Adapting global conservation strategies to climate ...

Biological Conservation 144 (2011) 2068–2080

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Adapting global conservation strategies to climate change at the European scale: The otter as a flagship species Carmen Cianfrani a,⇑, Gwenaëlle Le Lay a,b, Luigi Maiorano a, Héctor F. Satizábal c, Anna Loy d, Antoine Guisan a a

Department of Ecology and Evolution, University of Lausanne, CH-1015 Lausanne, Switzerland Landscape Dynamics Research Unit, Swiss Federal Research Institute WSL, Zürcherstrasse 111, CH-8903 Birmensdorf, Switzerland Reconfigurable & Embedded Digital Systems Institute, School of Business and Engineering Vaud (HEIG-VD), CH-1401 Yverdon-les-Bains, Switzerland d Department of Science and Technology for the Environment, University of Molise, I-86090 Pesche, Italy b c

a r t i c l e

i n f o

Article history: Received 19 July 2010 Received in revised form 25 March 2011 Accepted 28 March 2011 Available online 31 May 2011 Keywords: Long-term conservation plan Climate change Freshwater ecosystem Ensemble forecasting Species distribution models

a b s t r a c t Climate change has created the need for new strategies in conservation planning that account for the dynamics of factors threatening endangered species. Here we assessed climate change threat to the European otter, a flagship species for freshwater ecosystems, considering how current conservation areas will perform in preserving the species in a climatically changed future. We used an ensemble forecasting approach considering six modelling techniques applied to eleven subsets of otter occurrences across Europe. We performed a pseudo-independent and an internal evaluation of predictions. Future projections of species distribution were made considering the A2 and B2 scenarios for 2080 across three climate models: CCCMA-CGCM2, CSIRO-MK2 and HCCPR HADCM3. The current and the predicted otter distributions were used to identify priority areas for the conservation of the species, and overlapped to existing network of protected areas. Our projections show that climate change may profoundly reshuffle the otter’s potential distribution in Europe, with important differences between the two scenarios we considered. Overall, the priority areas for conservation of the otter in Europe appear to be unevenly covered by the existing network of protected areas, with the current conservation efforts being insufficient in most cases. For a better conservation, the existing protected areas should be integrated within a more general conservation and management strategy incorporating climate change projections. Due to the important role that the otter plays for freshwater habitats, our study further highlights the potential sensitivity of freshwater habitats in Europe to climate change. Ó 2011 Elsevier Ltd. All rights reserved.

1. Introduction Global change has created the need for new conservation strategies accounting for the dynamic factors threatening endangered species (Thomas et al., 2004; Sala et al., 2000). There is already compelling evidence that many species will move out of current conservation areas following climate changes (Hannah et al., 2007; Coetzee et al., 2009; Ohlemüller et al., 2006), thus questioning the appropriateness of current static reserve networks and strongly calling for proactive conservation strategies (Mawdsley et al., 2009; Hansen et al., 2010). In this context, species distribution models (SDM; Guisan and Zimmermann, 2000) are useful tools for predicting climate change impacts on species distributions (Guisan and Thuiller, 2005) and supporting conservation plans (Araujo et al., 2004; Thuiller et al., ⇑ Corresponding author. Tel.: +41 216924279 E-mail address: [email protected] (C. Cianfrani). 0006-3207/$ - see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.biocon.2011.03.027

2008; Cianfrani et al., 2010). However, substantial challenges still remain in the use and application of these models for conservation planning. Until recently, the first limitation came from the difficulty of selecting between different modelling techniques as well as different climate change scenarios. One possible solution is to explicitly account for prediction uncertainties by combining the different models and scenarios within an ensemble forecasting framework (Thuiller, 2004; Araujo and New, 2007; Thuiller et al., 2009). This approach relies on the idea that the different predictions represent plausible scenarios of future species distribution (Marmion et al., 2009) while, at the same time, accounting for possible uncertainties in the predictions and decreasing the risk of proposing conservation strategies on the basis of a single and potentially erroneous model (Araujo and New, 2007; Le Lay et al., 2010). Many different conservation measures already exist throughout Europe to protect species and habitats, but national protected areas (PAs), often integrated by multinational networks like Natura2000

C. Cianfrani et al. / Biological Conservation 144 (2011) 2068–2080

(e.g., Maiorano et al., 2007), still represent the main conservation measure. Ideally, all these national networks should be considered and managed as a holistic network through which species can move following climate changes as well as other types of habitat change. In such a scenario, it is particularly important to assess: (1) the structural connectivity between protected areas under the different climate change scenarios, (2) the potential distribution of species under current climatic conditions, with particular reference to protected areas and, (3) the potential distribution of the species after climate change, to evaluate the potential level of protection in the future. If protected areas match both present and future species distributions, these areas could then be considered key areas for the conservation of the target species. The approach of combining species distribution changes and conservation areas is crucial for conservation planning in the perspective of shifting species distributions (Araujo et al., 2004). Here, we propose a framework for such evaluation and illustrate it with the European otter (Lutra lutra), a flagship species for freshwater ecosystems (Saavedra and Sargatal, 1998). Otters are top predators whose home ranges extend over tens of kilometres of river stretches, and populations depend on the conditions in catchments and riparian environment. Otters suffered a severe decline in the 20th Century in most European countries (Robitaille and Laurence, 2002) due to the reduction of food supply, pollutants, persecution by humans, and destruction of riparian vegetation (Kruuk, 2006). As habitat conditions seem to improve again in some parts of Europe going towards a more natural landscape (e.g., Switzerland and the Alpine range in general; e.g., Falcucci et al., 2007), it is of particular importance to assess, where when and how suitable connected habitats for the otter will be located in Europe. In a climatically different future, freshwater ecosystems may experience strong perturbations due to changes in precipitation. Precipitation is predicted to either increase (e.g., in the Atlantic

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basin) or decrease (e.g., in the Mediterranean basin), and may also fluctuate more through time or occur with more frequent extreme events, increasing flood risks in temperate-cold regions (e.g., in central Europe) (Hall et al., 2008). Despite these general climate predictions, impact studies on freshwater ecosystems remain rare (e.g., Mulholland et al., 1997; Wrona et al., 2006; Prowse et al., 2009). The goal of this study was to assess climate change threats to the European otter using an ensemble forecasting approach (sensu Araujo and New, 2007) and taking into account the network of existing protected areas. In particular, by accounting for predicted shifts in the species’ distribution caused by climate change, the approach identifies priority areas for long-term conservation and assesses the efficacy of the current protected areas network for conserving the otter and, indirectly, freshwater ecosystems.

