Towards a better understanding of potential impacts of climate change ...

Global Ecology and Biogeography, (Global Ecol. Biogeogr.) (2014) bs_bs_banner

RESEARCH PA P E R

Towards a better understanding of potential impacts of climate change on marine species distribution: a multiscale modelling approach Tarek Hattab1,2*, Camille Albouy3, Frida Ben Rais Lasram1, Samuel Somot4, François Le Loc’h2,5 and Fabien Leprieur6

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UR 03AGRO1 Ecosystèmes et Ressources Aquatiques, Institut National Agronomique de Tunisie, 43 Avenue Charles Nicolle, 1082 Tunis, Tunisia, 2UMR 212 Ecosystèmes Marins Exploités (IRD-IFREMER-UM2), Avenue Jean Monnet, BP 171, 34203 Sète, France, 3 Département de Biologie, Chimie et Géographie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC, Canada G5L 3A1, 4Centre National de Recherches Météorologiques CNRM-GAME, Météo-France, 42 Avenue Gaspard Coriolis, 31057 Toulouse Cedex, France, 5UMR 6539 Laboratoire des Sciences de l’Environnement Marin (CNRS-UBO-IRD-IFREMER), Rue Dumont d’Urville, Pointe du Diable, Technopole Brest-Iroise, Brest, 29280 Plouzané, France, 6UMR 5119 ECOSYM (CNRS-UM2-IRD-IFREMER-UM1), Université de Montpellier 2, CC 093, 34095 Montpellier, France

*Correspondence: Tarek Hattab, UR 03AGRO1 Ecosystèmes et Ressources Aquatiques, Institut National Agronomique de Tunisie, 43 Avenue Charles Nicolle, 1082 Tunis, Tunisia. E-mail: [email protected]

ABSTRACT

Aim In this paper, we applied the concept of ‘hierarchical filters’ in community ecology to model marine species distribution at nested spatial scales. Location Global, Mediterranean Sea and the Gulf of Gabes (Tunisia). Methods We combined the predictions of bioclimatic envelope models (BEMs) and habitat models to assess the current distribution of 20 exploited marine species in the Gulf of Gabes. BEMs were first built at a global extent to account for the full range of climatic conditions encountered by a given species. Habitat models were then built using fine-grained habitat variables at the scale of the Gulf of Gabes. We also used this hierarchical filtering approach to project the future distribution of these species under both climate change (the A2 scenario implemented with the Mediterranean climatic model NEMOMED8) and habitat loss (the loss of Posidonia oceanica meadows) scenarios. Results The hierarchical filtering approach predicted current species geographical ranges to be on average 56% smaller than those predicted using the BEMs alone. This pattern was also observed under the climate change scenario. Combining the habitat loss and climate change scenarios indicated that the magnitude of range shifts due to climate change was larger than from the loss of P. oceanica meadows. Main conclusions Our findings emphasize that BEMs may overestimate current and future ranges of marine species if species–habitat relationships are not also considered. A hierarchical filtering approach that accounts for fine-grained habitat variables limits the uncertainty associated with model-based recommendations, thus ensuring their outputs remain applicable within the context of marine resource management. Keywords Climate change, exploited species, habitat loss, hierarchical filtering, Mediterranean Sea, spatial scale, species distribution modelling.

INTRODUCTION Changes in global climate are having significant ecological impacts on the world’s oceans (Barange et al., 2010). Both direct observations and model outputs suggest that the expected responses to global warming will include: (1) a latitudinal shift in the distributions of a wide range of species as they seek cooler locations (e.g. Pinsky et al., 2013), (2) high local extinction rates © 2014 John Wiley & Sons Ltd

(e.g. Cheung et al., 2009), and (3) the marked reorganization of local community assemblages (e.g. Harborne & Mumby, 2011; Albouy et al., 2012). These ecological responses may ultimately have significant consequences for the functioning of marine ecosystems and the services that they provide (Harborne & Mumby, 2011). Species distribution modelling, which is grounded in ecological niche theory (Hutchinson, 1957), has been widely used to DOI: 10.1111/geb.12217 http://wileyonlinelibrary.com/journal/geb

