Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes

Journal of Great Lakes Research xxx (xxxx) xxx

Contents lists available at ScienceDirect

Journal of Great Lakes Research journal homepage: www.elsevier.com/locate/ijglr

Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes Austin Happel a,b,⇑, Craig P. Stafford c, Jacques Rinchard d, Sergiusz Czesny a a

Lake Michigan Biological Station, Illinois Natural History Survey, University of Illinois, Zion, IL, USA Daniel P. Haerther Center for Conservation and Research, John G. Shedd Aquarium, Chicago, IL, USA c Division of Biological Sciences, The University of Montana, Missoula, MT, USA d Department of Environmental Science and Ecology, The College at Brockport - State University of New York, Brockport, NY, USA b

a r t i c l e

i n f o

Article history: Received 16 May 2019 Accepted 10 October 2019 Available online xxxx Communicated by: Erin Dunlop

Keywords: Fatty acids Tissue type Ontogenetic diet shift Lake trout Food webs

a b s t r a c t Fatty acid profiles increasingly are being used to quantify foraging patterns of consumers, but the associated interpretation may vary with the tissue type and its lipid content. For salmonids, lipid deposits can be found in both dorsal and ventral (‘‘belly flap”) areas of muscle tissues. However, it is uncertain whether belly flap and dorsal muscle fatty acid profiles are similar in natural populations of fish. We examined how fatty acid profiles of belly flap compared to those of dorsal muscle and the consequent impacts on dietary inferences. Fatty acid profiles were derived from lake trout (Salvelinus namaycush) caught in five North American lakes: Champlain, Flathead, Michigan, Ontario, and Swan. Fatty acid profiles were most similar between tissues when lipid content of muscle was > ~10%, the threshold below which similarities decreased and thus increasingly affected dietary inference. Some fatty acids commonly used as trophic indicators preferentially accrued in one tissue over the other depending on lipid content of the tissues. Regardless of tissue type, fatty acid profiles were specific to each lake indicating that food web structures were distinctive over a broad geographic range. Fatty acid profiles of tissues from lakes Michigan and Ontario were highly similar, so were those from Flathead and Swan lakes, whereas those from Lake Champlain were distinct, having comparatively high proportions of 18:1n-9. We conclude that lipid storage areas like belly flaps likely provide a more accurate signal than muscle when using fatty acids to investigate dietary patterns, particularly when muscle lipid levels are low. Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Introduction Tracing naturally occurring biomarkers through food webs is increasingly being used to provide information on trophic interactions in lieu of, or in conjugation with, stomach content analysis (Budge et al., 2006; Happel et al., 2017b; Iverson, 2009; Stafford et al., 2014). A major advantage of biochemical tracers is that they reveal long-term foraging patterns rather than the short-term perspective provided by stomach contents. Fatty acids are particularly useful in diet tracer studies as their diverse constituents and associated proportional compositions vary across taxa (Budge et al., 2002; Czesny et al., 2011; Kelly and Scheibling, 2012; Persson and Vrede, 2006). Although consumers may alter (i.e., biosynthesize, elongate, desaturate, catabolize, etc.) some fatty acids, profiles generally reflect dietary origins (Beckmann et al., 2013; Budge ⇑ Corresponding author at: Daniel P. Haerther Center for Conservation and Research, John G. Shedd Aquarium, Chicago, IL, USA E-mail address: [email protected] (A. Happel).

et al., 2011; Happel et al., 2016). Accordingly, investigations of predator and prey fatty acid profiles have aided the quantification of dietary patterns within a variety of food webs, including those in lakes (Galloway et al., 2015; Happel et al., 2017c; Iverson, 2009; Kelly and Scheibling, 2012; Maazouzi et al., 2007; Napolitano, 1999; Parrish et al., 2015). Proportional compositions of fatty acids vary among tissues within individual animals, and these differences warrant consideration in dietary tracer investigations. Fatty acids are essential components of cell membranes, are used as energy storage molecules, and predominately arise from diet but can be modified prior to assimilation (Budge et al., 2006; Henderson and Tocher, 1987). Tissues with high triacylglyceride concentrations, such as blubber and adipose tissues, serve as deposits rich in neutral lipids that correspond more to diet than membrane-associated phospholipids (Böhm et al., 2014; Budge et al., 2006; Iverson et al., 2004; Lazzarotto et al., 2015). Fishes, including salmonines, store lipids in both dorsal and ventral regions of musculature (Fengle et al., 2012; Jobling et al., 2002; Nanton et al., 2007; Weil et al., 2013),

https://doi.org/10.1016/j.jglr.2019.10.015 0380-1330/Ó 2019 International Association for Great Lakes Research. Published by Elsevier B.V. All rights reserved.

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

2

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

herein referred to as ‘‘muscle” and ‘‘belly flap”, respectively. Controlled feeding studies with Atlantic salmon (Salmo salar), which had relatively low (<4%) levels of muscle lipid, show that fatty acid profiles of belly flaps reflected diet compositions better than muscle tissues (Budge et al., 2011). However, it remains unclear if this pattern applies to species in the wild that have higher lipid concentrations in muscle tissues, such as lake trout (Salvelinus namaycush) (Kinsella et al., 1977). Lake trout occur across broad areas of northern North America, and their diets are of interest for a range of management and conservation issues (Guy et al., 2011; Jacobs et al., 2010; Madenjian et al., 1998). Within the Great Lakes, lake trout alevins have poor survival, primarily due to parental dietary effects on progeny (i.e., thiamine deficiency complex; Honeyfield et al., 2005). Adult diets in these lakes are composed almost entirely of non-native prey: round goby (Neogobius melanostomus), alewife (Alosa pseudoharengus), and rainbow smelt (Osmerus mordax) (Dietrich et al., 2006; Happel et al., 2017c, 2017b; Jacobs et al., 2010), the latter two of which have been implicated in poor alevin survival (Honeyfield et al., 2005; Riley et al., 2011). In Lake Champlain, survival of early life history stages is low (Ellrott and Marsden, 2004), however, the causes and exact mortality rates remain unknown. Lake trout diet is also poorly understood in Lake Champlain, however limited observations suggest alewife, rainbow smelt, and yellow perch (Perca flavescens) are the major food items of adults (Fisheries Technical Committee, 2009; Simonin et al., 2018). Conversely, introduced lake trout populations in lakes of the upper Flathead River Basin, Montana are thriving and have reduced the abundance of native fish populations (Ellis et al., 2011; Fredenberg, 2002). In a subset of these lakes, including Swan and Flathead, introduced mysid shrimp (Mysis diluviana) provide the main food source for lake trout
500 mm total length (Beauchamp et al., 2006; Guy et al., 2011). An investigation on the south half of Flathead Lake revealed that two major ecotypes predominate, leans (piscivores with a low fat content) and dwarfs [akin to ‘‘humpers” from the Great Lakes, with slow growth and a maximum size ~600 mm total length, (Eshenroder, 2008; Zimmerman et al., 2009)], that generally inhabit deeper water and, as adults, feed more heavily on invertebrates (i.e., Mysis; Stafford et al., 2014). The authors studied morphology, growth, and diet by depth, noting that in the 451–600 mm total length range, a divergence in diet occurred based on collections from early May to early July 2008. Fish captured from depths of 60–100 m (predominately dwarfs) consumed 75% Mysis and 25% fish (by mass). Collections made in <26 m of water (predominantly leans) consumed 30% fishes and 70% invertebrates; within the invertebrate fraction only 4% were Mysis versus 64% for chironomids (Stafford et al., 2014). Flathead leans become increasing piscivorous with size, wherein fish 625–750 mm total length consumed ~80% fish and were even more piscivorous at 751–1000 mm (Beauchamp et al. 2006). The varied ecotypes in Flathead Lake seem to be a response, at least in part, to high lake trout densities and the associated intra-specific competition. Accordingly, only leans are present in Swan Lake where lake trout recently (~1998) migrated from Flathead Lake and are in relatively low abundance (Cox, 2010; Stafford et al., 2014). Lake trout in Swan Lake primarily consume Mysis at smaller sizes, with a transition to piscivory (especially consuming kokanee, Oncorhynchus nerka) at about 500 mm total length (Guy et al., 2011). Overall, the varied diets, life histories, and management issues of lake trout in these systems offer a relevant venue to investigate foraging patterns and the associated geographic differences in food webs using fatty acids. The primary objectives of our study were to: (1) qualitatively describe fatty acid profile differences of lake trout captured across a broad spatial scale, (2) interpret how these differences relate to varied foraging patterns in the associated food webs across lakes,

