jo ur nal ho me page: www.elsev ier .com/ locate / ecol ind
axonomic surrogacy, numerical resolution and responses of streamacroinvertebrate communities to ecological gradients: Are the
nferences transferable among regions?
ani Heinoa,b,∗
Finnish Environment Institute, Natural Environment Centre, Ecosystem Change Unit, P.O. Box 413, FI-90014 University of Oulu, FinlandDepartment of Biology, P.O. Box 3000, FI-90014 University of Oulu, Finland
r t i c l e i n f o
rticle history:eceived 16 August 2012eceived in revised form 17 July 2013ccepted 19 July 2013
eywords:ommunity compositionacroinvertebrates
axonomic resolutionichnesstreams
a b s t r a c t
It is typically at the species level where the responses of organisms to natural environmental gradients arethe most clearly visible. However, due to the fact that many organismal groups are poorly resolved at thespecies level in various geographical regions, many studies have to still rely on supra-specific taxa whenanalyzing community–environment relationships. This study examined the community–environmentand richness–environment relationships of stream macroinvertebrates at three taxonomic levels (i.e.,species, genus, family) in three high-latitude drainage basins. Despite the fact that species-to-genus andspecies-to-family ratios were low and of similar magnitude in all drainage basins, each region showeddifferent rankings in terms of the species-, genus- and family-level data being best explained by environ-mental variables. Furthermore, within each region the three taxonomic levels did not respond similarlyto the underlying environmental gradients, which was evident with taxonomic richness and taxonomic
2
composition based on both quantitative and qualitative data (adjusted R of models varied from 0 to 0.604for taxonomic richness and from 0.068 to 0.307 for taxonomic composition). The present findings thusdo not support the views: (i) taxonomic surrogacy in stream macroinvertebrate communities is trans-ferable among regions and (ii) that higher taxon taxonomic surrogates can be used without restraintsto infer species-level community–environment and richness–environment relationships in studies of
ervat
community ecology, cons
. Introduction
Studying the responses of biodiversity to environmental gradi-nts is a key topic in community ecology, conservation biology andnvironmental assessment. These research fields typically utilizeata at the species level due to the notion that species are the gen-rally accepted units of ecological analyses (Gaston, 2000; Lenatnd Resh, 2001; Bertrand et al., 2006). Species are undoubtedlyuitable units of study in the context of community–environmentelationships, as it is typically at the species level where theesponses of organisms to natural environmental gradients arehe most clearly visible (Warwick, 1993; Bevilacqua et al., 2012).
owever, due to the fact that many organismal groups are poorly
esolved at the species level in various geographical regions, manytudies have to still rely on supra-specific taxa when analyzing
∗ Correspondence to: Finnish Environment Institute, Natural Environment Centre,cosystem Change Unit, P.O. Box 413, FI-90014 University of Oulu, Finland.el.: +358 505845977.
community–environment relationships. Evidence to date suggeststhat species richness varies quite predictably with richness athigher taxonomic levels, suggesting that taxonomic surrogatesmay perform well in regions where little or no species leveldata exist (Gaston and Williams, 1993; Williams and Gaston,1994). While early studies focused on congruence in richnesspatterns among different taxonomic levels, more recent stud-ies have also addressed variation in community composition andcommunity–environment relationships in the same context (Heinoand Soininen, 2007; Terlizzi et al., 2009).