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T. Hattab et al. predict the potential effects of climate change on both terrestrial (e.g. Thuiller et al., 2008) and marine species (Cheung et al., 2009; Albouy et al., 2013). Bioclimatic envelope models (BEMs) are commonly used to relate observations of species occurrence or known environmental tolerance limits to climatic variables (Araújo et al., 2011). By evaluating changes in bioclimatic envelopes, shifts in species distributions can be predicted under different climate change scenarios (e.g. Cheung et al., 2009; Albouy et al., 2013, for marine fishes). To ensure robust predictions of distribution changes it is important to develop realistic BEMs across the range of climatic conditions in which a given species occurs (Thuiller et al., 2004). This is particularly critical for marine species, as climate is the main driver shaping their distributions at large spatial scales (Pinsky et al., 2013). In addition, their geographical ranges closely correspond to their thermal tolerance limits (i.e. their fundamental climatic niche), which implies that, for marine species, range shifts caused by climate change can be accurately predicted at large spatial scales (Sunday et al., 2012). Species–environment relationships are strongly dependent on the scale at which the dependent and independent variables are observed (Cushman & McGarigal, 2004). For instance, habitat characteristics such as seafloor type strongly influence the distribution of marine species at small spatial scales (e.g. Moore et al., 2010; Hattab et al., 2013a). Consequently, BEMs that do not account for species–habitat relationships may give unrealistic predictions. The inclusion of habitat variables in BEMs is also important because (1) physical habitat changes are expected to have large effects on the distributions of marine species (e.g. Sundblad et al., 2014) and (2) there may be interaction between the effect of climate change and habitat loss on marine biodiversity (Crain et al., 2009). However, accounting for species– habitat relationships in BEMs is not straightforward. Habitat variables are only well described for small areas (see Moore et al., 2010; Hattab et al., 2013a) and such fine-scale data are

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generally not available over the full range of a species. In addition, habitat variables are generally described at finer spatial resolutions than climatic variables. For example, sea surface temperature (SST) and sea surface salinity (SSS) are typically modelled at a resolution of around 10 km (Albouy et al., 2013), while seafloor composition is commonly given at a much finer spatial resolution (e.g. 90 m; see Hattab et al., 2013a). In this study we propose that a two-stage hierarchical approach based on the on the concept of ‘hierarchical filters’ may address these limitations (Simpson, 1953; Lawton & Kinne, 2000; Heino et al., 2009). According to this concept, species assemblages are shaped by various abiotic, biotic and historical factors that constitute a series of filters for the fauna or flora that are nested across spatial and temporal scales. These filters act to progressively eliminate species from global, regional and local faunal pools according to their ecological requirements (Heino et al., 2009). This concept has been tested by numerous studies in both terrestrial and aquatic ecosystems (Keddy, 1992; Luoto et al., 2007; Heino et al., 2009). For instance, Luoto et al. (2007) showed that the determinants of bird species distributions are hierarchically structured; climatic variables determine largescale distribution, while land cover was important at the smaller scale. In the two-stage hierarchical filtering approach, BEMs are initially built at a global scale to account for the full range of climatic conditions a given species encounters. Therefore, climate becomes the first, coarse-resolution filter (layer 1; Fig. 1). Habitat models are then built using habitat variables at a finer spatial scale (i.e. a marine ecoregion). The physical habitat (e.g. seafloor composition) represents the second filter (layer 2; Fig. 1). This two-stage approach allows for species distributions to be described using both large-scale climatic variables and fine-scale habitat variables. This latter component was not previously considered when BEMs were fitted at large spatial scales (Cheung et al., 2009; Albouy et al., 2013). As such, scenarios in

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Figure 1 Schematic representation of the hierarchical ‘faunal filters’ concept applied to species distribution modelling of four hypothetical species. According to their life-history traits, species from the regional pool will be able to pass through a large-scale climatic filter (map at the top of figure) and a local-scale habitat filter (map at the centre of the figure). Hence, they will be present (species A and B) or absent (species C and D) from the local assembly of species (map at the bottom of figure). Climatic and habitat characteristics allow species A and B to pass through both filters while unsuitable habitat for species C and unsuitable climatic conditions for species D explain why they are absent locally.

Global Ecology and Biogeography, © 2014 John Wiley & Sons Ltd

Threatened coastal region under global change which climatic variables (e.g. temperature and salinity) change or physical habitats are modified can be combined to provide a better understanding of the potential cumulative effects on marine species distribution of these two threats. In this study, we applied this approach to determine both the current and future distributions of 20 marine species in the highly threatened Gulf of Gabes ecosystem, a coastal region of Tunisia in the Mediterranean Sea. METHODS Study area The Gulf of Gabes, located in the southern Mediterranean Sea, is characterized by unique geomorphological, climatic and oceanographic conditions that combine to support one of the most productive ecosystems in the Mediterranean Sea (Hattab et al., 2013b). The region also has significant economic and ecological importance, supporting high fisheries productivity and serving as a nursery, feeding and breeding ground for numerous populations of fish and crustacean species (Hattab et al., 2013b). For example, it supports one of the most extensive communities of seagrass (Posidonia oceanica) in the Mediterranean Sea, which acts as a major nursery site for several marine species (Ben Mustapha et al., 1999). Over the past 20 years, fish production has gradually declined simultaneously with the decline of littoral P. oceanica meadows (90% decline between 1924 and 1990 in the central gulf region; Zaouali, 1993).