and (3) evaluate the potential for tissue type to affect dietary inference based on fatty acid analysis. Fish were captured in five lakes differing in food web structure and lake trout genealogy to ensure our results were not population specific. We hypothesized that differences in fatty acid profiles across lakes would be related to their varying food webs, that our ability to infer these differences would be best when using tissue with a high lipid content, and accordingly that the similarity between fatty acid profiles generated from muscle and belly flap will increase as a function of muscle lipid content. Methods Sample collection Lake trout (n = 123) of various sizes were collected from lakes Champlain (44°320 N 73°200 W; Nov. 14th), Ontario (43°210 N 77°570 W; off Hamlin Beach, Oct. 26th & Nov 10th), Michigan (42°130 N 87°320 W; near Julian’s Reef, Oct. 24th), Flathead (47°540 N 114°60 W; Nov. 15th), and Swan (47°570 N 113°530 W; Oct. 22nd) in 2012. Collections were made in association with gill net monitoring and management activities conducted by: Illinois Department of Natural Resources (Lake Michigan), a crew led by Ellen Marsden of the University of Vermont (Lake Champlain), a crew led by Jacques Rinchard (Lake Ontario), Montana Fish, Wildlife & Parks (Swan Lake), and the Confederated Salish-Kootenai Tribes (southern half of Flathead Lake). Collected fish were euthanized and stored on ice after which total length and sex were recorded. Samples (~2.0 g) of dorsal muscle (immediately posterior to the cranium, left side of body) and belly flap (portion of the body’s ventral center, anterior to pelvic fins) were excised from consistent locations (relative to body size), immediately frozen using dry ice, and stored at 80 °C. Samples were shipped on dry ice to The College at Brockport - State University of New York and stored at 80 °C prior to fatty acid analysis. The exception was lake trout from Swan Lake, which were stored at 20 °C for two weeks before investigators arrived to conduct dissections. Concerns over storage effects of fatty acid oxidation were relieved when fatty acid profiles of stored and fresh fish were not substantially different (see Results), and in accordance with the findings of Rudy et al.(2016). Fatty acid analysis Lipids were extracted according to Folch et al. (1957) from 1.0 g skinless samples of homogenized frozen muscle and belly flap tissues, and lipid content was determined gravimetrically as a percentage of wet weight. Fatty acid methyl esters were prepared according to Metcalfe and Schmitz (1961), separated by gas chromatography/mass spectrometry (Agilent 7890A GC and 5975C inert XL EI/CI MSD, Agilent Technologies, Inc., Santa Clara, California, USA), and quantified as described by Happel et al., 2017b. Statistical analysis Lengths and lipid contents We tested for differences in the total lengths among collections to account for any size effects in our later analyses. Length differences were evaluated with ANOVAs and a Tukey-Kramer adjustment when significant effects were found (R Development Core Team, 2019). As lipid content is a percentage and the distribution resembled more closely a beta distribution, we tested for nonlinear correlations between lipid content and length as well as for differences among lakes with Beta Regression (Cribari-Neto and Zeileis, 2010). Beta regression with logit link function was used

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

with lipid content as the dependent variable wherein both length and lake of capture were factors (ANCOVA type test) and dispersion differences among lakes were quantified using the betareg package in R (Cribari-Neto and Zeileis, 2010; Zeileis et al., 2016). Separate analyses were conducted for muscle and belly flap samples, and plots of residuals indicated a better fit of betareg models compared to regular logit models (not reported). To evaluate the significance of length and lake factors, the car::Anova function was used on the resulting betareg fit to produce a Chi-Square statistic for each variable. A post hoc Tukey test was conducted for differences among lakes using the emmeans::emmeans and emmeans::pairs functions in R on the betareg output. Relationships between fatty acid profiles and lipid content Fatty acid profiles of belly flap and muscle, consisting of 26 fatty acids (consistently quantifiable and identifiable), were expressed as relative percentages of identified fatty acids and converted to Bray-Curtis resemblance matrices. Bray-Curtis was chosen as it is a readily interpretable scale from 0% to 100% similar, and has been shown to reflect dietary information with a relatively high fidelity, similar to Euclidean Distances (Happel et al., 2017a). To assess how similar fatty acid profiles are between tissue types and the inter-relationship with lipid content, Bray-Curtis similarities were calculated between each tissue for each individual lake trout. We regressed Bray-Curtis similarities against lipid contents using beta regressions with lake as an interacting categorical variable and different dispersions among lakes accounted for in the model. This analysis was conducted using the predictor variables of either belly flap lipid content, muscle lipid content, or the difference between the tissues’ lipid contents. Fatty acid profile differences within and among lakes We used a permutational MANOVA (PERMANOVA; vegan::adonis2) with body length as a covariate to assess differences in lake trout fatty acid profiles within and among lakes. This covariate was used to control for body size differences among collections, which was especially relevant for Flathead and Swan lakes where average body lengths were lower. We used Analaysis of Similarities (ANOSIM) to provide an assessment of the degree of similarity of fatty acid profiles between the tissue types within lakes, when significant differences were indicated by PERMANOVA. Differences in fatty acid profiles among lakes then were depicted by plotting axes produced by canonical analysis of principle coordinates (CAP; BiodiversityR::CAPdiscrim; Anderson and Willis, 2003). As a constrained ordination technique, CAP acts as linear discriminant analysis using any resemblance of choice (in our case: Bray-Curtis similarities), whereas linear discriminant function analysis is restricted to Euclidean Distances. The CAP analysis also provides a compliment to PERMANOVA tests in that its ordination routine includes an iterative leave-one-out routine that classifies samples to a priori groups, thus offering an evaluation of group distinctiveness. We used similarity percentages (SIMPER) routines to identify which fatty acids contributed to differences between tissue types. To identify which fatty acids contributed to differences among lakes, fatty acid vectors (vegan::envfit) were plotted on the resulting CAP plot. Vectors depicted were limited to fatty acids with correlations > 0.6 to either the first or second axis. Effect of lipid content differences on fatty acid proportions We used a ratio analysis to evaluate how differences between lipid contents of belly flap and muscle tissues may affect our interpretations of dietary information. Ratios of fatty acids were calculated by dividing the proportional abundance of each fatty acid recovered from the belly flap by that from the muscle tissue within a fish. Lipid contents of belly flap and muscle tissues were also divided, with that of muscle tissue as the denominator.

3

Subsequently, we regressed proportions of individual fatty acids against proportions of total lipid contents. As such, positive slopes represent fatty acids whose abundances increase in belly flap as more lipids are stored in that tissue. Conversely, negative slopes indicate a fatty acid which accumulates in high proportions in muscle tissues which simultaneously are lipid poor compared to belly flaps. Regressions first were calculated without the effect of lake. If a significant regression was returned, we used a post hoc ANCOVA (Type III sum of squares) to assess the effect of lake on the regressions between fatty acid and lipid content ratios. We report the main effect regression output, along with the F and p values for the interaction term of the ANCOVAs. We limited graphic depictions to two fatty acids commonly used as indicators of pelagic resources in freshwater systems which had and significant and contrasting slopes (positive slope for 18:3n-3 and negative slope for 22:6n-3; both often used to indicate fish’s reliance on pelagic food webs). To expand upon our single fatty acid analyses of tissue type on dietary inference, we investigated the ratio of n-3 to n-6 fatty acids, which not only summarizes many fatty acids but is putatively used to trace autochthonous (i.e., pelagic) vs allochthonous (terrestrial) primary production (Lau et al., 2012a; Taipale et al., 2015). Pelagic algal resources, zooplankton (i.e., Daphnia spp. and copepods), and Mysis are typically rich in n-3 fatty acids compared to more littoral primary producers and consumers, which are generally characterized by higher proportions of n-6 fatty acids (Bell et al., 1994; Czesny et al., 2011; Lau et al., 2012b; Taipale et al., 2015, 2014; Torres-Ruiz et al., 2007). Accordingly, the ratio of n-3 to n-6 fatty acids increases with the relative importance of pelagic production in a food chain, whereas lower values may indicate more reliance on littoral food sources. Our evaluation used simple linear and curvilinear regressions to describe relationships between the n-3: n-6 ratios and lipid content of both tissues versus body length.

