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Morphology and Blood Metabolites Reflect Recent Spatial Differences Among Lake Winnipeg Walleye, Sander vitreus

The invasive rainbow smelt (Osmerus mordax) was an abundant food source for Lake Winnipeg walleye (Sander vitreus), especially in the north basin of the lake, until the smelt’s collapse in approximately 2013. We quantified changing length-at-age (≈ growth rates) and relative mass (≈ body condition) in Lake Winnipeg walleye caught for a gillnet index data set. Here, walleye showed smaller length-at-age, particularly in the north basin with young fish, over time. This approach to assessing growth suggests a constraint in the north basin fish, possibly a nutritional limitation between 2017 and 2018, that was not present in the south. We then analyzed a separate group of walleye (≥452 mm in fork length) sampled in 2017 as part of a large-scale tracking study, which had a similar slope in length-mass relationship to large walleye caught in that year for the gillnet index data. A panel of metabolites associated with amino acid metabolism and protein turnover was compared in whole blood. These metabolites revealed elevated essential amino acids and suggest protein degradation may be elevated in north basin walleye. Therefore, based on both growth estimates and metabolites associated with protein balance, we suggest there were spatially distinct separations affecting Lake Winnipeg walleye with decreased nutritional status of walleye in the north basin of Lake Winnipeg being of particular concern.


Introduction 32
Walleye (Sander vitreus) are the largest component of the Lake Winnipeg fishery in 33 Manitoba, the second-largest freshwater fishery in Canada (Fisheries and Oceans Canada, 2018). 34 Sustainable management of these walleye is, therefore, of enormous importance to commercial 35 fishing, recreational angling, and the Lake Winnipeg ecosystem. However, annual commercial 36 yield from the Lake Winnipeg walleye fishery has been above maximum sustainable yield since To address conservation issues such as those that the Lake Winnipeg walleye may face, 45 resource managers require many pieces of information to forecast how actions affect the 46 probability of distinct alternative futures (Gattuso et al., 2015;Dudgeon et al., 2006). 47 Linking metabolites and spatial patterns 120 In the present study, we examined both temporal and spatial changes in Lake Winnipeg 121 walleye morphological measurements using gillnet index data collected by the Province of 122 Manitoba. First, length-at-age was used to assess longer-term trends in growth prior to 2017. 123 Differences in walleye length-at-age may be an effective metric to study the historical rainbow 124 smelt collapse (approximately 2013), since length-at-age is a summary of prior growth while 125 nutritional deprivation has little effect on length (Caruso et al., 2011;Einen and Thomassen, 126 1998;Sumpter et al., 1991). On the other hand, body condition or length-mass relationships can 127 vary with a single spawning or feeding event, each of which change a fish's mass. Spatial 128 patterns in length-at-age (≈ growth rate) and length-mass relationships (≈ body condition) are 129 thus assessed concurrently. We then linked length-mass relationships to a survey of walleye in 130 Lake Winnipeg in 2017, where blood was sampled non-lethally for metabolites included in our 131 nutritional biomarker development strategy (see above; Figure 2). Spatial differences in the 132 presence of chosen metabolites were then tested with estimated marginal means (Lenth, 2019; 133 Searle et al., 1980) and linear models incorporating length and site collected. We hypothesized 134 that because the rainbow smelt collapse was most pronounced in the north basin of Lake 135 Winnipeg (Manitoba Government, 2018), north basin walleye would exhibit the greatest decline 136 in relative mass over time and length-at-age. We also hypothesized that metabolites would 137 implicate higher rates of endogenous protein breakdown (i.e., elevated hydroxyproline, 138 dimethylarginine, and methionine sulfoxide) and amino acid oxidation (i.e., elevated dimethyl 139 sulfone, kynurenine, and α -aminoadipic acid) in the north basin than in the south basin. This 140 work thus 1) explored the effect of the rainbow smelt collapse on Lake Winnipeg walleye 141 morphology and 2) examined the potential for a suite of 9 metabolites to reflect walleye 142 nutritional status and be developed into a non-lethal