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Environmental Biology of Fishes ISSN 0378-1909 Environ Biol FishDOI 10.1007/s10641-013-0114-x

Variability in the functional role of Arcticcharr Salvelinus alpinus as it relates to lakeecosystem characteristics

Pamela J. Woods, Skúli Skúlason,Sigurður S. Snorrason, BjarniK. Kristjánsson, Finnur Ingimarsson &Hilmar J. Malmquist

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Variability in the functional role of Arctic charr Salvelinusalpinus as it relates to lake ecosystem characteristics

Pamela J. Woods & Skúli Skúlason &

Sigurður S. Snorrason & Bjarni K. Kristjánsson &

Finnur Ingimarsson & Hilmar J. Malmquist

Received: 13 May 2012 /Accepted: 10 February 2013# Springer Science+Business Media Dordrecht 2013

Abstract This study investigated how dietary habitsvary with lake characteristics in a fish species thatexhibits extensive morphological and ecological vari-ability, the Arctic charr Salvelinus alpinus. Iceland is ahotspot of geological activity, so its freshwater ecosys-tems vary greatly in physical and chemical attributes.Associations of dietary items within guts of charr wereused to form prey categories that reflect habitat-specificfeeding behavior. Six prey categories were defined anddominated by snails (Radix peregra), fish (Gasterosteusaculeatus), tadpole shrimp (Lepidurus arcticus),

chironomid pupae, pea clam (Pisidium spp.), and thecladoceran Bosmina sp.. These reflected different com-binations of feeding in littoral stone, offshore benthic,and limnetic habitats. Certain habitat-specific feedingstrategies consistently occurred alongside each otherwithin lakes. For example, zooplanktivory occurred inthe same lakes as consumption from offshore habitats;piscivory occurred in the same lakes as consumptionfrom littoral benthic habitats. Redundancy analyses(RDA) were used to investigate how lake environmentwas related to consumption of different prey categories.The RDA indicated that piscivory exhibited by Arcticcharr was reduced where brown trout were abundantand lakes were shallow, greater zooplanktivory occurredat lower latitudes and under decreased nutrient buthigher silicon dioxide concentrations, and benthic re-source consumption was associated with shallowerlakes and higher altitudes. This study showed that trendspreviously observed across fish species were supportedat the intraspecific level, indicating that a single specieswith flexible dietary habits can fill functional rolesexpected of multiple species in more diverse food webs.

Keywords Prey habitat . Diet . Lake ecosystem .

Piscivory . Zooplanktivory . Consumer

Introduction

Although species are considered singular units of bio-logical diversity, considerable ecological variabilityoccurs within species, especially in diet and feeding

Environ Biol FishDOI 10.1007/s10641-013-0114-x

Electronic supplementary material The online version of thisarticle (doi:10.1007/s10641-013-0114-x) containssupplementary material, which is available to authorized users.

P. J. Woods : S. Skúlason :B. K. KristjánssonHólar University College,Háeyri 1,551 Sauðárkrókur, Iceland

P. J. Woods (*) : S. S. SnorrasonUniversity of Iceland,Sturlugata 7, Askja,101 Reykjavík, Icelande-mail: pamelajwoods@gmail.com

P. J. WoodsSchool of Aquatic and Fishery Sciences,University of Washington,Box 355020, Seattle, WA 98195, USA

F. Ingimarsson :H. J. MalmquistNatural History Museum of Kópavogur,Hamraborg 6a,200 Kópavogur, Iceland

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behavior, morphology, life history, and reproductivecharacteristics. This intraspecific variation can haveimportant consequences on population structure andevolutionary dynamics (Bolnick et al. 2003; Knudsenet al. 2010), as well as the species’ role within itsencompassing ecosystem (Harmon et al. 2009). Thegoal of this study was to characterize dietary trends ofa diverse consumer across a broad geographical scaleto gain a better grasp of variation in the functional therole of a consumer, and how this variation relates tothe surrounding ecosystem.

Feeding is the main route by which fish can affectecosystem properties. Given the mobility of fishes,they can link processes occurring in disparate habitats(Vanni 2002). For example, fish populations can struc-ture carbon flow and change energy transfer pathwaysthrough food webs by causing or modifying trophiccascades (Schindler et al. 1997; Hulot et al. 2000;Jeppesen et al. 2003; Taylor et al. 2006), affect thenumber of trophic levels (Post et al. 2000; VanderZanden and Fetzer 2007), or incorporate allochtho-nous energy subsidies into food webs (Cole et al.2006). In addition, fish can affect nutrient cycling byeither recycling nutrients within water columns ortransferring digested nutrients from benthic prey tothe water column by excretion, thereby supporting nu-trient availability within the water column (Schindlerand Scheurell 2002; Vanni 2002). As dietary habitscan depend on the local environment, the mere presenceof fishes is not enough to understand how they affectecosystem properties.

The realized effects that fish have on an ecosystemdepends on the prey taxa consumed, which represents acomplex interaction of feeding behavior, functionalmorphology, and environmental and biogeographicalfactors. Morphological specialization may or may notreflect behavioral constraints, since fish with a special-ized morphology can nevertheless have diverse feedingcapabilities (Liem 1980). Diverse feeding within a spe-cies is accomplished through behavioral flexibility orswitching, leading to “generalist” feeding, “omnivory”(i.e., feeding at different trophic levels), or “resourcepolymorphism” (Post et al. 2000; Schaus et al. 2002;Vander Zanden and Vadeboncoeur 2002; McCann et al.2005). Past studies have focused mainly on the role oftemporal ecological changes in diverse feeding, butspatial variationmay also be apparent across ecosystems(McCann et al. 2005; Verant et al. 2007). For example,shoreline complexity affects use of the littoral area by

piscivores (Dolson et al. 2009), ecosystem size affectsthe development of high trophic positions (Post et al.2000; Vander Zanden and Fetzer 2007), and the pres-ence of submerged structures affects prey species abun-dances based on habitat availability (Okun et al. 2005).Furthermore, biotic interactions influence relative con-sumption of prey taxa through competition (Hesthagenet al. 1997; Forseth et al. 2003) or predation risk thataffects habitat use (Okun et al. 2005).

This study investigated how dietary habits varywith abiotic and biotic ecosystem characteristics in aspecies that exhibits extensive morphological and eco-logical variability, the Arctic charr Salvelinus alpinus(L.). Arctic charr dietary habits are diverse ontogenet-ically (Byström and Andersson 2005), seasonally orannually (Saskgård and Hesthagen 2004; Amundsenet al. 2008; Corrigan et al. 2011), and across lakes andpopulations (Skúlason et al. 1992; Jonsson andJonsson 2001; Alekseyev et al. 2002; Gantner et al.2010; Klemetsen 2010). In addition, they mayreflect resource polymorphism, in which phenotypicvariation yields a functional advantage to consumecertain prey within certain habitats (Malmquist 1992;Malmquist et al. 1992; Adams and Huntingford 2002;Andersson 2003; Snorrason and Skúlason 2004;Knudsen et al. 2010). An extensive array of datadetailing Arctic charr populations from the EcologicalSurvey of Icelandic Lakes (ESIL, Malmquist et al.2000; Karst-Riddoch et al. 2009; Kristjánsson etal. 2011) yields a unique opportunity to study dietvariation at the novel scale of an intraspecificstudy across locations that show great physical diversitydue to their various geological ages (Jónasson et al.1998).