Results Lengths and lipid contents The lengths of fish captured in our study exhibited some variation among lakes (Table 1; ANOVA; F4,118 = 37.49, p < 0.001); accordingly, length was used as a covariate in further analyses. Larger ranges of lake trout sizes (lengths) were collected in Flathead and Swan lakes in comparison to the other lakes. Mean lengths of lake trout in Flathead and Swan were similar to each other (p = 0.34) and smaller than in other lakes (all p < 0.001). Lengths of lake trout from lakes Champlain, Michigan, and Ontario were not statistically different (all p > 0.90) from each other. Across lakes, lipid content of muscle tissue (8.43 ± 5.47; mean ± SD) was generally lower than that of belly flap (27.73 ± 11 .04) and increased with body length in the muscle tissue but not the belly flaps (Fig. 1). Using all lake trout (without accounting for potential lake differences; see next paragraph) in the beta regression model, no relationship existed between lipid content of belly flaps and lake trout lengths (betareg; p = 0.87; Fig. 1a). Conversely, muscle lipid content was correlated with length (betareg; pseudo R-squared = 0.35; p < 0.001: dashed line Fig. 1b). Full betareg outputs are provided as Electronic Supplementary Material (ESM) Appendix S1. Variation in the lipid content of both tissues sampled (belly flap and muscle) was evident among lakes (Fig. 1). Belly flap lipid contents were significantly different among lakes (Chi-Square = 106. 99; p < 0.001) with body length as a covariate (Fig. 1a). Belly flap lipid content was significantly lower in Lake Champlain relative to other lakes (all p < 0.001); belly flap lipid content in Ontario was significantly higher than Michigan (p = 0.004); and all other

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

4

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

Table 1 Total length (L; mm; mean ± 1 SD), lipid content (% ww), and average fatty acid percentages (expressed as relative percentages; ± 1 SD) of lake trout belly flaps and dorsal muscle from five North American lakes. Significantly similar values for lengths and among tissue lipid contents share letters. Fatty acids determined to be highly influential to multivariate dissimilarities among lakes are bolded for each tissue (SIMPER).

*

n L (mm) L Range

Flathead 33 547.9 ± 104.6b 358–898

Champlain 14 729 ± 56.8a 648–864

Michigan 31 730 ± 65.7a 625–850

Ontario 24 742.8 ± 56.3a 652–830

Swan 21 585.2 ± 69.9b 435–745

Tissue

Belly Flap

Muscle

Belly Flap

Muscle

Belly Flap

Muscle

Belly Flap

Muscle

Belly Flap

Muscle

Lipid (%) 14:0 15:0 16:0 16:1n-9 16:1n-7 17:0 17:1n-9 18:0 18:1n-9 18:1n-7 18:2n-6 18:3n-3 18:4n-3 20:0 20:1* 20:2n-6 20:3n-6 20:3n-3 20:4n-6 20:4n-3 20:5n-3 22:1n-11 22:4n-6 22:5n-6 22:5n-3 22:6n-3

27.4 ± 11.2b 3.8 ± 0.4 0.5 ± 0.1 13.4 ± 0.8 0.5 ± 0.1 8.5 ± 0.9 0.3 ± 0.0 0.3 ± 0.0 2.0 ± 0.8 23.8 ± 1.9 4.3 ± 0.4 5.4 ± 0.4 3.2 ± 0.3 2.1 ± 0.3 0.1 ± 0.0 2.5 ± 0.3 1.3 ± 0.2 0.4 ± 0.1 2.8 ± 0.3 0.6 ± 0.1 2.1 ± 0.2 6.9 ± 1.4 0.3 ± 0.0 0.8 ± 0.3 1.5 ± 0.3 3.5 ± 0.5 9.1 ± 1.3

4.6 ± 2.1y 2.8 ± 0.4 0.4 ± 0.1 15.1 ± 1.2 0.5 ± 0.1 7.1 ± 1.4 0.3 ± 0.0 0.2 ± 0.1 2.6 ± 0.8 22.2 ± 2.5 4.0 ± 0.6 4.4 ± 0.7 2.7 ± 0.4 1.5 ± 0.4 0.1 ± 0.0 2.3 ± 0.6 1.0 ± 0.2 0.3 ± 0.1 3.2 ± 0.6 0.5 ± 0.1 1.7 ± 0.2 7.2 ± 1.3 0.2 ± 0.0 0.5 ± 0.2 1.5 ± 0.4 3.2 ± 0.5 14.7 ± 3.8

10.4 ± 4.6c 2.8 ± 0.1 0.3 ± 0.0 12.3 ± 1.7 0.9 ± 0.1 6.5 ± 0.7 0.3 ± 0.0 0.5 ± 0.1 3.2 ± 0.3 33.0 ± 1.6 3.8 ± 0.3 3.7 ± 0.3 3.0 ± 0.4 1.0 ± 0.2 0.2 ± 0.0 2.8 ± 0.7 0.6 ± 0.1 0.5 ± 0.1 3.2 ± 0.2 0.5 ± 0.1 2.1 ± 0.3 3.5 ± 0.5 0.3 ± 0.1 1.2 ± 0.4 1.8 ± 0.4 3.7 ± 0.6 8.2 ± 1.1

5.1 ± 2.5x 2.5 ± 0.2 0.3 ± 0.0 11.7 ± 1.4 0.9 ± 0.1 6.5 ± 0.7 0.3 ± 0.0 0.5 ± 0.1 3.1 ± 0.3 33.0 ± 1.7 3.9 ± 0.3 3.5 ± 0.3 3.1 ± 0.4 1.0 ± 0.2 0.3 ± 0.4 2.8 ± 0.6 0.5 ± 0.1 0.5 ± 0.1 3.2 ± 0.2 0.5 ± 0.1 2.1 ± 0.3 3.6 ± 0.5 0.2 ± 0.0 1.1 ± 0.2 1.8 ± 0.3 3.7 ± 0.5 9.4 ± 1.1

28.3 ± 7.1ab 3.0 ± 0.3 0.3 ± 0.0 12.8 ± 1.7 0.7 ± 0.1 8.2 ± 1.6 0.3 ± 0.0 0.3 ± 0.0 3.1 ± 0.5 26.2 ± 2.4 4.8 ± 0.3 4.4 ± 0.6 2.7 ± 0.6 1.2 ± 0.4 0.2 ± 0.0 2.2 ± 0.4 1.1 ± 0.2 0.5 ± 0.1 3.7 ± 0.2 0.8 ± 0.2 2.4 ± 0.5 4.8 ± 0.6 0.3 ± 0.0 1.4 ± 0.3 2.1 ± 0.3 4.0 ± 0.6 8.6 ± 1.0

10.0 ± 3.3x 2.7 ± 0.2 0.3 ± 0.0 13.4 ± 1.6 0.7 ± 0.1 8.0 ± 1.6 0.2 ± 0.0 0.3 ± 0.0 3.2 ± 0.4 25.9 ± 2.2 4.8 ± 0.3 4.2 ± 0.6 2.6 ± 0.5 1.1 ± 0.3 0.1 ± 0.0 2.2 ± 0.3 1.0 ± 0.2 0.5 ± 0.1 3.8 ± 0.2 0.8 ± 0.2 2.2 ± 0.5 4.9 ± 0.6 0.2 ± 0.0 1.1 ± 0.2 1.9 ± 0.4 3.7 ± 0.7 10.1 ± 1.0

36.1 ± 7.7a 3.1 ± 0.3 0.4 ± 0.0 11.8 ± 1.7 1.0 ± 0.1 7.1 ± 1.2 0.4 ± 0.1 0.5 ± 0.0 2.8 ± 0.5 25.6 ± 1.6 4.0 ± 0.3 4.8 ± 0.4 3.6 ± 0.4 1.4 ± 0.3 0.2 ± 0.0 2.7 ± 0.5 1.1 ± 0.1 0.5 ± 0.1 3.7 ± 0.2 1.1 ± 0.2 2.8 ± 0.4 4.9 ± 0.6 0.3 ± 0.0 1.4 ± 0.2 1.8 ± 0.2 4.4 ± 0.6 8.4 ± 0.9