2. Materials and methods 2.1. Species data We considered continental Europe, excluding the British Islands, spatially disconnected from the rest of Europe, and Scandinavia, where the geographic and ecological contexts require conservation strategies different from the rest of Europe. We also excluded Balearic, Sicily, Corsica, Sardinia, Crete and other smaller Mediterranean islands relatively far from the mainland, where otter have never existed. Across our study area, we obtained data on species occurrence from the Information System for Otter Survey (ISOS) database (Reuther and Krekemeyer, 2004) and from national field surveys on otter presence performed in Italy (Loy et al., 2009) and Spain (López-Martin & Jiménez, 2008). All records of presence were reported to a common 100 km2 square UTM grid cell, as

Fig. 1. (a) Otter occurrences referred to the UTM 10 10 km grid used in the modelling processes; (b) Otter occurrences referred to the UTM 50 50 km grid used to identify the distribution range (data source EIONET); (c) Bioregions; (d) Protected Areas and the Natura2000 network.

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recommended by the European standard for otter surveys (Reuther et al., 2000) for a total of 12,600 records of presence (Fig. 1 a). This large presence dataset allowed us to split data into eleven random subsets, each large enough to build reliable models and to provide an excellent way for assessing model stability by comparing the results obtained using different subsets. Moreover, by splitting the database we reduced data clustering and limited spatial autocorrelation in model residuals (Osborne and Suarez-Seoane, 2002). We assigned occurrence points randomly to one of the eleven subsets by choosing points separated by at least 30 km. We generated 3300 random pseudo-absences with points being at least 30 km from any other presence and/or pseudo-absence. We used these same pseudo-absences in combination with the 11 subsets of presences, giving to each absence a weight equal to 0.35, corresponding to the ratio (number of presences)/(number of pseudoabsences) (Gibson et al., 2007).

2.2. Environmental data We considered 13 environmental variables related to the otter’s ecological requirements (Kruuk, 2006) or to potential disturbances (Robitaille and Laurence, 2002; Barbosa, 2003; Reuther and Krekemeyer, 2004; Table 1). As water availability is a crucial parameter (Beja, 1992), we considered seven water related variables: annual precipitation, mean precipitation of the wettest quarter, mean precipitation of the driest quarter, percentage of small rivers, percentage of medium rivers, percentage of large rivers, and percentage of lakes. As otters need vegetation cover on river edges as potential resting sites, we also considered percentage of forest within each cell (to represent natural vegetation cover) calculated from the Corine Land Cover 2000. No reliable data on fish occurrence/density is available at the European scale, and we therefore considered elevation as a crude surrogate for fish assemblages and abundance as suggested by Remonti et al. (2009). Finally, we considered distance to cities with more than 100,000 inhabitants, distance to roads, percentage of industrial areas, and human population density as indices of the main human disturbances (Prenda, 1996). To predict annual precipitation, mean precipitation of the wettest quarter, and mean precipitation of driest quarter for the year

2080, we considered the A2 and B2 scenarios produced by the following climate models: CCCMA-CGCM2, CSIRO-MK2 and HCCPR HADCM3. The A2 scenario predicts a temperature increase of 2.0–5.4 °C, while the B2 predicts an increase of 1.4–3.8 °C; in any case the potential implications for water availability would be very important, with huge impacts on freshwater ecosystems (Hall et al., 2008; Kundzewicz et al., 2008). All variables were re-sampled using ArcGIS 9.3 (ESRI, Redlands, USA) to a spatial resolution of 100 km2, having the species distribution map as a reference (Table 1). 2.3. Species distribution models 2.3.1. Niche consistency throughout Europe The current distribution range of the European otter is divided into three disconnected portions (Fig. 1a and b). Following Osborne and Suarez-Seoane (2002), we tested for niche similarity between the three sub-distributions before modelling otter distribution over all of Europe. We performed a Principal Component Analysis (PCA) on the environmental predictors using the ‘‘ade4’’ library (Chessel et al., 2004) in R (The R Project for Statistical Computing). Following Broennimann et al. (2007), we compared the position of the three clouds of occurrences (one for each sub-distribution) in the PCA space. The magnitude and the statistical significance of the distance between the three clouds were assessed using the between-class inertia percentage (Doledec et al., 2000) with 99 Monte–Carlo randomisations (Romesburg, 1985). 2.3.2. Ensemble forecasting and variables’ importance We predicted current and future species distribution using an ensemble forecasting approach (Thuiller et al., 2009; Fig. 2). For the future, we projected the models to the six future climate scenarios (CCCMA-CGCM2_A2, CSIRO-MK2_A2, HCCPR HADCM3_A2, CCCMA-CGCM2_B2, CSIRO-MK2_B2 and HCCPR HADCM3_B2, downloaded from http://www.cgiar-csi.org). We used six modelling techniques: Artificial Neural Network (ANN), Generalised Additive Models (GAM), Generalised Linear Models (GLM), Flexible Discriminant Analysis (FDA) and Multivariate Adaptive Regression Splines (MARS), all implemented in the BIOMOD R package (version