Table 1 Percentage changes in species geographical range size when considering the habitat filter according to the two-stage hierarchical filtering approach for the three time periods considered in this study.

Species Balistes capriscus (Gmelin, 1789) Diplodus annularis (Linnaeus, 1758) Eledone moschata (Lamarck, 1798) Gobius niger (Linnaeus, 1758) Lithognathus mormyrus (Linnaeus, 1758) Loligo vulgaris (Lamarck, 1798) Melicertus kerathurus (Forskål, 1775) Merluccius merluccius (Linnaeus, 1758) Metapenaeus monoceros (Fabricius, 1798) Mullus barbatus (Linnaeus, 1758) Mullus surmuletus (Linnaeus, 1758) Mustelus mustelus (Linnaeus, 1758) Octopus vulgaris (Cuvier, 1797) Pagellus erythrinus (Linnaeus, 1758) Pagrus caeruleostictus (Valenciennes, 1830) Sepia officinalis (Linnaeus, 1758) Serranus hepatus (Linnaeus, 1758) Solea aegyptica (Chabanaud, 1927) Sparus aurata (Linnaeus, 1758) Squilla mantis (Linnaeus, 1758)

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Bioclimatic envelope modelling Global species occurrence data

In collating these data our intention was to develop global maps of key climatic variables based on long-term average conditions using comprehensive, publicly accessible raster data (Appendix S3). Four metrics were derived from monthly SST climatologies (1982–2009): annual maximum, minimum, mean and the annual range, which is the difference between the maximum and the minimum and represents a proxy of seasonality and temporal variation in SST (Appendix S4). These metrics and the mean monthly SSS climatologies (1961–2009) were used to model bioclimatic envelopes for each species. Each variable was prepared on a 5 arcmin (c. 9.2 km) global grid. Only

cells located on the continental shelf (414,721 cells per variable) were used to calibrate the BEMs. To obtain projected climatic data over the Mediterranean Sea we used the regional oceanographic circulation model NEMOMED8, which is a Mediterranean configuration of the NEMO ocean model. This model predicts SST and SSS based on water energy fluxes, river discharges and water exchanges with the surrounding seas (Beuvier et al., 2010). NEMOMED8 covers the entire Mediterranean Sea and includes a buffer zone in the adjacent Atlantic Ocean. The horizontal resolution of NEMOMED8 is 1/8° longitude, resulting in square grid cells of 9–12 km, depending on the latitude. Based on the Intergovernmental Panel on Climate Change (IPCC) A2 scenario, NEMOMED8 predicts values for SST and SSS anomalies for the middle (2040–59) and end (2080–99) of the 21st century (Appendices S5 & S6). Considered conservative (Solomon et al., 2007), the IPCC A2 scenario is the only one implemented in NEMOMED8 and is a standard scenario used in regional climate studies conducted in the Mediterranean Sea (Ben Rais Lasram et al., 2010; Albouy et al., 2012, 2013). The projected SST and SSS climatological anomalies were added to current SST and SSS values to infer future SST and SSS values over the continental shelf of the Mediterranean Sea.


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Twenty commercially exploited, benthic and demersal continental shelf species were selected for this study (Table 1). This included fish, cephalopods and crustacean species, which collectively represent the majority of species caught by bottom trawls in the Gulf of Gabes. Species presence data were obtained from the Ocean Biogeographic Information System (OBIS; http:// www.iobis.org/) and the Global Biodiversity Information Facility (GBIF; http://data.gbif.org) (both accessed on 5 April 2013; see Appendices S1 & S2 in Supporting Information). Current and projected environmental data

