Aim Techniques that predict species potential distributions by combining observed occurrence records with environmental variables show much potential for application across a range of biogeographical analyses. Some of the most promising applications relate to species for which occurrence records are scarce, due to cryptic habits, locally restricted distributions or low sampling effort. However, the minimum sample sizes required to yield useful predictions remain difficult to determine. Here we developed and tested a novel jackknife validation approach to assess the ability to predict species occurrence when fewer than 25 occurrence records are available.Location Madagascar.Methods Models were developed and evaluated for 13 species of secretive leaftailed geckos (Uroplatus spp.) that are endemic to Madagascar, for which available sample sizes range from 4 to 23 occurrence localities (at 1 km 2 grid resolution). Predictions were based on 20 environmental data layers and were generated using two modelling approaches: a method based on the principle of maximum entropy (Maxent) and a genetic algorithm (GARP). ResultsWe found high success rates and statistical significance in jackknife tests with sample sizes as low as five when the Maxent model was applied. Results for GARP at very low sample sizes (less than c. 10) were less good. When sample sizes were experimentally reduced for those species with the most records, variability among predictions using different combinations of localities demonstrated that models were greatly influenced by exactly which observations were included.Main conclusions We emphasize that models developed using this approach with small sample sizes should be interpreted as identifying regions that have similar environmental conditions to where the species is known to occur, and not as predicting actual limits to the range of a species. The jackknife validation approach proposed here enables assessment of the predictive ability of models built using very small sample sizes, although use of this test with larger sample sizes may lead to overoptimistic estimates of predictive power. Our analyses demonstrate that geographical predictions developed from small numbers of occurrence records may be of great value, for example in targeting field surveys to accelerate the discovery of unknown populations and species.

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There is an urgent need to develop e ective vulnerability assessments for evaluating the conservation status of species in a changing climate 1 . Several new assessment approaches have been proposed for evaluating the vulnerability of species to climate change 2-5 based on the expectation that established assessments such as the IUCN Red List 6 need revising or superseding in light of the threat that climate change brings. However, although previous studies have identified ecological and life history attributes that characterize declining species or those listed as threatened 7-9 , no study so far has undertaken a quantitative analysis of the attributes that cause species to be at high risk of extinction specifically due to climate change. We developed a simulation approach based on generic life history types to show here that extinction risk due to climate change can be predicted using a mixture of spatial and demographic variables that can be measured in the present day without the need for complex forecasting models. Most of the variables we found to be important for predicting extinction risk, including occupied area and population size, are already used in species conservation assessments, indicating that present systems may be better able to identify species vulnerable to climate change than previously thought. Therefore, although climate change brings many new conservation challenges, we find that it may not be fundamentally di erent from other threats in terms of assessing extinction risks.Attempts to quantify the threat that climate change poses to species' survival commonly infer extinction risk from changes in the area of climatically suitable habitat (the bioclimate envelope) 10,11 , but this approach ignores important aspects of species' biology such as population dynamics, vital rates and dispersal 12-16 , leading to high uncertainty 1,17 . To address this challenge, we coupled ecological niche models (ENMs) with demographic models [13][14][15][18][19][20] and expanded this approach by developing a generic life history (GLH) method. The coupled modelling approach estimates extinction risk as the probability of abundance falling to zero by the year 2100, rather than as the proportion of species committed to extinction due to contraction of bioclimate envelopes 10 (Methods).By matching ENMs for 36 amphibian and reptile species endemic to the US with corresponding GLH models (Supplementary Table 1), we estimate mean extinction risk by 2100 to be 28 ± 7% under a high CO 2 concentration Reference climate scenario 21 and 23 ± 7% under a Policy climate scenario that assumes substantive intervention 22 (Methods). In contrast, extinction risk is estimated by the same models to be <1% without climate change, showing that the methods are not biased towards predicting high risks. The contrast between predicted extinction risk with and without climate change suggests that climate change will cause a pronounced increase in extinction risk for these taxonomic groups over the coming century. Contrary to other stud...

Despite the importance of tropical biodiversity, informative species distributional data are seldom available for biogeographical study or setting conservation priorities. Modelling ecological niche distributions of species offers a potential solution; however, the utility of old locality data from museums, and of more recent remotely sensed satellite data, remains poorly explored, especially for rapidly changing tropical landscapes. Using 29 modern data sets of environmental land coverage and 621 chameleon occurrence localities from Madagascar (historical and recent), here we demonstrate a significant ability of our niche models in predicting species distribution. At 11 recently inventoried sites, highest predictive success (85.1%) was obtained for models based only on modern occurrence data (74.7% and 82.8% predictive success, respectively, for pre-1978 and all data combined). Notably, these models also identified three intersecting areas of over-prediction that recently yielded seven chameleon species new to science. We conclude that ecological niche modelling using recent locality records and readily available environmental coverage data provides informative biogeographical data for poorly known tropical landscapes, and offers innovative potential for the discovery of unknown distributional areas and unknown species.

Although the systematic utility of ecological niche modeling is generally well known (e.g., concerning the recognition and discovery of areas of endemism for biogeographic analyses), there has been little discussion of applications concerning species delimitation, and to date, no empirical evaluation has been conducted. However, ecological niche modeling can provide compelling evidence for allopatry between populations, and can also detect divergent ecological niches between candidate species. Here we present results for two taxonomically problematic groups of Phelsuma day geckos from Madagascar, where we integrate ecological niche modeling with mitochondrial DNA and morphological data to evaluate species limits. Despite relatively modest levels of genetic and morphological divergence, for both species groups we find divergent ecological niches between closely related species and parapatric ecological niche models. Niche models based on the new species limits provide a better fit to the known distribution than models based upon the combined (lumped) species limits. Based on these results, we elevate three subspecies of Phelsuma madagascariensis to species rank and describe a new species of Phelsuma from the P. dubia species group. Our phylogeny continues to support a major endemic radiation of Phelsuma in Madagascar, with dispersals to Pemba Island and the Mascarene Islands. We conclude that ecological niche modeling offers great potential for species delimitation, especially for taxonomic groups exhibiting low vagility and localized endemism and for groups with more poorly known distributions. In particular, niche modeling should be especially sensitive for detecting recent parapatric speciation driven by ecological divergence, when the environmental gradients driving speciation are represented within the ecological niche models.