Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors.

Conservation practitioners have long recognized ecological connectivity as a global priority for preserving biodiversity and ecosystem function. In the early years of conservation science, ecologists extended principles of island biogeography to assess connectivity based on source patch proximity and other metrics derived from binary maps of habitat. From 2006 to 2008, the late Brad McRae introduced circuit theory as an alternative approach to model gene flow and the dispersal or movement routes of organisms. He posited concepts and metrics from electrical circuit theory as a robust way to quantify movement across multiple possible paths in a landscape, not just a single least-cost path or corridor. Circuit theory offers many theoretical, conceptual, and practical linkages to conservation science. We reviewed 459 recent studies citing circuit theory or the open-source software Circuitscape. We focused on applications of circuit theory to the science and practice of connectivity conservation, including topics in landscape and population genetics, movement and dispersal paths of organisms, anthropogenic barriers to connectivity, fire behavior, water flow, and ecosystem services. Circuit theory is likely to have an effect on conservation science and practitioners through improved insights into landscape dynamics, animal movement, and habitat-use studies and through the development of new software tools for data analysis and visualization. The influence of circuit theory on conservation comes from the theoretical basis and elegance of the approach and the powerful collaborations and active user community that have emerged. Circuit theory provides a springboard for ecological understanding and will remain an important conservation tool for researchers and practitioners around the globe.Aplicaciones de la Teoría de Circuitos a la Conservación y a la Ciencia de la Conectividad Resumen: Quienes practican la conservación han reconocido durante mucho tiempo que la conectividad ecológica es una prioridad mundial para la preservación de la biodiversidad y el funcionamiento del * email: brett@csp-inc.org Article impact statement: Uses of circuit theory to understand connectivity have had a durable and global impact on conservation science and practice.

In the lower Sonoran Desert of south-western Arizona, climate change and non-native plant invasions have the potential to increase the frequency and size of uncommon wildfires. An understanding of where and why ignitions are more likely to become large fires will help mitigate the negative consequences of fire to native ecosystems. We use a generalised linear mixed model and fire occurrence data from 1989 to 2010 to estimate the relative contributions of fuel and other landscape variables to large fire probability, given an ignition. For the 22-year period we examined, a high value for the maximum annual Normalised Difference Vegetation Index was among the strongest predictors of large fire probability, as were low values of road density and elevation. Large fire probability varied markedly between years of moderate and high fine fuel accumulation. Our estimates can be applied to future periods with highly heterogeneous precipitation. Our map-based results can be used by managers to monitor variability in large fire probability, and to implement adaptive fire mitigation at a landscape scale. The approaches we present have global applications to other desert regions that face similar threats from changing climate, altered fuels and potential punctuated changes in fire regimes.