This study had two broad goals. In the first broadgoal, we analyzed how associations of prey taxa withinindividual guts of Arctic charr reflected habitat associa-tions and feeding strategies and whether individualsexhibited certain strategies as consistently co-occurringwithin lakes. For example, the consumption of benthicresources has been shown to sustain charr populationsthat otherwise specialize on resources that are morevariable, such as zooplankton and fish, by providing aconstant alternative resource (Schindler and Scheurell2002; Vander Zanden and Vadeboncoeur 2002; VanderZanden et al. 2005). Therefore, we expected that whenanalyzing diets across individuals, the distinction be-tween consuming benthic versus zooplankton or fishwould be quite strong, whereas this distinction would

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dull when analyzing diets across populations. This re-duced distinction would indicate the presence of indi-viduals consuming zooplankton or fish alongsideindividuals consuming zoobenthos; however, it wasunknown which benthic prey would be more closelyassociated with zooplankton versus fish consumption.

The second broad goal was to analyze the environ-mental conditions under which such feeding strategiesoccurred. As Arctic charr are highly variable, weexpected that relationships between their diet and en-vironment would follow many of the same trends asthose observed across species. First, because foodchains are in general longer in larger ecosystems(Post et al. 2000; Vander Zanden and Fetzer 2007),we expected to find greater piscivory in larger lakes.As Arctic charr populations can be cannibalistic, thispiscivory was not thought to be constrained by thepresence of other fish species. Second, deeper lakeshave a greater ratio of volume to benthic area than doshallower lakes, and therefore more pelagic habitat(Schindler and Scheurell 2002). Therefore, Arcticcharr were expected to consume more pelagic prey(zooplankton and possibly fish) in deep lakes andmore benthic invertebrates in shallow lakes. This trendshould be especially strong given that shallower lakeshave greater benthic production due to higher temper-atures, greater sunlight, and greater nutrient concen-trations (Hanson and Leggett 1982; Schindler andScheurell 2002; Jeppesen et al. 2003). However, thisrelationship would be weakened by the presence ofcompetitors, especially those of similar size and tro-phic status such as the brown trout (Salmo trutta).Brown trout are known to displace Arctic charr awayfrom shallow benthic habitat (e.g., Hesthagen et al.1997; Jansen et al. 2002; Forseth et al. 2003), sogreater consumption of zooplankton was expectedwhere brown trout were abundant. Finally, as Arcticcharr also tend to be opportunistic, greater consump-tion of all resources was expected at higher densitiesof that resource.

Methods

Data acquisition

Data from 62 lakes were used for this study; however,different subsets of lakes were used for each analysisdepending on data availability (Table A1). For all lakes

except Thingvallavatn, data were derived from the da-tabase of Ecological Survey of Icelandic Lakes, whichtook place in August – September each year during1992 – 2004. Data at each lake were collected at a singlesampling event, so temporal variation (within or be-tween years) may confound spatial effects. However,samples were all obtained within the same 2-monthperiod, and the large number of lakes, variables mea-sured, and contrasting environmental factors shouldprovide enough power to detect spatial patterns. Dataincluded information on benthic invertebrate abundan-ces from the shallow (0.2 – 0.5 m) near-shore zones withcourse sand to pebble-sized sediment or large rocks(“rocky littoral”) and off-shore fine-grained sedimenthabitat from a variety of depths (“offshore benthic”).Zooplankton abundances were sampled from thelimnetic habitat, and abundances of Arctic charr, browntrout, and Atlantic salmon (Salmo salar) were sampledusing sinking gill nets. Threespine sticklebackGasterosteus aculeatuswere caught using minnow trapsset on the bottom inshore. Only American eel Anguillarostrata and European eel A. anguilla otherwise inhabitIcelandic fresh water.

Zoobenthos from littoral habitats were sampled from4 to 6 stations spread evenly around the shore. At eachstation, five 10 – 15 cm stones from 20 to 50 cm depthwere taken and sampled for invertebrates (Malmquist etal. 2000). For off-shore benthic habitats, 5 Kajak coresamples were taken at each of 2 – 4 stations along atransect in the middle of each lake at varying depths, aslakes varied from 1 to 112 m deep at most. Zoobenthossamples were sorted with a 250 μm sieve. Retainedinvertebrates were preserved in 3 – 4 % buffered forma-lin and later transferred to 75 % isopropanol.Zooplankton were collected by tow net (125 μm mesh)at the same transect stations, where 3 vertical hauls(max. 15 m depth) were taken beginning at least 30 –50 cm from the bottom. Samples were fixed in ~1 %Lugol’s solution, stored in dark-brown glass bottles, andinvertebrates were identified and counted. Zoobenthosdensities were calculated as areal counts (m−2), andvolumetric zooplankton densities (m−3) were convertedto an areal measure (m−2) by multiplying volumetriccounts by mean lake depth (m).

To catch salmonids, 11 (22 for lakes>10 km2) ny-lon gill nets (Lundgren series) were set in the littoralzone perpendicular to shore, from ca. 2 m depth out-and downwards and then left overnight for 12 h. Thegill nets were 25 m long and 1.5 m high with mesh

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sizes 10, 12.5, 15.5, 19.5, 21.5, 24.0, 29.0, 35.0, 43.0,55.0, and 60.0 mm (knot to knot) (see Malmquist et al.2000, and Karst-Riddoch et al. 2009 for details).

Wet weight (to the nearest 1.0 g) was measured forevery Arctic charr, and stomachs were removed fordiet analysis from a subsample of up to100 individu-als. Stomach fullness was visually estimated and ratedas 0 (empty), 1 (trace, < 1/3 full), 2 (half, 1/3 – 2/3full), or 3 (entire, > 2/3 full). Food items were identi-fied and counted, and individuals with empty stom-achs were excluded, resulting in gut content data from57 lakes (Supplementary information: Table A1).

Prey taxa were identified mostly as taxonomic groups,but in some cases they were more general (Table 1).Invertebrate survey data were aggregated into the samegroups. “Small fish” were almost entirely threespinestickleback, although small Arctic charr were includedin rare cases (< 1 % guts). “Fish eggs” were most likelySalvelinus eggs, but were also rare (< 1 % guts). “Otherflies” included adult stoneflies, dagger flies (Empididae),heather flies (Bibio pomonae), or blowflies (Calliphoraspp.). In a few lakes, the category “Copepoda” was usedto indicate unidentified copepods, so this group was theonly one to taxonomically overlap with other groups (i.e.,Diaptomus spp. and Cyclops spp. were used instead inmost lakes). We did not combine Diaptomus spp. andCyclops spp. because they may have different habitataffinities or dominate different lakes under certain envi-ronmental conditions. In all cases, each dietary item onlybelonged to a single category. Data on Arctic charr fromThingvallavatn were derived from sampling in July andlate September – October 1997. Sampling followed theprotocol of the ESIL project in major details, except thatchironomids were only identified to family.

