Abstract
Habitat suitability models (HSMs) are popular statistical tools used to inform decision-making for conservation planning, using species location data to characterize species-environment relationships and identify important habitats. Suitable habitats may vary according to behavior-specific resource requirements (e.g., foraging, resting), yet HSMs generally ignore behavior because obtaining spatially explicit behavioral data from wild animals is challenging. As such, suitable habitats may be incorrectly identified, and processes determining habitat selection may be misinterpreted. Despite offering unprecedented behavioral insight, contemporary multi-sensor biologgers remain underutilized in this context. We incorporated behavior into HSMs using biologging data collected from adult flatback turtles Natator depressus (n = 42) at a macrotidal study site in Western Australia and subsequently identified and characterized suitable habitat for key in-water behaviors. Foraging and resting locations derived from high-resolution motion sensor data (e.g., accelerometer, magnetometer) coupled with animal-borne video and GPS data were combined with 10 environmental features (i.e., bathymetry, aspect, slope, terrain ruggedness, distance from the coast and currents from a bespoke hydrodynamic model of the study site). A series of random forest HSMs were implemented for each behavior, accounting for temporal variation in habitat use. Bathymetry, distance from the coast, and currents best determined both foraging and resting suitability, with observed differences in habitat selection between behaviors. Overall, spatiotemporal patterns of most suitable foraging and resting habitat were similar, with shallow (10-15 m deep) nearshore (5-10 km from the coast) waters most suitable for both behaviors; however, habitats nearest to the coast (<5 km) were more suitable for foraging than resting. Overall, for foraging and resting, as water level increased turtles selected increasingly nearshore habitats where current speed was low and more variable direction. Overlap between most suitable habitats and current spatial zoning at the study site varied both seasonally and with water level, likely reflecting strong tidal influence on distribution and hence highlighting considerable opportunity for dynamic management. Our approach facilitates mechanistic insight into habitat selection and is generalizable across behaviors, taxa, and study systems, advancing the application of biologging tools to enhance the utility of HSMs and providing crucial context for decision-makers in threatened species management.