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Efficient statistical mapping of avian count data

Environmental and Ecological Statistics

6349_Royle.pdf
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Abstract

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Efficient statistical mapping of avian count data
Series title:
Environmental and Ecological Statistics
Volume
12
Issue:
2
Year Published:
2005
Language:
English
Contributing office(s):
Patuxent Wildlife Research Center
Description:
225-243
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
Larger Work Title:
Environmental and Ecological Statistics
First page:
225
Last page:
243
Number of Pages:
19