All environmental variables were taken or calculat-ed from the ESIL database, including abiotic(physicochemical) and biotic variables. Abiotic varia-bles included lake mean depth (MD), lake volume(VOL), July-August mean precipitation (PR), altitude(ALT), July-August mean air temperature (AT), lakesurface temperature (ST), conductivity (COND), totalphosphorus (TP), total nitrogen (TN), total organiccarbon (TOC), silicon dioxide (SiO2), calcium (CA),pH (PH), and iron (FE). To correct for skewness, thesevariables were all transformed by log(x) or log(x+1) ifthe variable ranged<1, except for SiO2 which wassquare-root transformed and AT which needed notransformation. Biotic variables included brown troutand Arctic charr abundances, estimated as catch per

unit effort and transformed by log(x+1) (BTA, ACA),and stickleback presence as a binary factor (SP1 /SP0), as abundance was not recorded for this species.All continuous variables were scaled to have a meanof 0 and standard deviation of 1.

Habitat associations of prey taxa

This preliminary analysis was used to form a hypothesisof expected prey associations in the gut by indicatinghow prey taxa were associated based on habitat alone. IfArctic charr feed within a single habitat during a singlefeeding session, it is reasonable to expect that associa-tions among prey taxa in gut contents will reflect prey-habitat associations. Invertebrate abundance data fromthese habitats in the 45 lakes with invertebrate surveydata were included in this analysis. In this first hierar-chical cluster analysis, sample methods (i.e., planktonhauls, Kajak cores, and littoral stone collections) wereassumed to generally indicate potential prey from lim-netic, benthic offshore, and benthic littoral habitats re-spectively. To calculate the frequency that each taxonwas found in each habitat using data from all lakes,counts for each individual prey taxon were summedacross lakes within each habitat, and this sum wasdivided by the sum across all three habitats across lakes.These frequencies were analyzed in a hierarchical clus-ter analysis using Euclidean distance and McQuittylinkage (McQuitty 1966).

Forming prey categories

We used gut content data from 57 lakes to 1) analyzenatural associations of prey taxa within guts using adetrended correspondence analysis (DCA) of prey countdata in individual guts (as a “by-individual DCA”) and ahierarchical cluster analysis on resultant scores from thefirst four DCA axes using Euclidean distance withMcQuitty linkage, 2) compare this second cluster anal-ysis to expected patterns based on habitat associationsamong prey taxa (i.e., results of the first cluster analysis)and form habitat-associated prey categories using a cut-off distance that yielded relatively few (< 10) separatebranches, and 3) detect consistent co-occurrence ofhabitat-associated feeding habits within lakes by con-ducting a DCA on lake-aggregated prey count data (as a“by-lake DCA”) and comparing these patterns withthose in the first by-individual DCA. Associationsamong prey consumed in the “by-lake” DCA that were

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not found in the “by-individual DCA” would then indi-cate co-occurrence of individuals within lakes that con-sumed those prey (i.e., associated feeding strategies:“by-lake DCA”), rather than a co-occurrence of thoseprey within individual guts (i.e. associated prey withinfeeding strategies: “by-individual DCA”). All DCA andcluster analyses were implemented in R V. 2.9.2(R Development Team 2009) using the “vegan” pack-ages (Oksanen et al. 2009).

Summarizing relative prey consumption by lake

To compare prey consumption with lake characteristics,a measure of relative consumption of prey categoriesamong lakes was needed. This was accomplished by

estimating linear lake coefficients from generalized lin-ear models (GLMs) used to predict consumption of eachprey category with a categorical predictor that reflectedlake of origin. Because the biomass of prey within anycategory may differ by several orders of magnitude,consumption of each prey category would be betterreflected by a total biomass rather than a total count ofprey items within categories. Therefore, approximatebiomasses within prey categories were first calculatedby multiplying the counts of each prey taxon by anappropriate order of magnitude (i.e., 0.001, 0.01, 0.1,1, or 10 mg), as indicated by wet weights found inliterature and local expert knowledge (Table 1), and thensumming these weights within categories. This methoddoes not calculate a true biomass, but scales prey taxa by

Table 1 Indicator biomassconversions for prey taxa aregiven along with the expectedcapture habitat based on inver-tebrate surveys (from Fig. 1,top), and prey categories (fromFig. 1, bottom). Capture habitatsinclude stone (S), fine-grainedsediment (mud: M), or the watercolumn (W). Prey categoriesincluded snail (S), tadpoleshrimp (T), pea clam (P),Bosmina (B), chironomid pupae(C), and fish (F). Exclusions areindicated as 0. Capture habitatsare listed in order of importance,with backslashes indicatingsimilar frequencies(20 % – 80 %). Parenthesesindicate that expert knowledgewas used to fill informationfor taxa absent ininvertebrate surveys

Dietary taxa Common name Biomass (mg) Capture habitat Prey cat.

Chydorus sp. Cladoceran 0.001 W/M 0

Fish eggs Fish eggs 1 (S) 0

Gammarus sp. Amphipod 1 S 0

Limnephilus spp. L Caddisfly larvae 1 M/S S

Apatania zonella L Caddisfly larvae 1 M/S S

Radix peregra Snail 1 M/S S

Daphnia spp. Cladoceran 0.01 W, M T

Cyclops spp. Copepod 0.01 M, W T

Ostracoda Ostracod 0.001 M, W T

Macrothrix sp. Cladoceran 0.01 M, W T

Alona spp. Cladoceran 0.001 M, W T

Lepidurus arcticus Tadpole shrimp 10 M, W T

Coleoptera Beetle 1 S T

Other flies Other flies 0.1 S T

Eurycercus sp. Cladoceran 0.1 M, W M

Polyphemus sp. Cladoceran 0.001 (M, W) M

Simocephalus vetulus Cladoceran 0.001 M, W M

Chironomidae L Fly larvae 0.1 M/S M

Pisidium casertanum Pea clam 1 M, W M

Annelida Worm 0.1 M, W M

Diaptomus spp. Copepod 0.01 W, M B

Bosmina sp. Cladoceran 0.01 W, M B

Copepoda Copepod 0.01 (W/M) B

Trichoptera A Caddisfly 0.1 (S) C

Chironomidae A Fly 0.1 S C

Hemiptera True bug 0.1 (WC/M) C

Hydracarina sp. Water mite 0.1 WC/M C

Chironomidae P Fly pupae 0.1 M/S C

Small fish Small fish 10 M/S F

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a rough relative biomass. Because categorical predictorsin linear models require a baseline to which each level ofthe predictor can be compared, Thingvallavatn was usedas the baseline (coefficient=0). Therefore, lake coeffi-cient values indicate how mean consumption within alake differs from mean consumption in Thingvallavatn.

