Model‐based integration of observed and expert‐based information for assessing the geographic and environmental distribution of freshwater species

Freshwater ecosystems harbor specialized and vulnerable biodiversity, and the prediction of potential impacts of freshwater biodiversity to environmental change requires knowledge of the geographic and environmental distribution of taxa. To date, however, such quantitative information about freshwater species distributions remains limited. Major impediments include heterogeneity in available species occurrence data, varying detectability of species in their aquatic environment, scarcity of contiguous freshwater‐specific predictors, and methods that support addressing these issues in a single framework. Here we demonstrate the use of a hierarchical Bayesian modeling (HBM) framework that combines disparate species occurrence information with newly‐developed 1 km freshwater‐specific predictors, to account for imperfect species detection and make fine‐grain (1 km) estimates of distributions in freshwater organisms. The approach integrates a Bernoulli suitability and a Binomial observability process into a hierarchical zero‐inflated Binomial model. The suitability process includes point presence observations, records of site visits, 1 km environmental predictors and expert‐derived species range maps integrated with a distance‐decay function along the within‐stream distance as covariates. The observability process uses repeated observations to estimate a probability of observation given that the species was present. The HBM accounts for the spatial autocorrelation in species habitat suitability projections using an intrinsic Gaussian conditional autoregressive model. We used this framework for three fish species native to different regions and habitats in North America. Model comparison shows that HBMs significantly outperformed non‐spatial GLMs in terms of AUC and TSS scores, and that expert information when appropriately included in the model can provide an important refinement. Such ancillary species information and an integrative, hierarchical Bayesian modeling framework can therefore be used to advance fine‐grain habitat suitability predictions and range size estimates in the freshwater realm. Our approach is extendable in terms of data availability and generality and can be used on other freshwater organisms and regions.