Please use this identifier to cite or link to this item:
https://repositorio.inpa.gov.br/handle/1/17122
Title: | Estimating species richness in hyper-diverse large tree communities |
Authors: | ter Steege, H. Sabatier, Daniel Oliveira, Sylvia Mota de Magnusson, William Ernest Molino, Jean François Gomes, Vitor H.F. Pos, Edwin T. Salomão, Rafael Paiva |
Keywords: | Data Set Estimation Method Plant Community Species Richness Tropical Forest Amazonia Biodiversity Ecology Forest Tree Biodiversity Ecology Forests Trees |
Issue Date: | 2017 |
metadata.dc.publisher.journal: | Ecology |
metadata.dc.relation.ispartof: | Volume 98, Número 5, Pags. 1444-1454 |
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. © 2017 by the Ecological Society of America |
metadata.dc.identifier.doi: | 10.1002/ecy.1813 |
Appears in Collections: | Artigos |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.