Effects of salmon on the diet and condition of
Transcription
Effects of salmon on the diet and condition of
Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) 1 ARTICLE Effects of salmon on the diet and condition of stream-resident sculpins Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Noel R. Swain, Morgan D. Hocking, Jennifer N. Harding, and John D. Reynolds Abstract: Pacific salmon (Oncorhynchus spp.) can subsidize freshwater food webs with marine-derived nutrients from their eggs, juveniles, and carcasses. However, trophic interactions between spawning salmon and freshwater fish across natural gradients in salmon subsidies remain unclear. We tested how salmon affected the diets and condition of two dominant freshwater consumers — prickly and coastrange sculpins (Cottus asper and Cottus aleuticus, respectively) — across a wide gradient of pink salmon (Oncorhynchus gorbuscha) and chum salmon (Oncorhynchus keta) biomass from 33 streams in the Great Bear Rainforest of British Columbia, Canada. Sculpin diets shifted from invertebrates and juvenile salmonids to salmon eggs when salmon arrived in autumn, with salmon-derived nutrient contributions to diets and sculpin condition increasing with increasing biomass of spawning salmon among streams. Season, habitat, and individual sculpin body size and species also mediated the effects of salmon on sculpin diet as inferred from their carbon and nitrogen stable isotope signatures. This study shows the timing and pathways by which spawning salmon influence the diets and condition of freshwater consumers, and some of the individual and environmental factors that can regulate uptake of salmon nutrients in streams, thus informing ecosystem-based management. Résumé : Les saumons du Pacifique (Oncorhynchus spp.) peuvent contribuer aux réseaux trophiques d'eau douce par l'entremise de nutriments d'origine marine issus de leurs œufs, leurs juvéniles et leurs carcasses. Les interactions trophiques entre les saumons en frai et les poissons d'eau douce le long de gradients naturels d'apports trophiques des saumons demeurent toutefois mal définies. Nous avons voulu comprendre l'incidence des saumons sur le régime alimentaire et l'état d'embonpoint de deux consommateurs d'eau douce dominants, le chabot piquant et le chabot côtier (Cottus asper et Cottus aleuticus, respectivement), le long d'un large gradient de biomasse de saumons roses (Oncorhynchus gorbuscha) et kétas (Oncorhynchus keta) de 33 cours d'eau dans la forêt pluviale de Great Bear, en Colombie-Britannique (Canada). Les régimes alimentaires des chabots passaient d'invertébrés et de salmonidés juvéniles aux œufs de saumon après l'arrivée des saumons en automne, les contributions de nutriments provenant des saumons au régime alimentaire et à l'état d'embonpoint des chabots augmentant parallèlement à l'augmentation de la biomasse de saumons en frai dans les cours d'eau. La saison, l'habitat, ainsi que la taille du corps et l'espèce des chabots modulaient également les effets des saumons sur le régime alimentaire des chabots déterminé à la lumière de leurs signatures d'isotopes stables de carbone et d'azote. Ces résultats font la lumière sur les périodes où les saumons en frai influencent le régime alimentaire et l'état d'embonpoint de consommateurs d'eau douce et les voies par lesquelles cette influence s'exerce, ainsi que sur certains des facteurs individuels et du milieu ambiant qui peuvent réguler la consommation de nutriments dérivés des saumons dans les cours d'eau, ce qui peut éclairer la gestion écosystémique. [Traduit par la Rédaction] Introduction In coastal areas of the North Pacific Ocean, marine and aquatic ecosystems are linked through nutrient subsidies to fresh waters by annual spawning migrations of anadromous Pacific salmon (Oncorhynchus spp.) (Gende et al. 2002; Naiman et al. 2002). Salmon nutrient pulses provide food to freshwater consumers directly through salmon eggs, juvenile life stages, and adult flesh (Scheuerell et al. 2005, 2007; Moore et al. 2008). They may also support consumers by affecting primary production and the longer term recycling of limiting nutrients such as nitrogen in stream food webs (Wipfli et al. 1998; Verspoor et al. 2010, 2011). Because of the regular periodicity and large magnitude of these nutrient subsidies to coastal fresh waters, salmon streams are good model systems for understanding the impacts of such processes on recipient ecosystems and dynamics of consumer populations (Schindler et al. 2003; Janetski et al. 2009). Freshwater fish, including sculpins (Cottus spp.) and salmonids, consume salmon eggs and alevin, as well as invertebrates and other fish that also benefit from salmon (Foote and Brown 1998; Chaloner et al. 2002; Denton et al. 2010). This does not mean that there would necessarily be a strong correspondence between their dietary composition and the numbers of salmon in the streams, because this can depend on habitat size and physical habitat features, as shown recently for other impacts of salmon on ecosystems (Tiegs et al. 2008; Hocking and Reimchen 2009; Field Received 14 March 2013. Accepted 30 October 2013. Paper handled by Associate Editor John Richardson. N.R. Swain. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. M.D. Hocking. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada; Hakai Network for Coastal People, Ecosystems, and Management, Faculty of Environment, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada; School of Environmental Studies, University of Victoria, P.O. Box 1700, STN CSC, Victoria, BC V8W 2Y2, Canada. J.N. Harding and J.D. Reynolds. Earth to Ocean Research Group, Department of Biological Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada; Hakai Network for Coastal People, Ecosystems, and Management, Faculty of Environment, Simon Fraser University, 8888 University Drive, Burnaby, BC V5A 1S6, Canada. Corresponding author: Noel R. Swain (e-mail: [email protected]). Can. J. Fish. Aquat. Sci. 71: 1–12 (2014) dx.doi.org/10.1139/cjfas-2013-0159 Published at www.nrcresearchpress.com/cjfas on 28 November 2013. Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. 