) was tested with t-tests using Satterthwaite approximations (Luke 2017) via the *anova* function ([lmerTest R package, Kuznetsova et al. 2017]). Conditional and marginal R-squared estimates (Nakagawa and Schielzeth 2013) were calculated with the r-squared GLMM function via R-version 1.43.17; MuMln package (Barton 2009).

### 2.5.3 Environmental controls of fish condition

Considering the practical difficulty in including all 18 environmental variables in the analyses, we first performed principal component analysis (PCA) using standardized (scaled to unit variance) environmental variables to evaluate the correlations among them. Surface area, maximum depth, elevation, and temperature were included as supplementary variables in the PCA.

Based on literature (Katzenmeyer 2010; Rueda et al. 2015) and results of the PCA (correlation among variables, Figure S1), five main variables (surface area, maximum depth, alkalinity, chlorophyll *a*, and total phosphorus) known to influence fish body condition were selected as explanatory variables for the regression models. To quantify the relative importance of selected regressors, the “lmg” metric of the R package “relaimpo” was used (Grömping and Matthias 2013). The “lmg” metric decomposes R² into non-negative contributions attributable to each regressor based on averaging over all orderings of regressors in a multiple regression setting (Grömping 2006).

Bootstrapping (1000 runs) of models was done using functions boot and boot.ci from R package boot (Davison and Hinkley 1997). Due to the small sample size for pike, both small and large groups were combined for analysis. All analyses were performed using R version 4.0.3. (R Core Team 2020).
340 **3 RESULTS**

341 **3.1 Diet composition and ontogenetic dietary shifts**

342 Across 15 lakes and 283 individual fish stomachs, we identified 23 prey taxa comprising:
343 two amphipod species (*Gammarus lacustris* [Sars, 1863] and *Hyallela azteca*
344 [Saussure, 1858]); seven fish species; brook stickleback (*Culaea inconstans* [Kirtland,
345 1840]), ninespine stickleback (*Pungitius pungitius* [(Linnaeus, 1758)]), fathead minnow
346 (*Pimephales promelas* [(Rafinesque, 1820)]), johnny darter (*Etheostoma nigrum*
347 [Rafinesque, 1820]), walleye fry, perch fry, and pike fry; 13 taxa of benthic
348 macroinvertebrates, (Chironomidae, Corixidae, Physidae, Planorbidae, Asilidae,
349 Lestidae, Cambaridae, Diptera, Odonata, Hirudinea, Oligochaeta, Nematoda and
350 Notonecta); and one predatory zooplankton species, (*Chaoborus punctipennis* [Say,
351 1823]). Plant matter, stones and unidentified items were also found.

352 Approximately 40% of the sampled fishes had empty stomachs. Of the 82 small- and
353 106 large-sized walleye, 26 (32%) and 31 (29%) stomachs were empty respectively
354 (Table S4). Similarly, 5 (33%) of the 15 small-sized pike had empty stomachs, but the
355 proportion of empty stomachs increased to 53% (8 of 15) in large pike (Table S5). Small
356 perch exhibited a 48% incidence of empty stomachs (14 of 29), relative to 28% (10 of 36)
357 for large perch (Tables S6).

358 Prey importance varied between size groups of walleye, and to a lesser extent for pike
359 and perch. Smaller walleye (≤ 40 cm) relied heavily on amphipods (41.91 ± 10.54 %IRI),
360 whereas larger walleye (> 40 cm) consumed more fish (38.07 ± 11.20 %IRI; Table S4).
361 Vertebrate prey was the most important diets of both small (≤ 60 cm) and large (> 60
362 cm) pike (41.50 ± 20.82 %IRI and 51.26 ± 21.32 %IRI, respectively), although a
considerable portion of their diet was unidentifiable (Table S5). Perch primarily consumed invertebrates, particularly chironomids for small individuals (61.21 %IRI), while larger perch consumed appreciable proportions of both amphipods (27.81 ± 17.49 %IRI) and chironomids (25.28 ± 16.51 %IRI; Table S6).

MANOVA revealed significant differences between the diets of small and large walleye (*p* = 0.028, Figure S3, Table S4), but no differences in diet composition of pike (*p* = 1.00; Figure S4, Table S5) or perch (*p* = 1.00; Figure S5, Table S6). Walleye exhibited an ontogenetic dietary shift, transitioning from amphipod-based diets in smaller individuals (4-5 yrs.) to more fish-based diets in larger individuals (> 5 yrs.) (Table S4, Figure S3). By contrast, pike and perch showed less pronounced shifts in diet.

The proportions of individual dietary items did not differ substantially among gamefish groups (Wilcoxon rank-sum test: *p* > 0.05), except in perch, where amphipod contributions varied significantly (W = 9, *p* = 0.044), accounting for 27.81 ± 17.49 %IRI in the diet of large perch, but were entirely absent from that of small perch (Table S6, Figure 2).

## 3.2 Effects of total length on fish condition

We conducted condition assessments for 514 individual fish, (317 walleyes, 41 pike, 156 perch), with sizes from 14 to 86 cm in total length and weights from 28 to 3760 g (Table 1). The average body condition (Wr) for walleye, pike, and perch populations were 92, 87 and 105, respectively (Table 1).
After accounting for the variation in lakes (sites), *lme* models showed a significant negative correlation between total length and the condition of walleye (*p* = 0.04; Figures 3A, Table S7), and pike across lakes (*p* = 0.02; Figure 3B, Table S8). This translated to a decline of approximately 0.24% Wr ± 0.10 SE for walleye and 0.31% Wr ± 0.13 SE for pike (Figures 3A and 3B; Tables S7 and S8). In contrast, no significant link existed between total length and condition of perch (*p* = 0.26; Figure 3C and Table S9). The relatively large unexplained variance in the perch model (>80%), suggests sources of variability beyond the model's capacity to predict.

Respective estimated Marginal R² and Conditional R² values were 0.05 and 0.55 for walleye (Table S7; model 3), 0.11 and 0.43 for pike (Table S8; model 4), and 0.01 and 0.19 for perch (Table S9; model 7).

### 3.3 Relative importance of environmental regressors to fish body-condition

We concentrated on five key environmental variables—surface area, maximum depth, alkalinity, chl *a* (measure of phytoplankton biomass), and total phosphorus—as controls of gamefish body condition (Rudstam et al. 1996; Katzenmeyer 2010; Rueda et al. 2015).

Assessment of relative importance of the five regressors indicated that phytoplankton biomass and alkalinity were two major factors impacting the conditions of large walleye and perch. These parameters accounted for 50% and 16% of the variation in body condition for large walleye (Figure 4A; Adj R² = 0.68). Similarly, they explained 37% and 51% of the variability, respectively in large perch (Figure 4B; Adj R² = 0.91).

However, for their smaller counterparts no strong associations existed between body condition and environmental parameters (Figures S5 and S6). In addition, in large
walleye and perch we observed a positive relationship between body condition and chl a (Figures 2A and 2B). However, alkalinity had nuanced effects. While it positively correlated with large walleye condition (Figure 4A), it negatively impacted large perch (Figure 4B). In contrast, the model for pike explained only 7% of the variability in their condition (Figure S8).

412

413

## 4.0 DISCUSSION

414

### 4.1 Dietary importance

Understanding the dietary needs of co-existing gamefish species —walleye, pike, and perch— in SK prairie lakes is crucial for predicting their responses to imminent aquatic stressors such as the invasion of ZQM and the growing threat of climate change. In line with our hypothesis, this study recorded remarkable variations in diet composition and foraging strategies among species. Gamefishes relied on a broad range of prey taxa (Figure S2, Tables S4-6), suggesting their capacity to thrive amidst changing prey resources. While both invertebrates and fish constituted substantial components of walleye diets, pike slightly favored fish prey, while perch leaned towards an invertebrate-based diet, especially chironomids and amphipods. Surprisingly, even as walleye grew and transitioned towards more piscivorous diets, invertebrates remained a large part of their diet (Figure S4, Table S4). This finding challenges the classic notion that “early switching piscivores” or “specialists” rapidly shift to energetically beneficial vertebrate prey with increasing size/gape width (Graeb et al. 2005).

