Tue, Aug 16, 2022: 4:30 PM-4:45 PM
515A
Background/Question/MethodsAnimal-habitat relationships tend to occur at specific spatial scales. Accurately identifying these scales and accounting for their variance is crucial for linking habitat selection patterns to the ecological processes giving rise to them. Although this fundamental issue has long been recognized, issues relating to spatial scale remain under-examined in habitat selection studies. We used a dataset collected from 485 GPS-collared white-tailed deer (Odocoileus virginianus) across three study sites in Missouri, USA to model habitat selection at 65 spatial scales from 900 m2 to 15 km2 using integrated step selection functions. To investigate potential drivers of spatial scaling we used multiple linear regression to model how scale of effect, defined as the scale at which model AIC was minimized, could be predicted by intrinsic (age, sex, and home range size) and extrinsic factors (study site, season, mean percentage forest in home range, mean distance to nearest road in home range).
Results/ConclusionsScale of effect varied substantially among individuals, and individual variation in scale of effect was predicted by home range size, study site, and proportion of forest within a home range. In contrast, other intrinsic and extrinsic factors had little to no relationship with scale of effect. Parameter coefficients for forest cover and distance to nearest road varied strongly with opposing directionality of responses across spatial scales, revealing that spatial scale may bias habitat selection analyses. Coefficients were both positive and negative at different scales for an average of 86.7% individuals, and no single spatial scale resulted in the scale of effect more than 9.0% of the time. Our study demonstrates that spatial scale can strongly influence model parameter coefficients, thereby raising questions about the conventional interpretation of habitat selection analyses. We discuss outstanding issues regarding the comparability of results across study sites and the future of multi-scale habitat selection analyses.
Results/ConclusionsScale of effect varied substantially among individuals, and individual variation in scale of effect was predicted by home range size, study site, and proportion of forest within a home range. In contrast, other intrinsic and extrinsic factors had little to no relationship with scale of effect. Parameter coefficients for forest cover and distance to nearest road varied strongly with opposing directionality of responses across spatial scales, revealing that spatial scale may bias habitat selection analyses. Coefficients were both positive and negative at different scales for an average of 86.7% individuals, and no single spatial scale resulted in the scale of effect more than 9.0% of the time. Our study demonstrates that spatial scale can strongly influence model parameter coefficients, thereby raising questions about the conventional interpretation of habitat selection analyses. We discuss outstanding issues regarding the comparability of results across study sites and the future of multi-scale habitat selection analyses.