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Large Scale Relationship between Aquatic Insect Traits and Climate

  • Avit Kumar Bhowmik ,

    bhowmik@uni-landau.de

    Affiliation Quantitative Landscape Ecology, Institute for Environmental Sciences, University of Koblenz-Landau, Landau in der Pfalz, Germany

  • Ralf B. Schäfer

    Affiliation Quantitative Landscape Ecology, Institute for Environmental Sciences, University of Koblenz-Landau, Landau in der Pfalz, Germany

Large Scale Relationship between Aquatic Insect Traits and Climate

  • Avit Kumar Bhowmik, 
  • Ralf B. Schäfer
PLOS
x

Abstract

Climate is the predominant environmental driver of freshwater assemblage pattern on large spatial scales, and traits of freshwater organisms have shown considerable potential to identify impacts of climate change. Although several studies suggest traits that may indicate vulnerability to climate change, the empirical relationship between freshwater assemblage trait composition and climate has been rarely examined on large scales. We compared the responses of the assumed climate-associated traits from six grouping features to 35 bioclimatic indices (~18 km resolution) for five insect orders (Diptera, Ephemeroptera, Odonata, Plecoptera and Trichoptera), evaluated their potential for changing distribution pattern under future climate change and identified the most influential bioclimatic indices. The data comprised 782 species and 395 genera sampled in 4,752 stream sites during 2006 and 2007 in Germany (~357,000 km² spatial extent). We quantified the variability and spatial autocorrelation in the traits and orders that are associated with the combined and individual bioclimatic indices. Traits of temperature preference grouping feature that are the products of several other underlying climate-associated traits, and the insect order Ephemeroptera exhibited the strongest response to the bioclimatic indices as well as the highest potential for changing distribution pattern. Regarding individual traits, insects in general and ephemeropterans preferring very cold temperature showed the highest response, and the insects preferring cold and trichopterans preferring moderate temperature showed the highest potential for changing distribution. We showed that the seasonal radiation and moisture are the most influential bioclimatic aspects, and thus changes in these aspects may affect the most responsive traits and orders and drive a change in their spatial distribution pattern. Our findings support the development of trait-based metrics to predict and detect climate-related changes of freshwater assemblages.

Introduction

Freshwater ecosystems are among the most threatened in terms of biodiversity loss, because of overexploitation, water pollution, invasive species, flow modification and degradation of habitat [1,2]. While these are mainly local scale stressors, patterns of freshwater assemblages on large spatial scales are driven by environmental variables such as climate, geology and acid deposition [3,4]. Climate is the predominant environmental driver that directly affects the thermal and flow regimes of freshwater bodies and thus controls organismal growth and performance [5]. Moreover, climate may influence the biogeography of organisms and shape geology and acid deposition on large spatial scales [4]. Thus, quantifying the relationship between climate and large scale freshwater assemblages can help to understand and predict climate change effects on freshwater ecosystems [6].

Traits of organisms, defined as biological (life history) characteristics and ecological preferences that may evolve from a number of developmental, morphological, physiological and behavioral adaptations of organisms to their environment [7,8], have shown considerable potential as indicators of multiple stressor effects in freshwater ecosystems [9]. Traits were also shown to provide a link to important freshwater ecosystem functions and services [10,11]. Especially on large scales, trait variability is less than the taxonomic variability [12] and therefore traits are more suitable for quantifying the relationship between climate and freshwater assemblage composition.

Several biological and ecological traits of freshwater organisms have been associated with climate change in previous studies. For example, organisms that prefer cold temperature [13] and with low dispersal capacity [14] exhibited range contractions, large-bodied (>4 cm) and semivoltine organisms decreased [15], rheophil and rheobiont organisms declined or disappeared [16] and the distribution of organisms with narrow niche breadth, restricted resource distribution and short flight period shrinked [17]. Consequently, such traits were assumed to be vulnerable and employed to assess risk of individual organism groups, i.e. Ephemeroptera, Plecoptera and Trichoptera [1820], sites (streams and lakes) and ecoregions [4,21] from climate change. For example, rheobiont and cold temperature preferring organisms were assumed to be threatened by climate change and in concert with additional traits were used to identify potentially vulnerable European ephemeropterans, plecopterans and trichopterans [1820]. The same hypothesized climate-vulnerable traits and organism groups were used to identify the Swedish streams and lakes [21] and European eco-regions [4] that are at the highest risk of adverse climate change effects. However, the large scale relationship between the variability of freshwater assemblage trait composition and climate has rarely been quantified [22]. Quantification of the trait-climate relationship allows to identify the most vulnerable and tolerant organism groups and their traits as well as to examine whether organism groups or traits differ in their vulnerability to specific aspects of climate change, e.g. change in winter temperature or precipitation [5,13,16].

Freshwater assemblages are distributed non-randomly along spatial gradients, i.e. longitude, latitude and altitude on large scales, leading to spatial patterns in their trait composition [23]. Spatial autocorrelation, referring to the concept that organismal traits observed at a given stream site are more similar to traits in close sites than in distant sites, measures the strength of spatial pattern in the distribution of organismal traits [24]. Trait spatial autocorrelation can be endogenous, i.e. arises from ecological processes such as dispersal and reproduction, or exogenous, i.e. induced by environmental drivers like climate [23,25]. Climate shows a strongly positive autocorrelation, i.e. closer regions have a more similar climate than distant ones. The spatial patterns of freshwater organisms with climate-associated traits often reflect this spatial autocorrelation of climate. For example, a recent study on stream invertebrate taxonomic richness and composition suggested that spatial autocorrelation in organism groups with aerial dispersal ability (e.g. Ephemeroptera, Plecoptera and Trichoptera) is mainly related to large scale climate variability [24]. Moreover, organisms preferring cold temperature were shown to predominantly occur in alpine regions with high altitudes, whereas those preferring warm temperature tend to occur in lowland regions [26]. Hence, freshwater organisms with climate-associated traits that exhibit strong relationship with climate in their spatial autocorrelation are most likely to change their distribution pattern under future climate change [27]. However, little is known about the relationship between the spatial pattern in the assumed climate-associated traits and climate on large spatial scales.

