BIOMOD is a computer platform for ensemble forecasting of species distributions, enabling the treatment of a range of methodological uncertainties in models and the examination of species-environment relationships. BIOMOD includes the ability to model species distributions with several techniques, test models with a wide range of approaches, project species distributions into different environmental conditions (e.g. climate or land use change scenarios) and dispersal functions. It allows assessing species temporal turnover, plot species response curves, and test the strength of species interactions with predictor variables. BIOMOD is implemented in R and is a freeware, open source, package.Species distribution models (SDM, Guisan and Thuiller 2005) are being used in nearly all branches of life and environmental sciences. A quick search in ISI Web of Science (18/02/08) using ''species distribution models'' OR ''niche models'' OR ''habitat models'' OR ''bioclimatic models'' highlights 21 973 papers, 74% of which published in the past 10 yr, in fields as varied as environmental sciences (53% of the records), zoology (15%), marine and freshwater biology (15%), life sciences and biomedicine (9%), biodiversity and conservation (8%), evolutionary biology (8%), fisheries (6%), forestry (6%), oceanography (5%), genetics and heredity (5%), amongst others. Advancement of knowledge in these fields is now intertwined with technical innovation in species distribution modelling and dependent on the existence of suitable software for fitting models and examining results. One difficulty with the use of species distribution models is that the number of techniques available is large and is increasing steadily, making it difficult for ''non-aficionados'' to select the most appropriate methodology for their needs (Elith et al. 2006, Heikkinen et al. 2006. Recent analyses have also demonstrated that discrepancies between different techniques can be very large, making the choice of the appropriate model even more difficult. This is particularly true when models are used to project distributions of species into independent situations, which is the example of projections of species distributions under future climate change scenarios (Thuiller 2004, Pearson et al. 2006. A possible solution to account for this inter-model variability is to fit ensembles of forecasts by simulating across more than one set of initial conditions, model classes, model parameters, and boundary conditions (for a review see Araújo and New 2007) and analyse the resulting range of uncertainties with bounding box, consensus and probabilistic methodologies rather than lining up with a single modelling outcome New 2007, Thuiller 2007). BIOMOD offers such a platform for ensemble forecasting (Fig. 1) using freeware and open-source R software (R Development Core Team 2008). It overcomes some of the limitations of existing software (e.g. being able to fit and compare different models) and incorporates several features for testing models (e.g. k-fold cross validation) ...

Summary 1.Few examples of habitat-modelling studies of rare and endangered species exist in the literature, although from a conservation perspective predicting their distribution would prove particularly useful. Paucity of data and lack of valid absences are the probable reasons for this shortcoming. Analytic solutions to accommodate the lack of absence include the ecological niche factor analysis (ENFA) and the use of generalized linear models (GLM) with simulated pseudo-absences. 2. In this study we tested a new approach to generating pseudo-absences, based on a preliminary ENFA habitat suitability (HS) map, for the endangered species Eryngium alpinum . This method of generating pseudo-absences was compared with two others: (i) use of a GLM with pseudo-absences generated totally at random, and (ii) use of an ENFA only. 3. The influence of two different spatial resolutions (i.e. grain) was also assessed for tackling the dilemma of quality (grain) vs. quantity (number of occurrences). Each combination of the three above-mentioned methods with the two grains generated a distinct HS map. 4. Four evaluation measures were used for comparing these HS maps: total deviance explained, best kappa, Gini coefficient and minimal predicted area (MPA). The last is a new evaluation criterion proposed in this study. 5. Results showed that (i) GLM models using ENFA-weighted pseudo-absence provide better results, except for the MPA value, and that (ii) quality (spatial resolution and locational accuracy) of the data appears to be more important than quantity (number of occurrences). Furthermore, the proposed MPA value is suggested as a useful measure of model evaluation when used to complement classical statistical measures. 6. Synthesis and applications. We suggest that the use of ENFA-weighted pseudoabsence is a possible way to enhance the quality of GLM-based potential distribution maps and that data quality (i.e. spatial resolution) prevails over quantity (i.e. number of data). Increased accuracy of potential distribution maps could help to define better suitable areas for species protection and reintroduction.Key-words : ecological niche factor analysis (ENFA), Eryngium alpinum , generalized linear model (GLM), habitat suitability (HS) model, minimal predicted area (MPA), spatial resolution vs. data size.

The aim of the ecospat package is to make available novel tools and methods to support spatial analyses and modeling of species niches and distributions in a coherent workflow. The package is written in the R language (R Development Core Team) and contains several features, unique in their implementation, that are complementary to other existing R packages. Pre‐modeling analyses include species niche quantifications and comparisons between distinct ranges or time periods, measures of phylogenetic diversity, and other data exploration functionalities (e.g. extrapolation detection, ExDet). Core modeling brings together the new approach of ensemble of small models (ESM) and various implementations of the spatially‐explicit modeling of species assemblages (SESAM) framework. Post‐modeling analyses include evaluation of species predictions based on presence‐only data (Boyce index) and of community predictions, phylogenetic diversity and environmentally‐constrained species co‐occurrences analyses. The ecospat package also provides some functions to supplement the ‘biomod2’ package (e.g. data preparation, permutation tests and cross‐validation of model predictive power). With this novel package, we intend to stimulate the use of comprehensive approaches in spatial modelling of species and community distributions.

