Temperature and hydrologic alteration predict the spread of invasive Largemouth Bass (Micropterus salmoides)

Science of the Total Environment 639 (2018) 58–66

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Temperature and hydrologic alteration predict the spread of invasive Largemouth Bass (Micropterus salmoides) Mi-Jung Bae a,⁎, Christina A. Murphy a,b, Emili García-Berthou a a b

GRECO, Institute of Aquatic Ecology, University of Girona 17003, Girona, Catalonia, Spain Department of Fisheries and Wildlife, Oregon State University, Corvallis, OR 97331, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• The distribution of Largemouth Bass, an invasive species, was examined using species distribution models. • The most influential predictor of bass distribution in the Iberian Peninsula was temperature. • Larger volumes of local and upstream reservoirs also increased predicted presence. • Understanding the drivers promoting the establishment of this global invader will be important in identifying areas at risk.

a r t i c l e

i n f o

Article history: Received 30 January 2018 Received in revised form 16 April 2018 Accepted 1 May 2018 Available online xxxx Editor: Daniel Wunderlin Keywords: Disturbance Invasive alien species Ensemble forecasting Natural flow regime Reservoirs Species distribution modelling

a b s t r a c t The successful establishment of an aquatic invasive alien species can be mediated by a suite of environmental factors, including climate and anthropogenic disturbance. Dams and reservoirs are thought to promote freshwater fish invasion success through hydrological alterations but the evidence for their role in the global invasion of Largemouth Bass (Micropterus salmoides) on a landscape scale is limited. Here, we examine the distribution of Largemouth Bass, one of the most widely introduced fish in the world, from the Iberian Peninsula using species distribution models (SDMs), including an ensemble forecast. We used these models to test the role of twelve environmental predictors expected to influence the distribution of Largemouth Bass, including the reservoir storage capacity at local and upstream reaches. We found that the predictive accuracy, based on AUC criteria, of the ensemble model was higher than any of the six individual SDMs for Largemouth Bass. The most influential predictor of bass distribution included in our model of the Iberian Peninsula was temperature, where warmer temperatures were generally associated with bass presence, and cooler temperatures with absence. In addition to warmer temperatures, increasing storage of local and upstream reservoirs increased predicted presence, suggesting an important role of reservoirs in mediating the invasive success of this fish. Our results indicate that although natural climatic factors may be crucial in the successful invasion of Largemouth Bass, hydrological alteration (e.g., regulated flow regimes and lentic habitats associated with dams and reservoirs) may be important. Understanding the drivers promoting the establishment of this global invader will be important in identifying areas at risk and in developing future efforts to control its spread, especially when those drivers are ongoing anthropogenic disturbances such as the construction and operation of dams and reservoirs. © 2018 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: Freshwater Biodiversity Research Division, Nakdonggang National Institute of Biological Resources, Gyeongsangbuk-do 37242, Republic of Korea. E-mail address: [email protected] (M.-J. Bae).

https://doi.org/10.1016/j.scitotenv.2018.05.001 0048-9697/© 2018 Elsevier B.V. All rights reserved.

