PREDICTING FUTURE SPECIES DISTRIBUTION OF ODONATA IN WESTERNMOST MEDITERRANEAN REGION UNDER CLIMATE CHANGE

Master in Ecology, Environmental Management and Restoration

Author: Aida Viza Sánchez Tutor: Dr. Cesc Múrria i Farnós Department of Evolutionary Biology, Ecology and Environmental Sciences University of Barcelona 28th of September of 2016

PREDICTING FUTURE SPECIES DISTRIBUTION OF ODONATA IN WESTERNMOST MEDITERRANEAN REGION UNDER CLIMATE CHANGE

Master in Ecology, Environmental Management and Restoration

Author: Aida Viza Sánchez Internal Advisor: Dr. Núria Bonada, Tutor: Dr. Cesc Múrria i Farnós FEM Research Group, Department of Evolutionary University of Barcelona Biology, Main Advisor: Ecology and Environmental Dr. Cesc Múrria, Sciences FEM Research Group, University of Barcelona University of Barcelona 28th of September of 2016 Author: Aida Viza, MSc student, FEM Research Group, University of Barcelona

ABSTRACT A critic question in biodiversity conservation is how species will response in front of current rates of Climate Change. Such environmental alterations have the potential to modify habitat characteristics and, consequently, it is predicted that many species may shift their ranges to higher latitudes or altitudes to remain in a constant environmental niche. On another hand, those species with high evolutionary adaptation, phenotypic acclimation or plasticity are expected to have the ability to face new conditions. Finally, species with poor strategies are vulnerable and can become extinct. In this project, I focus on the evolutionary history, functional traits characteristics and Species Distribution Models (SDM) of Odonata to elucidate how species distribution of odonates in Iberian Peninsula and Morocco will be affected by Climate Change and the role that traits would play in future species responses. In general, I found that odonates potential distribution will be altered by an increase of temperature seasonality and drought events, as a result of anthropogenic impact. High emissions scenarios were dominated by a reduction of species potential distribution, while low emissions scenarios showed a trend to subtile displacement from current species distribution. The ecological distance between species including also closely related species was decoupled to their phylogenetic divergence. Therefore, phylogeny cannot predict the ecological requirements of species. Moreover, none clear pattern was found between traits (ecological and life-history), current habitat occupancy and future potential distribution under several models of climate change. Hence, I cannot elucidate species response based on the probability of their lineage to neither extinction, northward range expansion nor shift in its distribution range. Further studies modelling multi- species distribution considering intraspecific traits and genetic variability will be needed to infer future species-specific distribution and extinction risk in order to do a correct management of freshwater biodiversity under climate change.

RESUM Una qüestió crítica en la conservació de la biodiversitat és com les espècies respondran davant del Canvi Climàtic. Les alteracions ambientals poden modificar les característiques de l'hàbitat i, en conseqüència, s’espera que moltes espècies canviïn la seva distribució a latituds o altituds més elevades, per tal de romandre en un nínxol ambiental constant. S'espera que les espècies amb una alta capacitat d’adaptació evolutiva, d’aclimatació o de plasticitat fenotípica puguin fer front a les noves condicions. En canvi, les espècies amb estratègies limitades són vulnerables i poden arribar a extingir-se. Aquest projecte pretén entendre com afectarà el Canvi Climàtic a la distribució dels odonats de la Península Ibèrica i el Marroc, i quin paper juguen els trets biològics en la resposta futura de les espècies. En general, la distribució potencial dels odonats serà alterada com a resultat del canvi climàtic antropogènic. Els escenaris futurs amb majors emissions estan dominats per la reducció de la distribució potencial de les espècies, mentre que en els escenaris de baixes emissions aquest tendeix a desplaçar-se. No obstant això, la distància ecològica entre espècies no està acoplada a la seva divergència filogenètica, per tant la filogènia no pot predir els requeriments ecològics de les espècies. D'altra banda, no s'ha trobat cap pauta clara entre les característiques funcionals, l’ocupació actual i la predicció de la distribució potencial futura sota diversos models de canvi climàtic. Per tant, no puc aclarir com respondrà cada espècie ni atribuir a un llinatge la probabilitat de canvi en la seva àrea de distribució. Per tant, calen més estudis de modelització de distribució de múltiples espècies tenint en compte les característiques i la variabilitat genètica intraespecífica, ja que són necessaris per a inferir la futura distribució de les espècies i el grau d'amenaça, per tal de realitzar una correcta gestió de la biodiversitat d’ecosistemes fluvials sota el canvi climàtic.

INDEX

Introduction ...... 2

Methods ...... 6

 Study area, species occurrences and data specifications ...... 6

 Climatic models, bioclimatic variables and future species distribution ...... 6

 Compilation of DNA sequences and Phylogenetic analyses ...... 9

 Species-specific habitat preferences and trait conservatism ...... 10

Results ...... 11

Discussion ...... 22

Conclusions ...... 25

Acknowledgements ...... 25

References ...... 26

INTRODUCTION

Current predicted rates of climate warming will likely modify current habitat characteristics (Walther et al., 2002; Travis, 2003). As consequence, it is expected that many species may shift their ranges to higher latitudes and/or altitudes, where the temperature conditions will be more suitable to remain in a constant environmental niche; may locally adapt or phenotypically acclimatise to the new ecological conditions; or will go to extinct (Parmesan, 2006; Markovic et al., 2014; Stoks et al., 2014; Buckley & Kingsolver, 2016).

Aquatic ecosystems have showed high vulnerability to global change due to losses of habitat heterogeneity, reduction of connectivity and additional stressors such as pollution, river regulation, over-abstraction of water, and unpredictable consequences of alien species introduction (Sala et al., 2000; Woodward et al., 2010). Moreover, the expected increase of temperature will influence physiological processes of freshwater macroinvertebrates species such as increases in body size, development rate and growth rate (Burgmer et al., 2007; Markovic et al., 2014; Stoks et al., 2014). As a result, habitat suitability will decrease or shift for many species (Markovic et al., 2014) and freshwater macroinvertebrates communities’ composition will change (Daufresne et al., 2007). For this reason, to predict how habitats will shift in the future, dispersive abilities of species and their evolutionary potentials for adapting in new conditions is critical for the conservation and management of freshwater ecosystems and their associated freshwater biodiversity.

Functional traits and habitat preferences play an important role in species survival facing Climate Change because determinate if species is able to shift toward future suitable habitats or, on the contrary, can locally adapt to the new environmental conditions. Only those species with a high phenotypic plasticity are expected to have the ability to change its ecological preferences in response to environmental fluctuations (Parmesan, 2006; Wellenreuther et al., 2012). Then, determining what biological traits are favourable to locally face the global warming and whose that may promote a possible expansion to new habitats (Schloss et al., 2012), can provide critical information for elucidate the future of species. Once within the new habitat, population persistence is likely to be driven by traits that determines the species strategy, such that generalists might be more successful in meeting their needs for food and shelter than specialists (Jeschke & Strayer, 2006). Estimates of an organism’s fundamental niche following Climate Change

2 can be compared to its current realized niche to predict whether a species will need to move or adjust its phenotype to avoid extinction (Buckley & Kingsolver, 2016).

The ecological niche of a species is the set of biotic and abiotic conditions in which a given species is able to persist and maintain stable population sizes (Hutchinson, 1957). What determine which niche is likely to be occupied by a certain species are the biological and ecological traits that characterize a species. Species functional traits evolve over time as a response to species interaction and ecological conditions, but there is the tendency of species to fix their ancestral ecological characteristics and to retain aspects of their fundamental niche through time, which is known as niche conservatism (Webb et al., 2002; Wiens & Graham, 2005). Studying simultaneously niche conservatism and future potential distribution of species is possible to estimate extinction risk, northward range expansions or shifts in range distribution, and local adaptation (plasticity). In this project, I focus on the evolutionary history, functional traits characteristics and Species Distribution Models (SDM) of Odonata to elucidate how species distribution in the westernmost Mediterranean region will be affected by Climate Change and to determine the role that traits would play in future species responses of odonates.

Odonata is a good model taxon to predict effects of climate warning on freshwater biodiversity because of (1) their tropical evolutionary origins may limit their distribution by temperature, (2) medium-high local abundances that facilitates sampling, captures and experiments, (3) high specialization of larvae and adult for habitat usages and evident niche partition among species, (4) a long history of scientific research in ecology, behaviour and evolution because many species can be reared and crossed successfully in captivity, and (5) the extensive recording and abundant historical datasets, mostly by volunteer work (Hassall & Thompson, 2008; Ott, 2010).

