Experimental support of the scaling rule for demographic stochasticity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/73613/1/j.1461-0248.2006.00903.x.pd

Genetic factors in threatened species recovery plans on three continents

Around the world, recovery planning for threatened species is being applied in an attempt to stem the current extinction crisis. Genetic factors linked to small population processes (eg inbreeding, loss of genetic diversity) play a key role in species viability. We examined how often genetic factors are considered in threatened species recovery planning. We selected recent species recovery plans from Europe (n = 110), North America (the US only; n = 100), and Australia (n = 108), and reviewed three broad categories of genetic data they address: population‐genetic, fitness‐related, and life‐history data. We found that the host country, taxonomic group to which the species belonged, and several proposed management actions were important predictors of the inclusion of genetic factors. Notably, species recovery plans from the US were more likely to include genetic issues, probably due to legislative requirements. We recommend an international standard, similar to an IUCN Red List framework, that requires explicit consideration of genetic aspects of long‐term viability

Modelling range dynamics under global change: which framework and why?

1. To conserve future biodiversity, a better understanding of the likely effects of climate and land-use change on the geographical distributions of species and the persistence of ecological communities is needed. Recent advances have integrated population dynamic processes into species distribution models (SDMs), to reduce potential biases in predictions and to better reflect the demographic nuances of incremental range shifts. However, there is no clear framework for selecting the most appropriate demographic-based model for a given data set or scientific question. 2. We review the computer-based modelling platforms currently used for the development of either population- or individual-based species range dynamics models. We describe the features and requirements of 20 software platforms commonly used to generate simulations of species ranges and abundances. We classify the platforms according to particular capabilities or features that account for user requirements and constraints, such as (i) ability to simulate simple to complex population dynamics, (ii) organism specificity or (iii) their computational capacities. 3. Using this classification, we develop a protocol for choosing the most appropriate framework for modelling species range dynamics based in data availability and research requirements. We find that the main differences between modelling platforms are related to the way in which they simulate population dynamics, the type of organisms they are able to model and the ecological processes they incorporate. We show that some platforms can be used as generic modelling software to investigate a broad range of ecological questions related to the range dynamics of most species, and how these are likely to change in the future in response to forecast climate and land-use change. We argue that model predictions will be improved by reducing usage to a smaller number of highly flexible freeware platforms. 4. Our approach provides ecologists and conservation biologists with a clear method for selecting the most appropriate software platform that meets their needs when developing SDMs coupled with population-dynamic processes. We argue that informed tool choice will translate to better predictions of species responses to climate and land-use change and improved conservation management.Miguel Lurgi, Barry W. Brook, Frédérik Saltré and Damien A. Fordha

Incorporating evolutionary processes into population viability models

We examined how ecological and evolutionary (eco-evo) processes in population dynamics could be better integrated into population viability analysis (PVA). Complementary advances in computation and population genomics can be combined into an eco-evo PVA to offer powerful new approaches to understand the influence of evolutionary processes on population persistence. We developed the mechanistic basis of an eco-evo PVA using individual-based models with individual-level genotype tracking and dynamic genotype-phenotype mapping to model emergent population-level effects, such as local adaptation and genetic rescue. We then outline how genomics can allow or improve parameter estimation for PVA models by providing genotypic information at large numbers of loci for neutral and functional genome regions. As climate change and other threatening processes increase in rate and scale, eco-evo PVAs will become essential research tools to evaluate the effects of adaptive potential, evolutionary rescue, and locally adapted traits on persistence