T. Hattab et al. Model construction We modelled species climatic envelopes using an ensemble forecasting approach which accounts for the uncertainty associated with the outcomes of different BEMs (Thuiller et al., 2009). BEM outputs are sensitive to the fit of the model to the occurrence data. Consequently, it is preferable to fit multiple models to the data and combine them afterwards. To achieve this, we ran eight models individually: a generalized linear model, a generalized additive model, a general boosting method, a classification tree analysis, an artificial neural network, a flexible discriminant analysis, a multivariate adaptive regression splines analysis and a random forest analysis. Analyses were implemented using the BIOMOD package (Thuiller et al., 2009) in the R statistical programming environment (R Development Core Team, 2014). Models integrated in the BIOMOD platform require both presence and absence data; however, our datasets were obtained from online occurrence databases and, as such, do not include absences. Thus, we generated pseudo-absences in order to better characterize the environmental conditions experienced by a species within its current range (Hengl et al., 2009; Hattab et al., 2013a). How these pseudo-absences are constructed is a particularly important consideration because the selected method can influence the model quality (Engler et al., 2004; Hattab et al., 2013a). We used an environmentally and geographically weighted method to select pseudo-absence data points such that pseudoabsences were located in regions of low environmental suitability and far from positions of occurrence (see Hattab et al., 2013a, for more details). This weighting is based on both the environmental suitability of an area, estimated using the presence-only ecological niche factor analysis (ENFA; Hirzel et al., 2002) model and a buffer map that describes the distance of pseudo-absences from observations (see Hengl et al., 2009, for further details). The number of simulated pseudo-absences was equal to the number of presences in accordance with the statistical theory of model-based designs (d-designs; Hengl et al., 2009). According to this theory, the optimal design for minimizing prediction variance is when an equal number of observations are at opposite value extremes and there is a higher spreading in the feature space (Hengl et al., 2009). Once the pseudo-absences were simulated, they were combined with the occurrence data to build the BEMs (see Fig. 2 for more details about the modelling framework). For each species, the eight models were each calibrated using a random sample of the initial data (80%). The remaining 20% was then used to evaluate the model using the true skill statistic (TSS) criterion (three-fold cross-validation) to ensure that circularity was avoided such that the same data were not used to both construct and evaluate the model. As recommended by Allouche et al. (2006) we evaluated the models using the TSS criterion. TSS scores were interpreted using the accuracy classification scheme. The contribution of each statistical model to the ensemble was based on the weighted average consensus (WAC) method to account for model-based uncertainty (Thuiller et al., 2009). Using the SST and SSS projections, we predicted where the potential climatic niches (as 4

inferred by the WAC method) would be within the Mediterranean Basin for each species by the mid and late 21st century. A threshold that maximized the TSS score of the eight models was used to generate current and projected binary (presence/ absence) outputs (Thuiller et al., 2009). Habitat modelling Local species occurrence data Local species occurrence data (Appendix S2) were collected from the Tunisian bottom trawl survey database gathered by the National Institute of Marine Sciences and Technologies (INSTM, Tunisia). Central point georeferenced position data were extracted for each trawl haul (a total of 360 between 1998 and 2005) and information on their associated catches was obtained. Local habitat variables and habitat change scenarios Seafloor topography and seafloor type were selected to characterize the study area and develop the habitat models. Previous studies conducted at similar spatial scales showed that these characteristics have a strong influence on species distributions in coastal environments (e.g. Monk et al., 2011). These characteristics can be captured by five habitat variables: seafloor type, depth, slope and aspect (describing two derived variables: the eastness and northness of the slope). Habitat data were obtained from the INSTM database (Appendix S3). Maps for each of the five variables were prepared using a 90-m grid resolution (Appendix S7). Based on a seafloor type map (see Appendix S7) and a seagrass coverage map (produced by the INSTM using a side scan sonar survey), habitat change scenarios were simulated by replacing P. oceanica meadows occurring in each cell with circalittoral bioclastic muddy sand (CBMS). This mimics the observed declines in seagrass in the Gulf of Gabes (Ben Mustapha et al., 1999). By increasing the percentage seagrass loss by 10% increments from a baseline scenario, a total of nine scenarios were developed (from 10 to 90% loss; Appendix S8). In the low-loss scenarios, cells with low seagrass coverage were selected (i.e. cells where the P. oceanica meadows were already considered to be degraded; Ben Mustapha et al., 1999). As the percentage of simulated loss increased, cells with a high seagrass density began to be replaced with CBMS. For example, cells with seagrass coverage of 60 and 90% are replaced by CBMS in the 60 and 80% loss scenarios, respectively. At each stage, cell selection is made at random from the relevant cell grouping, as determined by their seagrass coverage. Model construction The habitat models were built using the same methodology used for developing the BEMs, i.e. an ensemble forecasting approach using BIOMOD, an environmentally and geographically weighted method to simulate pseudo-absences, and a crossvalidation procedure and binary transformation using the TSS criterion. The relative importance of each habitat variable was Global Ecology and Biogeography, © 2014 John Wiley & Sons Ltd

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Figure 2 Computational framework and data processing steps. BIOMOD is a computer platform for ensemble forecasting of species distributions. NEMOMED8 is a Mediterranean configuration of the NEMO ocean circulation climate model. Ecological niche factor analysis (ENFA) is a type of factor analysis that uses observed presences of a species to estimate which are the most favourable areas in the feature space.

For each species, data from grid cells (c. 9.2 km) within the study area (Gulf of Gabes) were extracted from both the current and projected potential climatic niche maps. The resulting presence/

absence maps constitute the first hierarchical faunal filter (i.e. large-scale climatic filter; Fig. 1). Once this filter is applied, a species can potentially occur anywhere within this predicted range. However, a species may be absent in some areas due to unsuitable habitats (Fig. 1). Therefore, these predicted ranges were refined using a second filter based on the habitat models (Fig. 1). To achieve this, the potential climatic niche maps were resampled to match the finer spatial resolution of the habitat models (i.e. a 90-m grid). A species was only considered as present if, for any given cell, both the first filter (climate; BEM) and the second filter (seafloor type and topography; habitat model) predicted its presence.


