Assessing the Data Requirements of Spatially Explicit Dispersal Models

We evaluated the consequences of parameter errors for predictions of spatially explicit population models. We examined a simple model for organisms dispersing in a fragmented landscape and assessed how errors in three model input parameters propagate into errors in model predictions: (1) misclassification of habitat suitability (landscape error); (2) incorrect estimation of how far a disperser can travel (mobility error); and (3) incorrect estimation of the mortality rate of dispersers (dispersal‐mortality error). The two‐dimensional landscape through which organisms dispersed was filled with patches of various shapes (square, linear, and elbow) and sizes (4, 9, and 16 cells), and we allowed the overall proportion of suitable habitat in the landscape (2, 8, 16, and 24%) to vary among runs. A single run consisted of 400 individuals dispersing through the landscape until they found suitable habitat patches, and the output was a frequency distribution of the number of steps taken before a patch was found (n = 400 individuals). In the error‐free model, dispersal success increased with the percentage of the landscape that was composed of suitable habitat and was greater in landscapes filled with more small patches than in those with fewer large patches. Errors in dispersal‐mortality parameters resulted in the greatest prediction errors (25–90%), followed by mobility errors (2–60%) and landscape errors (<1–17%). In general, prediction errors were higher in landscapes with a lower percentage of suitable habitat, precisely the type of habitat characterizing most species of conservation concern. Our results point to the need for better empirical estimates of errors in dispersal parameters. In addition, our results suggest that less detailed models would improve the match between the complexity of the model and the quality of available data.