Jost Diversity Measures for Community Data

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Description

This package computes diversity with uncertainty estimates for community data sets using the methods outlined by Jost (2006, 2007). While there are differing opinions on the ideal way to calculate diversity (e.g. Magurran 2004), this method offers the advantage of providing diversity 'numbers equivalents,' independent alpha and beta diversities, and the ability to incorporate 'order' (q) as a continuous measure of the importance of rare species in the metrics. The functions provided in this package largely correspond with the equations offered by Jost in the cited papers. The package computes alpha diversities, beta diversities, gamma diversities, and similarity indices. Uncertainty estimates for diversity measures are calculated using a bootstrap method described by Chao et al. (2008). Traditional diversity measures can also be output. For datasets with many samples (sites, plots), sim.table creates tables of all pairwise comparisons possible, and for grouped samples sim.groups calculates pairwise combinations of within- and between-group comparisons.

Details

Package: vegetarian
Type: Package
Version: 1.2
Date: 2009-08-23
License: GPL-2
LazyLoad: yes

The core of the vegetarian library is the d function, which calculates the basic alpha, beta, and gamma diversity 'numbers equivalents' from community data. H uses d to calculate the standard diversity indices. The functions similarity, M.homog, Rel.homog, and turnover call d to compare diversity across communities. use sim.table and/or sim.groups to produce multiple pairwise similarity comparisons among many sample sites. All functions can output standard errors by calling bootstrap internally. For more detailed bootstrapping outputs, the user can call bootstrap seperately. The function normalize.rows is called by d to convert count data into frequencies. The simple function, p.q.sum is called internally as core part of the diversity calculations, and is probably of little use to the average user, though it may be used to create more complex diversity measures. This update corrects an error in the similarity function and boostrap standard error estimates from earlier versions.

Author(s)

Noah Charney, Sydne Record

References

Chao, A, L. Jost, S. C. Chiang, Y.-H Jiang, R. L. Chazdon. 2008. A two-stage probabilistic approach to multiple-community similarity indices. Biometrics 64: 1178-1186.

Jost, L. 2006. Entropy and diversity. Oikos 113(2): 363-375.

Jost, L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88(10): 2427-2439.

Hill, M. 1973. Diversity and evenness: A unifying notation and its consequences. Ecology 54: 427-432.

Examples

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data(simesants)
d(simesants[,-1], boot=TRUE) 
#remove column with site names 
#calculates alpha diversity of entire data-set with standard error