site_species_metrics {bioregion} | R Documentation |
Calculate contribution metrics of sites and species
Description
This function calculates metrics to assess the contribution of a given species or site to its bioregion.
Usage
site_species_metrics(
bioregionalization,
comat,
indices = c("rho"),
net = NULL,
site_col = 1,
species_col = 2
)
Arguments
bioregionalization |
A |
comat |
A co-occurrence |
indices |
A |
net |
|
site_col |
A number indicating the position of the column containing
the sites in |
species_col |
A number indicating the position of the column
containing the species in |
Details
The \rho
metric is derived from Lenormand et al. (2019) with the
following formula:
\rho_{ij} = \frac{n_{ij} - \frac{n_i n_j}{n}}{\sqrt{\left(\frac{n - n_j}{
n-1}\right) \left(1-\frac{n_j}{n}\right) \frac{n_i n_j}{n}}}
where n
is the number of sites, n_i
is the number of sites in
which species i
is present, n_j
is the number of sites in
bioregion j
, and n_{ij}
is the number of occurrences of species
i
in sites of bioregion j
.
Affinity A
, fidelity F
, and individual contributions
IndVal
describe how species are linked to their bioregions. These
metrics are described in Bernardo-Madrid et al. (2019):
Affinity of species to their region:
A_i = \frac{R_i}{Z}
, whereR_i
is the occurrence/range size of speciesi
in its associated bioregion, andZ
is the total size (number of sites) of the bioregion. High affinity indicates that the species occupies most sites in its bioregion.Fidelity of species to their region:
F_i = \frac{R_i}{D_i}
, whereR_i
is the occurrence/range size of speciesi
in its bioregion, andD_i
is its total range size. High fidelity indicates that the species is not present in other regions.Indicator Value of species:
IndVal = F_i \cdot A_i
.
Cz
metrics are derived from Guimerà & Amaral (2005):
Participation coefficient:
C_i = 1 - \sum_{s=1}^{N_M}{\left(\frac{k_{is}}{k_i}\right)^2}
, wherek_{is}
is the number of links of nodei
to nodes in bioregions
, andk_i
is the total degree of nodei
. A high value means links are uniformly distributed; a low value means links are within the node's bioregion.Within-bioregion degree z-score:
z_i = \frac{k_i - \overline{k_{si}}}{\sigma_{k_{si}}}
, wherek_i
is the number of links of nodei
to nodes in its bioregions_i
,\overline{k_{si}}
is the average degree of nodes ins_i
, and\sigma_{k_{si}}
is the standard deviation of degrees ins_i
.
Value
A data.frame
with columns Bioregion
, Species
, and the desired summary
statistics, or a list of data.frame
s if Cz
and other indices are
selected.
Author(s)
Pierre Denelle (pierre.denelle@gmail.com)
Boris Leroy (leroy.boris@gmail.com)
Maxime Lenormand (maxime.lenormand@inrae.fr)
References
Bernardo-Madrid R, Calatayud J, González‐Suárez M, Rosvall M, Lucas P, Antonelli A & Revilla E (2019) Human activity is altering the world’s zoogeographical regions. Ecology Letters 22, 1297–1305.
Guimerà R & Amaral LAN (2005) Functional cartography of complex metabolic networks. Nature 433, 895–900.
Lenormand M, Papuga G, Argagnon O, Soubeyrand M, Alleaume S & Luque S (2019) Biogeographical network analysis of plant species distribution in the Mediterranean region. Ecology and Evolution 9, 237–250.
See Also
For more details illustrated with a practical example, see the vignette: https://biorgeo.github.io/bioregion/articles/a5_3_summary_metrics.html.
Associated functions: bioregion_metrics bioregionalization_metrics
Examples
comat <- matrix(sample(0:1000, size = 500, replace = TRUE, prob = 1/1:1001),
20, 25)
rownames(comat) <- paste0("Site",1:20)
colnames(comat) <- paste0("Species",1:25)
dissim <- dissimilarity(comat, metric = "Simpson")
clust1 <- nhclu_kmeans(dissim, n_clust = 3, index = "Simpson")
net <- similarity(comat, metric = "Simpson")
com <- netclu_greedy(net)
site_species_metrics(bioregionalization = clust1, comat = comat,
indices = "rho")
# Contribution metrics
site_species_metrics(bioregionalization = com, comat = comat,
indices = c("rho", "affinity", "fidelity", "indicator_value"))
# Cz indices
net_bip <- mat_to_net(comat, weight = TRUE)
clust_bip <- netclu_greedy(net_bip, bipartite = TRUE)
site_species_metrics(bioregionalization = clust_bip, comat = comat,
net = net_bip, indices = "Cz")