Oecologia

, Volume 167, Issue 2, pp 513–524 | Cite as

Species traits predict assembly of mayfly and stonefly communities along pH gradients

Community ecology - Original Paper

Abstract

Much recent ecological research has centred on the interrelations between species diversity and ecological processes. In the present study, I show how species traits may aid in comprehending ecology by studying the link between an environmental variable and functional traits. I examined the composition of species traits with a theoretically underpinned relationship to ecological processes along a pH gradient. I focused on body size, reproductive output, life cycle length and feeding habit of mayflies and stoneflies. In mayfly assemblages, I found smaller body size, greater reproductive output, faster life cycles and a larger proportion of gathering collectors and scrapers with increasing pH. In stonefly assemblages, I found smaller body size, greater reproductive output and faster life cycles at sites with a history of long-term natural acidification, but no clear trends in feeding habits and in most traits where acidification is anthropogenic. The results suggest that mayflies and stoneflies exhibit different ecological functions following different ecological strategies. Mayflies follow an opportunistic strategy relative to stoneflies, likely facilitating high rates of ecological processes with respect to the autotrophic resource base at neutral sites. Relative to mayflies, stoneflies follow an equilibrium strategy contributing to ecological functioning in heterotrophic ecosystems and likely maintaining heterotrophic processes despite the erosion of species diversity in response to acidification. The rules governing an ecological community may be more readily revealed by studying the distribution of species traits instead of species diversity; by studying traits, we are likely to improve our understanding of the workings of ecological communities.

Keywords

Biodiversity Ecological functioning Ephemeroptera Plecoptera Trait diversity 

Introduction

Traditionally, researchers have tried to comprehend the workings of ecosystems in two different ways (Loreau 2010): Community ecologists have attempted to unravel the functioning of ecosystems by studying ecological structure but solely describing the species composition of an assemblage is insufficient, if we want to comprehend the functioning of an ecosystem and understand how ecological processes, including production, consumption rates and decomposition, are controlled (Cummins 1974; McGill et al. 2006; Loreau 2010). Ecosystem ecologists, on the other hand, have tried to disentangle the mechanisms by focusing on ecological processes at the expense of structural detail and ignoring the idiosyncratic nature of species (Loreau 2010). However, the mechanics of an ecosystem may be more readily revealed by examining the trait composition of a community, as the trait that is manifested in a particular species, rather than the species identity, mediates a particular ecological process (McGill et al. 2006). In addition, trait composition appears to be structurally more stable than species composition (Fukami et al. 2005; McGill et al. 2006; Bonada et al. 2007; Schmera et al. 2009). The larger similarity in trait than species composition among communities may reflect similarities in the traits between species, i.e. some, often related, species may share the same traits, whereas others may have developed different traits (Felsenstein 1985; Bonada et al. 2007; Cadotte et al. 2008). Hence, the smaller variation in the trait than species composition between communities and the intimate bond between trait and function may aid in revealing links and relationships that have remained unnoticed or poorly understood (Fukami et al. 2005; McGill et al. 2006).

In freshwater ecosystems, macroinvertebrates have been identified as a functionally vital component stimulating a number of studies on their functional traits (Dolédec et al. 1999; Bonada et al. 2007; Statzner et al. 2007). However, a large proportion of the previous work has focused on traits in general regardless of whether and how they relate to ecological processes (e.g. Lamouroux et al. 2004; Bonada et al. 2007; Heino 2008; but see Schmera et al. 2009). In addition, in many studies, a traditional approach was chosen by selecting or developing trait diversity metrics––for instance, metrics that are related to Shannon’s diversity index, or measures of community (dis)similarity––to explore the effects that different variables have on trait diversity (e.g. Heino 2008; Schmera et al. 2009) rather than directly studying the effects that the environmental variables have on the magnitude of the traits or the proportional composition of the trait categories (cf. Lamouroux et al. 2004).

Much previous research has centred on the effects of pH on freshwater species diversity (Otto and Svensson 1983; Townsend et al. 1983; Rosemond et al. 1992), but a lot of the variation in ecological processes along an acidity gradient remains unaccounted for. For instance, it is still unclear why decomposition is impaired by low pH under some circumstances but not under others (Petrin et al. 2008a). Concurrently, little is known about how pH affects the composition of species traits that may control ecological processes (e.g. Ledger and Hildrew 2000, 2005). Furthermore, the acidic sites in most of the previous studies were selected to address the effects of anthropogenic acidification (Otto and Svensson 1983; Townsend et al. 1983; Rosemond et al. 1992), whereas few studies have included naturally acidic locations (Collier et al. 1990; Dangles et al. 2004; Petrin et al. 2007a, 2008b). Some studies suggest evolved adaptation to naturally low pH levels that may cause different functional trait responses than anthropogenic acidity (cf. Collier et al. 1990; Dangles et al. 2004; Petrin et al. 2007a, 2008b). None of these latter studies have included data on changes in trait composition along the pH gradients.

In the present study, firstly, I examined how pH affects species traits that are characterised by a theoretically underpinned relationship to ecological processes. Traits with an unclear relationship to ecological processes, such as body form, dispersal, locomotion, aquatic stages, resistant life stages and where and how eggs and egg masses are deposited, are discarded from the analyses. Secondly, I accounted for the differential effects of a pH gradient with a history of long-term natural acidification compared to a pH gradient characterised by recent anthropogenic acidification. Therefore, thirdly, I examined how the traits specifically varied along the environmental gradients rather than analysing a metric of trait richness or diversity that, by ignoring trait identity, may be difficult to interpret functionally.

