Parameter Estimation, Reliability, and Model Improvement for Spatially Explicit Models of Animal Populations

Abstract

We address model specification, parameter estimation, and model reliability for spatially explicit population models (SEPMs). We assume that these models have the complementary goals of understanding the processes that influence the number and distribution of animals in space and time, and forecasting the effect of management or other human activities on population abundance and distribution. Incorrect model structure, parameter estimates, or both will result in unreliable model output. Spatially explicit models require knowledge of population spatial structure, dispersal, and movement rates, in addition to the usual demographic parameters and structural assumptions such as density‐dependence, and are thus potentially very vulnerable to propagation of model uncertainty. Sensitivity analysis and validation can both be used to evaluate the reliability of SEPMs, but the level of spatiotemporal resolution at which the model should be evaluated is often not clear. Many SEPMs are very complex, and validation may only be possible or meaningful on a sub‐model basis. Forecasting, that is, prediction under a different set of conditions than that under which the model was built, will provide a stronger test of model reliability. Forecasts from SEPMs can be used to generate hypotheses that can then be tested as parts of large‐scale adaptive management experiments. In this way resource management goals can be achieved, while providing enhanced understanding of systems and improved predictability of future scenarios.