JSM 2011 Online Program

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Abstract Details

Activity Number: 356
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract - #302828
Title: Modeling Animal Abundance and Detection with a Hierarchical Catch-Effort Model
Author(s): Katherine St. Clair*+ and John Giudice and Eric Dunton
Companies: Carleton College and Minnesota Department of Natural Resources and Shiawassee National Wildlife Refuge
Address: 1 North College St., Northfield, MN, 55057, USA
Keywords: abundance estimation ; removal sampling ; harvest model ; catch-effort ; Bayesian analysis ; hierarchical models
Abstract:

A hierarchical modeling framework can produce estimates of animal abundance and detection from replicated removal counts taken at different locations in a region of interest. A common method of specifying variation in detection probabilities across locations or replicates is with a logistic model that incorporates relevant detection covariates. As an alternative to this logistic model, we propose using a catch-effort model to account for heterogeneity in detection when a measure of removal effort is available for each removal count. We model the probability of detection as a nonlinear function of the removal effort and a catchability parameter that can vary spatially and temporally. Simulation results demonstrate that Bayes estimates from our model are effective estimates of abundance and catchability. We also found that our catch-effort model fits better than logistic models when estimating wild turkey abundance using harvest and hunter counts collected by the Minnesota Department of Natural Resources.


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