RInSp (version 1.2.5)

overlap: Mean pairwise overlap

Description

This procedure calculates the pairwise diet overlap between all individuals in a sample.

Usage

overlap(dataset)

Arguments

dataset

Object of class RInSp with data.

Value

Return an matrix of three columns:

meanoverlap

The mean pairwise overlap for all pairs of individuals, excluding the diagonal.

meanindividualoverlap

The mean pairwise overlap for individual i and all others, excluding the diagonal.

meandissimilarity

The mean pairwise dissimilarity overlap for all pairs of individuals, excluding the diagonal.

overlapmatrix

The probability associated to the likelihood value.

parameter=0

Internal option for plotting results.

Details

For a sample size of N individuals, this will produce an N * N matrix, each cell \(o_{ik}\) representing the diet overlap between individual i and individual k. The diagonal is all ones because an individual has a 100% overlap with itself. Pairwise overlap is calculated by determining the proportional similarity between each individual.

The script reports the average pairwise diet overlap (not including diagonal values), the average pairwise diet dissimilarity (i.e., one minus the average pairwise diet overlap) and the complete diet overlap matrix. The overlap matrix can be useful for testing whether diet (dis)similarity is a function of various other metrics of between-individual difference or similarity (e.g., Bolnick and Paull 2009).

References

Bolnick, D.I., and Paull, J.S., 2009. Morphological and dietary differences between individuals are weakly but positively correlated within a population of threespine Stickleback. Evolutionary Ecology Research 11: 1217-1233.

Bolnick, D.I., L.H. Yang, J.A. Fordyce, J.M. Davis, and Svanback, R. 2002. Measuring individual-level resource specialization. Ecology 83: 2936-2941.

Examples

# NOT RUN {
 data(Stickleback)
# Select a single spatial sampling site (site D)
GutContents_SiteD = import.RInSp(Stickleback, row.names = 1,
info.cols = c(2:13), subset.rows = c("Site", "D"))
similarity = overlap(GutContents_SiteD)

# }
# NOT RUN {
# Generate a plot similar to Bolnick and Paull 2009, testing whether
# morphological differences between individuals are positively related
# to diet differences (or conversely are negatively related to diet similarity)
GutContents <- import.RInSp(Stickleback, row.names = 1,info.cols = c(2:13))
similarity <- overlap(GutContents)
diet.similarity <- as.dist(similarity$overlapmatrix)
size.dissimilarity <- dist(GutContents$info[,3])
plot(diet.similarity ~ size.dissimilarity, xlab = "Size dissimilarity",
ylab = "Diet similarity", pch = 16, cex = 0.4)
model <- lm(diet.similarity ~ size.dissimilarity)
abline(model, lwd = 3, col = "red")

# The ade4 package is needed to complete the analysis
# not run
# library(ade4)
# mantel.rtest(diet.similarity, size.dissimilarity)
# }