M.-J. Bae et al. / Science of the Total Environment 639 (2018) 58–66

1. Introduction Invasive alien species drive freshwater biodiversity loss and have enormous economic costs worldwide (Williamson, 1999; Mooney and Hobbs, 2000; Simberloff et al., 2013). Anthropogenic disturbances such as land use change, river channelization, fragmentation, and water abstraction produce changes in natural temperature and flow regimes of freshwater ecosystems (Poff et al., 2007; Bae et al., 2016). These and other anthropogenic alterations may promote the invasion of exotic species (Dudgeon et al., 2006; Vörösmarty et al., 2010). Dams create novel lentic habitats and modify the timing and magnitude of downstream flows, in general greatly reducing seasonal and interannual variability (Poff and Hart, 2002; Poff et al., 2007). Dams may also alter temperature regimes, in general reducing seasonal variability and often increasing temperatures during winter periods (Bunn and Arthington, 2002; Poff and Hart, 2002; Olden and Naiman, 2010). Dams and associated reservoirs may promote invasive alien species establishment because many successful freshwater invaders are more limnophilic (i.e. preferring lentic habitats) and thermophilic (i.e., thriving at relatively high temperatures) than the native species they replace in hydrologically altered systems (Bunn and Arthington, 2002; Vila-Gispert et al., 2005; Olden et al., 2006; Boix et al., 2010; Gido et al., 2013). There is an urgent need to improve our understanding of biological invasions, both to reduce future invasions and to predict their ecological effects (Shea and Chesson, 2002; Simberloff et al., 2013). Species distribution models (SDMs) are increasingly used as a tool to explain and predict the patterns and processes of biological invasions (Rodríguez et al., 2007; Smolik et al., 2010; Capinha and Anastácio, 2011). SDMs relate species distribution data (occurrence or abundance at known locations) with information on the environmental or spatial characteristics of those locations (Elith and Leathwick, 2009). In the field of invasion biology, SDMs can be used to predict the potential distribution of introduced species (Ficetola et al., 2007; Mika et al., 2008; Bradley, 2009), and to compare the invasive potential of different invasion stages (Václavík and Meentemeyer, 2012). SDMs also indicate important environmental drivers of distribution, which can inform invasive alien species management (Guisan et al., 2013). Here, we analyze the factors mediating the invasion of Largemouth Bass (Micropterus salmoides), a centrarchid species native to parts of North America (Page and Burr, 1991). Largemouth Bass are among the ten most frequently introduced aquatic species worldwide (GarcíaBerthou et al., 2005) and are now found on all continents except Antarctica (Lever, 1996). Largemouth Bass are an apex predator in most introduced streams and lakes (Carpenter and Kitchell, 1993; García-Berthou, 2002) and can cause trophic cascades that change community structure (Carpenter and Kitchell, 1993; Ahrenstorff et al., 2009). Largemouth Bass can also alter the foraging behavior of native fish (MacRace and Jackson, 2001), compete with native piscivores (Bacheler et al., 2004), and extirpate or decrease the abundance of native species (Maezono and Miyashita, 2003). Largemouth Bass are considered a warm-water species (Coutant, 1975; Brown et al., 2009; Cooke and Philipp, 2009), but species distribution models analyzing the influence of temperature and other environmental factors on Largemouth Bass distribution are very limited (see Iguchi et al., 2004 for the known example). Therefore, we assessed the influence of environmental factors on the invasion of Largemouth Bass outside of their native range, using the Iberian Peninsula as a case study. Largemouth Bass were first introduced in Spain in 1955 (Elvira and Almodóvar, 2001) for sport fishing activities. Many 20th century introductions favored preferred fishing locations such as reservoirs (Godinho et al., 1998; Marta et al., 2001; Copp et al., 2005) and Largemouth Bass are now estimated to occur in about 50% of all reservoirs in the Iberian Peninsula (Clavero et al., 2013). Although initial introductions likely favored lentic habitats, Largemouth Bass are now common in lotic areas as well (Hermoso et al., 2008).The Iberian Peninsula offers a large region (ca. 582,000 km2) with strong spatial