Given their high mobility, Odonata are currently experiencing a clear trend of northward range expansion from Morocco to the Iberian Peninsula favoured by climate warming and facilitated by the increasing frequency of the Saharan southern winds (Herrera-Grao et al., 2012). The first North-African species arrival registered was Orthetrum nitidinerve in 1842 (Jaquemin & Boudout, 1999). In the last 60 years, other species that arrived to Iberian Peninsula from North-Africa were Brachythemis impartita (Compte Sart, 1962), Paragomphus genei (Testard, 1975), Trithemis annulata (López, 1983), Diplacodes lefebvrei (Conesa García, 1985), Orthetrum trinacria (Hartung, 1985), and the most recently registered Trithemis kirbyi (Chelmick & Pickess, 2008), which was recorded in South-Catalonia in 2012 (Herrera-Grao et al., 2012).

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Since climate warming is expected to promote changes in the geographical distribution of odonates, I predicted 5 different categories of changes in geographical species range that differ in whether the potential future distribution area will increase, decrease or remain insignificantly alterable (fig.1). (1)”Displaced” potential distribution. Under this model, the mean of distribution range should displace to northern latitudes. (2) “Expansive” potential distribution that implies an overall increase of appropriate environmental areas keeping current distribution. (3) “Non-change” potential distribution is predicted when the future bioclimatic niche will remain subtlety altered, thus, the current and future potential range will overlap. (4) “Reduced” potential distribution when the current distribution will decline due to a reduction of suitable habitat. (5) “Extinct” potential distribution is expected when the potential area and suitable habitats could disappear in the future and, therefore, those species will be especially vulnerable to extinction.

Fig. 1. Scheme of the main hypothesis of the study. Species can be classified in 5 different categories depending on how their potential distribution will change in the future. (a) “Displaced”: potential area shifts northwards, (b) “Expansive”: expansion of the potential area, (c) “Non-change”: few changes in the location of potential area, (d) “Reduced”: potential area regression, (e) “Extinct”: potential area loss.

This study focuses in understanding how the distribution of Odonata species currently located at the westernmost Mediterranean region will change in 2050 and 2080 using environmental niche models approach and considering the most pessimistic (high emissions) and optimistic (low emissions) predictions of future climate. Since species responses facing climate warming are driven by ecological traits and evolutionary potential, this project also determines the species ecological niches and evolutionary

4 history (phylogeny and niche conservatism) of the odonates. I note three points that sum up the aims of the study:

(1) To predict future environmental conditions in the Iberian Peninsula and Morocco and to assess how Odonata species distribution will be altered when fitting predicted future conditions. In order to model current potential species distribution and after perform future predictions of species potential distribution on future climatic conditions, I used a compilation of occurrence data from most of the Iberian Peninsula and Morocco species in which this study focuses.

(2) To determine the evolutionary history of Odonata and the composition of the functional traits of each species for assessing niche conservatism. Odonata species appear in almost all type of freshwater habitats, but species showed high habitat- specificity (Suhling et al., 2015). If traits are phylogenetically conserved, i.e., the niche conservatism is supported, the relatedness of species should preserve the signature of habitat preference. As a consequence, I can assess which traits and lineages will be favoured by Climate Change, and thus, the predictions of extinction risk will be straightforward. Also, trait conservatism will allow me distinguish if the 5 different categories of changes in geographical species range are based on phylogenetic relations.

(3) To associate habitat-specificity with species traits for determining species vulnerability. Since the unanimity in predictions indicate an increase of temperature and seasonality of precipitations, I expect species that preferred temporary habitats such as ponds or streams, which should adapt to these habitat by fast development and growth rates (Perry et al., 2005), will be favoured by Climate Change. Greater ecological generalization may release species from being constrained by the distribution or phenology of species they associate with. Moreover, large body species, which commonly showed also large geographical range, may correlate directly with dispersal ability and long life-cycle duration, in some cases also with environmental tolerance and ecological generalization, and, inversely, with reproductive rates (Davies et al., 2009). If all these considerations are supported in odonates, large body species and species that preferred temporary habitats will expand their distribution and they should belong to categories 1 or 2 (future potential area shift or expansion, respectively). On the other hand, species with poor dispersal abilities or species located in higher elevations should be more sensible to Climate Change and they must adapt to great magnitude of warming. Moreover, their habitat will be more prone to disappear or be fragmented, and therefore these species will have major constraints for arriving to their suitable thermal conditions

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(Wrona et al., 2006). These species should belong to categories 4 or 5 (potential area reduction) and are probably threatened and very likely to disappear.

METHODS Study area, species occurrences and data specifications Total species richness in the studied area was 91 (Torralba-Burrial & Ocharan, 2007; Boudot et al., 2009). Species occurrence was compiled using four datasets: (1) Oxygastra, The Catalan odonatologist group; (2) ROLA’s project of AEA “El Bosque Animado”, an environmental education association from Andalucia; (3) published database from Aragon and Cantabria (BOS Arthropod Collection of University of Oviedo, Spain; Torralba-Burrial & Ocharan, 2013), and (4) African data gently offered by researcher Mohamed El Haissoufi from Université Abdelmalek Essaadi.

Before running the models, the occurrences list (species-by-site) was filtered following a series of rules: only taxonomical identification at the species level were considered, which implied the removal of larvae data; all occurrences were considered since 2003 when Oxygastra started a regular and systematic data collection; occurrences non- georeferenced were discarded; and localities where site description was available but lacked of precise location were georeferenced using the official website of “Institut Cartogràfic de Catalunya” (ICC, www.icc.cat/vissir3/). Since the main fuse zone in the Iberian Peninsula is 30N UTM, all geographic data were transformed and standardized.

Climatic models, bioclimatic variables and future species distribution To assess how the potential distribution areas would change in the future, Species Distribution Models (SDM) based on occurrences and current environmental variables as predictors were performed. Climatic conditions were described by 19 bioclimatic variables based on temperature and rainfall values. These bioclimatic variables are standard, commonly used in analyses of SDMs and were created in order to generate more biologically meaningful variables than traditionally environmental ones (Hijmans et al., 2005; O’Donnell & Ignizio, 2012). The 19 current bioclimatic variables used to run the models were downloaded from Worldclim.org. To reduce statistic complexity of the models, the number of bioclimatic variables were reduced to capture the entire environmental conditions using the lowest number of bioclimatic variables. Firstly, to reduce collinearity, the correlated variables were identified by Spearman’s correlation test and Variance Inflation Factors (VIF) analyses. After, the biological and ecological meaning of each variable were considered to remove one of each pair of correlated variables. All these analyses resulted in 5 selected predictor variables out of 19: (1) Bio 3 (Isothermality) quantifies the monthly mean diurnal range (Bio 2) relative to the annual

6 temperature oscillation (Bio 7), and then multiplying it by 100. If this value is close to 100, the daily diurnal temperature range is equivalent to the annual diurnal temperature range (small level of temperature oscillation compared to annual variability), while a smaller value indicates a large temperature variability. (2) Bio 4 (Temperature Seasonality) is a measure of annual temperature variability calculated as the standard deviation of monthly temperature averages multiplied by 100. The larger values indicate a greater variability of monthly mean temperature. (3) Bio 8 (Mean Temperature of Wettest Quarter) is calculated as the average temperature of the three consecutive months with the highest cumulative precipitation. (4) Bio 9 (Mean Temperature of Driest Quarter) is the average temperature of the three consecutive months with the lowest cumulative precipitation. Finally, (5) Bio 15 (Precipitation Seasonality) is a measure of monthly total precipitation variability estimated as the ratio of the standard deviation of the monthly total precipitation to the mean monthly total precipitation, expressed as a percentage (i.e., coefficient of variation). Larger values of Bio 15 indicate a greater variability of precipitation. For predicting changes in future potential habitat distribution, seasonality of both temperature and precipitation are important to be captured because species distribution should be strongly influenced by precipitation variability and droughts events, especially in freshwater ecosystems (Woodward et al., 2010). In general, all selected variables are commonly utilized for examining how temperature and precipitation variability may affect species seasonal distributions (O’Donnell & Ignizio, 2012).

For future predictions in 2050 and 2080, two climate global models that focused in the land component were selected: HadGEM2 ES (Met Office Hadley Centre, MOHC, and Instituto Nacional de Pesquisas Espaciais) and MPI ESM-MR (Max Planck Institute for Meteorology, MPI-M). For both models, the two climatic scenarios RCP 2.6 and RCP 8.5 were considered in order to capture the lowest (optimist model) and the highest (pessimist model) anthropogenic emissions, respectively. Future climatic conditions of the 5 selected bioclimatic variables were downloaded from ccafs-climate.org in ASCII format and 30 arc-second resolution.

In order to determine the current species distribution, the widely used Generalized Linear Models (GLM), Generalized Additive Models (GAM) and Boosted Regression Trees (BRT) were selected to running Species Distribution Models (SDM) for each species separately. These three models uses different methods to infer species potential distribution (Guisan et al., 2002; Franklin, 2009; Elith et al., 2008; Elith & Leathwick, 2009; Kienast et al., 2012): (1) GLM represents a flexible extension of linear models that allows for response variables that have non-normal distribution error; (2) GAM is a non- parametric extension of GLM. GAM models are very flexible because the linear predictor

7 is the sum of smoothing functions that are selected locally along the gradients of predictor variables to find the best solution for the data; (3) BRT combines regression trees and boosting algorithms. Regression trees results from classifications and decision tree, while boosting builds and combines many simple models to give the best prediction. All of these models were performed using “Stats” (Hastie & Pregibon, 1992; Venables & Ripley, 2002), “gam” version 1.14 (Hastie and Tibshirani, 1990) and “gbm” version 2.1.1 (Ridgeway, 1999) packages of R 2 (R Core Development Team, 2013).

