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...

Although humpback whales are among the best-studied of the large whales, population boundaries in the Southern Hemisphere (SH) have remained largely untested. We assess population structure of SH humpback whales using 1,527 samples collected from whales at fourteen sampling sites within the Southwestern and Southeastern Atlantic, the Southwestern Indian Ocean, and Northern Indian Ocean (Breeding Stocks A, B, C and X, respectively). Evaluation of mtDNA population structure and migration rates was carried out under different statistical frameworks. Using all genetic evidence, the results suggest significant degrees of population structure between all ocean basins, with the Southwestern and Northern Indian Ocean most differentiated from each other. Effective migration rates were highest between the Southeastern Atlantic and the Southwestern Indian Ocean, followed by rates within the Southeastern Atlantic, and the lowest between the Southwestern and Northern Indian Ocean. At finer scales, very low gene flow was detected between the two neighbouring sub-regions in the Southeastern Atlantic, compared to high gene flow for whales within the Southwestern Indian Ocean. Our genetic results support the current management designations proposed by the International Whaling Commission of Breeding Stocks A, B, C, and X as four strongly structured populations. The population structure patterns found in this study are likely to have been influenced by a combination of long-term maternally directed fidelity of migratory destinations, along with other ecological and oceanographic features in the region.

Habitat preference is driven by a complex interaction among behavioural patterns, biological requirements, and environmental conditions. These variables are difficult to determine for any species but are further complicated for migratory marine mammals, such as humpback whales Megaptera novaeangliae. Patterns of habitat use in relation to social organization potentially exist for this species on their wintering grounds. Using an integrated GIS approach, we examined the degree to which spatial patterns of habitat stratification are correlated within different humpback whale group types from 6 years of sighting data (1996)(1997)(1998)(1999)(2000)(2001) collected on the Antongil Bay, Madagascar, wintering ground. Stratification of humpback whale sightings by behavioural classification showed significant variation in depth and distance from shore. Distribution by depth could not be described as a function of group size but could be described as a function of social organization, with mother-calf pairs showing a strong preference for shallower water compared to all other group types. Group size and social organization seem to be factors in distribution by distance from shore. Significant diurnal patterns in distribution by depth and distance from shore also exist, where mother-calf groups maintain a relatively stable distribution and pairs and competitive groups are the most variable. Patterns of habitat preference on this wintering ground appear to be guided by social organization, where distribution by depth and distance from shore highlight areas critical to conservation.

Summary1. Methods used to predict shifts in species' ranges because of climate change commonly involve species distribution (niche) modelling using climatic variables, future values of which are predicted for the next several decades by general circulation models. However, species' distributions also depend on factors other than climate, such as land cover, land use and soil type. Changes in some of these factors, such as soil type, occur over geologic time and are thus imperceptible over the timescale of these types of projections. Other factors, such as land use and land cover, are expected to change over shorter timescales, but reliable projections are not available. Some important predictor variables, therefore, must be treated as unchanging, or static, whether because of the properties of the variable or out of necessity. The question of how best to combine dynamic variables predicted by climate models with static variables is not trivial and has been dealt with differently in studies to date. Alternative methods include using the static variables as masks, including them as independent explanatory variables in the model, or excluding them altogether. 2. Using a set of simulated species, we tested various methods for combining static variables with future climate scenarios. Our results showed that including static variables in the model with the dynamic variables performed better or no worse than either masking or excluding the static variables. 3. The difference in predictive ability was most pronounced when there is an interaction between the static and dynamic variables. 4. For variables such as land use, our results indicate that if such variables affect species distributions, including them in the model is better than excluding them, even though this may mean making the unrealistic assumption that the variable will not change in the future. 5. These results demonstrate the importance of including static and dynamic non-climate variables in addition to climate variables in species distribution models designed to predict future change in a species' habitat or distribution as a result of climate change.