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An Estimator of Number of Species from Quadrat Sampling
Author(s) -
Haas Peter J.,
Liu Yushan,
Stokes Lynne
Publication year - 2006
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.1541-0420.2005.00390.x
Subject(s) - jackknife resampling , estimator , mathematics , statistics , minimum variance unbiased estimator , bias of an estimator , bayes' theorem , efficient estimator , invariant estimator , trimmed estimator , quadrat , consistent estimator , econometrics , bayesian probability , biology , ecology , shrub
Summary We consider the problem of estimating the number of distinct species S in a study area from the recorded presence or absence of species in each of a sample of quadrats. A generalized jackknife estimator of S is derived, along with an estimate of its variance. It is compared with the jackknife estimator for S proposed by Heltshe and Forrester (1983, Biometrics 39, 1–12) and the empirical Bayes estimator of Mingoti and Meeden (1992, Biometrics 48, 863–875). We show that the empirical Bayes estimator has the form of a generalized jackknife estimator under a specific model for species distribution. We compare the new estimators of S to the empirical Bayes estimator via simulation. We characterize circumstances under which each is superior.