In the estimation of lake coefficients to describeconsumption of each prey category, 2 types of GLMswere used per prey category, following the delta-gamma method for fitting a model to abundance datacontaining many 0 s (Stefánsson 1996). The first GLMpredicted the probability of prey occurring within in-dividual guts using a logit link function and Bernoullierrors using data that reflected whether prey from acategory were present (1) or absent (0) in individualguts. The second GLM used indicator biomass data topredict biomass with a log link function and gamma-distributed error, but only using the subset of individ-uals with that prey category present (since gammadistributions contain no 0 s). This delta-gamma modelis useful because results may be extrapolated to allindividuals by multiplying the two predictions to yieldthe joint probability of biomass given its presence.

However, one drawback to this method was thatlakes where Arctic charr consumed none of a givenprey category were excluded from the GLMs thatpredict indicator biomass, so those lakes consequentlylacked coefficients. To analyze these coefficients infurther analysis, it was preferable to have lake coef-ficients available for all lakes. We therefore replacedthese missing values with a number that was lowerthan all estimated coefficients to scale this lack of con-sumption as a minimum. As long as the value was aminimum, it had no effect on analyses and was arbitrari-ly chosen as the minimum estimated lake coefficient,rounded to the next lowest integer.

Larger fish are expected to consume more biomass ofall prey categories simply due to their larger stomachsizes, and fewer of all prey should be consumed whenstomachs are not full. To control for this less meaningfulvariation, prey category biomasses were 1) corrected forfish body size (wet weight) and adjusted for stomachfullness before fitting GLMs and 2) diagnostically testedto ensure that gamma errors were appropriate. To ac-complish the first task, non-linear models were used todetermine whether a linear or power function betterfitted prey category prediction by body weight*stomachfullness adjustment (i.e., 1/3 for rating 1, 2/3 for 2, and 1for 3). This comparison was made because metabolism

generally scales to body weight with an exponent=0.75(Brown et al. 2004), so we expected that food intakerates may do the same. Model fits were compared usingan F-test, which indicated that including a third expo-nential parameter in a power function yielded a better fitover the 2 parameters in a linear function (F1,2101=8.145, P=0.004). Parameter estimates (parameter±S.E., t-value, P-value: intercept=−15.416±11.175,−1.379, 0.168; slope=2.137±1.240, 1.724, 0.085; ex-ponent=0.783±0.08862, 8.839, < 0.0001) were used toremove this variation by dividing prey category biomassby (body weight*stomach fullness adjustment)^expo-nent, yielding a measure used in GLMs of g prey cate-gory / g body weight. Only the 54 lakes with both gutcontent and body weight data were included in theseGLMs. To accomplish the second task, a linear relation-ship between log mean and log variance were fitted tothe subset of prey category biomass>0 (Stefánsson1996). As the calculation of these prey category bio-masses could only occur after prey categories weredefined, tests are given below in Results.

Comparison of diet with geographicand environmental trends

To compare diet with geographic and environmentaltrends, coefficient values were retained from GLMs.These were then analyzed in a third hierarchical clus-ter analysis to group lakes by similarities in dietaryhabits using Euclidean distance and McQuitty linkage,and trends of increasing latitude or longitude withinclusters were analyzed. To test for environmentaltrends with prey consumption, redundancy analyses(RDAs) were used to form correlations between con-sumption coefficients and a matrix of lake-specificenvironmental conditions.

All environmental variables were included inRDAs, which were done separately for abiotic andbiotic variables to minimize restrictions due to missingdata, yielding 53 lakes for the abiotic analysis and 42for biotic analysis (11 lacked invertebrate survey data,Table A1). The availability of each prey category wasalso included as a biotic variable by treating surveycounts m−2 the same as dietary counts: they weremultiplied by respective indicator biomasses and thensummed within the same defined prey categories(Table 1). Benthic offshore data for lakes 31, 39, 61,62, and 64 were lacking, so these were replaced withmean counts of benthic offshore taxa calculated over

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all other lakes. Forward and backward stepwise algo-rithms were used to select models by minimum AIC.RDAs were implemented using the “vegan” packagein R (Oksanen et al. 2009).

Results

Habitat associations of prey taxa

The cluster analysis of invertebrate sample frequencieswithin habitats (benthic littoral, benthic offshore, or lim-netic) yielded hypotheses for prey associations in guts.Limnetic zooplankton species (i.e., Diaptomus spp.,Bosmina sp., and Daphnia spp.) were mostly found inplankton tows, indicating a strong presence in the watercolumn (Fig. 1, top). Chydorus sp., Hydracarina sp.,Cyclops spp., Alona spp., and more distinctly benthiccrustacean species (i.e., Eurycercus sp., Simocephalusvetulus, Macrothrix sp., and ostracods) were partiallyfound in the water column, but were also frequentlysampled in fine-grained sediments (benthic off-shore“mud,”M) with infauna such as pea clams and annelids.Snails, caddisfly larvae, and chironomid larvae and pu-pae were found commonly in both fine-grained sediment(“mud”) and stone samples (benthic littoral). Gammarussp., Coleoptera, chironomid adults, and adult “other flies”were found only in stone samples (Fig. 1, Table 1).

Forming prey categories

The by-individual DCA ordination and first cluster anal-ysis yielded prey categories with strong habitat associa-tions, indicating 6 feeding strategies (Fig. 1, bottom).Category names of the feeding strategies were based onthe common name of the dominant prey, as defined bythe greatest indicator biomass of all constituent preytaxa: Bosmina (B), fish (F), snail (S), tadpole shrimp(T), pea clam (P, although chironomid larvae were aclose second), and chironomid pupae (C) categories.The first four axes (DCA1 - DCA4) of this by-individual DCA yielded eigenvalues of 0.8556, 0.7824,0.7417, and 0.7840, and axis lengths were 4.8103,3.8628, 5.4610, and 4.6952. Starting left in Fig. 1, thefirst category (Bosmina) indicated limnetic feeding with-in the water column (Bosmina sp., Diaptomus spp., andCopepoda). The second category (fish) was defined toinclude only fish, although fish clustered closely withsnails and caddisflies. This division was meant to reflect

behavioral modifications, since not all Arctic charr indi-viduals or populations have the same tendency to be-come piscivorous, despite availability of fish prey(Malmquist et al. 1992). The third category (snails)indicated feeding on zoobenthos in the stony littoral zone(i.e., snails and caddisflies). The fourth (tadpole shrimp)indicated a mixture of limnetic and benthic feeding onnon-sedentary crustaceans and aquatic and terrestrialinsects that are sometimes found in the water column,possibly in patchy near-shore habitats (i.e., Lepidurusarcticus, Daphnia spp., Cyclops spp., Macrothrix sp.,Alona spp., Ostracoda, Coleoptera, and other flies). The