2 and Reynolds 2011). Furthermore, individual characteristics of organisms such as body size can also mediate the diet and abundance of consumers in their response to subsidies (Phillips and Claire 1966; Sweeting et al. 2007a; Hocking et al. 2013). To date, no study has tested how salmon may affect the trophic ecology of freshwater fish across a wide natural range of spawning salmon densities, and there is little to no research of how individual fish body size and habitat features of streams may mediate the contribution of salmon to fish diets and condition. Freshwater sculpins often constitute most of the density and biomass of freshwater fish assemblages (Adams and Schmetterling 2007; Bellmore et al. 2013) and are known to consume salmon eggs and juveniles (Hunter 1959; Foote and Brown 1998). Sculpins are therefore likely to be strongly affected by salmon subsidies and to play important roles in freshwater ecosystem dynamics. However, little research has examined their trophic ecology or potential interactions within freshwater food webs (Bellmore et al. 2013). Resident freshwater prickly sculpin (Cottus asper) and coastrange sculpin (Cottus aleuticus) are commonly found in large, sympatric populations in coastal streams where Pacific salmon spawn. Where they coexist, prickly sculpins are typically less abundant in lower reaches of streams and prefer sheltered habitat such as pools and undercut banks, while coastrange sculpins tend to be smaller and more abundant riffle-dwelling fish (Mason and Machidori 1976; McPhail 2007). Here, we test how salmon affect the diets and physical condition of freshwater sculpins (C. asper and C. aleuticus) across a wide natural gradient in spawning biomass of pink salmon (Oncorhynchus gorbuscha) and chum salmon (Oncorhynchus keta) from 33 streams in the Great Bear Rainforest of coastal British Columbia. We account for mediating effects of habitat, season, and individual body size. We predicted that salmon-derived nutrient contributions to sculpin diets and lipid storage in sculpin livers (carbon:nitrogen (C:N) ratios) would increase with increasing salmon spawning density. We further predicted that these relationships would be stronger in autumn, when salmon are spawning; would be more apparent in larger sculpins; and would be mediated by habitat. To test these predictions we used (i) stomach contents analysis; (ii) tissuespecific nitrogen and carbon isotope analysis (fin versus liver tissues); (iii) single isotope linear mixed-effects models; and (iv) Bayesian C–N isotope mixing models to analyze shifts in sculpin diet as well as liver C:N as a measure of lipid storage and sculpin condition, across the gradient in salmon nutrient availability. Materials and methods Study sites We conducted our study across watersheds on coastal islands and mainland inlets near the Heiltsuk First Nation community of Bella Bella in the Great Bear Rainforest region on the Central Coast of British Columbia (Fig. 1). These watersheds span several natural gradients: (i) a wide range of spawning salmon abundance, from zero to more than 60 000 combined pink and chum salmon returning within a single year; (ii) watershed size; and (iii) habitat structure (e.g., large wood density) (Table 1). The streams run through coastal conifer-dominated temperate rainforest in the Coastal Western Hemlock biogeoclimatic zone, where there have been relatively few anthropogenic impacts aside from some selective logging in the 1940s (Hocking and Reynolds 2011). Sampling sites were in the lower reaches of salmon spawning streams, well above tidal influence, and included one control site located above an impassable waterfall. Where possible, sampled areas included a range of stream habitats such as pools, undercut banks, riffles, runs, and glides. Sculpin sampling We collected C. asper and C. aleuticus in the summers of 2007, 2008, and 2010 (before salmon arrived) and in September and Can. J. Fish. Aquat. Sci. Vol. 71, 2014 October of 2009 and 2010 (while salmon were spawning) using minnow traps (2007–2009) and backpack electrofishing units (2010). Twelve minnow traps, baited with perforated canisters containing a mixture of canned cat food and prawn oil, were set per stream for 6–24 h. Where possible, half of the traps were set in shallow high-discharge habitats (riffles, runs, glides), and the other half in deeper, low-discharge, protected habitats (undercut banks, pools, log jams). We used Smith-Root LR-24 and 12-B backpack electrofishers (Smith–Root, Vancouver, Washington, USA), with fish caught by one or two people using dip nets. We sampled 517 sculpins (258 C. asper and 259 C. aleuticus). Where possible, we collected 3–10 individuals of each species per stream per sampling period to analyze fin (n = 517) and liver (n = 352) isotopes, as well as stomach contents (n = 384). In 2007 and 2008, nonlethal fin clips were taken from the anterior portion of the second dorsal fin. In 2009 and 2010, sculpins were sampled lethally by clove oil overdose, retained, and stored frozen for later tissue-specific (fin and liver) isotope as well as stomach content analysis. We sampled a range of sculpin body sizes (30–155 mm standard length), and excluded young-of-the-year fish (<30 mm), because residual maternal signatures and marine inputs during estuary rearing could confound their tissue isotopic signatures (e.g., McPhail 2007). Stream habitat variation Steam habitat surveys were carried out from 2006 to 2011. Based on a review of the literature, we hypothesized that there were two main processes by which physical habitat characteristics of streams were likely to affect salmon-derived nutrient contributions to sculpin diets: (i) the extent to which a stream was likely to retain or discharge nutrients from salmon, and (ii) the extent to which foraging sculpins were able to access these resources during and after spawning periods. We hypothesized that watershed size and habitat complexity would be most likely to influence salmonderived nutrient contributions to sculpin diets and subsequently affect individual sculpin condition (Table 2). Habitat surveys were conducted for a reach of stream, the length of which was roughly 30 times the mean wetted width, starting just above the estuary margin. This reach was divided into four sections of equal length, from which three transects were selected randomly, for a total of 12 transects at which measurements were taken. Because many potential habitat variables could describe these processes, we derived a reduced set of variables using methods described by Zuur et al. (2010). We