Since the original data included ”presence-only”, data processing were simplified including background values ("random-absence") in each species database for GLM and GAM, and pseudo-absences for BRT, thereby obtaining a matrix with pseudo-absences (Franklin, 2009). Background and pseudo-absences values were generated with the R function “RandomPoints” of Dismo package version 1.1-1 (Hijmans et al., 2016). Pseudo- absences values differ of background because the spatial points with a present-data point are excluded in the former.

To create an input SDM matrix, the current bioclimatic information was extracted for each spatial point, i.e., for species occurrence and random-absences locations, and then the obtained data-environment matrix was divided and independently created for each species. To run the models, occurrence data were split into 70% as training set and 30% as testing set by random partition. Models were evaluated by means of Area Under Curve (AUC) statistics from a receiver-operating characteristic analysis, which is threshold-independent evaluation of model discrimination (Fielding & Bell, 1997). AUC values ranged from 0.5 to 1: 0.5 to 0.7 represents poor model performance, 0.7 to 0.9 represents moderate performance and values higher than 0.9 represents high performance of model. For each species, the three models of SDMs were calibrated with current environmental conditions and posteriorly compared by the AUC values. The model with the highest AUC value and with current predicted distribution overlapping the actual species distribution (Dijkstra et al., 2013) was chosen and used for predicting future potential habitat. The probability distribution maps of current and future projections were transformed into binary presence–absence maps to compare between them by applying a cut-off value that minimises the difference between sensitivity (true-positive predictions) and specificity (true-negative predictions, Lobo et al., 2007). The resulting comparisons between current and future scenarios for each model and species were used for sorting species in the five hypothesized categories (fig. 1).

Compilation of DNA sequences and Phylogenetic analyses DNA sequences of all Iberian and Moroccan species were searched and compiled from GenBank for the genes that were more frequently sequenced: the mitochondrial

8 cytochrome c oxidase subunit I gene (COI; 587 bps) and the ribosomal 12S RNA (12S rRNA; 1774 bp), 16S RNA (16S rRNA; 542 bp), 18S RNA (18S rRNA; 1813 bp) and 28S RNA (28S rRNA; 3933 bp). 4 Ephemeroptera species were used as outgroup (Baetis harrisoni, Callibaetis ferrugineus, Ephemera danica and E. orientalis).

The alignment procedure was executed in MAFFT 7 (Katoh & Standley, 2013) using the E-INS-i strategy (Very slow; recommended for <200 sequences with multiple conserved domains and long gaps). The best-fit partitioning scheme and individual models of molecular evolution for phylogenetic analyses were specified for gene partition, the best model of substitution was determined using the AIC (Akaike Information Criterion) in Partition Finder (Lanfear et al., 2012). Moreover, for the protein-coding gene COI independent model of nucleotide substitution were performed for each of the three codon positions that were treated as one partition. Gene partition was combined in a single data supermatrix using MEGA 5.0 (Tamura et al., 2011). Two methods of phylogenetic inference were used to reconstruct phylogenetic relationships. The maximum likelihood was implemented with RAxML (Randomized Axelerated Maximum Likelihood) (Stamatakis et al., 2008) under the GTR + Γ + I model with default number of Γ - categories implemented independently for each codon position. The best trees were selected from 100 multiple inferences, and clade support was assessed by means of 1000 nonparametric bootstrap resampling replicates of the original matrix. Bayesian inference was conducted using MrBayes 3.2.5 (Ronquist & Huelsenbeck, 2003). Two independent runs with four simultaneous Markov chain Monte Carlo (MCMC) chains (one cold and three heated), each with random starting trees, were carried out simultaneously, sampling 1000 generations until the standard deviation of the split frequencies of these two runs dropped below 0.01 (10 million generations). Tracer 1.4 (http://evolve.zoo.ox.ac.uk/) was used to ensure that the MCMC chains had reached stationarity by examining the effective sample size (ESS) values and to determine the correct number of generations to discard as burn-in. The two phylogenetic analyses were run remotely at the CIPRES Science Gateway (Miller et al., 2010). As conservative measures of node support, a value of bootstrap of 80% or greater might indicate substantial confidence for the maximum likelihood tree. In the Bayesian inference, posterior probabilities should only be considered reliable if were greater than 0.95.

Species-specific habitat preferences and trait conservatism Individual species information of adult habitat preferences were extracted from Dijkstra (2006). This information was used to delimit ecological trait space for each species. Trait information was quantified using a fuzzy coding approach (Chevene et al., 1994) and compiled in a matrix including 11 traits and 34 categories (table S1): 3 morphological

9 traits (body length, abdomen size and posterior wing longitude), 3 distributional characteristics (local abundance, distribution width and geographical distribution range), 4 traits of habitat preferences (lotic or lentic waters, seasonality of water, vegetation type, water chemistry) and period of adult flight. All scores within each trait were standardised so that the sums for a given species and a given trait were 1. In order to visualize how traits and families were distributed based on their trait composition, trait categories variance was measured conducting a Fuzzy Principal Component Analysis (FPCA, Chevene et al., 1994).

To assess how taxonomy influence species ecological distribution or, in other words, if families groups were ecologically differentiated or not, a Between-Classes Analyses (BCA) were performed based in FPCA results. BCA can be considered as a particular case of a Principal Component Analysis (because it is also a dimension reduction technique) but aims to discriminate groups maximizing the differences between them. BCA applied to PCA decomposes and orders only variance between groups, with the idea of obtaining a few dimensions in preserving the maximum variance between the centroids of the groups, not between individual observations (Dolédec & Chessel, 1987). Next, a permutation test was executed in order to estimate a p-value and hence detect differences between families distribution across the ecological space.

In order to assess character evolution and trait conservatism, the correlation of biological and ecological trait variation on the phylogenetic tree was tested. Two indices were calculated to infer possible patterns between traits and phylogeny: Pagel’s λ parameter (Pagel, 1999; Freckleton et al., 2002) and Blomberg’s K-statistic (Blomberg et al., 2003). Pagel’s λ parameter is based on maximum likelihood and gives a value between 0 and 1. If λ goes towards 1, the internal branches retain their original length indicating that there is a strong correlation between trait and the phylogenetic tree, so niche is preserved. Similarly, when the estimate of λ is close to 0, means that trait evolution has not followed the tree topology, therefore, there is no trait conservatism. On the other hand and for comparison purposes, the Blomberg’s K-statistic was also measured to test the existence of a phylogenetic signal. The higher the K statistic, the more phylogenetic signal in a trait. K values around 1 indicates that trait disparification follows the topology of the tree, which implies some degree of phylogenetic signal or conservatism of traits, whereas a little K means trait variation is independent to the phylogeny, which corresponds to a random pattern of evolution. The principal difference between the two methods is that Pagel’s λ compares all branches together while Blomberg’s K compares pairs of branches. The test of significance these indexes also differ: Pagel’s λ uses likelihood ratio tests against simpler models but Blomberg’s K makes randomizations of

10 the original trait data, comparing signal in a trait to the signal under a null model of trait evolution on a phylogeny, concretely, the Brownian motion. These analyses were carried out using two libraries of the R package: “geiger” (Harmon et al., 2008) and “picante” (Kembel et al., 2010).

RESULTS Final dataset included 85 Odonata species and 53845 individual records of “presence- only” (fig. 2). 10 species occurred exclusively in Morocco and 26 were found limited in the Iberian Peninsula. According to the Spain Red List of Invertebrates (Verdú et al. (Eds), 2011), 12 species included in the dataset were classified as vulnerable, 3 as endangered and another 3 as critical. Species distribution was strongly variable across species (fig. 3; Supplementary Material). For instance, in one of the extremes, Onychogomphus costae, Coenagrion scitulum and another 25 species were rare and had a small potential distribution area; these are some of the most vulnerable species. On the other extreme, Cordulegaster boltonii and Sympetrum fonscolombii were abundant and widely distributed across the studied area. In an intermediate situation, Aeshna cyanea and Calopteryx haemorrhoidalis were distributed mainly in the

Fig. 2. Map showing the 53845 Odonata species records (presence-only) distributed across the Iberian Peninsula and N-Africa. The four groups conforming the database are indicated.

