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Hierarchical multi‐scale occupancy estimation for monitoring wildlife populations
Author(s) -
Pavlacky David C.,
Blakesley Jennifer A.,
WHITE GARY C.,
Hanni David J.,
Lukacs Paul M.
Publication year - 2012
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.245
Subject(s) - occupancy , scale (ratio) , sampling (signal processing) , sample (material) , spatial ecology , environmental science , sampling design , statistics , geography , computer science , ecology , cartography , mathematics , biology , population , chemistry , demography , filter (signal processing) , chromatography , sociology , computer vision
Occupancy estimation is an effective analytic framework, but requires repeated surveys of a sample unit to estimate the probability of detection. Detection rates can be estimated from spatially replicated rather than temporally replicated surveys, but this may violate the closure assumption and result in biased estimates of occupancy. We present a new application of a multi‐scale occupancy model that permits the simultaneous use of presence–absence data collected at 2 spatial scales and uses a removal design to estimate the probability of detection. Occupancy at the small scale corresponds to local territory occupancy, whereas occupancy at the large scale corresponds to regional occupancy of the sample units. Small‐scale occupancy also corresponds to a spatial availability or coverage parameter where a species may be unavailable for sampling at a fraction of the survey stations. We applied the multi‐scale occupancy model to a hierarchical sample design for 2 bird species in the Black Hills National Forest: brown creeper ( Certhia americana ) and lark sparrow ( Chondestes grammacus ). Our application of the multi‐scale occupancy model is particularly well suited for hierarchical sample designs, such as spatially replicated survey stations within sample units that are typical of avian monitoring programs. The model appropriately accounts for the non‐independence of the spatially replicated survey stations, addresses the closure assumption for the spatially replicated survey stations, and is useful for decomposing the observation process into detection and availability parameters. This analytic approach is likely to be useful for monitoring at local and regional scales, modeling multi‐scale habitat relationships, and estimating population state variables for rare species of conservation concern. © 2011 The Wildlife Society.