PARAMETER-ESTIMATION, RELIABILITY, AND MODEL IMPROVEMENT FOR SPATIALLY EXPLICIT MODELS OF ANIMAL POPULATIONS

We address model specification, parameter estimation, and model reliab ility for spatially explicit population models (SEPMs). We assume that these models have the complementary goals of understanding the proces ses that influence the number and distribution of animals in space and time, and forecasting the effect of management or other human activit ies on population abundance and distribution. Incorrect model structur e, parameter estimates, or both will result in unreliable model output . Spatially explicit models require knowledge of population spatial st ructure, dispersal, and movement rates, in addition to the usual demog raphic parameters and structural assumptions such as density-dependenc e, and are thus potentially very vulnerable to propagation of model un certainty. Sensitivity analysis and validation can both be used to eva luate the reliability of SEPMs, but the level of spatiotemporal resolu tion at which the model should be evaluated is often not clear. Many S EPMs are very complex, and validation may only be possible or meaningf ul on a sub-model basis. Forecasting, that is, prediction under a diff erent set of conditions than that under which the model was built, wil l provide a stronger test of model reliability. Forecasts from SEPMs c an be used to generate hypotheses that can then be tested as parts of large-scale adaptive management experiments. In this way resource mana gement goals can be achieved, while providing enhanced understanding o f systems and improved predictability of future scenarios.