14.0 ± 7.3x 2.8 ± 0.2 0.4 ± 0.0 12.5 ± 1 0.9 ± 0.1 7.0 ± 1.0 0.3 ± 0.0 0.5 ± 0.0 2.7 ± 0.2 25.8 ± 1.2 4.0 ± 0.2 4.5 ± 0.4 3.4 ± 0.4 1.2 ± 0.3 0.2 ± 0.1 2.7 ± 0.9 1.0 ± 0.1 0.4 ± 0.1 3.8 ± 0.2 1.1 ± 0.2 2.6 ± 0.3 5.1 ± 0.5 0.2 ± 0.0 1.1 ± 0.3 1.7 ± 0.2 4.1 ± 0.6 9.8 ± 1.2

29.0 ± 8.2ab 3.2 ± 0.6 0.4 ± 0.1 13.8 ± 2.5 0.5 ± 0.1 7.6 ± 0.9 0.3 ± 0.0 0.3 ± 0.1 3.5 ± 0.6 23.4 ± 2.2 4.2 ± 0.5 5.2 ± 0.5 3.1 ± 0.3 1.8 ± 0.3 0.2 ± 0.0 1.9 ± 0.3 1.2 ± 0.1 0.4 ± 0.0 2.8 ± 0.3 0.5 ± 0.1 2.4 ± 0.4 5.1 ± 0.9 0.2 ± 0.0 0.8 ± 0.2 2.0 ± 0.4 3.2 ± 0.4 12.0 ± 1.4

9.0 ± 3.4y 2.5 ± 0.3 0.3 ± 0.0 15.6 ± 1.1 0.5 ± 0.1 6.3 ± 0.9 0.3 ± 0.0 0.3 ± 0.0 3.8 ± 0.4 21.2 ± 2 3.9 ± 0.4 4.3 ± 0.5 2.6 ± 0.4 1.4 ± 0.3 0.1 ± 0.0 1.6 ± 0.4 1.0 ± 0.1 0.4 ± 0.0 3.0 ± 0.2 0.5 ± 0.1 2.1 ± 0.5 5.2 ± 0.8 0.2 ± 0.0 0.6 ± 0.2 2.1 ± 0.3 2.8 ± 0.4 17.5 ± 2.3

Co-eluted 20:1n-9 and 11 peaks summed together

belly flap comparisons were not statistically significant (all p > 0.05; Table 1). Muscle lipid contents were also different among lakes (Chi-Square = 46.1946; p < 0.001) with length as a covariate. Muscle tissue lipid content was generally lower in Flathead and Swan than other lakes (all p < 0.05); muscle tissue lipid content in Champlain was lower than Ontario (p = 0.39); whereas muscle lipid content in Michigan did not differ from Champlain or Ontario (p > 0.05). Relationships between fatty acid profiles and lipid content Bray-Curtis similarities between the fatty acid profiles correlated with lipid content of each tissue type (betareg; Fig. 2). Generally, the fatty acid profiles of both tissues were more similar as their lipid content increased (Fig. 2). For each analysis, a significant interaction occurred between lake and the lipid content predictor (whether muscle, belly flap, or the difference between the two). Without the lake effect, the similarity between fatty acid profiles of the tissues generally decreased as the difference in lipid content between tissues increased, this was plotted as a dashed line in Fig. 2c. Full betareg outputs are available in ESM Appendix S1. Fatty acid profile differences within and among lakes

Fig. 1. Scatter plot of lake trout body length (mm) vs. lipid content (% ww) of belly flap (a.) and muscle tissue (b.) from five North American lakes. Lipid contents of lake trout differed primarily by lake (solid lines), although lipid content of muscle tissue correlated positively with length when fish from all lakes were pooled (dashed line).

Within lake variation in muscle and belly flap fatty acid profiles differed among lakes (Table 1; Fig. 3a & 3b). No significant differences existed between tissue types in Lake Champlain (PERMANOVA; t1,25 = 1.35, p = 0.11). Significant differences between tissues existed in Lake Ontario and Lake Michigan (PERMANOVA; t1,45 = 2.59, p < 0.001 and t1,59 = 2.07, p = 0.008, respectively), but

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

5

Fig. 2. Bray-Curtis similarity between belly flap and muscle tissue fatty acid profiles plotted against lipid content of the respective tissue (a. & b.) and the difference in lipid content between the tissues (c.). Muscle lipid content showed the strongest relationship with fatty acid similarity between tissues wherein values > ~10% were most similar. Belly flap lipid content was less predictive of how similar fatty acid profiles were between tissues, which generally decreased with greater differences between tissue lipid contents. Symbols match those in Fig. 1, except panel c. includes a dashed line representing the overall trend.

Table 2), illustrating that fatty acid profiles were distinctive in each lake regardless of tissue type. Misclassified individuals from Flathead and Swan were assigned to the other respective lake, except two larger (lengths 720 and 777 mm) fish from Flathead were misclassified to Michigan with the CAP classification using muscle tissue, but not when belly flap was used. Fatty acids that describe differences in lake trout fatty acid profiles among lakes included 18:1n-9, 18:2n-6, 18:4n-3, 20:3n-3, 20:4n-6, 20:5n-3, and 22:6n-3. Proportions of 18:1n-9 were higher in lake trout from Lake Champlain (Fig. 3), regardless of tissue (Table 1). Proportions of 20:4n-6 and 20:3n-3 were higher, and 22:6n-3 lower in lake trout from the Great Lakes compared to Montana lakes regardless of tissue type. Proportions of 18:2n-6, 18:4n-3, and 20:5n-3 were higher in lake trout from Montana than other lakes (Tables 1; Fig. 3). Effect of lipid content differences on fatty acid proportions

Fig. 3. Canonical analysis of principle coordinates plot of fatty acid profiles from muscle (a.) and belly flap (b.) of lake trout. Vectors correlated (multiple correlations) with axes with  ± 0.60 correlations are plotted below the respective graph (c. & d.). Symbols match those in Fig. 1.

overlap in fatty acid profile similarities was relatively high (ANOSIM; R = 0.14, p = 0.002 and R = 0.06, p = 0.02, respectively). In both Flathead and Swan lakes, fatty acid profiles between tissues were significantly different (PERMANOVA; t1,63 = 6.20, p < 0.001 and t1,39 = 5.41, p < 0.001, respectively) and relatively distinct (ANOSIM; R = 0.42, p < 0.001 and R = 0.59, p < 0.001, respectively). Of the difference between tissues in Flathead and Swan Lakes, > 50% was accounted for by differences in proportions of 16:0, 16:1n-7, 18:1n-9, and 22:6n-3 (SIMPER; Table 1). 16:0 and 22:6n-3 were higher in muscle samples while 16:1n-7 and 18:1n-9 were higher in belly flap samples from both Flathead and Swan lakes. Fatty acid profiles of lake trout were significantly distinct in each lake, within both muscle (PERMANOVA; all pairwise p < 0.001) and belly flap (PERMANOVA; all pairwise p < 0.001). Classification rates were similar and relatively high for both muscle and belly flap (misclassified n = 8 and n = 4 respectively;

Significant linear relationships existed between ratios of fatty acids and ratios of lipid content between tissue types for 19 of the 26 fatty acids included in our study (Table 3), and these patterns appear consistent among lakes (non-significant interaction terms). Significant negative slopes imply accumulation of a fatty acid in muscle tissue as lipid content of muscle decreased compared to belly flap. Higher (>0.20) r2 values were returned for 22:6n-3, 18:0, 14:0, 20:4n-6, and 18:3n-3. Few (2 out of 26) returned significant ANCOVA interactions between regressions and lake of origin; however, these were due to an outlier in the Lake Michigan data and was alleviated when this fish was removed. As fatty acids 18:3n-3 and 22:6n-3 are used as indicators of fishes utilization of pelagic resources (i.e., Strandberg et al., 2015) yet contrastingly distributed between belly flap and muscle relative to the lipid content ratio between tissues; we depicted their regressions using scatterplots (Fig. 4). The belly flap to muscle ratio of 18:3n-3 showed a positive relationship to the corresponding lipid content ratio (Fig. 4a), while 22:6n-3 showed a negative trend (Fig. 4b). Significant negative linear relationships existed between the ratio of n-3 to n-6 fatty acids in both tissues and the length of lake trout (p < 0.001; Fig. 5b & 5d). A negative curvilinear relationship existed between n-3:n-6 and non-transformed muscle lipid content (p < 0.001; Fig. 5c). Conversely, no relationship existed between n-3:n-6 ratios and lipid content of belly flap tissues (p = 0.09). Particularly of interest in Fig. 5b & 5d was the wide

