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A consumer's guide to nestedness analysis

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Autor(es):
Ulrich, Werner [1] ; Almeida-Neto, Mario [2] ; Gotelli, Nicholas J. [3]
Número total de Autores: 3
Afiliação do(s) autor(es):
[1] Nicholas Copernicus Univ, Dept Anim Ecol, PL-87100 Torun - Poland
[2] Univ Brasilia, IB Dept Ecol, BR-70910900 Asa Norte - Brazil
[3] Univ Vermont, Dept Biol, Burlington, VT 05405 - USA
Número total de Afiliações: 3
Tipo de documento: Artigo de Revisão
Fonte: OIKOS; v. 118, n. 1, p. 3-17, JAN 2009.
Citações Web of Science: 395
Resumo

Nestedness analysis has become increasingly popular in the study of biogeographic patterns of species occurrence. Nested patterns are those in which the species composition of small assemblages is a nested subset of larger assemblages. For species interaction networks such as plant-pollinator webs, nestedness analysis has also proven a valuable tool for revealing ecological and evolutionary constraints. Despite this popularity, there has been substantial controversy in the literature over the best methods to define and quantify nestedness, and how to test for patterns of nestedness against an appropriate statistical null hypothesis. Here we review this rapidly developing literature and provide suggestions and guidelines for proper analyses. We focus on the logic and the performance of different metrics and the proper choice of null models for statistical inference. We observe that traditional `gap-counting' metrics are biased towards species loss among columns (occupied sites) and that many metrics are not invariant to basic matrix properties. The study of nestedness should be combined with an appropriate gradient analysis to infer possible causes of the observed presence-absence sequence. In our view, statistical inference should be based on a null model in which row and columns sums are fixed. Under this model, only a relatively small number of published empirical matrices are significantly nested. We call for a critical reassessment of previous studies that have used biased metrics and unconstrained null models for statistical inference. (AU)

Processo FAPESP: 06/56889-2 - Diversidade e estrutura de interações tritróficas num gradiente de degradação antrópica: plantas, herbívoros e parasitóides em áreas de cerrado
Beneficiário:Mário Almeida Neto
Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado