A spatially explicit, multi-scale occupancy model for large-scale population monitoring

作者：Cro**y Andrew D^*; Porter William F

来源：Journal of Wildlife Management, 2018, 82(6): 1300-1310.

DOI：10.1002/jwmg.21466

• conditional autoregressive model
• hierarchical modeling
• Martes americana
• monitoring
• multiple scales
• occupancy modeling
• Pekania pennanti
• snow tracking
• spatial correlation

One of the continuing challenges in wildlife ecology and management is the ability to obtain reliable estimates of species' distributions at large spatial extents. Multi-scale occupancy models using a cluster sampling design offer the opportunity to increase the resolution of estimates and model processes occurring at multiple spatial scales, increasing the efficiency of large-scale monitoring and mitigating the tradeoff between extent and grain. However, accounting for spatial correlation among subsamples in a way that allows for the addition of covariates remains an issue. Using tracking transect surveys for carnivores as an example, we describe and evaluate a hierarchical, multi-scale occupancy model that integrates existing approaches to estimate occupancy at multiple spatial scales simultaneously, and uses a conditional autoregressive (CAR) process to account for spatial correlation in use between subsamples. We evaluated 3 versions of the model under a single-survey and a multi-survey sampling design: a non-spatial model, a model that accounted for spatial correlation in use between transect segments, and a model that also accounted for spatial correlation in the detection process. Simulations showed that accounting for spatial correlation gave better estimates of transect-level occupancy under both sampling designs, whereas accurate estimates of segment-level use required a multi-survey design. When applied to historical snow track data, the differences in estimates among models followed the same pattern found in the simulations. The multi-survey design was able to detect equivalent declines in segment use with much less survey effort than the single-survey design. The modeling framework presented here offers researchers and managers a powerful tool for monitoring populations at large spatial extents while being able to detect ecologically important dynamics at finer spatial scales.

• 出版日期2018-8