Regulation of the functional structure of aquatic communities acrossspatial scales in a major river network

ERIC HARVEY1,2,3,4

AND FLORIAN ALTERMATT1,2

1Department of Evolutionary Biology and Environmental Studies, University of Zurich, Zurich CH-8057 Switzerland2Eawag, Swiss Federal Institute of Aquatic Science and Technology, Duebendorf CH-8600 Switzerland

3D�epartement de Sciences Biologiques, Universit�e de Montr�eal, Montr�eal H2V2S9 Canada

Citation: Harvey, E., and F. Altermatt. 2019. Regulation of the functional structure of aquaticcommunities across spatial scales in a major river network. Ecology 100(4):e02633.10.1002/ecy.2633

Abstract. Moving beyond species count data is an essential step to better understand theeffects of environmental perturbations on biodiversity and ecosystem functions, and to eventu-ally better predict the strength and direction of those effects. Here, coupling an integrative pathanalysis approach with data from an extensive countrywide monitoring program, we tested themain spatial, environmental and anthropogenic drivers of change in the functional structure ofaquatic macroinvertebrate communities along the entire Swiss Rhine river catchment. Func-tional structure was largely driven by inherent altitudinal variation influencing and cascadingto regional scaled factors such as land use change and position in the riverine network, which,in turn, transformed local habitat structure variables. Those cascading effects across scalespropagated through the biotic community, first affecting prey and, in turn, predators. Ourresults illustrate how seemingly less important local factors can act as essential transmissionbelts, propagating through direct and indirect pathways across scales to generate the specificcontext in which each functional group will strive or not, leading to characteristic landscapewide variations in functional community structure.

Key words: altitude; aquatic biodiversity; indirect effects; land use change; macroinvertebrates;meta-community; path analysis; Rhine River, Switzerland; river network; riverscape; trophic structure.

INTRODUCTION

River ecosystems constitute iconic examples of spatialcomplexity with complex regional scale vertical struc-tures (from upstream to downstream; the river network)constraining organism and energy movement (Vannoteet al. 1980, Rodriguez-Iturbe and Rinaldo 1997, Alter-matt 2013, Abbott et al. 2018, Tonkin et al. 2018a, c),but also strong localized horizontal interactions with theterrestrial matrix influencing local habitat characteristicsthrough changes in cross-ecosystem subsidy (Bartelset al. 2012, Richardson and Sato 2015, Little andAltermatt 2018a). The shape of river networks, which allfollow the same geometric scaling properties (Rodri-guez-Iturbe and Rinaldo 1997), has been shown toinfluence biological community dynamics and localspecies richness patterns (Woodward and Hildrew 2002,Muneepeerakul et al. 2008, Carrara et al. 2012, Wood-ward et al. 2012, Tonkin et al. 2018a, c). However,recent studies have found that the relative importance ofthe regional river network and local habitat characteris-tics is somewhat context-dependent as a function ofspecies traits (e.g., dispersal mode) and location-specific

conditions such as terrestrial land use and biotic interac-tions (Romanuk et al. 2006, Tonkin et al. 2016, 2018b).Although those studies tend to emphasize the impor-tance of considering both local and regional factors tounderstand variations in aquatic community, totalexplanatory power remains generally low (Heino et al.2015).In part, the explanatory power issue might be caused

by current approaches tending to focus on the relativeimportance of regional vs. local factors to identify thedominant drivers while largely ignoring the inherentstructure of interdependences among regional and localfactors (Cottenie et al. 2003, Cottenie 2005, G€othe et al.2013, Kuglerov�a et al. 2014, Borthagaray et al. 2015,Heino et al. 2015, Mayfield and Stouffer 2017). Theoften-assumed dichotomy between regional and localfactors generally erodes when considering the mecha-nisms behind those effects (Kuglerov�a et al. 2014, Masa-hiro et al. 2018). For instance, many regional factors,such as altitude, do not have direct mechanistic effectson community structure, but rather their effect is indi-rect via influences on local factors that, in turn, will cau-sally impact communities. Other regional factors such asland use cover are likely to have both direct (e.g.,changes in habitat structure) and indirect (e.g., changesin water chemical quality) impacts on aquatic communi-ties. Thus, local factors that may seem less important at

Manuscript received 31 October 2018; accepted 7 January2019. Corresponding Editor: Elizabeth T. Borer.