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heterogeneity in climate and anthropogenic disturbance (Ferreira et al., 2007), providing an excellent opportunity to examine environmental factors influencing Largemouth Bass invasion and persistence. There are over 1200 large dams (storage capacity N 1 hm3) with a total capacity of ca. 64,000 hm3 in the Iberian Peninsula (BergaCasafont, 2003). The identification of environmental factors, especially factors related to anthropogenic alterations, that promote or mediate its spread is critical to develop effective management strategies and to reduce the effects of Largemouth Bass on freshwater ecosystems worldwide. Our objectives were: 1) to identify the influential environmental factors that regulate Largemouth Bass distribution in the Iberian Peninsula, 2) to test whether hydrologic alteration influences Largemouth Bass occurrence, and 3) to determine the potential for range expansion of Largemouth Bass in the region. These objectives were designed to inform management efforts to identify areas at risk and to limit the further spread of this species. Based on the known habitat preferences for Largemouth Bass, we hypothesized that warm temperatures and hydrologic alteration would promote their invasion success at a landscape scale. Specifically, we predicted that bass would be more frequent: 1) at temperatures known to maximize growth and performance without increasing mortality (i.e., around 15–25 °C) and 2) in reservoirs or in regulated rivers downstream of reservoirs which provide lentic habitats and reaches with fewer extreme hydrological events. 2. Methods 2.1. Largemouth Bass data Largemouth Bass occurrence data in the Iberian Peninsula were mainly obtained from the Spanish (Doadrio et al., 2011) and Portuguese (Ribeiro et al., 2007) national databases. This information was complemented with searches in the Global Biodiversity Information Facility (GBIF, http://www.data.gbif.org/, last accessed in February 2014), published papers and our own unpublished records (Table S1). We restricted data to occurrence records from 2000 to 2010. We used a 10 × 10 km UTM (Universal Transverse Mercator) resolution, which was the finest resolution available for the majority of the Largemouth Bass occurrence data. Overlapping or duplicate records within 10 km UTM cells were removed to allow only one occurrence per UTM unit. A total of 590 occurrence records of Largemouth Bass were thus obtained for the Iberian Peninsula and included in the final database (out of 6138 grid cells, Fig. 1e). 2.2. Environmental data We collected environmental data expected to determine the distribution of Largemouth Bass based on the literature. All the environmental data were obtained from online databases (Table 1) and extracted for SDM with the Spatial Analyst toolbox in ArcGIS 10 (ESRI, 2009). When available environmental data were finer grain than the records for Largemouth Bass occurrences (10 km UTM), average values for each 10 × 10 km grid cell were computed. Because collinearity can compromise the reliability of SDM (Heikkinen et al., 2006), only one variable from any pair of strongly correlated variables (i.e. Pearson's r ≥ |0.75|) was retained (Cord and Rödder, 2011; Dormann et al., 2013; Filipe et al., 2013). We based our decision on which to retain on literature and our expert opinion. A total of 12 environmental predictors passed the collinearity threshold (Table S2) and were used for SDM development (Table 1). Largemouth Bass occurrence can be influenced by many environmental variables across various spatial scales (Maceina and Bettoli, 1998; Suski et al., 2006; Taylor et al., 2014) (Table 1). Water temperature, water level and pool area have been reported as critical factors for survival (Aggus and Elliott, 1975; Garvey et al., 2000), spawning (Kramer and Smith, 1960; Post et al., 1998), growth (Olson, 1996), and density (Sowa and Rabeni, 1995) of Largemouth

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Fig. 1. The four environmental variables with highest variable importance in species distribution models (a) annual mean temperature, (b) upstream reservoir capacity, (c) flow accumulation, and (d) local reservoir capacity and (e) observed and (f) predicted distribution (ensemble model) of Largemouth Bass in the Iberian Peninsula. The predicted distribution of individual SDMs is provided in Fig. S1.

Bass in its native range (Parkos and Wahl, 2002). However, many of these variables vary strongly at finer spatial and temporal scales that could not be obtained or extracted for the Iberian Peninsula as a whole. In this case, we used air temperature as representative of water temperature and precipitation as representative of water level and discharge (Garvey et al., 2000). We also selected slope and flow accumulation, measured as the number of cells flowing into each down-slope cell, as a proxy of drainage area (Domisch et al., 2011). Solar radiation, previously reported as influential for spawning and growth, was also included (Havens et al., 2005). Finally, as indicators

of anthropogenic disturbance including hydrologic alteration due to dams and land use change, we compiled data on local reservoir capacity (the volume of water stored in each UTM cell), upstream reservoir capacity (the accumulated volume of water stored in reservoirs upstream of each UTM cell), human population density, and agricultural and urban land uses. Local reservoir capacity was included to measure the direct influence of reservoirs on bass occurrence, whereas upstream reservoir capacity was included to approximate the degree of hydrological alteration affecting a site, which might promote bass invasion by reducing extremes in flow (Batalla et al., 2004) and temperature (Prats et al.,