Fig. 1 Hierarchical cluster analysis of prey taxa frequencies (top,N=45 lakes) showed habitat-related patterns within stone (S),fine-grained sediment (mud: M), or water column (W) habitatsthrough invertebrate surveys. Lines indicate frequency: thick>80%, light<20%, or medium ~50%. Hierarchical cluster analysisof by-individual DCA scores (bottom, N=57 lakes) yielded preycategories with habitat associations under the horizontal cut-offline: snail (S), tadpole shrimp (T), pea clam (P), Bosmina (B),chironomid pupae (C), and fish (F). Prey taxa include:Alo=Alonaspp.; Ann=Annelida; Apa=Apatania zonella; Bos=Bosmina sp.;ChA=chironomid adults;ChL=chironomid larvae;ChP=chiron-omid pupae; Chy=Chydorus sp.; Col=Coleoptera; Cop=Cope-poda; Cyc=Cyclops sp.; Dap=Daphnia spp.; Dia=Diaptomusspp.; Egg=fish eggs; Eur=Eurycercus sp.; Fis=fish; Fly=otherflies; Gam=Gammarus sp.; Hem=Hemiptera; Hyd=Hydracar-ina sp.; Lep=Lepidurus arcticus; Lim=Limnephilus spp.; Rad=Radix peregra; Mac=Macrothrix sp.; Ost=Ostracoda; Pis=Pisi-dium spp.; Pol=Polyphemus sp.; Sim=Simocephalus vetulus;TrA=Trichopteran adults

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placement ofDaphniawith species less frequently foundin the water column likely reflects either 1) our inabilityto distinguish species within this genus with differinghabitat preferences or 2) a difference in limnetic feedinghabits of Arctic charr between lakes dominated byDaphnia spp. rather than Bosmina spp. The fifth catego-ry (pea clam) indicated feeding on prey that live in or onfine-grained sediment that settles further off shore (i.e.,pea clams Pisidium spp., annelids, chironomid larvae,Polyphemus sp., Eurycercus sp., and Simocephalus vetu-lus). Finally, the sixth category (chironomid pupae) in-dicated a combination of limnetic and benthic feeding,but may also reflect surface feeding since the surface wasnot sampled in invertebrate surveys (chironomid pupaeand adults, Hydracarina sp., Hemiptera, and trichopter-an adults). Fish eggs, Chydorus sp., and Gammarus sp.were found in few lakes and lacked strong affinities, sothey were excluded from prey categories.

The by-lake DCA indicated that habitat-related con-sumption patterns observable within individual gutsbecome lost for most prey categories when aggregatedwithin lakes (Fig. 2). This DCA of gut contents summedby lake yielded the first four axes with eigenvalues0.6501, 0.5665, 0.5339 and 0.4357, and axis lengthsof 4.8970, 2.7513, 3.0650, and 2.7497. Affinities withinprey categories of the by-individual DCA, indicated bylines drawn around prey category constituents (left,Fig. 2), showed a benthic-limnetic trend along the firstaxis, whereas the second axis mainly distinguished otherbenthic categories. In the by-lake DCA, the same styleof line was drawn around the same prey constituents(Fig. 2, right). Bosmina category constituents weremixed with constituents of the chironomid pupae andpea clam categories, indicating that lakes with individ-ual Bosmina-consumers also contained chironomid-pupae- and pea-clam-consumers. Lepidurus sp. becamesingularly positioned on the far right, likely reflectinghigh consumption of tadpole shrimp only in certainlakes, whereas other constituents of the tadpole shrimpcategory were mixed with other species. High overlapwas also apparent among zoobenthos categories. Thisgreater mixture of prey from various habitats indicatedthat analyzing individual variation, rather than acrosslake-level variation, was necessary for the detection ofhabitat-associated feeding patterns. The by-lake DCAs,on the other hand, showed that consumption of certaincategories was rare among lakes, but composed a sub-stantial portion of the diet when present (e.g., tadpoleshrimp). Certain prey categories were also commonly

consumed within the same lake (e.g., Bosminawith chironomid pupae or pea clam categories, snailswith fish).

Comparisons of diet with geographicand environmental trends

Gamma error distributions for biomass indicator GLMswere deemed appropriate as slopes (β) were not signif-icantly different from 2 (snail category: β=1.782±0.418SE, F1,2=18.20, P=0.051; tadpole shrimp category: β=2.058±0.134 SE, F1,2=235.1, P =0.004; pea clam cate-gory: β=2.016±0.199 SE, F1,3=102.9, P =0.002;Bosmina category: β=1.948±0.105 SE, F1,1=341.5,P=0.034; chironomid pupae category: β=2.182±0.296 SE, F1,2=54.39, P =0.018; and fish category:β=2.261±0.842 SE, F1,4=7.217, P=0.055). The cate-gorical lake predictor accounted for a substantial pro-portion of variation in each of the 12 GLMs (1 modelpredicting presence/absence and 1 indicator biomass perprey category: Table 2, for coefficient estimates seeSupplementary material, Tables A2 and A3).Therefore, coefficients gained from these models wereinformative in reflecting relative prey use among lakes.Lakes excluded from indicator biomass models wereassigned the following values: snail category=−5, tad-pole shrimp category=−12, pea clam category=−5,Bosmina category=−9, chironomid pupae category=−5,fish category=−2 (see Methods for calculation).

The cluster analysis of lake coefficients showed sim-ilarities among lakes in resource use (Fig. 3, Table 3).The first split distinguished lakes with no consumptionof prey in the Bosmina category by Arctic charr(Clusters 1 – 6) from those where there was Bosminacategory consumption (Clusters 7 – 11). In Clusters 1 –3, fish had similar extreme dietary habits: in Cluster 1,Arctic charr only ate snail category prey, whereas in 2they consumed only snail and fish categories, and in 3they consumed these and minimal amounts in the peaclam and chironomid pupae categories. Lakes inClusters 4 – 6 were similar in showing some consump-tion of prey in the tadpole shrimp category, but differedas Arctic charr in Cluster 4 lakes consumed no chiron-omid pupae, and Arctic charr in Cluster 5 lakes con-sumed no fish. Cluster 4 was spread across the westernhalf of Iceland, but Clusters 5 and 6 had a strongpresence in the north and northeast regions, where cool-er climates and older basaltic bedrock are common. Forlakes with some consumption within the Bosmina

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category, Clusters 7 – 8 split from 9 to 11 byArctic charrconsuming low frequencies of prey from the pea clamcategory and high biomass from the Bosmina category.Except for two valley lakes (3 and 6 in the same riversystem), these lakes were located within or had inflowsfrom young bedrock areas (< 0.7 myr of age). In addi-tion, 6 of the 8 lakes in Cluster 8 were located in thesouthwestern region of the country. Because of apparentlatitudinal trends in consumption of the Bosmina cate-gory within clusters 5 – 8 (i.e., 30 lakes), linear modelswere fit and indicated negative relationships between

latitude and both the Bosmina coefficient sets (biomass:F1,28=7.478,P=0.011; presence / absence: F1,28=9.239,P=0.005). Arctic charr from lakes in Cluster 9 weredistinguished as consuming large amounts from thetadpole shrimp category. Lakes in Cluster 10 differedfrom lakes in Cluster 11 by the consumption of fish andlarge quantities in the chironomid pupae category, asopposed to high frequencies of all prey categories ex-cept fish and snails (Cluster 11).