4 E-mail: eric.harvey@umontreal.ca

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Ecology, 100(4), 2019, e02633© 2019 by the Ecological Society of America

first might effectively act as transmission belts, propagat-ing a part or the total effects of some regional factors onlocal community structure. Those effects are then likelyto propagate within biological communities as a func-tion of biotic interactions (e.g., effects on prey, which, inturn, affect predators). Overall, we cannot rely onwhole-community endpoint biodiversity measurementsonly, such as local species richness, to understand thedirect and indirect pathways by which regional and localfactors interact and propagate through biological com-munities to influence their structure and function (Jack-son et al. 2016, Cernansky 2017, Hillebrand et al. 2017).While this is widely acknowledged (Tonkin et al. 2018c),studies on riverine metacommunities still mostly focuson species-richness as a descriptor of local communities(Altermatt 2013, Heino et al. 2015), and ignore ecologi-cally more relevant descriptors, such as the functionalstructure and the relative abundance of functionally dif-ferent trophic groups.Here, we disentangled the main spatial, environmen-

tal, and anthropogenic drivers shaping stream macroin-vertebrate functional structure across an entire rivercatchment. Starting from abundance data from a Swiss-wide biodiversity-monitoring program we collectedfunctional traits on each taxon to reconstruct the func-tional structure of each local community for 364 sitescovering the entire Swiss Rhine river catchment. Weused functional groups rather than taxa for three maincomplementary reasons. (1) Theoretical and empiricalevidences suggest that the regional context tends toaffect some functional groups more than others, eitherbased on trophic dependencies (Gravel et al. 2011, Har-vey and MacDougall 2014) or dispersal capacity (Tonkinet al. 2018a). Thus, the use of functional groups is notonly biologically more meaningful but should alsoimprove on the general issue of low explanatory powersdescribed above, because the response to regional andlocal effects will be better defined for each functionalgroup (e.g., all taxa within a functional group share thesame resources). (2) The use of functional groups is anefficient way to bridge from community- to ecosystem-level effects, because clearly defined functions can beassociated with different groups, such as predators,decomposers, or parasites, which could ultimately belinked with ecosystem processes. (3) Finally, all streamsdo not necessarily share the same taxa, but they do sharethe same general functional components (Vannote et al.1980), thus allowing generalization and larger inferenceof results outside the specific study system.Integrating data related to land use change, local

water chemical and physical properties, regional factorsrelated to altitude and position along the dendritic net-work, we used an integrative path analysis framework toidentify specific pathways by which factors interactacross spatial scales to affect the functional structure ofstream invertebrate communities. We started from ameta-model representing our initial predictions of thedirect and indirect effects of each predictor on each

functional group (Appendix S1: Fig. S1). The meta-model was built based on a priori knowledge about thesystem (Altermatt 2013, Altermatt et al. 2013, Kaelinand Altermatt 2016, Masahiro et al. 2018), and alsobased and facilitated by the use of functional groupsdefined by their main feeding resources (e.g., any factoraffecting a given resource will predictably affect eachtaxon within a specific functional group, seeAppendix S1: Fig. S1). The SEM approach allowed usto disentangle the specific pathways linking correlatedpredictors (i.e., either in the graphical form of a fork,mediator or collider) and their relative importance. Asopposed to many previous studies (Cottenie 2005, Alter-matt 2013, Heino et al. 2015, Tonkin et al. 2016), inter-actions between local and regional factors in our studyrepresent “causal” chains of indirect effects testingdirectly the “propagation” of effects “across scales” in ahierarchical sense.