Taxonomic surrogacy (i.e., the degree to which higher taxo-nomic levels portray species-level patterns; Bertrand et al., 2006)and taxonomic sufficiency (i.e., identifying organisms to the tax-onomic level needed to satisfy the objectives of a study; Ellis,1985) have been under considerable study in terrestrial, marineand freshwater environments, although no consensus surroundingthe two concepts exists (Ferraro and Cole, 1990; Pik et al., 1999;Lovell et al., 2007; Bevilacqua et al., 2012). The idea behind both
concepts is that higher taxonomic levels could be used to repro-duce patterns at the species level, which would also entail thatcommunities described at different taxonomic levels show similarresponses to environmental variation. Despite the fact that many
tudies have found higher taxa to portray community responseso ecological gradients similarly to species-level data (Dethier andchoch, 2006; Heino and Soininen, 2007; Terlizzi et al., 2009),here is currently no consensus whether species or higher taxahould be used in the examination of community–environmentelationships. Some researchers stress that we should attempt todentify most organisms to the lowest possible taxonomic level,ecause species should carry most information about the responsesf organisms to environmental variation (Lenat and Resh, 2001;arshall et al., 2006; Melo, 2005; Verdonschot, 2006). By contrast,
ther researchers doubt that species-level identifications are worthhe effort, because they provide little extra information over higheraxonomic levels about community responses to environmentalonditions (Warwick, 1993; Bowman and Bailey, 1997; Bailey et al.,001). The latter group of researchers often argues that there isot enough money, time and taxonomic expertise to devote topecies-level identifications of most organisms groups (Marshallt al., 2006; Jones, 2008). They also raise concerns about possibleisks of misidentifications when species-level data is the goal. It ishe objective of the study, however, which dictates which level ofdentification should be used (Ellis, 1985; Jones, 2008). For exam-le, conservation-oriented studies should preferably concentraten species-level data, whereas rapid bioassessment studies mighttilize higher taxa if they portray well enough the ecological statef a stressed ecosystem (Lenat and Resh, 2001; Jones, 2008).
The degree to which taxonomic surrogacy applies in a certainituation is likely to vary considerably among regions. Hawkins andorris (2000) suggested that higher taxa could be used to reproduceatterns of species-level data in regions where species-to-higheraxon ratios are low, implying that higher taxa have not radiatedonsiderably. By contrast, in regions where species-to-higher taxonatios are high, implying high degrees of adaptive radiation, higheraxa may perform poorly in portraying patterns of species levelata. This is because each species belonging to a higher taxon mayossess different ecological niches and respond differently to envi-onmental variation across a set of sites (Lenat and Resh, 2001),lthough some researchers have suggested that species within aigher taxon show some ecological coherence (Warwick, 1993).he suggestion of ecological coherence also pertains to the idea thataxonomic surrogates might perform well even in regions with highpecies-to-higher taxon ratios. This reasoning is also supportedy empirical evidence that community–environment relationshipsre largely invariant at different taxonomic levels (at least up tohe family level), suggesting that higher taxa could be used as short-uts for portraying responses of biodiversity to ecological gradientsDethier and Schoch, 2006; Heino and Soininen, 2007; Terlizzi et al.,009).
Another topic about community–environment relationshipsertains to the use of quantitative versus qualitative taxonomicata. Typically, quantitative abundance data should be preferredver qualitative presence–absence data, as the former containsore information about the responses of organisms to ecologi-
al gradients. In general, the lows and highs in the abundancesf organisms across a set of sites could inform us greatly aboutommunity–environment relationships, which presence–absenceata may not necessarily do as clearly. However, few suchests are available which have compared quantitative versusualitative data in the context of community–environment rela-ionships. These studies have typically reported equally strongommunity–environment relationships between quantitative andualitative data, although the details of these relationships mayiffer to some degree between the data types (Cushman and
cGarigal, 2004; Heino et al., 2010a,b; De Bie et al., 2012). However,
t would be interesting to examine quantitative and qualitative datan the context of taxonomic surrogacy, as the resolution of datan both numerical (abundance, presence–absence) and taxonomic
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(species, genus, family and others) terms may affect our con-clusions about community–environment relationships (Andersonet al., 2005; Heino, 2008; Landeiro et al., 2012).