Table 1 Environmental predictors used in the habitat suitability models. Biological interpretation

Predictors name

Description

Data source

Resolution/scale of original data

Up-scale at 10 km method

Water

p_driest p_wettest an_prec smal_riv

Mean precipitation driest quarter Mean precipitation wettest quarter Annual precipitation Rivers 1,2 Strahler order*

1 km 1 km 1 km 1:250.000

Mean Mean Mean Percentage

med_riv

Rivers 3–5 Strahler order*

1:250.000

Percentage

big_riv

Rivers 6–9 Strahler order*

1:250.000

Percentage

perc_lake

Lake

1:250.000

Percentage

dist_road dist_town

1:250.000 1:250.000

Mean Mean

perc_indus pop_dens

Distance from main roads Distance from cities comprising more than 100.000 inhabitants Industrial areas Human population density

Worldclim Worldclim Worldclim CCM rivers and catchment database CCM rivers and catchment database CCM rivers and catchment database CCM rivers and catchment database Edit geoplatform ESRI dataset database

100 m 1 km

Percentage Mean

perc_forest elev

Forest Altitude

Corine Land Cover 2000 Gridded population of the world (2000) Corine Land Cover 2000 Shuttle radar topography mission

100 m 100 m

Percentage Mean

Disturbance

Resting sites Food supply

*

Stream segments classification method. A stream with no tributaries (headwater stream) is considered a first order stream. A segment downstream of the confluence of two first order streams is a second order stream. Thus, a nth order stream is always located downstream of the confluence of two (n 1)th order streams.


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Fig. 2. Ensemble forecasting used to produce the otter’s distribution models.

2008.06.01; Thuiller et al., 2009), and the Maximum Entropy method (MAXENT; Phillips et al., 2006). All techniques were applied to each of the eleven subsets of presence data (and to the pseudo-absences for models developed in BIOMOD), yielding a total of 462 SMDs (66 SMDs under the current climate, 66 for CCCMACGCM2_A2, 66 for CSIRO-MK2_A2, 66 for HCCPR HADCM3_A2, 66 for CCCMA-CGCM2_B2, 66 for CSIRO-MK2_B2 and 66 for HCCPR HADCM3_B2). Each of the 66 SDMs developed for the current climate was evaluated measuring AUC (Fielding and Bell 1997). In particular, every model developed for a subset of presences was evaluated considering the remaining ten subsets, thus obtaining ten pseudo-independent evaluation measures for each model.

We used the results of the 66 current SDMs to analyse by randomisation the importance of the environmental variables in defining suitable areas for the otter at the European scale. The randomisation procedure was repeated 100 times for each variable and for each method, using the function ‘‘VarImport’’ in BIOMOD (Thuiller et al., 2009). 2.3.3. Final predictions We combined the predictions coming from each set of 66 individual models into a final SDM, both for the current and the future potential otter distributions (Fig. 2). In particular, we first combined the 11 SDMs fitted with each modelling technique obtaining

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Fig. 3. Environmental space with the positions of the otter’s occurrences in the three European sub-distributions along the two first axes of the PCA. The map on the top left shows the otter’s occurrence data referred to the UTM 10 10 km grid. Colours refer to the three sub-distributions; the solid line represents 100% kernel density estimate, the dashed line represents 75% kernel density estimate. The enclosed correlation graph indicates the importance of each environmental variable on the two significant axes of the PCA, for the variable description see Table 1.

six average predictions (ANNtot, GAMtot, GLMtot, FDAtot, MARStot, and MAXENTtot). Then we produced six binary maps (equal prevalence criteria; Freeman and Moisen 2008), and combined the six binary maps into a final SDM with three classes of habitat suitability (Fig. 2): (i) highly suitable habitat, where all models predict species’ presence, (ii) suitable habitat, where at least one model predicts presence, and (iii) unsuitable habitat, where all models predict absence. The same procedure was applied to the six future scenarios, and at the end we combined the predictions obtained for the A2 scenario and those for the B2 scenario, obtaining three future predictions (one for each climate model).

2.4. Species range shift and conservation under climate change To assess the potential shift in otter’s distribution under climate change, we overlaid the SDM predicting the current potential distribution with SDMs predicting the future potential distribution under the A2 scenario, and under the B2 scenario. We then calculated the proportions of pixels of the three habitat suitability classes (highly suitable, suitable and unsuitable) that remained in the same suitability class or in a different one between the three predictions, considering the whole study area, the current distribution range (obtained from the EIONET database; Fig. 1b), European

Fig. 4. Importance of each environmental variable in each modelling method used in the ensemble forecasting. The x-axis represents the variables (see Table 1 for variable description). The y-axis represents the importance of variable measured as 1 minus the correlation score computed between the prediction made with all the variables and the predictions made by deleting one focal variable.


Bioregions (Fig. 1a and c) and each European country. We defined the European Bioregions by merging the WWF Ecoregions (Olson et al., 2001) and the EEA’s (http://www.eea.europa.eu) (Fig. 1c). We defined all areas that are suitable or highly suitable according to both present and future SDMs as urgent priority areas for the conservation of the otter in Europe. The areas predicted as highly suitable only in the future were defined as priority areas, representing important opportunities for the conservation of the otter in the future. We considered existing and proposed national and international protected areas (PAs) in the study region as available in the World Database on Protected Areas (WDPA, 2009). We considered also all Natura2000 special protection areas specifically designated for aquatic and/or semi-aquatic birds and mammals (EU Habitat Directive 92/43/EEC) (Fig. 1 d). To assess how well the otter is currently protected in Europe, the map of PAs was overlaid with the otter’s current distribution. We also overlapped the network of PAs with the urgent priority