415

416

417

418

419

420

421

422

423

424

425

426

427
In this study, the reliance on invertivory of walleye and pike were higher than their typical trophic-specialist feeding patterns in other prairie lakes (Sammons et al. 1994). This suggests an adaptive shift toward generalist feeding, or a response to limited forage fish in these lakes. For example, the prevalence of *Gammarus* (abundant taxon in study lakes, data not shown) in adult walleye diets may reflect opportunistic feeding when preferred forage fishes are scarce (Kaufman et al. 2009). This warrants further investigation into the prevailing status of forage fishes in prairie lakes.

As expected, small and large perch showed a clear preference for invertebrates. While piscivory can occur in perch, it is less prominent as perch favors invertebrates over fish when both are available (Liao et al. 2001; Graeb et al. 2005). In an experimental study where perch were offered more fish prey than invertebrates, perch displayed a neutral selection for fish across all size groups. In contrast, walleye maintained a positive preference for fish even at lower densities of fish relative to alternate prey (Graeb et al. 2005). This suggests that morphology, feeding behavior, and life history contribute to the differences in dietary ontogenies in these two percid predators (Graeb et al. 2005).

Additionally, diel movements of forage fishes (moving inshore during the day and offshore at night) might impact their accessibility to adult walleye that forages in shallow zones at night. Observed cannibalism in large walleye (42.6 – 50 cm) supports the hypothesis of limited availability of forage fishes, like scenarios reported in Lake Oneida, New York, where low prey densities resulted in cannibalism (Forney 1974). To that end, Forney (1976) proposed augmenting the densities of young perch to mitigate the scarcity of forage fish and sustain walleye populations.
Overall, this study demonstrates dietary plasticity of gamefish communities in prairie lakes, and their ability to adapt to shifting prey resources by supplementing their diets with invertebrates when preferred fish prey is scarce. However, it also warns of trophic bottlenecks and stunted growth (Venturelli and Tonn 2006; VanDeValk et al. 2008).

The absence of forage fishes may impact higher trophic levels and increase vulnerability to invasive species. At the same time, cannibalism among walleye may further constrain population size.

## 4.2 Ontogenetic dietary shift

This study presents evidence of ontogenetic diet shifts within adult walleye populations.

Smaller individuals transition from invertebrates (amphipods) to fish as they mature. In contrast, pike and perch populations showed less pronounced dietary shifts, maintaining piscivorous and invertivorous foraging strategies, respectively. Despite broadly similar diets among gamefish groups, amphipods dominated the diets of large perch and also contributed notably to those of walleye and pike, whereas small perch largely avoided them. This suggests large perch predominantly forage in sediment (benthic) habitats, whereas small perch avoid shallow areas where amphipods are abundant. This strong reliance on amphipods has important implications for juvenile gamefish, particularly if ZQM create conditions that enhance amphipod availability.

Zooplanktivory is a precursor to invertivory and benthivory during the early life stages of pike, walleye, and perch (Frost 1954; Mathias and Li 1982; Slipke and Duffy 1997). In walleye, the transition from zooplankton to macroinvertebrates and later to fish prey occurs between 30-50 mm TL and 60-117 mm TL, respectively (Galarowicz and Wahl 2005; Uphoff et al. 2019; Yang 2022). Perch shifts from zooplankton to
473 macroinvertebrates at 20 to 60 mm TL (Wu and Culver 1992; Graeb et al. 2006).

474 However, piscivory varies in perch, ranging from 80 mm TL (Graeb et al. 2006) to over
475 150 mm TL (Keast 1985; Fullhart et al. 2002). From 100-200 mm TL, pike begins
476 foraging on fish while continuing to supplement their diet with macroinvertebrates as
477 they grow (Nolan and Britton 2025).

478 The hypothesis that shifts in diet are detectable as gamefish species grew was accepted
479 only for walleye. Piscivory levels were higher in larger individuals, while smaller ones
480 showed a prevalence of invertivory. Fish that transition early to piscivory benefit from
481 higher somatic growth, earlier maturation, and increased fitness (Sánchez-Hernández et
482 al. 2019), which was an option for pike and walleye in this study. Such a shift may
483 directly impact fish communities (e.g., fish abundance, biomass, and population or
484 community structure). Several factors, including ontogeny, vulnerable prey sizes,
485 competition, community structure or fish species richness (Sánchez-Hernández et al.
486 2017), and temperature (Amundsen et al. 1999) influence the prevalence of piscivory
487 within fish populations. Although the onset of piscivorous behavior has been associated
488 with high water temperatures, which promote growth during early development
489 (Mehner et al. 1996), it is likely triggered also by resource competition and the
490 availability of profitable small prey fish (Sánchez-Hernández et al. 2017).

491 This study affirms the influence of size structure on gamefish foraging habits,
492 particularly in walleye. Such dietary variations may reduce competition and promote
493 their ecological success (Graeb et al. 2005). In contrast, the shared diet across size
494 groups within pike and perch suggests intra-species competition (Venturelli and Tonn
495 2006), potentially affecting energy reserves, growth, and overwinter survival (Sogard
and Olla 2000; Grausgruber and Weber 2021). These findings underscore the ecological significance of size-related feeding dynamics and provide valuable insights for aquatic ecologists. As revealed in this research, predator-prey interactions carry critical ecological implications for food-web structure and the long-term survival of fish populations.

### 4.3 Body condition of gamefishes

Our findings unveil stark contrasts in the physiological condition of gamefish populations within the studied lakes. Remarkably, perch were the only species exhibiting optimal body condition, boasting an average Wr of 105 (Table 1). In contrast, walleye and pike populations fell well below the target range for healthy populations (Wr of 95-105; Blackwell et al. 2000). These findings echo previous reports of poor conditions in walleye and pike compared to perch (Nanayakkara et al. 2021). Similar patterns were observed in other prairie (Willis 1989) and boreal lakes (VanDeValk et al. 2008). However, our study reports substantially lower Wr values for walleye and pike and higher values for perch than those in other North American systems (Porath and Peters 1997; Flinders and Bonar 2008; D'Arco et al. 2015), suggesting further deterioration in these gamefishes' body condition over time.

Dietary analyses suggest a scarcity of forage fish or prevailing trophic bottlenecks, potentially contributing to the diminished body condition in adult walleye and pike. This decline in walleye Wr aligns with reports across three length classes over 23 years (1961–1983) in Oneida Lake, New York, where walleye reliance on small, age-0 prey fish was linked to decreased condition (VanDeValk et al. 2008). In contrast, walleye Wr increased due to the consumption of larger prey fish (160–200 mm long) and a lower
abundance of age-0 fish in Lake McConaughy, Nebraska (Porath and Peters 1997). In our study, those forage fish that were consumed were relatively smaller and perhaps more energetically costly to capture (22–183 mm TL, data not shown) than those promoting walleye well-being in Lake McConaughy (Graeb et al. 2005).

For northern pike, the negative regressions between Wr and TL observed in Upper Lake Mary, Arizona (Flinders and Bonar 2008), contrast with positive relationships reported in Long and Parker Canyon Lakes, Arizona, where catchable-sized rainbow trout (> 200 mm TL) were available. Our results mirror the challenges faced by adult pike in boreal lakes of northeast Alberta, experiencing slow growth possibly due to a shift from piscivory to invertivory following winterkills. Adult pike (> 45 cm) that relied on leeches experienced retarded growth, whereas juveniles (<30 cm) preying on invertebrates (amphipods, zygopterans, glossiphoniid leeches) demonstrated exceptional growth (Venturelli and Tonn 2006). The reliance on less-preferred invertebrates perhaps with less energy per unit mass (Venturelli and Tonn 2006) may contribute to the declining body condition of pike, compounded by the lack of thermal refugia in the study lakes during summer (Diana 1987).

