Dietary vs non-dietary fatty acid profiles of lake trout ecotypes from Lake Superior and Great Bear Lake: Are fish really what they eat?

: 31 Fatty acids are well-established biomarkers used to characterize trophic ecology, food-web linkages, and the ecological niche of many different taxa. Most often, fatty acids that are examined 33 include only those previously identified as “dietary” or “extended dietary” biomarkers. Fatty acids 34 considered as non-dietary biomarkers, however, represent numerous fatty acids that can be 35 extracted. Some studies may include non-dietary fatty acids (i.e., combined with dietary fatty 36 acids), but do not specifically assess them, whereas in other studies, these data are discarded. In 37 this study, we explored whether non-dietary biomarkers fatty acids can provide worthwhile information by assessing their ability to discriminate intraspecific diversity within and between lakes. Non-dietary fatty acids used as biomarkers delineated variation among regions, among 40 locations within a lake, and among ecotypes within a species. Physiological differences that arise 41 from differences in energy processing can be adaptive and linked to habitat use by a species’ ecotypes, and likely explains why non-dietary fatty acids biomarkers can be a relevant tool to delineate intraspecific diversity. Little is known about the non-dietary-mediated differences in fatty acid composition, but our results showed that non-dietary fatty acids biomarkers can be useful tool in identifying variation.

Abstract: 31 Fatty acids are well-established biomarkers used to characterize trophic ecology, food-web 32 linkages, and the ecological niche of many different taxa. Most often, fatty acids that are examined 33 include only those previously identified as "dietary" or "extended dietary" biomarkers. Fatty acids 34 considered as non-dietary biomarkers, however, represent numerous fatty acids that can be 35 extracted. Some studies may include non-dietary fatty acids (i.e., combined with dietary fatty 36 acids), but do not specifically assess them, whereas in other studies, these data are discarded. In 37 this study, we explored whether non-dietary biomarkers fatty acids can provide worthwhile 38 information by assessing their ability to discriminate intraspecific diversity within and between 39 lakes. Non-dietary fatty acids used as biomarkers delineated variation among regions, among 40 locations within a lake, and among ecotypes within a species. Physiological differences that arise 41 from differences in energy processing can be adaptive and linked to habitat use by a species'  (Ota and Yamada 1971). Thus, fatty acids not labelled as 85 "dietary" markers could be useful when the aim of a study is to delineate or better understand 86 intraspecific diversity. To investigate this, we compared non-dietary to dietary fatty acids 87 biomarkers among ecotypes of lake trout (Salvelinus namaycush) in Lake Superior and Great Bear 88 Lake, as these ecotypes represent important intraspecific diversity in these lakes.

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Salmonids, such as lake trout, inhabit young ecosystems believed to be 10,000 to 15,000 90 years old, e.g., post-glacial lakes colonized from non-glaciated refugia. The depauperate 1994, Smith and Skulason 1996). This high level of ecological opportunity, together with an 95 increase in intraspecific competition after colonization, can promote specialization and divergence 96 within a population, e.g., the development of groups of individuals with similar patterns of 97 resource use (Svanbäck et al. 2007

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Intraspecific diversity in lake trout has been mostly linked to differences in depth 103 distribution and, not surprisingly, is best known from large (> 500km 2 ), deep lakes, such as Lake    Great Bear Lake is the most northerly lake of its size (~31 000 km 2 ) and is the fifteenth deepest 137 freshwater lake in the world (  lake is ultra-oligotrophic and, despite its size, has a simple food web, supporting only 15 fish 140 species (Johnson 1975, MacDonald et al. 2004). The lake and its biota have remained relatively 141 isolated and unexploited and is one of the most pristine large lakes in North America. Great Bear 142 Lake has five semi-isolated arms, but due to sample sizes, data were pooled across multiple sites  Great Bear Lake sustains a noteworthy example of lake trout divergence (Fig. 2). With its 145 intraspecific diversity independent of depth-based segregation, the lake also presents an unusual    were found to be shared between the two lakes, and these were selected for further analyses (Table   220 A1 and A2).

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Unless noted otherwise, statistical analyses were conducted using R software version 3.5.3 223 (R Core Team 2017). Prior to analysis, fatty acid concentrations were logit transformed (log(p/(1-224 p)) to normalize the data, and then scaled and centered using a z-score transformation (z=xµ/σ).

