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.
Scheda prodotto non validato
Scheda prodotto in fase di analisi da parte dello staff di validazione
|Titolo:||The spatial domain matters: Spatially constrained species rarefaction in a Free and Open Source environment|
|Citazione:||Bacaro, G., Rocchini, D., Ghisla, A., Marcantonio, M., Neteler, M., & Chiarucci, A. (2012). The spatial domain matters: Spatially constrained species rarefaction in a Free and Open Source environment. ECOLOGICAL COMPLEXITY, 12, 63-69.|
|Appare nelle tipologie:||1.1 Articolo in rivista|