sampling approach for assessing the 143 nutritional state of wild-caught fish. https://www.gov.mb.ca/sd/fish_and_wildlife/fish/commercial_fishing/netting_data.html). This 150 gillnet index has been run annually by the province since 1979 to provide an alternative to 151 commercial fisheries data to better track trends in size and abundance for walleye and sauger 152 (Sander canadensis) in Lake Winnipeg. We focused on the data collected between 2009 and 153 2018 because there was consistent monitoring of the same six sites during that period and these 154 data included age estimates for each fish. To examine walleye length-mass and length-at-age 155 relationships over time, sauger and dwarf walleye (which possibly inhabit a different ecological 156 niche and are responding to selective pressures; see Moles et al., 2010) were filtered out. The 157 remaining data set included year, site, basin collected, gillnet mesh size, fork length, mass, and 158 sex for adult and sub-adult walleye. Fork length and mass were transformed on a log 10 scale. 159 Gillnet mesh size was included as an independent variable where possible to account for bias in 160 walleye length and mass caught in gillnets with different mesh sizes. For all linear models, we 161 chose variables based on their biological significance, as opposed to taking a stepwise model 162 selection approach. Statistical modeling was performed in R (R Core Team, 2019) using the 163 packages emmeans (Searle et al., 1980), sjstats ( Changes in growth rate over time were assessed for walleye from seven sites across Lake 169 Winnipeg using length-at-age data from the gillnet index, with a special focus on growth rates 170 before and after the rainbow smelt collapse (in approximately 2013). Length is a useful measure 171 because a fish facing nutritional deprivation decreases in mass, but decreases in length either 172 negligibly or not at all (Caruso et al., 2011;Einen and Thomassen, 1998;Sumpter et al., 1991). 173 Length-at-age therefore represents more long-term trends in growth than year-specific values for 174 body condition or length-mass relationships. A separate linear model was used for each site 175 sampled for the gillnet index data, with Dauphin River and Grand Rapids representing the north 176 basin, Matheson Island and Frog Bay representing the channel, and Riverton and Grand Beach 177 representing the south basin of Lake Winnipeg (Figure 1). These linear models used fork length 178 as the dependent variable, and the independent variables were age interacting with year, sex, and 179 gillnet mesh size. Results were therefore averaged over gillnet mesh sizes to account for 180 sampling bias. Only walleye ages two through six years old as estimated by counting annuli in 181 otoliths were included in the models, because not all sites had sufficient sample sizes available 182 for walleye of older and younger ages (n ≥ 10 individuals age class -1 year -1 site -1 , except the 183 Dauphin River age two fish in 2012 and 2014 were included, n=5 and 2, respectively). A total of 184 n=2164 individuals were used in the models for Dauphin River, n=2745 for Grand Rapids, 185 n=844 for Matheson Island, n=959 for Frog Bay, n=1732 for Riverton, and n=1729 for Grand 186 Beach collection sites. 187 Differences in walleye growth rates (i.e., length-at-age) among sampling sites were also 188 examined for the years surrounding 2017 (i.e., 2015-2018), to support connections with the 189 available metabolite data (i.e., from year 2017) and thus potential food availability during this 190 time. Age two and three walleye sampled in 2018 were of particular interest for this analysis 191 because length-at-age for young walleye in the growth phase of their life cycle in 2018 should 192 reflect food availability from 2017 through to their capture in 2018. Four similar linear models 193 (i.e., one for each year) were used for length-at-age comparisons among sites. In each model, 194 fork length was the dependent variable, while independent variables were the interaction of site 195 and age, sex, and gillnet mesh size. As above, only walleye ages two through six years old were 196 included in the models. For these analyses, n=1844 individuals were available for the year 2015, 197 n=1392 for 2016, n=805 for 2017, and n=624 for 2018. Estimated marginal means and estimated 198 marginal trends were used to investigate pairwise mean and slope differences between sites for 199 each model (Lenth, 2019;Searle et al., 1979). Briefly, the analysis of estimated marginal means 200 obtains predictions from a linear model using Tukey's post hoc test and finds meaningful 201 averages to summarize primary factor effects while estimated marginal trends follows the same 202 process but for the interaction between two predictors in a model (see Lenth, 2019). 