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

6

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

Table 2 Geographic classification of lake trout based on fatty acid profiles of belly flap and muscle tissue. Columns denote tissue type, lake of origin, and number of fish classified to each lake. Classification table produced from canonical analysis of principal coordinates on fatty acid profiles of each tissue. Classified as: Tissue

Origin

Flathead

Champlain

Michigan

Ontario

Swan

Muscle

Flathead Champlain Michigan Ontario Swan

27 0 0 0 2

0 14 0 0 0

2 0 31 0 0

0 0 0 24 0

4 0 0 0 19

Flathead Champlain Michigan Ontario Swan

32 0 0 0 3

0 14 0 0 0

0 0 31 0 0

0 0 0 19 0

1 0 0 0 18

Belly Flap

Table 3 Multiple fatty acids are found in greater quantities in muscle tissues as muscle lipid content decreases (negative slopes). Many other fatty acids accumulate in greater quantities in belly flap tissues when belly flap lipid content is much higher than that of muscle (positive slope). Regressions analyzed the linear relationship between ratios comparing proportions of fatty acids and lipid content in belly flaps vs muscle (quantity in belly flap divided by quantity in muscle). A post hoc interaction effect (ANCOVA) of lake evaluated to ensure relationships were consistent across lakes. Columns represent the outputs of both the linear regression model and the post hoc ANCOVA for each fatty acid. Linear Regression Output

*

ANCOVA Interaction Term 2

Fatty Acid

Slope

F

p

r

14:0 15:0 16:0 16:1n-9 16:1n-7 17:0 17:1 18:0 18:1n-9 18:1n-7 18:2n-6 18:3n-3 18:4n-3 20:0 20:1n* 20:2n-6 20:3n-6 20:4n-6 20:3n-3 20:4n-3 20:5n-3 22:1n-11 22:4n-6 22:5n-6 22:5n-3 22:6n-3

0.04 0.04 0.01 0.03 0.03 0.00 0.05 0.04 0.01 0.01 0.03 0.03 0.04 0.03 0.01 0.02 0.02 0.01 0.02 0.02 0.00 0.03 0.11 0.02 0.00 0.03

44.19 32.17 19.97 7.32 16.35 0.16 12.34 55.99 20.33 8.15 24.72 35.25 25.28 7.87 2.79 6.95 20.05 38.58 8.08 21.3 0.67 3.91 0.53 1.94 0.02 86.82

<0.01 <0.01 <0.01 0.01 <0.01 0.69 <0.01 <0.01 <0.01 0.01 <0.01 <0.01 <0.01 0.01 0.1 0.01 <0.01 <0.01 0.01 <0.01 0.42 0.05 0.47 0.17 0.88 <0.01

0.27 0.21 0.14 0.06 0.12 0.00 0.09 0.32 0.14 0.06 0.17 0.23 0.17 0.06 0.02 0.05 0.14 0.24 0.06 0.15 0.01 0.03 0.00 0.02 0.00 0.42

F

p

1.76 1.82 1.84 0.73 1.34 1.91 0.90 0.69 2.05 1.59 1.40 1.59 0.58 0.62 1.31 1.27 2.06 0.91 1.10 1.12 1.59 1.99 1.02 3.49 3.48 2.15

0.14 0.13 0.13 0.57 0.26 0.11 0.47 0.60 0.09 0.18 0.24 0.18 0.68 0.65 0.27 0.29 0.09 0.46 0.36 0.35 0.18 0.10 0.40 0.01 0.01 0.08

Co-eluted 20:1n-9 and 11 peaks summed together.

vertical spread in points corresponding to lake trout from Flathead Lake that were between 500 and 600 mm. Discussion Fatty acid profiles of 123 lake trout from five lake ecosystems across North America indicated that dorsal muscle and belly flap samples can provide similar fatty acid profiles, if the lipid content of muscle tissue is high enough (i.e., > ~10%; Fig. 2). At lower lipid levels, fatty acid profiles of the muscle samples contained greater proportions of fatty acids known to be prevalent in phospholipids (e.g., 16:0) which do not reflect diets well (Böhm et al., 2014; Budge et al., 2006; Iverson et al., 2004; Lazzarotto et al., 2015). Similarly, Schultz et al. (2018) found that fatty acid profiles of common carp (Cyprinus carpio) muscle tissue corresponded better to dietary profiles when fed higher lipid diets, which in turn increased muscle lipid content. To avoid issues with lipid content related

biases in fatty acid composition, we suggest that belly flap samples provide data that are more reflective of dietary origins for trophic studies using fatty acid profiles. Our study is the first to our knowledge to compare fatty acid distributions between tissues to their corresponding lipid contents from several wild fish populations. Kainz et al. (2017) illustrated that regardless of species analyzed, total n-3 and n-6 polyunsaturated fatty acids (mg FA g1 lipid) increased with lipid content (mg g1 dry mass) in the muscle tissue of fish from a Siberian reservoir. Similar to the feeding study presented by Böhm et al. (2014), we found that several individual fatty acids accumulated in lipid-dependent fashions specific to each tissue. Large differences in fatty acid proportions among tissues could surpass differences among species, suggesting tissue-specific interpretations of diet composition may occur. For example, the ranges of some fatty acids in lake trout herein surpass those reported among Great Lakes prey fish species (Czesny et al., 2011; Happel et al., 2017c).

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

Fig. 4. Examples of fatty acids that either accumulate in belly flap tissue (positive slope; top panel) or accumulate in muscle tissues (negative slope; bottom panel) as belly flap lipid content increases relative to that of muscle. Regression analysis outputs are located in Table 4. Ratios were generated by dividing the quantity of a variable in belly flap samples by its quantity in muscle tissue of the same fish. Symbols match those in Fig. 1.

As it is difficult to parse apart if dorsal muscle tissues of wild fish have different fatty acid profiles from belly flap due to differences in total lipid content or diet, for trophic studies we recommend the use of belly flap due to lower correlations between fatty acid profiles and both length and lipid status. The dissimilarity between fatty acid profiles of dorsal muscle and belly flap at low muscle lipid contents (<~10%) likely reflect the increasing proportion of phospholipids compared to neutral lipids, which more rapidly and accurately represent dietary lipids (Böhm et al., 2014; Lazzarotto et al., 2015; Sargent et al., 1993). For example, both 16:0 and 22:6n-3 are important components of phospholipid-rich cell membranes and are likely conserved in tissues as lipid levels decrease (Tocher, 2003). Where differences between fatty acid profiles of tissues were greatest, Flathead and Swan lakes, proportions of 16:0 and 22:6n-3 were higher in muscle tissue of smaller individuals with correspondingly lower lipid contents. Furthermore, the muscle samples showed a stronger curvilinear correlation between n-3:n-6 ratios and lipid contents than was shown with belly flap samples, suggesting a physiological control of the fatty acid profiles in muscle that is not related to dietary sources. For example, fasting for 27 days has been shown to induce