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and priority areas to consider options for conservation and management in the future. 3. Results 3.1. Otter’s habitat requirements The first two PCA components (Fig. 3) clearly revealed an overlap of the three sub-distribution ranges in the environmental space (between-class inertia percentage ranging between 0.015 and 0.0045, P-value = 0.001), suggesting strong similarities between the niche characteristics of the otters in these three European sub-distributions, and further supporting the use of a single model to predict the species distribution across all of Europe. The mean amount of precipitation in the driest quarter of the year (Fig. 4) was consistently the most relevant predictor, followed by annual precipitation and precipitation of the wettest quarter. Human population density was the most important variable

Fig. 5. (a) Otter distribution map under the current environmental conditions. (b) and (c) Otter’s distribution map predicted for the future environmental conditions under climate change following the A2 and B2 scenario. (d) and (e) Discrepancies between the maps.

*

8.8 71.8

*

5.0 41.8 14.3 10.6 10.2 7.2 4.7 0.6 16.1 300.0 10.7 34.4 20.4 81.5

B2pres

4.8 74.9 47.0 6.8 4.9 35.0 10.4 26.3 20.5 9.6 85.4 26.0 3.1 5.8 39.6 14.6 25.5 33.3

15.9 1.7 1.1 9.9 41.5 4.0 27.4 20.5 20.7 0.4 8.0 3.5 15.1 1.0 0.9 10.9 37.3 3.7 26.1 20.4 17.3 1.6 8.9 2.3 16.7 7.0 0.7 10.6 39.6 6.2 30.6 16.3 26.0 0.0 7.4 12.4 5.0 14.5 18.8 8.0 5.0 13.8 6.0 2.6 2.8 17.3 5.5 8.5 12.4 28.4 25.2 3.3 26.9 0.2 2.3 12.0 43.5 43.2 11.9 28.6 8.0 18.0 48.7 11.6 30.5 14.1 8.5 9.7 41.9 67.9 18.0 34.6 36.0 25.0 40.1 36.2 37.9 37.1 42.0 34.1 27.3 4.6 43.0 43.3 37.8 28.6 47.6 39.1 36.0 42.2 44.5 34.9 28.1 5.4 45.4 39.6 41.1 34.9 32.0 35.0 51.8 37.0 41.1 38.7 48.4 3.2 38.4 60.7 2.1 3.9 12.5 7.3 29.8 8.2 4.1 1.7 4.9 2.1 6.5 9.2 *

The proportion of change is infinite because for the present the percentage is 0.

13.9 26.0 12.5 0.8 139.7 3.7 7.9 0.8 102.6 1.8 9.6 97.3 11.4 21.0 23.5 8.0 211.1 4.8 3.6 0.9 112.6 3.9 15.5 115.4 48.0 73.2 58.8 53.9 20.6 58.9 30.6 45.4 52.0 95.0 49.0 53.2

A2B2 A2pres A2pres A2 A2

47.0 70.4 51.5 50.0 26.7 54.1 29.3 44.6 54.6 93.1 45.8 58.1

Changes

A2pres B2

Future

A2 A2

B2

A2pres

B2pres

A2B2

Unsuitable areas (%)

Present Changes Future

Suitable areas (%)

Present

42.2 58.1 67.3 54.4 8.6 56.8 28.3 45.0 25.7 96.8 54.2 27.0 100.0 33.3 1.2 65.5 0.0 20.0 14.9 31.8 11.5 1.9 11.5 8.4

In general, the priority areas defined under both climate scenarios are similar (Table 3). In the western European sub-distribution, the percent of urgent priority areas which are protected is only of 12.6%, where otter is present, and 15.6% for the areas not containing the otter. In the South-Italian sub-distribution the situation is worst with only the 9.1–9.7% of protected urgent priority areas, where otter occurs, and 14.2–15.3% for the urgent priority areas, where it does not. In the case of the eastern sub-distribution, the percent of urgent priority areas which are protected is 11.3% for

Study area West Italy East Centre Mediterranean Alpine Continental Atlantic Boreal Pannonic Steppic

3.4. Priority areas for conservation

Changes

The potential effects of climate change on the otter distribution were unevenly distributed in the whole study area. Generally, as expected, the effects were stronger under the climate change scenario A2 (Fig. 5, Table 2), with suitable habitats increasing from 42% to 47% under A2 and from 42% to 48% under B2. Considering the current otter distribution, highly suitable habitats increased by 21% under the A2 scenario and by 26% under the B2 scenario (Table 2) in western Europe. In southern Italy, consisting in a small and isolated population (only 1.2% of occurrences), the amount of highly suitable habitats should decrease by 23.5% under A2 and by 12.5% under B2. In the eastern part of the current distribution (65% of all European occurrences), highly suitable habitats would decrease by 8% under A2 and remain almost stable considering B2 (Table 2). In central Europe, outside of the current distribution range, we forecasted a strong increase of highly suitable habitats, with 211.1% under A2 and 139.7% under B2 (Table 2). Considering each biogeographic region, the Atlantic bioregion shows the highest increase of highly suitable habitats (112.6% for A2 and 102.6% for B2). In the Mediterranean bioregion (accounting for the 20% of the current otter occurrence) the results are not clear: according to the A2 scenario, the highly suitable habitats should decrease by 4.8% but according to the B2 scenario they should increase by 3.7% (Table 2). At countries level, France had the highest increase of suitability, with highly suitable habitats predicted to increase by 280% considering A2, and by 204.3% for B2 (Table A2 in Appendix A), especially in the western and in the southern parts of the country (Fig. 5). In Italy, the increase of suitability will occur in the centre and in the northern part of the country (Fig. 5). Spain and Portugal seem to follow an opposite trend, losing suitability according to both climate scenarios, but particularly under the A2 scenario (Table A2 in Appendix A). Specifically, Spain is supposed to experience a decrease of highly suitable habitats by 10.1% following the A2 scenario and by 1.8% following B2. In Portugal the contrast between the two scenarios is even greater, with a decrease in highly suitable habitat by 40.4% under the A2, and by 6.1% under the B2 (Table A2 in Appendix A).