Our findings affirm that larger piscivorous fishes (i.e., pike and walleye) should optimize net energy by targeting larger prey (i.e., forage fish) as they age. In contrast, perch does not require larger vertebrate prey to maintain a high Wr; rather, the availability / abundance of macroinvertebrates (Lott et al. 1996) influences their body condition. In addition to reflecting shifts in prey availability, size-specific patterns in Wr offer insights into prey population structure and the energetic benefits they provide to predators of different sizes.
## 4.4 Environmental controls of fish condition

Widespread evidence highlights the impact of environmental factors on fish body condition (Katzenmeyer 2010; Rueda et al. 2015). We observed distinct species- and size-specific responses of gamefish body conditions to environmental variables. Specifically, chl *a* positively correlated with the body condition of adult walleye and perch (Figures 4A and 4B), aligning with a North American study (Rudstam et al. 1996).

However, the declining condition of walleye, despite high productivity in our study lakes, may be attributed to elevated alkalinity levels. Optimal alkalinity levels between 75 and 200 mg/L are crucial for healthy fish populations. Outside this range, primary productivity declines, which negatively impacts aquatic ecosystems (Wurts 2002). Our investigation revealed significant differences in alkalinity levels among sampled lakes, *p* = 0.015: walleye lakes exhibited higher alkalinity (110–485 mg/L; mean = 239) compared to perch lakes (110–368 mg/L; mean = 208) (Table S1). Further analysis of 65 prairie lakes confirmed a substantial negative correlation between alkalinity and chl *a* (Figure S9). This suggests that elevated alkalinity may hinder primary productivity in walleye lakes, and in turn affect the body condition of adult walleye.

In contrast, lower alkalinity in perch lakes may enhance productivity, promoting better body conditions in adult perch. However, the relationship between alkalinity and adult gamefishes condition —positive for walleye (Figure 4A) and negative for perch (Figure 4B)— is complex and warrants further investigation. Future studies could benefit from examining how alkalinity impacts gamefish feeding behaviors and metabolic responses. Contrary to what was hypothesized, no discernible relationship existed between the five environmental parameters (chl *a*, alkalinity, maximum lake depth, surface area, and total phosphorus) and body condition of younger walleye and perch (Figure S6-S8). This
might suggest that the well-being of these cohorts may be more sensitive to other variables, thermal stress, parasite loads, or predation (Sammons et al. 1994; Flinders and Bonar 2008; Nanayakkara et al. 2021). Turbidity (Secchi depth) affects visual predators like pike, which has been linked to their poor growth due to impediments in feeding (Sammons et al. 1994; Flinders and Bonar 2008), though, it was not the case for pike in this study ($F_{(1, 8)}$ = [0.30], $p$ = 0.601).

Our findings underscore the sensitivity of different life stages of fish species to the biochemical properties of lakes, which interact synergistically to impact gamefish health. We anticipate that any future reduction in phytoplankton biomass associated with ZQM may detrimentally affect adult gamefishes, especially walleye, due to the strong influence of chl a on walleye adult body condition (Figure 4A). Excessively high alkalinity poses additional risks, such as respiratory difficulties and the dissolution of the protective mucus layer, rendering gamefishes vulnerable to bacterial attacks and infections (Quarters 2017). These factors can increase gamefish vulnerability to aquatic stressors, including invasive species.

## 4.5 Potential ecological impacts of ZQM

Future invasions by ZQM may exert indirect negative or stabilizing effects on gamefish, depending on species-specific foraging habits, lake morphology, and the ecological status of invaded systems (Strayer et al. 2004; Karatayev et al. 2012; Geisler 2015). However, the magnitude of these impacts varies widely, complicating efforts to draw simple generalizations (Strayer et al. 2004; Hansen et al. 2020). ZQM are well known for disrupting planktonic food webs while supporting littoral pathways. Acting as ecosystem engineers, they filter pelagic biomass (phytoplankton, zooplankton) and
redirect nutrients to the benthos. This has fueled a 97% increase in benthivorous fishes and a 28% decline in planktivorous fish populations (Strayer et al. 2004; Karatayev et al. 2007; Kelley et al. 2010).

Walleye may be disproportionately impacted in prairie lakes, as juveniles rely heavily on pelagic prey (E. Hillis & S. Davies, pers. comm., 2020). ZQM-driven prey declines could constrain walleye growth and recruitment, although perch may remain largely unaffected (Hansen et al. 2020). Increased water clarity may heighten predation risk and impair feeding for light-sensitive walleye that best forage under dim light (Ryder 1977). Varied outcomes have been observed across systems. In Manitoba prairie lakes, walleye yields increased by 2.63 kg/ha/yr, whereas boreal shield lakes experienced declines of 2.36 kg/ha/yr. Increases mainly occurred in lakes with lower pre-invasion Secchi depths (1.24 ± 0.57 m), while declines were observed in clearer lakes (1.88 ± 0.89 m), especially when post-invasion clarity exceeded 2 m (Geisler 2015). About 20% of our study lakes already exhibit high water clarity (3.53 ± 1.38 m), making them vulnerable to post-invasion declines.

Ambush predators such as northern pike may respond variably to ZQM-induced changes in environmental conditions. Increased water clarity and macrophyte growth can enhance macroinvertebrate abundance and boost foraging opportunities, as well as improve spawning habitat and reproductive success for these predators (Walker et al. 2013; Søndergaard et al. 2025). However, long-term reliance on invertebrates will be more energetically costly than piscivory (Venturelli and Tonn 2006). Persistent forage
fish trophic bottlenecks in these systems may further compromise the body condition of pike and walleye.

In contrast to pike and walleye, yellow perch may benefit more from shifts in prey assemblages toward benthic pathways. Invasive mussel beds (druses) provide food, via feces and pseudofeces, and shelter for littoral benthic invertebrates. These include amphipods, isopods, leeches, turbellarians, hydrozoans, and some oligochaetes and chironomids (Cobb and Watzin 2002; Beekey et al. 2004; Karatayev et al. 2007; Burlakova et al. 2012). Enhanced benthic production may improve perch condition and growth, as perch rely predominantly on invertebrates in prairie lakes, indirectly supporting pike and walleye populations where perch are abundant.

Nonetheless, invasive mussel biodeposits from feces and pseudofaeces can also induce anoxia, threatening key invertebrates such as oligochaetes, chironomids, and other burrowing organisms (Caraco et al. 2000). This can compromise perch nutrition and, in turn, affect predators dependent on perch. In the Great Lakes, similar trophic bottlenecks occurred when the loss of the benthic amphipod *Diporeia* spp.—a crucial prey for whitefish—cascaded to forage fish (e.g., alewife, sculpin *Cottus* spp. [Linnaeus, 1758], bloater *Coregonus hoyi* [Milner, 1874]) and salmonids, ultimately reducing whitefish condition and abundance (Nalepa 2010; Hoyle et al. 2008). Comparable outcomes may occur in prairie lakes if declines in both invertebrate and vertebrate prey assemblages disrupt the growth, reproduction, and recruitment of upper-trophic-level species, including pike and walleye.

Unlike the Great Lakes, where *Diporeia* declined, benthic amphipods such as *Gammarus lacustris* and *Hyalella azteca* in prairie lakes may persist or even increase
post-invasion (Burlakova et al. 2012). These amphipods serve as key prey for gamefish occupying a similar trophic niche as *Diporeia*. At the same time, structural complexity of ZQM beds can limit prey accessibility, lowering foraging success and leading to poorer feeding outcomes. Foraging barriers have been documented in lake sturgeon (*Acipenser fulvescens* [Rafinesque, 1817]), bluegill (*Lepomis macrochirus* [Rafinesque, 1819]), and perch, where feeding on amphipods and chironomids was inhibited under ZQM-dominated beds (Cobb and Watzin 2002; Beekey et al. 2004; McCabe et al. 2006).