examined multicollinearity among habitat variables using variance inflation factors (VIF), where highly collinear variables (VIF > 4) were either combined with similar variables through principal component analysis (PCA) or dropped from subsequent analyses. We used the first principal component of stream length, bankfull width, and catchment area as a metric of watershed size (Field and Reynolds 2011; Hocking and Reynolds 2011). This axis explained 87% of the variation in these variables, and all three variables loaded positively with eigenvalues >0.5. We measured bankfull width during habitat surveys at each transect and used the mean for the entire reach. Catchment areas and stream lengths were determined from area maps and iMapBC (http:// maps.gov.bc.ca/ess/sv/imapbc/). We characterized stream complexity using the first principal component of large wood per 100 m of stream channel, percent area of the reach made up of highgradient habitat (falls, riffles, runs, cascades, step pools, and step runs), percentage of transects having undercut banks, and percentage of the reach area made up of pools, which together explained 52% of the variation in these variables. Large wood per 100 m, percentage of undercut banks, and percent pool area all loaded positively on this axis with eigenvalues of 0.37, 0.56, and 0.54 respectively, while percent high-gradient habitat loaded negatively with an eigenvalue of –0.51. We also considered stream discharge, canopy cover, stream gradient, and total alder basal Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) Swain et al. 3 Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Fig. 1. Locations of the 33 streams (black dots) in the Great Bear Rainforest of coastal British Columbia, Canada. area per watershed in our analyses, as these variables have been shown to explain some natural variation in isotope levels in stream ecosystems and could potentially mediate an effect of salmon (e.g., Pinay et al. 2003). However, these variables were only available for a subset of our sites and did not meaningfully change the patterns in our results, so they were dropped from our final analyses to maximize sample sizes. Details on the collection of these data can be found in Hocking and Reynolds (2011) supplementary materials. Salmon population data From 2006 to 2010, autumn salmon enumeration was conducted jointly by personnel from Simon Fraser University, the Heiltsuk First Nation, and Fisheries and Oceans Canada. Spawning salmon were counted by stream surveys on foot, and where possible, streams were visited at least three times during the spawning season. Abundance estimates were calculated using area under the curve, or peak live plus dead abundance, which were highly correlated estimates with comparable means (Hocking and Reynolds 2011). In the few cases (n = 14) where pink or chum abundance estimates for a stream were missing for a given year, we used predicted values from hierarchical linear regressions of abundance estimates based on regional trends for 2006–2010. We derived an index of salmon spawning density (D) (kg salmon biomass·(m2 spawning area)–1) for each stream: (1) D! !"N × W # i i A where N is the number of adult salmon, i is the salmon species (chum or pink salmon), W is the regional adult mean mass (kg) (Field and Reynolds 2011), and A is salmon spawning area, measured as the distance from the mouth of the stream to the furthest point upstream where spawning salmon were observed × mean wetted width of the stream. We tested two salmon metrics: (i) the single previous year (for summer samples) or current year (for Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) 4 Can. J. Fish. Aquat. Sci. Vol. 71, 2014 Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Table 1. Habitat, chum and pink salmon population data (2006–2010), number of sculpins sampled, and other fish species present in the study streams. Stream Spawning length (m) Bankfull width (m) Chum salmon mean Pink salmon mean Salmon index (kg·m−2) C. aleuticus (n) C. asper (n) Other species present Ada Beales Left Beales Right Bolin Bay Bullock Main Bullock Square Clatse Codville Evans East Fancy Head Fancy Right Fannie Left Farm Bay Goat Bushu Hooknose Jane Kill Creek Kunsoot Main Lee Mosquito Bay Left Mosquito Bay Right Neekas Port John Quartcha Rainbow Ripley Bay Roscoe Main Roscoe Trib. 1 Sagar Spiller Trib. 1 Troupe North Troupe South Windy Bay 435 300 200 700 622 296 900 250 500 141 298 1500 590 1000 1800 500 453 1280 700 250 500 2100 262 5500 300 0 5800 250 800 80 332 489 2200 11.1 10.9 9.6 17.9 10.9 8.4 22.8 3.3 13.3 5.5 4.8 12.8 6.4 7.5 16.9 4.6 3.5 13.1 12.4 5.7 4 17.7 3.3 21.7 15.1 14.7 23.5 14.1 15.5 7.7 4.4 4.1 13.1 945.4 389.6 72.8 121.8 1416 248.4 3870 5.6 848.6 214 76.4 1935 0.0 75.4 1244.8 8.4 596.6 393.4 1110 91.8 416.8 17280 1.2 2360 189 0.0 10072 461.8 474.2 3.2 2.2 27.6 107 266.8 1187.6 322.2 1871.6 1418.4 119.8 9290 26.2 184.4 107 121.2 3439.4 0.0 662.6 1363.4 0.0 235.8 6238 177.8 202.8 606.2 24070.8 0.4 2040 8.4 0.0 219.8 388 461.2 7.2 0.6 156.4 283.6 0.75 0.93 0.38 0.27 1.25 0.61 1.19 0.06 0.6 1.22 0.28 0.56 0.0 0.19 0.29 0.01 1.25 0.56 0.54 0.54 1.28 3.15 0.05 0.1 0.17 0.0 0.29 0.77 0.2 0.07 0.01 0.12 0.03 5 1 0 0 20 13 7 1 5 4 4 3 9 5 20 7 14 17 14 2 6 10 6 18 3 5 2 10 3 15 14 6 0 8 11 6 6 9 2 15 18 1 15 5 23 0 0 16 7 0 9 2 2 0 26 0 5 0 14 10 0 5 0 15 17 6 CO, CT, RB CO, CT, DV CO, CT CO CO, CT, DV CO, RB CO, RB, DV CO, SB CO CO CO, SB CO CT CO CO, CT, DV, LA CO, CT CO, CT CO CO, RB, DV CO CO, CT, RB CO, RB CO CO, CT, RB, DV, LA CO, RB, DV DV CO, RB, DV NA CO CO, CT, DV CO CO, CT CO Note: CO, juvenile coho; CT, cutthroat trout; RB, rainbow trout; DV, Dolly Varden; SB, stickleback species; LA, lamprey species. autumn samples) of salmon spawning, reflecting the most recent contribution of salmon to sculpin diet, and (ii) a 5-year mean salmon density (2006–2010), which dampens out annual variation and reflects a more general and temporally robust characterization of the number of salmon spawning in each stream. Although present in several of these systems, adult steelhead and coho salmon were excluded from analyses owing to their extremely low abundance in the reaches of the streams where we worked. Sculpin diets The recent diet of sculpins was assessed from the stomach contents of lethally sampled fish. All recognizable prey items were sorted into broad groups: (i) aquatic invertebrates, (ii) terrestrial invertebrates, (iii) fish, and (iv) salmon eggs. Aquatic invertebrates were further sorted into Ephemeroptera, Plecoptera, Trichoptera, Diptera, and other aquatic invertebrates (e.g., mollusks, worms, beetle larvae, crustaceans). Diptera were further subdivided into Chironomidae and other dipterans. All groups were quantified as number of individuals. These counts were multiplied by mean individual masses of prey items calculated from our own sampling (for invertebrates and fish) or from published values for our sampling region (salmon eggs; Beacham and Murray 1993). Diet characterization using stable isotope analysis We used stable isotope analysis on both fin and liver tissue to assess short- versus long-term diets of sculpins. Fish tissues differ in the rate at which they incorporate the isotopic signatures of their diet, thereby enabling analyses that represent different diet windows in time. In this case, fin tissues turn over slowly, incorporating the isotopic signature of a fish’s diet over a long time period (months), while liver tissues turn over quickly and reflect diet over shorter time periods (weeks) (Pinnegar and Polunin 1999; Suzuki et al. 2005; Eloranta et al. 2010). Fin and liver samples were dried at 60 °C for 48 h and ground manually into a fine powder. Samples (fin: 0.25–4.4 mg; liver: 0.26–1.5 mg) were assayed for carbon and nitrogen stable isotopes using a PDZ Europa ANCAGSL elemental analyzer interfaced to a PDZ Europa 20–20 isotope ratio mass spectrometer (Sercon Ltd., Cheshire, UK) at the University of California Davis Stable Isotope Facility (http://stableisotopefacility. ucdavis.edu/). Carbon and nitrogen stable isotope signatures are expressed in delta notation (") and are measures of a parts per thousand (‰) difference between the sample isotope ratio and that of international standards (Pee Dee Belemnite carbon or atmospheric nitrogen, respectively) (Peterson and Fry 1987): (2) "13C or "15N ! $ % Rsample # Rstandard × 1000 Rstandard where R = 13C/12C or 15N/14N. We normalized fin and liver "13C values using a standardized lipid-normalization procedure (Logan et al. 2008): (3) "13Cnormalized ! "13Cuntreated $ " a ×C:NC:N$$c b# where C:N is the elemental carbon to nitrogen ratio of bulk tissue, and a, b, and c are averaged tissue-specific model parameters of 7.42, –22.73, and 0.75, respectively, for fin tissue and 6.06, –22.27, Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) Swain et al. 5 Table 2. A priori predictions for the potential influence of salmon metrics; sculpin body size, species, and season sampled; environmental variables; and potential interactions between salmon and covariates on salmon nutrients in sculpin diets (tissue "13C and "15N) and individual sculpin condition (liver C:N). Variable Hypothesis Direction Metric Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Salmon metrics Most recent salmon Most recent salmon nutrient input Positive density and related to direct input in diets and effect on sculpin condition 5-year mean salmon Indicates general and temporally Positive density robust salmon nutrient inputs to ecosystem Most recent autumn pink + chum Verspoor et al. 2011 adult salmon spawning density (kg·m–2) Mean 2006–2010 autumn pink + chum adult salmon spawning density (kg·m–2) Sculpin body size, species, and season sampled Sculpin size Size is associated with trophic Positive level and age, resulting in differences in tissue isotopic signatures and condition Sculpin species Differences in habitat and ecology See text among species may affect access to salmon subsidies Season The timing of salmon spawning, Positive (spawning), as well as patterns in Negative environment and primary (prespawning) productivity that can affect diet and condition of freshwater fish Environmental variables Watershed size Related to level of primary productivity, terrestrial input, spawning area, and foraging area Habitat complexity Increased habitat complexity and low channel gradient increase nutrient retention, primary productivity, and foraging habitat for sculpins, with carryover effects on salmon nutrients in diet and condition of sculpins Interactions between salmon and covariates Salmon × sculpin Salmon resources are more size accessible to larger fish Salmon × season Stronger effect of salmon in autumn during spawning on both contributions to diet and condition Salmon × watershed Increased terrestrial and in-stream size nutrient inputs in large watersheds reduce the effect of salmon on stream production, mediating effects on sculpin diet and condition Salmon × habitat As salmon density and the ability complexity for a watershed to retain nutrients increase, they should have an additive effect on dietary contribution of salmon nutrients and condition See text References Individual sculpin standard length (mm) Scharf et al. 2000; Shannon et al. 2001; Sweeting et al. 2007a, 2007b Individual species (C. asper or Jardine et al. 2005; C. aleuticus) Scheuerell et al. 2007. Season sampled: autumn; salmon Perga and Gerdeaux spawning (Sept.–Oct.); or 2005 summer (June–Aug.) PC axis 1 of main stem and tributary length, bankfull width, and catchment area Positive (undercut bank; PC axis 1 of % undercut banks, large woody debris; large wood per 100 m, % pool and % pool area), area, and % high gradient Negative (% high habitat gradient habitat) Positive Verspoor et al. 2011 Lamberti and Steinman 1997; Hocking and Reynolds 2011; Verspoor et al. 2011 Quinn and Peterson 1996; Kauffman et al. 2007; Milner et al. 2011 Salmon metrics × sculpin body size Salmon metrics × sampling season (prespawning or spawning) Foote and Brown 1998; Armstrong et al. 2010 Scheuerell et al. 2007 Negative Salmon metrics × watershed size PC axis 1 Hocking and Reynolds 2011 Positive Salmon metrics × habitat complexity PC axis 1 Milner et al. 2011 Positive and –1.40, respectively, for liver tissue derived from isotope data from three species of freshwater fish (see Logan et al. 2008). Fåhraeus-Van Ree et al. 2003, Heintz et al. 2004), we used liver C:N as an indirect metric of individual sculpin condition. Individual fish condition Tissue C:N ratios provide an indirect measure of lipid content in tissues (e.g., Post et al. 2007; Fagan et al. 2009). As liver tissue can be an important site for lipid storage in fishes, and increased lipid