Most studies on taxonomic surrogacy in the freshwater realmhave dealt with the bioassessment of anthropogenic environmen-tal changes (Bailey et al., 2001; Waite et al., 2004; see also areview by Jones, 2008). Fewer studies have been conducted acrosssets of streams that are near-pristine and considered to be in“reference condition” (Heino and Soininen, 2007; Heino, 2008).Studying if taxonomic surrogates can be applied in near-pristinestream ecosystems is important, given that if we can reproducespecies-level patterns using higher taxa across natural environ-mental gradients, we are also likely to do so across anthropogenicgradients. This is because species are likely to respond to subtleenvironmental variation, while higher taxa are mostly responsiveto more drastic anthropogenic effects (Ferraro and Cole, 1990;Warwick, 1993). However, very few stream studies have taken amulti-region approach on taxonomic surrogacy by examining ifthe responses to environmental gradients differ among species,genus and family levels. Furthermore, among-region differencesand within-region heterogeneity in community composition havenot been associated with taxonomic surrogacy in the freshwaterrealm, although such a study provided evidence of the similarity ofpatterns at the species, genus and family levels in the marine realm(Terlizzi et al., 2009).
The aims of the present study were to examine if among-regiondifferences in “average community composition” and “heterogene-ity in community composition” were similar at the species-, genus-and family levels in stream macroinvertebrate data sets. Further-more, the community–environment and richness–environmentrelationships at each of the three taxonomic levels were examinedwithin each region and across the regions. Building on previousfindings in the research on taxonomic surrogates, I assumed thatcommunity composition at each taxonomic level would vary signif-icantly among the regions and be related to different environmentalgradients within each of the three study areas. Furthermore, iftaxonomic surrogacy is directly supported, (i) species-, genus-and family-level data sets should all vary significantly among theregions (both with regard to average community composition andheterogeneity in community composition), (ii) taxonomic compo-sition at each taxonomic level should show similar relationships toenvironmental gradients in each region and (iii) taxonomic richnessat each taxonomic level should vary similarly along environmentalgradients in each region. Finally, (iv) ecological inferences abouttaxonomic surrogates could be transferable among regions if vari-ation in taxonomic composition and/or taxonomic richness atdifferent taxonomic levels was similarly related to environmen-tal variation in different regions (i.e., the same predictor variablesentering the models and similar amount of variation in compo-sition and/or richness explained at each taxonomic level in allregions). If strong support for these four assumptions was obtained,then higher taxonomic levels could be used as surrogates forspecies level patterns in terms of community–environment andrichness–environment relationships.
2. Materials and methods
The test data set comprised three northern drainage basins,where detailed surveys of stream macroinvertebrates have beenconducted. Partly the same data set as used in the present study hasbeen formerly utilized in assessing community traits–environment
relationships (Schmera et al., 2013) and metacommunity patternsat the species level (Heino et al., 2012). These studies found cleardifferences in assemblage traits among the regions, clearly dis-cernible variation in traits along environmental gradients and
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irtually no spatial structuring of community composition at thepecies level within each region.
.1. Study areas
The following description of the study areas and field methodss largely based on two previous studies (Heino et al., 2012; Heino,013). The details of the study areas will, however, be reiteratedere to facilitate understanding the ecological context of the threerainage basins.
The first study area is located in the Iijoki drainage basin (cen-red on 65◦N, 27◦E). The study area is characterized by middleoreal coniferous forests and peatlands. Headwater streams inhe drainage basin are often modified by forestry, drainage andog floating, although some near-pristine running waters are alsoresent. Only near-pristine sites were sampled. The streams areenerally slightly acidic, and nutrients range from low to moderateHeino et al., 2012; Heino, 2013). A total of 20 first to third ordertreams were surveyed in the Iijoki drainage basin.
The second study area is located in the Koutajoki drainageasin in northeastern Finland (centred on 66◦N, 29◦E). The bedrockf the study area is highly variable, with extensive occurrencesf calcareous rocks. Accompanied by considerable altitudinal dif-erences, this geological variability is mirrored in highly variableegetation, ranging from northern boreal coniferous forests toixed-deciduous riparian woodlands, and from nutrient-poor bogs
o fertile fens. Headwater streams in the drainage basin are gener-lly near-pristine, and they are characterized by circumneutral tolkaline water, low to high levels of humic substances, and low tooderate nutrient concentrations (Heino et al., 2012; Heino, 2013).
total of 20 first to third order streams were surveyed in the Finnishart of the Koutajoki drainage basin.