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Table 1 Environmental variables used (and their mean and standard deviation) in SDMs for Largemouth Bass in the Iberian Peninsula from all 10 km2 UTM grid cells. When environmental data were originally of finer grain than our species data, data were downsampled using the mean of values within each grid cell. All values indicate the mean and standard deviation (in parenthesis) values of environmental variables in the Iberian Peninsula, Presence reports the mean and standard deviation for cells with detected presence of Largemouth Bass and Absence represents those values for the pseudo-absences used in the SDMs. * indicates layers calculated from source data using ArcGIS geoprocessing tools. Environmental variables Climate Annual mean temperature (°C) Annual temperature range (°C) Annual precipitation (mm) Solar radiation (mm/day) Topography Slope (°) Topographic index Flow accumulation Anthropogenic disturbance Upstream reservoir capacity (km3) Local reservoir capacity (km3) Population density (people/km2) Agricultural land use (%) Urban land use (%)

Sourcea

Abbreviation

All

Presence

Absence

1 1* 1 1

AnMeanTemp AnRangeTemp AnPrecip SolarRad

13.6 (2.7) 11.9 (1.1) 713.9 (321.7) 2027.5 (28.8)

15.2 (1.8) 12.2 (0.9) 636.1 (205.0) 2032.3 (17.6)

13.4 (2.7) 11.9 (1.1) 729.4 (335.4) 2027.9 (28.0)

2* 2* 2*

Slope TopoIndex FlowAcc

5.4 (4.5) 2.1 (0.6) 1321.3 (5489.0)

4.2 (3.2) 2.3 (0.6) 4893.4 (10,044.6)

5.5 (4.6) 2.0 (0.5) 1054.5 (5139.3)

3,4* 3,4* 5 6 6

MaxResVol ResLocVol Population Agric% Urban%

410.9 (1685.2) 11.6 (110.0) 83.1 (322.1) 48.7 (32.7) 1.4 (5.8)

1561.0 (3186.6) 38.8 (208.9) 80.9 (242.2) 53.5 (29.1) 1.4 (5.1)

316.5 (1453.0) 8.5 (107.2) 86.5 (393.5) 47.3 (33.1) 1.5 (6.4)

a Sources: 1 = Universitat Autònoma de Barcelona, Atlas climático digital de la Península Ibérica (http://www.opengis.uab.es/); 2 = Spanish National Center for Geographic Information (http://centrodedescargas.cnig.es/); 3 = Melo and Gomes (1992); 4 = Spanish Ministry of Agriculture, Food and Environment (http://sig.magrama.es/); 5 = DIVA-GIS data (http://www. diva-gis.org/datadown); 6 = National Center of Geographical Information (https://www.cnig.es).

2010). All the environmental predictors were transformed into z scores to standardize the measurement scales of the inputs and reduce their effects on the SDM results (Vander Zanden et al., 2004).

2.3. Modelling approach To develop the SDM of Largemouth Bass, we used the ‘biomod2’ package (Thuiller, 2003; Thuiller et al., 2009) in R (http://cran.rproject.org) (R Core Team, 2013). We used six SDMs that have been frequently applied in a variety of taxa: Generalized linear models (GLM), generalized additive models (GAM), boosted regression trees (BRT), artificial neural networks (ANN), multiple adaptive regression splines (MARS), and Random Forests (RF). GLM are an extension of linear models to allow for non-normal errors and heteroscedasticity (McCullagh and Nelder, 1989) and we used them with binomial distribution and a logit function to model presence-absence data (Thuiller, 2003). GAM are a non-parametric extension of GLM that use a smoother to fit nonlinear functions (i.e., Spline function) (Hastie and Tibshirani, 1990), they have been widely applied in biogeographic studies (e.g., Araújo et al., 2004; Thuiller et al., 2006). BRT, more recently introduced in ecology, combine the strengths of regression trees and boosting (Ridgeway, 1999; Elith et al., 2008) by proceeding through sequential improvements using a numerical optimization algorithm to minimize a loss function (e.g., deviance) and add a new tree at each step (Elith et al., 2008). ANN are a powerful rule-based modelling technique (Lek and Guégan, 1999). Compared to logistic regression or linear discriminant analysis, ANN have displayed higher predictive power when modelling nonlinear relationships (Olden and Jackson, 2002). Because ANN can be applied to a variety of data types with nonlinear associations, ANN have been increasingly used in SDM (Thuiller, 2003; Heikkinen et al., 2006). MARS combine linear regression, mathematical construction of splines and binary recursive partitioning to fit local models in which the relationships between the response and predictors can be linear or nonlinear (Friedman, 1991). The purpose of MARS is to try to determine the appropriate intervals to run independent linear regressions, for each predictor, and identify interactions while avoiding overfitting the data (Briand et al., 2004). RF (Breiman, 2001) are model-averaging approaches where bootstrap samples are drawn to construct multiple trees, grown with a randomized subset of predictors (Prasad et al., 2006). RF have shown better prediction accuracy with minimal overfitting than many other SDM techniques (Cutler et al., 2007; Marmion et al., 2009).