7

fatty acid mobilization, altering fatty acid profiles of rainbow trout (Oncorhynchus mykiss) muscle, liver, and viscera (Jezierska et al., 1982). Salmonids are known to fast in the weeks prior to spawning, however fat content of belly flaps is conserved during this period compared to dorsal muscle, liver, and viscera which apparently act as metabolic fuel sources (Einen et al., 1998; Weil et al., 2013). As such, when inferring trophic relations from fatty acid profiles it is advisable to sample areas with high concentrations of triacylglycerides (i.e., belly flaps of salmonids). For larger, fattier, lake trout, a high degree of similarity existed between profiles of fatty acids generated from lake trout muscle tissues and more lipid-rich belly flaps. Similarly, above a lipid content of 6.0% dry mass, there was no increase in similarity between dorsal muscle tissue and dietary fatty acid profiles for common carp reared in ponds (Schultz et al., 2018). For Atlantic salmon, fatty acid profiles generated from belly flaps were more reflective of artificial diets than those from muscle tissues; interestingly, both tissues contained relatively low (~3.5% ww) lipid concentrations (Budge et al., 2011). Herein, a high degree of similarity was found between fatty acid profiles of belly flap and muscle from large, fatty, wild lake trout, suggesting that when tissues are rich in lipids, relatively similar dietary information can be derived. Regardless of the tissue sampled, fatty acid profiles of lake trout varied among lakes. Prey bases in lakes Michigan and Ontario are nearly identical, being composed primarily of alewife and round goby (USGS., 2018), and this is reflected in recent lake trout diet studies (Happel et al., 2017c, 2017b; Luo et al., 2019). In an apparent and expected response, fatty acid profiles of lake trout from lake Michigan and Ontario cluster together in ordinations, indicating similar diet compositions. Fatty acid profiles of lake trout from Champlain, were distinguished by low belly flap lipid contents and a large accumulation of 18:1n-9, a fatty acid thought to increase with trophic position (Kelly and Scheibling, 2012). This accumulation is consistent with lower 16:1n-7 and 18:2n-6, fatty acids noted to be in higher proportions when fish consume chironomids and other benthic invertebrates with a relatively low trophic position (Foley et al., 2016; Happel et al., 2016, 2015). The emergent view is that Champlain lake trout rely heavily on pelagic fishes, consistent with the available diet information (Fisheries Technical Committee, 2009; Simonin et al., 2018). The Montana collections provide insights into dietary differences associated with life history type. Collections from the southern half of Flathead Lake (Stafford et al., 2014) have shown that two major life history types are present: leans (common lake trout) and dwarfs (akin to humpers, see: Eshenroder, 2008). Swan Lake, in contrast, contains only leans (Cox, 2010; Stafford et al., 2014). Small lake trout in both lakes rely primarily on Mysis, and the Swan fish become piscivorous ~500 mm, but at larger sizes they rely primarily (>80% of fish prey, numerically) on kokanee (Beauchamp et al., 2006; Guy et al., 2011). In Flathead Lake, the dwarf ecotype continues to consume Mysis throughout life, but do eat some fishes (~25% of diet, by mass) based on collections of lake trout 451– 600 mm from water > 59 m deep (Stafford et al., 2014). A larger ontogenic diet shift is evident in the Flathead leans as they shift from Mysis to a varied diet (chironomids and fish) at 451– 600 mm and then are primarily piscivorous above 625 mm. Overall, the major fish prey species of lake trout in Flathead Lake are lake whitefish (Coregonus clupeaformis), yellow perch, pygmy whitefish (Prosopium coulterii), and lake trout (i.e. cannibalism) (Beauchamp et al., 2006; Stafford et al., 2014). The increased variation in n-3:n-6 ratios of Flathead Lake fish beginning at ~500 mm (Fig. 5b and d) is consistent with the continued consumption of Mysis by the dwarfs and the shift to inshore foods by the leans. Lake trout from Swan Lake, in contrast, show more even distribution of fatty acid profiles with body lengths 500 to 600 mm, consistent with a single life history type that uses a modest variety of

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

8

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

Fig. 5. Ratios of n-3 to n-6 fatty acids of lake trout putatively used to indicate reliance on nearshore versus offshore production (lower ratio = more nearshore/terrestrial) versus lipid content of the tissues (a. = belly flap and c. = muscle) and fish length (b. = belly flap and d. = muscle). Significant regressions are shown. Of note is the wide vertical spread of points from Flathead Lake between 500 and 600 mm, a size range where that population is thought to diverge into differing ecotypes.

food sources throughout its size range, similar to most Great Lake populations. Although lake trout from Swan Lake transition to fish forage at a smaller size than those from Flathead Lake, this is not evident in the n-3:n-6 ratio. A potential explanation is that lake trout fish forage is dominated by zooplanktivorous (i.e., pelagic feeding) kokanee in Swan Lake, a species that is nearly absent from Flathead Lake (Ellis et al. 2011). Concomitant analysis of stable isotopes, which are used to study species’ trophic relationships, could aide in resolving some current questions pertaining to fatty acids as dietary indicators. Compared to stable isotopes, fatty acids offer a larger number of tracers, but they reflect dietary origins to varying degrees. Further, some fatty acids are modified by various endogenous processes (e.g., biosynthesis, selective retention, etc.) to account for diets lacking certain fatty acids, simultaneously obscuring interpretations of trophic relationships while offering insights that other methods may not allow (Böhm et al., 2014; Happel et al., 2016; Lazzarotto et al., 2015; Murray et al., 2014; Schultz et al., 2018). The rates at which these processes occur, the fatty acids involved, and the connections to diet compositions remain unclear (Sawyer et al., 2016). Similar diet-specific issues are currently being raised within stable isotope studies which have a longer history of use in ecology (e.g., Britton and Busst, 2018; Dodds et al., 2014; Gorokhova, 2018). Thus, pairing stable isotope and fatty acid analyses with each other and/or with traditional direct observational methods (i.e., stomach contents) is recommended to aid in further establishing specific trophic markers. In conclusion, our analyses indicate that care should be taken when selecting tissue types when using fatty acids for trophic investigations. For our lake trout, proportions of fatty acids from dorsal muscle had strong correlations and curvilinear relationships with the lipid content of the tissue. Samples taken from the more

lipid rich belly flap area of the body did not have similar patterns and thus is thought to more accurately reflect dietary origins. Separation of neutral and phospholipid fractions may provide a work around for studies taking only samples of dorsal musculature. Continued examination of factors affecting fatty acid variation within individuals and populations is critical before management actions should rely on trophic interpretations from fatty acid data alone.

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements We wish to thank the Illinois Department of Natural Resources, the crew led by Ellen Marsden of the University of Vermont, Montana Fish, Wildlife & Parks, and the Confederated Salish-Kootenai Tribes for supplying fish for this project. We further thank the numerous technicians and undergraduates that helped with sample collection and processing. We extend thanks to two anonymous reviewers who helped improve the manuscript. All work was conducted in accordance to IACUC standards.

Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.jglr.2019.10.015.