METHODS

Data

Our study used the aquatic macroinvertebrate abun-dance data from 364 sites across the whole Rhine rivercatchment in Switzerland, covering about 30,000 km2.The data are collected and curated by a Swiss govern-mental monitoring program (“Biodiversity Monitoringin Switzerland BDM”; BDM Coordination Office 2014).Sampling is done following a systematic sampling gridand was conducted in wadeable streams, second order orlarger in size, thus excluding standing waterbodies, first-order streams, and large rivers inaccessible by wading(Stucki 2010). Each site was sampled once between 2009and 2014 with seasonal timing of sampling adjusted withrespect to elevation: the sampling period for a site wasbased on local phenology so as to collect as manymacroinvertebrate taxa as possible for a given elevation(Stucki 2010). Because the yearly subsets of sites wererandomly sampled across the catchment, variations inannual climatic conditions are not expected to affectcommunity composition in a systematic direction. Wealso validated this by including the number of years ofsampling as a factor in our models; it was never identi-fied as a significant/relevant covariable and thus couldbe ignored and data were pooled across years.The survey was done using a standard kick-net

(25 9 25 cm, 500 lm mesh) sampling procedure definedin the Swiss Macrozoobenthos Level I module forstream benthic macroinvertebrates (BDM CoordinationOffice; see Stucki 2010, Altermatt et al. 2013). Briefly, atotal of eight kick-net samples were taken at each site tocover all major microhabitats within a predefined sectionof the river (area covered per site was width 9 10 timesthe average width in length; subsamples were pooled).Therefore, all locally represented habitat types (includingvarious sediment types such as rocks, pebbles, sand,mud, submerged roots, macrophytes, leaf litter, and

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artificial river-beds) and water velocities were sampled.Samples were preserved in 80% ethanol and returned tothe laboratory for processing. In the laboratory, allmacroinvertebrates used in this study were sorted andidentified to the family level by trained taxonomists(total of 63 families see Appendix S1: Table S1 for a list).For further details on the sampling method and thedatabase (see also Altermatt et al. 2013, Seymour et al.2016, Kaelin and Altermatt 2016).

Predictors

We used 38 predictors representative of regional, localand hydrological conditions, as well as land use coverageand position in the dendritic network (see Appendix S1:Table S2 for a complete list of each variable with descrip-tion). Regional predictors included altitude at the sam-pling site and catchment size. Local predictors representinstream habitat conditions that were measured directlyat sampling site. Local predictors included features ofchannel cross-section (e.g., width, depth, and their vari-ability), riverbed conditions (e.g., mud deposition andattached algae), aquatic conditions (e.g., turbidity anddissolved iron sulfide concentration), and a discreteranking of human alterations to riverbank and riverbed(see Stucki [2010] for details). Hydrological predictorsare factors representing geometry conditions of the rivernetwork in the upstream catchment of a sampling site.Those predictors included geomorphological (e.g., riv-erbed slope), hydrological (e.g., mean discharge) andchemical (e.g., inflowing wastewater volume) conditions.Land use predictors represent terrestrial conditions sur-rounding a sampling site. Those predictors included sixland use classes considering adjacent influences to thelocal site with a lateral buffer distance of 500 m, 1, 5, 10,100, or 1,000 km (Seymour et al. 2016). We know fromprevious work on this data that the 5-km scale is mostsignificant in affecting stream invertebrate diversity(Masahiro et al. 2018); thus, we used only the six landuse classes with lateral buffer distance of 5 km in ouranalyses. Network predictors were measured using adirected graph and represent the position of each sam-pling site in the river dendritic network (e.g., centralityand distance to the outlet). Centrality was measured asthe out-closeness centrality, which measures how manysteps are required, from a focal vertex, to access everyother vertex in the directed network (Csardi and Nepusz2006).Many land use predictors were strongly skewed

toward zero leading to important loss of informationand degrees of freedom when analyzing each variableindividually. Instead, to emphasize a more continuoustransition between each land use type, for further analy-sis, we used first axis scores from a canonical correspon-dence analysis representing a gradual shift in land usefrom high proportion of human settlement and agricul-tural lands to high proportion of natural meadows (seeAppendix S1: Fig. S2). Such a gradient is dominant in

Switzerland with lowlands representing most of theurban and agricultural lands. Grouping our land usedata this way reduced our total number of predictors to34 for 364 sites.