The performance and spatial predictions of SDMs depend on uncertainties from a number of factors such as measurement errors, sample size, sample representativeness (Edwards et al., 2006; Marmion et al., 2009) and the statistical techniques used (Thuiller et al., 2004a, 2004b). These uncertainties can be minimized by including multiple environmental drivers, using appropriate ranges of data (Filipe et al., 2013), applying the suitable spatial resolution data reflecting the ecological knowledge (e.g., dispersal ability) of the study taxon and conducting standardization or normalization of environmental variables prior to SDM construction (Peterson et al., 2011). In addition, ensemble forecasting, combining the output of multiple individual SDMs (e.g., using means, medians or weighted averages, Araújo and New, 2007), is used to overcome “prediction uncertainty” from different modelling techniques (Pearson et al., 2006; Carvalho et al., 2010) and generally increases prediction accuracy compared with any individual SDM (e.g., Marmion et al., 2009; Grenouillet et al., 2011). Our data did not contain completely reliable absence locations of Largemouth Bass because of imperfect capturability and inconsistent sampling effort, so we generated pseudo-absences among nonpresence or background grid cells (n = 1850 of 6138 total cells) (Phillips et al., 2009; Barbet-Massin et al., 2012). Following BarbetMassin et al. (2012), we used random selection of pseudo-absences as this method yields the most reliable distribution models. Species occurrence data were divided into a training set (70%) and a testing set (30%) (Araújo et al., 2005) and each model was replicated 10 times to avoid bias from the data split. Model performance was evaluated based on the area under the curve (AUC) of the receiver-operating characteristic (ROC) (Swets, 1988), which ranges from 0 to 1. As a rule of thumb, AUC values above 0.9 indicate an excellent prediction model, whereas values between 0.7 and 0.9 indicate a fair model, and values below 0.7 indicate poor model performance (Swets, 1988). We estimated the importance of the environmental predictors from all species distribution models with a permutation procedure available in the “variables_importance” function of biomod2 (Thuiller et al., 2009). This procedure starts with the predictions from the trained (i.e. calibrated) model, randomizes each variable separately, and compares the new predictions using a randomized variable with the original predictions based on the Pearson correlation coefficient (r). The variable importance measure is obtained as 1 − r where higher values indicate more influential variables (Thuiller et al., 2009). The probability of Largemouth Bass occurrence across the range of variation for each environmental predictor was also examined using response curves for each individual SDM (Elith et al., 2005).

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We then applied ensemble modelling, which can increase the reliability of predictions as well as decrease model-based uncertainty (Thuiller, 2004; Grenouillet et al., 2011). For the ensemble model a minimum AUC value of 0.7 was used to select adequate models (Araújo et al., 2005), the average of these SDM was then used to construct the ensemble model and provide more robust forecasts (Marmion et al., 2009, Grenouillet et al., 2011). Finally, the AUC statistic was also used to analyze the accuracy of the ensemble model and to compare its performance to the individual SDM.