For organisms depending on autochthonous production, food resource levels should be relatively low under acidic conditions (Otto and Svensson 1983; Maurice et al. 1987; Meegan and Perry 1996). Hence, I hypothesised (Fig. 1) that at acidic sites macroinvertebrate assemblages would comprise overall larger individuals––reflecting their more efficient metabolism (Brown et al. 2004)––and a larger proportion of detritivores relying on allochthonous organic material (Cummins 1973; Cummins and Klug 1979; Wallace et al. 1997). In a context with relatively abundant resources, such as in neutral freshwater ecosystems (Otto and Svensson 1983; Maurice et al. 1987; Meegan and Perry 1996), I hypothesised that macroinvertebrate assemblages would largely comprise small organisms with high reproductive rates and fast life cycles (cf. MacArthur and Wilson 1967; Roughgarden 1971; Pianka 1972), obviously in addition to a larger proportion of herbivores and predators relying on autochthonous production (Fig. 1).
Fig. 1

Conceptual diagram illustrating the hypothesised changes in the mean trait levels along the pH gradient. I hypothesised faster life cycles (voltinism), higher fecundity and smaller body size with increasing pH. I also hypothesised a shift from relatively ‘brown’ food webs at acidic sites to relatively ‘green’ food webs at circumneutral sites and hence a decreasing proportion of detritivory and increasing proportions of herbivory and predation with increasing pH reflecting a supposedly more productive environment at neutral relative to acidic sites

Materials and methods

I studied the distribution of traits that affect ecological processes, such as consumption rates, as these traits are most likely to link ecological structure to functioning. The traits include body size, fecundity, voltinism and feeding habit. Generally, the metabolic rate of the whole organism scales as ¾ power of its body mass (Brown et al. 2004). Hence, given a constant population biomass, larger organisms tend to be more efficient requiring less food per unit biomass, whereas smaller organisms, in need of more energy per unit biomass, tend to interact more strongly with their resource base (Brown et al. 2004). Fecundity is a proxy of the potential population growth describing how quickly the abundance of an organism may change when the supply of resources is unlimited. Abundance may then greatly influence ecological functioning. The potential population growth rate––the change in the number of individuals over time––may be approximated as the product between fecundity and voltinism, the number of generations per year (Clifford 1982). Lastly, the feeding habit directly affects the trophic position of an organism and its relationships to other components of the community through the type and size of food items consumed (Cummins 1973; Merritt et al. 2008).

I assembled data on the maximum and mean body size, fecundity, voltinism and feeding habits of Swedish mayfly and stonefly species from the literature (Online Resource 1). The biology and ecology of mayflies and stoneflies have been intensively studied in Scandinavia during the past decades and are therefore well understood. In contrast, other diverse aquatic invertebrate taxa, for instance caddisflies, have received less attention, both ecologically and taxonomically. To facilitate the interpretation of the results, I preferred focusing on the better researched mayflies and stoneflies. Hence, other taxa were disregarded in the present study.

Where multiple trait measurements were available, I calculated the average value. Fecundity measurements were unavailable for 43% of the species. For the remaining species, the reported values are based on one or several publications. The authors variously reported fecundity values either individually for several females, or as an average, species-specific value. When calculating species-specific average fecundity values for the present study, I treated each dataset reported in the literature as one replicate. The life cycle categories included bivoltine, univoltine, semivoltine and merovoltine, meaning that the organisms completed two, one, half and less than half a generation per year (Clifford 1982). Flexible life cycles have been documented for 29% of the studied species, and they were therefore classified in multiple categories. None of the stoneflies were bivoltine. The feeding habit categories included filtering collectors, gathering collectors, scrapers, shredders and predators (Cummins 1973; Merritt et al. 2008). Collectors consume fine detrital particles and microscopic algae, scrapers feed on periphyton largely consisting of algae and microbes, shredders process coarse particulate organic matter, and predators capture living prey (Cummins 1973). Feeding habits were flexible in 71% of the species. They were classified in multiple categories. None of the stoneflies were filtering collectors. The present approach disregards intra-specific variation in trait values among sites due to limitations in the available data. Hence, I was unable to examine whether intra-specific variation among sites may have affected the observed ecological patterns.

I analysed data from two Swedish surveys of 700 streams in 1995 and 2000 (Wilander et al. 1998, 2003). The databases comprised species abundances for macroinvertebrates, including mayflies and stoneflies, and hydrochemical measurements, including pH, that emerged as the major chemical variable directly controlling species composition (Sandin 2003). The mean pH values changed by 0.1–0.2 pH units across latitudes and longitudes suggesting that pH varied mainly within rather than between regions. At each stream, riffles were sampled once during autumn following Swedish standard procedures (Wilander et al. 1998, 2003; Sandin 2003). Macroinvertebrates were sampled using a kick net (500 μm mesh, 1 m2 sampling area, 1 min sampling time), preserved in the field and sorted and identified by professional taxonomists at accredited and inter-calibrated laboratories (Wilander et al. 1998, 2003; Sandin 2003). The hydrochemical analyses were done at the Department of Environmental Assessment, Swedish University of Agricultural Sciences, Uppsala, following international standards (Wilander et al. 1998, 2003).

Previous research indicated that the hydrochemistry and ecology differ between streams in south-western and northern Sweden. The differences reflect variation in hydrochemical and biogeochemical processes (Laudon and Bishop 1999, 2002; Bishop et al. 2000), dissimilarities in acid deposition (Warfvinge and Bertills 1999), disparities in natural acidification following the past glaciations across the Scandinavian peninsula (Renberg et al. 1993; Korsman 1999) and likely diverging biogeographic and evolutionary processes (Petrin et al. 2007a, 2008b). To address the effects of the history and causes of acidification, the dataset was divided into northern Sweden, comprising the northern half of the country, and southern Sweden, comprising the south-west (see Petrin et al. 2007a for a map illustrating the locations of the regions). In addition, limed sites and sites affected by agricultural activities were removed from the dataset (see Petrin et al. 2007a for further details). The remaining dataset comprised 1,008 different sites.