We empirically quantified the large scale relationship of the German stream macroinvertebrate assemblage trait composition with climate. Our research questions were two-fold: (i) which of the climate-associated traits and organism groups show the highest response to climate and highest potential for changing distribution pattern under future climate change?, and (ii) which are the most influential climatic aspects for the traits and organism groups showing the highest response and potential for changing distribution? We selected climate-associated traits from six grouping features, i.e. four biological and two ecological grouping features (“grouping feature” and “trait” follow the unified terminologies suggested by [28]) that have been used in previous large scale studies to indicate vulnerability [4,1821] and five orders of stream macroinvertebrates that are aerial dispersers, i.e. aquatic insects [29]. Climate was measured as 35 global bioclimatic indices (BIs) that are biologically and ecologically relevant [30] and vary considerably over Germany due to its diverse topography [31]. The large scale variability and spatial distribution pattern of the aquatic insect assemblage trait composition were quantified and checked for their relationship with the combined and individual BIs.

Materials and Methods

Concepts of scale

We covered two concepts of spatial scale: (i) spatial extent or size of the study area and (ii) spatial resolution or wavelength of variability of the variables [25]. Ours is a large scale study from both conceptual points of view, i.e. large extent (Germany, area approximately 357,000 km²) and large (coarse) resolution (approximately 18 km (10 arcminutes) based on the variability of the BIs). We use the terms “large scale” and “scale” for both concepts. When we refer to the scale of Germany, we mean extent; but when we refer to the scale of the relation, e.g. variability and pattern, we mean resolution. Moreover, when we refer to the scale of relationship, we refer to both concepts.

Data and processing

Aquatic insect data.

We used governmental biomonitoring data on macroinvertebrates from 4,752 stream sites (i.e. stream reaches with a maximum of 20 meters length) sampled during 2006–2007 that covered the whole spatial extent of Germany (Fig 1). Data coverage in terms of number of sites was lower (approximately 1% of the total number of sites) in the southeast and northwest (in proximity to the North and Baltic sea) than in the other regions. The biomonitoring data was produced following a standardized protocol, where a pooled sample was taken from all major habitat types in a stream site [32]. Samples were collected from the middle and small sized streams in each ecoregion of Germany. For more details on biological sampling, subsampling and sorting see AQEM CONSORTIUM, Rolauffs et al. and Biss et al. [3234]. Given the semi-quantitative nature of macroinvertebrate data, the data were originally reported as abundance classes where the classes approximated log-transformed abundance data following the classes of the saprobic index (for the description of the abundance classes see AQEM CONSORTIUM and Rolauffs et al. [32,33]). Thus, the abundance classes varied on a scale of zero to seven; zero meaning no abundance, i.e. absence and seven the highest abundance [33]. Overall, abundance classes for 2,099 stream macroinvertebrates were available. Abundance data were preferred over presence-absence data because more powerful hypothesis tests are available for abundance data in spatial pattern analysis of assemblage compositions and studies of turnover rates [35].

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Fig 1. Distribution of the 4,752 stream sites sampled by the German national bio-monitoring program during 2006–2007.

Spatial reference system is WGS 1984.

http://dx.doi.org/10.1371/journal.pone.0130025.g001

We examined the homogeneity of taxonomic resolution and found that the organisms were reported at different taxonomic levels (from class to species). We took the subset of 1,901 organisms (91%) that were reported at genus (660) and species (1,241) levels. From this subset, we selected the aquatic insect orders, namely Diptera (True flies), Ephemeroptera (Mayflies), Odonata (Dragonflies and Damselflies), Plecoptera (Stoneflies) and Trichoptera (Caddisflies) (Table 1). Aerial dispersers are more suitable for large scale analyses than exclusive aquatic dispersers, because they can disperse through the landscape and are not limited to the stream network [24,29]. Moreover, these orders were also used in previous large scale studies to indicate climate vulnerability [4,1821] and information for the selected traits were available for all organisms in these orders. This resulted in 782 species [and 395 genera] that comprised 384 (216) dipterans, 101 (39) ephemeropterans, 42 (33) odonates, 52 (36) plecopterans and 203 (71) trichopterans. Next, in case that a taxon was identified at genus level for more than 1% of stream sites, we converted all species belonging to this genus to genus level. This was the case for 73% of the species and was done to avoid artifacts from potential spatial pattern linked to the taxonomic resolutions, for instance mainly genus level identification in regions with low data coverage.

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Table 1. Explained variances and spatial autocorrelation in the traits of each order and full data by the bioclimatic indices.

http://dx.doi.org/10.1371/journal.pone.0130025.t001

Biological and ecological traits data.

Biological and ecological traits of aquatic insects were taken from two databases: (i) the freshwater ecology database (www.freshwaterecology.info) [36] and (ii) the Tachet database [37]. The trait information is recorded at species level in the freshwater ecology database, whereas they are recorded mostly at genus and species levels in the Tachet database. In both databases, the membership state (see Schmera et al. [28] for terminology) of a taxon for a particular trait is generally described on a scale from zero to 10 (with exceptions for the Tachet database); zero indicates no membership and 10 the highest membership state. We selected the climate-associated traits from six grouping features (for details see Table 1) and converted the membership state of the traits into percentages as suggested by Schmera et al. [28]. These traits were selected because they were used in previous large scale studies to indicate vulnerability [4,1821] and have the highest data coverage for the macroinvertebrates in German streams. We also compared the membership states of the insect orders for each of the selected traits (Table A in S2 File).

Calculation of assemblage trait composition.

The biomonitoring data were linked to the trait data using the codes of “The development and testing of an integrated assessment system for the ecological quality of streams and rivers throughout Europe using benthic macroinvertebrates” (AQEM) project to avoid discrepancies in naming conventions [38]. Each of the species was assigned with the traits using their corresponding percentage membership states that were multiplied with the absolute abundance classes of the species for a site to compute relative abundance classes for the traits (Fig 2). To assign trait information to genera, we calculated the median of the related species level information following Schmidt-Kloiber and Nijboer [39] except for maximal body size where genus level information were available in the Tachet database for all genera. Subsequently, the assemblage trait composition, i.e. abundance weighted trait (AWT) was calculated following the procedure described in [40] and as outlined in Fig 2. The AWT was calculated as a measure of assemblage trait composition because it is the most frequently used metric to assess the relationship between assemblage traits and environmental variables [41,42]. Note that we use the term assemblage trait composition to improve readability, although the assemblage data was restricted to aquatic insects, and hence does not represent the complete macroinvertebrate assemblage. The calculation resulted in annual averaged abundance-weighted traits (AWT) for each insect order (Fig A in S1 File) and for the combined (full) data (Figs 3 and 4) for each stream site. The calculation was omitted for the dispersal capacity of ephemeropterans and plecopterans because the grouping feature consisted of only one trait (low dispersal). However, they were included in the calculation for the full data.