The third study area is located in the Tenojoki drainage basincentred on 70◦N, 27◦E). This subarctic study area is characterizedy arctic-alpine vegetation, comprising mountain birch woodlandst low altitudes and barren fell tundra at higher altitudes. Streamaters are circumneutral, and nutrient levels are indicative ofltraoligotrophic systems (Heino, 2013). Headwater streams in therainage basin range from pristine to near-pristine, as forestry andssociated land uses are not generally feasible at these latitudes. Aotal of 30 first to fourth order headwater tributary streams wereurveyed in the Tenojoki drainage basin, draining into the maintem of the River Tenojoki.
.2. Environmental variables
Several riparian, in-stream habitat and water chemistry vari-bles were measured at each site. Percentage cover of deciduousrees was assessed in a 50-m section on both banks directlypstream of the sampling site. Shading was estimated visually asercent canopy cover at 20 locations along transects (the numberf which depended on stream width) at the whole study sec-ion. Current velocity (at 0.6 × depth) and depth were measuredt 30 random locations along cross-stream transects, the numberf which depended on stream width. Stream wetted width waseasured at each site based on five cross-stream transects. Moss
over (%) and substratum particle class cover (%) were assessedt ten randomly spaced 50 cm × 50 cm plots. Visual estimates ofhe percentage cover of five particle size classes were made forach plots using a modified Wentworth scale: (i) sand (diame-er 0.25–2 mm), (ii) gravel (2–16 mm), (iii) pebble (16–64 mm),iv) cobble (64–256 mm), and (v) boulder (256–1024 mm). Water
amples were collected simultaneously with the field sampling,nd they were analyzed for pH, conductivity, water colour, andotal phosphorus using Finnish national standards (National Boardf Waters and the Environment, 1981). Water colour and total
rs 36 (2014) 186–194
phosphorus were not measured in the Tenojoki drainage basin, asthere is little variability in colour, and total phosphorus is typicallybelow easily detectable limits.
2.3. Macroinvertebrate data
Stream macroinvertebrates were sampled in the Koutajokidrainage basin in the last week of May in 2008, in the Iijoki drainagebasin in the last week of May in 2009, and in the Tenojoki drainagebasin in the second week of June in 2010. As the resources for thisstudy did not allow sampling all the sites within a short periodof time in a single year, the sites had to be sampled in differentregions in different years. I strongly argue that it is more importantto sample the sites in the same season (i.e., soon after the snowmeltin the spring) than in the same year. If the sites are not sampledwithin a short period of time in the same season, the results maynot portray spatial differences but seasonal differences in streammacroinvertebrate communities. Such seasonal variation related toaquatic insect life cycles among spring, summer and autumn sam-ples is typical in northern streams (J. Heino, pers. obs.). The springseason after the snowmelt is also the season when the majorityof macroinvertebrates in high latitude streams are still in the lar-val stage. This was evidenced during the field sampling by the factthat we observed only occasional adult stoneflies flying around thestreams (Heino et al., 2009). The timing of sampling also facilitatedthe identification of aquatic insect larvae, most of which are closeto their maximum size at this time of the year.
At each site, we took a two-minute kick-net (net mesh size0.3 mm) sample covering most microhabitats present in a riffle ofapproximately 100 m2. This sampling effort typically yields morethan 70% of species occurring at a headwater site in a given season,mainly missing rare tourist species that occur only sporadically inheadwater streams (Mykrä et al., 2006). Macroinvertebrates andassociated material were immediately preserved in 70% ethanol inthe field, and the samples were taken to the laboratory for furtherprocessing and identification. All macroinvertebrates were identi-fied to the lowest feasible level. A few individuals of various wormsthat were not identified to species or genus level were omittedfrom the analyses. The data set was carefully harmonized acrossthe three basins, so that among-basin differences could not be dueto differences in the identification.