storage is often related to increased physical condition (e.g., Data analysis We used linear mixed-effects (LME) modeling (Zuur et al. 2008) and model selection via Akaike’s information criterion corrected for small sample sizes (AICc) to evaluate the relative effects of salmon density, habitat, sculpin species, size, season, and several Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) 6 Can. J. Fish. Aquat. Sci. Vol. 71, 2014 Table 3. Results for model selection using Akaike’s information criterion corrected for small sample sizes (AICc) showing top linear mixed-effects models (%AICc < 3), to a maximum of four models for sculpin fin and liver "13C and "15N isotope signatures and condition (liver C:N). Response Model k %AICc wi Pseudo R2 Fin " N Salmon × season, body size, species, watershed size Salmon × body size, salmon × season, species, watershed size Salmon × season, salmon × watershed size, body size, species Salmon × season, body size, species, watershed size, habitat complexity Salmon, body size, species, season, watershed size, habitat complexity Salmon, body size, species, season, habitat complexity Salmon × habitat complexity, body size, species, season Salmon, body size, species, watershed size, habitat complexity Salmon × body size, salmon × season Salmon × body size, salmon × season, watershed size Salmon × body size, salmon × season, species Salmon × body size, salmon × season, species, watershed size Salmon × body size, species, season, habitat complexity Salmon × body size, season, habitat complexity Salmon × body size, species, season, watershed size Salmon × body size, season, watershed size Salmon × season, body size, species Salmon × season, salmon × watershed size, body size, species Salmon × body size, salmon × season, species Salmon × season, body size, species, watershed size 6 7 7 7 6 5 7 5 5 6 6 7 6 5 6 5 5 7 6 6 0.00 1.67 1.90 2.03 0.00 0.27 0.94 1.05 0.00 0.11 0.30 0.41 0.00 0.39 0.81 0.89 0.00 0.62 0.98 1.90 0.29 0.12 0.11 0.10 0.09 0.08 0.06 0.05 0.20 0.19 0.17 0.16 0.14 0.11 0.09 0.09 0.17 0.13 0.11 0.07 0.61 — — — 0.48 — — — 0.73 — — — 0.62 — — — 0.28 — — — 15 Fin "13C Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Liver "15N Liver "13C Liver C:N Note: Candidate sets contained all combinations of parameters — 5-year mean salmon density (kg·m–2) (salmon), sculpin length, species, season sampled (before or after salmon spawning), watershed size, and habitat complexity (see Table 2) — and were restricted to including only interactions between salmon metrics and covariates, and up to three stream-level parameters. The value k is the number of parameters (fixed-effects plus interactions) included in a each model. a priori hypothesized interactions on the isotopic signatures and liver C:N of sculpins. The following explanatory variables (fixed effects) were included based on a priori hypotheses (Table 2): single-year (previous or current autumn) salmon density; multiyear (2006–2010) mean salmon density; season (prespawning or during spawning); watershed size (PC1 scores); habitat complexity (PC1 scores); sculpin standard length (mm); sculpin species (C. asper or C. aleuticus); salmon × season; salmon × watershed size; salmon × habitat complexity; and salmon × sculpin length. We included stream as a random effect to account for the lack of independence among individual sculpin measurements. Salmon terms were transformed (log (salmon + 0.1)) to meet model assumptions of normality. We evaluated the relative support for these hypotheses using an approach combining all models (n = 149 models), restricting models to include up to one salmon term, only prehypothesized interactions, and a maximum of four stream-level explanatory variables (salmon, watershed size, habitat complexity, and interactions). Model performance and uncertainty were assessed using AICc, which ranks models based upon the principle of parsimony (Burnham and Anderson 2002; Anderson 2008). The lower the AICc score for a given model, the better the trade-off between complexity and optimal fit for that model. We used the MuMIn package in R (Barton 2012) to compare competing models based on %AICc values and AICc weights (wi). Because there was often little distinction among high-ranking models based on these values (Table 3), we also calculated multimodel averaged parameter estimates and relative variable importance (the sum of AICc weights from all models containing the variable of interest) for fixed effects from 95% confidence sets of models for each of our five response variables (Burnham and Anderson 2002; Grueber et al. 2011). These averaged values were then used to assess the effects of individual independent variables. We also calculated pseudo R2 as a measure of the explanatory power of top models (%AICc < 3) (derived from regressions of the observed data versus fitted values) (see Magee 1990; Nagelkerke 1991; Piñeiro et al. 2008 for details). To test the relative support for our two salmon metrics, we conducted AICc model selection with each salmon metric separately and compared the top model from each set for each response variable. Because 5-year mean salmon density performed considerably better than single-year density, and because multi- model averaging was not possible when comparing model combinations containing both salmon metrics, we present model-averaged results for only 5-year mean salmon density. We used nonstandardized continuous explanatory variables to maintain the predictive utility of averaged models. To compare among all parameters and interpret the main effects in conjunction with interaction terms, we also conducted the above analyses after standardizing continuous explanatory variables by subtracting global means from each value (centering) and dividing by two times the standard deviation (SD) (scaling) (Gelman 2008). All statistical analyses were conducted in R (R Development Core Team 2012). Isotope mixing models While analyses of stomach contents and tissue-specific isotopic signatures can provide evidence for salmon-derived nutrients in the diets of freshwater consumers from different time periods, isotopic mixing models are required to estimate the proportion of diets attributable to alternative prey sources (Perga and Gerdeaux 2005; Moore and Semmens 2008; Martinez del Rio et al. 2009). We assessed the contribution of salmon-derived nutrients to