For data analysis, I employed linear mixed models with generalised least squares (GLS) and multinomial logistic regression models. Separate mixed models were fitted for the dependent variables maximum body size, mean body size, fecundity and potential reproductive rate. For each assemblage, I first calculated the average of each dependent variable using the species-specific values weighted by the respective species abundances. To account for unequal relatedness between species and hence statistical non-independence between taxa (Felsenstein 1985; Harvey 1996), I employed a phylogenetic GLS method (PGLS) when calculating the means of the dependent variables for the different assemblages (Garland et al. 2005). The evolutionary relationships among the taxa were derived from published phylogenies (e.g. Zwick 2000; Ogden et al. 2009). Disregarding statistical non-independence between taxa from the statistical analyses changed the effect sizes by up to 21%, on average by 10%. I therefore report phylogenetically corrected model results. For the species lacking fecundity data, fecundity was set equal to the estimate for the next higher taxonomic level. For instance, no fecundity data was available for Amphinemura borealis. Hence, the fecundity of A. borealis was set equal to the fecundity of Amphinemura that was estimated as the average of A. standfussi and A. sulcicollis. The potential reproductive rate was calculated as the product between fecundity and voltinism. I then used mixed models to study the effects of pH and region on the dependent variables across assemblages. In these models, pH constituted a covariate, region a fixed factor with two levels (northern and southern Sweden) and study year a random factor with two levels (1995 and 2000). I assessed the significance of the variables pH, region and their interaction with a conditional F test (Pinheiro and Bates 2000). Similarly to the linear mixed models, multinomial logistic regression models were fitted to study the effects of pH and region on the dependent variables voltinism and feeding habit. For each assemblage, I calculated the proportions of the different voltinism and feeding habit categories again, using PGLS to account for differential relatedness among species as disregarding statistical non-independence between taxa changed the model results by up to 79%, on average by 8% (Garland et al. 2005). To avoid repeatedly counting individuals with flexible life cycles and feeding habits, I weighted the individuals’ contributions to each category by the inverse of the number of categories recorded for the respective species. I analysed the dependent variables voltinism and feeding habit separately and assessed the significance of the independent variables and their interaction with log likelihood ratio tests (Pinheiro and Bates 2000; Venables and Ripley 2002). The mayfly and stonefly data were analysed separately for all dependent variables.

I also included a spherical model of the semi-variogram by implementing the GLS method (S-GLS) for the dependent variables of maximum body size, mean body size, fecundity and potential reproductive rate to account for spatial autocorrelation (Beale et al. 2010). Due to the lack of multinomial logistic regression models that allow for specifying a spatial autocorrelation structure, I fitted surrogate GLS models to study the effects of pH and region on the dependent variables voltinism and feeding habit and at the same time account for spatial autocorrelation (cf. Venables and Ripley 2002; Beale et al. 2010). Comparison of the models that accounted for spatial autocorrelation with the models that disregarded spatial autocorrelation revealed very similar trends and suggested that spatial autocorrelation affected the results only marginally. I therefore only report the more parsimonious models disregarding spatial autocorrelation.

To examine whether physicochemical variables may have confounded the effects of pH, I employed redundancy analyses (RDA; Jongman et al. 1995). RDA is similar to canonical correspondence analysis (CCA) relating a set of response variables (species) to a set of environmental variables. Hence, RDA allows the assessment of the proportion of the variation in the response variables that is explained by environmental variables. However, the assumptions between RDA and CCA differ; and as preliminary analyses suggested that the response curves were linear, rather than unimodal, I preferred RDA to CCA. Results from RDA are presented and interpreted similarly to results from CCA. In the present study, I constrained the ordinations to a set of 20 environmental variables including pH, study region, water temperature and different chemical variables. The complete set of environmental variables was available for 427 sites for mayflies and for 421 sites for stoneflies. As response variables, I used the trait data. I employed an ANOVA-like permutation test with the ratio of the constrained and unconstrained total inertia (F) as test statistic to assess the significance of the complete set of the constraints (Oksanen et al. 2008). I separately analysed the data for mayflies and stoneflies. pH emerged as the major environmental variable affecting trait composition (Online Resource 2). Further important predictors were the acidity related variables absorbance (at 254 nm, a measure of the dissolved organic carbon content), total organic carbon content and phosphorous content. Phosphorous content is correlated with the dissolved organic carbon content in Sweden, a major variable controlling pH (Laudon and Bishop 1999, 2002; Bishop et al. 2000; Fölster et al. 2004). Hence, trait composition was largely related to pH and other acidity-related variables.

Most of the study sites were characterised by pH values ranging from 5.5 to 8.0. To examine whether the results may reflect the influence of outliers beyond either side of that range, I repeated all analyses limiting the data set to sites with pH levels from 5.5 to 8.0. Most results of the new analyses were similar to the original ones using the complete dataset, and I therefore only report the new results based on the limited dataset when the analyses indicated that the original results may not be robust.

Where macroinvertebrate abundances were overall lower, the variation in the trait composition between sites would be larger, which would increase uncertainty masking any ecological patterns rather than generating spurious results. Hence, the analyses are conservative and likely to be robust with respect to potentially low macroinvertebrate abundances.

The statistical models were fitted using the software package R 2.9.2 including the nlme, nnet and vegan packages (Pinheiro and Bates 2000; Venables and Ripley 2002; Oksanen et al. 2008; Pinheiro et al. 2009; R Development Core Team 2009). All tests were performed at a probability level of 5% for type I errors.

Results

The maximum and mean body size of mayfly assemblages decreased with increasing pH, albeit by less than 1 mm per unit of pH (Table 1; Fig. 2). The effect size for assemblages in the circumneutral range was on average 4%. Body size appeared to be larger in mayfly assemblages in southern Sweden. In contrast, the maximum and mean body size of stonefly assemblages increased with increasing pH in northern Sweden, but remained similar throughout the pH gradient in southern Sweden (Table 1; Fig. 3). The average effect sizes equalled 9% in northern Sweden and 3% in southern Sweden for stonefly assemblages at circumneutral sites. However, the effect of the interaction on the body size was insignificant when only sites with pH values from 5.5 to 8.0 were considered (maximum body size: F1,682 = 3.3, p = 0.070; mean body size: F1,682 = 2.7, p = 0.102). Body size also appeared to be slightly larger in the north than in the south.
Table 1

Effect of pH and region on the maximum body size, mean body size, fecundity, potential reproductive rate, voltinism and feeding habit of mayfly and stonefly assemblages