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Mediterranean and Atlantic coast with patched occurrence on the Meseta Central. The comprehension of changes in the bioclimatic data is important to understand future prediction results (fig. 4). Despite seasonality will be greater in the future, the coast and inland regions in the Iberian Peninsula presented strong differences with reference to future isothermality (Bio 3) and temperature seasonality (Bio 4). Coast areas presented higher isothermality and lower temperature seasonality, which means less annual variability than inlands regions likely due to the sea effect, noteworthy wider in Atlantic than in Mediterranean coasts. Regarding to the mean temperature of the wettest quarter (Bio 8), the entire eastern half of the Peninsula presented the highest values, while Plana de Vic in northeast of Peninsula and highlands showed the lowest value of the mean temperature of the driest quarter (Bio 9). It means that the wettest months will be colder than in present-day and that the driest months will be hotter, except in Plana de Vic and highlands. Precipitation seasonality (Bio 15) had the highest values in south-west of the Iberian Peninsula and along the Moroccan coast, but this seasonality will decrease and, therefore, precipitations will vary less during the year. Also, bioclimatic variables will change over time. For instance, isothermality will decrease and, therefore, temperature seasonality will increase from 2050 to 2080. Moreover, the mean temperature of the

Fig. 3. Potential distribution area represented as a probability (0-1) for a couple of abundant and wide-distributed species (Cordulegaster boltonii and Boyeria irene), two Mediterranean (Sympetrum fonscolombii Calopteryx haemorrhoidalis) and a pair of rare and with a constrained distribution (Coenagrion scitulum and Onychogomphus costae).

12 wettest quarter will tend to decrease as a result of a shift in the rainfall season to either a delay in autumn or an advanced in spring. In contrast, the mean temperature of the driest quarter will increase. Finally, precipitation seasonality is expected to decrease in future scenarios owing to more drought events.

BRT models provided contribution percentages of variables fitted in the model. Bio4 and Bio15 were the predictors that commonly influenced the most to SDMs, whereas Bio9 had the lowest influence. Among species, in general, both the seasonality (i.e., annual variability) in temperature and precipitation influenced more than predictors based exclusively in temperature range per se.

Geographical occurrence allowed to run SDMs for 64 species out of 85 included in the dataset, the discarded species were rare and locally distributed (e.g., Cordulegaster bidentata that was recorded in 30 nearby sites), many classified as “vulnerable” in ecological traits. From those 64, 41 species were modelled using GAM, another 12 species were modelled using GLM and, finally, 11 were modelled using BRT (table S2). Remarkably, species modelled under BRT were confined in small areas at either Morocco, Morocco plus south of the Iberian Peninsula or northern Spain, whereas many species modelled using GLM or GAM showed a larger geographical range. The analysed 64 species fell into one of the predicted categories, however high discrepancies were found between pessimist (high emissions, RCP 8.5) and optimist (low emissions, RCP 2.6) future scenarios under both future models, as expected (fig. 5, table 1 & S3). Predictions of MPI in 2050th for both RCP scenarios showed a half of species as “expansive” (category 2), while HadGEM2 showed a 40.6% of species as “displaced” (category 1) for RCP 2.6 scenario, whereas a 43.8% of species were assigned as “reduced” (category 4) for RCP 8.5 scenario. In 2080th, predictions for RCP 2.6 scenario of MPI showed a 42.2% of species as “non-change” (category 3), while predictions of HadGEM2 showed a 42% of species as “displaced”. Predictions of both future models for RCP 8.5 showed half of species as “reduced” (category 4). Notably, RCP 8.5 scenarios showed larger percentages of species classified as “extinct”, and the highest value was detected in 2080 for HadGEM2 under RCP 8.5, as predicted.

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Fig. 4. Evolution of bioclimatic variables selected using HadGEM2 model under a RCP 8.5 scenario. Bio3: Isothermality (%), Bio4: Temperature seasonality (stdev x100), Bio8: Mean temperature of wettest quarter (ºC x10), Bio9: Mean temperature of driest quarter (ºC x10), Bio15: Precipitation seasonality (CV x100).

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2050 2080 MPI HadGEM2 MPI HadGEM2 RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5 RCP 2.6 RCP 8.5 1 29,6875 34,375 40,625 29,6875 15,625 40,625 42,1875 20,3125

2 57,8125 46,875 37,5 21,875 34,375 10,9375 4,6875 6,25 3 7,8125 3,125 1,5625 1,5625 42,1875 1,5625 26,5625 10,9375 4 4,6875 12,5 18,75 43,75 7,8125 42,1875 26,5625 54,6875

Categories 5 0 3,125 1,5625 3,125 0 4,6875 0 7,8125 Table 1. Percentages of species potential distribution categories for each model (MPI and HadGEM2 ES) and scenario (RCP 2.6 and RCP 8.5). Values >40% in bold. Categories: 1: potential area is latitudinally or longitudinally displaced, 2: potential area is being expanded, 3: no significant differences between current and future potential areas distribution, 4: habitat lost, 5: significant reduction of potential area. RAxML phylogenetic tree (fig. 6) included 2 suborders, 9 families and 73 species (80.2% of the species list was covered), and the final matrix contained 8649 bp. The species coverage was 67.12%, 58.90%, 72.60%, 72.60%, and 57.53%, for COI, 12S, 16S, 18S and 28S genes, respectively (table S4). The two suborders Anisoptera and Zygoptera were monophyletic, in agreement with other previous phylogenetic analyses (Dumont et al., 2010; Dijkstra & Kalkman, 2012; Suhling et al., 2015). Zygoptera was divided in two supported clades “Lestomorphs” and “non-Lestomorphs”. In the Iberian Peninsula, Lestomorphs were composed by three genera: Chalcolestes, Lestes and Sympecma, whereas the remaining genera Platycnemis, Ceriagrion, Pyrrhosoma, Ischnura, Enallagma, Coenagrion, Erythromma and Calopteryx were clustered together in “non- Lestomorphs”. Anisoptera order was separated into two clades: (1) families Cordulegasteridae, Gomphidae, and Aeshnidae, and (2) families Corduliidae and Libellulidae as indicated in Dumont et al. (2010). The first clade was well-resolved, and all genera were placed as previously showed in Ware et al. (2007) and Dumont et al. (2010). However, the monophyletic family Libellulidae did not have much supported nodes, but genera position corresponds to Dijkstra & Kalkman (2012), except for the genera Zygonyx and Crocothemis that were placed in the same clade. Finally, Corduliidae was placed paraphyletic as Dijkstra et al. (2013) stated, but in contrast of Dumont et al. (2009) that found this family as monophyletic.

Traits conservatism was rejected because Blomberg’s K and Pagel’s λ values in both axes were close to 0 (K=0.000192 and λ=0.50 for the first axis, and K=0.000694 and λ=0.37 for the second axis; p-value>0.05; table 2). In general, individual traits were neither preserved in the phylogeny, except large body, wings and abdomen sizes that were phylogenetically conserved in the two indexes (large body: K=3.15, λ=1; large abdomen: K=1.27, λ=1; large wings: K=1.11, λ=1). All in all, these results indicated no trait correlation in the phylogenetic tree and no phylogenetic signal of traits evolution. In

15 other words, the ecological similarity of closely related species was decoupled to their phylogenetic relations.

Odonata species commonly showed a high habitat specificity (Hassall & Thompson, 2008; Dijkstra, 2006; Suhling et al., 2015). It means that each species inhabited one of the categories for each defined habitat preference. For instance, species that preferred lentic water never were found in lotic habitats, or species located in riparian forest were exclusive for this habitat. For example, Calopteryx virgo was only found in permanent forested streams, Sympetrum meridionale preferred permanent vegetated ponds and Cordulegaster boltonii was found in pristine highland streams. We also found few generalist species, for instance Anax imperator inhabited in all types of lotic habitats, or all Trithemis species inhabited permanent but also temporary lotic waters.

Distribution of trait categories across niche space explained a 23.59% of the total variability on axis 1, whereas axis 2 and 3 explained a 19.73% and a 14.99%, respectively (fig. 7). The morphological small sizes and also abundant and continuous distributed species were the main contributors in the trait variance on the positive sides on axis 1, whereas morphological medium sizes and also rare and fragmented distributed species contributed negatively. In contrast, on axis 2 medium body size and abundant and continuous distributed species were the main contributors on the positive sides, while small body size and fragmented and rare distributed species contributed negatively. Large morphological sizes contributed positively on axis 3, while medium morphological sizes were negatively (fig. 7a).

The FPCA of traits disparity within species revealed how ecological characters are distributed among families, indicating that each family was characterised by a certain biological and ecological preferences and identity. In fact, permutation test of variance between groups indicated significant differences between families in relation to traits disparity (p-value = 0.01). Given the high specificity for preferred habitat, the families of Odonata showed high variability of how individual species were distributed in relation to traits disparity. Some families had high trait disparity (i.e., large circle area in fig. 7b) such as Lestidae and Libellulidae, whereas other families showed low trait disparity such as Platycnemididae.

