Bayesian Learning of Biodiversity Models Using Repeated Observations

Predictive biodiversity distribution models (BDM) are useful for understanding the structure and functioning of ecological communities and managing them in the face of anthropogenic disturbances. In cases where their predictive performance is good, such models can help fill knowledge gaps that could only otherwise be addressed using direct observation, an often logistically and financially onerous prospect. The cornerstones of such models are environmental and spatial predictors. Typically, however, these predictors vary on different spatial and temporal scales than the biodiversity they are used to predict and are interpolated over space and time. We explore the consequences of these scale mismatches between predictors and predictions by comparing the results of BDMs built to predict fish species richness on Australia’s Great Barrier Reef. Specifically, we compared a series of annual models with uninformed priors with models built using the same predictors and observations, but which accumulated information through time via the inclusion of informed priors calculated from previous observation years. Advantages of using informed priors in these models included (1) down-weighting the importance of a large disturbance, (2) more certain species richness predictions, (3) more consistent predictions of species richness and (4) increased certainty in parameter coefficients. Despite such advantages, further research will be required to find additional ways to improve model performance.