sculpin diets through dual isotope ("13C and "15N), two-source Bayesian mixing models implemented in the program SIAR (stable isotope analysis in R; Parnell and Jackson 2011). SIAR takes isotope data from consumers (sculpins) and sources (diet items) along with estimates of diet to tissue isotopic fractionation, and then fits Bayesian models based on Gaussian likelihoods with a Dirichlet prior mixture on the mean, which provide posterior distribution estimates of source contributions (Parnell et al. 2010). We used the isotope signatures from all sampled sculpin individuals and the mean and standard deviation of isotope signatures from three potential diet sources: (i) averaged benthic macroinvertebrate values collected from summer and autumn, (ii) prey fish (juvenile coho salmon) fin tissues, and (iii) dorsal muscle samples from spawning pink and chum salmon adults collected from streams in autumn. We categorized streams by salmon spawning density into three equal bins based on the spread of the data: low (0– 0.03 kg·m−2), medium (0.1–0.2 kg·m−2), or high (0.5–1.3 kg·m−2). Because isotopic signatures for potential diet sources were not available for each stream, benthic invertebrate and prey fish values were averaged across several streams in each category, with Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) Swain et al. Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Fig. 2. Mean proportion of diet by mass (g) of identifiable prey items in stomachs of prickly (C. asper) and coastrange (C. aleuticus) sculpins sampled in summer before salmon arrived and in autumn during salmon spawning. these values representing values for these diet sources in all streams in that category. The use of separate signatures based on levels of salmon spawning density accounted for potential indirect salmon inputs through these lower trophic levels. As egg and juvenile salmon isotopic signatures are very similar to that of adult flesh (Scheuerell et al. 2007), this latter diet source represents all of the above as direct salmon nutrient inputs. Because no data on sculpin tissue fractionation values were available, we used mean values of "13C (fin = 2.2 ± 0.43 (mean ± SD); liver = 0 ± 0.89) and "15N (fin = 2.5 ± 0.52; liver = 1.75 ± 0.46) for fish (Sweeting et al. 2007a, 2007b). We ran individual models for each season–stream combination for each tissue. All salmon and juvenile coho tissues were also lipid normalized (see eq. 3; Logan et al. 2008). Results Sculpin stomach contents Before adult salmon arrived, trichopterans, dipterans (predominantly chironomids), and other aquatic invertebrates made up the majority of stomach contents in both sculpin species (Fig. 2). In the autumn, when the salmon were spawning, salmon eggs became the most common diet items, averaging almost 50% of diets by mass. While less common in both seasons, juvenile salmonids, when present, outweighed all other prey items in individual stomach samples. 1 7 Fig. 3. Scaled parameter estimates (circles) with 95% unconditional confidence intervals (lines) from averaged predictive linear mixedeffects models of "13C and "15N isotope signatures in freshwater sculpins based on fin (slow turnover; A, B) and liver (fast turnover; C, D) tissues. Parameters (indicated on left) are ordered for each response variable by their relative importance (indicated on right) to the averaged model on a scale of 0–1. Only parameters with relative importance values >0.5 are included in this figure. Sculpin carbon and nitrogen stable isotope signatures As predicted, pink and chum salmon spawning density was typically the best variable at explaining "13C and "15N isotope enrichment in sculpin fin and liver tissues, with strong positive effects (Figs. 3, 4). We also found that multiyear mean salmon density performed considerably better than single-year density (Table S11). Further in line with our predictions, the combination of salmon density, season, sculpin body size, species, and either watershed size or habitat complexity explained much of the variation in sculpin tissue isotopic enrichment among individuals and streams, particularly for "15N (pseudo R2 = 0.61–0.73) (Table 3). Sculpins in larger watersheds had higher "13C and "15N enrichment in both tissues (relative variable importance range = 0.42– 0.83; Fig. 3). Contrary to our predictions, increasing habitat complexity resulted in lower fin and liver "13C enrichment (relative importance = 0.86 and 0.57, respectively), but had a negligible influence on "15N enrichment (relative importance < 0.3; Fig. 3). Liver "13C Supplementary data are available with the article through the journal Web site at http://nrcresearchpress.com/doi/suppl/10.1139/cjfas-2013-0159. Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) 8 Can. J. Fish. Aquat. Sci. Vol. 71, 2014 Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Fig. 4. Relationship between multiyear mean salmon density (kg·m−2) and tissue-specific "15N and "13C from individual sculpins, with predicted lines from linear mixed-effects models (A–D). Interaction plots showing relationship between multiyear mean salmon density (kg·m−2) and tissue-specific "15N and "15C for 40 and 140 mm fish (E–H). Although other covariates and interactions were important (season, sculpin species, watershed size), we only show body size, as it was consistently important. signatures were higher during salmon spawning than before they arrived, consistent with a recent diet of salmon eggs and tissues. In contrast, and counter to our predictions, fin and liver "15N signatures were higher in sculpins prior to salmon spawning. Fin isotope enrichment was also considerably higher in C. asper, while differences in liver isotopic signatures between the two species were negligible (Fig. 3). As predicted, apart from salmon spawning density, sculpin body size was typically the most important variable in describing differences in isotopic signatures among individual sculpins and streams, with enrichment in both isotopes being consistently higher in larger individuals (Figs. 3, 4). Stronger positive relationships were observed between salmon density and isotope signatures in smaller sculpins than in larger ones in short-turnover liver tissue, but not fin tissue (relative importance for salmon × body size interaction in livers = 1; Figs. 3, 4). Further, for both fin and liver tissues, the