Largemouth Bass were much less frequent in the northwest, corresponding to the Atlantic climate zone with much higher annual precipitation (N1000 mm) and cooler temperatures, and in the southeast, corresponding to a semi-arid climate with some of the lowest rainfall in Europe (b300 mm per year). The mainstem of large Mediterranean rivers (Douro, Tagus, Guadiana, Guadalquivir, and Ebro), with high upstream reservoir capacity (i.e. hydrologic alteration) had the highest suitability (Fig. 1). 4. Discussion

3. Results 4.1. Largemouth Bass invasion For the six modelling techniques applied, average AUC scores were fair (around 0.8; Fig. 2). RF overall showed the highest predictive performance based on AUC (0.838), followed by BRT (0.828), MARS (0.815), ANN (0.789), GLM (0.778), and GAM (0.774). As expected, the predictive accuracy (0.908) of the ensemble model based on AUC was higher than any individual SDM. Of the 12 environmental predictors, annual mean temperature (average importance ± standard error: 0.320 ± 0.013), flow accumulation (0.120 ± 0.011), upstream reservoir capacity (0.143 ± 0.013), and local reservoir capacity (0.108 ± 0.005) had the greatest effects on predicted bass presence (Fig. 3). Other predictors, more related to the position along the river network, such as slope (0.035 ± 0.006) and topographic index (0.054 ± 0.006) or to anthropogenic disturbance, such as population density: 0.013 ± 0.003, agricultural: 0.048 ± 0.006 and urban: 0.015 ± 0.003 land uses were poor predictors of Largemouth Bass presence. The shape of the response curves of RF and BRT, which had the highest individual model performances based on AUC, were also the most similar among the models (Fig. 4). Most response curves of GLM and GAM, which had comparatively poor predictive performances, were also similar, though some predictors presented different shapes (e.g., flow accumulation, upstream reservoir capacity, local reservoir capacity and urban land use) and contrasting predicted probabilities. The best performing model (i.e. ensemble model) predicted Largemouth Bass to more frequently occur in locations with relatively high mean annual temperatures (from ca. 14.9 to 17.4 °C), intermediate levels of annual precipitation, and thermal ranges above 12.1 °C (Fig. 1). Largemouth Bass were less likely to occur at lower flow accumulations (i.e. headwaters), and where few large reservoirs were found locally or upstream. The ensemble model projection reproduced the reported occurrences of Largemouth Bass in the Iberian Peninsula (Fig. 1). The geographical areas with the highest environmental suitability were mostly in the south-western and eastern parts of the Iberian Peninsula.

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Fig. 2. Model performance as determined by the mean area under the curve (AUC) of the receiver-operating characteristic for species distribution models of Largemouth Bass: Generalized linear model (GLM), generalized additive model (GAM), boosted regression trees (BRT), artificial neural networks (ANN), multiple adaptive regression splines (MARS), and Random Forests (RF). Error bars are standard deviations.