The abiotic RDA indicated that consumption ofprey categories was related to mean depth (MD), pH,

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Fig. 2 DCA ordinations ofprey item counts forindividuals guts (top,N=2,551 individuals)and guts summed bylake (bottom,N=57 lakes). Affinitieswithin prey categories,indicated by surroundinglines (top), are degradedwhen individual variation isremoved (bottom). Linetypes match the samecategory in each panel.Weighted average values foreach taxon are indicated bytext: Alo=Alona spp.;Ann=Annelida; Apa=Apa-tania sp.; Bos=Bosmina sp.;ChA=chironomid adults;ChL=chironomid larvae;ChP=chironomid pupae;Chy=Chydorus sp.;Col=Coleoptera;Cop=Copepoda; Cyc=Cyclops spp.; Dap=Daphniaspp.; Dia=Diaptomus spp.;Egg=fish eggs; Eur=Eurycercus sp.; Fis=fish;Fly=other flying insects;Gam=Gammarus sp.;Hem=Hemiptera; Hyd=Hydracarina sp.; Lep=Lepi-durus arcticus; Lim=Limne-philus spp.; Rad=Radixperegra; Mac=Macrothrixsp.; Ost=Ostracoda;Pis=Pisidium spp.; Pol=Polyphemus sp.; Sim=Simo-cephalus vetulus; TrA=Trichopteran adults

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and silicon dioxide (SiO2) altitude (ALT), and ratio oftotal nitrogen to total phosphorus (TNTP), all of whichwere retained in the final model. Altitude was selectedonly during forward model selection, and TNTP wasselected only during backward selection, so both wereretained in the final model to indicate their similarimportance. Permutation tests indicated that this finalmodel was significant (F5,47=2.580, P=0.005 with199 permutations). The constrained portion accountedfor 21.5 % of the variation, of which the first axis(RDA1) accounted for 41.9 % and the second axis(RDA2) accounted for 33.2 % (eigenvalues RDAs 1 –4 were 1.083, 0.857, 0.369, and 0.243). Weightedaverage positions of GLM coefficients for a givenmodel and prey category were labeled beginning withthe prey category (snail=S, tadpole shrimp=T, peaclam=P, Bosmina=B, chironomid pupae=C, fish=F)followed by the model code (presence / absence=D;gamma-distributed biomass=G, Fig. 4). In general,correlations with environmental variables indicatedthat more fish were consumed in lakes with higherpH and higher ratio of total nitrogen to total phospho-rus (TNTP) but less silicon dioxide (SiO2). Snail cat-egory coefficients (SD and SG) and to a lesser extentfish and chironomid pupae category coefficients (FG,FD, CG, and CD) were positioned away from theTNTP arrow, indicating that they were consumedmore when TNTP was lower. Fish, snail, and chiron-omid pupae category coefficients (FD, FG, SD, SG,CD, and CG) were placed in the same direction as the

mean depth (MD) vector and the opposite direction asthe altitude (ALT) vector indicated that Arctic charr inlakes at lower altitudes and deeper lakes had greatertendency to consume fish and invertebrates in the snailand chironomid pupae categories, whereas Arcticcharr in high altitude and shallow lakes predominantlyconsumed prey in the tadpole shrimp or pea clamcategories (Fig. 4, left). The placement of Bosminaand chironomid pupae category coefficients (BD,BG, CD, and CG) near the tip of the SiO2 vectorindicated that higher silicon dioxide concentrationswere associated with consumption of prey from theBosmina and chironomid pupae categories. In addi-tion, the Bosmina category was negatively correlatedwith the ratio of total nitrogen to total phosphorus, asindicated by its placement in the opposite direction asthe variable’s vector points.

The biotic RDA showed that consumption of preycategories was related to brown trout abundance(BTA), stickleback presence (SP), and abundance ofprey in the snail (S) and chironomid pupae categories(C). The best model was the same using forward andbackward model selection, and permutation testsshowed it to be significant (F4,37=4.430, P=0.005with 199 permutations). The constrained portionaccounted for 32.3 % of the variance, of which, thefirst axis (RDA1) accounted for 43.0 % of the vari-ance, and the second axis (RDA2) accounted for34.4 % (eigenvalues for RDAs 1 – 4 were 1.702,1.362, 0.630, and 0.262). The brown trout abundance

Table 2 Results of GLMs to predict presence/absence andindicator biomass using a categorical Lake predictor for eachprey category: snail (S), tadpole shrimp (T), pea clam (P),

Bosmina (B), chironomid pupae (C), and fish (F). Fifty-fourlakes were included; the number of individuals per lake can befound in Table A1

Model Prey cat. N Residual deviance Null deviance Residual df. Variance explained

Presence/ absence S 2104 1989.80 2731.30 2050 27.15 %

T 2104 1929.00 2807.20 2050 31.28 %

P 2104 2009.00 2913.60 2050 31.05 %

B 2104 757.60 1262.10 2050 39.97 %

C 2104 2026.40 2829.10 2050 28.37 %

F 2104 978.47 1452.11 2050 32.62 %

Indicator biomass S 742 912.81 1224.02 692 25.43 %

T 813 2676.70 6341.60 765 57.79 %

P 1011 2166.20 3314.60 961 34.65 %

B 187 542.56 879.01 162 38.28 %

C 838 1426.30 2018.70 795 29.35 %

F 230 139.70 345.28 200 59.54 %

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vector (BTA) pointed away from the fish categoryconsumption coefficients (FD and FG, Fig. 4), indi-cating that piscivory among Arctic charr was nega-tively associated with brown trout abundance, but

positively associated with the presence of stickleback(SP). The snail category abundance vector (S) pointedtoward snail consumption coefficients (SD and SG),indicating that consumption within the snail category

Fig. 3 The hierarchicalcluster analysis of lake co-efficient values (top)estimated from GLMs(N=54 lakes) and geo-graphic spread of the Clusterassignments (1 – 11) areillustrated in the map(bottom), with lake numbersreferenced in Table A1. Thesymbols at the branch ofeach cluster in the dendro-gram (top) indicate the clus-ter to which the lake numberbelongs in the map (bottom).These symbols correspondleft to right with Clusters1 – 11 (labeled below thedendrogram)

Table 3 For each lake cluster (Fig. 3, N=54 lakes), averagecoefficient values are given fromGLMs to predict presence/absenceof prey in the snail (SD), tadpole shrimp (TD), pea clam (PD),Bosmina (BD), chironomid pupae (CD), and fish (FD) categories,

as well as to predict indicator biomass of the same categories (SG,TG, PG, BG, CG, FG). For the indicator biomass GLMs, values of−5, −12, −5, −9, −5, −2 were respectively used as replacements forlakes where Arctic charr ate none of that prey category

Cluster SD TD PD BD CD FD SG TG PG BG CG FG

1 17.67 −16.01 −15.94 −16.71 −18.50 −18.15 −0.09 −12.00 −5.00 −9.00 −5.00 −2.002 0.66 −16.01 −15.94 −16.71 −18.50 1.97 −1.41 −12.00 −5.00 −9.00 −5.00 1.04

