Abstract
Contemporary ecologists often find themselves with an overwhelming amount of data to analyze. For example, it is now possible to collect nearly continuous spatiotemporal data on animal locations via global positioning systems and other satellite telemetry technology. In addition, there is a wealth of readily available environmental data via geographic information systems and remote sensing. We present a modeling framework that utilizes these forms of data and builds on previous research pertaining to the quantitative analysis of animal movement. This approach provides additional insight into the environmental drivers of residence and movement as well as resource selection while accommodating path uncertainty. The methods are demonstrated in an application involving mule deer movement in the La Sal Range, Utah, USA. Supplemental materials for this article are available online.
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Hooten, M.B., Johnson, D.S., Hanks, E.M. et al. Agent-Based Inference for Animal Movement and Selection. JABES 15, 523–538 (2010). https://doi.org/10.1007/s13253-010-0038-2
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DOI: https://doi.org/10.1007/s13253-010-0038-2