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Fig 2. Conversion steps from abundance classes of the selected aquatic insects to trait compositional (annual averaged abundance weighted traits) data.

http://dx.doi.org/10.1371/journal.pone.0130025.g002

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Fig 3. Annual averaged abundance weighted traits across 4,752 stream sites in Germany for the biological traits of the full data.

The figure sub-captions and panel captions indicate names of grouping features and traits, respectively. The gray dots indicate the zero abundance, i.e. trait absence.

http://dx.doi.org/10.1371/journal.pone.0130025.g003

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Fig 4. Annual averaged abundance weighted traits across 4,752 stream sites in Germany for the ecological traits of the full data.

The figure sub-captions and panel captions indicate names of grouping features and traits, respectively. The gray dots indicate the zero abundance, i.e. trait absence.

http://dx.doi.org/10.1371/journal.pone.0130025.g004

Bioclimatic indices and altitude data.

The 35 bioclimatic indices (BI, denoted as “Bio01” to “Bio35”, see Table 2 for details) for temperature, precipitation, radiation and moisture were collected from the global climatologies for bioclimatic modeling (CliMond) database (www.climond.org) [30]. A previous study showed that these BIs can provide an approximation of climate impact on assemblage patterns, despite the omission of confounding endogenous factors such as biotic interactions, evolutionary change and dispersal potential [43]. The scale of variability was determined by the spatial resolution of the BI raster, which is 10 arc-minutes (approximately 18 km). The digital elevation model (giving altitude over mean sea level) for Germany was collected from the ASTER GDEM on one arc-second (approximately 30 m) resolution [44]. The altitude raster was resampled to the resolution of the BI rasters to extract altitude information for each BI raster cell.

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Table 2. Explained variances and spatial autocorrelation by the individual bioclimatic indices in the traits and orders with the highest climate response and potential for changing distribution pattern.

http://dx.doi.org/10.1371/journal.pone.0130025.t002

Although no clear gradient in the BIs was found for Germany, lower temperatures and higher precipitation were mostly observed in the southern regions, whereas higher temperatures and lower precipitation were mostly observed in the northern regions (Fig B in S1 File). For example, observed ranges of the annual mean temperature (Bio01) and annual precipitation (Bio12) are 2 to 5°C and 7 to 12°C, and 1200 to 1600 mm and 600 to 800 mm in the southern and northern regions, respectively. The northern and southern regions are portrayed as flat (zero to 250 m above sea level) and mountainous (600 to 1800 m above sea level), respectively (Fig C in S1 File). The BIs showed significant (p < 0.001) spatial autocorrelation, i.e. average Moran's I = 0.28 (Table B in S2 File). Significant spatial gradients were also observed for the BIs (Table B in S2 File). Longitudinal (North—South) and altitudinal (high—low) gradients were both stronger than the latitudinal gradient (East—West) for most of the BIs. Longitude and altitude of the BI cells showed significantly high correlation (r = -0.8, p < 0.001) with each other and thus indicates that the dominant climatic variation along the North—South (longitudinal) gradient on the scale of Germany (also observed in Fig B in S1 File) may be attributed to topography, i.e. altitude (low—high).

Pre-processing of BI and AWT data.

The stream sites covered 72% of the total BI raster cells within the boundary of Germany (Fig D in S1 File). However, given the relatively coarse resolution of the BI data, multiple sites were often located in one BI raster cell. Therefore, we aggregated the AWTs in all sites within a BI raster cell via averaging and assigned the result to that cell to avoid pseudo replication. The BIs exhibited considerable multicollinearity (Fig E in S1 File) and therefore we conducted a principal component analysis (PCA) to arrive at independent variables and extracted the scores of the 35 orthogonal principal components, as suggested by Graham [45], for latter analysis. PCA was preferred over residual and sequential regression as this also obliterates the likely effects of the latent spatial variables (as described above) on the BIs [45]. All data processing and PCA of BIs were done in R software environment [46] using the packages “sp” [47], “vegan” [48], “raster” [49] and “maptools” [50].

Analyses of the spatial relationship between traits and climate

The spatial relationship between the aggregated AWT per BI cell and the BIs was analyzed in four steps (Fig F in S1 File). First, we checked for spatial autocorrelations in the AWT (Table C in S2 File). The spatial autocorrelation was analyzed using Global Moran's I (see Bonada et al. [24] for details on computation), where great circle distances among BI cell pairs were given as weights based on the simplified assumption that the selected species disperse symmetrically during their terrestrial life stage [29]. The computations of spatial autocorrelations were done using the R package “ape” [51].

Second, zero-or-one inflated beta regression models were fitted with the AWT as response and 35 principal component scores of the BIs as explanatory variables [52]. This was done to identify the traits and insect orders with the highest climate response. We used zero-or-one inflated beta regression because the response variables were proportional data and included many zeros and ones [53]. The models were fitted for the AWT of each order and the full data and the adjusted R2s were calculated to identify the explained variance by the BIs. The zero-or-one-inflated beta regression model fitting was done using the R package “gamlss” [54].

In the third step, we checked for spatial autocorrelation in the residuals of the trait-climate models using Moran's I as outlined above. The Moran's I values for the residuals of the trait-climate models were subtracted from the complete Moran's I values for the AWT (computed at the first step). Thus, the percentage of trait spatial autocorrelation that is associated with the BIs was identified. This was done to identify the traits and orders that show the highest potential for changing their distributional pattern, i.e. redistribution under future climate change.

In a final step, the zero-or-one inflated beta regression models were re-fitted with the AWT for the previously identified traits and orders with the highest climate response and potential for redistribution as response variables and 35 BIs (original values) separately as explanatory variables. The BIs with the highest explanatory power in terms of R2 were identified for the traits and insect orders with the highest climate response. To identify the BIs explaining the highest amount of spatial autocorrelation in the traits and in the insect orders with the highest potential for redistribution, we computed the Moran's I in the residuals of the trait-individual BI models and subtracted them from the complete Moran's I computed at the first step.