In other systems, however, amphipod densities increased and perch predation on them remained unaffected, but perch required greater foraging effort (González and Downing 1999). These divergent outcomes highlight that ZQM impacts on gamefish–prey interactions, particularly with amphipods, are species- and system-specific. Ultimately, the benefits of increased benthic production may depend on the trade-off between higher prey availability and reduced foraging efficiency in these structurally complex mussel habitats (Beekey et al. 2004).

In a nutshell, foraging conditions may move closer to optimal for yellow perch through enhanced benthic prey. For walleye and pike, conditions will improve only if forage fish increase but may become less ideal where forage resources remain limited in prairie lakes post-ZQM invasion. To sustain these valuable yet vulnerable ecosystems, fisheries managers should prioritize protecting shallow littoral habitats that support key invertebrates (e.g., amphipods, chironomids, corixids) and monitor forage-fish availability. Long-term monitoring of gamefish diets and body condition—especially in aging adults will be critical to safeguarding population health.

660
661 **5.0 CONCLUSION**

662 The findings of this study support previous research on prairie lake gamefishes and 663 deepen our knowledge about species-specific diets and size-related body condition 664 trends on a spatial scale. Assessment of three gamefish species unveil unique strategies 665 for adapting to changing environmental conditions. Particularly, walleye exhibited 666 ontogenetic dietary shifts and the ability to adjust their diets in response to changing 667 prey availability compared to native pike and perch. Starting with macroinvertebrates as 668 young adults, they progressively incorporate more fish as they mature. Despite this shift, 669 walleye persistently rely heavily on macroinvertebrates, which may be a response to the 670 limited availability of preferred forage fish in hardwater prairie lakes. Adult pike and 671 walleye populations that depend heavily on macroinvertebrates experience declining 672 body conditions and perhaps an increased vulnerability to parasites. Additional strains 673 on body conditions such as high alkalinity, and eutrophication in these lakes could 674 further jeopardize fish health and increase the risk of winterkills. These challenges will 675 likely intensify with the dual threats of climate change and the invasion of ZQM and may 676 directly or indirectly affect fish populations by impeding dominant processes like 677 nutrient cycling and the depletion of deepwater dissolved oxygen in uninvaded systems. 678 Changes to the chemical, biophysical, and deepwater habitat quality can constrain or 679 distort the habitability of these environments, making survival increasingly challenging 680 for fish.

681 Addressing these challenges requires urgent and coordinated action. By proactively 682 mitigating ZQM impacts, we can safeguard the vital role these gamefishes play in our 683 aquatic environment and local economies. For instance, implementing strategies to 684 enhance habitat for forage fish and closely monitoring water quality (temperature,
oxygen, alkalinity) will be vital for walleye conservation and help sustain these
ecosystems for future generations.

This research was conducted on Treaty 4 and Treaty 6 lands, which consist of the
traditional territories of the Cree, Dene, Blackfoot, Saulteaux, Nakoda, Dakota, and
Lakota peoples and the homeland of the Métis Nation. Special thanks to Gord
Sedgewick, James Villeneuve, Jennifer Merkowsky, Rebecca Eberts, and the
Saskatchewan Ministry of Environment for their collaboration on this project. Field
support was provided by Buddhist Meegahage, Afruza Begum, Xingzi Zhou, and Alison
Walker.

The authors declare there are no competing interests.

BW and CBO co-designed the study, developed the conceptual framework, and secured
funding for the project. EH and CBO conducted field sampling, coordinated logistics,
and managed all technical details. ZQR assisted with water chemistry analysis and
numerical computations.

CBO performed fish stomach content analyses, related laboratory work, numerical
calculations, and data analyses. BW, MUM, EH, and CBO contributed to the
interpretation of the results. MUM and BW supervised the project and validated the
findings. CBO drafted the original manuscript. All authors reviewed, edited, and discussed the final manuscript, providing constructive feedback that shaped its content.

FUNDING STATEMENT

Funding was provided through NSERC Discovery Grant # RGPIN-06475-2018 to Björn Wissel and the SK Fish and Wildlife Development Fund (no grant number) to Caroline B. Ofosu and Björn Wissel.

DATA AVAILABILITY STATEMENT

Data generated and/or analyzed during this study are available in the DYRAD repository, [confidential Reviewer URL, http://datadryad.org/stash/share/AqWBdUoeyGmQhOHZinmXCb889RSOL5LhgO7PQqkTrNs].
732 REFERENCES

733 Ahlbeck, I., Hansson, S., and Hjerne, O. 2012. Evaluating fish diet analysis methods by
734 individual-based modelling. Can. J. Fish. Aquat. Sci. **69**(7): 1184-1201.
735 doi:10.1139/f2012-051.

736 Amundsen, P.A., Svenning, M.A., and Slikavuoplo, S.I. 1999. An experimental
737 comparison of cannibalistic response in different Arctic charr (*Salvelinus alpinus*
738 (L.)) stocks. Ecol. Freshw. Fish **8**(1): 43-48. doi:10.1111/j.1600-7399.1999.tb00051.x.

740 Arrington, D.A., Davidson, B.K., Winemiller, K.O., and Layman, C.A. 2006. Influence of
741 life history and seasonal hydrology on lipid storage in three neotropical fish
742 species. J. Fish Biol. **68**(5): 1347-1361. doi:10.1111/j.0022-1112.2006.00996.x.

743 Baker, R., Buckland, A., and Sheaves, M. 2014. Fish gut content analysis: robust
744 measures of diet composition. Fish. Fish. **15**(1): 170-177. doi:10.1111/faf.12026.

745 Barton, K. 2009. MuMln: multi-model inference. Available from http://R-Forge.R-project.org/projects/mumin/ [accessed 26 September 2025].

747 Bates, D., Mächler, M., Bolker, B., and Walker, S. 2015. Fitting linear mixed-effects
748 models using lme4. J. Stat. Softw. **67**(1): 1-48. doi:10.18637/jss.v067.i01.

749 Beekey, M.A., McCabe, D.J., and Marsden, J.E. 2004. Zebra mussels affect benthic
750 predator foraging success and habitat choice on soft sediments. Oecologia **141**(1):
751 164-170. doi:10.1007/s00442-004-1632-1.

752 Blackwell, B.G., Brown, M.L., and Willis, D.W. 2000. Relative weight (Wr) status and
753 current use in fisheries assessment and management. Rev. Fish. Sci. **8**(1): 1-44.
754 doi:10.1080/10641260091129161.
755 Borcard, D., Gillet, F., and Legendre, P. 2011. Numerical Ecology with R. 1st edition.
756 Springer Nature, New York, NY. doi:10.1007/978-1-4419-7976-6.

757 Bukontaite, R., and Zaiko, A. 2008. *Dreissena polymorpha* (zebra mussel). CABI
758 Compendium. Available from https://doi.org/10.1079/cabicompendium.85295
759 [accessed 20 April 2020].

760 Burlakova, L.E., Karatayev, A.Y., and Karatayev, V.A. 2012. Invasive mussels induce
761 community changes by increasing habitat complexity. Hydrobiologia 685(1):
762 121-134. doi:10.1007/s10750-011-0791-4.

763 Caraco, N.F., Cole, J.J., Findlay, S.E.G., Fischer, D.T., Lampman, G.G., Pace, M.L., and
764 Strayer, D.L. 2000. Dissolved oxygen declines in the Hudson River associated
765 with the invasion of the zebra mussel (*Dreissena polymorpha*). Environ. Sci.
766 Technol. 34(7): 1204-1210. doi:10.1021/es990565z.

