Appendix 9 National-Scale Responses of Macroinvertebrates

Home , Girardia, Girardia tigrina, Hydrometra stagnorum, Molanna, Rhynchobdellida

Appendix 9: National-scale responses of river macroinvertebrates species to changes in temperature and precipitation

Abstract Global climate change is expected to have a large impact on the biodiversity and functioning of freshwater ecosystems because of shifts in temperature, seasonality and weather. The response of freshwater organisms to climate change is likely to vary according to their environmental optima, with some species thriving under new conditions, while some at risk species may decline in abundance. These changes could significantly alter biodiversity, trophic interactions and key ecological processes, affecting current and future management and conservation regimes, as well as compliance with current environmental legislation such as the Water Framework Directive. This study examines the response of 137 river macroinvertebrate species to two climatic variables (temperature and precipitation) from 1,588 sampling sites across the United Kingdom over 15 to 25 years (1983-2007). Using a bespoke modelling method, the sensitivity of each species to changes in temperature and precipitation was identified, with the aim of inferring likely changes in the abundance of particular species in response to climate change. The characterisations of species responses are also used to demonstrate that a combination of species-specific traits and environmental preferences may be a systematic way to predict impacts.

Introduction

Freshwater ecosystems are considered one of the richest ecosystems globally in terms of biodiversity, sustaining a disproportionate high fraction of species per surface area relative to other ecosystems (Dudgeon et al., 2006, Balian et al., 2008). This biodiversity supports a range of important ecosystem processes (Woodward, 2009) , many of which provide key goods and services, such as the supply of clean drinking water, the dilution of pollution and the harvest of fish and other produce, to name but a few (Millennium Ecosystem Assessment, 2005). Despite their inherent value and importance, freshwater ecosystems are especially susceptible to degradation and climate change (Hart & Calhoun, 2010, Ormerod et al., 2010), manifesting in freshwater biodiversity declining at a much faster rate than either terrestrial or marine ecosystems (Ricciardi & Rasmussen, 1999, Sala et al., 2000, Jenkins, 2003, Heino et al., 2009). Stream and rivers, particularly, rank among the most threatened freshwater networks owing to the combined effects of multiple pressures. These include warming temperatures, increased frequency of extreme hydrological fluctuations, habitat destruction and fragmentation, alien species invasion and point and diffuse pollution (Malmqvist & Rundle, 2002, Vorosmarty et al., 2010). Reduced biodiversity may disrupt the functioning of ecosystems, threatening their intrinsic resilience to change (Loreau et al., 2001, Hooper et al., 2005), which may directly impact the ecosystem services on which human communities rely (Strayer & Dudgeon, 2010).

Evidence that climate change is occurring and impacting freshwater biodiversity is now unequivocal (IPCC, 2013), with increasing vulnerability projected for the future due to the interaction of climatic stressors (temperature, precipitation) with other stressors such as pollution and habitat loss (Domisch et al., 2013, Floury et al., 2013, Khamis et al., 2014). Any increase in air temperature is likely to translate directly into warmer water temperatures (Mohseni & Stefan, 1999, Morrill et al., 2005). In line with this, the temperatures of flowing waters have risen in Europe. For example water temperature in the Danube has increased by up to 1.7 oC since 1901 (Webb & Nobilis, 2007), and

temperature has increased by 2.6 oC in French rivers between 1979 and 2003 (Daufresne & Boet, 2007), and by 1.4 oC in Welsh streams between 1981 and 2005 (Durance & Ormerod, 2007). Warmer temperatures are likely to change species distributions, growth rates and phenology (Root et al., 2005, Friberg et al., 2009), in turn affecting food web dynamics and ecosystem processes (Kishi et al., 2005). Water quality may decrease as microbial activity and decomposition of organic matter increase, aggravating the reduced dissolved oxygen levels associated with higher temperatures. Aquatic species unable to migrate (regionally to cooler climes or within a river to the cooler headwaters) may face local extinctions. Conversely, there is a strong risk that non native invasive species, with broader temperature tolerances, may spread to new territories and establish themselves rapidly, applying further stress to native species. (Poff et al., 2002, Rahel & Olden, 2008).

Climatic changes to air and water temperature will cause shifts in the timing and intensity of precipitation and changes in the rates of evapotranspiration. Because these affect the volume and timing of runoff, and modify groundwater recharge, changes to the hydrology of freshwater systems are expected. These include a greater frequency, intensity and duration of extreme events such as storms/floods and droughts, increased peak flows and reduced base flows (IPCC, 2007) . These changes mediated by the supply and the quality of water, when combined with higher water temperature and further anthropogenic stressors, make freshwater ecosystems amongst the most vulnerable to climatic change (Allen & Ingram, 2002).