Results and Discussion

Which of the climate-associated traits and organism groups show the highest response to climate and highest potential for changing distribution pattern under future climate change?

We quantified the amount of large scale variability and spatial autocorrelation in the assumed climate-associated traits from six grouping features and five aquatic insect orders of the freshwater assemblages that is explained by 35 global BIs. The BIs explained 19% of the large scale variability in the AWT of the full data on average (Table 1). Traits of the temperature preference grouping feature were the most responsive (32% on average) to the BIs, and the insects with very cold temperature preference (50%) showed the highest response. Among the insect orders, Ephemeroptera and Plecoptera (16%) showed the highest response to the BIs on average, and the ephemeropterans with very cold temperature preference (33%) showed the highest response in particular (Table 1).

The highest response of the traits of the temperature preference grouping feature, particularly of the very cold and cold preference may be due to traits of temperature preference grouping feature being the product of several underlying climate-associated biological traits [19,55,56]. For example, cold temperature preference of the selected aquatic insects in our study was significantly related to low dispersal capacity, large body size (>4 cm), low reproductive capacity (semivoltine) and resistance to drought (egg diapause) (Table D in S2 File), and together they explained 55% of the variability in cold temperature preference. Likewise, warm temperature preference of the insects was related to high dispersal capacity, small body size (≤0.5 cm), high reproductive capacity (multivoltine) and resistance to drought (adult diapause) (Table D in S2 File), and together they explained 48% of the variability in warm temperature preference. These findings are in agreement with other studies on the association of traits with climate change. For example, insects with low dispersal are often characterized by a restricted temperature (cold) niche and hence are more affected by change in temperature regimes, e.g. contractions of alpine regions than the insects with high dispersal ability [1820,57]. Large-bodied insects generally lack efficient respiration and thus have high ectotherm oxygen demand and hence typically inhabit streams with high oxygen supply, i.e. cold water streams [15,58,59]. Hence, we argue that the highest response of the traits of temperature preference grouping feature to the BIs in our study rather follows from the response of several underlying climate-biological traits relationships. Thus, we envisage an adverse effect of global warming on the insects inhabiting cold water streams in Germany because their biological and ecological niche will be contracted. This prediction is in line with Poff et al. [5], where temperature has been shown to be mostly accountable for the differences in the sensitivity of stream macroinvertebrate traits across geographic space and also with Lawrence et al. and Stamp et al. [15,56] where major declines in macroinvertebrates that inhabit cold water streams were reported as a result of climate change.

The differences in the response of insect orders observed in our study are related to their biological and ecological traits (Tables A and D in S2 File) [4,1820,58]. Although European ephemeropterans were found to be generally tolerant to climate change [4], we observed the highest BI response in the German ephemeropterans with very cold temperature preference (Table 1). This indicates that ephemeropterans inhabiting very cold water streams in Germany are also vulnerable to climate change because of shrinking ecological niche [60]. Plecopterans showed equally high response as ephemeropterans because they show high membership state for the very cold and cold preference traits, which showed the highest response to the BIs (Table 1 and Table A in S2 File). Generally, plecopterans have a very narrow environmental tolerance with nymphs living mainly in cold and well-oxygenated running water and adults showing low flight ability [18,59]. Hence, plecopterans have never transitioned to thermally variable lentic water and are thus vulnerable to increasing temperature and severe drought episodes [58]. Thus, we also anticipate an adverse effect of climate change on plecopterans in Germany. Overall, our results indicate that insects with traits such as preference for cold water (due to several underlying traits), and from certain orders, i.e. Ephemeroptera and Plecoptera may indeed be more vulnerable to climate change than others (Table 1). Thus, we suggest that future studies on the vulnerability of macroinvertebrate assemblage traits to climate change should particularly focus on traits and orders exhibiting the strongest signal to climate.

Regarding the potential for changing distribution pattern, i.e. redistribution, on average, 59% of the spatial autocorrelation in the AWT of the full data was associated with the BIs (Table 1). The BIs explained the highest spatial autocorrelation in the temperature preference (81%), particularly in the insects with cold temperature preference (91%) (Table 1). More than 50% of the spatial autocorrelation for the majority (62%) of the traits in the insect orders was associated with the BIs. The BIs explained the highest amount of spatial autocorrelation for the insect order Ephemeroptera (59%) in general, and for the Trichoptera with moderate temperature preference (97%). The amount of large scale variability explained by the BIs (described above) in insect traits and orders showed positive significant correlation (r = 0.5, p < 0.001) with the amount of explained spatial autocorrelation. This indicates that the traits and orders showing higher response to the BIs also exhibit a higher potential for changing spatial distribution pattern under changing BIs and vice-versa. Overall, the spatial distribution pattern, i.e. patchiness in the aquatic insects on large scales mostly originate from their high response to spatially autocorrelated climate that is line with Bonada et al. and Domisch et al. [24,26].

The highest potential for redistribution in the traits of temperature preference grouping feature and insect order Ephemeroptera, and trichopterans preferring moderate temperature also presumably relates to their strong covariation with underlying climate-associated biological and ecological traits as discussed above (Tables A and D in S2 File). For example, trichopterans showed high membership state for the underlying biological traits of the moderate temperature preference, i.e. small body size (< 0.5 cm) and high drought resistance (adult diapause) (Tables A and D in S2 File), and hence moderate temperature preferring trichopterans showed the highest potential for redistribution. The redistribution of traits and orders may occur through local extinction of vulnerable insects and thus range contraction [19], or by expansion of the range of tolerant macroinvertebrates in response to climate change [61,62]. Moreover, given that there is a strong association of the spatial distribution pattern of AWT of the insect orders individually (Fig A in S1 File) and of the full data (Figs 3 and 4) with the longitudinal gradient (which is coherent with the observed longitudinal spatial distribution pattern in the climate sensitive European stream macroinvertebrates [4,19,20]), and the BIs also showed a major longitudinal gradient with high correlation to altitude (Fig B in S1 File and Table B in S2 File), the redistribution may occur along the longitudinal (altitudinal) gradient. For example, a higher proportion of insects (0.4) and ephemeropterans (0.3) with cold temperature preference were observed in the cooler southern mountainous regions than in the warmer flat North of Germany (Fig 4 and Fig A in S1 File) that may shrink their distribution range. By contrast, trichopterans with moderate temperature preference that predominantly (0.5) occur in the warmer flat northern regions than in the cooler South may extend their range from North to South because more streams will be suitable for their habitat due to increasing temperature. A similar phenomenon was observed in Hering et al. [19] where most of the European trichopterans were suggested to benefit from increasing stream temperature (78%) and decreasing current (77%). Overall, climate change may alter the trait distribution pattern especially with respect to temperature preference and for the insect order Ephemeroptera, Plecoptera, and for trichopterans with moderate temperature preference in Germany, though adaptations may occur and ameliorate the ecological effects.

