Evaluating coyote management strategies using a spatially explicit, individual-based, socially structured population model
Managing canid predation on livestock is the leading challenge facing canid conservation worldwide. However, removing canids, and coyotes in particular, to reduce livestock predation is environmentally and socially controversial. In addition, it can be expensive and logistically difficult to field evaluate the myriad of potential selective, spatial, and temporal canid management strategies. Here, we develop a spatially explicit, individual-based simulation model to evaluate commonly used or promoted coyote control strategies. We began with an already constructed non-spatial, individual-based stochastic coyote population model that incorporated behavioral features, such as dominance and territoriality. We added a spatial component and enhanced the social rule set to more realistically model coyote movement and territory replacement. This model merges coyote spatial, social, and population ecology into a management framework. The development, structure, and parameterization of this model are described in detail. For lethal methods, model results suggest that spatially intensive removals are more efficient and long lasting compared to random removal methods. However, sterilization appears to be the management strategy offering the largest and most lasting impact on coyote population dynamics. We recommend adding spatial prey/livestock density and environmental components to this model to further enhance its ecological reality and management usefulness. Although this model is applied to coyotes in particular, it is applicable to many canid species of conservation concern. This model provides a tool to assist in the development of more effective and socially acceptable livestock predation management strategies.
Conner, M. M., M. R. Ebinger, and F. F. Knowlton. 2008. Evaluating coyote management strategies using a spatially explicit, individual-based, socially structured population model. Ecological Modelling 219:234-247.