Benthic macroinvertebrates are one of most common indicators for biomonitoring the health of lotic ecosystems (Wright et al., 1993, Friberg et al., 2011) and are used in the United Kingdom (UK) and elsewhere to assess compliance with environmental regulations such as the Water Framework Directive (WFD) (European Commission, 2000). Macroinvertebrate communities are known to respond strongly to water temperature (Hawkins et al., 1997, Caissie, 2006), flow alterations (Poff & Zimmerman, 2010) and extreme drought/flood events (Ledger et al., 2013b), therefore provide an ideal system for the study of climate change impacts (Wilby et al., 2010). Three relatively consistent results from studies on macroinvertebrate responses to metrics of a changing climate are (i) alterations in the timing and duration of life cycle phases, such as pupation and emergence periods(Kotiaho et al., 2005, Leberfinger et al., 2010), (ii) the losses of species and trophic interactions, especially predators (Ledger et al., 2013a), and (ii) the geographical distribution of biota, such as shifts in altitudes according to thermal tolerances (Daufresne et al., 2003, Hering et al., 2009) However, the results of most studies are difficult to extrapolate at regional and national scales because they are often constrained to the analysis of macroinvertebrate data in specific habitat types (Zivic et al., 2014) or specific catchment (Daufresne et al., 2003, Durance & Ormerod, 2007) that usually have unique local stressors other than climate. These (e.g. nutrient pollution, oxygen concentrations) may exacerbate, reduce or offset the direct influence of climate change, making it harder to detect (Floury et al., 2013, Vaughan & Ormerod, 2014). For the purpose of improving conservation and management plans, and the prioritisation of interventions and mitigation measures, a better understanding of the sensitivity of macroinvertebrate communities to climate change is necessary at regional or national scales.

Despite their advantages to national management programmes, large-scale or regional studies are often limited to the analysis of macroinvertebrate data at a higher level of biological organisation than species, e.g. family level (Floury et al., 2013, Vaughan & Ormerod, 2014). As a result, few studies have examined differences in the responses of individual species within the same taxonomic

groups, across a wide range of taxa. Intra-group heterogeneity in species traits (e.g. ecological preferences and life cycle events), and interactions between these traits, may mask contrasting or stronger species responses to climate that are not observed at the higher group level (Hering et al., 2009, Tierno de Figueroa et al., 2010, Conti et al., 2014). This study presents the first comparative assessment of climatic sensitivity using the most comprehensive dataset of lotic macroinvertebrate species abundances, comprising 23 orders, across the UK. A bespoke modelling approach developed in Appendix 2 was used, where the annual spring population abundance of 137 species were modelled as a function of metrics describing local monthly mean air temperature and precipitation. A broad-scale approach was adopted, focusing on evidence for systematic trends across multiple sites over 15-25 years, while excluding any linear trends that may be explained by alternative stressors.

The modelling approach proposed in this study assumes that (i) the response of species population abundances to local climate varies throughout the 12 months prior to spring sampling, and is captured by a single oscillating pattern, and (ii) species abundance is likely to be influenced by the local climate in the preceding three years, necessitating the inclusion of a decaying lagged effect. Once models were calibrated, statistically significant relationships were examined and species responses were used to classify any observable trends according to each species’ traits. Model outputs yield a measure of directional change that incorporates month on month local climatic effects on species population abundance, providing a tool to assess the future impact of climate change (e.g. increases in temperature or precipitation) on the abundance of each species, including two invasive and one threatened species, in the UK.

Materials and methods Macroinvertebrate data Long-term data on species-level macroinvertebrate population abundances were supplied from two independent sources: the Environment Agency (EA) in England and the Scottish Environment Protection Agency (SEPA) in Scotland. The data are based on regular samples taken at 1,588 sites (Fig. 1) using a standardised three-minute kick sampling methods (Moss et al., 1999) and form part of the database developed by the agencies in their routine monitoring programmes (GQA, now WFD). Typically, taxa are identified to the family level, however for the current study we sought those that were further identified to species level. Data were checked for anomalies, coded using the same taxonomic reference system and merged to form the study database. Species that had abundance data for less than 15 years during the 25 year timeframe (1983-2007), and those that occurred in less than 20 sites (1,588 sites in total) over the time series were omitted from the database. The final database quantified the population abundance of 137 individual species, from 106 genera, 60 families, 22 orders, 7 classes and 4 phyla (Fig. 2, Table 1). The phyla were Annelida (worms and leeches), Arthopoda (crustaceans and insects), Mollusca (bivalves and snails) and Platyhelminthes (flatworms).

Local climate data Long-term local data on air temperature and precipitation for the 1km 2 grid of square each of the 1,588 sample sites is situated were extracted from CHESS (Climate, Ecological and Hydrological research Support System), a comprehensive database held by the Centre for Ecology and Hydrology (CEH). The CHESS database offers daily modelled values for both climate metrics, based on observed

Met Office data. Monthly mean estimates for air temperature and precipitation were calculated from the daily values for each site for the time series to match macroinvertebrate sample dates.

Data analysis Null and climate models All analyses were carried out using R software and the nlme package (R Development Core Team, 2008). A national spring sampled population index (April) for all 137 macroinvertebrate species was developed for 15-25 years to act as the response variable in the models. The species abundance indices were calculated using a linear mixed effect model, fitting time and site as effects for each species. As some species display strong geographic patterns, this approach accounted for the spatial variation by using site as a random effect. Species-specific averages of monthly climate data were also calculated, depending on the geographical distribution of the sites at which the species were sampled.