Variable

Mayfly assemblages

Stonefly assemblages

Test statistic

p value

Test statistic

p value

Maximum size

 pH

F1,739 = 17.6

<0.001

F1,694 = 29.4

<0.001

 Region

F1,739 = 20.6

<0.001

F1,694 = 16.2

0.001

 pH × region

F1,739 = 0.5

0.483

F1,694 = 5.4

0.020

Mean size

 pH

F1,739 = 22.7

<0.001

F1,694 = 30.1

<0.001

 Region

F1,739 = 22.5

<0.001

F1,694 = 19.0

<0.001

 pH × region

F1,739 = 0.5

0.502

F1,694 = 4.8

0.029

Fecundity

 pH

F1,739 = 72.0

<0.001

F1,694 = 18.2

<0.001

 Region

F1,739 = 1.2

0.281

F1,694 = 0.7

0.415

 pH × region

F1,739 < 0.1

0.873

F1,694 = 8.3

0.004

Potential reproductive ratea

 pH

F1,739 = 138.9

<0.001

F1,694 = 11.7

0.001

 Region

F1,739 = 19.1

<0.001

F1,694 = 0.2

0.663

 pH × region

F1,739 < 0.1

0.837

F1,694 = 12.1

0.001

Voltinismb

 pH

LR = 9.6

0.023

LR = 0.9

0.642

 Region

LR = 5.6

0.132

LR = 1.5

0.479

 pH × region

LR = 0.4

0.930

LR = 6.0

0.051

Feeding habitc

 pH

LR = 28.9

<0.001

LR = 7.3

0.064

 Region

LR = 12.8

0.012

LR = 0.5

0.912

 pH × region

LR = 2.1

0.717

LR = 2.2

0.525

The effects on body size and reproductive output were assessed by fitting linear mixed effects models using generalised least squares (GLS) with pH as covariate and region as fixed factor. Conditional F tests were employed as statistical tests. The effects on voltinism and feeding habit were assessed by fitting multinomial logistic regression models. The effects of pH and region were assessed using log-likelihood ratio tests

F Conditional F statistic with numerator and denominator degrees of freedom, LR log-likelihood ratio statistic

aThe potential reproductive rate constitutes the product between fecundity and voltinism and hence represents the annual reproductive output

bMayflies were classified as bivoltine, univoltine, semivoltine and merovoltine; stoneflies were classified as univoltine, semivoltine and merovoltine. Some of the taxa fall into multiple categories as the same species may adopt different life cycle strategies

cMayflies were classified as filtering collectors, gathering collectors, scrapers, shredders and predators; stoneflies were classified as gathering collectors, scrapers, shredders and predators. Some of the taxa fall into multiple categories as the same species may adopt different feeding habits

Fig. 2

Effects of pH and region on a maximum body size, b mean body size, c fecundity and d potential reproductive rate of mayfly (Ephemeroptera) assemblages. Note that the potential reproductive rate constitutes the product between fecundity and voltinism and hence represents the annual reproductive output. Data are from northern Sweden (blue lines and triangles) and southern Sweden (red lines and dots); dotted lines delimit standard errors

Fig. 3

Effects of pH and region on a maximum body size, b mean body size, c fecundity and d potential reproductive rate of stonefly (Plecoptera) assemblages. Note that the potential reproductive rate constitutes the product between fecundity and voltinism and hence represents the annual reproductive output. Data are from northern Sweden (blue lines and triangles) and southern Sweden (red lines and dots); dotted lines delimit standard errors

Fecundity and potential reproductive rate of mayfly assemblages increased with increasing pH by 296 and 412 eggs per unit of pH, respectively (Table 1; Fig. 2), corresponding to an average effect size of 19 and 24% for assemblages in the circumneutral range. The reproductive output was slightly larger in northern than southern Sweden. In stonefly assemblages, the reproductive output decreased by 149 eggs per unit of pH in the north, but remained similar along the whole acidity gradient in southern Sweden (Table 1; Fig. 3). The average effect sizes equalled 29 and 3%, respectively, for assemblages at circumneutral sites.

The proportion of bivoltine mayflies increased with increasing pH, whereas the proportion of univoltine mayflies decreased (Table 1; Fig. 4; Online Resource 3). The proportion of semivoltine mayflies remained similar along the whole pH gradient. The proportions of the different life cycles were similar between the two regions (Table 1). In stonefly assemblages, the changes in the proportions of the different life cycle lengths along the acidity gradient tended to differ between the regions: the proportion of semivoltine stoneflies increased at low pH values in the south, but decreased in the north, whereas univoltine stoneflies exhibited the reverse pattern (Table 1; Fig. 5; Online Resource 4). The pattern was strengthened when only sites with pH values from 5.5 to 8.0 were considered (LR = 8.5, p = 0.015).
Fig. 4

Effects of pH and region on a, c voltinism and b, d feeding habits of mayfly assemblages in a, b northern and c, d southern Sweden. The mayflies were classified as a, c bivoltine, univoltine, semivoltine and merovoltine and as b, d filtering collectors (FC), gathering collectors (GC), scrapers (Sc), shredders (Sh) and predators (P). Some of the taxa fall into multiple categories as the same species may adopt different life cycle strategies and feeding habits. Note that for clarity only the model results have been plotted. The variation in the data is evident in the figures in the supplementary electronic material (Online Resource 3), where the data for the different life cycle and feeding habit categories are illustrated separately

Fig. 5

Effects of pH and region on a, c voltinism and b, d feeding habits of stonefly assemblages in a, b northern and c, d southern Sweden. The stoneflies were classified as a, c univoltine, semivoltine and merovoltine and as b, d gathering collectors (GC), scrapers (Sc), shredders (Sh) and predators (P). Some of the taxa fall into multiple categories as the same species may adopt different life cycle strategies and feeding habits. Note that for clarity only the model results have been plotted. The variation in the data is evident in the figures in the supplementary electronic material (Online Resource 4), where the data for the different life cycle and feeding habit categories are illustrated separately

Shredders constituted a relatively large proportion of mayflies at low pH values in both regions, while gathering collectors and scrapers dominated at higher pH levels (Table 1; Fig. 4; Online Resource 3). Filtering collectors and predators were only recorded at neutral sites in the south albeit at small proportions. There were no clear regional differences in the patterns. In stonefly assemblages, no clear patterns were revealed except for a trend towards different proportions of feeding habits with changing pH (Table 1; Fig. 5; Online Resource 4). However, restricting the analysis to sites with pH levels ranging from 5.5 to 8.0 suggests no effect of pH (LR = 6.0, p = 0.111).

Discussion

For mayflies, the results were in agreement with the expectations. The mayflies constituting assemblages at acidic and presumably less productive sites were slightly larger than those at neutral, more productive localities possibly reflecting differences in metabolic efficiency (cf. Otto and Svensson 1983; Maurice et al. 1987; Meegan and Perry 1996; Brown et al. 2004). The reproductive output of mayfly assemblages and the proportion of fast life cycles increased with increasing pH as predicted (cf. MacArthur and Wilson 1967; Roughgarden 1971; Pianka 1972). Mayfly assemblages at acidic sites also comprised a larger proportion of shredders relying on allochthonous organic material (Cummins 1973; Cummins and Klug 1979; Wallace et al. 1997), while the assemblages at neutral sites constituted a large proportion of gathering collectors and scrapers depending more strongly, though not exclusively, on autochthonous primary production (Cummins 1973; Cummins and Klug 1979). Low macroinvertebrate abundances at some sites should reduce resource scarcity relaxing the expected relationships. Hence, finding relationships with respect to pH suggests robustness of the results. Similarly, the flexible classification of mayflies in different life cycle and feeding habit categories is conservative as it may mask ecological patterns (Rawer-Jost et al. 2000).