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estimated using a randomization procedure (see Thuiller et al., 2009). Using the 10 different habitat scenarios (a baseline scenario and nine habitat change scenarios), we also predicted the potential habitat locations for each of the 20 species studied (Fig. 2). Combining the models: a two-stage hierarchical filtering approach

T. Hattab et al. Gulf of Gabes scale

Mediterranean continental shelf scale Mustelus mustelus Merluccius merluccius Sparus aurata Mullus surmuletus Loligo vulgaris Pagrus caeruleostictus Diplodus annularis Gobius niger Sepia officinalis Peanaeus kerathurus Lithognathus mormyrus Pagellus erythrinus Mullus barbatus Eledone moschata Octopus vulgaris Squilla mantis Solea aegyptiaca Balistes carolinensis Metapenaeus monoceros Serranus hepatus

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R E S U LT S Future climate conditions According to NEMOMED8 model projections (using the IPCC A2 scenario), mean SST and mean SSS are expected to rise steadily during the 21st century across the entire Mediterranean Sea (Appendices S5 & S6). By the middle of the century (2040– 59), SST is projected to rise by 1.11 °C while SSS will increase by 0.25 practical salinity units (PSU). By the end of the century (2080–99), SST is predicted to be 2.44 °C higher while SSS will increase by 0.48 PSU. In the Gulf of Gabes, mid-century increases were estimated to be 1.05 °C for SST and 0.44 PSU for SSS with increases of 2.42 °C for SST and 0.7 PSU for SSS by the end of the century.

Figure 3 Relative gains (positive values) and losses (negative values) in the geographical range sizes (as measured by the difference in the number of cells) for all species studied at the Mediterranean continental shelf scale (right column) and the Gulf of Gabes scale (left column). Values were determined by calculating the differences between ranges predicted for current climate conditions (1982–2009, baseline scenario) and ranges predicted by the Intergovernmental Panel on Climate Change A2 future climate scenario evaluated for the end of the century (2080–99). Predictions were made using the NEMOMED8 ocean circulation climate model.

the predictive accuracy of the ensemble habitat models was lower on average (mean TSS = 0.79, n = 20) than the predictive accuracy calculated for the best single models (mean TSS = 0.88). We therefore selected the best single model for each species (all model performance statistics are provided in Appendix S2). Among the habitat variables, depth was the most important determinant of the current distribution of the species studied (mean correlation score 0.53). The remaining variables (seafloor type, eastness, northness and slope) had weaker explanatory powers with mean correlation scores ranging from 0.09 to 0.15. Current and projected climatic niches

The predictive accuracy of the ensemble BEMs was classified as ‘excellent’ with a mean TSS criterion score of 0.96. The most accurate model was the random forest (mean TSS = 0.98) and the least accurate was flexible discriminant analysis (mean TSS = 0.95). In terms of predictive accuracy, no significant differences were observed between the eight BEMs (Appendix S9; pairwise Wilcoxon rank sum tests, P < 0.001). We therefore used the WAC method to predict and project the potential climatic niches. However, large differences were observed in the predictive accuracy within habitat models (Appendix S9). In addition,

At the Mediterranean Basin scale, mid-century BEM projections suggest that geographical ranges would increase for 14 species and decrease for a further six species. By the end of the century, projections suggest that 12 species would have increased their geographical ranges, while eight species would experience a range decrease (Fig. 3). Although the proportion of ‘winners’ and ‘losers’ is similar for both periods, the extent of projected changes is much more pronounced at the end of the 21st century. For instance, Mustelus mustelus is projected to lose only 13% of its geographical range by the middle of the century, but this figure increases significantly to 40% at the end of the century (Fig. 4, left column). Similarly, Sparus aurata is initially projected to lose 17% of its range by the middle of the century, but this projection rises sharply to 57% by the end of the century (Fig. 4, right column).

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Figure 4 The predicted geographical ranges of gilthead seabream (Sparus aurata; right column) and smooth-hound (Mustelus mustelus; left column) off and over the Mediterranean continental shelf as determined by bioclimatic envelope model for the (a) current climate conditions (1982–2009, baseline scenario), (b) mid-century climate scenario (2040–59), and (c) end-century climate scenario (2080–99).

