Species distribution models (SDMs) are increasingly proposed to support conservation decision making. However, evidence of SDMs supporting solutions for on-ground conservation problems is still scarce in the scientific literature. Here, we show that successful examples exist but are still largely hidden in the grey literature, and thus less accessible for analysis and learning. Furthermore, the decision framework within which SDMs are used is rarely made explicit. Using case studies from biological invasions, identification of critical habitats, reserve selection and translocation of endangered species, we propose that SDMs may be tailored to suit a range of decision-making contexts when used within a structured and transparent decision-making process. To construct appropriate SDMs to more effectively guide conservation actions, modellers need to better understand the decision process, and decision makers need to provide feedback to modellers regarding the actual use of SDMs to support conservation decisions. This could be facilitated by individuals or institutions playing the role of ‘translators’ between modellers and decision makers. We encourage species distribution modellers to get involved in real decision-making processes that will benefit from their technical input; this strategy has the potential to better bridge theory and practice, and contribute to improve both scientific knowledge and conservation outcomes.

Biodiversity in agricultural landscapes can be increased with conversion of some production lands into 'more-natural'- unmanaged or extensively managed - lands. However, it remains unknown to what extent biodiversity can be enhanced by altering landscape pattern without reducing agricultural production. We propose a framework for this problem, considering separately compositional heterogeneity (the number and proportions of different cover types) and configurational heterogeneity (the spatial arrangement of cover types). Cover type classification and mapping is based on species requirements, such as feeding and nesting, resulting in measures of 'functional landscape heterogeneity'. We then identify three important questions: does biodiversity increase with (1) increasing heterogeneity of the more-natural areas, (2) increasing compositional heterogeneity of production cover types and (3) increasing configurational heterogeneity of production cover types? We discuss approaches for addressing these questions. Such studies should have high priority because biodiversity protection globally depends increasingly on maintaining biodiversity in human-dominated landscapes.

Climate changes have profound effects on the distribution of numerous plant and animal species 1-3 . However, whether and how different taxonomic groups are able to track climate changes at large spatial scales is still unclear. Here, we measure and compare the climatic debt accumulated by bird and butterfly communities at a European scale over two decades (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008). We quantified the yearly change in community composition in response to climate change for 9,490 bird and 2,130 butterfly communities distributed across Europe 4 . We show that changes in community composition are rapid but different between birds and butterflies and equivalent to a 37 and 114 km northward shift in bird and butterfly communities, respectively. We further found that, during the same period, the northward shift in temperature in Europe was even faster, so that the climatic debts of birds and butterflies correspond to a 212 and 135 km lag behind climate. Our results indicate both that birds and butterflies do not keep up with temperature increase and the accumulation of different climatic debts for these groups at national and continental scales.Species are not equally at risk when facing climate change. Several species-specific attributes have been identified as increasing species' vulnerability to climate change, including diets, migratory strategy, main habitat types and ecological specialization [5][6][7] . Moreover, although phenotypic plasticity may enable some species to respond rapidly and effectively to climate change 8,9 , others may suffer from the induced spatial mismatch and temporal mistiming with their resources 10,11 . For instance, species such as great tits and flycatchers have been shown to become desynchronized with their main food supply during the nesting season 12 .However, beyond individual species' fates, climate change should also affect species interactions and the structure of species assemblages within and across different taxonomic groups over large spatial scales [13][14][15] . For instance, ectotherms should be more directly affected by climate warming and taxonomic groups with short generation time should favour faster evolutionary responses to selective pressures induced by climate changes 13 . Yet, whether different taxonomic groups are tracking climate change at the same rate over large areas is still unclear, and methods to routinely assess the mismatch between temperature increases and biodiversity responses at different spatial scales are still missing 16 .Here, we used extensive monitoring data of birds and butterflies distributed across Europe to assess whether, regardless of their species-specific characteristics, organisms belonging to a given group are responding more quickly or more slowly than organisms belonging to another group over large areas. We characterized bird and butterfly communities in 9,490 and 2,130 sample sites respectively by their community temperature index (CTI) for ea...

International audienceAim Many attempts to predict the potential range of species rely on environmental niche (or 'bioclimate envelope') modelling, yet the effects of using different niche-based methodologies require further investigation. Here we investigate the impact that the choice of model can have on predictions, identify key reasons why model output may differ and discuss the implications that model uncertainty has for policy-guiding applications. Location The Western Cape of South Africa. Methods We applied nine of the most widely used modelling techniques to model potential distributions under current and predicted future climate for four species (including two subspecies) of Proteaceae. Each model was built using an identical set of five input variables and distribution data for 3996 sampled sites. We compare model predictions by testing agreement between observed and simulated distributions for the present day (using the area under the receiver operating characteristic curve (AUC) and kappa statistics) and by assessing consistency in predictions of range size changes under future climate (using cluster analysis). Results Our analyses show significant differences between predictions from different models, with predicted changes in range size by 2030 differing in both magnitude and direction (e.g. from 92% loss to 322% gain). We explain differences with reference to two characteristics of the modelling techniques: data input requirements (presence/absence vs. presence-only approaches) and assumptions made by each algorithm when extrapolating beyond the range of data used to build the model. The effects of these factors should be carefully considered when using this modelling approach to predict species ranges. Main Conclusions We highlight an important source of uncertainty in assessments of the impacts of climate change on biodiversity and emphasize that model predictions should be interpreted in policy-guiding applications along with a full appreciation of uncertainty