Albeit in disagreement with the expectations, the results for stoneflies were revealing. Whereas the trait composition of mayfly assemblages changed along the pH gradient in the same way in both regions, in stoneflies the outcome for reproductive output and voltinism depended on the region. For changes in body size and feeding habit along the pH gradient, no unequivocal regional differences were detected. Most abundant stoneflies are shredders, especially at acidic sites, although many exhibit flexible feeding habits alternatively functioning as gathering collectors or scrapers (Cummins 1973; Hynes 1976; Lillehammer 1988). Hence, except for predators, many stoneflies are independent of autochthonous, primary production. Instead, they may rely on the influx of allochthonous organic material that should be largely unrelated to pH (Cummins 1973; Cummins and Klug 1979; Wallace et al. 1997). The allocation of numerically dominating stoneflies to multiple categories (Cummins 1973; Hynes 1976; Lillehammer 1988), their independence of autochthonous primary production (Cummins 1973; Cummins and Klug 1979; Wallace et al. 1997) and the observation that many stoneflies are acid-tolerant (Brinck 1949; Lillehammer 1988; Malmqvist 1999) should together contribute to masking any patterns implying limited systematic change in the composition of feeding habits along an acidity gradient.

Contrary to the expectations, stonefly body size was larger at neutral sites in northern Sweden, and the reproductive output was lower. However, macroinvertebrate abundance often is larger at neutral sites (e.g. Townsend et al. 1983; Rosemond et al. 1992). Thus, competition for resources should also be larger there, and a larger body size, entailing a more efficient metabolism and hence greater competitiveness (cf. Brown et al. 2004), would prove advantageous. This would also explain the lower reproductive output and slower life cycles of stoneflies at neutral sites, as competitiveness tends to inversely covary with fecundity and voltinism (cf. MacArthur and Wilson 1967; Roughgarden 1971; Pianka 1972). In the south, where tolerance to anthropogenic acidity is less widespread (Petrin et al. 2007a, 2008b), extinction due to low pH levels may have been random rather than having covaried with particular traits. A random order of extinction would explain the absence of a clear relationship between body size or reproductive output and pH in the south. However, in the north, the distribution of stoneflies in response to pH may have covaried with functional traits as at least some of the stonefly taxa are likely adapted to acidic water in northern Sweden (Dangles et al. 2004; Petrin et al. 2007a, 2008b). The finding for voltinism in southern Sweden, that life cycles are faster at neutral sites, corresponded to the expectation.

The available data did not allow examining whether intra-specific variation in the trait levels among sites may have contributed to the reported results. However, a comprehensive study of plant traits revealed that changes in trait means along environmental gradients largely reflect species turnover rather than intra-specific variation (Cornwell and Ackerly 2009) and may suggest that the patterns reported in the present paper are largely unaffected by intra-specific variation. The insects’ relative mobility should also favour species turnover rather than intra-specific variation as the mechanism controlling trait levels. Although plausible, the suggested mechanism that species turnover affects trait levels more strongly than intra-specific variation remains to be demonstrated for aquatic insects.

Disregarding differential relatedness and hence statistical non-independence between taxa may result in reduced or inflated effect size estimates. The differences between the phylogenetically corrected and uncorrected results were on average small in the present study, but larger differences between the results do occur and may affect the conclusions (Felsenstein 1985; Harvey 1996). We can only be confident that the results reflect underlying ecological principles if the findings are supported by data comprising several unrelated taxa that represent different evolutionary lineages. Closely related organisms, due to the inheritance of similar traits from a common ancestor, may constitute little more than one replicate, independent observation. If the pattern is driven by closely related taxa, the result may be spurious (Felsenstein 1985; Harvey 1996). Phylogenetic corrections should thus be especially important when studying assemblages that include many related taxa such as the often numerically dominant and relatively diverse mayfly Baetis spp. in fluvial ecosystems.

At neutral sites, the larger proportion of herbivorous mayflies, faster life histories, higher reproductive output and smaller body size all would likely contribute to higher rates of ecological processes and hence a stronger interaction with the autotrophic resource base. Most stonefly assemblages in northern Sweden––where the stoneflies may be adapted to conditions of low pH (Petrin et al. 2007a, 2008b)––comprise smaller organisms, a higher reproductive output and probably also faster life cycles at acidic sites. This, in addition to the stoneflies’ reliance on allochthonous detritus (Cummins 1973; Cummins and Klug 1979; Wallace et al. 1997), should contribute to relatively higher rates of ecological processes and therefore a stronger interaction with the heterotrophic resource base at acidic sites (Petrin et al. 2007b). The attributes of mayfly and stonefly ecology discussed here may explain why ecological functioning in heterotrophic ecosystems is maintained at low pH levels despite reductions in species diversity (Petrin et al. 2007b, 2008a, b). Furthermore, the finding that the trait composition in mayfly assemblages varied in the same way in both northern and southern Sweden is in agreement with the widely accepted view that mayflies are generally acid-sensitive regardless of the causes of acidity (Otto and Svensson 1983; Townsend et al. 1983; Rosemond et al. 1992). In contrast, the trait composition of stonefly assemblages along a pH gradient changed in different ways in the two regions in accordance with the hypothesis of an evolved tolerance to low pH levels in stoneflies that inhabit naturally acidic freshwater ecosystems including those in northern Sweden (Dangles et al. 2004; Petrin et al. 2007a, 2008b). Finally, the results suggest that mayflies and stoneflies follow different ecological strategies in addition to having different ecological functions. Mayflies exhibited relatively fast life cycles, a high reproductive output and a tendency towards exploiting abundant food resources, suggesting they primarily followed an opportunistic strategy (Brittain 1982; Winemiller and Rose 1992). Stoneflies, characterised by slower life cycles, a lower reproductive output and a tendency towards exploiting partly limited food resources, appeared to rely more on an equilibrium strategy relative to mayflies (Hynes 1976; Winemiller and Rose 1992; Wallace et al. 1997).

