beta.decay: Fit an exponential distance decay model for beta diversity

In chihlinwei/ddecay: Fit an exponential distance decay model to assemblage similarities

Description

Fit a generalized dissimilarity model to the spatial turnover, nestness or dissmilarity of the ecological assemblages(including zero similarity points) between two sites as a function of their distance apart along an environmental gradient.

Usage

beta.decay(gradient, counts, coords, nboots = 1000, dis.fun = "beta.pair",
  dis = 1, like.pairs = T, ...)

Arguments

gradient	A vector of environmental values of interests
counts	A dataframe of community matrix. Presence/absence if dis.fun="beta.pair"
coords	A dataframe of 2 column with x and y coordinates of the sampling site. The same sites will be remove from bootsrap procedures if like.pairs = TRUE
nboots	A integer number indicating the numbers of bootsrap resampling
dis.fun	Matching function to compute dissimilarity. The default is beta.pair
dis	An integer of 1, 2 or 3, indicating either the spatial turnover, nestness or total dissmilarity are used in the distance decay modeling.
like.pairs	Logical, whether to remove the like pairs or not. During bootstrap resampling, because the sites are randomyl resampled with replacement and hence there may be multiple copies of some sites. like.pairs = TRUE removes those site pairs having zero separation distance.
...	Arguments to be passed to the matching function

Details

This funtion extends dist.decay to fit a generalized linear models (GLMs) using quasibinomial distribution (with a log-link function) for the partition or total beta diversity in an ecological assemblage as a function of an environmental gradient. The beta diversity is partitioned into spatial turnover, nestness and totdal dissimilarity using beta.pair, bray.part, functional.beta.pair, or phylo.beta.pairt following the methods decribed by Baselga (2010, 2012). A modified bootstrap method was used to estimate the bootraped mean and standard error of model parameters (alpha, beta), s=1-alpha*exp(-beta*d) for the spatial turnover and dissimilarity and s=alpha*exp(-beta*d) for the nestedness component of betat diversity, where s is assemblage sptail turnover, nestedness or disimilarity and d is environmental distance, and then use these parameters to calculate the turnover, nestedness or disimilarity at zero distance (s0) and the halving distance (halfd) for which the turnover, nestedness or disimilarity between sites decreased/increase by 50% following the method decribed by Millar et al. (2010).

Value

A list of data frames, including:

Results

A data frame of bootstrap estimate of GLM coefficients, (intercept) and x, as well as beta diversity at zero distance (s0) and halving distance (halfd)

Summary

A data frame of bootsrap summary statistics

Predictions

A data frame of distance difference, bootstrap predictions and 95% confidence intervals

CtrlList

Other information

Note

This function models the complement of dissimilarity (similarity).

Author(s)

Chih-Lin Wei <[e-mail]>

References

• Millar, R.B., Anderson, M.J., Tolimieri, N., 2011. Much ado about nothings: using zero similarity points in distance-decay curves. Ecology 92, 1717-1722. doi:10.1890/11-0029.1

• Baselga, A. 2010. Partitioning the turnover and nestedness components of beta diversity. Global Ecology and Biogeography 19:134-143

• Baselga, A. 2012. The relationship between species replacement, dissimilarity derived from nestedness, and nestedness. Global Ecology and Biogeography 21, 1223-1232

See Also

dist.decay

Examples

data(os)
dd <- beta.decay(gradient=os$dist, counts=decostand(os[, -1:-7], method="pa"),
                 coords=os[, c("longitude", "latitude")], nboots=1000,
                 dis.fun = "beta.pair", index.family = "sorensen", dis = 1, like.pairs=T)
x <- vegdist(os$dist, method = "euclidean")
y <- 1 - dist(decostand(os[,-1:-7], method="pa"), index.family = "sorensen")[[1]]
plot(x, y)
lines(dd$Predictions[, "x"], dd$Predictions[,"mean"], col="red", lwd=2)

chihlinwei/ddecay documentation built on May 13, 2019, 4:48 p.m.