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

References Anderson, M.J., Willis, T.J., 2003. Canonical analysis of principal coordinates: a useful method of constrained ordination for ecology. Ecology 84, 511–525. Beauchamp, D.A., Kershner, M.W., Overman, N.C., Rhydderch, J., Lin, J., Hauser, L., 2006. Trophic interactions of nonnative lake trout and lake whitefish in the Flathead Lake food beb, Final Report to the Salish and Kootenai Tribes, Pablo, Montana. Beckmann, C.L., Mitchell, J.G., Seuront, L., Stone, D.A.J., Huveneers, C., 2013. Experimental evaluation of fatty acid profiles as a technique to determine dietary composition in benthic elasmobranchs. Physiol. Biochem. Zool. 86, 266– 278. https://doi.org/10.1086/669539. Bell, J.G., Ghioni, C., Sargent, J.R., 1994. Fatty acid compositions of 10 freshwater invertebrates which are natural food organisms of Atlantic salmon parr (Salmo salar): a comparison with commercial diets. Aquaculture 128, 301–313. Böhm, M., Schultz, S., Koussoroplis, A.M., Kainz, M.J., 2014. Tissue-specific fatty acids response to different diets in common carp (Cyprinus carpio L.) e94759 PLoS One 9. https://doi.org/10.1371/journal.pone.0094759. Britton, J.R., Busst, G.M.A., 2018. Stable isotope discrimination factors of omnivorous fishes: influence of tissue type, temperature, diet composition and formulated feeds. Hydrobiologia 808, 219–234. https://doi.org/10.1007/s10750-017-34239. Budge, S.M., Iverson, S.J., Bowen, W.D., Ackman, R.G., 2002. Among-and withinspecies variability in fatty acid signatures of marine fish and invertebrates on the Scotian Shelf, Georges Bank, and southern Gulf of St Lawrence. Can. J. Fish. Aquat. Sci. 59, 886–898. Budge, S.M., Iverson, S.J., Koopman, H.N., 2006. Studying trophic ecology in marine ecosystems using fatty acids: a primer on analysis and interpretation. Mar. Mammal Sci. 22, 759–801. https://doi.org/10.1111/j.1748-7692.2006.00079.x. Budge, S.M., Penney, S.N., Lall, S.P., 2011. Response of tissue lipids to diet variation in Atlantic salmon (Salmo salar): Implications for estimating diets with fatty acid analysis. J. Exp. Mar. Bio. Ecol. 409, 267–274. Cox, B.S., 2010. Assessment of an invasive lake trout population in Swan Lake. Montana State University-Bozeman, College of Letters & Science, Montana. Cribari-Neto, F., Zeileis, A., 2010. Beta regression in R. J. Stat. Softw. 34, 1–24. Czesny, S.J., Rinchard, J., Hanson, S.D., Dettmers, J.M., Dabrowski, K., 2011. Fatty acid signatures of Lake Michigan prey fish and invertebrates: among-species differences and spatiotemporal variability. Can. J. Fish. Aquat. Sci. 68, 1211– 1230. https://doi.org/10.1139/f2011-048. Dietrich, J.P., Morrison, B.J., Hoyle, J.A., 2006. Alternative ecological pathways in the Eastern Lake Ontario food web-round goby in the diet of lake trout. J. Great Lakes Res. 32, 395–400. Dodds, W.K., Collins, S.M., Hamilton, S.K., Tank, J.L., Johnson, S., Webster, J.R., Simon, K.S., Whiles, M.R., Rantala, H.M., McDowell, W.H., 2014. You are not always what we think you eat: selective assimilation across multiple whole-stream isotopic tracer studies. Ecology 95, 2757–2767. Einen, O., Waagan, B., Thomassen, M.S., 1998. Starvation prior to slaughter in Atlantic salmon (Salmo salar): I. Effects on weight loss, body shape, slaughterand fillet-yield, proximate and fatty acid composition. Aquaculture 166, 85– 104. Ellis, B.K., Stanford, J.A., Goodman, D., Stafford, C.P., Gustafson, D.L., Beauchamp, D. A., Chess, D.W., Craft, J.A., Deleray, M.A., Hansen, B.S., 2011. Long-term effects of a trophic cascade in a large lake ecosystem. Proc. Natl. Acad. Sci. U.S.A. 108, 1070–1075. Ellrott, B.J., Marsden, J.E., 2004. Lake trout reproduction in Lake Champlain. Trans. Am. Fish. Soc. 133, 252–264. Eshenroder, R.L., 2008. Differentiation of deep-water lake charr (Salvelinus namaycush) in North American lakes. Environ. Biol. Fishes 83, 77–90. Fengle, Z., Jiyu, P., Junfeng, G., Yanru, Z., Keqiang, Y., Yong, H., 2012. Determination and visualization of fat contents in salmon fillets based on visible and nearinfrared hyperspectral imagery. Trans. Chinese Soc. Agric. Eng. 30. https://doi. org/10.3969/j.issn.1002-6819.2014.23.040. Fisheries Technical Committee, 2009. Strategic Plan for Lake Champlain Fisheries. Lake Champlain Fish Wildl. Manag. Coop. USFWS. Folch, J., Lees, M., Sloane-Stanley, G.H., 1957. A simple method for the isolation and purification of total lipids from animal tissues. J. Biol. Chem. 226, 497–509. Foley, C., Hook, T., Happel, A., Bootsma, H., Czesny, S.J., Janssen, J., Jude, D., Rinchard, J., Henebry, M.L., 2016. Patterns of integration of invasive round (Neogobius melanostomus) into a nearshore food web. Food Webs. https://doi.org/10.1016/ j.fooweb.2016.10.001. Fredenberg, W., 2002. Further evidence that lake trout displace bull trout in mountain lakes. Intermt. J. Sci. 8, 143–152. Galloway, A.W., Brett, M.T., Holtgrieve, G.W., Ward, E.J., Ballantyne, A.P., Burns, C. W., Kainz, M.J., Muller-Navarra, D.C., Persson, J., Ravet, J.L., Strandberg, U., Taipale, S.J., Alhgren, G., 2015. A fatty acid based bayesian approach for inferring diet in aquatic consumers e0129723 PLoS One 10. https://doi.org/10.1371/ journal.pone.0129723. Gorokhova, E., 2018. Individual growth as a non-dietary determinant of the isotopic niche metrics. Methods Ecol. Evol. 9, 269–277. https://doi.org/10.1111/2041210X.12887. Guy, C.S., McMahon, T.E., Fredenberg, W.A., Smith, C.J., Garfield, D.W., Cox, B.S., 2011. Diet overlap of top-level predators in recent sympatry: bull trout and nonnative lake trout. J. Fish Wildl. Manag. 2, 183–189. Happel, A., Creque, S., Rinchard, J., Jude, D., Höök, T., Janssen, J., Bootsma, H., Czesny, S., 2015. Exploring yellow perch diets in Lake Michigan through stomach