203 204 Lake Winnipeg Walleye Relative Mass Over Time 205 Relative mass over time was examined for walleye across Lake Winnipeg. These relative 206 mass measures were used as metrics analogous to body condition, which is an imperfect 207 approach to our data because walleye mass may change in a single feeding or spawning event, 208 thus changing a fish's body condition and length-mass relationship. However, an examination of 209 relative mass remains useful because it represents a link between inter-annual growth rate trends 210 and metabolite presence, which can vary on much faster timescales. Only walleye ≥ 375 mm in 211 fork length from the gillnet index data, n=5,838 individuals (mean 583.80 walleye ± 220.17 s.d. 212 year -1 ), were included in the model because this value represents the smallest estimate of fork 213 length for mature individuals among Manitoba lakes (Craig et al., 1995), and is a conservative 214 threshold for modeling the fish that were sampled for metabolites (minimum fork length 452 215 mm). After filtering, a total of n=5,838 individuals (mean 583.80 walleye ± 220.17 s.d. year -1 ) 216 remained. 217 To study how relative mass has changed over time, a linear model was used with mass as 218 the dependent variable, and independent variables were fork length and its interactions with site, 219 year and sex. Eta squared is reported for the independent variables in this model, where analysis 220 of variance applied to the model outputs returns the percentage of variance in the final model 221 accounted for by an independent variable (Levine and Hullett, 2002). 222 223

Spatial Differences Among Walleye in 2017 224
In addition to exploring length-mass relationships over time, we modeled spatial 225 relationships among Lake Winnipeg walleye using the gillnet index data set in 2017 to establish 226 possible connections between length-mass relationships and metabolite presence in that year. 227 Here, n=286, 127, and 227 individuals remained from the Dauphin River, Matheson Island, and 228 Riverton sites, respectively. These three sites were chosen because they are most similar to the 229 three sites sampled for metabolites (i.e. Dauphin River, Matheson Island, and the Red River). 230 This model used mass as the dependent variable, with the independent variables as the 231 interaction of mass with fork length, site collected, sex, age, and gillnet mesh size. 232 Another linear model was used to assess the length-mass relationship of walleye caught 233 as part of the blood metabolite study in 2017. The smallest walleye in this data set was 452 mm 234 in fork length. Mass was the dependent variable, with the independent variables fork length 235 interacting with site collected, sex, and the interaction of fork length and sex. Note that gillnet 236 mesh size and age were not included in the model since fish were caught by electrofishing and 237 age was not known for these fish. 238 To examine how similar the length-mass relationships were of walleye were between 239 those collected for metabolite analysis and those for the gill net index data, we used a linear 240 model with an independent variable describing which study an individual was collected for. 241 Here, mass was the dependent variable, which was related to fork length, basin collected, sex, 242 study (metabolite or gillnet index), the interaction of fork length and basin, the interaction of fork 243 length and sex, and the interaction between fork length and study. In this model, only gillnet 244 index samples from the Dauphin River, Matheson Island, and Riverton were included. Riverton 245 from the gillnet index data and the Red River from the metabolite data were used to jointly 246 represent the south basin of Lake Winnipeg. One milliliter of whole blood was collected for metabolite analysis from anaesthetized 265 walleye by caudal puncture using a heparinized needle and 3 ml syringe, flash frozen in liquid 266 nitrogen immediately after sampling, and stored at -80˚C. Of note, logistics prevented separation 267 of plasma or serum and therefore all metabolite data are for whole blood. This effort was part of 268 a larger study assessing the physiological health, movement, and genetic structure