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- Supporting Information
There are major challenges to improving the ecological integrity of freshwater ecosystems across the globe, and climate change will potentially exacerbate many existing problems (Dudgeon et al., 2006; Rosenzweig et al., 2008; Pittock & Finlayson, 2011). Changes in species phenology (Hassall et al., 2007), distribution (Daufresne et al., 2004; Hickling et al., 2005) and assemblage structure (Flenner & Sahlén, 2008; Chessman, 2009; Daufresne et al., 2009) of freshwater species have already been recorded, consistent with being responses to recent climatic change. To meet the challenge of improving or maintaining the ecological integrity of rivers, we must consider climate change effects (Palmer et al., 2009; Turak et al., 2011). Acting before significant ecological change occurs will increase the likelihood of success and reduce the risk of inefficient resource allocation (Heller & Zavaleta, 2009). To provide an informed basis for adaptive management action (e.g. revegetation (Davies, 2010; Thomson et al., 2012), ecologically relevant indicators are required that improve the prediction of species responses (e.g. range shifts) and provide rapid feedback of observed changes (Hering et al., 2010).
The state of freshwater ecosystems is frequently assessed by monitoring the diversity and/or structure of freshwater communities (e.g. Bunn et al., 2010; Davies et al., 2010). Many biological monitoring programmes in freshwater use the deviation of an observed assemblage from a notionally undisturbed reference state to reflect the effects of various stressors such as eutrophication or hydrological degradation (Hering et al., 2010). The effects of climate change are more difficult to interpret because without historic reference conditions and long-term data collection, there is no baseline with which to reference ecological response (Jackson & Füreder, 2006). The resolution of sampling and taxonomy that has proven satisfactory for previous monitoring to detect changes in water quality may also be insufficient to recognize the potentially complex network of effects predicted because of climate change (Hering et al., 2010). Given the significant additional impact that climate change is expected to have on freshwater ecosystems (Daufresne & Boët, 2007; Hassall & Thompson, 2008; Daufresne et al., 2009; Woodward et al., 2010), it is urgent that we consider specific indicators and establish baseline conditions with which to compare future changes (Morecroft et al., 2009; Lawrence et al., 2010).
The term ‘indicator’ is used here to describe a simple measure that acts as a signal of a more complex process, response to climate change (Fleishman & Murphy, 2009). Ideally, the response of an indicator (such as a single species) will be congruent with the wider system of interest (such as multiple, co-occurring species within a community), and its sensitivity to climate should not only be sufficient to observe a measurable response, but also exceed its sensitivity to other environmental conditions such as changing land use and pollution. In addition, an indicator will be more useful if it represents a single functional group (e.g. predators) because inferring the likely relationships with other species is more straightforward (Hughes, 2003). Finally, the choice of an indicator in a monitoring programme depends largely on costs, so one that is readily and consistently observed, measured and identified will be more useful (Marshall et al., 2006; Jones, 2008).
Freshwater biomonitoring programmes are typically designed to identify specimens only to family level, as part of a trade-off between cost and information requirements (Beattie & Oliver, 1994; Lenat & Resh, 2001). Low-resolution taxonomy assumes that species within higher levels, especially within genera and families, have similar ecological preferences (Marshall et al., 2006). However, in cases where ecological similarity of species does not correspond closely to their phylogenetic relatedness, the overall response of those species grouped at family level may be misleading (Lenat & Resh, 2001; Heino & Soininen, 2007; Bevilacqua et al., 2012). Further, when species are combined into families, potentially valuable information for discriminating between samples may be lost. Deciding whether the loss of information by aggregating species at family level is acceptable depends on the data required and the level of discrimination needed. Whether families are taxonomically sufficient to discern the important environmental drivers of assemblage change is largely dependent on scale, as well as region and amount of species radiation within a group (Hewlett, 2000; Marshall et al., 2006; Heino et al., 2007). Therefore, in selecting indicators to monitor climate effects, it is important to consider taxonomic resolution (Lawrence et al., 2010).
Amongst freshwater invertebrates, the dragonflies (Order: Odonata) receive the same ‘flagship’ recognition that butterflies offer for terrestrial ecosystems (Hawking & New, 2002; Fleishman & Murphy, 2009). In comparison with other freshwater invertebrates, dragonflies have a long history of research that provides a solid basis for understanding the implications of climate change (Corbet, 1999; Córdoba-Aguilar, 2008; Hassall & Thompson, 2008). Dragonflies originated and spread from the tropics and display a multitude of thermodynamic adaptations in both adult and larval stages that have allowed them to colonize temperate and subarctic environments (Hassall & Thompson, 2008). In the absence of fish, dragonfly larvae are often the top aquatic predators and may be key to maintaining diverse communities (Fox, 1977). Their development rate is strongly correlated with temperature, including the ability to complete multiple life cycles per year at lower latitudes (higher voltinism) (Corbet, 1999; Braune et al., 2008; Hassall & Thompson, 2008; Flenner et al., 2009). Where long-term records exist, phenological changes have been observed that are consistent with climate change predictions, showing an advance in the timing of emergence (Hassall et al., 2007). Most importantly, dragonflies are mobile and have the potential to disperse widely, readily colonizing new habitats (e.g. Suhling et al., 2004). As a result, a number of studies have demonstrated range shifts amongst dragonflies, consistent with being an adaptive response to climate change (Aoki, 1997; Hickling et al., 2005, 2006; Ott, 2007; Flenner & Sahlén, 2008). Dragonflies have been proposed as indicators of environmental quality in many circumstances (Chovanec & Waringer, 2001; Sahlén & Ekestubbe, 2001; Foote & Rice Hornung, 2005; Smith et al., 2007; Simaika & Samways, 2009, 2010). Given the interest in using dragonflies, we empirically tested whether they could be extended to representing climate change effects (Fleishman & Murphy, 2009).
