A grant is awarded to University of Maryland, College Park to develop informatics tools that allow scientists and conservation managers to use animal relocation and tracking data to study movement processes at the population level. Technological advances such as GPS tracking devices have facilitated much recent progress in understanding the movements of individual animals, but scientists' understanding of the emergent spatial dynamics at the population level has not kept pace, in large part due to an absence of appropriate tools for data handling and statistical analysis. To bridge this key gap and study such processes as spatial learning, social interactions vis-Ã -vis aggregation, and population level movement patterns (e.g., migration, nomadism), detailed analyses of individual movement paths are not sufficient. Researchers must, in addition, attend to the relationships that exist between moving animals. This project will develop new and innovative data management and analysis tools focusing on the interrelationship of multiple moving individuals. These include measures that calculate 1) realized mobility (quantifying the relationship of individual to population ranges), 2) population dispersion (quantifying the spatial relationship among individuals), 3) movement coordination (quantifying the coordination of movements among individuals), and 4) intra-individual concordance (quantifying the spatial relationship of relocations of individuals over time). These innovative ways of treating animal movement data will allow researchers to investigate a broad range of new research questions. For example, by statistically analyzing the interrelationships of relocation data among individuals, it will be possible to distinguish and quantify population-level movement patterns such as migration, range residency, and nomadism. The same tools can be used to analyze interrelationships of relocation data among individuals but across time, thereby examining how animal movements change as individuals age and gain experience. Finally these same tools may be applied to analyze social networks and use animal relocations to understand fission-fusion dynamics of grouping behavior and characterize the timing and consistency of aggregations. Using existing data, they will develop and test these new tools using datasets on Mongolian gazelles, whooping cranes, and blacktip sharks. These species represent not only different types of movement (on land, in air, in water) but also different types of relocation data (from visual observations of individually marked animals to GPS relocations to relocations obtained from networked sensor arrays). They will focus on spatial learning and changes in migratory patterns in whooping cranes, nomadic long-distance movement in gazelles, and group formation in sharks.
The project will develop an analysis package in the open-source language R and complement it with a step-by-step hands-on manual to make tools available to a broad, international user community that includes academics, scientists working for governments and non-governmental organizations, and professionals directly engaged in conservation practice and land management. The software package will be made publicly available under www.clfs.umd.edu/biology/faganlab/movement/. Efforts will also include a major emphasis on graduate and undergraduate research and training, through assistantships for PhD students and undergraduates. Additional broader impacts will emerge from analyses of the whooping crane dataset. Through collaborations with endangered species biologists in the US Geological Survey, these analyses will have direct relevance to specific management actions for the whooping crane, such as the timing, group size, and composition of crane reintroductions and potentially their training with ultra-light aircraft.
