Environmental and Ecological Statistics

, Volume 12, Issue 2, pp 225–243 | Cite as

Efficient statistical mapping of avian count data



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.


breeding bird survey mapping count data poisson model random effects spatial prediction spatial modeling spatial statistics 


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Copyright information

© Springer Science+Business Media, Inc. 2005

Authors and Affiliations

  1. 1.Division of Migratory Bird ManagementU.S. Fish and Wildlife ServiceLaurelUSA
  2. 2.Department of StatisticsUniversity of MissouriColumbiaUSA
  3. 3.USGS Patuxent Wildlife Research CenterLaurelUSA

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