3 2.59 −16.01 1.12 −16.71 −1.57 −0.62 0.57 −12.00 −0.98 −9.00 −0.85 1.11

4 −0.44 1.77 2.96 −16.71 −18.50 −5.13 −0.36 −9.39 0.76 −9.00 −5.00 −0.365 −0.24 1.45 −1.37 −16.71 −0.62 −18.15 −0.36 −7.79 −1.73 −9.00 −1.34 −2.006 −2.60 1.57 −0.08 −16.71 −0.13 −0.65 −0.98 −5.43 −0.88 −9.00 −0.14 0.21

7 0.80 −16.01 −0.12 1.39 −0.81 −18.15 −0.05 −12.00 −3.62 −1.88 −0.66 −2.008 −3.39 1.57 4.88 1.95 0.43 −18.15 −0.90 −2.58 0.10 −3.06 −0.40 −2.009 −6.85 21.12 1.21 −4.72 −0.37 −18.15 −3.03 −6.14 −1.78 −5.66 −1.67 −2.0010 −0.91 2.31 1.20 1.81 −2.09 −0.41 −0.76 −5.80 −0.39 −2.69 −0.10 −0.3611 −2.65 11.72 6.15 1.80 −18.50 −18.15 −3.53 −4.40 −1.28 −6.49 −5.00 −2.00

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was closely related to snail category abundance. Incontrast, the chironomid pupae abundance vector (C)pointing toward Bosmina category consumption coef-ficients (BD and BG) indicated that consumptionwithin the Bosmina category was instead related tothe abundance of prey in the chironomid pupaecategory.

Discussion

This study focused on characterizing dietary trends inArctic charr at a broad geographical scale, across manyecologically diverse lakes within Iceland, to gain a bettergrasp of how the functional role of Arctic charr as aconsumer varies with the environment. A number ofimportant conclusions can be drawn. First, prey catego-ries can be interpreted as habitat-associated feedingstrategies that were apparent in diet variation amongindividuals, but not lakes. Second, geographical analy-ses showed that prey consumption in the Bosmina cat-egory was generally more prevalent in the south,indicating either that a limnetic feeding strategy is lesscommon in the north or that it occurs during a seasonthat is shorter or shifted in timing. Finally, we found that

feeding was related to both biotic and abiotic conditionswithin the lake environment (i.e., depth, altitude, totalnitrogen to phosphorus ratio, pH, silicon dioxide, browntrout abundance, stickleback presence, snail abundance,and chironomid pupae abundance). The strongest trendsoccurred on the scale of the prey’s environment. Forexample, piscivory was most related to fish community(i.e., negatively with brown trout abundance and posi-tively with the presence of stickleback), zooplanktivorywas related to nutrient availability (i.e., negatively toratio of total nitrogen to total phosphorus and positivelyto silicon dioxide concentrations), and benthivory (al-though common) was related to physical characteristics(i.e., positively with mean depth and snail abundance,negatively with altitude).

By comparing these environmental trends to paststudies, we found support for a number of expectedpatterns. First, Arctic charr exhibiting limnetic feedingstrategies occurred in the same lake alongside fish thatconsumed from prey categories in fine-grained sediment(as indicated by the overlap of these categories in the by-lake DCA). Fish consumption was also closely associ-ated with snail consumption in DCA and cluster analy-ses. This data set contains some polymorphic Arcticcharr populations composed of one piscivorous morph

Fig. 4 Redundancy analysis ordinations show how diet wascorrelated with abiotic (left, N=53 lakes) and biotic (right, N=42 lakes) variables. Positions of numbered lakes (Table A1)reflect axis scores. SD, TD, PD, BD, CD, and FD representthe weighted average scores of lake coefficients, scaled relativeto axis eigenvalues, from models predicting presence/absence ofthe snail, tadpole shrimp, pea clam, Bosmina, chironomid pu-pae, and fish prey categories respectively. SG, TG, PG, BG, CG,

and FG represent the same but from models predicting indicatorbiomass. Vector direction and length reflect value and strengthof correlations. Environmental variables include mean depth(MD), altitude (ALT), total nitrogen : total phosphorus(TNTP), silicon dioxide (SiO2), pH (PH), snail category abun-dance (S), chironomid pupae category abundance (C), browntrout abundance (BTA), threespine stickleback presence (SP1)or absence (SP0) indicated by weighted average positions

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and one benthivorous morphs (likely no more than 7,Woods et al. 2013), as well as monomorphic lakes withindividuals that consume either food source. Therefore,this pattern in our results support the idea that thebehavioral specialization of piscivory likely requiressome degree of benthic feeding as a supplement to thesepopulations during periods in which fish resources arescarce (Schindler and Scheurell 2002; Vander Zandenand Vadeboncoeur 2002; Vander Zanden et al. 2005). Asimilar pattern could be seen in the consumption of theBosmina category, which was associated with consump-tion of the pea clam category in the “by-lake” DCA(Fig. 1) and RDAs (Fig. 4), indicating that benthicoffshore feeding may supplement consumption of themore variable zooplankton resource. Therefore, thesepatterns indicate that certain benthic habitats are prefer-entially used as supplemental for certain pelagic feedingtypes over others: zooplanktivores utilize zoobenthosliving in fine-grained sediment whereas piscivores uti-lize zoobenthos living on coarse-sediment prey in litto-ral stone habitats. The piscivorous diets of brown troutare also likely to be subsidized by snails and caddisfies,since they also consume a combination of fish andzoobenthos in littoral areas (Hesthagen et al. 1997;Björnsson 2001). Therefore, although piscivory is fre-quently studied as a component of the limnetic foodchain (Vander Zanden et al. 2005), in this case perhapsit would be more appropriately studied within a littoralbenthic food chain, which can be further differentiatedfrom a benthic offshore food chain.

In this study, higher brown trout abundances werealso associated with reduced fish consumption in Arcticcharr, likely indicating habitat displacement of Arcticcharr away from littoral zones (Langeland et al. 1991).In contrast, greater brown trout abundance was notfound to correspond with greater zooplanktivory, ashas been found previously (Hesthagen et al. 1997;Jansen et al. 2002; Forseth et al. 2003). Instead, thisapparently resulted in greater offshore benthic consump-tion. However, the lack of detection of a relationshipbetween brown trout abundance and zooplankton con-sumption may also be due to other factors not accountedfor in our study, such as seasonal variation, temperature,ice cover, and productivity that may influence Arcticcharr / brown trout interactions (Finstad et al. 2011;Helland et al. 2011).

Although we did not find a relationship with lakevolume, lake depth was positively correlated withgreater piscivory in the RDA with abiotic variables.

