On the Bayesian estimation of species richness and related quantities from quadrat sampling

Jérôme Dupuis (1)

1 LSP - Laboratoire de Statistique et Probabilités

We consider the problem of estimating the number of species of a biological community located in a region R divided into J quadrats. Recently, two parametric approaches have been developed which both take into account in a same modeling framework the detectability and the occurence of species in the quadrats. One developed by Dorazio and Royle (2005, J.A.S.A. 100, 389-398) ignores the unsampled part of R (thus also J) and models the occurence of species only in the sampled quadrats. We show that this approach can be used only if J is large, which limits its use in practice, since the value of J can strongly vary from one study to another. The other developed by Dupuis and Joachim (2006, Biometrics 62, 706-712) does not have this limitation but it applies only in the presence of prior information. In this paper, we propose a new approach which extends these two approaches since it can be used in non informative setups, and applies without limitation on J. We develop our approach within a simple model, which assumes that the species population is homogeneous. This constitutes the suitable framework for examining the effect that ignoring J has on the Bayesian estimate of species richness. In particular, a simulation study is undertaken which shows that the approach of Dorazio and Royle generates an error which can be important for small or moderate values of J, when species are spatially rare or hard to detect.

Mots clés: Species richness; Bayesian estimate; Biodiversity; Identifiability; Missing data; Quadrat sampling; Species richness.

Domaines: Théorie [stat.TH]; Statistiques [math.ST]; Méthodologie [stat.ME]

Jérôme Dupuis. On the Bayesian estimation of species richness and related quantities from quadrat sampling. 2008. ⟨hal-00281829v3⟩