9

content, fatty acids, and stable isotope ratios. J. Great Lakes Res. 41, 172–178. https://doi.org/10.1016/j.jglr.2015.03.025. Happel, A., Czesny, S., Rinchard, J., Hanson, S.D., 2017a. Data pre-treatment and choice of resemblance metric affect how fatty acid profiles depict known dietary origins. Ecol. Res. 32, 757–767. https://doi.org/10.1007/s11284-0171485-9. Happel, A., Jonas, J.L., McKenna, P.R., Rinchard, J., He, J.X., Czesny, S.J., 2017b. Spatial variability of lake trout diets in Lakes Huron and Michigan revealed by stomach content and fatty acid profiles. Can. J. Fish. Aquat. Sci. 75, 95–105. https://doi. org/10.1139/cjfas-2016-0202. Happel, A., Pattridge, R., Walsh, M., Rinchard, J., 2017c. Assessing diet compositions of Lake Ontario predators using fatty acid profiles of prey fishes. J. Great Lakes Res. 43, 838–845. https://doi.org/10.1016/j.jglr.2016.12.008. Happel, A., Stratton, L., Pattridge, R., Rinchard, J., Czesny, S., 2016. Fatty-acid profiles of juvenile lake trout reflect experimental diets consisting of natural prey. Freshw. Biol. 61, 1466–1476. https://doi.org/10.1111/fwb.12786. Henderson, R.J., Tocher, D.R., 1987. The lipid composition and biochemistry of freshwater fish. Prog. Lipid Res. 26, 281–347. Honeyfield, D.C., Hinterkopf, J.P., Fitzsimons, J.D., Tillitt, D.E., Zajicek, J.L., Brown, S. B., 2005. Development of thiamine deficiencies and early mortality syndrome in lake trout by feeding experimental and feral fish diets containing thiaminase. J. Aquat. Anim. Health 17, 4–12. Iverson, S.J., 2009. Tracing aquatic food webs using fatty acids: from qualitative indicators to quantitative determination. In: Brett, M.T., Kainz, M. (Eds.), Lipids in Aquatic Ecosystems. Springer, New York, N.Y, pp. 281–308. Iverson, S.J., Field, C., Don Bowen, W., Blanchard, W., 2004. Quantitative fatty acid signature analysis: a new method of estimating predator diets. Ecol. Monogr. 74, 211–235. Jacobs, G.R., Madenjian, C.P., Bunnell, D.B., Holuszko, J.D., 2010. Diet of lake trout and burbot in northern Lake Michigan during spring: evidence of ecological interaction. J. Great Lakes Res. 36, 312–317. Jezierska, B., Hazel, J.R., Gerking, S.D., 1982. Lipid mobilization during starvation in the rainbow trout, Salmo gairdneri Richardson, with attention to fatty acids. J. Fish Biol. 21, 681–692. https://doi.org/10.1111/j.1095-8649.1982.tb02872.x. Jobling, M., Larsen, A.V., Andreassen, B., Sigholt, T., Olsen, R.L., 2002. Influence of a dietary shift on temporal changes in fat deposition and fatty acid composition of Atlantic salmon post-smolt during the early phase of seawater rearing. Aquac. Res. 33, 875–889. Kainz, M.J., Hager, H.H., Rasconi, S., Kahilainen, K.K., Amundsen, P., Hayden, B., 2017. Polyunsaturated fatty acids in fishes increase with total lipids irrespective of feeding sources and trophic position. Ecosphere 8. e01753. Kelly, J.R., Scheibling, R.E., 2012. Fatty acids as dietary tracers in benthic food webs. Mar. Ecol. Prog. Ser. 446, 1–22. Kinsella, J.E., Shimp, J.L., Mai, J., Weihrauch, J., 1977. Fatty acid content and composition of freshwater finfish. J. Am. Oil Chem. Soc. 54, 424–429. Lau, D.C.P., Vrede, T., Pickova, J., Goedkoop, W., 2012a. Fatty acid composition of consumers in boreal lakes - variation across species, space and time. Freshw. Biol. 57, 24–38. https://doi.org/10.1111/j.1365-2427.2011.02690.x. Lau, D.C.P., Vrede, T., Pickova, J., Goedkoop, W., 2012b. Fatty acid composition of consumers in boreal lakes–variation across species, space and time. Freshw. Biol. 57, 24–38. Lazzarotto, V., Corraze, G., Leprevost, A., Quillet, E., Dupont-Nivet, M., Médale, F., 2015. Three-year breeding cycle of rainbow trout (Oncorhynchus mykiss) fed a plant-based diet, totally free of marine resources: consequences for reproduction, fatty acid composition and progeny survival. PLoS One 10. Luo, M.K., Madenjian, C.P., Diana, J.S., Kornis, M.S., Bronte, C.R., 2019. Shifting Diets of Lake Trout in Northeastern Lake Michigan. North Am. J. Fish. Manag. 39, 793–806. Maazouzi, C., Masson, G., Izquierdo, M.S., Pihan, J.-C., 2007. Fatty acid composition of the amphipod Dikerogammarus villosus: feeding strategies and trophic links. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 147, 868–875. https://doi. org/10.1016/j.cbpa.2007.02.010. Madenjian, C.P., DeSorcie, T.J., Stedman, R.M., 1998. Ontogenic and spatial patterns in diet and growth of lake trout in Lake Michigan. Trans. Am. Fish. Soc. 127, 236–252. https://doi.org/Doi 10.1577/1548-8659(1998)127<0236:Oaspid>2.0. Co;2 Metcalfe, L.D., Schmitz, A.A., 1961. The rapid preparation of fatty acid esters for gas chromatographic analysis. Anal. Chem. 33, 363–364. Murray, D.S., Hager, H., Tocher, D.R., Kainz, M.J., 2014. Effect of partial replacement of dietary fish meal and oil by pumpkin kernel cake and rapeseed oil on fatty acid composition and metabolism in Arctic charr (Salvelinus alpinus). Aquaculture 431, 85–91. Nanton, D.A., Vegusdal, A., Rørå, A.M.B., Ruyter, B., Baeverfjord, G., Torstensen, B.E., 2007. Muscle lipid storage pattern, composition, and adipocyte distribution in different parts of Atlantic salmon (Salmo salar) fed fish oil and vegetable oil. Aquaculture 265, 230–243. https://doi.org/10.1016/j.aquaculture.2006.03.053. Napolitano, G.E., 1999. Fatty acids as trophic and chemical markers in freshwater ecosystems, in: Arts and Wainman, B. C, M.T. (Ed.), Lipids in Freshwater Ecosystems. Springer, New York, N.Y, pp. 21–44. Parrish, C.C., Nichols, P.D., Pethybridge, H., Young, J.W., 2015. Direct determination of fatty acids in fish tissues: quantifying top predator trophic connections. Oecologia 177, 85–95. Persson, J., Vrede, T., 2006. Polyunsaturated fatty acids in zooplankton: variation due to taxonomy and trophic position. Freshw. Biol. 51, 887–900. https://doi. org/10.1111/j.1365-2427.2006.01540.x. R Development Core Team, 2019. R: A language and environment for statistical computing.

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015

10

A. Happel et al. / Journal of Great Lakes Research xxx (xxxx) xxx

Riley, S.C., Rinchard, J., Honeyfield, D.C., Evans, A.N., Begnoche, L., 2011. Increasing thiamine concentrations in Lake Trout eggs from Lakes Huron and Michigan coincide with low Alewife abundance. North Am. J. Fish. Manag. 31, 1052–1064. Rudy, M.D., Kainz, M.J., Graeve, M., Colombo, S.M., Arts, M.T., 2016. Handling and storage procedures have variable effects on fatty acid content in fishes with different lipid quantities e0160497 PLoS One 11. https://doi.org/10.1371/ journal.pone.0160497. Sargent, J.R., Bell, J.G., Bell, M.V., Henderson, R.J., Tocher, D.R., 1993. The metabolism of phospholipids and polyunsaturated fatty acids in fish. Aquac. Fundam. Appl. Res., 103–124 Sawyer, J.M., Arts, M.T., Arhonditsis, G., Diamond, M.L., 2016. A general model of polyunsaturated fatty acid (PUFA) uptake, loss and transformation in freshwater fish. Ecol. Modell. 323, 96–105. Schultz, S., Koussoroplis, A.-M., Kainz, M.J., 2018. Dietary fatty-acid compositions are more strongly reflected in fatty than lean dorsal fillets of Common Carp (Cyprinus carpio L.). Lipids 53, 727–735. https://doi.org/10.1002/lipd.12080. Simonin, P.W., Rudstam, L.G., Parrish, D.L., Pientka, B., Sullivan, P.J., 2018. Piscivore diet shifts and trophic level change after Alewife establishment in Lake Champlain. Trans. Am. Fish. Soc. 147, 939–947. Stafford, C.P., Eby, L.A., McPhee, M.V., Allendorf, F.W., 2014. Introduced lake trout exhibit life history and morphological divergence with depth. Can. J. Fish. Aquat. Sci. 71, 10–20. https://doi.org/10.1139/cjfas-2013-0115. Strandberg, U., Hiltunen, M., Jelkänen, E., Taipale, S.J., Kainz, M.J., Brett, M.T., Kankaala, P., 2015. Selective transfer of polyunsaturated fatty acids from

phytoplankton to planktivorous fish in large boreal lakes. Sci. Total Environ. 536, 858–865. https://doi.org/10.1016/j.scitotenv.2015.07.010. Taipale, S.J., Brett, M.T., Hahn, M.W., Martin-Creuzburg, D., Yeung, S., Hiltunen, M., Strandberg, U., Kankaala, P., 2014. Differing Daphnia magna assimilation efficiencies for terrestrial, bacterial, and algal carbon and fatty acids. Ecology 95, 563–576. https://doi.org/10.1890/13-0650.1. Taipale, S.J., Kainz, M.J., Brett, M.T., 2015. A low x-3:x-6 ratio in Daphnia indicates terrestrial resource utilization and poor nutritional condition. J. Plankton Res. 37, 596–610. https://doi.org/10.1093/plankt/fbv015. Tocher, D.R., 2003. Metabolism and functions of lipids and fatty acids in teleost fish. Rev. Fish. Sci. 11, 107–184. Torres-Ruiz, M., Wehr, J.D., Perrone, A.A., 2007. Trophic relations in a stream food web: importance of fatty acids for macroinvertebrate consumers. J. North Am. Benthol. Soc. 26, 509–522. https://doi.org/10.1899/06-070.1. USGS., 2018. Compiled reports to the Great Lakes Fishery Commission of the annual bottom trawl and acoustics surveys for 2017 [online]. Available from http// www.glfc.org/pubs/lake_committees/common_docs/ CompiledReportsfromUSGS2017.pdf. Weil, C., Lefèvre, F., Bugeon, J., 2013. Characteristics and metabolism of different adipose tissues in fish. Rev. Fish Biol. Fish. 23, 157–173. Zeileis, A., Cribari-Neto, F., Gruen, B., Kosmidis, I., Simas, A.B., Rocha, A. V, Zeileis, M. A., 2016. Package ‘betareg.’ R Packag. Zimmerman, M.S., Schmidt, S.N., Krueger, C.C., Vander Zanden, M.J., Eshenroder, R.L., 2009. Ontogenetic niche shifts and resource partitioning of lake trout morphotypes. Can. J. Fish. Aquat. Sci. 66, 1007–1018.

Please cite this article as: A. Happel, C. P. Stafford, J. Rinchard et al., Fatty acid profiles of lake trout reveal the importance of lipid content for interpreting trophic relationships within and across lakes, Journal of Great Lakes Research, https://doi.org/10.1016/j.jglr.2019.10.015