Temperature was the most important factor for predicting Largemouth Bass distribution. Largemouth Bass were less prevalent in the cool, wet northeastern Iberian Peninsula, and in the hot, dry region of southeastern Spain. This is not surprising, as many Largemouth Bass life history traits such as spawning, breeding, growth and activity are known to depend on water temperature (Coutant, 1975; Brown et al., 2009; Cooke and Philipp, 2009). All our models predicted that the occurrence of bass is unlikely below 10 °C mean air temperature and that it is most likely to occur in areas with temperatures from 14 to 18 °C. Temperature partly explains the more invasive character of this fish species in Mediterranean countries in contrast to many parts of northern Europe. The Iberian Peninsula has a mostly Mediterranean climate with mild winters and dry, warm summers; the temperatures required for Largemouth Bass growth are maintained for many months. Water temperature would probably be a better predictor of Largemouth Bass occurrence than air temperature, but was not generally available. Nearly all SDMs for freshwater species have used air instead of water temperatures for this reason (e.g., Buisson et al., 2010; Capinha and Anastácio, 2011; Comte and Grenouillet, 2013). Although air and water temperatures are often well correlated, this is not always the case (Carmona-Catot et al., 2014; Bae et al., 2016) and we would recommend the use of water temperature for SDM construction where possible. Although temperature ranges, including minimum temperatures, may be important for Largemouth Bass, we did not use minimum (air) temperature because it was strongly correlated to mean air temperature and its effects likely depend on acclimation temperature (Beitinger et al., 2000). In addition, more data of species presence-absence and temperatures at finer grains would be needed to separate the differential effects of minimum vs. mean temperatures (e.g. overwinter mortality vs. seasonality in growth; Post et al., 1998, Fullerton et al., 2000, Lookingbill and Urban, 2003, Cooke and Philipp, 2009). Following temperature, the upstream and local reservoir capacities were among the most influential variables predicting Largemouth Bass occurrence. These anthropogenic indicators of hydrologic alteration have been generally neglected in previous SDM studies even though it has been continuously reported that these alterations may facilitate species invasions (Murphy et al., 2015). Local reservoir capacity indicated the presence and size of local reservoirs, whereas upstream reservoir capacity, the cumulative volume of reservoirs upstream, was aimed at describing the degree of modification of the natural flow regime and other ecological features resulting from upstream impoundment. In our study, both local and upstream reservoir capacities appear to affect the prevalence of bass, although the latter appeared slightly more important. The strong relationship between reservoir-related factors and the occurrences of Largemouth Bass can be explained by altered environmental conditions (i.e., the modification of natural flow and thermal regimes; Poff et al., 2007) and increased propagule pressure (i.e., the size and frequency of introductions) (Williamson and Fitter, 1996; Simberloff, 2009; Woodford et al., 2013). The presence of local reservoirs likely modifies available habitat to the detriment of native species which are more adapted to riverine conditions, and provides

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Fig. 3. Mean variable importance and standard error from the six species distribution models for Largemouth Bass (GLM, GAM, BRT, ANN, MARS, and RF). Abbreviations of environmental variables are provided in Table 1.

and reducing their flood and drought induced mortality rates (Propst and Gido, 2004; Kiernan et al., 2012; Gido et al., 2013; Taylor et al., 2014).

lentic habitat for invasive alien species, frequently introduced for recreational opportunities including sport fishing (Havel et al., 2005; Rahel and Olden, 2008). In the Iberian Peninsula, sport fishing activities are common in reservoirs (Marta et al., 2001) where Largemouth Bass have been stocked since the 1950s (Leunda, 2010). It is assumed that Largemouth Bass and other game fish continue to be illegally introduced into reservoirs (Clavero and Hermoso, 2011). According to Clavero and Hermoso (2011), invasive species, such as Largemouth Bass, dominate Iberian reservoirs (71% of species richness) but not rivers and streams (33% of species richness). Most unimpounded Iberian freshwater ecosystems display Mediterranean extremes; during the summer dry season stream flow is often low or ceases, whereas unpredictable floods are common during spring and autumn. These extremes structure Mediterranean freshwater communities (Gasith and Resh, 1999; Magalhães et al., 2002) and exotic species are less likely to occur where these extremes are the greatest (e.g., Marchetti and Moyle, 2001; Bernardo et al., 2003; Vila-Gispert et al., 2005; Olden et al., 2006). By contrast, it has been proposed that Largemouth Bass thrive in Iberian regulated streams and reservoirs because of damped flow variation, availability of naive prey, and low predation pressure (Godinho et al., 2000; Almeida et al., 2012). Hydrologic alteration favors Largemouth Bass and similar limnophilic species by producing more suitable habitats

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4.2. Species distribution modelling Modelling accuracy (based on AUC and ecological interpretation of results) of all individual SDM was weaker than for the ensemble model, indicating that the various modelling techniques did not produce equivalent and equally plausible predictions (Roura-Pascual et al., 2009; Meller et al., 2013). The newer techniques of machine learning, such as RF and BRT, consistently outperformed other modelling methods, as in many other applications (e.g., Cutler et al., 2007; Marmion et al., 2009; Markovic et al., 2012). This is partly because these machine learning techniques combine several modelling algorithms, average the results of many models, and have fewer assumptions than more classical techniques such as GLM (Prasad et al., 2006). An individual model's suitability (e.g., a model with the most accurate prediction) can depend on features of the study species (e.g., specialist vs. generalist, invasive vs. native species) or its spatial distribution (e.g., geographical range modelled) (e.g., Thuiller et al., 2004a; Meller et al., 2013). Therefore, ensemble modelling often provides more

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Fig. 4. Largemouth Bass response curves with four influential environmental predictors for the six spatial distribution modelling techniques. The response curves of all the predictors are provided in Fig. S2. Abbreviations of environmental variables are provided in Table 1.