positive relationship between salmon density and nitrogen isotope enrichment was stronger in the summer, before adult salmon arrived, than in the autumn, during spawning (relative importance for salmon × season interaction = 0.76–0.87; Fig. 3). An opposite but weaker interaction was present for "13C (relative importance of salmon × season < 0.18). Finally, and contrary to our predictions, the interactions with habitat of salmon × watershed size and salmon × habitat complexity had low variable importance (<0.16). Sculpin tissue-specific mixing models "15N and "13C signatures of sculpins predictably followed that of their diets (Fig. 5). Benthic macroinvertebrates consistently had the lowest isotopic signatures, regardless of season, with both "15N and "13C in invertebrates increasing across streams with higher densities of spawning salmon. Resident prey fish (juvenile coho salmon) had intermediate isotope signatures, below those of adult pink and chum salmon in streams with low to medium salmon densities, and above those of adult pink and chum salmon in streams with a high densities of spawning salmon. For fin tissue, representing a longer term diet of sculpins (months), mean estimates of the contribution of pink and chum salmon to the diet of !30% changed little with increasing salmon spawning density regardless of season (Fig. 6). In contrast, liver tissue, which represents short-term diet (weeks), showed a posi- tive relationship with salmon spawning density, with mean estimates indicating that up to 80% of the diet is derived from salmon during spawning in high-density streams. For both long- and short-term diets, the contribution of prey fish to sculpin diets increased in summer and decreased in autumn, while the contribution of benthic invertebrates generally decreased, across the gradient of increasing salmon spawning density. Sculpin individual condition As predicted, sculpin liver C:N ratio increased with multiyear mean salmon density and sculpin body size and was much higher during pink and chum salmon spawning in autumn (variable importance = !1 for all three variables; Fig. 7). The effect of pink and chum salmon density on liver C:N ratio was also considerably stronger for sculpins caught when salmon were spawning than those caught prior to spawning (salmon × season interaction relative importance = 0.81; Fig. 7). In general, all explanatory variables were poor at explaining differences in liver C:N ratio among individual sculpins and streams (pseudo R2 < 0.29). Discussion We show the timing and pathways by which freshwater resident fish can acquire salmon nutrients, and how individual and environmental factors may mediate the uptake and effects of these nutrients, in 33 coastal streams. Freshwater sculpins shifted from eating invertebrates and fish in the summer to largely salmon eggs in the autumn, when salmon were spawning. The relative contribution of salmon-derived nutrients to sculpin diets rose consistently across the wide gradient of increasing salmon spawning densities. Sculpin body size, species, season, and physical habitat also affected their isotope signatures, and in some cases mediated the effects of salmon on these signatures. Distinctions in the responses of different measures of diet and physical condition highlight the potential utility of using multiple metrics and analyses in the study of trophic dynamics. Our results show a range of pathways by which sculpins access salmon-derived nutrients. One line of evidence was the differing response of "13C and "15N enrichment of sculpin tissues to increasing salmon spawning densities. "15N enriches considerably with Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) Swain et al. 9 Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Fig. 5. Carbon–nitrogen biplots of sculpin long-term, prespawning (fin, summer) and short-term, during spawning (liver, autumn) isotope signatures. Small circles show mean sculpin values for individual streams. Symbol shading indicates salmon density (white, low; grey, medium; black, high). Means and standard deviations of isotope signatures of potential diet sources are also shown: pink and chum adult salmon (large open circle), prey fish (juvenile coho) (triangles), and benthic macroinvertebrates (squares). These are for reference and are the same in both panels. Adult salmon signatures are averaged across all available streams, while prey fish and invertebrate signatures are averaged within the three levels of salmon density. Fig. 6. Relationship between estimated proportional contributions to sculpin diets of pink and chum salmon eggs, carcass tissue or alevins (panels A and D), resident prey fish (juvenile coho) (panels B and E), and benthic macroinvertebrates (panels C and F) and multiyear mean salmon spawning density (kg·m−2), prior to salmon spawning (circles, solid line) and during spawning (triangles, dotted line). Estimates are means of posterior probability distributions from carbon–nitrogen Bayesian mixing models based on isotopic signatures from sculpin fin tissue (top row, long-term diet) and liver tissue (bottom row, short-term diet). increasing trophic level compared with "13C (e.g., McCutchan et al. 2003). Furthermore, much of the sculpin prey base of macroinvertebrates and juvenile fish can have higher "15N in streams with high salmon densities, because the periphyton base of production is enriched in "15N, but not "13C, from the legacy of past spawning (e.g., Verspoor et al. 2010). Sculpins that consume lower trophic levels with elevated "15N signatures would therefore have even higher "15N themselves, but not necessarily enriched "13C. Direct consumption of salmon nutrients, however, would result in similar patterns of enrichment of both isotopes. Our results provide evidence of both scenarios; the finding that salmon density typically had the highest positive effect on enrichment of both isotopes in sculpin tissues is consistent with our mixing models and stomach content analyses showing that salmon eggs Published by NRC Research Press Pagination not final (cite DOI) / Pagination provisoire (citer le DOI) 10 Can. J. Fish. Aquat. Sci. Downloaded from www.nrcresearchpress.com by 174.1.194.237 on 03/05/14 For personal use only. Fig. 7. Scaled parameter estimates (circles) with 95% unconditional confidence intervals (lines) from the averaged predictive linear