Estimating species richness in hyper-diverse large tree communities
2017
ter Steege, Hans | Sabatier, Daniel | Mota de Oliveira, Sylvia | Magnusson, William E. | Molino, Jean-François | Gomes, Vitor F. | Pos, Edwin T. | Salomão, Rafael P. | Naturalis Biodiversity Center [Leiden] | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie]) | Instituto Nacional de Pesquisas da Amazônia = National Institute of Amazonian Research (INPA) | Museu Paraense Emílio Goeldi [Belém, Brésil] (MPEG) | ANR-10-LABX-0025, PVE–MEC/MCTI/CAPES/CNPq/FAPs. Grant Number: 407232/2013-3 | ANR-10-LABX-0025,CEBA,CEnter of the study of Biodiversity in Amazonia(2010)
Species richness estimation is one of the most widely used analyses carried out by ecologists, and nonparametric estimators are probably the most used techniques to carry out such estimations. We tested the assumptions and results of nonparametric estimators and those of a logseries approach to species richness estimation for simulated tropical forests and five datasets from the field. We conclude that nonparametric estimators are not suitable to estimate species richness in tropical forests, where sampling intensity is usually low and richness is high, because the assumptions of the methods do not meet the sampling strategy used in most studies. The logseries, while also requiring substantial sampling, is much more effective in estimating species richness than commonly used nonparametric estimators, and its assumptions better match the way field data is being collected.
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