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Ecological processes and biodiversity patterns are strongly affected by how animals move through the landscape. However, it remains challenging to predict animal movement and space use. Here we present our new r package enerscape to quantify and predict animal movement in real landscapes based on energy expenditure. enerscape integrates a general locomotory model for terrestrial animals with GIS tools in order to map energy costs of movement in a given environment, resulting in energy landscapes that reflect how energy expenditures may shape habitat use. enerscape only requires topographic data (elevation) and the body mass of the studied animal. To illustrate the potential of enerscape, we analyse the energy landscape for the Marsican bear (Ursus arctos marsicanus) in a protected area in central Italy in order to identify least-cost paths and high-connectivity areas with low energy costs of travel. enerscape allowed us to identify travel routes for the bear that minimize energy costs of movement and regions that have high landscape connectivity based on movement efficiency, highlighting potential corridors. It also identifies areas where high energy costs may prevent movement and dispersal, potentially exacerbating human–wildlife conflicts in the park. A major strength of enerscape is that it requires only widely available topographic and body size data. As such, enerscape permits a first cost-effective way to estimate landscape use and movement corridors even when telemetry data are not readily available, such as for the example with the bear. enerscape is built in a modular way and other movement modes and ecosystem types can be implemented when appropriate locomotory models are available. In summary, enerscape is a new general tool that quantifies, using minimal and widely available data, the energy costs of moving through a landscape. This can clarify how and why animals move in real landscapes and inform practical conservation and restoration decisions.

Original publication




Journal article


Methods in Ecology and Evolution

Publication Date