The explained variability and spatial autocorrelation for the traits and orders by the BIs in our study are similar (with a few exceptions) to previous studies using aerial and exclusive aquatic dispersers on comparable spatial scales [5,24]. A study dealing with the Mediterranean basin found that climate and environmental variables together explained < 19% variability for the same insect orders (except Diptera) [24]. Moreover, a lower percentage (< 30%) of spatial autocorrelation was associated with climate and other environmental variables than in our study, and in many cases significant spatial autocorrelation remained in the residuals. This discrepancy may be explained by the fact that the study considered only two climate variables (average precipitation and temperature) whereas we considered 35 BIs. The 35 BIs used in our study better captured the climate gradient in Germany and consequently are associated with higher variability and spatial autocorrelation in the AWTM. The use of different biological endpoints, i.e. taxonomic richness in [24] and trait abundance in our study may also explain this discrepancy. In another study on the catchment scale, climate and hydrological variables together explained a similar (19%) trait variability [5] although this study was conducted on a largely different set of traits of macroinvertebrates. Overall, the differences between the studies presumably relate to the traits, organism groups and the number (dimension) of climate variables used as input in models [27].

The inclusion of other environmental drivers such as geology and stream size may decrease the amount of trait variability and spatial autocorrelation that can be attributed to the BIs, especially if drivers exhibit collinearity with the BIs. Nevertheless, other environmental drivers explained much lower taxonomic and trait variation than climate in previous studies [5,24]. Moreover, in our study, the BIs explained more than half of the spatial autocorrelation for the majority of traits, and no statistically significant (all p ≥ 0.08) spatial autocorrelation was observed in the residuals of the trait-climate models (Table 1). This indicates that the remaining trait variability and spatial autocorrelation that can be explained by other environmental drivers are either statistically insignificant or have already been captured by climate, and thus these drivers are of lower importance for the traits under scrutiny [27].

The results may bear some uncertainty regarding the northwestern and southeastern regions of Germany, which were represented by a relatively lower number of stream sites and in turn a lower coverage of BI raster cells than other regions (Fig 1 and Fig D in S2 File). However, previous studies on comparable spatial scales successfully captured macroinvertebrate trait and taxonomic variabilities and their relationships with climate and other environmental drivers, despite relying on less stream sites (lower density) [5,12,24]. Thus, we suggest that our results are sufficiently robust on the scale of Germany, though more stream sites may be required for smaller scale studies in some regions.

Which of the climatic aspects show the strongest relationship with the traits and organism groups showing the highest response and potential for redistribution?

The explained variance and spatial autocorrelation in the most responsive traits and orders by individual BIs was on average 50% lower than by the combined BIs (Table 2). The BIs precipitation of the driest week (18%) and radiation seasonality (17%) exhibited the strongest relationship with insects preferring very cold temperature (Table 2). Precipitation and moisture indices, i.e. annual moisture index and precipitation of the driest week (both 14%), and moisture seasonality, moisture of the wettest and driest quarter (all 13%) explained the highest variance in the very cold preferring ephemeropterans. The radiation seasonality (46%), and radiation (65%) and mean temperature (64%) of the driest quarter explained the highest amount of spatial autocorrelation in the cold temperature preferring insects and moderate temperature preferring trichopterans, respectively (Table 2). Overall, these results suggest that aquatic insects in Germany may mainly be affected in response to potential changes in seasonal radiation and moisture.

In the coming decades, the winter and summer temperatures are highly likely to increase, with the strongest increase predicted for the South of Germany [60]. Moreover, winter precipitation has been predicted to increase with a larger increase in the North. By contrast, summer precipitation has been predicted to decrease in Germany with the strongest decrease in the South [60]. Thus, we anticipate an increase in winter radiation and decrease in summer moisture for the South of Germany where the majority of very cold and cold temperature preferring insects occur (Fig 4), particularly the very cold and cold temperature preferring ephemeropterans and plecopterans (Fig A in S1 File). Thus the increasing winter radiation and decreasing summer moisture may drive climate change effects on insects in general and ephemeropterans and plecopterans in particular that prefer cold water streams in Germany, and may eventually shrink their distribution range. These findings are in line with [13,15], where cold preferring stream macroinvertebrates were shown to be the most adversely affected by increasing winter temperature and decreasing summer precipitation. However, insects may also adapt to increasing temperature and decreasing precipitation [7,8]. For example, adaptations such as decreasing body size [61] and color lightening of adults [62] have been observed in insects. Trichopterans with moderate temperature living in the flat North of Germany (Fig 4 and Fig A in S1 File) may benefit from increasing radiation and recolonize upstream [19], and thus extend their distribution range from the North to the South. Overall, we anticipate a substantial change in the aquatic insect distribution pattern along the longitudinal gradient in Germany because of increasing seasonal radiation and decreasing moisture, especially in ephemeropterans and plecopterans with very cold and cold temperature preference and trichopterans with moderate temperature preference.

Concluding remarks

The relationship of the aquatic insect assemblage trait composition with climate identified in our study can contribute to the development of trait-based metrics for predicting climate-related assemblage changes [10,11]. For example, insights from the relationship between the traits and climate could help to predict their responses to seasonal discharge, torrential floods and droughts [12]. Such insights will also support freshwater management with respect to global climate change, i.e. bio-monitoring based on climate priority traits.

Supporting Information

S1 File. Supporting Figures.

Annual averaged abundance weighted traits across 4,752 stream sites in Germany for each order. The figure captions, sub-captions and panel captions indicate the names of orders, grouping features and traits, respectively. The gray dots indicate zero abundance, i.e. trait absence (Fig A). Extracted 35 global bioclimatic indices within the border of Germany. The indices are grouped according to their value ranges and units (°C, mm, W m-2 and no unit). The panel captions indicate the IDs of the indices (Bio_ID). Details on the indices and their IDs and units can be found in Table 2 and https://www.climond.org/Resources.aspx (Fig B). Altitudes from the mean sea level (m) within the border of Germany. Details can be found in http://asterweb.jpl.nasa.gov/gdem.asp (Fig C). Bioclimatic indices (BIs) raster cells that are covered (72%) by the bio-monitoring steam sites (Fig D). Observed multicollinearity among the 35 bioclimatic indices (BIs). Statistically significant (p<0.001) pairwise correlation coefficients (Pearson) are reported with scatterplots and histograms showing distribution. Details on the indices and their IDs and units can be found in Table 2 and https://www.climond.org/Resources.aspx (Fig E). Steps of the trait-climate spatial relationship analysis (Fig F).

doi:10.1371/journal.pone.0130025.s001

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S2 File. Supporting Tables.

Membership states of the five insect orders (%) for the traits of each grouping feature. The membership state (%) of an order for a trait was computed as the median of the membership states of all taxa in that order for that trait. The membership states were then scaled by the total of the membership states of an order for the traits of a grouping feature so that the membership states sum to 100% for each grouping feature (Table A). Spatial autocorrelations (Moran's I values) and gradients (Pearson correlations with longitude, latitude and altitude) for the bioclimatic indices (BIs) extracted at the stream sites. The Moran's I values and Pearson correlation coefficients are statistically significant at p<0.001. Details on the indices and their IDs and units can be found in Table 2 and https://www.climond.org/Resources.aspx (Table B). Spatial autocorrelations (global Moran's I) for abundance weighted traits in each stream macroinvertebrate order and in the full data. Observed Moran's I values are statistically significant at p<0.001 (Table C). Relationship between the traits of temperature preference grouping feature and the traits of remaining grouping features in terms of explained variance (%). The explained variances are the R2s of the zero-or-one-inflated beta regression models fitted with the abundance weighted traits (AWT) of the temperature preference grouping feature as response and the AWT of the remaining grouping features separately as predictor variables (Table D).

doi:10.1371/journal.pone.0130025.s002

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Acknowledgments

We thank the German state authorities for providing the stream macroinvertebrate data. Valuable comments from John Mbaka and an anonymous reviewer, and an initial analysis from Gunnar Oehmichen helped to improve the manuscript.

Author Contributions

Conceived and designed the experiments: AKB RBS. Performed the experiments: AKB. Analyzed the data: AKB. Contributed reagents/materials/analysis tools: AKB RBS. Wrote the paper: AKB RBS.