767 Chipps, S.R., and Garvey, J.E. 2007. Assessment of diets and feeding patterns. In
768 Analysis and interpretation of freshwater fisheries data. American Fisheries
769 Society, Bethesda, Maryland, USA. pp. 473-514.

770 Cobb, S.E., and Watzin, M.C. 2002. Zebra mussel colonies and yellow perch foraging:
771 spatial complexity, refuges, and resource enhancement. J. Great Lakes Res.
772 28(2): 256-263. doi:10.1016/S0380-1330(02)70581-9.

773 Cooper, R.N., and Wissel, B. 2012a. Interactive effects of chemical and biological
774 controls on food-web composition in saline prairie lakes. Aquat. Biosyst. 8(29).
775 doi:10.1186/2046-9063-8-29.

776 Cooper, R.N., and Wissel, B. 2012b. Loss of trophic complexity in saline prairie lakes as
777 indicated by stable-isotope based community-metrics. Aquat. Biosyst. 8(6).
778 doi:10.1186/2046-9063-8-6.
779 Cortés, E. 1997. A critical review of methods of studying fish feeding based on analysis of stomach contents: application to elasmobranch fishes. Can. J. Fish. Aquat. Sci. 780 **54**(3): 726-738. doi:10.1139/f96-316.

782 D'Arco, B.D., Farrell, J.L., Nierzwicki-Bauer, S.A., and Boylen, C.W. 2015. Are the condition, growth and diet of yellow perch (*Perca flavescens*) different between the three major basins of Lake George, New York? The Open Fish Science Journal 784 **8**(1): 30-36. doi:10.2174/1874401X01508010030.

786 Davison, A.C., and Hinkley, D.V. 1997. Bootstrap methods and their application, Number 1. Cambridge University Press, New York. Available from http://dx.doi.org/10.1017/CBO9780511802843.

789 Desmond, J.S., Deutschman, D.H., and Zedler, J.B. 2002. Spatial and temporal variation in estuarine fish and invertebrate assemblages: analysis of an 11-year data set. Estuaries **25**(4): 552-569. doi:10.1007/BF02804890.

792 Diana, J.S. 1987. Simulation of mechanisms causing stunting in northern pike populations. Trans. Am. Fish. Soc. **116**(4): 612-617. doi:10.1577/1548-8659(1987)116%3C612:SOMCSI%3E2.0.CO;2.

795 Essington, T.E., Moriarty, P.E., Froehlich, H.E., Hodgson, E.E., Koehn, L.E., Oken, K.L., Siple, M.C., and Stawitz, C.C. 2015. Fishing amplifies forage fish population collapses. Proc. Natl. Acad. Sci. **112**(21): 6648-6652. doi:10.1073/pnas.1422020112.

799 Flinders, J.M., and Bonar, S.A. 2008. Growth, condition, diet, and consumption rates of northern pike in three Arizona reservoirs. Lake Reserv. Manag. **24**(2): 99-111. doi:10.1080/07438140809354054.
802 Forney, J.L. 1974. Interactions between yellow perch abundance, walleye predation, and
803 survival of alternate prey in Oneida Lake, New York. Trans. Am. Fish. Soc.

804 **103**(1): 15-24. doi:10.1577/1548-8659(1974)103%3C15:IBYPAW%3E2.0.CO;2.

805 Forney, J.L. 1976. Year-class formation in the walleye (*Stizostedion vitreum vitreum*)
806 population of Oneida Lake, New York, 1966-73. Can. J. Fish. Aquat. Sci. **33**(4):
807 783-792. doi:10.1139/f76-096.

808 Frost, W.E. 1954. The food of pike, *Esox lucius* L., in Windermere. J. Anim. Ecol. **23**(2):
809 339-360. doi:10.2307/1985.

810 Fullhart, H.G., Parsons, B.G., Willis, D.W., and Reed, J.R. 2002. Yellow perch piscivory
811 and its possible role in structuring littoral zone fish communities in small
812 Minnesota lakes. J. Freshwater Ecol. **17**(1): 37-43.

813 doi:10.1080/02705060.2002.9663866.

814 Galarowicz, T.L., and Wahl, D.H. 2005. Foraging by a young-of-the-year piscivore: the
815 role of predator size, prey type, and density. Can. J. Fish. Aquat. Sci. **62**(10):
816 2330-2342. doi:10.1139/f05-148.

817 Geisler, M.E. 2015. Forecasting the effects of invasive dreissenid mussels on walleye
818 (*Sander vitreus*) habitat occupancy and yield. M.Sc. thesis, University of
819 Manitoba, Winnipeg, Man. Available from http://hdl.handle.net/1993/30327
820 [accessed 26 September 2025].

821 Gelwick, F.P., and McIntyre, P.B. 2017. Chapter 22 - Trophic relations of stream fishes.
822 *In* Methods in Stream Ecology, Volume 1: Ecosystem Structure, Third Edition.

823 *Edited by* R.F. Hauer and G.A. Lamberti. Academic Press. pp. 457-479.
824 doi:10.1016/B978-0-12-416558-8.00022-6.
825 González, M.J., and Downing, A. 1999. Mechanisms underlying amphipod responses to
826 zebra mussel (*Dreissena polymorpha*) invasion and implications for fish-
827 amphipod interactions. Can. J. Fish. Aquat. Sci. **56**(4): 679–685.
828 doi:10.1139/f98-211.

829 Graeb, B.D., Galarowicz, T., Wahl, D.H., Dettmers, J.M., and Simpson, M.J. 2005.
830 Foraging behavior, morphology, and life history variation determine the
831 ontogeny of piscivory in two closely related predators. Can. J. Fish. Aquat. Sci.
832 **62**(9): 2010–2020. doi:10.1139/f05-112.

833 Graeb, B.D.S., Mangan, M.T., Jolley, J.C., Wahl, D.H., and Dettmers, J.M. 2006.
834 Ontogenetic changes in prey preference and foraging ability of yellow perch:
835 insights based on relative energetic return of prey. Trans. Am. Fish. Soc. **135**(6):
836 1493–1498. doi:10.1577/T05-063.1.

837 Grausgruber, E.E., and Weber, M.J. 2021. Shift happens: Evaluating the ability of
838 autumn stocked walleye *Sander vitreus* to shift to natural prey. Fish. Manag.
839 Ecol. **28**(6): 516–527. doi:10.1111/fme.12492.

840 Grömping, U. 2006. Relative importance for linear regression in R: the package
841 relaimpo. J. Stat. Softw. **17**(1). doi:10.18637/jss.v017.i01.

842 Grömping, U., and Matthias, L. 2013. relaimpo: Relative importance of regressors in
843 linear models. R package version: 1–1.

844 Hansen, G.J.A., Ahrenstorff, T.D., Bethke, B.J., Dumke, J.D., Hirsch, J., Kovalenko,
845 K.E., LeDuc, J.F., Maki, R.P., Rantala, H.M., and Wagner, T. 2020. Walleye
846 growth declines following zebra mussel and *Bythotrephes* invasion. Biol.
847 Invasions **22**(4): 1481–1495. doi:10.1007/s10530-020-02198-5.
848 Higgins, S.N., and Vander Zanden, M.J. 2010. What a difference a species makes: a
849 meta-analysis of dreissenid mussel impacts on freshwater ecosystems. Ecol.
850 Monogr. **80**(2): 179–196. doi:10.1890/09-1249.1.

851 Hoyle, J.A., Bowlby, J.N., and Morrison, B.J. 2008. Lake whitefish and walleye
852 population responses to dreissenid mussel invasion in eastern Lake Ontario.
853 Aquat. Ecosyst. Health Manage. **11**(4): 403–411.
854 doi:10.1080/14634980802530392.

