OPTIMAL ALLOCATION OF POINT-COUNT SAMPLING EFFORT

Citation
Rj. Barker et al., OPTIMAL ALLOCATION OF POINT-COUNT SAMPLING EFFORT, The Auk, 110(4), 1993, pp. 752-758
Citations number
8
Language
INGLESE
art.tipo
Article
Categorie Soggetti
Ornithology
Journal title
ISSN journal
0004-8038
Volume
110
Issue
4
Year of publication
1993
Pages
752 - 758
Database
ISI
SICI code
0004-8038(1993)110:4<752:OAOPSE>2.0.ZU;2-R
Abstract
Both unlimited and fixed-radius point counts only provide indices to p opulation size. Because longer count durations lead to counting a high er proportion of individuals at the point, proper design of these surv eys must incorporate both count duration and sampling characteristics of population size. Using information about the relationship between p roportion of individuals detected at a point and count duration, we pr esent a method of optimizing a point-count survey given a fixed total time for surveying and travelling between count points. The optimizati on can be based on several quantities that measure precision, accuracy , or power of tests based on counts, including (1) mean-square error o f estimated population change; (2) mean-square error of average count; (3) maximum expected total count; or (4) power of a test for differen ces in average counts. Optimal solutions depend on a function that rel ates count duration at a point to the proportion of animals detected. We model this function using exponential and Weibull distributions, an d use numerical techniques to conduct the optimization. We provide an example of the procedure in which the function is estimated from data of cumulative number of individual birds seen for different count dura tions for three species of Hawaiian forest birds. In the example, opti mal count duration at a point can differ greatly depending on the quan tities that are optimized. Optimization of the mean-square error or of tests based on average counts generally requires longer count duratio ns than does estimation of population change. A clear formulation of t he goals of the study is a critical step in the optimization process.