Modeling and Conservation of Wildlife Populations in Managed Landscapes: A Trade-Off Between Effort and Results

Abstract

The mathematical modeling of ecological interactions is an essential tool in predicting the behavior of complex systems across managed landscapes. The literature abounds with examples of models used to explore predator-prey interactions, resource selection, population growth, and the relationship between population density and disease transmission. These models provide managers with an efficient alternative means of testing new management and control strategies without resorting to empirical testing that is often costly, time-consuming, and impractical. However, because models are abstractions of reality that make a large number of simplifying assumptions, their results are substantially less accurate than those of empirical testing. This illustrates a fundamental challenge in conservation biology – the trade-off between effort and the validity or impact of results. Because wildlife managers and researchers have a limited number of resources with which to conserve and study wildlife populations (e.g., time, funding, number of field technicians), this trade-off is a matter of efficiency. Using mathematical models to test a hypothesis, for example, requires substantially less effort and fewer resources than empirically testing, but at the expense of the validity of the results. Understanding the dynamics of this trade-off in different situations and landscapes can help managers and researchers better allocate their limited resources. My intention is to explore this trade-off in addressing questions on individual-level resource selection, state-level estimates of population abundance, and population-level assessments of alternative management strategies in controlling the spread of infectious diseases.