pielou: Pielou's index of non-randomness

In johngodlee/compInd: Forest tree-tree competition indices

Pielou's index of non-randomness

Description

Pielou's index of non-randomness

Usage

pielou(x, xmin, xmax, ymin, ymax, k)

Arguments

x	two column matrix of individual x and y coordinates
xmin	minimum x coordinate in plot
xmax	maximum x coordinate in plot
ymin	minimum y coordinate in plot
ymax	maximum y coordinate in plot
k	number of randomly allocated sample points

Details

The sum of squared nearest neighbour distances normalised by the number of sample points and the number of individuals in the structural unit. Defined by the equation:

\pi \frac{n}{A} \frac{1}{k} \sum_{1}^{k} r_{i}^{2}

where n is the number of individuals in the structural unit, A is the structural unit area, k is the number of sample points, and r_{i} is the nearest neighbour distance to individual i.

As the sample points are randomly allocated within the bounds of xmin,xmax,ymin,ymax, the mean of a number of runs of this function could be used to further constrain the estimate of Pielou's index.

Value

value of the competition index for the structural unit, i.e. plot.

References

Pielou, E. C. (1959). The use of point to plant distances in the study of the pattern of plan populations. Journal of Ecology. Volume 47. Pages 607-613.

Examples

data(bicuar)
pielou(bicuar[,c("x", "y")], 0, 100, 0, 100, 50)

johngodlee/compInd documentation built on Aug. 5, 2024, 8:44 a.m.