2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Á/ Ecography 27: 437 Á/448.Habitat suitability models can be generated using methods requiring information on species presence or species presence and absence. Knowledge of the predictive performance of such methods becomes a critical issue to establish their optimal scope of application for mapping current species distributions under different constraints. Here, we use breeding bird atlas data in Catalonia as a working example and attempt to analyse the relative performance of two methods: the Ecological Niche factor Analysis (ENFA) using presence data only and Generalised Linear Models (GLM) using presence/absence data. Models were run on a set of forest species with similar habitat requirements, but with varying occurrence rates (prevalence) and niche positions (marginality). Our results support the idea that GLM predictions are more accurate than those obtained with ENFA. This was particularly true when species were using available habitats proportionally to their suitability, making absence data reliable and useful to enhance model calibration. Species marginality in niche space was also correlated to predictive accuracy, i.e. species with less restricted ecological requirements were modelled less accurately than species with more restricted requirements. This pattern was irrespective of the method employed. Models for wide-ranging and tolerant species were more sensitive to absence data, suggesting that presence/absence methods may be particularly important for predicting distributions of this type of species. We conclude that modellers should consider that species ecological characteristics are critical in determining the accuracy of models and that it is difficult to predict generalist species distributions accurately and this is independent of the method used. Being based on distinct approaches regarding adjustment to data and data quality, habitat distribution modelling methods cover different application areas, making it difficult to identify one that should be universally applicable. Our results suggest however, that if absence data is available, methods using this information should be preferably used in most situations.L. Brotons (brotons@cefe.cnrs-mop.fr),

Thuiller, W., Brotons, L., Araú jo, M. B. and Lavorel, S. 2004. Effects of restricting environmental range of data to project current and future species distributions.Á/ Ecography 27: 165 Á/172.We examine the consequences of restricting the range of environmental conditions over which niche-based models are developed to project potential future distributions of three selected European tree species to assess first, the importance of removing absences beyond species known distributions (''naughty noughts'') and second the importance of capturing the full environmental range of species. We found that restricting the environmental range of data strongly influenced the estimation of response curves, especially towards upper and lower ends of environmental ranges. This induces changes in the probability values towards upper and lower environmental boundaries, leading to more conservative scenarios in terms of changes in distribution projections.Using restricted data analogous to not capturing the fun species' environmental range, reduces strongly the combinations of environmental conditions under which the models are calibrated, and reduces the applicability of the models for predictive purposes. This may generate unpredictable effects on the tails of the species response curves, yielding spurious projections into the future provided that probability of occurrence is not set to zero outside the environmental limits of the species. Indeed, as the restricted data does not capture the whole of the response curve, projections of future species distributions based of ecological niche modelling may be only valid if niche models are able to approach the complete response curve of environmental predictors. W. Thuiller (thuiller@cefe.cnrs-mop.fr), L. Brotons and M. B. Araújo, Centre d'Ecologic Fonctionelle et Evolutive, CNRS, 1919 route de Mende,

Some alien species cause substantial impacts, yet most are innocuous. Given limited resources, forecasting risks from alien species will help prioritise management. Given that risk assessment (RA) approaches vary widely, a synthesis is timely to highlight best practices. We reviewed quantitative and scoring RAs, integrating > 300 publications into arguably the most rigorous quantitative RA framework currently existing, and mapping each study onto our framework, which combines Transport, Establishment, Abundance, Spread and Impact (TEASI). Quantitative models generally measured single risk components (78% of studies), often focusing on Establishment alone (79%). Although dominant in academia, quantitative RAs are underused in policy, and should be made more accessible. Accommodating heterogeneous limited data, combining across risk components, and developing generalised RAs across species, space and time without requiring new models for each species may increase attractiveness for policy applications. Comparatively, scoring approaches covered more risk components (50% examined > 3 components), with Impact being the most common component (87%), and have been widely applied in policy (> 57%), but primarily employed expert opinion. Our framework provides guidance for questions asked, combining scores and other improvements. Our risk framework need not be completely parameterised to be informative, but instead identifies opportunities for improvement in alien species RA.

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