Future

3.3. Climate change and potential distribution shifts

Highly suitable areas (%)

Overall, all SDMs that we produced are potentially useful (sensu Swets, 1988), with a mean AUC of 0.746 estimated with pseudoindependent validation and of 0.754 with internal validation. The minimum AUC measured for the pseudo-independent evaluation was 0.703 (for GLM), and the maximum was 0.805 (for ANN). Considering results from internal validation, the minimum and maximum values were 0.700 (for GLM) and 0.780 (for ANN) respectively (Table A1 in Appendix A).

Present

3.2. SDMs evaluation

Otter’s occupancy (%)

related to disturbance across all modelling techniques, while distance to cities was relevant only for ANN (Fig. 4).

A2B2

C. Cianfrani et al. / Biological Conservation 144 (2011) 2068–2080 Table 2 Percentage of otter occurrences (calculated considering the EIONET database) and proportions of the highly suitable, suitable and unsuitable areas under the current conditions (present) and under climate change (scenario A2 and B2), and proportions of changes between present and future using A2, present and future using B2 and between A2 and B2 calculated for the study area, the three otter sub-distributions and the European bioregions.

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0.3 1.1 6 4.2 2.5 2.5 0.7 5.4 4.1 11 4.1 9.5

A2B2

11.8 16.2 15.6 8.4 15.8 14.1 17.5 10.4 15 3.9 8.4 2.8

B2 A2

11.9 16.4 14.6 8 15.4 14.5 17.6 11 14.3 3.5 8.1 2.5 0 0 7.5 0.4 0 0 3.9 2 0 11.2 0 2.4

Our study demonstrates the importance of using general climate variables for modelling the distribution of a freshwater species of high conservation value in Europe. Mean precipitation of the driest quarter of the year, mean precipitation of the wettest quarter of the year and the mean annual precipitation were shown as the most important climatic predictors for the otter at large scale (Fig. 4). Even if, at small scale, otter distribution is better predicted by the human variables (Clavero et al., 2010), these results strengthen the potential for considering large scale climate factors in conservation plans, as complements or surrogates for local freshwater body variables that are rarely available at large scales. Interestingly, no major niche difference was observed between the three separated sub-distributions in Europe, suggesting that the same macro-climatic characteristics limit the otter distribution throughout Europe.

*

4.2 2.3 0 1.3 17.8 6.3 0

11.6 15.6 14.2 9.3 16 14.2 18.4 10.4 12.1 3.5 9 4.8 0.6 1 5.7 0.5

Otter not present. *

*

13 12.7 11 12.5 7 10.3 15.2 12.4 12.4 11 12.7 8.2 11 15.2

* *

5.4 0.4 0.4 1.7 14.9 5.2 0

* *

10.9 12.4 10.9 13 6.3 11 14.9

*

10.2 13.4 12.5 14.1 9.5 15.5 14.3

11.5 12.5 11 12.7 5.5 10.4 14.9

12.3 12.9 11.4 11.4 12.2 12.8 10.7 11.5 0 0 6.4 0 12.1 12.6 9.7 11.3 12.1 12.6 9.1 11.3 12.5 12.6 9.1 13.1

B2

A2B2

B2

4.2. Effects of climate change on the otter distribution

Study Area Western South-Italian Eastern Centre Europe Mediterranean Alpine Continental Atlantic Boreal Pannonic Steppic

Priority areas (%)

4.1. Modelling otter distribution in Europe

11.6 15.6 15.3 9.4 16 14.2 17.7 10.2 12.1 3.9 9 4.9

A2B2 B2

4. Discussion

A2

Urgent priority areas (%)

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the areas with otter, and 9.3% for the areas without otter. In the central Europe this percent reaches 16% (Table 3).

A2B2

Current otter occupancy in priority areas (%)

A2

Current otter occupancy in urgent priority areas (%)

A2

Current otter’s occupancy (%)

Protected areas in

Table 3 Percentage of current otter occupancy (calculated considering the EIONET database), current otter occupancy overlapped with urgent priority areas, urgent priority areas and priority areas protected by the current network of protected areas or included in Natura2000, calculated over the whole study area, (2) for each European sub-distribution and the central Europe and (3) for each European bioregion. The analyses were computed using both the A2 and B2 scenario. The proportions of changes between A2 and B2 were calculated.


As already stated in the results section, the geographic distribution of habitats climatically suitable for the otter should be reshuffled in continental Europe (Fig. 5, Table 2). This is evident considering both the A2 and the B2 scenarios, even with the obvious differences existing between the two. The overall increase of highly suitable areas in the central part of Europe (Table 2) leads to a potential reconnection between the eastern and the western sub-distributions. However, taking into consideration the geographic distribution of the highly suitable habitat, the increase of suitability in the central part is located mainly in France (Fig. 5). In the rest of the central Europe, the highly suitable habitats decrease. Moreover in the eastern sub-distribution, it is evident that in the northern part corresponding to the Pannonic bioregion, the suitability should decrease (Fig. 5). That situation would eventually compromise the potential reconnection between the western and the eastern sub-distributions. For this reason, it is of critical importance to promote the reconnection before the decrease of the suitability occurs. Within the Mediterranean bioregion (the second most important region according to the current distribution of the otter; Table 2), results from scenario A2 suggest that the decrease in suitability mainly affects the Iberian Peninsula. This is probably linked to a potential increase in droughts as the climate warms, which can lead in some cases to the disappearance of shallow water bodies including small rivers. Moreover, the lower water availability could result from a higher water use by human activities (Jiménez and Lacomba, 1991). Similar results can be expected for many other freshwater species in this part of Europe, possibly associated with increased risks of local extinctions for small and isolated populations, as a result of catastrophic events (such as droughts) or habitat degradation (Lawton and May, 1995; Erasmus et al., 2002). A decrease in the fitness of local populations could also compromise dispersal potential and prevent colonisation of new areas that would have become suitable after climate change. The increase of highly suitable areas from central Italy up to southern France (Fig. 5) will favour the possible use of these areas as stepping stones for expanding distribution, thus increasing chances to reconnect the southern-Italian and the western sub-distributions. This is important also considering the decrease of highly