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In the Gulf of Gabes, eight species are projected to lose climatically suitable areas by the middle of the century and this rises to 13 species by the end of the century (Fig. 3). Later losses were more significant, with projections indicating that no suitable areas may remain within the gulf for six species (i.e. Diplodus annularis, Loligo vulgaris, Mullus surmuletus, S. aurata, Merluccius merluccius and Mustelus mustelus; Fig. 3). However, these species are projected to persist in the northern Mediterranean continental shelf region. Thermophilic species (i.e. those that thrive on high temperatures), such as Balistes capriscus, Solea aegyptiaca and Serranus hepatus, are projected to extend their geographical ranges at the Mediterranean Basin scale and the currently suitable climate conditions in the Gulf of Gabes are projected to be maintained (Fig. 3). Conversely, a northward migration is predicted for Pagrus caeruleostictus as areas of suitable climate are lost in the coastal regions of the southern Mediterranean Sea.

Results of the two-stage hierarchical filtering approach for the Gulf of Gabes Based on current conditions, a combination of bioclimatic and habitat models revealed smaller geographical ranges than when only the BEMs were used (e.g. Fig. 5). Accordingly, species prevalence declined on average by 56% in the Gulf of Gabes. This pattern was also observed under the climate change scenario (Table 1). For instance, by the end of the century, the geographical range of the sand steenbras (Lithognathus mormyrus) as projected by the hierarchical filtering approach is 70% smaller than the range predicted using the BEM only (Table 1). Consequently, projected losses in species richness with future climate conditions were lower when using the twostage hierarchical filtering approach as opposed to the BEM alone (Fig. 6). For both future scenarios, the projected losses in Global Ecology and Biogeography, © 2014 John Wiley & Sons Ltd

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species richness showed a marked east–west gradient (Fig. 6), which is related to bathymetry in the Gulf of Gabes. Losses were greatest at depths between 20 and 50 m. Under the habitat change scenarios, the gradual replacement of seagrass meadows with CBMS led to increasing changes in the geographical ranges of species (Fig. 7). Species associated with Posidonia oceanica (e.g. S. hepatus and Sparus aurata) progressively lost potential habitat, while the geographical ranges of species associated with CBMS (e.g. Merluccius merluccius and Gobius niger) gradually expanded within the Gulf of Gabes. Aggregating the species-level projections showed a weak decrease in species richness along the gradient of regression of seagrass (Appendix S10). Combining the habitat loss and climate change scenarios indicated that the magnitude of range shifts due to climate change was larger than from habitat loss alone (Fig. 7). When considering climate change alone, 84% of the continental shelf area in the Gulf of Gabes was projected to lose at least one species by the end of the 21st century. If a 90% reduction in P. oceanica meadows was also taken into consideration, this value increased by only 5% to 89% (Appendix S11).

DISCUSSION Projections given by BEMs in this study indicate that most of the Mediterranean coastal species considered here will experience a range shift by the end of the 21st century. Specifically, we found that these range shifts will be mainly related to the loss and gain of suitable climatic conditions. These changes may result in high local extinction rates in the warmest Mediterranean continental shelf regions, including the Gulf of Gabes. These results agree with previous studies that also used BEMs to forecast the potential impacts of climate change on Mediterranean coastal fish assemblages (Ben Rais Lasram et al., 2010; Albouy et al., 2012, 7

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Figure 5 The predicted geographical ranges of speckled shrimp (Metapenaeus monoceros; right column) and surmullet (Mullus surmuletus; left column) in the Gulf of Gabes using the bioclimatic envelope model only, the habitat model only and both models within a two-stage hierarchical filtering approach.

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Figure 6 Differences between species richness values predicted under current climate conditions (1982–2009, baseline scenario) and values predicted under a mid-century climate scenario (2040–59, upper panel) and an end-century climate scenario (2080–99; lower panel). (a), (c) Predictions based on bioclimatic envelope models and (b), (d) predictions based on a two-stage hierarchical filtering approach mapped for the Gulf of Gabes.


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Baseline scenario Pagrus caeruleostictus Metapenaeus monoceros Serranus hepatus Sparus aurata Mustelus mustelus Peanaeus kerathurus Diplodus annularis Pagellus erythrinus Loligo vulgaris Mullus barbatus Balistes carolinensis Sepia officinalis Solea aegyptiaca Lithognathus mormyrus Squilla mantis Octopus vulgaris Mullus surmuletus Eledone moschata Merluccius merluccius

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−30%

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Gobius niger −80%

Figure 7 Relative gains (positive values) and losses (negative values) in geographical range sizes (as measured by the difference in the number of cells) for all studied species in the Gulf of Gabes under habitat change scenarios. Range predictions for each habitat loss scenario (simulated by gradually replacing Posidonia oceanica seagrass meadow with circalittoral bioclastic muddy sand, as represented by the horizontal axes) were made under current climate conditions (1982–2009, baseline scenario; right column), a mid-century climate scenario (2040–59; middle column) and an end-century climate scenario (2080–99; left column).

