Using step and path selection functions for estimating resistance to movement: pumas as a case study

摘要: GPS telemetry collars and their ability to acquire accurate consistently frequent locations have increased the use of step selection functions (SSFs) path (PathSFs) for studying animal movement estimating resistance. However, previously published SSFs PathSFs often do not accommodate multiple scales or multi-scale modeling. We present a method that allows be analyzed with SSF PathSF models. also explore sensitivity model results resistance surfaces whether are used, scale, prediction framework, collar sampling interval. 5-min data from pumas (Puma concolor) in southern California at scales, predict using two frameworks (paired unpaired), potential bias intervals. Regression coefficients were extremely sensitive scale exhibited during movement. found produced stronger regression coefficients, larger values, superior performance than SSFs. observed more heterogeneous when was predicted paired framework compared an unpaired framework. Lastly, we habitat interval longer 5 min. The methods presented provide novel way data. Due method, schedule, care should used modeling corridors conservation purposes these methods.