The Future of the Brazilian Amazon ed individuals.These efforts, however, pale in comparison to the scale of ongoing and planned development activities in the Amazon. Under the auspices of its "Avanpa Brasil" (Advance Brazil) program (7), the Brazil-

Aim and Location We assessed the effects of biophysical and anthropogenic predictors on deforestation in Brazilian Amazonia. This region has the world's highest absolute rates of forest destruction and fragmentation.Methods Using a GIS, spatial data coverages were developed for deforestation and for three types of potential predictors: (1) human-demographic factors (rural-population density, urban-population size); (2) factors that affect physical accessibility to forests (linear distances to the nearest paved highway, unpaved road and navigable river), and (3) factors that may affect land-use suitability for human occupation and agriculture (annual rainfall, dry-season severity, soil fertility, soil waterlogging, soil depth). To reduce the effects of spatial autocorrelation among variables, the basin was subdivided into >1900 quadrats of 50 · 50 km, and a random subset of 120 quadrats was selected that was stratified on deforestation intensity. A robust ordination analysis (non-metric multidimensional scaling) was then used to identify key orthogonal gradients among the ten original predictor variables.Results The ordination revealed two major environmental gradients in the study area. Axis 1 discriminated among areas with relatively dense human populations and highways, and areas with sparse populations and no highways; whereas axis 2 described a gradient between wet sites having low dry-season severity, many navigable rivers and few roads, and those with opposite values. A multiple regression analysis revealed that both factors were highly significant predictors, collectively explaining nearly 60% of the total variation in deforestation intensity (F 2,117 ¼ 85.46, P < 0.0001). Simple correlations of the original variables were highly concordant with the multiple regression model and suggested that highway density and rural-population size were the most important correlates of deforestation.Main conclusions These trends suggest that deforestation in the Brazilian Amazon is being largely determined by three proximate factors: human population density, highways and dryseason severity, all of which increase deforestation. At least at the spatial scale of this analysis, soil fertility and waterlogging had little influence on deforestation activity, and soil depth was only marginally significant. Our findings suggest that current policy initiatives designed to increase immigration and dramatically expand highway and infrastructure networks in the Brazilian Amazon are likely to have important impacts on deforestation activity. Deforestation will be greatest in relatively seasonal, south-easterly areas of the basin, which are most accessible to major population centres and where large-scale cattle ranching and slash-andburn farming are most easily implemented.

Concern about the future of Amazonian forests is growing as both the extent and rate of primary forest destruction increase. We combine spatial information on various biophysical, demographic and infrastructural factors in the Brazilian Amazon with satellite data on deforestation to evaluate the relative importance of each factor to deforestation in the region. We assess the sensitivity of results to alternative sampling methodologies, and compare our results to those of previous empirical studies of Amazonian deforestation. Our findings, in concert with those of previous studies, send a clear message to planners: both paved and unpaved roads are key drivers of the deforestation process. Proximity to previous clearings, high population densities, low annual rainfall, and long dry seasons also increase the likelihood that a site will be deforested; however, roads are consistently important and are the factors most amenable to policymaking. We argue that there is ample evidence to justify a fundamental change in current Amazonian development priorities if additional largescale losses of forests and environmental services are to be avoided. Published by Elsevier Ltd.

In much of the world, the persistence of long-distance migrations by mammals is threatened by development. Even where human population density is relatively low, there are roads, fencing, and energy development that present barriers to animal movement. If we are to conserve species that rely on long-distance migration, then it is critical that we identify existing migration impediments. To delineate stopover sites associated with anthropogenic development, we applied Brownian bridge movement models to high-frequency locations of pronghorn (Antilocapra americana) in the Greater Yellowstone Ecosystem. We then used resource utilization functions to assess the threats to long-distance migration of pronghorn that were due to fences and highways. Migrating pronghorn avoided dense developments of natural gas fields. Highways with relatively high volumes of traffic and woven-wire sheep fence acted as complete barriers. At crossings with known migration bottlenecks, use of high-quality forage and shrub habitat by pronghorn as they approached the highway was lower than expected based on availability of those resources. In contrast, pronghorn consistently utilized high-quality forage close to the highway at crossings with no known migration bottlenecks. Our findings demonstrate the importance of minimizing development in migration corridors in the future and of mitigating existing pressure on migratory animals by removing barriers, reducing the development footprint, or installing crossing structures.

Highlights d Ungulates moved to track forage in landscapes with wavelike spring green-up d Patterns of green-up explained where migratory behavior occurred in many ecosystems d At the species level, migrants and residents received equivalent foraging benefits d Movement tactics represent behavioral adaptations to specific landscapes