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Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

Author:
Eskelson, Bianca N.I., Temesgen, Hailemariam, Barrett, Tara M.
Source:
Canadian journal of forest research = 2009 v.39 no.9 pp. 1749–1765
ISSN:
0045-5067
Subject:
forest trees, tree cavities, snags, statistical models, regression analysis, equations, spatial data, nesting sites, forest stands
Abstract:
Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods. The models were developed and fit to data collected by the Forest Inventory and Analysis program of the US Forest Service in Washington, Oregon, and California. For predicting cavity tree and snag abundance per stand, all three NB regression models performed better in terms of mean square prediction error than the NN imputation methods. The most similar neighbor imputation, however, outperformed the NB regression models in predicting overall cavity tree and snag abundance.
Agid:
35182
Handle:
10113/35182