Models were run for each of the 137 species in an attempt to explain inter-annual variations in the population abundance observed over the time series. The first, simpler model ( null model) placed the annual species abundance index values as a function of linear annual variation in which year was the only explanatory variable. This model was calibrated in order to control for a systematic linear trend that may account for stressors other than climate. The second, more complex model type (climate model), also contained year as a predictor but used various metrics of monthly mean precipitation or temperature as an explanatory variable for the abundance index for each species. Metrics included the average level of effect, Fourier oscillations to model a repetitive single wave pattern over 12 months and a lagged period of this wave decaying to zero over three years. For the latter two metrics, a series of regression coefficients were constrained to follow the cyclic wave pattern (linear sum of sine and cosine terms) determined by the data and an additional parameter was then used to control the decay of the cyclic pattern towards zero. The climate models allowed for differences in the direction and magnitude of species responses across the 12 months. Further information on the background to this modelling method can be found in Appendix 2

As the null model for each climate-species combination is nested within the corresponding climate model, the Likelihood Ratio Test (LRT) was used to compare model fits. The LRT expresses how many times more likely the data are under one model structure than the other. However, the climate model contained the same plus more explanatory parameters than the null and will always fit at least as well. In order to test if the climate model provided a statistically significant better fit, the p- value computed during the LRT was compared to a critical value (chi-squared distribution with appropriate degrees of freedom) to decide whether to reject the null model in favour of the alternative, climate model. The climate models that proved a statistically significant better fit to the data than their corresponding null models were then examined for coefficients (µ) indicating the magnitude and directional effect exerted by temperature and precipitation on spring time abundance from the 12 months preceding the April samples. The Akaike information criterion (AIC) was used to measure the relative quality of the explanatory data in the climate models for explaining each species abundance index, providing a coarse means for assessing the dominance of one climate metric over the other.

Classifying trends in climate sensitivities Trait data Over the last decade an open-access database containing taxonomic and ecological information on biota, including macroinvertebrates, in European freshwaters has been developed. The online database ( www.freshwaterecology.info ) contains information on species geographic preferences, biological and ecological traits based on published studies across Europe, including the UK (Schmidt- Kloiber & Hering, 2012). Available data on traits for those species that showed statistically significance responses to climate metrics were extracted from this database and are given in Table 2. Within the database, data on species traits are given in several formats such as presence/absence, distinct categories or using a ten point assignment system. Both emergence and reproduction traits are in distinct categories but the temperature preferences of species was considered on a gradient, allowing a 0-10 score to reflect the affinity of the taxon with that particular modality of trait. In the case of temperature preferences the stenothermal gradient extended from very cold (< 6 o C), to cold (< 10 o C), moderate (< 18 o C), warm (> 18 o C) and eurytherm (no specific preference, can exist over a wide range). In order to create a variable representing temperature preferences capable of acting as an explanatory variable, the scores given for each temperature preference were weighted and an index developed to reflect species vulnerabilities to increasing temperature (high values indicate extreme sensitivity). Additional traits (feeding groups, length of life cycle, number of generations per year, the presence/absence of a terrestrial life cycle, BMWP scores and LIFE flow groups) were sourced outside of the www.freshwaterecology.info database (Chesters, 1980, Moog, 1995, Merritt & Cummins, 1996, Extence et al., 1999, Tachet et al., 2000).

Boosted regression trees Modelling techniques, such as Machine Learning (ML), are particularly suitable for describing ecological behaviour. The advantage of these methods include their flexibility to account for the typical characteristics of ecological data (complex, non-linear relationships, non-normality, missing data, variable data formats and intercorrelated explanatory data) without having to meet the assumptions necessary for traditional parametric methods. One such ML method, Boosted Regression Trees (BRT), is a progressive ensemble approach that combines the strengths of two algorithms: regression trees (models that relate a response to their predictors by recursive binary splits) and boosting (an adaptive method for combining many simple models to give improved predictive performance) (Elith et al., 2008). This approach creates new regression trees by iteratively fitting the new trees to the residual errors of existing trees, i.e. each successive tree focuses on modelling unexplained response deviance of the existing tree assemblage. Interactions between predictors are automatically modelled owing to the hierarchical nature of a regression tree so that the response to one input variable relies on values in the upper part of the tree.

The BRT approach was used here to quantify those species traits that may account for or help explain trends observed in the response of macroinvertebrate abundances to temperature and precipitation fluctuations. Using the sign (positive or negative) of the coefficient (µ) extracted from the models as the response variable (defined by binary variables (1 and 0) with a Bernoulli distribution) and the traits listed in Table 2 as the explanatory variables, a BRT was fit to the data (n=804 for temperature and n=852 for precipitation) using the gbm and dismo packages in R. The relative importance of each trait was estimated, based on the number or times each are split and weighted by the squared improvement as a result of each split, averaged over all trees. Appropriate

variable selection in BRT is achieved as the process mainly ignores non-informative explanatory variables when ﬁtting trees. Measures of relative inﬂuence quantify the importance of explanatory variables while irrelevant ones are typically shown to have negligible effect (Elith et al., 2008). The importance of each trait is scaled so that the sum adds to 100, with higher values reflecting a stronger influence on the response variable. Two-dimensional partial dependency plots (response curves) to show the probability of an increase in abundance with increasing temperature as a function of each explanatory variable, after accounting for the average effects of all the other explanatory variables were generated (Elith et al ., 2008). Significant interactions between explanatory variables that impact on the fitted values were identified by comparing variance explained by subsets of trees with specific variables separately with subsets of trees including both variables. The two most dominant interactions for both climate metrics were examined using three- dimensional partial dependence plots to illustrate the influence of the interacting traits on the probability of species abundances increasing with increasing temperatures.

Results A phylogenetic tree based on the taxonomy of the 137 macroinvertebrate species was constructed (Fig. 2). The species for which the climate models showed a more highly statistically significant explanation of abundance than the (based on the LRT) the null models are given a colour. Those species for which a linear model explained abundance just as well as a climate model are in black text. The species that showed a response to temperature only were coloured orange, and those for which precipitation had a significant impact only were coloured green. Out of 137 species, climate models for 71 and 67 species showed a statistically significant better explanation of abundances for precipitation and temperature, respectively. In the cases where both temperature and precipitation models better explained abundances compared to the null models (46 species), the AIC score determined which stressor was stronger: temperature (blue) or precipitation (red).

The outputs from the models showed widespread intra-group variability in the responses of macroinvertebrate abundances to monthly fluctuations in temperature and precipitation. For instance, the cased caddisfly larvae Limnephilus extricatus McLachlan, 1865 and Allogamus auricollis (Pictet, 1834) (both family Limnephilidae) both showed significant sensitivity to temperature (Fig. 2), however, based on AIC values variation in the abundance of the cold stenotherm Limnephilus extricatus is much better explained by temperature fluctuations compared with the eurytherm Allogamus auricollis. When the monthly coefficients for temperature are examined for both species, seasonal differences are apparent: high temperature in the winter months increases the spring abundance of Allogamus auricollis but reduces the spring abundance of Limnephilus extricatus.

The three species for which models best explained abundance as a function of temperature or precipitation were all predators with a preference for low flow conditions; the leech Theromyzon tessulatum (O.F. Muller, 1774), the true bug Hydrometra stagnorum (Linnaeus, 1758) and larvae of the caddis-fly Molanna angustata Curtis, 1834. For example, increases in winter temperatures rates have a large negative impact on the population abundances of T. tessulatum in spring samples, whereas increases in temperature at other times of the year gives rise to a greater abundance in the spring. For the same species, increases in precipitation rates have a negative influence on spring abundances for a much longer period during the year, with increases in abundances only occurring as a result of high precipitation rates in late summer and autumn.

Two non-native species were included in the analysis. Precipitation, rather than temperature was shown to exert a statistically significant influence on the population abundance of the well established freshwater New Zealand Mudsnail Potamopyrgus antipodarum (J.E. Gray, 1843). The spring abundance of this snail shows different sensitivities to precipitation, increasing with increasing precipitation in autumn/winter and declining during the same conditions over the spring/summer months. The population abundance of the non-native flatworm Girardia tigrina Girard, 1850, showed a significant response to variations in both temperature and precipitation, although temperature appeared to exert more of an influence. For this species, increasing temperatures in all months except late winter increased the spring abundance. The one species of threatened status studied was the white clawed crayfish Austropotamobius pallipes (Lereboullet, 1858), the only crayfish native to the UK. The models showed that this crayfish is sensitive to fluctuations throughout the year in precipitation only, rather than temperature, especially in March when increases in precipitation are manifested in low population abundances in April. However, high precipitation events at other times of the year, winter in particular, have a positive impact on abundance in the following spring.

Attempts to classify the responses of macroinverbertate abundances observed from the climate model outputs by species-specific environmental preferences or traits were carried out using BRT analysis. The outputs from this analysis produced information on the relative importance of each factor, the directional effect and any possible interactions with other variables. For example, Figs. 3 and 6 rank the 9 variables according to their influence on the species response to increasing temperature and precipitation, respectively. For temperature, species functional feeding group followed by temperature index exert the most influence. The BMWP score for sensitivity to organic pollution followed by the temperature index are shown to be the major determinants of responses to increasing precipitation.

The response curves in Fig. 4 show that a species has a higher likelihood of increasing in abundance with increasing temperatures when the species is a shredder or collector-filter, is tolerant of drought conditions, and high temperatures, has a long emergence duration, and lays down groups of eggs in a fixed position. Species abundances tend to increase with increases in precipitation when species are moderately sensitive to organic pollution, have a high to moderate preference for high temperatures, are either collector filterers, predators or scrapers, prefer faster flows, have less than one life cycle per year and lay down groups of eggs in the water freely or in the riparian zone (Fig. 7). Caution is required in interpreting these responses where there is less data (i.e. oviposition, and possibly temperature index and duration of emergence) and in isolation, especially when interactions between explanatory variables occur (Figs. 5 and 8).

The interactions between LIFE flow groups (flow preferences) with both temperature index and duration of emergence period accounted for over 60% of the deviance attributed to interactions in the temperature models. These interactions are important to consider as they show that species with a preference for drought conditions and (i) a tolerance for high temperatures or (ii) longer emergence durations are likely to increase in abundance with rising temperature. The remaining two interactions (40%) showed that, although species abundances tend to increase with temperature when a species is tolerant of high temperatures or has long emergence duration, this is

dependent on functional feeding group. For example, with increases in temperature a scraper with a long emergence duration time will show lower abundances than a shredder with a short emergence time.

Interactions between species BMWP score, temperature index and other explanatory variables were important in shaping species responses to increases in precipitation. The dominant interaction, between BMWP score and temperature index (Fig. 8), indicated species are more likely to increase in abundance with greater precipitation when they are moderately sensitive to organic pollution but able to survive in warm conditions. An association between temperature index and number of life cycles per year followed, showing that species with a low temperature index value and a life cycle of less than one year are more likely to increase in abundances with increasing precipitation. Less influential interactions between oviposition mode and BMWP score, as well as between number of life cycles per year and BMWP score were also observed.

Discussion The future UK climate will comprise wetter, milder winters and hotter, drier summers, together with more frequent extreme events such as the drought seen in early 2012 and the widespread flooding over the winter of 2013-2014 (Kendon et al., 2014). In this context, conservation planning for freshwater biodiversity not only requires high-quality information on the sensitivity of the biota currently occupying rivers and streams, but also needs details on how the distribution and abundances of these species may change as a result of future climate change. The results from this study go some way into identifying some of these impacts on a range of freshwater species, including two invasive and one endangered in the UK. Another key output is a demonstration of the ability to classify species-specific trends in relative sensitivity to changes in temperature and precipitation using species environmental preferences and species traits.

Our results showed that most freshwater macroinvertebrate species have the potential to be affected in some way by changes in temperature and precipitation due to climate change (Fig. 2). Responses in species abundances varied strongly within higher taxonomic groupings, and could not be predicted fully using this type of biological organisation. However, species abundance was, to some degree, accounted for by environmental preferences and functional traits that can influence species’ vulnerability to climate change, such as feeding modes, thermal tolerances and life cycle lengths.

The BRT approach adopted here was able to identify and classify the importance of relevant explanatory variables and automatically identify interactions, giving substantial advantage over more traditional statistical methods. Efficient variable selection means that large suites of candidate explanatory variables will be managed more appropriately than a traditional stepwise selection (Elith et al., 2008). However, despite the significant relationships identified, interpretation of the results here should consider correlated traits and indirect effects (Statzner & Beche, 2010). For instance, predators show sensitivity to increasing temperatures, with a decline in abundance. This may be explained by several factors not included in the study, for example macroinvertebrate predators tend to have relatively larger body sizes (Woodward et al., 2010b) and hence greater thermo- regulatory demands, but are also exotherms, so that they are more sensitive to water temperature fluctuations. They also require greater quantities of food and if prey species become depleted, the

predators that depend on them for survival will also decrease in abundance (Sih et al., 1985). In contrast, the two functional feeding groups that show increases in abundance with rising temperatures are further down the food chain, exploiting the less limited basal resources such as organic detritus. Shredders mainly feed on decaying vegetation, reducing it to smaller particles while collector filterers feed on fine particles by filtration from the water. Moreover, higher temperatures increase microbial activity on decaying vegetation, which in turn increases the palatability of detritus for shredding macroinvertebrates, and may accelerate the breakdown to smaller particles that may be captured by collecting-filtering invertebrates (Graça, 2001, Artigas et al., 2009, Boyero et al., 2011). It is clear from these few examples that trophic status can play an important role to the sensitivity of a given species to climate change, and that an understanding of the feeding links of each species allows a better prediction of climate change impacts at the scale of whole food webs and ecosystems (Stouffer & Bascompte, 2010, Woodward et al., 2010a).

The use of traits provided a framework to classify impacts on species. It was clear from the modelling results that certain traits affected the ability of species to avoid, resist or be resilient to climate driven stressors, and thus modulated species sensitivity to these stressors. Many of these traits are shared across a wide range of species, and across higher level taxonomic groupings, indicating that climate change may have a selective impact on macroinvertebrate communities, with a discrete subset of species within them at risk of extinction (Conti et al., 2014). Because species traits underpin the ecological function of that species, it is therefore likely that specific aspects of ecosystem functioning could be impacted by climate change, with consequences for the flow of goods and services for humans (Lecerf & Richardson, 2010). The extent of functional redundancy within a biological community (i.e. the number of species that fill similar ecological niches) has been put forward as a potential buffer for the impacts of climate change (Rosenfeld, 2002), however this redundancy is provided by taxa from different biological groupings, but that have similar trait assemblages. Thus, if these traits characteristics are the primary source of species vulnerability, then functional redundancy is unlikely to buffer the community from the impacts of climate change. In addition there is a strong debate amongst ecologists as to whether functional redundancy occurs at all within a biological communities (Loreau, 2004) , as it is unlikely that different species occupy exactly the same niche, unless they are spatially segregated (Micheli & Halpern, 2005, Griffen & Byers, 2006, Hoey & Bellwood, 2009).

Attention should now focus on using appropriate functional traits and environmental preferences to gain a better understanding of the shifting geographical distribution of macroinvertebrate populations across the UK in respect of a changing climate. Considering the multi-stressor environment of rivers, the overall response of a combination of trait descriptors to climatic drivers (as indicated by the interactions in this study) may be more suitable to describe fine-scale changes in species abundances (Statzner & Beche, 2010). Furthermore, a similar approach may be used to investigate species resiliencies to warming, droughts and flooding. However, the list of traits examined here is not exhaustive, and there are many others that may, or may not, better explain responses of species (Tachet et al., 2000). Species traits may follow a complex gradient, i.e. may not be easily assigned to discrete categories, and for many species, certain trait types have yet to be resolved.

Our analyses only included very well established and widespread non–native species (a flatworm and a snail), that are not usually viewed as problematic invasive species in the UK, because they were common in the datasets. However, it was clear that climate change descriptors could be linked to changes in abundance of non-native species in the same way that they could be linked to the abundance of native species. Virulent invasive species such as the killer shrimp Dikerogammarus villosus Sowinsky, 1894 and the signal crayfish Pacifastacus leniusculus (Dana, 1858) are known to have wider environmental tolerances than their native equivalents, hence their success in invading new systems (Nystrom, 1999, Pockl, 2009). It is therefore crucial to review the traits and environmental preference of these species with respect to climate change. Any potential increases in the abundance of invasive species (which typically are very strong competitors for resources) may have serious consequences for other species and communities, and could significantly worsen any direct impacts of climate change on native populations (Rahel & Olden, 2008, Hänfling et al., 2011).

Similarly, rare and protected species will be subject to the impacts of climate change too. In this study, sufficient data was available only for modelling the response of the white-clawed crayfish A. pallipes , but it was clear from this example that at risk species may decrease in abundance under certain climate change scenarios. In this study we found that the occurrence of climatic stressors at key times of the year could reduce the abundance of A. pallipes , which was explained by increased flow events occurring at the same time as gravid (egg carrying) females emerge from winter burrows (Holdich, 2003). Thus very detailed knowledge of the ecology of rare and protected species is necessary to predict the impacts of climate change on their abundance, in addition to detailed, seasonally and geographically explicit modelling of the occurrence of stressors linked to climate change.

Investigation of changes in the spatial or altitudinal distribution of species as a result of climate change was not possible in this study owing to inherent limitations of species datasets (limited spatial range, temporal distribution or taxonomic identification) for this type of analysis. The key focus of this study was to describe species sensitivity and describe systematic trends attributable to traits. This information can be used to infer likely changes in the abundance of particular species as a function of future alterations in the temperature or precipitation regime of an area (see Appendix 9). However, the outputs given here are not without caveats.

Temperature and precipitation were considered in isolation in the climate models, but these two pressures are inherently correlated, with their impact occurring at different times of the year (IPCC, 2013). Indeed, over one third of the species studied showing significant sensitivities to both temperature and precipitation. Further weight is given to this correlation by the fact that a measure of flow preference (expressed as LIFE flow group) was shown to be an important factor in the response of species to increases in temperature and, similarly, temperature tolerances were significant when attributing traits to the response of species to increased precipitation. However, the broad-scale approach of focusing on evidence for systematic trends across multiple sites over 15-25 years minimised the risk of incorrectly interpreting effects.

Although a linear trend was included in the model in an attempt to capture the effects of potential confounding factors that may exert an influence on macroinvertebrate abundances (e.g. altitude, habitat changes, pollution), the climate models were trained on air temperature or precipitation

data only. Terms to account for interactions between external confounding factors and the metrics for climate were lacking, leading to potential uncertainty in the modelled outputs (Vaughan & Ormerod, 2012, Floury et al., 2013). For instance, shading by riparian tree cover has a strong moderating influence on stream temperature, and this is likely to buffer warming effects and influence the growth, distribution and life cycle of macroinvertebrates (Broadmeadow et al., 2011, Bowler et al., 2012). In addition, the physical modification of streams and rivers, e.g. through channel straightening and clearing, creates a less resilient habitat for macroinvertebrates and other fauna, which become more vulnerable to stressors and change (Newson & Large, 2006). Future research should consider the combined effect of multiple stressors and include climate change among these.

Many freshwater macroinvertebrates are juvenile life stages (larvae, nymphs, pupae) of terrestrial insects. This poses a fundamental problem because climate change may have a direct impact on the adult stage in the terrestrial environment, as well as an effect on the larval stages in the lotic environment, leading to complex patterns of population abundance (Fuller, 2009, Wesner, 2012). Many such taxa undergo full or partial metamorphosis so that the juvenile and adult life stages have very different morphologies, environmental preferences and trait characteristics, and thus differ in their vulnerability to climatic stressors. In addition rivers are inherently dependent on their riparian zone, which provides a large proportion of the organic detritus that supports the food web, so that changes in terrestrial vegetation with climate change may influence riverine ecological processes (Clews & Ormerod, 2010, Broadmeadow et al., 2011). Further work is needed to disentangle the relative effects of climate change on the two types of environment to be able to understand how a species will respond to climate change (Holland et al., 2011).

Conclusion Models investigating species abundance change as a function of fluctuations in air temperature and precipitation were run across a wide range of individual freshwater species from the UK’s river and streams. Outputs from these models have provided evidence that most lotic macroinertebrate species, including protected species and non native species, respond to either or both metrics but also show differences in the magnitude and direction of their response. Because these changes impact upon key ecological processes such as food web stability, consequences at the scale of whole communities and ecosystems are likely to occur, though are difficult to predict solely from changes in abundance. Changes in macroinvertebrate communities also has a fundamental implication for compliance with the WFD, as many of the species used in this study contribute to the biomonitoring systems used to assess the ecological quality of rivers (Environment Agency, 2006). Outputs can inform catchment management and biodiversity conservation plans as to which species are most vulnerable. The models may be used to predict changes in species abundances in a changing climate scenario (Appendix 8). Further investigation demonstrated that species-specific responses may be attributed to a combination of species-specific traits and environmental preferences (e.g. thermal tolerances, life cycle lengths and functional feeding groups) that make them more vulnerable or tolerant to a changing climate. However, caution is advised in the interpretation of models owing to the complex multiple stressor environment of rivers and their fundamental interaction with the terrestrial environment. The outputs from this study should help towards building a framework for better understanding the influence of climate change in the freshwater landscape.

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Figure 1. Location of the 1,588 sampling sites (red dots) for species-level macroinvertebrate data across 25 years (1983-2007)

Figure 2. Phylogenetic tree based on the taxonomy of the 137 macroinvertebrate species examined in the study. The species for which the climate models showed a statistically significant better explanation of the abundance indices compared with the null models are given a colour. The species that showed a response to temperature only were coloured orange, and green was given to those for which precipitation had a significant impact only. In the case of if models for both climate metrics better explained abundances compared to the null models, the dominant metric is coded blue (temperature) or red (precipitation).

Table 1. Nomenclature of the 4 phyla, 7 classes and 22 orders (with some common names) of the 137 species used in the study

Phylum Annelida (worms) Arthropod a Mollusca (bivalves Platyhelminthes (crustaceans and and snails) (flatworms) insects

Class Hirudinea (leeches) Malacostraca Bivalvia (bivalves) Turbellaria (crustaceans)

Arhynchobdellida Decapoda (crayfish) Unionoida (mussels) Seriata (proboscisless leeches)

Rhynchobdellida Isopoda Veneroida (clams and (jawless leeches) cockles)

Class Oligochaeta (aquatic Insecta (insects) Gastropoda (snails earthworms) and slugs)

Crassiclitellata Coleoptera (beetles) Architaenioglossa

Diptera (true flies) Ectobranchia

Ephemeroptera Hygrophila (mayflies)

Hemiptera (true bugs) Neotaenioglossa

Megaloptera Neritopsina (alderflies)

Odonata (dragonflies Pulmonata and damselflies)

Plecoptera (stoneflies)

Trichoptera (caddisflies)

Table 2. Species environmental preferences and functional traits used in classifying trends observed in species abundance responses to temperature and precipitation

Parameter Description and categories % data available for species

BMWP score Family -level score of tolerances to organic 97 pollution 1 - tolerant 10 - intolerant

LIFE flow group Family -level grouping of flow preferences 96 1 -rapid flows 10 - drought conditions

Tem perature index Vulnerability to high temperatures 35 1 - not vulnerable (eurytherm or warm stenotherm) 5 - vulnerable (cold stenotherm)

Reproduction Means of reproduction 15 1 - groups of eggs are laid down and fixed 2 - groups of eggs are laid down in the water freely 3 - groups of eggs are laid down in the riparian zone

Feeding group Functional feeding strategies 100 Pr - Predator Cg - Collector gatherer (deposit feeder) Sc - Scraper Sh - Shredder Cf - Collector filterer

Life cycle length Duration of one life cycle 100 0 - one year or less 1 - more than one year

Life cycles per year Number of life cycles per year 100 0 - less than one 1 - one 2 - more than one

Terrestrial Occurrence of a terrestrial life stage 100 0 - no 1 - yes

Emergence Duration of emergence period (time between 36 first observed emergence (flight) and last emergence of a species) 1 - short (< approximately 2 months) 2 -long ( > approximately 2 months)

