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Calculate metrics for bioregions

Source: R/bioregion_metrics.R
bioregion_metrics.Rd

This function calculates the number of sites, the number of species, the number of endemic species and the proportion of endemism per bioregion. The spatial coherence can be optionally computed if a spatial object is provided.

Usage

bioregion_metrics(bioregionalization, comat, map = NULL, col_bioregion = NULL)

Arguments

bioregionalization

A bioregion.clusters object.

comat

A site-species matrix with sites as rows and species as columns.

map

A spatial object that can be handled by sf or terra. The first attribute or layer should correspond to the sites' ID (see Details). Needed only for the spatial coherence (NULL by default).

col_bioregion

Deprecated.

Value

A data.frame with 5 columns (Bioregion ID and metrics described below) or 7 if spatial coherence is computed.

  • NbSites: Number of sites per bioregion

  • Richness: Number of distinct species per bioregion.

  • Rich_Endemics: Number of species found only in the bioregion.

  • Prop_Endemics: Fraction of endemics species.

  • SC_size: Spatial coherence based on size, fraction of the number of site contained in the bioregion's largest contiguous patch.

  • SC_area: Spatial coherence based on area, fraction of the bioregion area contained in its largest contiguous patch.

Note that if bioregionalization contains multiple partitions (i.e., if dim(bioregionalization$clusters) > 2), a list will be returned.

Details

map should be the output of map_bioregions(bioregionalization, geometry, write_clusters = TRUE)

See also

For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a5_2_summary_metrics.html.

Associated functions: site_species_metrics bioregionalization_metrics

Author

Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)
Maxime Lenormand (maxime.lenormand@inrae.fr)

Examples

comat <- matrix(sample(1000, 50), 5, 10)
rownames(comat) <- paste0("Site", 1:5)
colnames(comat) <- paste0("Species", 1:10)

net <- similarity(comat, metric = "Simpson")
clust <- netclu_louvain(net)

bioregion_metrics(bioregionalization = clust, 
                  comat = comat) 
#>   Bioregion NbSites Richness Rich_Endemics Prop_Endemics
#> 1         1       5       10            10             1