In fig. 7c species were grouped as currently vulnerable because they inhabit high elevations (blue labels) or their habitat distribution was in regression (red labels) against species that will expand geographical range (yellow label) or have the capacity to do it (green labels). None of these groups were plotted nearby on the FPCA axes. Similarly, we also labelled species according to their category in the hypotheses (fig. 7d for 2050

16 and fig. 7e for 2080): blue labels showed species grouped as “favoured” because potential distribution will expand, in contrast to vulnerable or critical species that their potential distribution will be reduced (orange labels), and in some cases their potential habitats will disappear (red labels). Some species were classified as “stable” distribution, since potential distribution maybe shift, but the total area is equal (green label if shifts and yellow label if is the same region). It did not show any pattern, i.e., species distributed across the trait space did not reveal if a species is going to expand or reduce their potential distribution.

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Fig. 5. Examples of each hypothesized category. Square colours correspond to the code indicated in fig. 1. The colour of the areas showed in current, 2050th and 2080th maps have their legend below (colours key).

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Fig. 6. Maximum-likelihood phylogenetic tree of 5 genes of Order Odonata including 31 genera belonging to 9 families. Comprises 77 taxa including 4 outgroup genera of Ephemeroptera. Branch support percentage is indicated: maximum likelihood bootstrap (>80) / Bayesian inference (>0.95), *refers to branches with discordances between the two phylogenetic inferences. Suborder classification is given by coloured branches: Zygoptera and Anisoptera.

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Fig. 7. Fuzzy Principal Component Analysis (FPCA) on trait data. (a) Distribution of functional traits on FPCA axes. See Table S1 for traits labels code. (b) Distribution of the families on FPCA axes. Family names code is: Ca, Calopterygidae; Ce, Coenagrionidae; Le, Lestidae; P, Platycnemidae; A, Aeshnidae; Cg, Cordulegasteridae; Cl, Corduliidae; G, Gomphidae; Li, Libellulidae. See Table S4 for species code. The two suborder groups are represented in the same colours as phylogenetic tree in figure 5. (c) Current species status. Red labels: vulnerable, blue: high altitude habitats, green: potentially expansive, yellow: expanding. (d-e) Species labelled as the category which they belong in 2050 (d) or 2080 (e). Colours correspond to the code indicated in fig. 1. Axes 1 and 2 explained 23.59 and 19.73% of the total variability, respectively.

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Category Traits Code K PIC.variance.P λ <40 BS 0.00 0.23 0.87 Body Size >40 - <60 BM 0.00 0.30 0.89

>60 BL 3.15 0.00 1 <30 AS 0.44 0.00 0.86 Abdomen Size >30 - <45 AM 0.30 0.00 0.88 >45 AL 1.27 0.00 1

Morphology <30 WS 0.00 0.31 0.81 Wing (posterior) Size >25 - <38 WM 0.00 0.25 0.88 >38 WL 1.11 0.00 1 Abundant AB 0.00 0.60 0 Density Rare RA 0.00 0.63 0

Continuum CO 0.09 0.13 0 Distribution Fragmented FR 0.09 0.14 0 Vulnerable VU 0.13 0.51 0 North-Africa NA 0.00 0.81 0

Distribution Iberian Peninsula PI 0.00 0.68 0.54 Range Europe EU 0.00 0.12 0 Africa AF 0.44 0.00 1 River RV 0.15 0.08 0.64 Streams ST 0.25 0.00 0.81 Habitat Ponds PD 0.00 0.41 0.55

Lakes LK 0.00 0.40 0 Temporal TP 0.00 0.83 0 Seasonality Permanent PR 0.00 0.84 0 Submergent SB 0.16 0.09 0 Floating-leaf FL 0.09 0.23 0.14 Vegetation

Habitat preferences Ruderal RO 0.12 0.03 0.55 Forest, riparian FO 0.26 0.02 0.84 Water Acid AC 0.08 0.57 0 Chemistry Saline SA 0.00 0.97 0 Winter: I-II WI 0.00 0.99 0

Spring: III-V SP 0.00 0.77 0 Flight Summer: VI-IX SU 0.00 0.79 0.21

Adults Autumn: X-XII AU 0.00 0.77 0.43 Axis 1 of the FPCA 0.000192 0.60 0.50 Axis 2 of the FPCA 0.000694 0.22 0.37

Table 2. Niche conservatism results expressed as Blomberg’s K and Pagel’s λ indices. The higher the K statistic, the more phylogenetic signal in a trait: K = 0 means a random pattern of evolution, trait variation has not followed the phylogeny; K = 1 indicates some degree of conservatism; when K > 1 there is a strong phylogenetic signal and conservatism of traits (bold numbers). Traits with PIC.variance.P < 0.05 have non-random phylogenetic signal. In the same way, when λ is close to 1, internal branches retain their original length indicating a strong trait correlation with the tree; whereas when λ go towards 0, trait evolution has not followed the tree topology.

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DISCUSSION Future climate predictions under the four scenarios evidenced an increase in seasonality of temperature and a decrease in rainfall variability. In general, the predicted environmental characteristics of Mediterranean basin and inland of Iberian Peninsula will be harsher than the Atlantic coast due to the increase of drought events, which will be buffered by change in the atmospheric circulation (Giorgi & Lionello, 2008). As expected, the predicted future climate conditions will directly impact on future species distribution of odonates, but these responses will be highly variable across species and predictions. For instance, several species will lose their potential distribution area in Iberian Peninsula (e.g., Sympetrum flaveolum), in opposition, other species will shift or increase their potential distribution areas (e.g., Anax ephippiger). Across predictions, differences were evident between the most pessimist model HadGEM2 versus MPI global climate models and between the highest RCP 8.5 versus the lowest RCP 2.6 emissions scenarios. In general, if only the environmental conditions are considered, the predictions for 2080 for the most severe conditions showed that the majority of species (54.69%) will reduce their potential distribution (category 4) and around 7.81% will go extinct (category 5). In contrast, the most favourable conditions revealed a subtle dominance of species that expand their potential distribution (category 2) or species that will displace their geographical distribution (category 1) to habitats where the ecological conditions will be more suitable Hence, the climatic models used to predict the future distribution of odonates indicate that human measures for reducing anthropogenic emissions are critical for ensuring habitat conservation and preserving the present-day diversity of species.

Despite I revealed how important is to model species-specific effects of global warming on abiotic conditions for multiple species, the high variability across species disallow me to establish generalizations. Since the majority of species will reduce their distribution range in similar or higher emissions than currently, the species-specific biotic responses of odonates facing climate change will be critical for occupying or not the new potential distribution. In general, species could respond to climate change (1) moving in space or time to remain in a constant environmental niche, or by (2) evolutionary adaptation or (3) phenotypic acclimation (plasticity) (Parmesan, 2006; Buckley & Kingsolver, 2016). Odonata is one of the freshwater lineage that shows more abilities to face climate change for their multiple pathways for adaptive thermoregulation, such as the production of body pigment (Hassall & Thompson, 2008), but also for their aerial long-dispersal capacity that favours northward expansion (Heino et al., 2009; Markovic et al., 2014). Empirically, northward expansions as response of climate change are common for many taxa of high-

22 dispersive insects (Parmesan et al., 1999; Parmesan & Yohe, 2003; Hitch & Leberg, 2007) and it is also well documented in odonates from North-Africa to South-Europe (Cano-Villegas & Conesa-García, 2009) and from South-Europe to North-Europe (Hickling et al., 2006; Ott, 2010). However, my results did not support a massive northward expansion of any specific linage or trait characteristics of odonates following habitat change, in contrast, obtained patterns of future potential species distribution indicated idiosyncratic responses when the entire diversity of odonates form the westernmost Mediterranean regions is considered.

The Iberian Peninsula harbours a high diversity of odonates that differ in distribution ranges and habitat specificity, but I did not find a clear pattern between analysed biological traits, current habitat preferences and future suitable habitats. For many species, Iberian Peninsula is either the southernmost region of their European distribution (e.g., Brachytron pratense), the northernmost region of their African distribution (e.g., Diplacodes lefebvrii) or the centre (e.g., Oxygastra curtisii) of their Mediterranean distribution (Dijkstra, 2006). It is expected varying ecological and functional traits across species with disparate distribution range, which suggests the high diversity of differences in ecological requirements among these species. Biological and ecological traits determine niche occupancy. For instance, freshwater species and also odonates could be classified by their preferred water temperature conditions in warm-, cool- and cold-water types. Climate warming will favour warm-water species opposite to cold-water species because temperature will tend to increase (Heino et al., 2009). In fact, theoretically species can be classified as vulnerable (e.g., bad flyers, low tolerance to eutrophication) or favourable (e.g., mechanism of resistance to drought events or high temperatures) depending on their response facing climate change (Ott, 2010), but I found here that analysed biological traits and future SDMs are decoupled. It means that I cannot predict future direction of the species responses facing climate change and therefore there are not favourable traits to face global warming expanding to new habitats. For instance, species with favourable traits in front of drought events (e.g., preference for temporary habitats such as Sympetrum flaveolum) will not expand if their future potential distribution area is not expected to increase in parallel. This study reveals that ecological and life-history traits are not good predictors of species shifts for odonates, which was also previously suggested for American songbird species (Auer and King, 2014).

