The spatial domain matters: Spatially constrained species rarefaction in a Free and Open Source environment

被引：20

作者：

Bacaro, Giovanni ^{[2
,3
]}

Rocchini, Duccio ^{[1
]}

Ghisla, Anne ^{[1
]}

Marcantonio, Matteo ^{[2
]}

Neteler, Markus ^{[1
]}

Chiarucci, Alessandro ^{[2
]}

机构：

[1] Fdn Edmund Mach, Res & Innovat Ctr, Dept Biodivers & Mol Ecol, I-38010 San Michele All Adige, TN, Italy

[2] Univ Siena, Dept Environm Sci G Sarfatti, Biodivers & Conservat Network, BIOCONNET, I-53100 Siena, Italy

[3] CNR IRPI, Ist Ric Protezione Idrogeol, I-06128 Perugia, Italy

来源：

ECOLOGICAL COMPLEXITY | 2012年 / 12卷

关键词：

Biodiversity assessment; Free and Open Source software; R statistical environment; Sampling effort; Spatially Constrained Rarefaction curves; Species sampling; BETA-DIVERSITY; AREA; CURVES;

D O I：

10.1016/j.ecocom.2012.05.007

中图分类号：

Q14 [生态学（生物生态学）];

学科分类号：

071012 ; 0713 ;

摘要：

Rarefaction curves represent a powerful method for comparing species richness among habitats on an equal-effort basis. Three assumptions are required to correctly perform rarefaction analysis: (i) data collection should be a representative sample of the community under study, (ii) individuals are randomly dispersed, and (iii) species are independently dispersed. However, the community structure is spatially organized, and these criteria cannot be guaranteed. Recently, Chiarucci et al. (2009) proposed a new type of rarefaction, named Spatially Constrained Rarefaction (SCR), which allows to include the autocorrelated structure of the samples in the construction of a rarefaction curve. Here we present an easy-to-use procedure to calculate Spatially Constrained Rarefaction curve in the R environment. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：63 / 69

页数：7

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