References

  1. 1. Dudgeon D, Arthington AH, Gessner MO, Kawabata Z-I, Knowler DJ, Lévêque C, et al. Freshwater biodiversity: importance, threats, status and conservation challenges. Biological Reviews. 2005;81: 163–182. doi: 10.1017/S1464793105006950. pmid:16336747
  2. 2. Vörösmarty CJ, McIntyre PB, Gessner MO, Dudgeon D, Prusevich A, Green P, et al. Global threats to human water security and river biodiversity. Nature. 2010;467: 555–561. doi: 10.1038/nature09440. pmid:20882010
  3. 3. Vorosmarty CJ. Global Water Resources: Vulnerability from Climate Change and Population Growth. Science. 2000;289: 284–288. doi: 10.1126/science.289.5477.284. pmid:10894773
  4. 4. Conti L, Schmidt-Kloiber A, Grenouillet G, Graf W. A trait-based approach to assess the vulnerability of European aquatic insects to climate change. Hydrobiologia. 2013;721: 297–315. doi: 10.1007/s10750-013-1690-7.
  5. 5. Poff NL, Pyne MI, Bledsoe BP, Cuhaciyan CC, Carlisle DM. Developing linkages between species traits and multiscaled environmental variation to explore vulnerability of stream benthic communities to climate change. Journal of the North American Benthological Society. 2010;29: 1441–1458. doi: 10.1899/10-030.1.
  6. 6. Parmesan C, Yohe G. A globally coherent fingerprint of climate change impacts across natural systems. Nature. 2003;421: 37–42. doi: 10.1111/j.1365-2486.2010.02370.x. pmid:12511946
  7. 7. Lancaster J, Downes BJ. Linking the hydraulic world of individual organisms to ecological processes: Putting ecology into ecohydraulics. River Research and Applications. 2010;26: 385–403. doi: 10.1002/rra.1274.
  8. 8. Lancaster J, Downes BJ. Ecohydraulics needs to embrace ecology and sound science, and to avoid mathematical artefacts. River Research and Applications. 2010;26: 921–929. doi: 10.1002/rra.1425.
  9. 9. Statzner B, Bêche LA. Can biological invertebrate traits resolve effects of multiple stressors on running water ecosystems? Freshwater Biology. 2010;55: 80–119. doi: 10.1111/j.1365-2427.2009.02369.x.
  10. 10. Vandewalle M, Bello F, Berg MP, Bolger T, Dolédec S, Dubs F, et al. Functional traits as indicators of biodiversity response to land use changes across ecosystems and organisms. Biodiversity and Conservation. 2010;19: 2921–2947. doi: 10.1007/s10531-010-9798-9.
  11. 11. Mlambo MC. Not all traits are “functional”: insights from taxonomy and biodiversity-ecosystem functioning research. Biodiversity and Conservation. 2014;23: 781–790. doi: 10.1007/s10531-014-0618-5.
  12. 12. Bonada N, DoléDec S, Statzner B. Taxonomic and biological trait differences of stream macroinvertebrate communities between mediterranean and temperate regions: implications for future climatic scenarios. Global Change Biology. 2007;13: 1658–1671. doi: 10.1111/j.1365-2486.2007.01375.x.
  13. 13. Durance I, Ormerod SJ. Climate change effects on upland stream macroinvertebrates over a 25-year period. Global Change Biology. 2007;13: 942–957. doi: 10.1111/j.1365-2486.2007.01340.x.
  14. 14. Brittain JE. Mayflies, biodiversity and climate change. International Advances in the Ecology, Zoogeography and Systematics of Mayflies and Stoneflies. California: University of California Publications in Entomology; 2008. pp. 1–14.
  15. 15. Lawrence JE, Lunde KB, Mazor RD, Bêche LA, McElravy EP, Resh VH. Long-term macroinvertebrate responses to climate change: implications for biological assessment in mediterranean-climate streams. Journal of the North American Benthological Society. 2010;29: 1424–1440. doi: 10.1899/09-178.1.
  16. 16. Floury M, Usseglio-Polatera P, Ferreol M, Delattre C, Souchon Y. Global climate change in large European rivers: long-term effects on macroinvertebrate communities and potential local confounding factors. Global Change Biology. 2013;19: 1085–1099. doi: 10.1111/gcb.12124. pmid:23504886
  17. 17. Kotiaho JS, Kaitala V, Komonen A, Päivinen J. Predicting the risk of extinction from shared ecological characteristics. Proceedings of the National Academy of Sciences of the United States of America. 2005;102: 1963–1967. doi: 10.1073/pnas.0406718102. pmid:15671171
  18. 18. Tierno de Figueroa JM, López-Rodríguez MJ, Lorenz A, Graf W, Schmidt-Kloiber A, Hering D. Vulnerable taxa of European Plecoptera (Insecta) in the context of climate change. Biodiversity and Conservation. 2010;19: 1269–1277. doi: 10.1007/s10531-009-9753-9.
  19. 19. Hering D, Schmidt-Kloiber A, Murphy J, Lücke S, Zamora-Muñoz C, López-Rodríguez MJ, et al. Potential impact of climate change on aquatic insects: A sensitivity analysis for European caddisflies (Trichoptera) based on distribution patterns and ecological preferences. Aquatic Sciences. 2009;71: 3–14. doi: 10.1007/s00027-009-9159-5.
  20. 20. Hershkovitz Y, Dahm V, Lorenz AW, Hering D. A multi-trait approach for the identification and protection of European freshwater species that are potentially vulnerable to the impacts of climate change. Ecological Indicators. 2015;50: 150–160. doi: 10.1016/j.ecolind.2014.10.023.
  21. 21. Sandin L, Schmidt-Kloiber A, Svenning J-C, Jeppesen E, Friberg N. A trait-based approach to assess climate change sensitivity of freshwater invertebrates across Swedish ecoregions. Current Zoology. 2014;60.
  22. 22. Heino J, Schmera D, Erős T. A macroecological perspective of trait patterns in stream communities. Freshwater Biology. 2013;58: 1539–1555. doi: 10.1111/fwb.12164.
  23. 23. Peres-Neto PR, Legendre P. Estimating and controlling for spatial structure in the study of ecological communities: Spatial structure in ecological communities. Global Ecology and Biogeography. 2010;19: 174–184. doi: 10.1111/j.1466-8238.2009.00506.x.
  24. 24. Bonada N, Dolédec S, Statzner B. Spatial autocorrelation patterns of stream invertebrates: exogenous and endogenous factors. Journal of Biogeography. 2012;39: 56–68. doi: 10.1111/j.1365-2699.2011.02562.x.
  25. 25. Fortin M-J, Dale MRT. Spatial analysis: a guide for ecologists. Cambridge: Cambridge University Press; 2005.
  26. 26. Domisch S, Araújo MB, Bonada N, Pauls SU, Jähnig SC, Haase P. Modelling distribution in European stream macroinvertebrates under future climates. Global Change Biology. 2013;19: 752–762. doi: 10.1111/gcb.12107. pmid:23504833
  27. 27. Dray S, Pélissier R, Couteron P, Fortin M-J, Legendre P, Peres-Neto PR, et al. Community ecology in the age of multivariate multiscale spatial analysis. Ecological Monographs. 2012;82: 257–275. doi: 10.1890/11-1183.1.
  28. 28. Schmera D, Podani J, Heino J, Erős T, Poff NL. A proposed unified terminology of species traits in stream ecology. Freshwater Science. 2015; 000–000. doi: 10.1086/681623.
  29. 29. Wikelski M, Moskowitz D, Adelman JS, Cochran J, Wilcove DS, May ML. Simple rules guide dragonfly migration. Biology Letters. 2006;2: 325–329. doi: 10.1098/rsbl.2006.0487. pmid:17148394
  30. 30. Kriticos DJ, Webber BL, Leriche A, Ota N, Macadam I, Bathols J, et al. CliMond: global high-resolution historical and future scenario climate surfaces for bioclimatic modelling: CliMond: climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution. 2012;3: 53–64. doi: 10.1111/j.2041-210X.2011.00134.x.
