OOS 53-2: Modeling animal movements in response to human disturbance using stochastic differential equations and potential functions
Haiganoush K. Preisler, US Forest Service, David R. Brillinger, University of California Berkeley, Michael Wisdom, Pacific Northwest Research Station, and Alan A. Ager, USDA Forest Service.
Background/Question/Methods The advent of GPS technology has made it possible to study human - wildlife interactions on large landscapes and quantify behavioral responses to recreation and other anthropogenic disturbances at increasingly fine scales. Of particular interest are the potential impacts on habitat use patterns, energetics, and cascading impacts on fecundity and other life history traits for key wildlife species that are increasingly exposed to human activities. However, the dynamics of free-ranging animal movements are complex, even without disturbances, and quantifying reactions to disturbance on heterogeneous landscapes is difficult. A modeling framework is required that can sort out diel and random movements that mask short and long-term behavioral response to disturbances. In this talk I will discuss how stochastic differential equations (SDE’s) are used to build potential function surfaces for describing movement patterns and responses to disturbance. I will demonstrate the methods using GPS data for free ranging elk within a 1450 ha study area on the Starkey Experimental Forest located in eastern Oregon. The telemetry data were collected as part of a long-term controlled experiment to understand the response of free ranging Rocky Mountain elk (Cervus elaphus) and deer (Odocoileus hemionus) to multiple anthropogenic disturbances including all terrain vehicles, hikers, mountain bikers and equestrians. This work includes the development of statistical procedures to estimate the simultaneous effects of two or more objects (combinations of animals and disturbance agents) on the potential surfaces, to understand the effects of multiple interacting disturbances on observed patterns of movement. Results/Conclusions We parameterized stochastic differential equations to quantify effects of topography; distance to roads; distance to disturbance, and random movements on elk locations. The GPS technology provided evidence that: 1) elk appear to know the location of roads and the routing of traffic on roads through the forest; 2) hikers and equestrians seem to generate a larger movement response compared to mountain bikers and ATV riders; and, 3) The threshold response distance appears to be well beyond those previously reported in studies that relied on visual methods. The study demonstrated the utility of SDE’s and the motivating concept of potential functions to model animal movements. These methods have also been applied by the authors to study movement patterns in yellow legged frogs (Rana sierrae), elephant seals (Mirounga angustirostris), Monk seals (Monachus schauinslandi) and whale sharks (Rhincodon typus ).