Functional structure

We built the functional structure of each streammacroinvertebrate community for each site, using thefreshwaterecology European database (Schmidt-Kloiberand Hering 2015) and extracting the feeding type metric(sensu Moog 1995) for each of our 63 stream macroin-vertebrate families. The data from the freshwaterecologydatabase were at the species level. Thus, we used aver-aged values across all species within family to determinethe dominant feeding type of each of the 63 families. Atthe end, our data were comprised of abundance data for63 families across seven feeding groups (following defini-tion by Moog [1995], see Appendix S1: Table S1) defin-ing overall functional structure. The seven groups weregrazer scrapers (13 families, mainly feeding on particu-late organic matter from endolithic and epilithic algaltissues and biofilm), shredders (10 families, mainly feed-ing on coarse particulate organic matter from fallenleaves and plant tissue), gatherer collectors (10 families,mainly feeding on sedimented fine particulate organicmatter), active filter feeders (one family, mainly feedingon suspended particulate organic matter actively filteredfrom the water column), passive filter feeders (two fami-lies, mainly feeding on suspended particulate organicmatter passively trapped from running water), predators(24 families, mainly feeding on prey), and parasites (twofamilies, mainly feeding from hosts).

Analyses

Ordination.—To identify the main environmental andspatial drivers of the functional structure of streammacroinvertebrate communities, we used a distance-based redundancy analysis on Euclidean distances(db-RDA, following Legendre and Anderson [1999])followed by an automatic stepwise model buildingapproach for constrained ordination based on theadjusted R2 of the full model (499 permutations, follow-ing Blanchet et al. [2008]). The significance level atP < 0.05 of the final model and of each selected termwas tested using a permutation ANOVA (200 permuta-tions, see Table 1). The uses of pairwise Euclidean dis-tances ensure that our analyses really emphasize changesin the relative proportion of each functional groupwithin each community rather than between site changesin absolute abundance or composition (Anderson et al.2011). Because we did not have any a priori knowledgeon which predictors might be most important, we usedall 34 predictors into our analytical pipeline. At the end,eight predictors were selected with first and second axesrespectively explaining 67% and 18% of the total vari-ance for the constrained axes.

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Structural equation model.—Ordination approaches pro-vide insightful information on main drivers; however,they do not provide information on the potential inter-actions and pathways by which each driver affects differ-ent functional groups. For instance, a regional factorsuch as altitude does not have any direct ecological rele-vance. Rather, altitude will affect functional groups viaits effects on local factors (e.g., temperature or decidu-ous forest cover). Thus, even variables that may seem lessimportant at first might act as transmission belts for theeffects of other factors on stream invertebrate functionalstructure. Moreover, factors affecting predators can doso by affecting the predator directly (e.g., high turbiditydecreasing hunting efficiency) or indirectly by affecting

its prey. Based on the information from the db-RDAanalysis, we built a meta-model representing the poten-tial links of importance in the system and how theyaffect functional structure. We hypothesized that effectswould mainly cascade from regional factors affectinglocal factors, which in turn, affect different functionalgroups (Fig. 1a). For this exercise, variables were classi-fied as either regional or local based on the inherenthierarchy in their effects. Local scale variables are vari-ables measured at the local scale and that directly influ-enced biological processes. Regional scale variables arefeatures of the landscape and that can indirectly influ-ence biological processes through their effects on lower-level variables. This classification is more like a gradientthan a bimodal (regional vs. local) classification andallowed us to properly represent the hierarchical natureof the predictors across scales. We then used structuralequation modeling to test the fit of this initial meta-model against the data. Subsequently, we used the resid-ual covariance matrix and modification indices (Rosseel2012) to identify potentially important missing links thatwere not included in the original meta-model. Afteradding those links to the model, we then identifiedand pruned least important links (based on P valuesand effect on model fit) to avoid over-parameterizationand over estimation of explanatory power. Because weused categorical factors, we measured the fit of our modelto the data with a robust diagonally weighted least squareestimator (DWLS; see Rosseel 2012). Our final model

TABLE 1. Permutation ANOVA (200 permutations) on thefinal db-RDA model.

df SS F P

Altitude 1 3.63 22.75 0.001Human modification indices 3 2.16 4.51 0.001Foam level 2 0.97 3.04 0.006Mud level 2 0.88 2.75 0.004Land use gradient 1 0.81 5.10 0.001Depth variation 2 0.95 2.98 0.012Deciduous cover 1 0.60 3.80 0.011Turbidity level 2 0.72 2.27 0.031Residual 193 30.86

FIG. 1. Spatial variation in the functional structure of riverine macroinvertebrate communities. The figure shows the Rhine riverbasin. All third-order streams or larger are shown (arrow indicates direction of flows). Each pie chart represents the functionalstructure (relative abundance of each functional group in the community) for one of the 364 sampling sites across the river basin.Each functional group is represented by a silhouette of one of its iconic taxon: gatherer-collector (Oligochaeta), grazer-scraper(Limnaeidae), predator (Cordulegaster), passive filter feeder (Simuliidae), active filter feeder (Sphaeridae), parasite (Hydracaria),shredder (Gammaridae).

