Abstract. 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 a easy-to-use procedure to calculate Spatially Constrained Rarefaction curve in the R environment.

Bacaro, G.; Rocchini, D.; Ghisla, A.; Marcantonio, M.; Neteler, M.G.; Chiarucci, A. (2012). The spatial domain matters: spatially constrained species rarefaction in a Free and Open Source environment. ECOLOGICAL COMPLEXITY, 12: 63-69. doi: 10.1016/j.ecocom.2012.05.007 handle: http://hdl.handle.net/10449/21655

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

Rocchini, Duccio;Ghisla, Anne;Marcantonio, Matteo;Neteler, Markus Georg;
2012-01-01

Abstract

Abstract. 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 a easy-to-use procedure to calculate Spatially Constrained Rarefaction curve in the R environment.
Biodiversity assessment
Free and Open Source software
R statistical environment
Sampling effort
Spatially Constrained Rarefaction curves
Species sampling
Biodiversità
Ecologia spaziale
Settore BIO/07 - ECOLOGIA
Bacaro, G.; Rocchini, D.; Ghisla, A.; Marcantonio, M.; Neteler, M.G.; Chiarucci, A. (2012). The spatial domain matters: spatially constrained species rarefaction in a Free and Open Source environment. ECOLOGICAL COMPLEXITY, 12: 63-69. doi: 10.1016/j.ecocom.2012.05.007 handle: http://hdl.handle.net/10449/21655
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10449/21655
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