of walleye in 269 Lake Winnipeg. Other tissues collected at the time of blood sampling include a fin clip, gill 270 filaments, the first dorsal spine, scales, and a muscle biopsy. All fish were sampled non-lethally 271 and a VEMCO acoustic tag (VEMCO, Bedford, Nova Scotia, Canada) was surgically implanted 272 prior to release back into the water near the collection site. 10 -16 , adjusted R 2 = 0.80) showed significant differences in estimated marginal mean of length-322 at-age between the Dauphin River (north basin) and Riverton (south basin), Grand Beach (south 323 basin), and Matheson Island (channel) sites in age two walleye (Table 1). Slopes in length-at-age 324 relationships, as described using estimated marginal trends, were also significantly more steep 325 between the Dauphin River and the Matheson Island, Riverton, and Grand Beach sites, 326 respectively (Table 1, Figure 4). Based on these results, we suggest that young Dauphin River 327 walleye grew more slowly in 2017 than young walleye at the Matheson Island (channel) and the 328 south basin sites.  (Table 3; Figure 5B). This suggests a different length-mass relationship for the 345 north basin fish compared to walleye caught in the channel. 346 Similar to the larger fish sampled as part of the gill net index in 2017, for length-mass 347 relationships among walleye collected for metabolites (F = 86, p < 2.2 x 10 -16 , adjusted R 2 = 348 0.94), estimated marginal trends were also lower in the Dauphin River compared to Matheson 349 Island (Table 3; Figure 5C). However, unlike the linear model used with the gillnet index data, 350 the Red River (south basin) showed a higher estimated marginal mean mass than the Matheson 351 Island (channel) site (Table 3; Figure 5C). 352 When comparing length-mass relationship of larger walleye collected in 2017 as part of 353 the gill net index and metabolite studies, no significant effect of study and of study interacting 354 fork length was found (F = 889, p < 2.2 x 10 -16 , adjusted R 2 = 0.97, Table 4). However, estimated 355 marginal means of mass based on fork length were higher in the walleye from the metabolite 356 data (p = 0.00070) while estimated marginal trends for fork length-mass relationships between 357 studies were not different (p = 0.90). A plot of fork length and mass with these data reveals that 358 the metabolite-measured walleye lie at the upper end of the distribution in length-mass for 359 walleye collected for the gillnet index data, while following a similar slope ( Figure 5D). 360 361

Modeling Metabolites Differences 362
Metabolite presence of the three essential amino acids varied significantly across 363 sampling sites for Lake Winnipeg walleye sampled in 2017. The models predicting the presence 364 of each of the three free essential amino acids were each significant (F = 4.3, 4.7, and 3.7, p = 365 0.011, 0.0071, and 0.020, adjusted R 2 = 0.21, 0.23, and 0.18 for methionine, tryptophan, and 366 lysine respectively, Table 5). In addition, site was a significant independent variable within the 367 overall models for the three essential amino acids while fork length was not, with eta squared 368 higher for site than for fork length in each case (Table 5). Estimated marginal means for each of 369 the three essential amino acids revealed significant differences in predicted essential amino acid 370 presence based on site, with higher values in the Dauphin River (north basin) than in the Red 371 River (south basin) ( Figure 6A, B, and C). 372 Overall, there was no effect of sampling location or fish size (i.e., fork length) on 373 essential amino acid breakdown biomarkers. Linear models for α -aminoadipic acid, kynurenine, 374 and dimethyl sulfone were not significant (F = 0.32, 2.6, and 3.3, p = 0.81, 0.070, and 0.031, 375 adjusted R 2 = 0.057, 0.11, and 0.15, respectively, Table 5), indicating a failure to fit fork length 376 and site to essential amino acid breakdown metabolite presence. The model for dimethyl sulfone 377 was significant (p = 0.031), but neither site nor fork length were significant independent 378 variables (p = 0.36 and 0.088, respectively). Predicted metabolite presence from estimated 379 marginal means showed no significant differences in amino acid breakdown metabolites among 380 sites, as well ( Figure 6D, E, and F). 