Knowledge of species’ geographic distributions is critical for many ecological and evolutionary questions and underpins effective conservation decision-making, yet usually remains limited in spatial resolution or unreliable. Over large spatial extent, range predictions are typically derived from expert knowledge or, increasingly, species distribution models based on presence records. Expert maps are useful at coarse resolution where they are suitable for delineating unoccupied regions. In contrast, point records typically provide finer-scale occurrence information that can be characterized for its environmental association, but often suffers from observer biases and does not address the geographic or environmental range occupied by a species representatively or fully.
We develop new modeling methodology to combine the complementary informative attributes of both data types to enable improved fine-scale, large extent predictions. Specifically, we use expert delineations to constrain predictions of a species distribution model parameterized with incidental point records. We introduce a maximum entropy approach for combining the two data types and generalize it to Poisson point process models. We illustrate critical decision making during model construction using a detailed case study and illustrate features more generally with applications to species with vastly different range/data attributes. We highlight an application in which ~10,000 expert maps for plant species in South Africa are updated with presence data to reflect novel spatial patterns of diversity.
The presented modeling strategy flexibly accommodates expert maps with different levels of bias and precision. The approach can also be useful with other coarse sources of spatially explicit information, including habitat associations, elevational bands, or vegetation types. The flexible nature of this methodological innovation is likely able to support improved characterization of species distributions for a variety of applications.