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Fig. 6. Map of urgent priority areas (i.e., high suitability in current and in future conditions) and priority areas (i.e., high suitability only in the future conditions) overlapped with Protected Areas and the Natura2000 network identified for (a) A2 scenario and (b) B2 scenario.

suitable habitats in the south-Italian sub-distribution. As this subdistribution is small and isolated, it is important to reconnect it. We considered here only the effects of temperature and precipitation changes on the otter distribution, such as increase in extreme and periodic events like floods and droughts (Hannah et al., 2001). Moreover, water warming may also increase water eutrophication (Hall et al., 2008). Following such changes, some fish species would migrate, others would not survive due to metabolic limits (Mohseni et al., 2003) or to the development of diseases (Hari et al., 2006). Water warming could affect fish species yield both positively or negatively, thus affecting the availability of food resources for the otter (Hall et al., 2008). Cold water species of fish are vulnerable to the effects of water warming, whereas warm water fishes are able to establish and invade as the thermal constraint, increasing fish biomass upstream.

4.3. Effectiveness of existing protected areas for otter conservation under climate change Our results showed that urgent priority areas and priority areas for otter’s conservation, identified by overlapping the current and future potential distributions, are unevenly and unfortunately not well protected by the current network of protected areas. Identifying critical areas for species’ conservation considering climate change is essential for maintaining biodiversity (Vos et al., 2008). We identified ‘‘urgent priority areas’’ and ‘‘priority areas’’ for the long-term conservation of the otter as those that are highly suitable now and under climate change and areas that will become highly suitable in the future (Fig. 6). In a long-term conservation perspective, urgent priority areas that currently contain otter populations are important areas to ensure the species’

2077


survival. These areas could also be considered as potential ‘‘sources’’ from which the species can spread to newly suitable areas (Clavero et al., 2010) after climate change occurs. Urgent priority areas and priority areas not yet colonised by the species are also important as potentially colonisable areas or suitable areas for the species reintroduction. Reconnecting otter sub-distributions is a crucial goal for the long term persistence of the species (Reuther and Krekemeyer, 2004) in Europe, because it would support a higher gene flow and compensate for local extinction risks related to local decreases in habitat suitability. For these reasons, it is essential to ensure the protection of those identified areas, where the species could expand to in the future. Our results show that few urgent priority areas are currently under protection (Table 3). The south-Italian sub-distribution hosts the lower proportion of urgent priority areas with and without otter, representing a danger for the conservation of the smallest and most isolated sub-distribution of otters in Europe. Conservation strategies should be devoted to maintain viable populations there. In the central part of the eastern distribution, efforts should be devoted to mitigate the effects of global climate change on habitat quality for otters. Here the percentage of protection of urgent priority area is low. In this area, otters are present but models predict a decrease in suitable areas due to climate change (Fig. 5). The conservation of the Iberian otter distribution may be critical in the near future because of the unclear situation due to the divergent prediction using the A2 and B2 scenario. Only a small proportion of urgent priority areas currently occupied by otter are protected in Spain, even less in Portugal (Table A3 in Appendix A). From a long-term conservation perspective, it is essential to ensure the protection of those areas, where the otter is not present but, where the models predict an increase in suitability. It is through these areas (e.g., centre-north of Italy, south of France and east of France; Fig. 6), that the species could potentially expand in the future. Here efforts should be devoted to ensure a good habitat quality. Knowing that climate change is already occurring and will likely continue (Coetzee et al., 2009), conservation responses to climate change have to be anticipatory and systematic (Hannah et al., 2001) and, as such, should consider human development and the fact that available resources (or those that our societies are ready to invest) are limited for biodiversity conservation (Joseph et al., 2009). The type of assessment proposed here constitutes one part of a decision process. To be efficient, conservation plans taking into account climate change should coordinate conservation actions at international levels (e.g., the otter’s sub-distribution areas in our example, or at the European scale) and across political boundaries and agency jurisdictions, because these are the scales at which climate change will act. Our analyses provide a basis for an informed decision on, where to perform management actions and what type of actions should be considered to achieve a reasonable conservation status for the otter in a human dominated landscape. Considering the key role of the otter in both freshwater and riparian European environments, many other species would likely benefit from management actions that would maintain or restore both functionality and connectivity of these ecosystems. In this context, our results are also consistent with the objectives of the Water Framework Directive (CEE 60/2000), i.e., the protection, enhancement and restoration of all European river basins.