855 Hoyle, J.A., Schaner, T., Casselman, J.M., and Dermott, R. 1999. Changes in lake
856 whitefish (*Coregonus clupeaformis*) stocks in eastern Lake Ontario following
857 *Dreissena* mussel invasion. Great Lakes Research Review **4**(2): 5–10.

858 Hyslop, E.J. 1980. Stomach contents analysis - a review of methods and their
859 application. J. Fish Biol. **17**(4): 411–429. doi:10.1111/j.1095-
860 8649.1980.tb02775.x.

861 Karatayev, A.Y., Claudi, R., and Lucy, F.E. 2012. History of *Dreissena* research and the
862 ICAIS gateway to aquatic invasions research. Aquat. Invasions **7**(1): 1–5.
863 doi:10.3391/ai.2012.7.1.001.

864 Karatayev, A.Y., Padilla, D.K., Minchin, D., Boltovskoy, D., and Burlakova, L.E. 2007.
865 Changes in global economies and trade: the potential spread of exotic freshwater
866 bivalves. Biol. Invasions **9**(2): 161–180. doi:10.1007/s10530-006-9013-9.

867 Katzenmeyer, E.D. 2010. Fish growth responses to a changing environment: effects of
868 aquatic nuisance species and environmental conditions in a shallow, eutrophic
869 lake. Master of Science, Iowa State University, Ames, Iowa. Available from
870 https://lib.dr.iastate.edu/etd/11827/ [accessed 29 September 2025].
871 Kaufman, S.D., Morgan, G.E., and Gunn, J.M. 2009. The role of ciscoes as prey in the
872 trophy growth potential of walleyes. N. Am. J. Fish. Manag. **29**(2): 468–477.
873 doi:10.1577/M07-117.1.

874 Keast, A. 1985. The piscivore feeding guild of fishes in small freshwater ecosystems.
875 Environ. Biol. Fish. **12**(2): 119–129. doi:10.1007/BF00002764.

876 Kelley, D.W., Herborg, L.-M., and MacIsaac, H.J. 2010. Ecosystem changes associated
877 with *Dreissena* invasions: recent developments and emerging issues. *In* The
878 Zebra Mussel in Europe. *Edited by* G. van der Velde, S. Rajagopal, and A. Bij de
879 Vaate. Backhuys Publishers, Leiden. pp. 199–209.

880 Kuznetsova, A., Brockhoff, P.B., and Christensen, R.H.B. 2017. lmerTest package: tests
881 in linear mixed effects models. J. Stat. Softw. **82**(13): 1–26.
882 doi:10.18637/jss.v082.i13.

883 Liao, H., Pierce, C.L., and Larscheid, J.G. 2001. Empirical assessment of indices of prey
884 importance in the diets of predacious fish. Trans. Am. Fish. Soc. **130**(4): 583–
885 591. doi:10.1577/1548-8659(2001)130%3C0583:EA0IOP%3E2.0.CO;2.

886 Lott, J.P., Willis, D.W., and Lucchesi, D.O. 1996. Relationship of food habits to yellow
887 perch growth and population structure in South Dakota lakes. J. Freshwater Ecol.
888 **11**(1): 27–37. doi:10.1080/02705060.1996.9663491.

889 Luke, S.G. 2017. Evaluating significance in linear mixed-effects models in R. Behav. Res.
890 Methods **49**(4): 1494–1502. doi:10.3758/s13428-016-0809-y.

891 Lumb, C.E., Johnson, T.B., Cook, H.A., and Hoyle, J.A. 2007. Comparison of lake
892 whitefish (*Coregonus clupeaformis*) growth, condition, and energy density
893 between Lakes Erie and Ontario. J. Great Lakes Res. **33**(2): 314–325.
894 doi:10.3394/0380-1330(2007)33%5B314:COLWCC%5D2.0.CO;2.
895 Madenjian, C.P., Pothoven, S.A., Dettmers, J.M., and Holuszko, J.D. 2006. Changes in
896 seasonal energy dynamics of alewife (*Alosa pseudoharengus*) in Lake Michigan
897 after invasion of dreissenid mussels. Can. J. Fish. Aquat. Sci. **63*(4): 891-902.
898 doi:10.1139/f06-017.

899 Manko, P. 2016. Stomach content analysis in freshwater fish feeding ecology. University
900 of Prešov, Prešov, Slovakia. Available from
901 https://share.google/DUyM8KVOvwJ2S1dLy [accessed 29 September 2025].

902 Mathias, J.A., and Li, S. 1982. Feeding habits of walleye larvae and juveniles:
903 comparative laboratory and field studies. Trans. Am. Fish. Soc. **111**(6): 722-735.
904 doi:https://doi.org/10.1577/1548-8659(1982)111<722:FHOWLA>2.0.CO;2.

905 Mavrin, A.S., and Strel'nikova, A.P. 2011. Feeding, development, and growth of juvenile
906 perch *Perca fluviatilis* in mesocosms in the presence of filter-feeding zebra
907 mussel *Dreissena polymorpha* Pallas. Inland Water Biol. **4**(2): 232-241.
908 doi:10.1134/S1995082911020143.

909 McCabe, D.J., Beekey, M.A., Mazloff, A., and Marsden, J.E. 2006. Negative effect of
910 zebra mussels on foraging and habitat use by lake sturgeon (*Acipenser
911 fulvescens*). Aquatic Conservation **16**(5): 493-500. doi:10.1002/aqc.754.

912 Mehner, T., Schultz, H., Bauer, D., Herbst, R., Voigt, H., and Benndorf, J. 1996.
913 Intraguild predation and cannibalism in age-0 perch (*Perca fluviatilis*) and age-0
914 zander (*Stizostedion lucioperca*): Interactions with zooplankton succession, prey
915 fish availability and temperature. Ann. Zool. Fenn. **33**(3/4): 353-361.

916 Murphy, B.R., Willis, D.W., and Springer, T.A. 1991. The relative weight index in
917 fisheries management: status and needs. Fisheries **16**(2): 30-38.
918 doi:10.1577/1548-8446(1991)016%3C0030:TRWIIF%3E2.0.CO;2.
919 Nakagawa, S., and Schielzeth, H. 2013. A general and simple method for obtaining R²
920 from generalized linear mixed-effects models. Methods Ecol. Evol. **4**(2): 133–142.
921 doi:10.1111/j.2041-210x.2012.00261.x.

922 Nalepa, T.F. 2010. An overview of the spread, distribution, and ecological impacts of the
923 quagga mussel, *Dreissena rostriformis bugensis*, with possible implications to
924 the Colorado River system. *In* Proceedings of the Colorado River Basin Science
925 and Resource Management Symposium. U.S. Geological Survey Scientific
926 Investigations Report 2010-5135, Scottsdale, Arizona. Available from
927 https://digitalcommons.unl.edu/usdeptcommercepub/412 [accessed 29
928 September 2025].

929 Nanayakkara, L., Starks, E.R., Cooper, R.N., Chow, S., Leavitt, P.R., and Wissel, B. 2021. Resource-use, body condition and parasite load metrics indicate contrasting health of stocked and native game fishes in Canadian prairie lakes. Fish. Manag. Ecol. **28**(1): 18–27. doi:10.1111/fme.12447.

933 Nanayakkara, L., and Wissel, B. 2017. Preliminary investigation of lake-use patterns in prairie lakes, stakeholder perceptions, and resulting management implications. Lake Reserv. Manag. **33**(1): 49–61. doi:10.1080/10402381.2016.1264513.

936 Nienhuis, S., Haxton, T., and Dunkley, T. 2014. An empirical analysis of the consequences of zebra mussel invasions on fisheries in inland, freshwater lakes in Southern Ontario. Manag. Biol. Invasions. **5**(3): 287–302. doi:10.3391/mbi.2014.5.3.12.