This yields partial support for the idea that food chainsare longer in larger lakes (Post et al. 2000; VanderZanden and Fetzer 2007). Food chain length has longbeen thought of as a fundamental property of ecosys-tems that reflects the number of energy transfersthrough the food web and links species diversity toecosystem function (Post et al. 2000; Vander Zandenand Fetzer 2007). However, the mechanism relatingfood-chain lengthening to depth in our study remainsunclear. For example, deeper lakes may yield 1) great-er fish prey diversity or abundance facilitated throughgreater habitat heterogeneity or prey refugia (Post etal. 2000), or 2) reduced omnivory in Arctic charr dueto reduced habitat proximity or greater cascadingeffects (Vander Zanden and Vadeboncoeur 2002;McCann et al. 2005). Even when brown trout havedisplaced Arctic charr, food chains are likely to remainlong, as piscivory is common in brown trout. Morespecific studies of dietary habits within these lakeswould help to clarify the mechanisms behind food-chain lengthening.

Depth was not well associated with consumption ofthe Bosmina category, yielding no support for greaterconsumption of zooplankton under greater availabilityof limnetic volume (Schindler and Scheurell 2002).However, further explorations (not shown) indicatedthat this resulted from the presence of many deep lakescontaining Arctic charr that consumed no zooplank-ton. When excluding lakes with no zooplankton con-sumption, a positive trend was observed, indicatingthat a relationship between prey consumption(zooplankton) and availability of prey habitat (lakedepth) should not be completely discounted. Possiblyan interaction between depth and nutrient availabilityor water clarity resulting from glacial silt could exist,which was not accounted for in our study. We alsofound support for greater consumption of zoobenthosin shallow lakes (Hanson and Leggett 1982; Schindlerand Scheurell 2002; Jeppesen et al. 2003), but only forzoobenthos associated with offshore habitats withfine-grained sediment rather than stony littoral habitats(i.e., pea clam and chironomid pupae categories).Perhaps fine-grained sediments are more prevalent inshallow lakes than deep lakes, yielding more habitatavailable for prey within these categories. Finally, wefound no support for greater zooplanktivory in shallowlakes, as has been found by others (Jeppesen et al.2003), since neither zooplanktivory nor greater Arcticcharr abundance was notably correlated with depth.

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Zooplanktivory was instead related to nutrient avail-ability through a negative correlation with the totalnitrogen to phosphorus ratio and positive correlationwith silicon dioxide in the RDAwith abiotic variables.As nitrogen is frequently more limiting than phospho-rus in Icelandic freshwater systems (Adalsteinsson etal. 1992; Einarsson et al. 2004; Friberg et al. 2009;Guðmundsdóttir et al. 2011), this indicated that excessdissolved nitrogen is likely being removed from thewater column by large phytoplankton blooms, and thathigh availability of silicon dioxide could facilitatediatom blooms. Notably, qualitative patterns from thecluster analysis of lakes suggested that zooplanktivoryfrequently occurred in lakes with geologically youngbedrock that would be expected to contain high siliconlevels. Therefore, our results loosely support the ideathat greater consumption of zooplankton was due togreater availability via greater limnetic productivity,but this could not be detected through direct correla-tions. A similar interpretation of why low nutrientlevels were associated with greater zooplankton con-sumption in lake whitefish (Coregonus lavaretus) wasmade by Siwertsson et al. (2010), indicating thatdynamical effects on zooplankton consumption arelikely common.

Alternatively, zooplanktivory may be consistentlystronger under nutrient-poor conditions for a variety ofreasons outlined by Jeppesen et al. (2003). Theseinclude longer pre-reproductive vulnerability of clado-cerans due to colder waters, higher visual acuity ofpredators due to clearer water, or higher benthic pro-duction due to further light penetrance that may alter-nately sustain fish populations. Fish may switch fromzooplanktivory to benthic feeding during spells of lowzooplankton density or by utilizing benthic insect pu-pae as they ascend to the surface prior to emerging asadults. Our data support this idea through the above-mentioned co-occurrence of zooplankton and chiron-omid pupae consumption, as well as the correlationbetween zooplanktivory and chironomid pupae abun-dance. Given the relatively nutrient-poor status ofmost lakes, fast erosion of andic soils in Iceland(Karst-Riddoch et al. 2009), and the high dependenceof zooplanktivory on nutrient availability in our study,nutrient availability may be especially important forthe limnetic food chain. Therefore, variation amonglakes either in the transfer of nutrients to the watercolumn through the consumption of benthic prey or innutrient loading due to variation in surrounding

terrestrial vegetation are likely important factorsexplaining nutrient cycles in Icelandic lakes (Schindlerand Scheurell 2002; Jeppesen et al. 2003).

Trophic cascades have traditionally been studied aslinkages through the limnetic food chain that ulti-mately affect the trophic status and nutrient cyclingwithin lakes (Schindler et al. 1997; Hulot et al. 2000).Arguments have been made both for and against thecase of stronger trophic cascades in nutrient-poorlakes. A broad empirical study indicated that despiteheavier predation pressure on zooplankton in nutrient-poor lakes, this pressure only translated into strongercascades in eutrophic systems (Jeppesen et al. 2003).We cannot directly detect trophic cascades with ourdata, but the generally high dependence of Arctic charron zoobenthos in our study also leaves the possibilityfor littoral trophic cascades to occur. Although rarelystudied, littoral cascades can affect prey abundancesand benthic algal production (Brönmark 1994), andhave been described under similar subarctic conditionsthrough the consumption of snails (Hershey et al.1999). In our study, the trend of greater snail con-sumption with snail abundance may indicate that con-sumption by Arctic charr may actually be causingreductions in snail populations.

We conclude that intraspecific diet variation allowsArctic charr in Icelandic freshwater systems to fillmany functional roles normally expected from multi-ple species in regions with greater fish diversity.Although dietary variation may be extensive within aspecies, how it affects ecosystem processes will de-pend strongly on whether diet variation was temporal-ly stable, possibly as an adapted trait (Bolnick et al.2003; Knudsen et al. 2010), or whether individualvariation simply reflected mobility, which integratesspatial linkages into food webs (McCann et al. 2005).In either case, our study is novel in that it presents arare case in which enough detailed and standardizeddata were available to yield a geographically broadanalysis of how a species functional role can vary withthe environment and potentially affect ecosystem char-acteristics. Recognizing the importance of this intra-specific diversity in regions of low species diversitymay be an important consideration in management ofthese freshwater systems. Further work would espe-cially benefit by the inclusion of temporal populationdynamics, such as density effects, as well as ontoge-netic shifts in diet to more fully understand thesesystems.

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Acknowledgments This research has been supported by theEuropean Marie Curie Research Training Network Fish ACE(Fisheries-induced Adaptive Changes in Exploited Stocks),funded through the European Community’s Sixth FrameworkProgramme (Contract MRTN-CT-2004-005578), and a researchgrant provided by the Icelandic Centre for Research and theUniversity of Washington Graduate School Fund for Excellenceand Innovation. The ESIL project was funded with grants fromthe Icelandic Research Council, the Ministry of the Environ-ment, the Ministry of Agriculture, and the Icelandic FisheriesResearch Fund. Special thanks are given to those who partici-pated in the Ecological Survey of Icelandic lakes, especially theIcelandic Institute of Freshwater Fisheries. We also thank Thom-as Quinn, Daniel Schindler, Ulf Dieckmann, Åke Brännström,and 3 anonymous reviewers for discussing and reviewing earlierversions of this study.

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