2.4. Statistical methods
I conducted canonical analysis of principal coordinates (CAP;Anderson and Robinson, 2003; Anderson and Willis, 2003) to testfor average differences in community composition among thebasins. CAP is a variant of principal coordinates analysis (PCoA;Gower, 1966), and it aims to find axes through the multivariatecloud of points that are best in discriminating among a priori groups(Anderson et al., 2008). I used Hellinger distance for construc-ting resemblance matrices prior to CAP. CAP helps one to discoveramong-group differences in constrained ordination space, and inthe present case “basin” was the constraining factor in all analy-ses of each taxonomic level. CAP was also used to allocate the sitesto correct classification groups (leave-one-out procedure) and totest for among-basin differences in community composition usingrandom permutations. I also tested for the null hypothesis of no dif-ferences among basin centroids using permutation tests with 999runs.
I conducted tests of homogeneity of dispersion (PERMDISP;Anderson, 2006) to examine the multivariate dispersions within
the basins. PERMDISP was further used to compare among-basindifferences in the distance from observations (i.e., streams) to theirgroup centroid (i.e., basin). Significance of among-group differencesis tested through permutation of least-squares residuals. I tested
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Table 1Summaries of (a) CAP and (b) PERMDISP analyses at species (S), genus (G) and family(F) levels. Mean distance to centroid was measured as mean Hellinger distance.
(a) CAP Percentage correct classifications Among-basindifferences
best explained, followed by family richness and genus richness.Taxonomic evenness was poorly explained by the measured envi-ronmental variables in the Iijoki and Tenojoki regions, while
Table 2Significant environmental variables and descriptive statistics of the final DistLMmodels. Variable selection was based on the option “forward”.
or the null hypothesis that there are no differences in within-asin biological dispersion among the basins using permutationests with 999 runs.
I used distance-based redundancy analysis (dbRDA; Legendrend Anderson, 1999) to analyze variation in assemblage com-osition along environmental gradients across the three basinsnd within each basin. This analysis was also based on Hellingeristance on macroinvertebrate data and the whole set of envi-onmental variables. Environmental variables were selected to thenal models using forward selection ( = 0.05) based on 999 per-utations. Prior to the analyses, stream width and conductivityere log(x + 1) transformed, whereas other environmental vari-
bles were left untransformed. To facilitate comparisons amongifferent taxonomic data sets, basins and other studies, adjustedoefficients of determination were reported in all analyses (Peres-eto et al., 2006).
Finally, I used distance-based linear models (DistLM; Andersont al., 2008) to examine the relationships of taxa richness andielou’s evenness (J′ = H′/log(S)) to environmental gradients. DistLMan be understood as a non-parametric procedure that performs
distance-based analysis on a linear model for any dissimilarityatrix (Gioria et al., 2010). I used a forward selection of the predic-
or variables based on 999 permutations. The flow of analysis wasimilar to that of dbRDA, except that Euclidean distance on richnessr evenness was used. I reported adjusted coefficient of determi-ation in all analyses. For comparison, I also used the option “best”,hereby the analysis programme searched for the best model in
erms of adjusted coefficient of determination of all the modelsossible.
To facilitate the comparisons among the different types of anal-ses, Hellinger distance was used in all community compositionnalyses, as it is recommended in the context of RDA (Legendrend Gallagher, 2001) and can be used for both abundance andresence–absence data (Legendre et al., 2005). Euclidean distanceas used in all DistLM analyses of taxa richness and taxa evenness,
s is typically done with univariate response variables in the con-ext of DistLM (Gioria et al., 2010; Sevastou et al., 2012). All analysesere conducted with PRIMER 6 and PERMANOVA+ (Anderson et al.,
008), using routines CAP, PERMDISP, DISTLM and dbRDA.
