Improving the forecast for biodiversity under climate change.

New biological models are incorporating the realistic processes underlying biological responses to climate change and other human-caused disturbances. However, these more realistic models require detailed information, which is lacking for most species on Earth. Current monitoring efforts mainly document changes in biodiversity, rather than collecting the mechanistic data needed to predict future changes. We describe and prioritize the biological information needed to inform more realistic projections of species' responses to climate change. We also highlight how trait-based approaches and adaptive modeling can leverage sparse data to make broader predictions. We outline a global effort to collect the data necessary to better understand, anticipate, and reduce the damaging effects of climate change on biodiversity.

Density-regulated population dynamics and conditional dispersal alter the fate of mutations occurring at the front of an expanding population.

There is an increasing recognition that the interplay between ecological and evolutionary processes shapes the genetic footprint of populations during and after range expansions. However, more complex ecological processes regularly considered within spatial ecology remain unexplored in models describing the population genetics of range expansion. In this study we integrate flexible descriptions of population growth and competition as well as conditional dispersal into a model that simulates the fate of mutations occurring at the wave front of an expanding population. Our results show that the survival and distribution of a mutation is not only affected by its bias (that is, whether it is deleterious, neutral or beneficial) but also by the mode of local density regulation and conditional dispersal of the simulated populations. It is in particular the chance of a mutation to establish at the front of advance and 'surf' to high frequencies that critically depends on the investigated ecological processes. This is because of the influence of these processes on demographic stochasticity in the system and the differential responses of deleterious, neutral and beneficial mutations to this stochasticity. Generally, deleterious mutations rely more on chance and thus profit the most from ecological processes that enhance demographic stochasticity during the period of establishment. Our study emphasizes the importance of incorporating more ecological realism into evolutionary models to better understand the consequences of shifting geographic ranges for the genetic structure of populations and to find efficient adaptation strategies to mitigate these effects.

Extinction risk in a temporally correlated fluctuating environment.

Usually extinction risk due to environmental stochasticity is estimated under the assumption of white environmental noise. This holds for a sufficiently short correlation time tauc of the fluctuations compared to the internal time scale of population growth r-1 (tauc/r-1<<1). Using a time-discrete simulation model we investigate when the white noise approximation is misleading. Environmental fluctuations are related to fluctuations of the birth and death rates of the species and the temporal correlation of these fluctuations (coloured noise) is described by a first-order autoregressive process. We found that extinction risk increases rapidly with correlation time tauc if the strength of noise is large. In this case the white noise approximation underestimates extinction risk essentially unless temporal correlation is very small (tauc/r-1<<0.1). Extinction risk increases only slowly with correlation time if the strength of noise is small. Then the white noise approximation may be used even for stronger temporal correlations (tauc/r-1>/=0.1). Thus, the estimation of extinction risk on the basis of white or coloured noise must be justified by time scale and strength of the fluctuations. Especially for species that are sensitive to environmental fluctuations the applicability of the white noise approximation should be carefully checked.