5. Conclusions 1. Variables expressing the general European climate proved to be important for modelling the distribution of a freshwater ecosys-

tem’s species. As a side result, no niche difference was detected between the three separated otter sub-distributions in continental Europe, allowing a single model to be fit for the whole European distribution. 2. Potential effects of climate change on the otter distribution were unevenly distributed across Europe by 2080. These effects could be more or less evident if we use the climatic scenario A2 or B2. The differences between the predictions obtained using the one or the other scenario are not uniformly distributed in the study area (Fig. 5, Table 2). In the Mediterranean bioregion the highly suitable habitats decrease under the A2 and on the contrary, increase under B2. This discrepancy makes difficult to take conservation strategies. The increase in highly suitable areas in the central-northern part of Italy and in the southern France (Fig. 5) is crucial for a reconnection of the western and south-Italian subdistributions. 3. Urgent priority areas and priority areas for conservation of the otter, identified by matching the current and future potential distributions, are unevenly and not well protected by the current network of protected areas. 4. Given the coarse resolution of this analysis, we assessed only the overall climatic suitability of the otter and thus, we did not address land use and connectivity issues. Our study illustrates a method for integrating climate change effects in the design of protected areas networks, both over a long term perspective and at the European scale. Such assessment of existing protected areas may be more systematically incorporated in the process of conservation decision (e.g., Joseph et al., 2009), and could be applied to many species and ecosystems worldwide to help managers in prioritising conservation actions.

Acknowledgements We thank anonymous referees, D. Pio, R. Engler, O. Broennimann and J. Dwyer for their useful comments on the manuscript. This study was supported by a grant from the Swiss Federal Office for the Environment and from MAVA Foundation to CC and AG. AG and LM were also supported by ECOCHANGE (FP6-ECOCHANGE project, FP6-036866). CC was also partially supported by a grant from the University of Molise. AL was funded by Italian Ministry of the Environment, Project ‘‘Rete Ecologica per la lontra – fase I’’. GL was funded by the MAVA Foundation and the ENHANCE project.

Appendix A Tables A1–A3

Table A1 Performance of the models evaluated through independent and internal validation using the Area Under the Curve (AUC).

ANN GAM GLM MARS FDA MAXENT

IntAUC_mean

IntAUC_sd

ExtAUC_mean

ExtAUC_sd

0.780 0.740 0.700 0.760 0.750 0.740

0.010 0.010 0.013 0.010 0.009 0.009

0.805 0.750 0.703 0.768 0.753 0.705

0.011 0.001 0.011 0.004 0.004 0.004

2078 Table A2 Percentage of otter occurrences (calculated considering the EIONET database) and proportions of the highly suitable, suitable and unsuitable areas under the current conditions (present) and under climate change (scenario A2 and B2), and proportions of changes between present and future using A2, present and future using B2 and between A2 and B2 calculated for the European countries. Otter’s occupancy

Highly suitable areas (%) Present

# *

1.3 2.2 #

1.6 2.2 2.7 1.2 9.1 8.3 3.7 4.8 1.8 3 #

0.8 0.4 0.1 18.2 4.1 2.9 2.4 1.1 19 #

5.5 3.3

30.5 23.8 0.1 6.2 38.6 58.3 77.4 18.1 23.9 39.6 75.4 27.9 97.2 9.1 35.7 4.7 0 84.1 95.5 4.7 54 11.6 68.6 0 24.1 36.3

Suitable areas (%) Changes

Present

A2

B2

A2pres

B2pres

A2B2

42.6 8.3 6.8 31.6 56.9 43.2 21 68.9 9.4 28.1 61.2 32.5 93.7 28.7 44.7 46.3 0.1 73.1 57 29.7 23.3 1.2 61.7 0.2 73.4 41.7

48.6 13.9 1.7 18.9 47.2 50.6 44.8 55.2 14.1 36.6 67.3 33.3 95.8 16.1 51.9 30.1 0.8 80.5 89.6 18.3 32.2 8 67.4 0 68.7 40

39.7 65 8613.5 411.1 47.5 25.9 72.8 280 60.6 29.2 18.8 16.4 3.5 216.6 25.3 876.3

59.6 41.5 2118.9 204.8 22.3 13.2 42.2 204.3 41 7.7 10.8 19.3 1.4 77.7 45.4 534

12.3 40.3 300 67.2 20.6 14.6 53.1 24.8 33.3 23.2 9.1 2.4 2.2 78.3 13.9 53.8 87.5 9.2 36.4 62.3 27.6 85 8.5

*

13.1 40.4 537.7 56.9 89.6 10.1 *

205 15

The otter is not present. The proportion of change is infinite because for the present the percentage is 0.

*

4.2 6.1 293.9 40.4 31.7 1.8 *

185.6 10.3

*

6.8 4.3

52.3 29.8 19.2 38.3 40.3 39.8 21.2 61.3 49.4 52.3 23.8 45.4 2.8 70 50.1 39.2 40.3 15.6 4.4 62.9 42.1 57.5 28.1 12.7 69.9 56.3

Unsuitable areas (%)

Future

Changes

Present

A2

B2

A2pres

B2pres

A2B2

51.8 25.5 40 58.8 37.2 49.3 64.1 25.1 45.8 62.2 36.6 44.3 5.2 54.2 52.2 52.5 45.5 24.6 42.9 64.8 48.5 37.3 36.5 16.4 25.1 51.3

46.5 27 30 55.6 43 45.4 51.1 34.6 41.7 55.7 31 42.4 4 61.3 44.4 57.7 37.1 18.7 10.3 72 51.2 27.9 30.5 13.6 29.9 51.2

1 14.4 108 53.5 7.8 24 203 59 7.3 18.9 53.9 2.5 81.1 22.6 4.3 33.8 13.1 57.3 872 3.1 15.2 35.1 30 29 64.1 8.9

11.1 9.4 55.9 45.3 6.6 14.1 141.6 43.6 15.6 6.5 30.4 6.6 39.3 12.5 11.3 47.1 7.9 19.6 132.3 14.6 21.6 51.5 8.8 6.9 57.2 9.1

11.4 5.6 33.3 5.8 13.5 8.6 25.4 27.5 9.8 11.7 18.1 4.5 30 11.6 17.6 9 22.6 31.6 316.5 10 5.3 33.7 19.7 20.6 16.1 0.2