In conclusion, where researchers have failed to derive more general laws in community ecology, species traits may comprise the component where the laws that rule an ecological community become evident. Studying species traits of putatively functional importance may provide novel insights into the workings of an ecosystem because species traits provide information complementary to measurements of species diversity and ecological functioning (McGill et al. 2006). When neglecting species traits, data comprising lists of species identities and abundances may indeed remain silent about the species’ functional significance even when combined with data on the rates of ecological processes. For instance, drawing inferences on the differential functional importance of mayflies and stoneflies along a pH gradient may be difficult if not impossible when solely based on studies of species diversity and ecological functioning (cf. Dangles et al. 2004; Petrin et al. 2007a, 2008a, b). Whether the goal of a research project is to test hypotheses and ecological theory (Townsend et al. 1997; Fukami et al. 2005; McGill et al. 2006), assess anthropogenic impacts on biodiversity and ecological functioning (Haybach et al. 2004; Bracken et al. 2008), monitor environmental and land use change (Fortunel et al. 2009), or manage environmental resources and ecosystem services (Merritt et al. 2002; Díaz et al. 2007), species traits will improve our comprehension of the principles that govern life-sustaining ecosystems.

Notes

Acknowledgments

I thank Edwige Bellier, Núria Bonada, John Edward Brittain, Grégoire Certain, Ola Diserud, Richard Hedger, Ingeborg Palm Helland, Frank Johansson, Odd Terje Sandlund, Ann Kristin Schartau and Maxim Teichert for discussing data analysis, the results and data presentation. Joel Trexler’s, the anonymous referees’ and the editor’s helpful comments are gratefully acknowledged. This paper is a contribution to the BIOCLASS-FRESH project (VANN: Biological indicators for classification of ecological status in freshwater, 184002) funded by the Norwegian Research Council (the MILJØ2015 programme), the Norwegian Energy Directorate (NVE), the Climate and Pollution Agency (KLIF, formerly SFT) and the Norwegian Directorate for Nature Management (DN).

Supplementary material

442_2011_2003_MOESM1_ESM.pdf (133 kb)
Online Resource 1: Functional traits of mayfly and stonefly species. (PDF 132 kb)
442_2011_2003_MOESM2_ESM.pdf (101 kb)
Online Resource 2: Redundancy analyses of the effects of environmental variables on mayfly and stonefly trait composition. (PDF 100 kb)
442_2011_2003_MOESM3_ESM.pdf (352 kb)
Online Resource 3: Effects of pH and region on the life cycles and feeding habits of mayfly assemblages. (PDF 351 kb)
442_2011_2003_MOESM4_ESM.pdf (372 kb)
Online Resource 4: Effects of pH and region on the life cycles and feeding habits of stonefly assemblages. (PDF 372 kb)

