Document: STE-3-26-2

Modeling movement, perception, and memory of cowbirds at the landscape scale.

HARPER, S.J.* ¹, J.D.WESTERVELT ² and A.SHAPIRO ³

Miami University, Oxford, OH 45056, USA ¹
University of Illinois, Urbana, IL 61801, USA ²
U. S. Army Construction Engineering Research Laboratory, Champaign, IL 61821, USA ³

Abstract:
Brood parasitism by brown-headed cowbirds (_Molothrus ater_) reduces the reproductive success of two endangered bird species at Fort Hood, TX. A control program has focused on trapping cowbirds at feeding locations, grassland areas almost always associated with the presence of cattle. To enhance the efficacy of the control program, we developed an individual-based model that predicted visitation rates by cowbirds to all potential feeding locations across the landscape (58 X 58 km). Utilizing GRASS-GIS and Swarm software, the model captured the spatial arrangement of habitats, daily movements of cattle, and daily movements of cowbirds from their breeding sites to feeding locations. We simulated four types of movements by cowbirds as they searched for cattle: 1) random walk, 2) direct return to previous locations (i.e., memory), 3) return to previous locations with en route assessment (i.e., memory with limited perception of the current state of the system), and 4) omniscience. Straight-line distances between breeding and feeding locations for cowbirds with memory and perception (type 3) closely matched independent telemetry results; further, total search distances for this movement type approached those of omniscience. While it seems plausible that cowbirds are able to remember foraging locations and to evaluate their surroundings as they travel, additional studies are needed to document their cognitive abilities. Simulating movements of individual cowbirds across a dynamic landscape may help to strengthen ongoing cowbird control efforts.

Keywords: Individual-based modeling, movement, spatial learning, cowbirds, GIS

This abstract is being presented at: 11:25 AM in session:
Symposium # 27: Advancing the Individual-Based Modeling Approach: New Tools and Concepts.