Mountain ecosystems will likely be affected by global warming during the 21st century, with substantial biodiversity loss predicted by species distribution models (SDMs). Depending on the geographic extent, elevation range, and spatial resolution of data used in making these models, different rates of habitat loss have been predicted, with associated risk of species extinction. Few coordinated across-scale comparisons have been made using data of different resolutions and geographic extents. Here, we assess whether climate change-induced habitat losses predicted at the European scale (10  10 0 grid cells) are also predicted from local-scale data and modeling (25 m  25 m grid cells) in two regions of the Swiss Alps. We show that local-scale models predict persistence of suitable habitats in up to 100% of species that were predicted by a European-scale model to lose all their suitable habitats in the area. Proportion of habitat loss depends on climate change scenario and study area. We find good agreement between the mismatch in predictions between scales and the fine-grain elevation range within 10  10 0 cells. The greatest prediction discrepancy for alpine species occurs in the area with the largest nival zone. Our results suggest elevation range as the main driver for the observed prediction discrepancies. Local-scale projections may better reflect the possibility for species to track their climatic requirement toward higher elevations.

Continental-scale assessments of 21st century global impacts of climate change on biodiversity have forecasted range contractions for many species. These coarse resolution studies are, however, of limited relevance for projecting risks to biodiversity in mountain systems, where pronounced microclimatic variation could allow species to persist locally, and are ill-suited for assessment of species-specific threat in particular regions. Here, we assess the impacts of climate change on 2632 plant species across all major European mountain ranges, using high-resolution (ca. 100 m) species samples and data expressing four future climate scenarios. Projected habitat loss is greater for species distributed at higher elevations; depending on the climate scenario, we find 36-55% of alpine species, 31-51% of subalpine species and 19-46% of montane species lose more than 80% of their suitable habitat by 2070-2100. While our high-resolution analyses consistently indicate marked levels of threat to cold-adapted mountain florae across Europe, they also reveal unequal distribution of this threat across the various mountain ranges. Impacts on florae from regions projected to undergo increased warming accompanied by decreased precipitation, such as the Pyrenees and the Eastern Austrian Alps, will likely be greater than on florae in regions where the increase in temperature is less pronounced and rainfall increases concomitantly, such as in the Norwegian Scandes and the Scottish Highlands. This suggests that change in precipitation, not only warming, plays an important role in determining the potential impacts of climate change on vegetation

Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.

Vittoz P. and Engler R. 2007. Seed dispersal distances: a typology based on dispersal modes and plant traits. Bot. Helv. 117: 109 -124.The ability of plants to disperse seeds may be critical for their survival under the current constraints of landscape fragmentation and climate change. Seed dispersal distance would therefore be an important variable to include in species distribution models. Unfortunately, data on dispersal distances are scarce, and seed dispersal models only exist for some species with particular dispersal modes. To overcome this lack of knowledge, we propose a simple approach to estimate seed dispersal distances for a whole regional flora. We reviewed literature about seed dispersal in temperate regions and compiled data for dispersal distances together with information about the dispersal mode and plant traits. Based on this information, we identified seven "dispersal types" with similar dispersal distances. For each type, upper limits for the distance within which 50 % and 99 % of a species seeds will disperse were estimated with the 80 th percentile of the available values. These distances varied 5000-fold among the seven dispersal types, but generally less than 50-fold within the types. Thus, our dispersal types represented a large part of the variation in observed dispersal distances. The attribution of a dispersal type to a particular species only requires information that is already available in databases for most Central European species, i.e. dispersal vector (e.g. wind, animals), the precise mode of dispersal (e.g. dyszoochory, epizoochory), and species traits influencing the efficiency of dispersal (e.g. plant height, typical habitats). This typology could be extended to other regions and will make it possible to include seed dispersal in species distribution models.

Aim  Many studies have forecasted the possible impact of climate change on plant distributions using models based on ecological niche theory, but most of them have ignored dispersal‐limitations, assuming dispersal to be either unlimited or null. Depending on the rate of climatic change, the landscape fragmentation and the dispersal capabilities of individual species, these assumptions are likely to prove inaccurate, leading to under‐ or overestimation of future species distributions and yielding large uncertainty between these two extremes. As a result, the concepts of ‘potentially suitable’ and ‘potentially colonizable’ habitat are expected to differ significantly. To quantify to what extent these two concepts can differ, we developed MigClim, a model simulating plant dispersal under climate change and landscape fragmentation scenarios. MigClim implements various parameters, such as dispersal distance, increase in reproductive potential over time, landscape fragmentation or long‐distance dispersal.Location  Western Swiss Alps.Methods  Using our MigClim model, several simulations were run for two virtual species by varying dispersal distance and other parameters. Each simulation covered the 100‐year period 2001–2100 and three different IPCC‐based temperature warming scenarios were considered. Results of dispersal‐limited projections were compared with unlimited and no‐dispersal projections.Results  Our simulations indicate that: (1) using realistic parameter values, the future potential distributions generated using MigClim can differ significantly (up to more than 95% difference in colonized surface) from those that ignore dispersal; (2) this divergence increases under more extreme climate warming scenarios and over longer time periods; and (3) the uncertainty associated with the warming scenario can be as large as the one related to dispersal parameters.Main conclusions  Accounting for dispersal, even roughly, can importantly reduce uncertainty in projections of species distribution under climate change scenarios.