940 Nolan, E.T., and Britton, J.R. 2025. Individual variability in ontogenetic dietary shifts and their influence on the trophic interactions of native and non-native
942 freshwater piscivorous fishes. Hydrobiologia **852**(18): 4715-4730.

943 doi:10.1007/s10750-025-05888-0.

944 Oksanen, J., Blanchet, F.G., Kindt, R., Legendre, P., Minchin, P.R., O'Hara, R.B.,
945 Simpson, G.L., Solymos, P., Stevens, M.H.H., Wagner, H., and Oksanen, M.J.

946 2013. Package 'vegan'. Community Ecology Package, version 2(9): 1-295.

947 Ozersky, T., Evans, D.O., and Barton, D.R. 2012. Invasive mussels alter the littoral food
948 web of a large lake: stable isotopes reveal drastic shifts in sources and flow of
949 energy. PloS one 7(12): e51249. doi:10.1371/journal.pone.0051249.

950 Pace, M.L., Findlay, S.E.G., and Fischer, D. 1998. Effects of an invasive bivalve on the
951 zooplankton community of the Hudson River. Freshw. Biol **39**(1): 103-116.
952 doi:10.1046/j.1365-2427.1998.00266.x.

953 Pham, S.V., Leavitt, P.R., McGowan, S., and Peres-Neto, P. 2008. Spatial variability of
954 climate and land-use effects on lakes of the Northern Great Plains. Limnol.
955 Oceanogr. **53**(2): 728-742. doi:10.4319/lo.2008.53.2.0728.

956 Pinkas, L., Oliphant, M.S., and Iverson, I.L.K. 1970. Food habits of albacore, bluefin
957 tuna, and bonito in California waters. Fish Bulletin 152, California Digital
958 Library, University of California. Available from
959 https://escholarship.org/uc/item/7t5868rd [accessed 29 September 2025].

960 Porath, M.T., and Peters, E.J. 1997. Use of walleye relative weights (wr) to assess prey
961 availability. N. Am. J. Fish. Manag. **17**(3): 628-637. doi:10.1577/1548-8675(1997)017%3C0628:UOWRWW%3E2.3.CO;2.

963 Pothoven, S.A., Nalepa, T.F., Schneeberger, P.J., and Brandt, S.B. 2001. Changes in diet
964 and body condition of lake whitefish in southern Lake Michigan associated with
965 changes in benthos. N. Am. J. Fish. Manag. **21**(4): 876-883. doi:10.1577/1548-8675(2001)021%3C0876:CIDABC%3E2.0.CO;2.

967 Quarters, C. 2017. What happens if the alkalinity rises too high in an aquarium?
968 Available from https://animals.mom.com/happens-alkalinity-rises-high-aquarium-6802.html [accessed 10 October 2021].

970 R Core Team. 2020. R: The R Project for Statistical Computing. R Foundation for Statistical Computing. Available from https://www.r-project.org/ [accessed 30 September 2025].

973 Rudstam, L.G., Green, D.M., Forney, J.L., Stang, D.L., and Evans, J.T. 1996. Evidence of interactions between walleye and yellow perch in New York State lakes. Ann. Zool. Fenn. **33**(3/4): 443-449.

976 Rueda, L., Massutí, E., Alvarez-Berastegui, D., and Hidalgo, M. 2015. Effect of intra-specific competition, surface chlorophyll and fishing on spatial variation of gadoid's body condition. Ecosphere **6**(10): 1-17. doi:10.1890/ES15-00087.1.

979 Ryder, R.A. 1977. Effects of ambient light variations on behavior of yearling, subadult, and adult walleyes (*Stizostedion vitreum vitreum*). J. Fish. Res. Board Can. **34**(10): 1481-1491. doi:10.1139/f77-213.

982 Sammons, S.M., Scalet, C.G., and Neumann, R.M. 1994. Seasonal and size-related changes in the diet of northern pike from a shallow prairie lake. J. Freshwater Ecol. **9**(4): 321-329. doi:10.1080/02705060.1994.9664902.

985 Sánchez-Hernández, J., Eloranta, A.P., Finstad, A.G., and Amundsen, P.-A. 2017. Community structure affects trophic ontogeny in a predatory fish. Ecol. Evol. **7**(1): 358-367. doi:10.1002/ece3.2600.
988 Sánchez-Hernández, J., Nunn, A.D., Adams, C.E., and Amundsen, P.-A. 2019. Causes and consequences of ontogenetic dietary shifts: a global synthesis using fish models. Biol. Rev. 94(2): 539-554. doi:10.1111/brv.12468.

991 Saskatchewan Fish Facts. 2019. Ministry of Environment, 2019 Fish Facts. Available from https://publications.saskatchewan.ca/#/products/105005 [accessed 30 September 2025].

994 Saskatchewan Ministry of Environment. 2017. 2017 Stocked Waters Guide. Available from https://publications.saskatchewan.ca/#/products/87895 [accessed 29 September 2025].

997 Saskatchewan Ministry of Environment. 2018. 2018 Stocked Waters Guide. Available from https://publications.saskatchewan.ca/#/products/92127 [accessed 29 September 2025].

1000 Saskatchewan Ministry of Environment. 2019. 2019 Stocked Waters Guide. Available from https://publications.saskatchewan.ca/#/products/102844 [accessed 29 September 2025].

1003 Saskatchewan Watershed Authority. 2010. Fish Species of Saskatchewan.

1004 Sauchyn, D., and Kulshreshtha, S. 2008. Chapter 7: Prairies. In *From Impacts to Adaptation: Canada in a Changing Climate 2007*. Edited by Lemmen, D.S., F.J. Warren, J. Lacroix, and E. Bush. Government of Canada, Ottawa, ON. pp. 275-328. Available from https://natural-resources.canada.ca/sites/nrcan/files/earthsciences/pdf/assess/2007/pdf/ch7_e.pdf.

1010 Scott, W.B., and Crossman, E.J. 1973. Freshwater fishes of Canada. Fisheries Research Board of Canada. Bulletin 184, Ottawa, ON.
1012 Simberloff, D., and Gibbons, L. 2004. Now you see them, now you don't! - population crashes of established introduced species. Biol. Invasions **6**(2): 161-172.
1013
1014 doi:10.1023/B:BINV.0000022133.49752.46.

1015 Slipke, J.W., and Duffy, W.G. 1997. Food habits of walleye in Shadehill Reservoir, South Dakota. J. Freshwater Ecol. **12**(1): 11-17. doi:10.1080/02705060.1997.9663504.

1017 Sogard, S.M., and Olla, B.L. 2000. Endurance of simulated winter conditions by age-0 walleye pollock: effects of body size, water temperature and energy stores. J. Fish Biol. **56**(1): 1-21. doi:10.1111/j.1095-8649.2000.tb02083.x.

1020 Søndergaard, M., Johansson, L.S., Andersen, P., and Jeppesen, E. 2025. Ecosystem changes in eight Danish lakes after zebra mussel invasion. Hydrobiologia **852**(2): 305-322. doi:10.1007/s10750-024-05542-1.

1023 Starks, E., Cooper, R., Leavitt, P.R., and Wissel, B. 2014. Effects of drought and pluvial periods on fish and zooplankton communities in prairie lakes: systematic and asystematic responses. Glob. Chang. Biol. **20**(4): 1032-1042.
1024
1025
1026 doi:10.1111/gcb.12359.

1027 Strayer, D.L., Hattala, K.A., and Kahnle, A.W. 2004. Effects of an invasive bivalve (Dreissena polymorpha) on fish in the Hudson River estuary. Can. J. Fish. Aquat. Sci. **61**(6): 924-941. doi:10.1139/f04-043.

