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Improving Removal‐Based Estimates of Abundance by Sampling a Population of Spatially Distinct Subpopulations
Author(s) -
Dorazio Robert M.,
Jelks Howard L.,
Jordan Frank
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00360.x
Subject(s) - abundance (ecology) , poisson distribution , covariate , abundance estimation , negative binomial distribution , sampling (signal processing) , statistics , population , count data , ecology , mathematics , biology , computer science , demography , filter (signal processing) , sociology , computer vision
Summary A statistical modeling framework is described for estimating the abundances of spatially distinct subpopulations of animals surveyed using removal sampling. To illustrate this framework, hierarchical models are developed using the Poisson and negative‐binomial distributions to model variation in abundance among subpopulations and using the beta distribution to model variation in capture probabilities. These models are fitted to the removal counts observed in a survey of a federally endangered fish species. The resulting estimates of abundance have similar or better precision than those computed using the conventional approach of analyzing the removal counts of each subpopulation separately. Extension of the hierarchical models to include spatial covariates of abundance is straightforward and may be used to identify important features of an animal's habitat or to predict the abundance of animals at unsampled locations.