coleman {sars}

Fit Coleman's Random Placement Model

Description

Fit Coleman's (1981) random placement model to a species-site abundance matrix: rows are species and columns are sites. Note that the data must be abundance data and not presence-absence data. According to this model, the number of species occurring on an island depends on the relative area of the island and the regional relative species abundances. The fit of the random placement model can be determined through use of a diagnostic plot (see plot.coleman) of island area (log transformed) against species richness, alongside the model’s predicted values (see Wang et al., 2010). Following Wang et al. (2010), the model is rejected if more than a third of the observed data points fall beyond one standard deviation from the expected curve.

Usage

coleman(data, area)

Arguments

data

A dataframe or matrix in which rows are species and columns are sites. Each element/value in the matrix is the abundance of a given species in a given site.

area

A vector of site (island) area values. The order of the vector must match the order of the columns in data.

Value

A list of class "coleman" with four elements. The first element contains the fitted values of the model. The second element contains the standard deviations of the fitted values, and the third and fourth contain the relative island areas and observed richness values, respectively. plot.coleman plots the model.

References

Coleman, B. D. (1981). On random placement and species-area relations. Mathematical Biosciences, 54, 191-215.

Matthews, T. J., Cottee-Jones, H. E. W., & Whittaker, R. J. (2015). Quantifying and interpreting nestedness in habitat islands: a synthetic analysis of multiple datasets. Diversity and Distributions, 21, 392-404.

Wang, Y., Bao, Y., Yu, M., Xu, G., & Ding, P. (2010). Nestedness for different reasons: the distributions of birds, lizards and small mammals on islands of an inundated lake. Diversity and Distributions, 16, 862-873.

Examples

data(cole_sim)
fit <- coleman(cole_sim[[1]], cole_sim[[2]])
plot(fit, ModTitle = "Hetfield")