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

Abstract

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. 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. 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. The Maxent predictions showed excellent performance, and the multi‐state SDM framework highlighted 36–77 potential suitable breeding sites in the study area. 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.

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

“…Accounting for such variation in habitat models can provide useful information for wildlife management and conservation (Brambilla and Saporetti ; Frans et al. ). Combining high‐resolution wildlife tracking data (Kays et al.…”

“…This is important, as many animals adapt their habitat use in reaction to changing habitat conditions throughout the year (Godvik et al 2009;Dupke et al 2016;Filla et al 2017). Accounting for such variation in habitat models can provide useful information for wildlife management and conservation (Brambilla and Saporetti 2014;Frans et al 2017). Combining highresolution wildlife tracking data (Kays et al 2015;Pimm et al 2015) with satellite time series holds great potential in this regard, since it allows simultaneously describing how habitat conditions vary within a year, and how animals respond to these changes (Bischof et al 2012;Gschweng et al 2012).…”

Avian SDMs: current state, challenges, and opportunities

“…However, new frameworks that incorporate such diverging species-environment relationships in time and space (e.g. multi-state SDMs; Frans et al 2017) may facilitate future studies. Ignoring these issues could in turn induce an underestimation of areas needed for effective conservation (Runge et al 2016) or an overprediction of ranges in general (Reside et al 2010).…”

“Tracking the Rain Bird”: Modeling the monthly distribution of Pied cuckoo in India

“…2012, Sardà-Palomera et al 2012). However, new frameworks that incorporate such diverging species-environment relationships in time and space (Frans et al 2018) may enable future studies. Ignoring these issues could, in turn induce an underestimation of areas needed for effective conservation (Runge et al 2016) or an over-prediction of ranges in general (Reside et al 2010).…”