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Hierarchical models and the analysis of bird survey information

Ornis Hungarica

Proceeding of the Bird Numbers Conference, Nyiregyhaza, Hungary, 2001. 6492_Sauer.pdf
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Abstract

Management of birds often requires analysis of collections of estimates. We describe a hierarchical modeling approach to the analysis of these data, in which parameters associated with the individual species estimates are treated as random variables, and probability statements are made about the species parameters conditioned on the data. A Markov-Chain Monte Carlo (MCMC) procedure is used to fit the hierarchical model. This approach is computer intensive, and is based upon simulation. MCMC allows for estimation both of parameters and of derived statistics. To illustrate the application of this method, we use the case in which we are interested in attributes of a collection of estimates of population change. Using data for 28 species of grassland-breeding birds from the North American Breeding Bird Survey, we estimate the number of species with increasing populations, provide precision-adjusted rankings of species trends, and describe a measure of population stability as the probability that the trend for a species is within a certain interval. Hierarchical models can be applied to a variety of bird survey applications, and we are investigating their use in estimation of population change from survey data.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
Hierarchical models and the analysis of bird survey information
Series title:
Ornis Hungarica
Volume
12-13
Issue:
1-2
Year Published:
2003
Language:
English
Contributing office(s):
Patuxent Wildlife Research Center
Description:
217-222
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
217
Last page:
222
Number of Pages:
6