ABI Innovation: Advanced mathematical, statistical, and software tools to unlock the potential of animal tracking data

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This project aims to develop a statistically rigorous analytical foundation for the nascent field of movement ecology. An understanding of animal movement can inform a wide range of biological topics including population and community ecology, animal physiology, disease spread, gene flow, and wildlife management and conservation. Historically, a lack of movement data limited progress, but advances in tracking technology have facilitated the collection of high-quality movement data for an ever-growing number of species. Now, the key bottleneck is the dearth of good statistical tools for extracting information from these accumulating data sources. This research addresses this gap by combining movement-modeling techniques from physics with statistical methods for non-independent data from geostatistics. These next-generation analytical tools will allow biologists to tackle the four key analysis categories in movement ecology: 1) modeling movement paths, 2) estimating kinematic quantities such as velocity and distance travelled, 3) identifying driving relationships between resources and movement, and 4) quantifying animal space use. Outreach efforts will target the diverse array of biologists collecting and asking questions of animal movement data. Specifically, a series of tutorial papers will demonstrate methods covering the four analysis categories on conservation-focused case studies including African bush elephants in Kenya and the endangered khulan in the Mongolian Gobi desert. Additionally, an in-depth training course aimed at conservation practitioners and wildlife managers will be offered, and project results will be incorporated into the graduate and undergraduate curriculum at University of Maryland.This work combines recent advances in modeling movement as a continuous space, continuous time stochastic process with kriging techniques from geostatistics. Kriging is a statistically optimal method of probabilistically "filling in the blanks" between a limited number of autocorrelated data points. Kriging revolutionized geostatistics and is now the gold standard for interpolating between autocorrelated spatial point observations. Analogously, adapting kriging to movement data will allow researchers to probabilistically reconstruct movement paths from a limited number of location observations. Knowledge of the continuous path an animal traversed is the critical nexus linking animal movement data to a wide range of ecologically-informative and conservation-relevant analyses. These transformative methods therefore have the potential to remove the key roadblock that is currently holding movement ecology back. To fully capitalize on that potential, this project will develop an integrated suite of freely available software packages for the R environment for statistical computing. These packages will enable users to answer movement related questions for a broad range of species with the powerful krige-based analytical tools the project develops. Project results will be available at http://biology.umd.edu/movement.html.