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converged after 105 iterations and showed a good fit tothe data (n = 364, DWLS = 63.36, df = 62, P = 0.428).All analyses were conducted with R 3.1.2 (R Core

Team 2016), using the vegan package (Oksanen et al.2015) for the db-RDA (capscale function) and stepwisemodel building (ordistep function), the igraph package(Csardi and Nepusz 2006) to compute network metrics,and the lavaan package (Rosseel 2012) for SEM analysis.The code to reproduce analysis and results is available athttps://zenodo.org/badge/83574106.svg

RESULTS

Testing the main spatial, environmental, and anthro-pogenic drivers of the functional structure of aquaticmacroinvertebrate communities along the Swiss Rhineriver catchment, we found that variations in relative(Figs. 1, 2) and absolute (Fig. 3) abundances of eachfunctional group across the whole river basin were lar-gely driven by altitudinal variations (Table 1 and Fig. 2).In turn, altitude influenced several other regional andlocal scale factors leading to a complex array of directand indirect pathways across spatial scales, eventuallyleading to landscape wide variations in functional com-munity structure (Figs. 1, 3).

More specifically, altitude led to a decline in decidu-ous forest cover, was associated with an increase in dis-tance to river outlet and drove land use change fromhigh settlement and agricultural lands to high altitudinalnatural meadows (Fig. 3). In turn, those regional factorsinfluenced local habitats with transition to naturalmeadows leading to lower water foam levels (a proxy ofeutrophication), and increased distance to outlet leadingto higher turbidity level (Fig. 3). Lowland upstream siteswere associated with higher probability of finding modi-fied streams (see negative effects of altitude and positiveeffects of distance to outlet on river modification indexon Fig. 3). Local habitat factors then affected variousfunctional groups with mud-level negatively impactingshredder, passive filter feeder and grazer-scraper abun-dances (Fig. 3), foam (proxy of eutrophication) posi-tively impacting gatherer-collector and passive filterfeeder, river habitat modification positively influencinggrazer-scraper, gatherer-collector and parasite, andhigher riverbed variations in depth positively affectingpassive filter feeder (Fig. 3). Finally, all those regionaland local factors affected predator abundance throughaffecting their prey (Fig. 3).We also found evidence for direct effects of regional

factors on some functional groups. Altitude had a direct

FIG. 2. Main environmental and spatial drivers of the functional structure of riverine macroinvertebrate communities. The ordi-nation figure is the final distance-based redundancy analysis (db-RDA) model selected by an automatic stepwise model buildingapproach based on adjusted R2. The first and second axes respectively explain 67% and 18% of the total variation in functionalstructure (relative abundance of each functional group per community, see Methods). A specific geometric shape represents eachcategorical predictor, with the gray gradient representing the level of each predictor. For the explanation of the silhouette of eachfunctional group and their abbreviations, see Fig. 1.

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negative impact on passive filter feeder and shredderabundances (Fig. 3), potentially mediated by unmea-sured (i.e., missing) local variables. Land use transitionto natural meadows had a direct positive effect onpredator abundance suggesting that predators tend tofare better in high altitudinal streams surrounded by nat-ural meadows than in low altitude zones characterizedby a matrix of agricultural lands and human settlements,and a negative effect on grazer-scraper (Fig. 3). Distanceto outlet directly influenced gatherer-collector and para-site abundances negatively (Fig. 3), illustrating that loca-tion along the river network has both indirect (mediatedby local habitat factors) and direct effects on aquaticinvertebrate functional structure.

There were also causal pathways among variables ateach spatial scale as described above for altitude andregional factors. Among local factors, river modificationsnegatively impacted stream depth variation, which inturn, negatively influenced turbidity (Fig. 3). Thus, rivermodifications had an indirect positive effects on turbiditymediated by a change in riverbed depth variation.Overall, our results illustrate the complex interactions

among local and regional scale predictors in shapingfunctional structure along an entire catchment (Fig. 3)and how the outcome of those interactions across scalegenerates the specific context in which each functionalgroup will strive or not, leading to large spatial scale vari-ations in functional community structure (Figs. 1, 2).

