Hierarchical modeling of cluster size in wildlife surveys |
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| Authors: | J Andrew Royle |
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| Institution: | 1.United States Geological Survey,Patuxent Wildlife Research Center,Laurel |
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| Abstract: | Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial
surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance
is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to
increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than
in the population at large. Thus, failure to account for the relationship between detectability and cluster size will tend
to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting
for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional
on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally,
a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation,
model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is
described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models
within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos). |
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