Understanding what determines species’ geographic distributions is crucial for assessing
global change threats to biodiversity. Measuring limits on distributions is usually, and
necessarily, done with data at large geographic extents and coarse spatial resolution.
However, survival of individuals is determined by processes that ...
Understanding what determines species’ geographic distributions is crucial for assessing
global change threats to biodiversity. Measuring limits on distributions is usually, and
necessarily, done with data at large geographic extents and coarse spatial resolution.
However, survival of individuals is determined by processes that happen at small spatial
assembly processes occurring at small scales, and are often available for relatively extensive
areas, so could be useful for explaining species distributions. We demonstrate that Bayesian
Network Inference (BNI) can overcome several challenges to including community structure
into studies of species distributions, despite having been little used to date. We hypothesized
that the relative abundance of coexisting species can improve predictions of species
distributions. In 1570 assemblages of 68 Mediterranean woody plant species we used BNI to
incorporate community structure into Species Distribution Models (SDMs), alongside
environmental information. Information on species associations improved SDM predictions
of community structure and species distributions moderately, though for some habitat
specialists the deviance explained increased by up to 15%. We demonstrate that most species
associations (95%) were positive and occurred between species with ecologically similar traits. This suggests that SDM improvement could be because species co-occurrences are a
proxy for local ecological processes. Our study shows that Bayesian Networks, when
interpreted carefully, can be used to include local conditions into measurements of species’
large-scale distributions, and this information can improve the predictions of species
distributions