References

  1. Beale CM, Lennon JJ, Yearsley JM, Brewer MJ, Elston DA (2010) Regression analysis of spatial data. Ecol Lett 13:246–264. doi:10.1111/j.1461-0248.2009.01422.x PubMedCrossRefGoogle Scholar
  2. Bishop KH, Laudon H, Köhler S (2000) Separating the natural and anthropogenic components of spring flood pH decline: a method for areas that are not chronically acidified. Water Resour Res 36:1873–1884. doi:10.1029/2000WR900030 CrossRefGoogle Scholar
  3. Bonada N, Dolédec S, Statzner B (2007) Taxonomic and biological trait differences of stream macroinvertebrate communities between mediterranean and temperate regions: implications for future climatic scenarios. Global Change Biol 13:1658–1671. doi:10.1111/j.1365-2486.2007.01375.x CrossRefGoogle Scholar
  4. Bracken MES, Friberg SE, Gonzalez-Dorantes CA, Williams SL (2008) Functional consequences of realistic biodiversity changes in a marine ecosystem. Proc Natl Acad Sci USA 105:924–928. doi:10.1073/pnas.0704103105 PubMedCrossRefGoogle Scholar
  5. Brinck P (1949) Studies on Swedish Stoneflies [Plecoptera]. In: Opuscula Entomologica Supplementum XI. Entomologiska Sällskapet i Lund, LundGoogle Scholar
  6. Brittain JE (1982) Biology of Mayflies. Annu Rev Entomol 27:119–147. doi:10.1146/annurev.en.27.010182.001003 CrossRefGoogle Scholar
  7. Brown JH, Gillooly JF, Allen AP, Savage VM, West GB (2004) Toward a metabolic theory of ecology. Ecology 85:1771–1789. doi:10.1890/03-9000 CrossRefGoogle Scholar
  8. Cadotte MW, Cardinale BJ, Oakley TH (2008) Evolutionary history and the effect of biodiversity on plant productivity. Proc Natl Acad Sci USA 105:17012–17017. doi:10.1073/pnas.0805962105 PubMedCrossRefGoogle Scholar
  9. Clifford HF (1982) Life cycles of mayflies (Ephemeroptera), with special reference to voltinism. Quaest Entomol 18:15–90Google Scholar
  10. Collier KJ, Ball OJ, Graesser AK, Main MR, Winterbourn MJ (1990) Do organic and anthropogenic acidity have similar effects on aquatic fauna? Oikos 59:33–38CrossRefGoogle Scholar
  11. Cornwell WK, Ackerly DD (2009) Community assembly and shifts in plant trait distributions across an environmental gradient in coastal California. Ecol Monogr 79:109–126. doi:10.1890/07-1134.1 CrossRefGoogle Scholar
  12. Cummins KW (1973) Trophic relations of aquatic insects. Annu Rev Entomol 18:183–206. doi:10.1146/annurev.en.18.010173.001151 CrossRefGoogle Scholar
  13. Cummins KW (1974) Structure and function of stream ecosystems. Bioscience 24:631–641CrossRefGoogle Scholar
  14. Cummins KW, Klug MJ (1979) Feeding ecology of stream invertebrates. Annu Rev Ecol Syst 10:147–172. doi:10.1146/annurev.es.10.110179.001051 CrossRefGoogle Scholar
  15. Dangles O, Malmqvist B, Laudon H (2004) Naturally acid freshwater ecosystems are diverse and functional: evidence from boreal streams. Oikos 104:149–155. doi:10.1111/j.0030-1299.2004.12360.x CrossRefGoogle Scholar
  16. Díaz S, Lavorel S, de Bello F, Quétier F, Grigulis K, Robson M (2007) Incorporating plant functional diversity effects in ecosystem service assessments. Proc Natl Acad Sci USA 104:20684–20689. doi:10.1073/pnas.0704716104 PubMedCrossRefGoogle Scholar
  17. Dolédec S, Statzner B, Bournard M (1999) Species traits for future biomonitoring across ecoregions: patterns along a human-impacted river. Freshw Biol 42:737–758. doi:10.1046/j.1365-2427.1999.00509.x CrossRefGoogle Scholar
  18. Felsenstein J (1985) Phylogenies and the comparative method. Am Nat 125:1–15. doi:10.1086/284325 CrossRefGoogle Scholar
  19. Fölster J, Sandin L, Wallin M (2004) A suggestion to a typology for Swedish inland surface waters according to the EU Water Framework Directive. Department of Environmental Assessment, Swedish University of Agricultural Sciences, report 13, Uppsala, SwedenGoogle Scholar
  20. Fortunel C et al (2009) Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe. Ecology 90:598–611. doi:10.1890/08-0418.1 PubMedCrossRefGoogle Scholar
  21. Fukami T, Bezemer TM, Mortimer SR, van der Putten WH (2005) Species divergence and trait convergence in experimental plant community assembly. Ecol Lett 8:1283–1290. doi:10.1111/j.1461-0248.2005.00829.x CrossRefGoogle Scholar
  22. Garland T, Bennett AF, Rezende EL (2005) Phylogenetic approaches in comparative physiology. J Exp Biol 208:3015–3035. doi:10.1242/jeb.01745 PubMedCrossRefGoogle Scholar
  23. Harvey PH (1996) Phylogenies for ecologists. J Anim Ecol 65:255–263CrossRefGoogle Scholar
  24. Haybach A, Schöll F, König B, Kohmann F (2004) Use of biological traits for interpreting functional relationships in large rivers. Limnologica 34:451–459. doi:10.1016/S0075-9511(04)80012-4 Google Scholar
  25. Heino J (2008) Patterns of functional biodiversity and function-environment relationships in lake littoral macroinvertebrates. Limnol Oceanogr 53:1446–1455CrossRefGoogle Scholar
  26. Hynes HBN (1976) Biology of Plecoptera. Annu Rev Entomol 21:135–153. doi:10.1146/annurev.en.21.010176.001031 CrossRefGoogle Scholar
  27. Jongman RHG, ter Braak CJF, van Tongeren OFR (eds) (1995) Data analysis in community and landscape ecology. Cambridge University Press, CambridgeGoogle Scholar
  28. Korsman T (1999) Temporal and spatial trends of lake acidity in northern Sweden. J Paleolimnol 22:1–15. doi:10.1023/A:1008003218065 CrossRefGoogle Scholar
  29. Lamouroux N, Dolédec S, Gayraud S (2004) Biological traits of stream macroinvertebrate communities: effects of microhabitat, reach, and basin filters. J North Am Benthol Soc 23:449–466. doi:10.1899/0887-3593(2004)023<0449:BTOSMC>2.0.CO;2 CrossRefGoogle Scholar
  30. Laudon H, Bishop KH (1999) Quantifying sources of acid neutralisation capacity depression during spring flood episodes in Northern Sweden. Environ Pollut 105:427–435. doi:10.1016/S0269-7491(99)00036-6 CrossRefGoogle Scholar
  31. Laudon H, Bishop KH (2002) Episodic stream water pH decline during autumn storms following a summer drought in northern Sweden. Hydrol Process 16:1725–1733. doi:10.1002/hyp.360 CrossRefGoogle Scholar
  32. Ledger ME, Hildrew AG (2000) Herbivory in an acid stream. Freshw Biol 43:545–556. doi:10.1046/j.1365-2427.2000.t01-1-00534.x Google Scholar
  33. Ledger ME, Hildrew AG (2005) The ecology of acidification and recovery: changes in herbivore-algal food web linkages across a stream pH gradient. Environ Pollut 137:103–118. doi:10.1016/j.envpol.2004.12.024 PubMedCrossRefGoogle Scholar
  34. Lillehammer A (1988) Stoneflies (Plecoptera) of Fennoscandia and Denmark. Brill, LeidenGoogle Scholar
  35. Loreau M (2010) Linking biodiversity and ecosystems: towards a unifying ecological theory. Philos Trans R Soc Lond B 365:49–60. doi:10.1098/rstb.2009.0155 CrossRefGoogle Scholar
  36. MacArthur HH, Wilson EO (1967) The theory of island biogeography. Princeton University Press, PrincetonGoogle Scholar
