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Estimating Species Richness from Quadrat Sampling Data: A General Approach
Author(s) -
Dupuis Jérôme A.,
Goulard Michel
Publication year - 2011
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.1541-0420.2011.01595.x
Subject(s) - quadrat , markov chain monte carlo , sampling (signal processing) , computer science , species richness , bayesian probability , fraction (chemistry) , markov chain , parametric statistics , mathematics , bayesian inference , statistics , algorithm , ecology , biology , chemistry , organic chemistry , filter (signal processing) , shrub , computer vision
Summary We consider the problem of estimating the number of species (denoted by S ) of a biological community located in a region divided into n quadrats. To address this question, different hierarchical parametric approaches have been recently developed. Despite a detailed modeling of the underlying biological processes, they all have some limitations. Indeed, some assume that n is theoretically infinite; as a result, n and the sampling fraction are not a part of such models. Others require some prior information on S to be efficiently implemented. Our approach is more general in that it applies without limitation on the size of n , and it can be used in the presence, as well as in the absence, of prior information on S . Moreover, it can be viewed as an extension of the approach of Dorazio and Royle (2005, Journal of the American Statistical Association 100, 389–398) in that n is a part of the model and a prior distribution is placed on S . Despite serious computational difficulties, we have perfected an efficient Markov chain Monte Carlo algorithm, which allows us to obtain the Bayesian estimate of S . We illustrate our approach by estimating the number of species of a bird community located in a forest.