1030 Therriault, T.W., and Orlova, M.I. 2010. Invasion success within the family *Dreissenidae*: prerequisites, mechanisms, and perspectives. In *The Zebra Mussel in Europe*. Edited by G. Van der Velde, S. Rajagopal, and A. Bij de Vaate. Backhuys Publishers, Leiden. pp. 59-68.

1034 Therriault, T.W., Weise, A.M., Higgins, S.N., Guo, Y., and Duhaime, J. 2013. Risk assessment for three dreissenid mussels (*Dreissena polymorpha*, *Dreissena*
1036 *rostriformis bugensis, and Mytilopsis leucophaeata*) in Canadian freshwater ecosystems. Research Document 2012/174, Canadian Science Advisory Secretariat, National Capital Region. Available from https://publications.gc.ca/site/eng/445835/publication.html [accessed 29 September 2025].

1041 Thorp, J.H., and Rogers, D.C. 2011. Field guide to freshwater invertebrates of North America. 1st edition. *In* 1st edition. Academic Press, Amsterdam.
doi:10.1016/C2009-0-61804-3.

1044 Uphoff, C.S., Schoenebeck, C.W., Koupal, K.D., Pope, K.L., and Wyatt Hoback, W. 2019. Age-0 walleye *Sander vitreus* display length-dependent diet shift to piscivory. *J. Freshwater Ecol.* **34**(1): 27-36. doi:10.1080/02705060.2018.1529637.

1047 VanDeValk, A.J., Forney, J.L., and Jackson, J.R. 2008. Relationships between relative weight, prey availability, and growth of walleyes in Oneida Lake, New York. N. Am. J. Fish. Manag. **28**(6): 1868-1875. doi:10.1577/M08-064.1.

1050 Venturelli, P.A., and Tonn, W.M. 2006. Diet and growth of northern pike in the absence of prey fishes: initial consequences for persisting in disturbance-prone lakes. Trans. Am. Fish. Soc. **135**(6): 1512-1522. doi:10.1577/T05-228.1.

1053 Walker, P.D., Wijnhoven, S., and van der Velde, G. 2013. Macrophyte presence and growth form influence macroinvertebrate community structure. Aquat. Bot. **104**: 80-87. doi:https://doi.org/10.1016/j.aquabot.2012.09.003.

1056 Werner, E.E., and Gilliam, J.F. 1984. The ontogenetic niche and species interactions in size-structured populations. Annu. Rev. Ecol. Syst. **15**(1): 393-425. doi:10.1146/annurev.es.15.110184.002141.
1059 Wetzel, R.G., and Likens, G.E. 1991. Limnological Analyses, 2nd edition. Springer, New York, NY. doi:10.1007/978-1-4757-4098-1.

1061 Willis, D.W. 1989. Proposed standard length-weight equation for northern pike. N. Am. J. Fish. Manag. **9**(2): 203-208. doi:10.1577/1548-8675(1989)009%3C0203:PSLWEF%3E2.3.CO;2.

1064 Willis, D.W., Guy, C.S., and Murphy, B.R. 1991. Development and evaluation of a standard weight (WS) equation for yellow perch. N. Am. J. Fish. Manag. **11**(3): 374-380. doi:10.1577/1548-8675(1991)011%3C0374:DAEOAS%3E2.3.CO;2.

1067 Wissel, B., Cooper, R.N., Leavitt, P.R., and Pham, S.V. 2011. Hierarchical regulation of pelagic invertebrates in lakes of the northern Great Plains: a novel model for interdecadal effects of future climate change on lakes. Glob. Chang. Biol. **17**(1): 172-185. doi:10.1111/j.1365-2486.2010.02291.x.

1071 Wu, L., and Culver, D.A. 1992. Ontogenetic diet shift in Lake Erie age-0 yellow perch (*perca flavescens*): a size-related response to zooplankton density. Can. J. Fish. Aquat. Sci. **49**(9): 1932-1937. doi:10.1139/f92-214.

1074 Wurts, W.A. 2002. Alkalinity and hardness in production ponds. World Aquaculture **33**(1): 16-17.

1076 Yang, T. 2022. Age-0 walleye diet shift reflects food web changes in western Lake Erie. M.Sc. thesis, University of Toledo, Toledo, Ohio. Available from http://rave.ohiolink.edu/etdc/view?acc_num=toledo1651833708001111 [accessed 29 September 2025].

1080 Zacharia, P.U., and Abdurahiman, K.P. 2004. Methods of stomach content analysis of fishes-Winter school on towards ecosystem based management of marine
1082 fisheries-Building mass balance trophic and simulation models. Available from

1083 http://eprints.cmfri.org.in/5282/ [accessed 29 September 2025].

1084

1085
1 **Table 1:** Number (N), total length (TL) and weight (W) of walleye, northern pike and

2 yellow perch sampled from May – August 2019, from 15 Prairie lakes for the calculation

3 of gamefishes' body condition, Wr. Perch exhibited the highest body condition while

4 walleye and pike fell below target range of healthy populations, Wr < 95-105.

<table><thead><tr><th rowspan="2">Fish species</th><th colspan="2">Walleye</th><th colspan="2">Northern pike</th><th colspan="2">Yellow perch</th></tr><tr><th>TL (cm)</th><th>W (g)</th><th>TL (cm)</th><th>W (g)</th><th>TL (cm)</th><th>W (g)</th></tr></thead><tbody><tr><td>Metrics</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Mean</td><td>41.3</td><td>790.9</td><td>60.3</td><td>1433.6</td><td>21.1</td><td>161.9</td></tr><tr><td>Range</td><td>16.4-75.2</td><td>40.0-3760.0</td><td>30.9-86.2</td><td>180.0-3910.0</td><td>14.1-32.5</td><td>28.0-666.0</td></tr><tr><td>Average Wr</td><td colspan="2">92</td><td colspan="2">87</td><td colspan="2">105</td></tr><tr><td>N</td><td colspan="2">317</td><td colspan="2">41</td><td colspan="2">156</td></tr></tbody></table>

5

6

7

8

9
<figure><img src="image_8.png" /></figure>

1

2 **Figure 1:** A) Map of Canada highlighting the proximity of Saskatchewan to zebra and

quagga mussels (ZQM) infested provinces particularly, Manitoba to the east. B)

4 Location of the 18 study lakes in south-central Saskatchewan. Lakes are coded by

5 ecoregions in stars, Red = moist mixed grassland, White = mixed grassland. Green =

6 boreal transitional zone, Blue = Aspen Parkland. Black circles indicate the major urban

7 cities.

8

9

10
<figure><img src="image_9.png" /><figcaption>**Figure 2:** Diet composition of three gamefish species (May-August 2019). Significant diet shifts were observed in walleye (p = 0.028, Figure S3) but not in pike or perch (Figures S4–S5). The colored legend depicts prey items.</figcaption></figure>

14
<figure><img src="image_10.png" /></figure>

15

<figure><img src="image_11.png" /></figure>

16
<figure><img src="image_12.png" /></figure>

17

18 **Figure 3:** Linear mixed-effects model (Tables S7-9) of predicted fit of the relationship

19 between A) walleye, B) pike and C) perch body condition (Wr) and total length (size).

20 Each point represents the Wr of individuals and the corresponding length

21 measurements in each lake. The light grey area around the line depicts standard error,

22 and the colored dots represent lakes. There was a significant negative relationship

23 between the size and condition of walleye and pike but not perch.
<figure><img src="image_13.png" /></figure>

**Figure 4:** Relative contribution of selected environmental variables to A) large walleye, R² = 82.5% and B) large perch R² = 97.4% body condition. Vertical lines represent 90% bootstrap confidence intervals. Chl *a* and Alkalinity positively correlated (*p* < 0.05) with walleye condition. However, alkalinity was negatively correlated with condition of large perch with Chl *a* positively correlated (*p* < 0.05). Metrics were not normalized.