Habitat loss scenario Changes in species range (%)

-100%

-50%

0%

50%

100%

2013). However, none of these studies accounted for species– habitat relationships, which can be particularly significant at small spatial scales (Moore et al., 2010; Hattab et al., 2013a). In this study, we considered these relationships by combining habitat model predictions with those from the BEMs, using a two-stage hierarchical filtering approach. This combined approach predicted current species geographical ranges to be on average 56% smaller than those predicted using the BEMs alone (Table 1). These findings highlight that BEMs may overestimate the extent of species ranges if species–habitat relationships are not considered as well (Pearson & Dawson, 2003). For example, the species whose ranges were most overestimated (i.e. G. niger, L. mormyrus, Metapenaeus monoceros and Pagrus caeruleostictus; Table 1) are known to have a narrow bathymetric range and inhabit areas with a specific seafloor type. In conclusion, our results suggest that, at the Gulf of Gabes scale, using BEM forecasts alone may not be sufficiently realistic to inform marine resource managers or policy makers. Our results showed that the availability of suitable habitat plays an important role in shaping a species’ response to climate change, and as such, local-scale habitat conditions are an important consideration (Pearson & Dawson, 2003; Buisson et al., 2008). The outcomes of this study have important implications for marine biodiversity studies, particularly in the context of climate change for which our results highlight that a failure to consider habitat variables is likely to result in less accurate predictions of both species distributions and community assemblage composition. For instance, the future patterns of species richness which were inferred by geographically aggregating the results of species-level projections (the ‘predict first, assemble later’ strategy; Ferrier & Guisan, 2006) differed depending on which modelling approach was used (Fig. 6). When BEMs alone

were used, species loss tended to be overestimated for large parts of the Gulf of Gabes (Fig. 6). This is because the BEMs also tended to overestimate species geographical ranges for the current period. These findings have important implications, since species distribution modelling is increasingly being used to support decision making for conservation purposes (Guisan et al., 2013). Habitat loss is one of the major global threats facing coastal biodiversity (Crain et al., 2009). This study showed that our habitat loss scenarios, in which Posidonia oceanica meadows were gradually reduced, had a weak effect on species richness (Appendix S10). However, this does not mean that these projected losses would have a weak impact on the community assemblages in the Gulf of Gabes. Indeed, we found that the conversion of seagrass meadows to areas of muddy sand stimulated a high level of species replacement. For instance, under the most severe habitat loss scenario (90% reduction), a number of species were projected to lose significant percentages of their geographical range within the Gulf of Gabes, e.g. the bluespotted seabream (Pagrus caeruleostictus; 43%), speckled shrimp (M. monoceros; 41%) and brown comber (S. hepatus: 19%). These species were projected to be subsequently replaced by the black goby (G. niger), European hake (Merluccius merluccius) and musky octopus (Eledone moschata). These species are commonly found in silted habitats and their ranges were expected to expand by 41, 32 and 25%, respectively, as the habitat changed. These results highlight that although habitat loss or modification may only effect weak changes in species richness, species composition may undergo important changes (see also Larsen & Ormerod, 2014). Finally, when the climate change and habitat loss scenarios were combined, our results showed that potential range shifts due to climate change


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T. Hattab et al. were larger. This confirms that climate, or more particularly temperature, is a major determinant of the distribution of marine species (Sunday et al., 2012), even at localized scales such as the Gulf of Gabes. However, it is important to note that the 20 species examined in this study are not strictly associated with Posidonia oceanica seagrass meadows and can colonize other habitats. This habitat plasticity may explain the weak impact of habitat loss on their distribution. Indeed, we can expect that climate change and habitat loss have impacts of similar magnitude on the distribution of habitat specialists, especially those with poor dispersal capacities. For these species, the combined effect of climate change and habitat loss may be disastrous (Travis, 2003). Species distribution models can be important for making robust conservation management decisions (Guisan et al., 2013). We have identified two major management considerations for which our hierarchical filtering approach can help environmental managers in the Gulf of Gabes: the implementation of marine protected areas (MPA) and assessing the potential range shifts of exotic species as the climate changes. Like most southern Mediterranean coastal regions (Mouillot et al., 2011), there is no MPA in the Gulf of Gabes. Site selection is critical to the design of robust MPAs, as inappropriate habitat protection can lead to species being more vulnerable to extinction (Guisan et al., 2013). In the context of climate change, the combination of projections from bioclimatic and habitat models could be used to achieve adequate habitat protection for threatened species by being used to set priorities in establishing conservation areas (Araújo et al., 2011). Like many other Mediterranean coastal regions, the Gulf of Gabes is also under pressure from invasive species (Galil, 2007). For instance, the exotic speckled shrimp (Metapenaeus monoceros; Galil, 2007; Ben Hadj Hamida-Ben Abdallah et al., 2009), a species with low commercial value, has rapidly colonized the Gulf of Gabes. Concurrently, catches of an important target species, Melicertus kerathurus (a native shrimp), have decreased (Ben Hadj Hamida-Ben Abdallah et al., 2009), raising concerns for both the species and its fishery in the Gulf. Applying the hierarchical filtering approach in this context could assist in identifying which areas will become suitable for known invasive species under climate change and habitat modification scenarios. In the present study, we made the assumption that ecosystems are a simple collection of independent species. This assumption is one of the major research gaps in the field of species distribution modelling (Wisz et al., 2013). However, the hierarchical approach we proposed can be used to account for the influence of biotic interactions on species distribution. For instance, the influence of prey availability on the distribution of predators can be incorporated as a biotic filter for species inhabiting similar habitats. Recently, Gravel et al. (2013) proposed a framework in which body size was used to infer trophic interactions between marine species that had not previously co-occurred. This new framework could be used to determine the presence or absence of a given species according to the presence or absence of other species (Gravel et al., 2013). The inclusion of this biotic

