CNESS: The Chord-distance Normalized Expected Specis Shared (CNESS)

In Lenaick-Menot/ness: Hypergeometric standardization and probabilistic measures of similarity for community ecology

Description

The function converts raw community data to hypergeometric probabilities from a random draw of NESSm individuals followed by station normalization. The CNESS distance can then be obtained by computing the Euclidean distance on the transformed data (see Trueblood et al., 1994; Gallagher 1996).

Usage

CNESS(X, m)

Arguments

X	Community data matrix, samples as rows, species as column
m	NESSm value, can range from 1 (high weight to adundant species) to the minimum sample total (high weight to rare species). The NESSm function compute the best trade-off.

Value

Standardized data frame

References

Gallagher, E.D., 1996. COMPAH documentation. 65 p. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.9.1334&rep=rep1&type=pdf. Trueblood, D.D., Gallagher, E.D., Gould, D.M., 1994. Three stages of seasonal succession on the Savin Hill Cove mudflat, Boston Harbor. Limnology and Oceanography 39, 1440-1454.

Examples

library(vegan)
data(varespec)
m <- NESSm(varespec)
cness.mat <- CNESS(varespec, m)
cness.dist <- dist(cness.mat, "euclidean")

Lenaick-Menot/ness documentation built on June 25, 2019, 12:05 a.m.