17.2 46.4 80.7 55.5 21.1 1.9 1.4 20.6 26.7 8.1 0.8 26.7 0 20.9 14.2 56.1 59.7 0.3 0.1 32.5 3.9 30.9 3.3 87.3 6.1 7.4

Future

Changes

A2

B2

A2pres

B2pres

A2B2

5.7 66.2 53.2 9.6 5.9 7.4 14.9 6 44.8 9.7 2.2 23.2 1.1 17.1 3.1 1.2 54.4 2.4 0.1 5.5 28.2 61.5 1.9 83.5 1.6 7

4.9 59.1 68.3 25.5 9.8 4 4.1 10.2 44.1 7.7 1.7 24.3 0.3 22.6 3.7 12.3 62.1 0.8 0.1 9.6 16.7 64.2 2.1 86.4 1.4 8.8

67.2 42.5 34 82.7 71.9 294.6 948.1 71.1 67.9 20.8 167.3 12.9

71.6 27.3 15.4 54.1 53.4 111 189.5 50.3 65.6 3.9 111.2 8.9

16.3 12 22.1 62.4 39.8 85 263.4 41.2 1.6 26 29.4 4.5 266.7 24.3 16.2 90.2 12.4 200 0 42.7 68.9 4.2 9.5 3.4 14.3 20.5

*

18.4 78.5 97.8 8.9 611 0 83.1 615.4 99 44.1 4.4 74.2 5.7

*

8 74 78.1 4 133.6 0 70.4 322.6 107.7 36.9 1 76.4 18.4


Albania Austria Belgium Bosnia Croatia Czech Denmark France Germany Greece Hungary Italy Lithuania Luxemburgo Macedonia Montenegro Netherlands Poland Portugal Serbia Slovakia Slovenia Spain Switzerland Bulgary Romania

Future

2079


Table A3 Percentage of current otter occupancy, current otter occupancy overlapped with urgent priority areas, urgent priority areas and priority areas protected by the current network of protected areas or included in the Natura2000, calculated for each European country. The analyses were computed using both the A2 and B2 scenario. The proportions of changes between A2 and B2 were calculated. Percentage of protected areas in Current otter’ s occupancy

Albania# Austria Belgium Bosnia# Croatia# Czech Denmark France Germany Greece Hungary Italy Lithuania Luxemburgo# Macedonia Montenegro# Netherlands Poland Portugal Serbia# Slovakia Slovenia Spain Switzerland# Bulgary# Romania#

10.4 17

Current otter occupancy in urgent priority areas

Change

Current otter occupancy in priority areas

Change

Urgent priority areas

Change

Priority areas

A2

B2

A2B2

A2

B2

A2B2

A2

B2

A2B2

A2

B2

A2B2

12 12

12.8 11.3

11.4 12.5

12.7 11.4

5 13.3 40.5 0 4 2.6 6 17.4 34.3 11.4 12.3 10 4.6 11.4 1.7 1

4.3 12.4 48 0 3.7 2.6 4.9 17.1 29.7 10.3 12.3 10.4 4.8 9.6 1.7 1

5 13.3 22.7 0 2.8 2.3 6 16.4 34.2 11.6 12.2 10.9 4.6 16.3 3.1 3.4 2.7 7.9 11 0.3 23.4 1.7 17.9 0 2.4 4.3

4.6 12.4 24.5 0 2.6 2.5 4.9 16.5 29.4 10.4 12.2 11 4.8 13.8 2.8 0.4 19.8 7.8 10.6 0.4 19.8 2.6 18.3

8.8 6.6 7.4 0 8 5.3 21.4 0.9 16.3 11.1 0.4 0.8 4.9 18.5 12.9 762.8 86.3 2.3 3.3 27.4 17.9 36.1 2.1

6.4 6.6

11 9.9

*

*

*

*

*

*

*

17.1 15.7 13.5 11.1 15.9 12.6 8 15 10.6 9.3

14.4 13.8

14.1 12.5 9.1 7.3 13 12.2 7.5 8.8 11.9 5.4

2.4 10.4 1.3 9.2 4.2 1.9 0.5 0.3 7 17

14.8 13.6 9.5 12.2 13.2 12.1 9.6 9.5 11.5 7.5

14.4 12.3 10.1 10.8 12.7 12.2 9.6 9 12.1 6.2

3.2 10 6.2 13 4.1 0.7 0 4.7 5.3 21

7.9 13.5 12 7.5 8.8 11.1 6.3

*

*

*

8.5 13.6 11.7 12.1 9.5 16.1 14.9 13.4 10.9

11.8 12.4 10.2 10.8 8.4 15.4 12.8 9.4 12.5

12.9 11.9 10.5 11.1 8.9 14.9 11.7 9.3 12.9

*

*

*

13.2 16.6

14.4 13.7

14.6 13.3

*

8.4 4.4 3.6 2.3 5.7 3.4 9.4 1 3.1 *

0.8 2.8

*

*

*

10.9 10.3 10.3 10.8 11.6 15.3 12.4 9.1 12.8

12.1 10.5 10.5 11 11.9 14.9 11.9 9 13.3

11 1.4 1.4 1.6 2.9 2.4 4.2 1.9 3.8

*

*

14.5 13.5

14.8 12.9

*

1.8 4

17 6.6 16 0 8.6 2.2 21.4 1.7 15.5 11 0.3 4.1 4.9 18.9 1.2 0

*

*

*

8.1 11 0.3 23.5 0.8 17.1

7.9 10.6 0.3 19.9 2.6 17.5

2.4 3.3 0 18 68 2.6

*

3.8 3.7

*

3.8 3.6

*

1.7 1.8

Change

*

2.4 3.8

*

0.5 12.5

# *

Natura2000 sites not available here. Otter not present in these countries.

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