filter should improve how the realized niches of species are estimated, thus refining the species distribution and assemblage forecasts obtained from the climatic and habitat filters (for terrestrial ecosystems see Boulangeat et al., 2012; Pellissier et al., 2013). We also assumed that under future climate scenarios there were no dispersal limitations towards newly suitable areas for any of the species we studied. This assumption is probably valid at small spatial scales (e.g. the Gulf of Gabes) but not at the Mediterranean Basin scale. Therefore, applying a hierarchical approach to the entire Mediterranean continental shelf would require a dispersal filter to be added before considering any climate filters. For example, a filter relating to the capacity of a species to colonize suitable areas could be built using data which quantified its dispersal potential during both adult and larval stages (Andrello et al., 2013). Overall, accounting for these scaledependent processes in a hierarchical framework would lead to a better understanding of both fundamental and realized niches (Pearson & Dawson, 2003). Finally, we propose that our hierarchical filtering approach be extended to terrestrial ecosystems. Recent terrestrial studies have already attempted to account for both climate and habitat variables when modelling current and future species distributions (e.g. Barbet-Massin et al., 2012). However, the spatial resolution of the habitat variables was probably too coarse over large areas to precisely account for changes in species composition within a specific habitat (Barbet-Massin et al., 2012). A major advantage of our approach is that fine-scale habitat variables that are not available over the full range of a species can be taken into account. This allows for the combined effects of climate change and habitat loss on species distribution to be quantified while limiting the model-based uncertainty associated with the recommendations, thus ensuring their suitability for environmental managers and policy makers.

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ACKNOWLEDGEMENTS This work was partly funded by the Institut de Recherche pour le Développement (IRD, France), the Total Foundation (Charmmed Project), and the EMIBIOS project through a grant from the French Foundation for Research on Biodiversity (FRB), ‘Modeling and Scenarios of Biodiversity’ (grant number APP-SCEN-2010-II). C.A. is funded by a MELS-FQRNT postdoctoral fellowship. We would like to thank H. Demarcq (IRDSète) for drawing the monthly climatologies maps and F. Sevault (Météo-France/CNRM) and R. Aznar (Puertos de l’Estado) for providing climate model data. We are very grateful to Jane Alpine for English editing.

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S U P P O RT I N G I N F O R M AT I O N Additional supporting information may be found in the online version of this article at the publisher’s web-site. Appendix S1 Additional data information. Appendix S2 Table of species names, number of occurrences used, true skill statistic values for the bioclimatic envelope model, habitat model and best model selected for habitat modelling. Appendix S3 Climate and habitat predictors used in the bioclimatic envelope model and the habitat model. Appendix S4 Global maps of current climatic variables. Appendix S5 Climatological anomalies of the Mediterranean Sea by 2040–59. Appendix S6 Climatological anomalies of the Mediterranean Sea by 2080–99. Appendix S7 Maps of the six habitat variables used in the habitat model. Appendix S8 Maps of habitat change scenarios. Appendix S9 True skill statistic values for the 20 species modelled with bioclimatic envelope models and habitat models. Appendix S10 Differences in species richness between the baseline scenario (i.e. 0% of reduction in seagrass meadows) and two habitat loss scenarios (i.e. 50%, and 90% of reduction in seagrass meadows) for current climate conditions (1982–2009). Appendix S11 Net differences in fish species richness between the baseline scenario (1982–2009) and the end-of-the-century scenario (2080–99) by simulating a loss of 0, 50 and 90% of Posidonia oceanica seagrass.


Threatened coastal region under global change BIOSKETCH Tarek Hattab is a PhD student at the University of Montpellier 2 and the National Agronomic Institute of Tunisia. His research is focused on marine spatial ecology with a special emphasis on the application of macroecological and spatial ecology concepts to marine ecosystems. Editor: Jonathan Belmaker


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