The evolutionary signature of trait conservatism is not preserved, except size-related traits such as abdomen, body and wings length. The only conserved traits are associated also to wing patterns that can easily distinguish the two monophyletic suborders of

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Odonata (Anisoptera and Zygoptera) (Dumont et al., 2010; Dijkstra & Kalkman, 2012). The non-preserved ecological traits can be explained by several mechanisms. Odonata have tropical origins and are one of the oldest winged insects that still inhabit the earth. These taxa belong to Odonatoptera, first appearing back minimum to the Upper Carboniferous period (~300Ma) (Suhling et al., 2015). Such old lineages must have faced multiple climate changes and adverse environmental conditions over millions of years that could promote repeated and independent extinction and speciation across clades, which likely explain some of the lack of phylogenetic signal. In contrast, young insect orders such as Trichoptera have been appeared more recently and the phylogenetic signal is still preserved in current species (Garcia-Raventós et al., in preparation). Moreover, Odonata diversification is more related to sexual morphology, reproductive behaviour and interactions between species than adaptive ecological divergence (Wellenreuther et al., 2012), which in turn can affect the loss of a phylogenetic signal of niche conservatism. As a result, the ecological space delimited by species traits and the high habitat-specificity of odonates did not show significant patterns across lineages. In other words, habitat preference such as temporary pond non-vegetated can be occupied by different species across lineages indistinctively. Thus, the phylogeny of Odonata is not useful to elucidate which traits are characteristic in each lineage and if this given trait can predict species vulnerability facing climate warming. Consequently, I cannot attribute to a lineage the probability of neither extinction, northward range expansion nor shift in its distribution range. However I did not test species plasticity and local adaptation, therefore, there are multiple unexplored biotic factors like species interaction, trophic networks, hot-tolerance, resistance to drought events, etc. (Hassall & Thompson, 2008) that must influence species colonization and establishment to new habitats. Further studies modelling multi-species distribution considering intraspecific traits and genetic variability are needed to infer future species- specific distribution and extinction risk in order to do a correct management of freshwater biodiversity under climate change.

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CONCLUSIONS As a main result of this project, I found odonates future potential distribution will be affected by Climate Change. Many species will shift their potential distribution to new suitable habitat, but the rates and directions at which species will achieve the new localities are species-specific. Other species will reduce their potential distribution and their locally adaptability or phenotypically acclimatise will allow them avoid extinction risk.

Despite each Odonata family showed their own ecological space, which was differentiated between families, the niche conservatism was rejected because traits were not preserved in phylogenetic tree of Iberian and Moroccan odonates. These results indicated that the ecological distance between species including also closely related species was decoupled to their phylogenetic divergence. Therefore, phylogeny cannot predict the ecological requirements of species.

Although species have high habitat-specificity, none clear pattern was found between traits (ecological and life-history), current habitat occupancy and future potential distribution under several models of Climate Change.

Understanding how Odonata will response in front of a Climate Change is critical to carry out a correct management in order to protect vulnerable species and maintain freshwater biodiversity.

ACNOWLEDGEMENTS I would like to acknowledge the help of Oxygastra and AEA El Bosque Animado groups who gently provided the occurrences data. I am especially gratefull to Xavier Maynou and Ricard Martín from Oxygastra for field training and their sound advices, and also to Florent Prunier from AEA El Bosque Animado for his recommendations. I am very grateful to María Ángeles Pérez for her great help with SDM and also to Dr. Dani Sol and his group for share their server-resources. Dr. Núria Bonada and Tony Herrera are thanked for their valuable comments and contacts, and also the research group FEM (Freshwater Ecology Management) for providing the space, equipment and unconditional encouragement for the investigation. Finally, I really appreciate the harsh job, collaboration and nearby support of my lab team (Macro&EvoLAB), my advisor Dr. Cesc Múrria and Aina Garcia-Raventós.

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29

Zygoptera Suborder

Lestidae Coenagrionidae Calopterygidae Family Sympecmafusca Lestes viridis numidicus)(+ L. Lestes virens Lestes sponsa Lestesmacrostigma Lestesdryas Lestesbarbarus Pyrrhosomanymphula Pseudagrion sublacteum Ischnurasaharensis Ischnurapumilio Ischnura Ischnurafountaineae Ischnuraelegans Erythrommaviridulum lindenii Erythromma(Cercion) Enallagmadeserti Enallagmacyathigerum Coenagrionscitulum Coenagrionpuella Coenagrionmercuriale Coenagrionhastulatum Coenagrioncaerulescens Ceriagriontenellum Calopteryx Calopteryxvirgo haemorrhoidalis Calopteryx Calopteryxexul

Species graellsii xanthostoma

Ce Ce Ce Ca Le Ce Ce Ca Ca Ce Ce Ce Ce Ce Le Le Ca Le Ce Ce Le Ce Le Ce Le Ce Ce Ce Code ------Lma Cme - - - - Lnu Lba - Cpu Cha Cha Lsp - - Ede Cca Cxa Cex Pny Psu - Csc Ecy Sfu Ldr Cte Lvi Cvi Ipu Evi Isa Igr Ifo Eli Iel

34 39 30 35 39 35 40 33 32 26 26 26 27 30 26 30 32 29 30 33 27 31 30 25 45 45 45 45 ------48 50 39 48 39 39 48 40 45 36 39 31 31 31 34 34 32 36 37 36 33 35 31 33 33 35 48 49 TO

Bodysize 30 34 25 29 25 25 31 26 26 25 25 19 22 20 21 22 22 24 24 22 20 22 19 22 18 22 35 31 ------43 36 30 39 32 33 38 33 35 30 33 25 25 25 25 29 25 28 29 28 27 31 27 26 27 30 37 42 AB

18 23 19 17 24 20 20 19 17 12 14 13 19 14 16 19 19 15 14 15 12 16 14 15 28 24 23 27 ------37 29 23 28 23 24 27 25 27 24 23 17 18 19 24 21 20 21 23 21 20 24 21 22 21 21 31 36 AP

1 0 1 1 1 1 0 1 1 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 0 1 1 1

AB Ab.

0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0

RA 1 0 1 1 0 1 0 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 0

CO Distrib. 0 1 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1

FR

0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

VU 1 1 1 1 1 0 0 0 1 1 1 1 1 1 1 0 1 0 1 1 1 1 1 0 1 1 0 0

NA Range 1 0 1 1 1 1 1 0 1 1 0 0 1 1 0 1 1 0 0 1 1 1 1 0 1 1 1 1

PI 0 0 1 1 1 1 1 1 1 1 0 0 1 0 0 1 1 0 0 1 0 1 0 1 0 1 0 1

EU 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

AF 0 0 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1

RV Habitat 0 0 0 0 0 0 1 1 0 1 1 1 1 1 0 0 0 0 0 1 1 0 1 1 1 1 1 1

ST 1 1 1 1 1 1 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 1 0 1 0 0 0 0

PD 1 0 1 1 0 1 0 0 0 1 0 1 0 1 1 1 1 1 1 1 0 1 0 0 0 0 0 0

LK 1 0 1 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Seas.

TP 1 1 0 1 1 0 0 1 0 1 0 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

PR 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 0 1 0 0 0 0

SB Vegetation 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 0 1 0 0 0 0

FL 1 0 1 1 1 1 1 1 0 1 1 1 0 1 0 0 0 1 1 1 1 1 1 1 0 0 0 0

RO

0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 1 1

FO Chem 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0

AC 0 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 .

SA Flight III Adult V V IV IV IV IV IV IV IV IV IV IV III III III III III V IV V I II III III II III V ------XII - - VIII VIII - - - - - VIII ------IX XI -

XI VI IX IX IX IX XI X IX IX IX IX IX IX IX IX IX IX X X X X

Platycnemis acutipennis P-Pac 34-37 24-28 18-19 1 0 1 0 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 V-VIII

Platycnemis latipes P-Pla 33-37 25-30 18-22 1 0 1 0 0 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 0 VI-IX

Platycnemis pennipes P-Ppe 35-37 27-31 19-23 1 0 0 1 0 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 0 V-IX Zygoptera

Platycnemididae Platycnemis subdilatata P-Psu 33-36 22-28 17-21 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 0 0 1 0 0 IV-IX Aeshna affinis A-Aaf 57-66 39-49 37-42 0 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 0 V-VIII Aeshna cyanea A-Acy 67-76 51-61 43-53 1 0 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 1 1 0 0 IV-X Aeshna (Anaciaeschna) A-Ais 62-66 47-54 39-45 1 0 0 1 0 1 1 1 0 0 0 1 1 0 1 0 1 1 0 0 0 V-VIII isoceles (isosceles)

Aeshna juncea A-Aju 65-80 50-59 40-48 0 1 0 1 0 0 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 VI-XI Aeshna mixta A-Ami 56-64 43-54 37-42 1 0 1 0 0 1 1 1 1 0 0 1 1 0 1 0 1 1 0 0 1 V-XII