This study investigated the potential for dragonflies to be used as indicators of climate change effects in freshwater environments and as surrogates for the responses of other stream macroinvertebrates. Initially, we asked whether spatial turnover of dragonfly assemblages is related to climate, and whether this group shows a higher degree of turnover in response to climate than other macroinvertebrate assemblages. On the basis of the results of these analyses, we asked whether the utility of dragonflies as indicators can be improved by increasing the taxonomic resolution at which they are identified. Finally, we asked whether changes to dragonfly assemblages are congruent with shifts in other aquatic macroinvertebrate assemblages. This investigation used data collected as part of an extensive monitoring programme of rivers and streams from subtropical to temperate climates, across 9.1° of latitude in eastern Australia. The region is well suited for studying the effects of climate change on range shifts in freshwater taxa because it contains multiple large catchments, all draining west–east, that potentially constrain migration across the latitudinal gradient.
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Over 92,000 specimens from 91 families were collected, and 3754 dragonflies identified (Table 1). From family through to species level, climate and water factors were the most important for explaining turnover, both as group-only and pure-components (Table 2 and Fig. 2). Much less variation could be explained by distance between sites or the degree of disturbance.
Table 2. Proportion of variation (%) explained in macroinvertebrate groups by partitioning four groups of environmental factors; climate, spatial distance, disturbance and water
The largest amount of variation that could be explained in family-level assemblage turnover was amongst the dragonflies and the Crustacea (Table 2). The influence of climate-only was also greatest amongst dragonflies and Crustacea, and even after partitioning other variation, their pure-climate fraction was similar (6–6.4%). Spatial separation was also influential for Crustacea assemblages, with a greater proportion confounded with climate than when partitioning dragonfly assemblages. In contrast, spatial factors were not important for assemblage turnover of either Trichoptera or Diptera families. Disturbance could potentially be highly influential for the distribution of Mollusca, but the variation explained was again largely correlated with other groups of factors. Dragonfly families showed equal sensitivity to stream and water factors as the EPT, although based on pure fractions, Trichoptera were the most sensitive taxon. For each taxonomic group, the potential explained variation for each factor, and the explanatory variables ranked most important are included in Tables S1 and S2 in Supporting Information.
From the 10 dragonfly families, we identified 46 genera and 97 species across a total of 791 sites. Although at family level, the variation in dragonfly assemblages that could be explained was comparable to other taxonomic groups, this increased significantly at higher taxonomic resolution (Fig. 2). Almost half the variation in dragonfly species assemblage composition could be explained by the tested factors, and the Climate-only component rose to 27%, comparable with the total variation explained by all factors amongst any taxonomic group at family level. Most importantly, the pure-climate fraction of this variation tripled from family to species-level resolution, due largely to a separation of previously covarying spatial factors. Although selection priority could not determine the importance for some variables, those associated with summer extremes such as precipitation of the warmest quarter and the temperature of the hottest month were consistently influential. The distribution of some dragonflies clearly demonstrates the importance of climate. Dendroaeschna conspersa, Cordulephya pygmaea, Nannophlebia risi, Pseudagrion ignifer and Rhadinosticta simplex appear to be warm-adapted and experience strong declines with increasing latitude or altitude, whereas Synthemis eustalacta and Austrolestes cingulatus appear cool-adapted and become increasingly common at higher altitudes.
When comparing congruence across all samples, dissimilarity amongst assemblages of dragonflies was significantly correlated with that in all the other taxa (P ≤ 0.001) (Table 3). However, the strength of the relationship was weak across all groups (r2 ≤ 0.25), including comparisons amongst non-dragonfly assemblages. The congruence between assemblages was stronger when comparing amongst catchments, although still not sufficient for prediction (ANOSIM r = 0.4–0.5). The use of dragonflies at genus or species level did not improve their performance as surrogates for assemblage turnover in families from other taxonomic groups.
Table 3. Mantel test of correlation in dissimilarity of dragonfly families, genera and species with other taxa
|Taxon||Scale||Dragonfly families||Dragonfly genera||Dragonfly species|
|Dragonfly families||L||n/a|| || |
|Dragonfly genera||L||0.6625***||n/a|| |
|All other taxa||L||0.2022***||0.2644***||0.2478***|