  31. 31. Deutscher Wetterdienst (DW). Weather and Climate—Deutscher Wetterdienst: Klimaüberwachung Deutschland [Internet]. 2014 [cited 15 Jan 2014]. Available: http://www.dwd.de/
  32. 32. AQEM CONSORTIUM. Manual for the application of the aqem system. A comprehensive method to assess european streams using benthic macroinvertebrates, developed for the purpose of the water framework directive. 2002.
  33. 33. Rolauffs P, Hering D, Sommerhäuser M, Jähnig S, Rödiger S. Entwicklung eines leitbildorientierten Saprobienindexes für die biologische Fließgewässerbewertung [Internet]. Essen: Umweltforschungsplan des Bundesministeriums für Umwelt, Naturschutz und Reaktorsicherheit; 2003 pp. 1–137. Report No.: 200 24 227. Available: http://www.umweltdaten.de/publikationen/fpdf-l/2253.pdf
  34. 34. Biss R, Kübler P, Pinter I, Braukmann U. Leitbildbezogenes biozönotisches Bewertungsverfahren für Fließgewässer in der Bundesrepublik Deutschland-Ein erster Beitrag zur integrierten ökologischen Fließgewässerbewertung [Internet]. Berlin: Umweltforschungsplan des Bundesministeriums für Umwelt, Naturschutz und Reaktorsicherheit; 2006 pp. 1–175. Report No.: 298 24 777. Available: http://www.fliessgewaesserrenaturierung.de/downloads/abschlussbericht_20060331.pdf
  35. 35. Legendre P, Borcard D, Peres-Neto PR. Analyzing beta diversity: partitioning the spatial variation of community composition data. Ecological Monographs. 2005;75: 435–450.
  36. 36. Schmidt-Kloiber A, Hering D. www.freshwaterecology.info —An online tool that unifies, standardises and codifies more than 20,000 European freshwater organisms and their ecological preferences. Ecological Indicators. 2015;53: 271–282. doi: 10.1016/j.ecolind.2015.02.007.
  37. 37. Usseglio-Polatera P, Bournaud M, Richoux P, Tachet H. Biomonitoring through biological traits of benthic macroinvertebrates: how to use species trait databases? Assessing the Ecological Integrity of Running Waters. Springer; 2000. pp. 153–162.
  38. 38. Department of Applied Zoology/Hydrobiology, University Duisburg-Essen, Germany (DZHUDE). The Development and Testing of an Integrated Assessment System for the Ecological Quality of Streams and Rivers throughout Europe using Benthic Macroinvertebrates. Acronym: AQEM [Internet]. 2008 [cited 10 Feb 2014]. Available: http://www.aqem.de/
  39. 39. Schmidt-Kloiber A, Nijboer RC. The effect of taxonomic resolution on the assessment of ecological water quality classes. Integrated Assessment of Running Waters in Europe. Springer; 2004. pp. 269–283.
  40. 40. Schmera D, Podani J, Erős T, Heino J. Combining taxon-by-trait and taxon-by-site matrices for analysing trait patterns of macroinvertebrate communities: a rejoinder to Monaghan & Soares (). Freshwater Biology. 2014;59: 1551–1557. doi: 10.1111/fwb.12369.
  41. 41. Larsen S, Ormerod SJ. Combined effects of habitat modification on trait composition and species nestedness in river invertebrates. Biological Conservation. 2010;143: 2638–2646. doi: 10.1016/j.biocon.2010.07.006.
  42. 42. Dolédec S, Phillips N, Scarsbrook M, Riley RH, Townsend CR. Comparison of structural and functional approaches to determining landuse effects on grassland stream invertebrate communities. Journal of the North American Benthological Society. 2006;25: 44–60. doi: 10.1899/0887-3593(2006)25[44:COSAFA]2.0.CO;2.
  43. 43. Araújo MB, Peterson AT. Uses and misuses of bioclimatic envelope modeling. Ecology. 2012;93: 1527–1539. pmid:22919900
  44. 44. National Aeronautics and Space Administration (NASA), Japan’s Ministry of Economy, Trade and Industry (METI). ASTER Global Digital Elevation Map [Internet]. 2009 [cited 18 Dec 2013]. Available: http://asterweb.jpl.nasa.gov/gdem.asp
  45. 45. Graham MH. Confronting multicollinearity in ecological multiple regression. Ecology. 2003;84: 2809–2815.
  46. 46. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing [Internet]. Vienna, Austria; 2015. Available: http://www.R-project.org/
  47. 47. Bivand RS, Pebesma EJ, Gómez-Rubio V. Applied spatial data analysis with R. New York: Springer; 2008.
  48. 48. Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community ecology package. 2007.
  49. 49. Hijmans RJ, Van Etten J. Raster: geographic analysis and modeling with raster data. R package version 2.1–66. 2010.
  50. 50. Lewin-Koh NJ, Bivand R, Pebesma E, Archer E, Baddeley A, Bibiko H, et al. maptools: Tools for reading and handling spatial objects. R package version 0.8–27 [Internet]. 2011. Available: http://CRAN. R-project. org/package = maptools
  51. 51. Paradis E, Claude J, Strimmer K. APE: Analyses of Phylogenetics and Evolution in R language. Bioinformatics. 2004;20: 289–290. doi: 10.1093/bioinformatics/btg412. pmid:14734327
  52. 52. Ospina R, Ferrari SL. A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis. 2012;56: 1609–1623.
  53. 53. Nishii R, Tanaka S. Modeling and inference of forest coverage ratio using zero-one inflated distributions with spatial dependence. Environmental and Ecological Statistics. 2012;20: 315–336. doi: 10.1007/s10651-012-0227-y.
  54. 54. Rigby RA, Stasinopoulos DM. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2005;54: 507–554.
  55. 55. Verberk WCEP, Bilton DT. Respiratory control in aquatic insects dictates their vulnerability to global warming. Biology Letters. 2013;9: 20130473–20130473. doi: 10.1098/rsbl.2013.0473. pmid:23925834
  56. 56. Stamp JD, Hamilton AT, Zheng L, Bierwagen BG. Use of thermal preference metrics to examine state biomonitoring data for climate change effects. Journal of the North American Benthological Society. 2010;29: 1410–1423. doi: 10.1899/10-003.1.
  57. 57. Domisch S, Jähnig SC, Haase P. Climate-change winners and losers: stream macroinvertebrates of a submontane region in Central Europe: Climate change effects on stream macroinvertebrates. Freshwater Biology. 2011;56: 2009–2020. doi: 10.1111/j.1365-2427.2011.02631.x.
  58. 58. Harrison JF, Kaiser A, VandenBrooks JM. Atmospheric oxygen level and the evolution of insect body size. Proceedings of the Royal Society B: Biological Sciences. 2010;277: 1937–1946. doi: 10.1098/rspb.2010.0001. pmid:20219733
  59. 59. Verberk WCEP, Atkinson D. Why polar gigantism and Palaeozoic gigantism are not equivalent: effects of oxygen and temperature on the body size of ectotherms. Konarzewsk M, editor. Functional Ecology. 2013;27: 1275–1285. doi: 10.1111/1365-2435.12152.
  60. 60. Stocker TF, Dahe Q, Plattner G-K. Climate Change 2013: The Physical Science Basis. Intergovernmental Panel on Climate Change (IPCC); 2013 pp. 1–1506.
  61. 61. Daufresne M, Lengfellner K, Sommer U. Global warming benefits the small in aquatic ecosystems. Proceedings of the National Academy of Sciences. 2009;106: 12788–12793. doi: 10.1073/pnas.0902080106. pmid:19620720
  62. 62. Zeuss D, Brandl R, Brändle M, Rahbek C, Brunzel S. Global warming favours light-coloured insects in Europe. Nature Communications. 2014;5. doi: 10.1038/ncomms4874.