Anax (Hemianax) ephippiger A-Aep 61-70 43-56 43-48 0 1 1 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 I-XII Aeshnidae Anax imperator A-Aim 66-84 50-61 45-52 1 0 1 0 0 1 1 1 1 1 0 1 1 1 1 0 1 1 0 0 0 II-X Anax parthenope A-Apa 62-75 46-53 44-51 1 0 1 0 0 1 1 1 1 0 0 0 1 0 1 1 1 1 0 0 0 II-XI Boyeria irene A-Bir 63-71 44-48 39-45 1 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 V-X

Brachytron pratense A-Bpr 54-63 37-46 34-37 0 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 0 0 III-VIII

Cordulegaster bidentata Cg-Cbi 69-78 52-60 41-46 0 1 1 0 0 0 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 V-VIII

Cordulegaster boltonii Cg-Cbo 74-80 52-64 40-47 1 0 1 0 0 1 1 1 0 0 1 0 0 0 1 0 0 0 1 0 0 V-VIII

Cordulegastridae Cordulegaster princeps Cg-Cpr 75-86 56-65 45-49 0 1 0 1 1 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 V-IX Anisoptera

Cordulia aenea Cl-Cae 47-55 30-39 29-35 1 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 1 1 0 0 IV-VII Macromia splendens Cl-Msp 70-75 48-55 42-49 0 1 0 1 1 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 V-VIII

Oxygastra curtisii Cl-Ocu 47-54 33-39 33-36 1 0 0 1 0 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 V-VIII Corduliidae Somatochlora metallica Cl-Sme 50-55 37-44 34-38 0 1 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 1 0 0 V-IX Gomphus graslinii G-Ggr 47-50 33-38 27-30 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 V-VIII Gomphus pulchellus G-Gpu 47-50 34-38 27-31 1 0 1 0 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 III-VIII

Gomphus simillimus G-Gsi 45-50 33-36 29-33 1 0 1 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 V-VII Gomphus vulgatissimus G-Gvu 45-50 33-37 28-33 1 0 1 0 0 0 1 1 0 1 1 0 1 0 1 0 0 0 1 0 0 IV-VI Onychogomphus costae G-Oco 43-46 30-34 22-27 0 1 0 1 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 0 V-VIII

Gomphidae Onychogomphus forcipatus G-Ofo 46-50 31-37 25-30 1 0 1 0 0 1 1 1 0 1 1 0 0 0 1 0 0 0 0 0 0 V-IX Onychogomphus uncatus G-Oun 50-53 34-42 29-33 1 0 1 0 0 1 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 V-IX Paragomphus genei G-Pge 37-50 30-36 21-26 0 1 1 0 0 1 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 IV-X

Acisoma panorpoides Li-AAr 24-31 16-22 19-25 0 1 0 1 0 1 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 V-IX Brachythemis leucosticta Li-Ble 25-34 16-21 20-26 1 0 1 0 0 1 1 0 1 1 0 1 1 1 1 0 0 0 0 0 0 IV-X Crocothemis erythraea Li-Cer 36-45 18-33 23-33 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 II-XI Diplacodes lefebvrii Li-Dle 25-34 15-25 19-29 1 0 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 IV-XI (lefebvri) Leucorrhinia dubia Li-Ldu 31-36 21-27 23-28 1 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 1 1 0 IV-IX Libellula depressa Li-Lde 39-48 22-31 32-38 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 0 0 IV-IX Libellula (Ladona) fulva Li-Lfu 42-45 25-29 32-38 1 0 0 1 0 0 1 1 0 1 0 1 1 0 1 0 0 1 0 0 0 IV-VIII Libellula (Ladona) Li-Lqu 40-48 27-32 32-40 1 0 1 0 0 1 1 1 0 0 0 1 1 0 1 1 1 1 0 0 0 V-IX quadrimaculata Orthetrum brunneum Li-Obr 41-49 25-32 33-37 1 0 1 0 0 1 1 1 0 0 1 1 0 0 1 0 0 0 0 0 0 IV-IX Orthetrum cancellatum Li-Oca 44-50 29-35 35-41 1 0 1 0 0 1 1 1 0 1 0 0 1 0 1 0 0 0 0 0 0 IV-IX Orthetrum chrysostigma Li-Och 39-48 26-33 27-32 1 0 1 0 0 1 1 0 1 1 0 1 0 0 1 0 0 1 0 0 0 IV-X Orthetrum coerulescens Li-Oco 36-45 23-38 28-33 1 0 1 0 0 1 1 1 1 0 1 1 0 0 1 1 0 1 0 0 0 IV-XI

Orthetrum nitidinerve Li-Oni 46-50 28-33 31-38 0 1 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 IV-XI

Orthetrum sabina Li-OAr 43-50 31-36 28-33 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1 0 0 1 0 0 0 IV-X

Orthetrum trinacria Li-Otr 51-67 38-44 34-38 0 1 0 1 0 1 1 0 1 0 0 1 1 0 1 0 0 1 0 0 0 III-X Anisoptera

Libellulidae Pantala flavescens Li-Pfl 45-55 26-37 38-42 0 1 1 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 VI-IX Selysiothemis nigra Li-Sni 30-38 21-26 24-27 0 1 0 1 0 1 1 0 1 0 0 1 0 1 0 0 0 1 0 0 0 V-IX Sympetrum flaveolum Li-Sfl 32-37 19-27 23-32 1 0 0 1 0 0 1 1 0 0 0 1 0 1 0 1 0 1 0 0 0 V-X Sympetrum fonscolombii Li-Sfo 33-40 22-29 26-31 1 0 1 0 0 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 0 I-XII (fonscolombei) Sympetrum meridionale Li-Sme 35-40 22-28 25-30 1 0 1 0 0 1 1 1 0 0 0 1 0 0 1 0 0 1 0 0 0 V-X Sympetrum pedemontanum Li-Spe 28-35 18-24 21-28 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1 1 0 0 0 VII-X Sympetrum sanguineum Li-Ssa 34-39 20-26 23-31 1 0 0 1 0 1 1 1 0 0 0 1 1 1 1 1 1 1 0 0 0 V-VIII Sympetrum sinaiticum Li-Ssi 34-37 21-26 24-29 1 0 1 0 0 1 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 VI-III Sympetrum striolatum Li-Sst 35-44 20-30 24-30 1 0 1 0 0 1 1 1 0 0 0 1 1 1 1 0 0 0 0 0 1 I-XII Sympetrum vulgatum Li-Svu 35-40 23-28 24-29 1 0 1 0 0 0 1 1 0 0 0 1 1 0 1 1 0 1 0 0 0 VI-XI (decoloratum) Trithemis annulata Li-Tan 32-38 17-29 20-35 1 0 1 0 0 1 1 0 1 0 0 1 1 1 1 0 0 1 0 0 0 III-X Trithemis arteriosa Li-Tar 32-38 20-26 23-30 1 0 1 0 0 1 0 0 1 0 0 1 1 1 1 0 0 1 0 0 0 V-X Trithemis kirbyi Li-Tki 30-34 19-23 23-29 0 1 0 1 0 1 1 0 1 0 1 1 0 1 0 0 0 0 0 0 0 V-XI Zygonyx torridus Li-Zto 50-60 35-43 45-50 0 1 1 0 0 0 1 0 1 1 0 0 0 0 1 0 0 0 0 0 0 IV-VIII

Table S1. Quantification of traits categories using a fuzzy code approach. Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera

Suborder

Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Gomphidae Gomphidae Gomphidae Gomphidae Gomphidae Gomphidae Gomphidae Gomphidae Gomphidae Gomphidae Corduliidae Corduliidae Corduliidae Corduliidae Cordulegastridae Cordulegastridae Cordulegastridae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Aeshnidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae

Family

Aeshna cyanea Aeshnaaffinis Orthetrumransonnetii Orthetrumnitidinerve Orthetrumcoerulescens Orthetrumchrysostigma Orthetrumcancellatum Orthetrumbrunneum Libellulaquadrimaculata Libellulafulva Libelluladepressa Leucorrhinia Diplacodeslefebvrii Crocothemiserythraea Brachythemisimpartita Acisomapanorpoides Paragomphusgenei Onychogomphusuncatus Onychogomphus forcipatus Onychogomphus costae Onychogomphusboudoti Gomphusvulgatissimus Gomphussimillimus Gomphus Gomphuslucasii Gomphusgraslini Somatochlorametallica Oxygastracurtisii Macromiasplendens Corduliaaenea Cordulegasterprinceps Cordulegasterboltonii Cordulegasterbidentata Brachytronpratense Boyeria irene Anaxparthenope Anaximperator Anaxephippiger Anaciaeschnaisosceles Aeshnamixta Aeshna juncea Sympetrumvulgatum Sympetrumstriolatum Sympetrumsinaiticum Sympetrumsanguineum Sympetrumpedemontanum Sympetrummeridionale Sympetrumfonscolombii Sympetrumflaveolum Selysiothemisnigra Pantala flavescens Orthetrumtrinacria Orthetrumsabina pulchellus dubia