381 Endogenous protein breakdown metabolite presence varied significantly with collection 382 site in Lake Winnipeg walleye sampled in 2017. Linear models run with hydroxyproline, 383 dimethylarginine, and methionine sulfoxide, potential endogenous protein breakdown 384 biomarkers, were each significant (F = 24.12, 6.2, and 6.3, p = 1.2 x 10 -8 , 0.0017, and 0.0016, 385 adjusted R 2 = 0.65, 0.29, and 0.29, respectively, Table 5). Moreover, site collected was both a 386 significant independent variable and had a higher effect size than fork length in each model 387 (Table 5). Predicted presence for each candidate metabolite associated with protein breakdown 388 was higher in the northern Dauphin River than the southern Red River ( Figure 6G, H, and I). 389 Hydroxyproline and dimethylarginine were also higher in fish from the Dauphin River than at 390 Matheson Island in the channel. 391

Morphological Observations 393
In the current study, we aimed to understand how changing food availability may be 394 affecting walleye and could potentially contribute to large-scale changes in the size and 395 abundance of walleye that have been observed in Lake Winnipeg in recent years. We used the 396 Lake Winnipeg gillnet index data set to examine length-at-age and relative mass to assess trends for as many possible confounding variables in the analyses, however the overall trend showed a 403 decrease in relative mass regardless of sex or gillnet mesh size, a pattern that is consistent with 404 previous analyses (Manitoba Government, 2018). Contrary to our predictions, the general trend 405 of decreasing relative mass was not more severe in the north basin (see Figure 5A) where 406 rainbow smelt contributed to a larger portion of the diet of walleye (Sheppard et al., 2015). 407 Given that the decrease in relative mass occurred at all sites sampled, these data suggest 408 ecosystem-wide changes in Lake Winnipeg have contributed to decreases in walleye size over 409 the past decade. impacts on walleye abundance in Lake Winnipeg as the length of fish and fecundity is highly 423 correlated (Craig et al., 1995;Wolfert, 1969). Therefore, the population-level impacts of the 424 decrease in the size of walleye in Lake Winnipeg may become a bigger issue in the future. 425 While the present study is focused on the collapse of the rainbow smelt, additional 426 differences between basins may underlie observed spatial patters. Some of these basin-level 427 differences include higher temperatures, precipitation, river discharge, suspended solids, We observed from a preliminary analysis of a large-scale targeted metabolomics study 448 (with 163 unique metabolites) that essential amino acids varied in whole blood from walleye 449 caught from different regions of Lake Winnipeg (Wiens, Jeffrey, Treberg unpublished data), 450 which provided the impetus to pursue the nutritional biomarkers strategy that we employed in the 451 current study. We focused on three essential amino acids (methionine, tryptophan and lysine), 452 that were each elevated in the Dauphin River fish relative to the more southern Red River fish 453 ( Figure 6). Whole blood was necessary because it was not possible to separate plasma or serum 454 in the field, and we therefore cannot distinguish between differences at the cellular level (mainly 455 red blood cells), or extracellular component of blood. Despite this limitation, because blood acts 456 as a connection between all organs and tissues, we are nevertheless confident that the more north 457 basin walleye had elevated essential amino acids in circulation. 458 Interpretation of changes in circulating levels of amino acids is complicated by the 459 dynamic nature of amino acid levels in the blood. Most fish nutritional studies focus on the 460 plasma (extracellular component) of blood, and show that essential amino acids may be high due 461 to two mutually exclusive reasons. During periods of high feeding success, the removal of 462 circulating amino acids may not be sufficient to prevent the amino acid levels from elevating in 463 the circulation. In other words, high amino acid levels can reflect high food intake. Alternatively, 464 if the animal must rely on increased protein breakdown due to insufficient feeding, then That north basin fish in 2017 displayed lower length-at-age, or less growth, indicates the 468 northern fish may have had lower feeding success in that year compared to the faster growing 469 south basin walleye (Figure 4). Even though the growth estimates support the idea that higher 470 amino acid levels do not reflect greater feeding success for 2017, more information is needed to 471 provide sufficient context for interpreting plasma amino