Integrating multi‐method surveys and recovery trajectories into occupancy models

Abstract

Abstract Conservation and management of animal populations requires knowledge of their occurrence and drivers that influence their distribution. Noninvasive survey methods and occupancy models to account for imperfect detection have become the standard tools for this purpose. Simultaneously addressing both occurrence and occurrence–environment relationships, however, presents multiple challenges, particularly for species with reduced ranges or those recovering from historical declines. Here, we present a comprehensive framework to satisfy the assumption of organism–environment equilibrium, map the range of a species, incorporate camera traps and detection dogs as complementary data sources, and make inference about wildlife space use at multiple scales. To meet these goals, we developed a Bayesian spatial occupancy model for Pacific fishers (Pekania pennanti) in Oregon using data from a large‐scale (64,280 km2) empirical effort combining 1240 camera traps (74,219 trap nights) and 196 detector dog surveys (3 × 3 km units, survey average = 17.3 km/unit). We deployed this model with and without a geoadditive term to improve predicted range map generation and covariate inference, respectively. We used reaction–diffusion models to project recovery trajectories to determine both plausible spatial extents for inclusion in our occupancy model and whether the current distribution can be explained by time‐limited population expansion from historical refugia. To assess nonstationary effects where species–habitat relationships vary spatially, we fit separate models within distinct ecological regions. We confirmed the presence of the native and introduced fisher populations, but populations occupy less area than previously believed. The spatial extent of the introduced population was less than expected except under our lowest growth model, suggesting limiting factors were preventing population expansion. The native population extent matched expectations under several growth scenarios, suggesting that the contemporary distribution is plausibly due to time‐limited expansion. The relationship of fisher occupancy to environmental covariates varied with scale, spatial extent, and ecological region, but fishers consistently selected for old forests at fine spatial scales in the detection model across spatial extents and detection modalities. Collectively, we provide an integration of camera traps and detection dogs into spatial occupancy models and demonstrate how to generate plausible spatial extents to improve inferences for species recovering from range contractions.

Keywords

• camera traps
• detection dogs
• multi‐scale
• nonstationarity
• occupancy models
• Pekania pennanti