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Combined lakes analyses 245 The first two axes of the PCAs explained 42.4 % and 41.5%, respectively, of the variation 246 among individuals in their non-dietary and dietary fatty acid biomarkers (Fig. 3). In both PCAs, 247 lake trout from Great Bear Lake were largely distinct from Lake Superior trout (only ~30 248 individuals from Great Bear Lake overlapped with individuals from Lake Superior), but trout from the dietary PCA appeared to be driven by fatty acids associated with pelagic habitat (14:0, 20:1n-253 9, 20:1n-7, 20:1n-11, and 22:1n-9; toward Lake Superior) versus one dietary fatty acid associated 254 with cannibalism or/and carnivory (20:5n-3; toward Great Bear Lake) (Appendix, Table 1). 255 Finally, the first two axes of the PCA based on all fatty acids combined explained 39.0% of the 256 variation among lake trout ecotypes from Great Bear Lake and Lake Superior. As before, lake trout 257 from Great Bear Lake were largely separated from the Lake Superior trout, whereas lake trout 258 from Stannard Rock and Superior Shoal in Lake Superior overlapped completely. to ecotype based on dietary fatty acids biomarkers (Fig. 4). significantly among the four ecotypes (one-way PERMANOVA, F 3,116 = 2.5, P < 0.01); leans 292 differed from redfins (P < 0.01) and differences were marginally between redfin and siscowet and 293 between redfin and humper (P < 0.06). The ten most discriminating non-dietary fatty acids 294 biomarkers from SIMPER explained ~69.9% of the dissimilarity among groups ( Table 1). The 295 first two axes of the linear discriminant analysis explained 64.3% and 25.6% of the variation, and 296 45.0% individuals were correctly classified to ecotype using non-dietary fatty acids biomarkers 297 (Fig. 4). Similar to what was found at Stannard Rock, we found no differences among the four  Table 2). The first two axes of the linear discriminant analysis 301 explained 38.6% and 35.6% of the variation, respectively, and 31.7% of fish were correctly 302 classified to ecotype based on their dietary fatty acids (Fig. 4).

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In this study, non-dietary fatty acids showed variation geographically (between lakes), 305 between locations within a lake (i.e., Stannard Rock versus Superior Shoal), and among ecotypes 306 within a lake or location. Although some overlap existed (which reduced the power to 307 discriminate), our results showed that when investigating intraspecific diversity, non-dietary fatty 308 acids biomarkers can be a useful tool to delineate groups, and that sometimes, such as at sites in 309 Lake Superior, were more discriminatory than dietary fatty acids. While characterizing trophic    The concept that dynamics of energy processing and storage are adaptive along a gradient 353 associated with depth does not apply to the intraspecific diversity of lake trout in Great Bear Lake.

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This diversity is limited to shallow-water habitat, and appears to be independent of major habitat between Great Bear Lake and Lake Superior is thus perplexing, as we were expecting greater 357 differences in non-dietary fatty acid biomarkers among ecotypes in Lake Superior than in Great 358 Bear Lake, due to known buoyancy variation associated with a depth gradient in Lake Superior. If Superior. Another question raised by our results is the extent to which ecotypes are independent 362 of major habitat or resource axes (the same question is pertinent for Lake Superior), especially 363 because dietary fatty acid biomarkers in Great Bear Lake were slightly better at delineating 364 intraspecific groups than non-dietary biomarkers than in Lake Superior.

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Differences between morphs and sites may be influenced by the total content of fatty acids to alter lipid composition in fish tissue (Olsen 1999). Third, multiple sizes and life-stages, e.g., 402 juvenile, mature, and resting individuals, were included in the Lake Superior analysis; different 403 life-stages can vary in lipid metabolism (Sheridan 1989) and large lake trout can rely more on 404 nearshore-benthic food web resources than small lake trout (Happel et al. 2017a). Finally, some 405 fatty acids exist at very low amounts (≤ 2%), which can introduce error when interpreting 406 differences among fatty acids that are found in only trace amounts (e.g., peak shouldering) 407 (Christie 1998). Despite these limitations, we found some consistent patterns with regards to 408 intraspecific diversity between lakes and among ecotypes within lakes from two distinct datasets.  intraspecific variation within a lake, but also in examining differences between lakes. Non-dietary 427 fatty acid biomarkers can provide useful information; therefore, one should carefully consider if 428 such information is superfluous or not before data from these fatty acids are discarded.  All data presented are available by request via e-mail to the first author.

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The authors declare that they have no conflict of interest.    Great Bear Lake, Stannard Rock (Lake Superior), and Superior Shoal (Lake Superior). The 745 95% ellipse of each morph is also provided.