acid levels. Our metabolite screening 472 yielded results for metabolites specific to the degradation pathways each of the three essential 473 amino acids we found to be elevated in the blood of Northern basin fish ( Figure 6D, E, and F). Because we focused on amino acids in the blood, and protein turnover is a major source 488 of those circulating amino acids, we needed a means to investigate spatial patterns of protein 489 degradation. To distinguish between amino acids in general and those amino acids that have 490 already been incorporated into proteins, we focused on amino acids that have been modified after 491 they were incorporated into proteins. Three unique protein modifications that arise from three 492 separate routes are focused on: two that come from enzymatic processes (hydroxyproline and 493 dimethylarginine), and one that occurs spontaneously as exposed methionines are oxidized by 494 reactive molecules such as hydrogen peroxide (methionine sulfoxide). While methionine 495 sulfoxide can be formed from free methionine, most methionine from tissues is bound in 496 proteins, so we assume that the bulk of circulating methionine sulfoxide is of proteinaceous 497 origin. Because the means of forming these three post-translational modifications from amino 498 acids represent independent pathways, we had no a priori expectation that the levels of all three 499 post-translational modifications would be observed to vary in a consistent fashion across Lake 500 Winnipeg. Nevertheless, all three modifications were higher in fish from the north than the south 501 basin ( Figure 6G, H, and I). The signal of protein degradation, based on the consistent presence 502 of post-translationally modified amino acids, could therefore suggest a higher rate of protein 503 turnover in walleye from the north basin of Lake Winnipeg than the south. 504 If an increased reliance on protein oxidation for energy is not driving the elevated amino 505 acids in north basin fish, then the question remains of why there was an increase in protein 506 turnover in those northern fish. While protein metabolism is controlled by many factors, both 507 protein synthesis and degradation rates in fish are known to increase with the level of swimming 508 activity (Houlihan and Laurent, 1987). If the north basin walleye must spend more time 509 swimming to find sufficient nutritional resources, then elevated levels of post-translationally 510 modified amino acids could reflect a greater level of activity for the fish in the north. Relevant to 511 these results, and contrary to what may be intuitive for carnivorous fishes, careful estimations of 512 fuel usage in relation to swimming speed indicates that protein is likely not a major fuel source 513 to supply the increased energy demand of increased swimming in rainbow trout and Nile Tilapia 514 during short-term swimming (Alsop and Wood, 1997; Alsop et al., 1999); although, using the 515 same strategies in Nile Tilapia indicated that prolonged swimming (at ~ 2.7 body lengths/second) 516 over the course of over 48 hours did increase the relative reliance on protein to fuel swimming in 517 unfed fish (Alsop et al., 1999). Since walleye in the wild appear to spend most of their time 518 swimming at speeds below 1.0 body lengths/second (Kelso, 1978), it seems that the extended 519 swimming response in tilapia is not likely applicable to the walleye sampled from the wild in the 520 present study. We therefore suggest that elevated signals of protein turnover in wild walleye may 521 be due to increased swimming activity, which may be undertaken to compensate for decreased 522 food availability. 523 524

Linking Data Sets 525
The metabolite and gillnet index datasets were consistent in patterns of possible 526 decreased food availability in the north basin in 2017 (when metabolites were measured), based 527 on analyses of spatially varying growth rates in the 2017 gillnet index data and slopes of length-528 mass relationships in both datasets. Moreover, data set origin (gillnet index or metabolite-529 measured walleye captured via electrofishing) was not a significant predictor of overall length-530 mass relationship in a combined linear model, providing additional evidence for no systematic 531 bias in length-mass relationship in one data set or another, even if the walleye used for 532 metabolites were large. Altogether, we argue that the walleye used for assessing metabolite 533 levels are likely representative of large walleye captured in the gillnet index data, and that spatial 534 differences in the slopes of length-mass relationships (≈ body condition) and length-at-age (≈ 535 growth rate) are consistent with spatially varying metabolite presence. 