A joint model for the estimation of species distributions and environmental characteristics from point-referenced data

Predicting and explaining species occurrence using environmental characteristics is essential for nature conservation and management, especially for rare species that are under threat from climate change, acidification and eutrophication. Species distribution models consider species occurrence as the dependent variable and environmental conditions as the independent variables. Suitable conditions are estimated based on a sample of species observations, where one assumes that the underlying environmental conditions are known. This is not always the case, as environmental variables at broad spatial scales are regularly extrapolated from point-referenced data. A two-stage approach is then used, where the missing environmental variables are predicted before a species distribution model is fitted. However, treating the predicted independent variables as accurate surveys of the environmental conditions at a specific point does not take into account prediction uncertainty or consider that the species occurrence may inform us about the environmental variables. To address both issues, we present a joint hierarchical Bayesian model where models for the environmental variables, rather than a set of predicted values, are input to the species distribution model. All models are fitted together based only on point-referenced observations, which results in a correct propagation of uncertainty. We use plant species and soil samples taken during field visits in the Netherlands as a case study. We find that predicted maps of species occurrence in the study area and abiotic associations sometimes differ from the two-stage approach