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accurate results than the best individual modelling techniques (RF in our study) (Marmion et al., 2009). In spite of the high predictive accuracy of our model, the performance of our model might be improved should additional data become available. First, the number and location of pseudo-absences can influence model predictions (e.g., Graham et al., 2008; Lomba et al., 2010). Confirming “true” absences is very difficult in mobile species and requires higher sampling effort to assure their reliability compared with presence data (Elith and Leathwick, 2009). In our study, we applied larger numbers of pseudo-absences (i.e. 1/3 of background data) than presences based on the recommendation of Barbet-Massin et al. (2012) and pseudo-absences were downweighted in order to emulate an equal number of presences and pseudo-absences (Ferrier et al., 2002; Lomba et al., 2010). Second, even though it is well known that climatic factors have a more significant role at larger spatial scales (Guisan and Thuiller, 2005), it is reasonable that biological factors (e.g., competition and predation) as well as unmeasured environmental factors also contribute to Largemouth Bass distribution. Physical habitat (e.g., substrate composition and riparian vegetation) and water quality (e.g., turbidity, pH, dissolved oxygen, and eutrophication) were not directly considered in our study although they are known to affect the local distribution of bass in individual lakes and reservoirs (Hanson and Butler, 1994; Shoup and Wahl, 2009; Gaeta et al., 2011). Third, dispersal limitation is generally not considered in SDM (Filipe et al., 2013) which may result in an overestimation of the occurrence probability in some places. However, our predictions seem statistically accurate and ecologically sound. The importance of temperature and hydrologic alteration on Largemouth Bass invasion has many management implications. For instance: i) temperature is a key factor in habitat suitability for bass and thermal pollution might promote bass invasion (Bae et al., 2016); ii) building new reservoirs may increase the distribution of bass and other invasive limnophilic species, whereas removing dams might act as a controlling measure; iii) mimicking the natural flow regime and preserving floods and droughts might provide an avenue for managing Largemouth Bass invasions in regulated rivers; iv) the spatial projections based on the ensemble model results identify highly suitable areas for bass where it has not been recorded, supporting the need for targeted surveys (Guisan et al., 2006) and providing basic information for managers of areas at risk (Farnsworth and Ogurcak, 2006). Our results are also relevant for over 45 countries where Largemouth Bass appear to have been successfully introduced (García-Berthou et al., 2005), by showing the environments where this species is likely to invade and how reservoir construction is likely to promote its invasion. Although natural abiotic factors such as temperature and habitat are important, hydrologic alteration through reservoir construction creates favorable conditions for this highly invasive alien species. Managing ongoing anthropogenic disturbances such as dams and reservoirs may be critical to future efforts to control the spread of Largemouth Bass. Acknowledgements We gratefully thank everybody who contributed with data on presence of Largemouth Bass, particularly Ignacio Doadrio and Filipe Ribeiro, and two anonymous reviewers for helpful comments on the manuscript. This research was financially supported by the Spanish Ministry of Economy and Competitiveness (projects: CGL201343822-R; CGL2015-69311-REDT; CGL2016-80820-R; and ODYSSEUS, BiodivERsA3-2015-26, PCIN-2016-168) and the Government of Catalonia (ref. 2014 SGR 484 and 2017 SGR 548). MJB benefited from a postdoctoral grant from the European Commission (Erasmus Mundus Partnership “NESSIE”, 372353-1-2012-1-FR-ERA MUNDUS-EMA22). Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2018.05.001.

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