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A spatially explicit, multi‐scale occupancy model for large‐scale population monitoring
Author(s) -
Crosby Andrew D.,
Porter William F.
Publication year - 2018
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.1002/jwmg.21466
Subject(s) - occupancy , transect , sampling (signal processing) , scale (ratio) , spatial analysis , covariate , spatial ecology , statistics , sampling design , autoregressive model , spatial correlation , computer science , kriging , econometrics , population , geography , cartography , ecology , mathematics , demography , filter (signal processing) , sociology , computer vision , biology
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. © 2018 The Wildlife Society.