536 The time scale in response variables should be considered when relating ecology to length-at-537 age, length-mass relationships, and metabolite levels across data sets. Length-at-age estimates 538 are summaries of one or more prior years of growth for a cohort of walleye, while mass may 539 change from a single feeding or spawning event, thus changing length-mass relationships. In  Tables  770  771  Table 1  to walleye (Sander vitreus) growth, diet, and energy requirements. Throughout this manuscript, 842 amino acid breakdown refers to breaking down an amino acid to use it as an energy source. 843 Metabolites specific to the breakdown pathways of specific essential amino acids, indicated 844 below, were selected as markers of these processes. Meanwhile protein degradation refers to 845 proteins that are being separated back into their constituent amino acids. For the current study, 846 we used metabolites in the blood that would come specifically from post-translationally modified 847 amino acids because their release is the result or proteins that have been synthesized and 848 subsequently degraded. 849 walleye (Sander vitreus). These data are pulled from a linear model relating log 10 mass as a 869 dependent variable to log 10 fork length, sex, site, and mesh size as independent variables (F = 870 2686, p < 2.2 x 10 -16 , adjusted R 2 = 0.98). 95% confidence intervals are provided as error bars at 871 each year. For panel B, the linear model related log 10 mass to the interaction of log 10 fork length 872 and basin collected, sex, mesh size, and age (F = 222, p < 2.2 x 10 -16 , adjusted R 2 = 0.97) using 873 the gillnet index data from the Dauphin River, Matheson Island, and Riverton sites. Panel C 874 represents a linear model on walleye used for fish from the metabolite measurements, and this 875 linear model related log 10 mass in kg to log 10 fork length interacting with site collected, sex, and 876 the interaction of log 10 fork length and sex (F = 86, p < 2.2 x 10 -16 , adjusted R 2 = 0.94). Panel D 877 shows a linear model comparing length-mass relationships in walleye between those used for 878 metabolites and from the gillnet index data. This model relates log 10 mass as the dependent 879 variable, with log 10 fork length, basin collected, sex, study (metabolite or gill net index), the 880 interaction of log 10 fork length and basin, the interaction of log 10 fork length and sex, and the 881 interaction between log 10 fork length and study as independent variables (F = 889, p < 2.2 x 10 -882 16 , adjusted R 2 = 0.97). Standard error is shown with filled colors. Overall model significance and 883 adjusted R 2 is provided in each panel. The gillnet index data were collected by the Government 884 of Manitoba and only walleye ≥ 375 millimeters in fork length are included in this models, while 885 in the metabolite data are large fish (≥452 mm in fork length) caught by electrofishing, from 886 which whole blood metabolites were measured. Log 10 mass was calculated in kilograms, and 887 log 10 fork length in millimeters. 888 889 Figure 6. Estimated metabolite concentration (µmol l -1 in whole blood) for Lake Winnipeg 890 walleye (Sander vitreus) by site, predicted by linear models that incorporate metabolite as the 891 dependent variable, with log 10 fork length and site collected as the independent variables. The 892 three sites on the horizontal axis are the Red River (south basin), Matheson Island (channel), and 893 Dauphin River (north basin). Here, n=39 large fish (≥452 mm in fork length) were caught by 894 electrofishing in 2017. On the vertical axis are estimated marginal means for each metabolite. 895 The light blue bars represent 95% confidence intervals, while the black arrows represent 896 significance for pairwise comparisons between sites. Overall linear model significance and 897 adjusted R 2 is provided in each panel. Panels A, B, and C represent essential amino acids. Panels 898 D, E, and F represent respective amino acid breakdown metabolites that in conjunction with plots 899 A, B, and C, may describe amino acid oxidation. Panels G, H, and I represent metabolites 900 associated post-translational modification and therefore may reflect endogenous protein 901 degradation (see Figure 2 for additional details). 902 Log 10 fork length