mixed-effects model of liver C:N ratio in freshwater sculpins. Parameters (indicated on left) are ordered for each response variable by their relative importance (indicated on right) to the averaged model on a scale of 0–1. Only parameters with relative importance values >0.5 are included in this figure. were consumed directly by sculpins. However, the effect of salmon density was stronger for "15N, suggesting that sculpins were also obtaining salmon nutrients indirectly through stream invertebrates and fish. We were also surprised to find that "15N values in both tissues were usually higher prior to spawning, especially in highdensity streams. This could be due to the legacy of direct consumption of a pulse of pink and chum alevins in spring (e.g., Hunter 1959; Phillips and Claire 1966; Foote and Brown 1998) or, in line with results from our mixing models, could reflect high consumption of enriched invertebrates and resident prey fish in summer in highdensity streams. Additional variation in isotopic signatures could reflect intrinsic differences among streams in geology, inputs from water or land, or fractionation (Pinay et al. 2003). As predicted, larger sculpins had higher isotopic enrichment in both tissues. This was expected owing to trophic level shifts with body size (e.g., Shannon et al. 2001). Also, gape size is closely related to length, which determines whether sculpins can feed directly on large isotopically enriched salmon eggs, alevins, and juveniles (Foote and Brown 1998; Armstrong et al. 2010). Interestingly, the relationship between salmon spawning density and isotope enrichment also weakened sharply with increasing sculpin body size for liver tissue, but not fin tissue. This suggests that larger sculpins always have access to pink and chum salmon eggs and alevins where available, whereas smaller sculpins either only have access when salmon spawning densities are high or have high isotope signatures by feeding on isotopically enriched invertebrates. We found diet differences between sculpin species. Prickly sculpins had higher fin isotopic enrichment than coastrange sculpins, suggesting that they feed at higher relative trophic levels. This matched our findings that prickly sculpins tend to be larger and more commonly have prey fish in their diets, likely owing to greater habitat overlap with other fish in pools and sheltered areas of streams (McPhail 2007). In contrast, the negligible differences in liver isotope enrichment between the two species suggests that both species can access nutrients from salmon eggs and carcasses. Habitat played less of a role than we expected in determining salmon-derived nutrient contributions to sculpin diets. Although watershed size generally had a positive effect on isotope enrichment in both tissues, this was only consistent in fin "15N. Increased ecosystem size has been linked to higher productivity and longer food chains (Vander Zanden et al. 1997; Post 2002; Sabo et al. 2010), which would explain "15N enrichment in sculpin tissues with increasing watershed size. We confirmed our prediction that sculpins in streams with higher salmon spawning densities would have higher lipid storage as shown by liver C:N ratios (e.g., Heintz et al. 2004). Although C:N is only an indirect measure of lipid storage, which in turn may differ among age classes of fish and season, this result suggests Can. J. Fish. Aquat. Sci. Vol. 71, 2014 that increased access to high-quality salmon subsidies carries over to increased condition of individual sculpins, similar to results shown for juvenile salmonids in some experimental studies (e.g., Wipfli et al. 2003). Experimental manipulations of salmon subsidies have helped our understanding of mechanisms by which spawning salmon influence freshwater fish (e.g., Bilby et al. 1998; Wipfli et al. 2010). However, there is a need to test these effects under natural conditions to confidently attribute apparent treatment effects to the presence of salmon (Schindler et al. 2003; Janetski et al. 2009). This is the first study to test the effects of salmon on freshwater fish other than salmonids, despite the important ecological roles of sculpins in salmon spawning streams (Adams and Schmetterling 2007; Janetski et al. 2009). This study is also among the first to test for such effects across wide natural gradients in salmon density and habitat. However, future research should aim to access populationlevel effects across such natural gradients to clarify varying results from recent experimental studies (e.g., Wilzbach et al. 2005; Denton et al. 2009; Harvey and Wilzbach 2010). While management of wild Pacific salmon stocks has traditionally focused on maximizing salmon production for commercial fisheries, there is a need to recognize the importance of salmon subsidies to recreational and nonrecreational fish populations and the overall productivity of freshwater systems. Understanding how salmon subsidies influence recipient freshwater fish populations across these natural conditions and gradients is key to implementing effective ecosystem-based management of wild salmon and associated freshwater systems. Acknowledgements We thank N. Dulvy and M. Bradford for constructive input during this study and we thank all those who assisted us in the field, including M. Arseneau, T. Brochmeier, D. Brown, C. Bruce, J. Bruce, R. Field, J. Harding, H. Hermanek, H. Jackson, R. Midgley, H. Recker, J. Slade, M. Spoljaric, and M. Wagner. We also appreciate help in the lab from M. Chung, G. Gu, L. Ogston, J. Pendray, M. Segal, and M. Stubbs. We also thank S. Anderson, K. Artelle, J. Moore, and D. Swain for help in analyzing and presenting our data. We appreciate the support of our colleagues in the Earth to Ocean Research Group throughout the study. We thank the Natural Sciences and Engineering Research Council of Canada (NSERC), the Simon Fraser University Pacific Scholarship Fund, the Tom Buell BC Leadership Chair endowment, and the Pacific Salmon Foundation for financial support. We also thank the Heiltsuk Integrated Resource Management Department and Fisheries and Oceans Canada staff for logistical support and autumn salmon enumeration and the Heiltsuk First Nation for allowing us to work with them in their traditional territory. References Adams, S.B., and Schmetterling, D.A. 2007. 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