Species

Iberian 1 1 0 1 1 1 1 1 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 1 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 1 1 1 1 0 1

Peninsula Dataset 1 0 1 0 0 1 0 0 1 1 0 0 1 1 1 1 1 1 0 0 1 0 1 1 1 0 1 1 0 1 1 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0 1 1 1 1 0 0 Morocco

Dataset GLM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...... ------90 91 97 87 98 82 81 91 91 89 90 90 91 87 90 97 87 88 97 95 90 93 93 89 90 91 97 80 82 97

AUC GAM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...... ------94 93 98 96 99 86 90 94 92 94 93 94 93 94 99 93 91 98 98 93 95 95 93 94 93 93 99 93 91 97

BRT 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 ...... ------96 98 00 97 96 99 98 99 89 98 98 00 97 97 00 98 00 97 99 98 97 98 00 98 97 00 98 99

1173 1806 1019 2862 1573 3267 1319 1032 1232 2810 1450 1344 214 242 162 706 137 248 283 708 266 716 175 132 141 154 218 296 117 978 299 264 583 187 253 227 28 23 48 30 78 53 43 83 60 0 2 1 0 4 0 0 2

Occurrence

Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Anisoptera Anisoptera Anisoptera Anisoptera Anisoptera Table S Table Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera Zygoptera

Suborder 2

.

AUC values of each species and model selected. D selected. and model species each of values AUC Platycnemididae Platycnemididae Platycnemididae Platycnemididae Lestidae Lestidae Lestidae Lestidae Lestidae Lestidae Lestidae Lestidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Coenagrionidae Calopterygidae Calopterygidae Calopterygidae Calopterygidae Libellulidae Libellulidae Libellulidae Libellulidae Libellulidae

Family

Trithemisarteriosa Trithemisannulata Platycnemis subdilatata Platycnemispennipes Platycnemislatipes Platycnemisacutipennis Sympecmafusca Chalcolestes Lestes virens Lestes sponsa Lestesnumidicus Lestesmacrostigma Lestesdryas Lestesbarbarus Pyrrhosomanymphula Pseudagrion sublacteum Ischnurasaharensis Ischnurapumilio Ischnuragraellsii Ischnurafountaineae Ischnuraelegans Erythrommaviridulum Erythrommalindenii Enallagmadeserti Enallagmacyathigerum Coenagrionscitulum Coenagrionpuella Coenagrionmercuriale Coenagrionhastulatum Coenagrioncaerulescens Ceriagriontenellum Calopteryxxanthostoma Calopteryxvirgo Calopteryxhaemorrhoidalis Calopteryxexul Zygonyx Urothemisedwardsii Trithemis kirbyi torridus

viridis

Species

Iberian 0 1 1 1 0 1 1 1 1 1 1 1 1 1 1 0 1 0 1 0 1 0 1 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1

Peninsula Dataset 1 0 1 1 1 1 1 1 1 0 1 1 0 1 1 1 1 0 1 1 1 1 0 0 0 1 1 1 0 0 0 1 1 1 1 1 0 1 Morocco

ataset origin is also indicated. is also origin ataset Dataset GLM 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ...... ------99 89 91 82 89 89 90 92 90 87 89 96 91 95 93 93 90 92 86 89 86 86 89 77 92 90

AUC GAM 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 ...... ------93 94 87 94 91 90 95 92 90 91 00 97 93 96 96 96 93 95 92 92 94 92 95 94

BRT 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 ...... ------00 98 99 99 97 99 97 98 98 99 98 98 98 00 98 99 99 99 97 99 98 99 98 98 97

1093 1703 1108 1367 2263 1873 2080 1817 1037 2548 288 745 135 695 390 409 511 172 900 540 616 366 114 174 300 359 26 24 22 90 68 16 76 6 8 0 4 0

Occurrence

Species MPI RCP 2.6 MPI RCP 8.5 HadGEM2 RCP 2.6 HadGEM2 RCP 8.5 Aeshna affinis 2/2 1*/1 2/1 1/1 Aeshna cyanea 1/3 4/4 4/3 4/4 Aeshna mixta 2/3 2/1 2/3 1/3 Anaciaeschna isosceles 2/3 2/1 3/1 3/2 Anax ephippiger 2/3 2/2 2/2 2/2 Anax imperator 2/2 2/1 2/3 1/3 Anax parthenope 2/3 2/4 1/4 3/1 Boyeria irene 1/2 1/1 4/2 4/4 Brachythemis impartita 2/3 2/1 2/3 2/4 Calopteryx haemorrhoidalis 2/2 1/1 1/1 4/4* Calopteryx virgo 1*/2 1/4 1/1 4/5 Calopteryx xanthostoma 1*/2 1/4 1/1 4/4 Ceriagrion tenellum 2/3 1*/4 1/4* 4/4 Coenagrion caerulescens 3/1 4/1 4/1 4/4 Coenagrion mercuriale 1/3 1/4 1/4 4/4 Coenagrion puella 4/4 5/5 4/4 4/5 Coenagrion scitulum 1/3 1/4 1/4* 4/4* Cordulegaster boltonii 3/2 1/1 4/1 4/4 Crocothemis erythraea 2/3 2/1 2/1 3/1 Diplacodes lefebvrii 2/3 2/1 2/3 2/1 Enallagma cyathigerum 3/2 1/4 4/1 4/4 Erythromma lindenii 2/3 2/1 2/1 3/1 Erythromma viridulum 2/3 2/1 1/1 4/4 Gomphus graslini 1/4* 4/4 1/4 4/4 Gomphus pulchellus 2/1* 2/2 2/1 1/1 Gomphus simillimus 1/3 1/4* 1/4* 1/4 Ischnura elegans 2/3 1*/4 1/4 5/1 Ischnura graellsii 2/1* 2/1 1/1 1/4* Ischnura pumilio 1/1* 4*/4 4*/4* 4/4 Ischnura saharensis 2/3 2/3 2/4* 2/3 Lestes barbarus 1/3 1/4* 1/4* 4/4 Lestes dryas 3/2 4*/4 4/1 4/4 Lestes sponsa 4/4* 4/5 4/2 4/5 Lestes virens 1/2 1/4 1/1 4/4 Chalcolestes viridis 2/1* 2/1 1/1 3/1 Libellula depressa 2/2 1/4 1/1 4/4 Libellula quadrimaculata 1/3 1/4 1/1 1/4 Macromia splendens 1/3 1/1 1/4 4/4 Onychogomphus costae 2/3 2/1 2/3 2/3 Onychogomphus forcipatus 1/2 1/4 1/1 4/4 Onychogomphus uncatus 4/2* 4/4* 4/1 4/4 Orthetrum brunneum 1/2 1/4 1/1 4/4 Orthetrum cancellatum 2/2 2/1 2/1 1/4 Orthetrum chrysostigma 2/2* 2/1 2/3 2/3 Orthetrum coerulescens 2/1 2/1 1/4* 4/4* Orthetrum nitidinerve 1/3 1/1 2/3 1/4 Orthetrum trinacria 2/3 2/4 2/4 2/4 Oxygastra curtisii 2/4* 2/4 1/4 1/4* Paragomphus genei 2/3 3/1 2/3 2/4 Platycnemis acutipennis 2/2* 2/2 2/1 2/2* Platycnemis latipes 2/1* 2/1 1/1 1/1 Platycnemis subdilatata 3/3 3/4 2/3 2/4 Pyrrhosoma nymphula 2/2 1/4* 4*/1 4/4 Selysiothemis nigra 2/2 2/2 2/1 3/1 Sympecma fusca 2/2* 2*/1 1*/4* 1/4* Sympetrum flaveolum 1/1 5/5 5/3 5/5 Sympetrum fonscolombii 2/1* 1/1 1/3 4/1 Sympetrum meridionale 1/4 2/4 2/4* 1/4 Sympetrum sanguineum 1/1 4/4 4/3 4/5 Sympetrum sinaiticum 2/2* 2/2 2/3 1/3 Sympetrum striolatum 2/2* 2/1 1/1 1/3 Trithemis annulata 2/3 2/2 2/3 2/3 Trithemis kirbyi 2/3 2/4 2/3 2/1 Zygonyx torridus 2/3 2/2 2/3 2/2*

Table S3. Species potential distribution categories for each model (MPI and HadGEM2) and scenario (RCP 2.6 and RCP 8.5). Left number in 2050, right number in 2080. Categories: 1: potential area is latitudinally or longitudinally displaced, 2: potential area is being expanded, 3: no significant differences between current and future potential areas distribution, 4: habitat lost, 5: significant reduction of potential area. 1*: there is a displacement of the potential area but some regions maintain the same environmental conditions, 2*: expansion but with area lost, 4*: area reduction but with new habitat gain.