. Novel methods improve prediction of species' distributions from occurrence data. Á/ Ecography 29: 129 Á/151.Prediction of species' distributions is central to diverse applications in ecology, evolution and conservation science. There is increasing electronic access to vast sets of occurrence records in museums and herbaria, yet little effective guidance on how best to use this information in the context of numerous approaches for modelling distributions. To meet this need, we compared 16 modelling methods over 226 species from 6 regions of the world, creating the most comprehensive set of model comparisons to date. We used presence-only data to fit models, and independent presence-absence data to evaluate the predictions. Along with well-established modelling methods such as generalised additive models and GARP and BIOCLIM, we explored methods that either have been developed recently or have rarely been applied to modelling species' distributions. These include machine-learning methods and community models, both of which have features that may make them particularly well suited to noisy or sparse information, as is typical of species' occurrence data. Presence-only data were effective for modelling species' distributions for many species and regions. The novel methods consistently outperformed more established methods. The results of our analysis are promising for the use of data from museums and herbaria, especially as methods suited to the noise inherent in such data improve. J. Elith

No abstract

Distributions of Earth's species are changing at accelerating rates, increasingly driven by human-mediated climate change. Such changes are already altering the composition of ecological communities, but beyond conservation of natural systems, how and why does this matter? We review evidence that climate-driven species redistribution at regional to global scales affects ecosystem functioning, human well-being, and the dynamics of climate change itself. Production of natural resources required for food security, patterns of disease transmission, and processes of carbon sequestration are all altered by changes in species distribution. Consideration of these effects of biodiversity redistribution is critical yet lacking in most mitigation and adaptation strategies, including the United Nation's Sustainable Development Goals.

A recently developed integrative framework proposes that the vulnerability of a species to environmental change depends on the species' exposure and sensitivity to environmental change, its resilience to perturbations and its potential to adapt to change. These vulnerability criteria require behavioural, physiological and genetic data. With this information in hand, biologists can predict organisms most at risk from environmental change. Biologists and managers can then target organisms and habitats most at risk. Unfortunately, the required data (e.g. optimal physiological temperatures) are rarely available. Here, we evaluate the reliability of potential proxies (e.g. critical temperatures) that are often available for some groups. Several proxies for ectotherms are promising, but analogous ones for endotherms are lacking. We also develop a simple graphical model of how behavioural thermoregulation, acclimation and adaptation may interact to influence vulnerability over time. After considering this model together with the proxies available for physiological sensitivity to climate change, we conclude that ectotherms sharing vulnerability traits seem concentrated in lowland tropical forests. Their vulnerability may be exacerbated by negative biotic interactions. Whether tropical forest (or other) species can adapt to warming environments is unclear, as genetic and selective data are scant. Nevertheless, the prospects for tropical forest ectotherms appear grim.