Using Space-for-Time Substitutions in Predictive Models

Ecological forecasting uses scientific data to model how future environmental changes will affect an ecosystem. Carefully applied models are valuable in fisheries management because they allow decisions to be based on the best available science, and improve communication between scientists and managers.  The predictive power of these models depends on the quantity and quality of the data used to determine the statistical relationship between an environmental driver and the ecosystem response. Ideally this relationship would be determined by looking at replicated study sites over the course of an extended period of time. However, in many cases long-term time-series data are not available. Instead, spatial variation of an environmental driver is used to determine the relationship. A potential problem of this method is that factors other than the target driver could be affecting ecosystem response, and these factors may vary spatially. This could produce a misleading correlation between the target variable and ecosystem response.  We use temporal and spatial monitoring data from the Florida Everglades to examine how scaling affects the performance of spatial-substitution models that predict bluefin killifish (Lucania goodei) population response to a drying event.  Bluefin killifish were used because their small size and short lifecycle is amenable to comprehensive sampling, though the results should also be applicable to larger species that are the traditional focus of fisheries management.  Preliminary results suggest that spatial models produce r-squared values comparable to temporal models, and work best when results are not extrapolated outside the range of variation encompassed by the spatial dataset. However, an understanding of the underlying biology of the focal species (such as the shape of the population growth curve) can inform workers of circumstances in which extrapolation can still provide useful information.  Substitution of temporal data with spatial data, while not always ideal, is often the best point of reference we have for making management decisions.  Development of guidelines for making this substitution will help managers avoid pitfalls that may come with indiscriminate application of the models.