Quantifying apart what belongs together: A multi- state species distribution modelling framework for species using distinct habitats

Species distribution models (SDMs) have been used to inform scientists and conservationists about  the  status  and  change  in  occurrence  patterns  in  threatened species. Many mobile species use multiple functionally distinct habitats, and cannot occupy one habitat type without the other being within a reachable distance. For such species, classical applications of SDMs might lead to erroneous representations of habitat suitability, as the complex relationships between predictors are lost when merging occurrence information across multiple habitats. To better account for the spatial arrangement of complementary—yet mandatory—habitat types, it is important to implement modelling strategies that partition occurrence information according to habitat use in a spatial context. Here, we address this issue by introducing a multi-state SDM framework.
2.  The multi-state SDM framework stratifies occurrences according to the temporal or behavioural use of distinct habitat types, referred to as “states.” Multiple SDMs are then run for each state and statistical thresholds of presence are used to combine these separate predictions. To identify suitable sites that account for distance between habitats, two optional modules are proposed where the thresholded output is aggregated and filtered by minimum area size, or through moving windows across maximum reachable distances.
3.  We illustrate the full use of this framework by modelling the dynamic terrestrial breeding habitat preferences of the New Zealand sea lion (NZSL) (Phocarctos hookeri), using Maxent and trialling both modules to identify suitable sites for possible recolonization.
4.  The Maxent predictions showed excellent performance, and the multi-state SDM framework highlighted 36–77 potential suitable breeding sites in the study area.
5.  This framework can be applied to inform management when defining habitat suitability for species with complex changes in habitat use. It accounts for temporal and behavioural changes in distribution, maintains the individuality of each partitioned SDM, and considers distance between distinct habitat types. It also yields one final, easy-to-understand output for stakeholders and managers.