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Estimating species richness in hyper‐diverse large tree communities
Author(s) -
Steege Hans,
Sabatier Daniel,
Mota de Oliveira Sylvia,
Magnusson William E.,
Molino JeanFrançois,
Gomes Vitor F.,
Pos Edwin T.,
Salomão Rafael P.
Publication year - 2017
Publication title -
ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.144
H-Index - 294
eISSN - 1939-9170
pISSN - 0012-9658
DOI - 10.1002/ecy.1813
Subject(s) - species richness , estimator , nonparametric statistics , sampling (signal processing) , ecology , estimation , statistics , field (mathematics) , econometrics , mathematics , computer science , biology , economics , management , filter (signal processing) , pure mathematics , computer vision
Abstract 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 data sets 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.