FIG. 3. Direct and indirect pathways by which regional and local drivers influences riverine macroinvertebrate functional struc-ture. (A) We hypothesized that most regional (dotted lines) factors would influence the biotic community (dashed lines) indirectlyvia an effect on local habitat factors (solid lines). We also expected within spatial scale interaction structure at both regional andlocal scales (looped arrows). Changes to biotic communities are usually analysed assuming that each predictor influence each taxaor functional group independently in the community; however, we hypothesized that the specific structure of interactions within abiotic community would rather drive the propagation of effects from specific entry points (prey) to the entire community (loopedarrow on the community box). (B) Final structural equation model illustrating the different direct and indirect pathways by whichregional (dotted lines) and local (solid line) factors interact and then propagate through the functional community (dashed lines).Each value is the standardized coefficient (standardized estimate from each partial regression, all significant at P < 0.05), represent-ing the strength of the effect of one variable on another. Red arrows, negative effects; black arrows, positive effects. Each functionalgroup is represented by a silhouette of one of its iconic taxon: GAT, gatherer-collector (Oligochaeta); GSC, grazer-scraper (Lim-naeidae); PRED, predator (Cordulegaster); PFF, passive filter feeder (Simuliidae); AFF, active filter feeder (Sphaeridae); PAR, par-asite (Hydracaria); SHR, shredder (Gammaridae).

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DISCUSSION

It is increasingly acknowledged that local species rich-ness, still probably the most commonly used descriptorof ecological communities, may not be an adequateproxy beyond a pure biodiversity perspective, and beespecially limited for understanding the ecosystem pro-cesses and dynamics of natural communities (Harveyet al. 2016, Hillebrand et al. 2017). Consequently,describing communities from the perspective of speciestraits, functional roles, or interactions may be morerewarding to understand and eventually to conserve theintegrity of ecological communities (McGill et al. 2006,Hillebrand and Matthiessen 2009, Tylianakis et al. 2010,Harvey et al. 2016).Riverine ecosystems offer a prime case example in

which the study of both drivers as well as responses of thefunctional structure of communities may be very insight-ful. Rivers are among the most biodiverse ecosystemsworldwide with respect to species richness (Dudgeonet al. 2006), but this diversity is often not attributable toenvironmental factors (Heino et al. 2015). In contrast,however, the functional role of major species groups inthese systems, such as macroinvertebrates, is welldescribed, with species covering functions from decom-posers, to predators and parasites, and the functional roleof these species is also linked to major processes andfunctions at the ecosystem level (Little and Altermatt2018b). Thus, including and analyzing the drivers of thefunctional structure of aquatic macroinvertebrate com-munities may not only give a better understanding of bio-diversity, but also of associated ecosystem functions.Testing for the main environmental and spatial drivers

of functional structure in stream macroinvertebrates, wefound a complex array of direct and indirect pathwaysby which regional and local drivers interact to influencerelative and absolute abundances of aquatic macroinver-tebrate functional groups, eventually leading to land-scape wide variations in functional community structureacross a major river network. More specifically, cascad-ing effects across spatial scales starting with altitude as akey driver influencing other regional factors, which inturn affected various local habitat characteristics directlyto influence functional group abundances. Most effectspropagated through the community by first affectingprey, which then affected predator abundances.The importance of the river network has been shown

to be context-dependent as a function of location-speci-fic conditions such as terrestrial land use and bioticinteractions (Tonkin et al. 2016). Our results suggestthat those location-specific conditions can, in part, inter-act with some river network properties because they arenot distributed randomly along the network but ratherlocated at specific substructures in the network. Thoseeffects constitute in themselves indirect effects of theriver network rather than the absence of effect. Morespecifically, we showed that distance to outlet affectedfunctional groups directly but also indirectly via its

positive effect on the human modification index. A maincomponent of this result is the observation that lowlandheadwater locations are systematically more affected byhuman-induced riverbed and riverbank modificationsthan headwaters at higher elevations. Consequently, ourresults illustrate how the significance of spatial andregional factors can be masked by location-specific con-ditions when indirect pathways are not being taken intoaccount (Grace et al. 2016).Our results also emphasize the complex response of

