evenness.contribution {BAT} | R Documentation |
Contribution of each observation to the regularity of abundances and distances (if PD/FD) between species in a community (or individuals in a species).
evenness.contribution(
comm,
tree,
distance,
method = "expected",
func = "camargo",
abund = TRUE
)
comm |
A sites x species matrix, with either abundance or incidence data. If missing, the evenness using the full tree or distance matrix is calculated. |
tree |
A phylo or hclust object (used only for PD or FD) or alternatively a species x traits matrix or data.frame to build a functional tree. |
distance |
A dist or matrix object representing the phylogenetic or functional distance between species. If both tree and distance are missing, taxonomic evenness is calculated. |
method |
Calculate evenness using "expected" values (default) or values based on "contribution" of species to the tree. |
func |
Calculate evenness using "Camargo" (1993; default) or "Bulla" (1994) index. |
abund |
A boolean (T/F) indicating whether evenness should be calculated using abundance data. |
Contribution to evenness is calculated using a leave-one-out approach, whereby the contribution of a single observation is the total evenness minus the evenness calculated without that observation. Evenness is based on the index of Camargo (1993) or Bulla (1994) using the values of both species abundances and edge lengths in the tree (if PD/FD). Note that the contribution of a species or individual can be negative, if the removal of an observation increases the total evenness.
If no tree or distance is provided the result is calculated for taxonomic evenness using the original index.
A matrix of sites x species (or a vector if no comm is given).
Bulla, L. (1994) An index of evenness and its associated diversity measure. Oikos, 70: 167-171.
Camargo, J.A. (1993) Must dominance increase with the number of subordinate species in competitive interactions? Journal of Theoretical Biology, 161: 537-542.
comm <- matrix(c(1,2,0,5,5,1,1,0,0,0,0,2,2,0,0,1,1,1,1,100), nrow = 4, byrow = TRUE)
distance <- dist(c(1:5), method = "euclidean")
tree <- hclust(distance, method = "average")
evenness.contribution(comm)
evenness.contribution(tree = tree, func = "bulla")
evenness.contribution(comm, tree)
evenness.contribution(comm, tree, method = "contribution")
evenness.contribution(comm, tree, abund = FALSE)