  37. Malmqvist B (1999) Lotic stoneflies (Plecoptera) in northern Sweden: patterns in species richness and assemblage structure. In: Friberg N, Carl JD (eds) Biodiversity in Benthic Ecology, NERI Technical Report No. 266. National Environmental Research Institute, Denmark, Silkeborg, DenmarkGoogle Scholar
  38. Maurice CG, Lowe RL, Burton TM, Stanford RM (1987) Biomass and compositional changes in the periphytic community of an artificial stream in response to lowered pH. Water Air Soil Poll 33:165–177. doi:10.1007/BF00191385 CrossRefGoogle Scholar
  39. McGill BJ, Enquist BJ, Weiher E, Westoby M (2006) Rebuilding community ecology from functional traits. Trends Ecol Evol 21:178–185. doi:10.1016/j.tree.2006.02.002 PubMedCrossRefGoogle Scholar
  40. Meegan SK, Perry SA (1996) Periphyton communities in headwater streams of different water chemistry in the central Appalachian Mountains. J Freshw Ecol 11:247–256CrossRefGoogle Scholar
  41. Merritt RW et al (2002) Development and application of a macroinvertebrate functional-group approach in the bioassessment of remnant river oxbows in southwest Florida. J North Am Benthol Soc 21:290–310CrossRefGoogle Scholar
  42. Merritt RW, Cummins KW, Berg MB (eds) (2008) An introduction to the aquatic insects of North America, 4th edn. Kendall/Hunt, DubuqueGoogle Scholar
  43. Ogden TH, Gattolliat JL, Sartori M, Staniczek AH, Soldán T, Whiting MF (2009) Towards a new paradigm in mayfly phylogeny (Ephemeroptera): combined analysis of morphological and molecular data. Syst Entomol 34:616–634. doi:10.1111/j.1365-3113.2009.00488.x CrossRefGoogle Scholar
  44. Oksanen J, Kindt R, Legendre P, O’Hara B, Simpson GL, Stevens MHH (2008) vegan: Community Ecology Package. In. http://cran.r-project.org/, http://vegan.r-forge.r-project.org/, R package version 1.11-4
  45. Otto C, Svensson BS (1983) Properties of acid brown water streams in South Sweden. Arch Hydrobiol 99:15–36Google Scholar
  46. Petrin Z, Laudon H, Malmqvist B (2007a) Does freshwater macroinvertebrate diversity along a pH-gradient reflect adaptation to low pH? Freshw Biol 52:2172–2183. doi:10.1111/j.1365-2427.2007.01845.x CrossRefGoogle Scholar
  47. Petrin Z, McKie B, Buffam I, Laudon H, Malmqvist B (2007b) Landscape-controlled chemistry variation affects communities and ecosystem function in headwater streams. Can J Fish Aquat Sci 64:1563–1572. doi:10.1139/f07-118 CrossRefGoogle Scholar
  48. Petrin Z, Englund G, Malmqvist B (2008a) Contrasting effects of anthropogenic and natural acidity in streams: a meta-analysis. Proc R Soc Lond B 275:1143–1148. doi:10.1098/rspb.2008.0023 CrossRefGoogle Scholar
  49. Petrin Z, Laudon H, Malmqvist B (2008b) Diverging effects of anthropogenic acidification and natural acidity on community structure in Swedish streams. Sci Total Environ 394:321–330. doi:10.1016/j.scitotenv.2008.01.055 PubMedCrossRefGoogle Scholar
  50. Pianka ER (1972) r and K selection or b and d selection? Am Nat 106:581–588CrossRefGoogle Scholar
  51. Pinheiro JC, Bates DM (2000) Mixed-effects models in S and S-PLUS. Springer, New YorkCrossRefGoogle Scholar
  52. Pinheiro J, Bates D, DebRoy S, Sarkar D, the R Core Team (2009) nlme: Linear and nonlinear mixed effects models. R package version 3:1–93Google Scholar
  53. Rawer-Jost C, Böhmer J, Blank J, Rahmann H (2000) Macroinvertebrate functional feeding group methods in ecological assessment. Hydrobiologia 422:225–232. doi:10.1023/A:1017078401734 CrossRefGoogle Scholar
  54. Renberg I, Korsman T, Anderson NJ (1993) A temporal perspective of lake acidification in Sweden. Ambio 22:264–271Google Scholar
  55. Rosemond AD, Reice SR, Elwood JW, Mulholland PJ (1992) The effects of stream acidity on benthic invertebrate communities in the south-eastern United States. Freshw Biol 27:193–209. doi:10.1111/j.1365-2427.1992.tb00533.x CrossRefGoogle Scholar
  56. Roughgarden J (1971) Density-dependent natural selection. Ecology 52:453–468. doi:10.2307/1937628 CrossRefGoogle Scholar
  57. Sandin L (2003) Benthic macroinvertebrates in Swedish streams: community structure, taxon richness, and environmental relations. Ecography 26:269–282. doi:10.1034/j.1600-0587.2003.03380.x CrossRefGoogle Scholar
  58. Schmera D, Erős T, Podani J (2009) A measure for assessing functional diversity in ecological communities. Aquat Ecol 43:157–167. doi:10.1007/s10452-007-9152-9 CrossRefGoogle Scholar
  59. Statzner B, Bonada N, Dolédec S (2007) Conservation of taxonomic and biological trait diversity of European stream macroinvertebrate communities: a case for a collective public database. Biodivers Conserv 16:3609–3632. doi:10.1007/s10531-007-9150-1 CrossRefGoogle Scholar
  60. R Development Core Team (2009) R: A language and environment for statistical computing. R Foundation for Statistical Computing, http://www.R-project.org/, Vienna, Austria
  61. Townsend CR, Hildrew AG, Francis J (1983) Community structure in some southern English streams: the influence of physicochemical factors. Freshw Biol 13:521–544. doi:10.1111/j.1365-2427.1983.tb00011.x CrossRefGoogle Scholar
  62. Townsend CR, Dolédec S, Scarsbrook MR (1997) Species traits in relation to temporal and spatial heterogeneity in streams: a test of habitat templet theory. Freshw Biol 37:367–387. doi:10.1046/j.1365-2427.1997.00166.x CrossRefGoogle Scholar
  63. Venables WN, Ripley BD (2002) Modern applied statistics with S, 4th edn. Springer, New YorkGoogle Scholar
  64. Wallace JB, Eggert SL, Meyer JL, Webster JR (1997) Multiple trophic levels of a forest stream linked to terrestrial litter inputs. Science 277:102–104. doi:10.1126/science.277.5322.102 CrossRefGoogle Scholar
  65. Warfvinge P, Bertills U (1999) Recovery from acidification in the natural environment: present knowledge and future scenarios. Swedish Environmental Protection Agency, report 5034, StockholmGoogle Scholar
  66. Wilander A, Johnson RK, Goedkoop W, Lundin L (1998) Riksinventering 1995––En synoptisk studie av vattenkemi och bottenfauna i svenska sjöar och vattendrag. Swedish Environmental Protection Agency, report 4813, UppsalaGoogle Scholar
  67. Wilander A, Johnson RK, Goedkoop W (2003) Riksinventering 2000––En synoptisk studie av vattenkemi och bottenfauna i svenska sjöar och vattendrag. Department of Environmental Assessment, University of Agricultural Sciences, report 2003:1, UppsalaGoogle Scholar
  68. Winemiller KO, Rose KA (1992) Patterns of life-history diversification in North American fishes: Implications for population regulation. Can J Fish Aquat Sci 49:2196–2218CrossRefGoogle Scholar
  69. Zwick P (2000) Phylogenetic system and zoogeography of the Plecoptera. Annu Rev Entomol 45:709–746PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Norwegian Institute for Nature Research (NINA)TrondheimNorway

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