each individual functional group (see Fig. 3) to eachindividual environmental and spatial factor. Interpretingany of these patterns independently can be misleadingand only an integrative approach allows a coherentunderstanding of community structure, and eventuallypredicting shifts in response to environmental changes(Peterson et al. 2014, Grace et al. 2016, Masahiro et al.2018). Although our predictors are hierarchically orga-nized (e.g., regional factors influencing local factorsinfluencing prey, which in turn impact predators) ratherthan multiplicative, our study echoes recent calls to takea more integrative approach to the study of multiple-stressors and environmental changes, especially in aqua-tic ecosystems (Ormerod et al. 2010, Elbrecht et al.2016, Jackson et al. 2016, Beermann et al. 2018).Shifts in functional structure are a well-known driver

of ecosystem processes (Emmett Duffy et al. 2005,Harvey et al. 2013, Poisot et al. 2013, Trzcinski et al.2016). Predicting those shifts, however, is a challengingendeavor because of multiple stressors interacting at dif-ferent spatial scales and potentially affecting differentfunctional groups simultaneously. By breaking down themany indirect pathways by which main effects (e.g., alti-tude or land use change) are affecting functional groups,it is much easier to understand the direction of effects.For instance, shredders feed on dead plant material andthus any factors reducing the amount (altitude) or access(mud) to that resource will impact their abundances and,in turn, a reduction in the relative importance of shred-ders can be directly taken as evidence of ecosystem-levelchanges (see next paragraph). In that context, predictingthe response and the effect of environmental changesbecomes a lot less challenging. We observed importantshifts in functional group’s relative abundances. Forinstance, our ordination analysis identified an importantgradient from gatherer-collector-dominated to shredder-dominated communities (Figs. 1, 2). This is corrobo-rated by the structural equation modeling where highergatherer-collector abundance is mainly associated withhigh levels of riverbed and bank modifications, whileshredders seem to strive in less disturbed environments(Figs. 2, 3).At the ecosystem level, we postulate that this shift

from coarse (shredder) to fine particle (gatherer-collec-tor) feeders along those environmental gradients islinked to variations in the type of resource available(Lepori and Malmqvist 2007). Such shifts in functionalstructures have also implications for energy transfer and

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stoichiometric constraints in the community becauseshredders mainly feed on allochthonous leaf particles,which tend to be rich in carbon but nitrogen poor, whilefine particles associated to agricultural lands tend to benutrient rich but a poorer source of carbon.Looking at the functional or trophic structure of com-

munities is an essential step to better understand theeffects of environmental perturbations on biodiversityand ecosystem functions, but also to eventually betterpredict the strength and direction of those effects (Har-vey et al. 2016, Cernansky 2017, Hillebrand et al. 2017).Our results illustrate the complex interactions amonglocal and regional scale predictors in driving functionalstructure and how the outcome of those interactionsacross scale generates the observed large-scale variationsin aquatic functional community structure.

ACKNOWLEDGMENTS

The Swiss Federal Office for the Environment (BAFU) pro-vided the BDM data. We thank Roman Alther for discussionand for help in extracting GIS data. We thank the many peoplewho conducted field and laboratory work within the biodiver-sity monitoring program, and Nicolas Martinez and TobiasRoth for help in data provisioning. Funding was provided bythe Swiss National Science Foundation Grants PP00P3_150698and PP00P3_179089 and the University of Zurich Priority Pro-gram (URPP) on Global Change and Biodiversity to F. Alter-matt and by the University of Zurich Forschungskredit to E.Harvey. E. Harvey and F. Altermatt designed the research; F.Altermatt provided and prepared the data; E. Harvey processedand analysed the data; E. Harvey wrote the first draft of themanuscript; E. Harvey and F. Altermatt contributed for furthermanuscript revisions.

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SUPPORTING INFORMATION

Additional supporting information may be found in the online version of this article at http://onlinelibrary.wiley.com/doi/10.1002/ecy.2633/suppinfo

DATA AVAILABILITY

Data are available on Zenodo: https://doi.org/10.5281/zenodo.2533394

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