Rarefy: an R package for the calculation of taxonomic, functional and phylogenetic sample-based spatially explicit rarefaction curves

AbstractRarefaction methods are statistical techniques used to estimate diversity for an incremental number of samples, generating rarefaction curves that depict diversity as a function of sample size. These methods are widely applied in ecological research to compare taxonomic, functional, and phylogenetic diversity across samples with varying collection efforts. However, incorporating spatially explicit rarefaction methods has become essential, as accounting for spatial autocorrelation substantially influences results and alters how biodiversity hotspots or conservation priorities are identified.This paper describes Rarefy, an R package that introduces novel functions capable of handling any diversity metric, allowing users to compute the expected values of taxonomic, functional, or phylogenetic indices for reduced sample sizes under spatially constrained, distance-based sampling unit arrangements.To demonstrate its utility, we applied the package to assess the functional diversity of plant communities along the northern Adriatic coastline. The spatially explicit rarefaction functions consistently produced lower diversity values compared to their non-spatial counterparts, reflecting the functional redundancy typical of spatially adjacent plant communities. These differences provide an indirect yet effective measure of spatial autocorrelation in the dataset.The Rarefy package offers ecologists a robust and straightforward tool to account for spatial constraints in rarefaction analyses, yielding more ecologically meaningful diversity estimates. Future developments will include the integration of null models, enabling comparisons between observed and randomized community diversity patterns, an approach that is exemplified in this study.