. Results
The species-to-genus and species-to-family ratios were veryow. The species-to-genus ratios were 1.25, 1.13 and 1.11 for theijoki, Koutajoki and Tenojoki regions. The species-to-family ratios
ere 3.02, 2.77 and 2.80 for the Iijoki, Koutajoki and Tenojokiegions. Across the regions, the species-to-genus ratio was 1.45 andpecies-to-family ratio was 3.89.
All taxonomic levels showed significant among-region differ-nces in average community composition in the CAP analysisTable 1a and Fig. 1). There were slight differences in the correctlassifications of sites to their parent group, with species-level dataeing best classified, followed by genus-level and family-level data.owever, these results were to some extent region specific, withenus-level data performing best with regard to correct classifi-ation of sites in the Koutajoki region. There were no consistentifferences between abundance and presence–absence data in thisegard, although slight among-region differences were visible espe-ially with family-level data.
All taxonomic levels also showed significant among-region dif-erences in the heterogeneity of community composition in the
ERMDISP analysis (Table 1b). Heterogeneity was slightly higher inresence–absence than abundance data in each region, and varied
n descending order from species to genus to family levels. Hetero-eneity was highest for both data types and all taxonomic levels in
the Koutajoki region, followed by the Iijoki region and the Tenojokiregion.
Taxonomic richness–environment relationships varied amongthe regions, with both the significant explanatory variables andadjusted coefficient of determination varying among the regions(Table 2). In the Iijoki region, taxonomic richness was bestexplained at the family level, followed by genus level and specieslevel. In the Koutajoki, the order was reversed, with specieslevel data being best explained, followed by genus level andfamily level data. In the Tenojoki region, species richness was
190 J. Heino / Ecological Indicators 36 (2014) 186–194
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ig. 1. Canonical analysis of principal coordinates (CAP) ordination plots based onell as presence–absence data (upper row) and abundance data (lower row).
pecies and genus evenness were almost equally well explainedy environmental variables as taxonomic richness at the respec-ive taxonomic levels in the Koutajoki region. The across-regionsnalyses indicated that family richness was best explained bynvironmental variables, followed by genus level and speciesevel. No such pattern was found for taxonomic evenness, ashere were no clear differences between species, genus andamily levels. For both taxonomic richness–environment andaxonomic–evenness–environment relationships, the adjusted R2
alues varied among the regions for each taxonomic level whenhe option “best” was used (Appendix 2). The order the taxonomicevels in terms of which level was best explained by environmen-al variables also varied among the regions. Hence, these resultstrengthened the findings based on forward selection.
Community composition–environment relationships also var-ed among the data types, taxonomic levels and regions. This wasvident in both the identities of the significant explanatory vari-bles and adjusted coefficient of determination (Table 3). Overall,bundance data were better explained than presence–absenceata by the environmental variables at each taxonomic level,he only exception being the Koutajoki region where differencesetween the data types were negligible. Among-region differences
n the community–environment relationships were also evident,ith higher explanatory power of environmental variables in
he Iijoki and Tenojoki regions than in the Koutajoki region atach taxonomic level. In the among-taxonomic level comparison,
amily-level data were best explained by environmental variables,ollowed by genus and species-level data. The Koutajoki region wasgain the only exception, with there being negligible differencesmong the taxonomic levels. In the across-regions analyses, there
es- (left column), genus- (middle column) and family-level (right column) data, as
were no clear differences among the taxonomic levels in thecoefficient of determination. There was, however, a differencebetween abundance and presence–absence data, with the formerbeing better explained than the latter. When the option “best” wasused, there was also some variation among the taxonomic levelsand among the regions in the adjusted R2 values (Appendix 3).
4. Discussion
There were clear differences among the three taxonomic lev-els and both data types (abundance vs. presence–absence) in thespatial patterns of community variation. First, among-region dif-ferences were clearly discernible with regard to both averagecommunity composition and heterogeneity in community com-position, corroborating previous findings from marine systems(Terlizzi et al., 2009). Although data sets at all taxonomic levelsshowed significant differences among the regions, the order ofcorrect classifications in the CAP analysis and mean distance tocentroid in the PERMDISP analysis could be ordered in descen-ding order from species to genus to family levels. This suggeststhat species-level data is most sensitive to portray among-regiondifferences and heterogeneity in community composition. This isbecause the distributions of species should be less wide than thoseof genera and families, which applies to both among-region andwithin-region scales. Among regions, dispersal limitation shouldbe evident, leading to some species being absent in some regions.
This finding is also supported by previous studies in the freshwa-ter realm where spatial structuring of community composition atthe species level increases from a single drainage basin to multi-ple drainage basins (Mykrä et al., 2007). By contrast, within a small
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Table 3Significant environmental variables and descriptive statistics of the final dbRDA models. Variable selection was based on the option “forward”.
rainage basin, dispersal limitations should be negligible, leadingo little or no spatial structuring of community composition (Heinond Mykrä, 2008; Brown and Swan, 2010; Heino et al., 2012). How-ver, it is much more difficult to interpret the results that alsoenera and families showed significant among-region differencesn community composition. A possible reason for this finding is that,lthough almost all families and genera are distributed across thehree study regions, their frequency of occurrence differs amonghe regions. This means that a family or a genus may be either veryommon or very rare in different regions and may thus differentlyontribute to among-region differences in community compositiont the respective taxonomic level.
Spatial heterogeneity in community composition within eachegion was highest at the species level, suggesting that speciesre more patchily distributed than genera or families. This find-ng also suggests that species within genera or families do notespond similarly to the underlying environmental gradients (Lenatnd Resh, 2001; Bevilacqua et al., 2012). If congeneric and confa-ilial species had strictly similar response to the environmental
ariation, suggesting a high degree of ecological coherence (cf.arwick, 1993), the higher taxonomic levels would show the
ame degree of heterogeneity in community composition andimilar responses to environmental variation as species-levelata. This was not exactly the situation, as slightly differentnvironmental factors were important in determining the com-unity composition at the three taxonomic levels within each
egion.Slightly different sets of environmental factors were also
mportant in determining the community composition in differentegions. Although variables such as current velocity, stream widthnd pH were important in most individual analyses, there wereome nuances in the significant variables entering the models in
ifferent region, taxonomic level and data type combinations. Thisuggests that the details of community–environment relationshipsary depending on what kind of response data is used, althought should also be noted that some predictor variables may, by
chance, enter the significant models in forward selection. Dis-regarding possible methodological effects, this finding is similarto those from other regional and environmental settings, wherethe details of community–environment relationships differedbetween abundance and presence–absence data (Cushman andMcGarigal, 2004; Heino et al., 2010a,b; De Bie et al., 2012). What ismore important, however, is that there were clear among-regiondifferences in the explanatory power of environmental variables,which was again contingent on the taxonomic level and datatype. For example, variation in community composition was worstexplained in the Koutajoki region irrespective of the taxonomiclevel or data type. In contrast, variation in community compo-sition was best explained at the species and family levels in theIijoki region, but genus-level data were best explained in theTenojoki region. These findings suggest that taxonomic surrogacywith regard to community–environment relationships is notdirectly transferable among regions.
Similarly to community composition analyses, also taxonomicrichness and taxonomic evenness showed different environmentalrelationships in different regions, which was evidenced by both for-ward selection and the best set of environmental variables selected.Furthermore, the explained variation varied considerably amongthe regions, which was again contingent on the taxonomic level. Inthe Iijoki region, adjusted coefficients of determination were rel-atively high at each taxonomic level, varying in descending orderfrom family to genus to species levels. By contrast, in the Kouta-joki region, the order was reversed, with the explained variationvarying from species to genus to family levels. In the Tenojokiregion, the situation was again different from the other two regions,with species richness showing strongest environmental relation-ships, followed by family richness and genus richness. It is difficultto decipher reasons for these among-region differences in the
richness–environment relationships at different taxonomic levels,although such findings suggest either faunal- or environment-related context dependency among regions. More importantly,however, these results also imply that taxonomic surrogacy with
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egard to richness–environment relationships detected in oneegion is not directly applicable in other regions.
