Abstract
Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub‐disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species’ presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change.
Highlights
Ecological studies at different spatial scales tend to ask different questions and use different methods
37 species had fewer than 30 presence records, so we focused on the 14 species with the most presence records (36–94 occurrences; Table 1) when validating model performance and assessing range changes because model performance is better with more records (Pearson et al 2007; names and functional groups of all 54 species are listed in Table S1 in Supporting Information)
The approach addresses a longstanding problem in macroecology: that models of species distributions have focused on the abiotic constraints imposed on species distributions and failed to take into account biotic interactions
Summary
Ecological studies at different spatial scales tend to ask different questions and use different methods. Random variables in our BNs are probabilities of species occurrence and biotic interactions are modelled as positive and negative conditional dependencies among random variables. In this way, the probability of species occurrence from an SDM at a particular location can be modified up or down given the expected presence of any interaction partners and their combined effect on the focal species. Values for shape parameters could be set based on priors, i.e. ai;x 1⁄4 fðpi;xÞ and bi;x 1⁄4 fðpi;xÞ, as well as separately for positive and negative conditional dependencies Even with this extra flexibility, notice that the SUM model in eqn 3 still assumes that species identity does not matter. Species ranges for 2010 represent the ‘present day’ and those for 2050 represent predictions under environmental change
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
