Spatially-Explicit Population Models with Complex Decisions

Abstract

Many approaches exist for modeling the response of animals to environmental condition and change. Regardless of the model selected, uncertainty is a major component in the modeling of complex physical-biological relationships. Structured methods exist for handling uncertainty in these modeling studies, and can facilitate decision-making among stakeholders with differing values. We describe two different approaches for modeling population response to environmental pattern. Then, we propose a simple means for incorporating uncertainty into the modeling process using structured and transparent means. First, a model formula is selected and applied with a structured uncertainty analysis during parameterization. Second, Monte Carlo simulation is applied to propagate the uncertainties in the model outputs induced by the uncertain inputs. Finally, multi-criteria decision analysis (MCDA) is applied to prioritize model forecasts (i.e., of the likely input conditions) according to perceived value, relevance, accuracy, and uncertainty. The structure discussed is simple and can be modified in many ways to meet the demands of a particular study. This paper provides (1) a brief look at alternatives for modeling animal populations and (2) how these types of models can be applied within a structured and transparent framework for handling uncertainty that saves time, money, and effort.