In the across-regions analyses of richness–environment rela-ionships, it was evident that the explanatory power of environ-
ental variables decreased from species to genus to family levelesolution. By contrast, taxonomic evenness was overall similarlyeakly related to environmental gradients at each taxonomic level.
his was also the case with community–environment relationships,hich showed no clear differences among the taxonomic levels.
hese findings suggest that also the response variable (i.e., rich-ess, evenness, composition) used in a study may have considerable
mportance on inferring responses of taxonomic levels to environ-ental gradients across large spatial extents.Abundance-weighted community composition was better
xplained than presence–absence data by environmental variablest each taxonomic level. This was the case in the Iijoki and Teno-oki regions as well as across the three regions, but there wereo discernible differences between the data types in the Kouta-
oki region. This finding suggests that the divisions of individualso taxa vary among the regions, with macroinvertebrate commu-ities in the Koutajoki region showing the closest correspondenceetween abundance and presence–absence data. This was also sug-ested by the environmental relationships of taxonomic richnessersus taxonomic evenness, which were equally strongly relatedo environmental gradients in the Koutajoki region.
A potential confounding factor in this study was related to tim-ng of sampling the streams. As the resources for this study didot allow sampling all the sites within a short period of time in
single year, the sites had to be sampled in different regions inifferent years. Although it is strongly advisable that one samplesll the sites in the same season (i.e., soon after the snowmelt in thepring) rather than in the same year, it can also be easily argued thatmong-region differences portray, at least to some degree, year-to-ear variation in community structure. This argument may or mayot be true, although based on an extensive experience with north-rn headwater streams, it is more important to sample sites within
short temporal window in different years rather than over a longeremporal period within a single year. If the sites are not sampledithin a short period of time in the same season, the results mayot portray spatial differences but seasonal differences in commu-ity structure. Such seasonal variation related to aquatic insect lifeycles among spring, summer and autumn samples is typical inorthern streams (J. Heino, pers. obs.). Hence, I strongly believehat the samples taken in the exactly same season portray wellrue among-region patterns in taxonomic surrogacy.
. Conclusions
Taxonomic surrogacy in stream macroinvertebrate communi-ies is not directly transferable among regions. Despite the facthat species-to-genus and species-to-family ratios were low andf similar magnitude in all regions, each region showed differ-nt rankings in terms of the species-, genus- and family-levelata being best explained by environmental variables. Further-ore, within each region, the three taxonomic levels did not
espond similarly to the underlying environmental gradients,hich was evident with taxonomic richness and taxonomic com-osition based on both quantitative and qualitative data. It waslso evident that different factors were responsible for varia-ion in richness and/or composition at each taxonomic level inifferent regions. The present findings thus do not support the
iew that higher taxon taxonomic surrogates can be used with-ut restraints to infer species-level community–environment andichness–environment relationships in studies of community ecol-gy, conservation biology and environmental assessment.
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Acknowledgements
I thank Mira Grönroos, Jari Ilmonen, Tommi Karhu, Heikki Mykräand Lauri Paasivirta for help with the field or laboratory work, andanonymous reviewers for comments on the manuscript drafts. Thispaper is part of the project “Spatial scaling, metacommunity struc-ture and patterns in stream communities” funded by the Academyof Finland.
Appendix 1. A list of the families detected in the study andthe numbers of genera and species within each family at theacross-regions extent. The total number of familiesincluded in this study was 57, that of genera 153 and that ofspecies 222
ppendix 2. Number of environmental variables anddjusted R2 of the final DistLM models for richness andvenness. Variable selection was based on the option “best”,hereby the best subset of variables increasing the adjusted2 were included in the model
ppendix 3. Number of environmental variables anddjusted R2 of the final DistLM models for communityomposition. Variable selection was based on the optionbest”, whereby the best subset of variables increasing thedjusted R2 were included in the model
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