Feeding_group 21.42 %

Temp_index 19.52 %

LIFE 18.10 %

BMWP 15.11 %

Emergence 11.18 %

Reproduction 6.75 %

Life_cycles_peryear 3.09 %

Life_cycle_length 3.01 %

Terrestrial 1.83 % 0 5 10 15 20

Relative influence Figure 3. Relative importance of the 9 explanatory variables considered to influence the trend of a species to show a response to increases in temperature. Percentages are given.

fitted function fitted function fitted function fitted function fitted -1.0 0.0 0.5 -1.0 0.0 0.5 -1.0 0.0 0.5 -1.0 0.0 0.5

Cf Cg Pr Sc Sh 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 2 4 6 8 10

Feeding_group (21.4%) Temp_index (19.5%) LIFE (18.1%) BMWP (15.1%) fitted function fitted fitted function fitted function fitted function fitted -1.0 0.0 0.5 -1.0 0.0 0.5 -1.0 0.0 0.5 -1.0 0.0 0.5

1.0 1.2 1.4 1.6 1.8 2.0 1.0 1.5 2.0 2.5 3.0 0.0 0.5 1.0 1.5 2.0 0.0 0.2 0.4 0.6 0.8 1.0

Emergence (11.2%) Reproduction (6.7%) Life_cycles_peryear (3.1%) Life_cycle_length (3%) fitted function fitted -1.0 0.0 0.5

0.0 0.2 0.4 0.6 0.8 1.0

Terrestrial (1.8%)

Figure 4. Functions fitted for the 9 explanatory variables influencing the tendency of a species to show an increase in abundance with increasing temperature. A common scale is used on the Y axis, which is centred to have zero mean over the data distribution. Rug plots on the inside of the X axis show distribution of deciles for that variable. The relative importance of each variable (Table 2) is given in parentheses.

0.8

f i t t 0.6 e

a 0.4

l u e 0.2

0.0 1

T e 2 m 5 p _ in 4 d 3 e x 3 FE LI 4 2 1 0.8

f i t 0.6 t e d

a 0.4

l u

e 0.2

0.0 1.0 1.5 2.0 2.0 L 1.8 IF 2.5 E 1.6 3.0 ce en 1.4 rg 3.5 e 1.2 Em

4.0 1.0

Figure 5. Three-dimensional partial dependency plots for the interaction of (top) temperature index with LIFE flow group and (bottom) duration of emergence period with LIFE flow group. The probability of an increase in species abundance with increasing temperature is shown as a fitted value on the Y axis. The interacting explanatory variables are described in Table 2.

BMWP 24.25 %

Temp_index 20.19 %

Feeding_group 16.68 %

LIFE 11.07 %

Life_cycles_peryear 10.21 %

Life_cycle_length 5.59 %

Emergence 5.24 %

Reproduction 4.78 %

Terrestrial 1.98 % 0 5 10 15 20

Relative influence Figure 6. Relative importance of the 9 explanatory variables considered to influence the trend of a species to show a response to increases in precipitation. Percentages are given in Table 4

fitted function fitted function fitted function fitted function fitted -1.5 -0.5 0.5 -1.5 -0.5 0.5 -1.5 -0.5 0.5 -1.5 -0.5 0.5 2 4 6 8 10 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 Cf Cg Pr Sc Sh 1 2 3 4 5

BMWP (24.3%) Temp_index (20.2%) Feeding_group (16.7%) LIFE (11.1%) fitted function fitted fitted function fitted function fitted function fitted -1.5 -0.5 0.5 -1.5 -0.5 0.5 -1.5 -0.5 0.5 -1.5 -0.5 0.5 0.0 0.5 1.0 1.5 2.0 0.0 0.2 0.4 0.6 0.8 1.0 1.0 1.2 1.4 1.6 1.8 2.0 1.0 1.5 2.0 2.5 3.0

Life_cycles_peryear (10.2%) Life_cycle_length (5.6%) Emergence (5.2%) Reproduction (4.8%) fitted function fitted -1.5 -0.5 0.5 0.0 0.2 0.4 0.6 0.8 1.0

Terrestrial (2%)

Figure 7. Functions fitted for the 9 explanatory variables influencing the tendency of a species to show an increase in abundance with increasing precipitation. A common scale is used on the Y axis, which is centred to have zero mean over the data distribution. Rug plots on the inside of the X axis show distribution of deciles for that variable. The relative importance of each variable (Table 2) is given in parentheses.

0.6

f i t t e

d 0.4

l u 0.2 e 0.0 1

T e 2 m 10 p _ 8 In d 3 e 6 P x W 4 BM 4 2 0.8

f i t t 0.6 e

v a 0.4

l u e 0.2

0.0 1

T e 2 2.0 m p _ in 1.5 r d 3 ea e ry x 1.0 pe s_ le 0.5 yc 4 _c ife 0.0 L

Figure 8. Three-dimensional partial dependency plots for the interaction of (top) temperature index with BMWP scores and (bottom) temperature index with number of life cycles per year. The probability of an increase in species abundance with increasing temperature is shown as a fitted value on the Y axis. The interacting explanatory variables are described in Table 2.