BiodiversityR (version 2.16-1)

diversityresult: Alternative Diversity Results

Description

Provides alternative methods of obtaining results on diversity statistics than provided directly by functions diversity, fisher.alpha, specpool and specnumber (all from vegan), although these same functions are called. Some other statistics are also calculated such as the reciprocal Berger-Parker diversity index and abundance (not a diversity statistic). The statistics can be calculated for the entire community, for each site separately, the mean of the sites can be calculated or a jackknife estimate can be calculated for the community.

Usage

diversityresult(x, y = NULL, factor = NULL, level = NULL, 
    index=c("Shannon", "Simpson", "inverseSimpson", "Logalpha", "Berger",
        "simpson.unb", "simpson.unb.inverse", 
        "richness", "abundance", "Jevenness", "Eevenness", 
        "jack1", "jack2", "chao", "boot"),
    method=c("pooled", "each site", "mean", "sd", "max", "jackknife"), 
    sortit = FALSE, digits = 8)
diversityvariables(x, y, digits=8)
diversitycomp(x, y = NULL, 
    factor1 = NULL ,factor2 = NULL,
    index=c("Shannon", "Simpson", "inverseSimpson", "Logalpha", "Berger", 
        "simpson.unb", "simpson.unb.inverse",
        "richness", "abundance", "Jevenness", "Eevenness", 
        "jack1", "jack2", "chao", "boot"),
    method=c("pooled", "mean", "sd", "max", "jackknife"),
    sortit=FALSE, digits=8)

Value

The functions provide alternative methods of obtaining diversity results. For function diversitycomp, the number of sites is provided as "n".

Arguments

x

Community data frame with sites as rows, species as columns and species abundance as cell values.

y

Environmental data frame.

factor

Variable of the environmental data frame that defines subsets to calculate diversity statistics for.

level

Level of the variable to create the subset to calculate diversity statistics.

index

Type of diversity statistic with "richness" to calculate species richness, "abundance" to calculate abundance, "Shannon" to calculate the Shannon diversity index, "Simpson" to calculate 1-Simpson concentration index, "inverseSimpson" to calculate the reciprocal Simpson diversity index, "simpson.unb" to calculate the unbiased Simpson index, "simpson.unb.inverse" to calculate the unbiased inverse simpson index, "Logalpha" to calculate the log series alpha diversity index, "Berger" to calculate the reciprocal Berger-Parker diversity index, "Jevenness" to calculate one Shannon evenness index, "Eevenness" to calculate another Shannon evenness index, "jack1" to calculate the first-order jackknife gamma diversity estimator, "jack2" to calculate the second-order jackknife gamma diversity estimator, "chao" to calculate the Chao gamma diversity estimator and "boot" to calculate the bootstrap gamma diversity estimator.

method

Method of calculating the diversity statistics: "pooled" calculates the diversity of the entire community (all sites pooled), "each site" calculates diversity for each site separetly, "mean" calculates the average diversity of the sites, "sd" calculates the standard deviation of the diversity of the sites, "max" calculates the maximum diversity of the sites, whereas "jackknife" calculates the jackknifed diversity for the entire data frame.

sortit

Sort the sites by increasing values of the diversity statistic.

digits

Number of digits in the results.

factor1

Variable of the environmental data frame that defines subsets to calculate diversity statistics for.

factor2

Optional second variable of the environmental data frame that defines subsets to calculate diversity statistics for in a crosstabulation with the other variable of the environmental data frame.

Author

Roeland Kindt (World Agroforestry Centre)

Details

These functions provide some alternative methods of obtaining results with diversity statistics, although functions diversity, fisher.alpha, specpool, estimateR and specnumber (all from vegan) are called to calculate the various statistics.

Function diversityvariables adds variables to the environmental dataset (richness, Shannon, Simpson, inverseSimpson, Logalpha, Berger, Jevenness, Eevenness).

The reciprocal Berger-Parker diversity index is the reciprocal of the proportional abundance of the most dominant species.

J-evenness is calculated as: H / ln(S) where H is the Shannon diversity index and S the species richness.

E-evenness is calculated as: exp(H) / S where H is the Shannon diversity index and S the species richness.

The method of calculating the diversity statistics include following options: "all" calculates the diversity of the entire community (all sites pooled together), "s" calculates the diversity of each site separatedly, "mean" calculates the average diversity of the sites, whereas "Jackknife" calculates the jackknifed diversity for the entire data frame. Methods "s" and "mean" are not available for function diversitycomp. Gamma diversity estimators assume that the method is "all".

Functions diversityresult and diversitycomp allow to calculate diversity statistics for subsets of the community and environmental data sets. Function diversityresult calculates the diversity statistics for the specified level of a selected environmental variable. Function diversitycomp calculates the diversity statistics for all levels of a selected environmental variable separatedly. When a second environmental variable is provided, function diversitycomp calculates diversity statistics as a crosstabulation of both variables.

References

Kindt, R. & Coe, R. (2005) Tree diversity analysis: A manual and software for common statistical methods for ecological and biodiversity studies.

https://www.worldagroforestry.org/output/tree-diversity-analysis

Examples

if (FALSE) {

library(vegan)
data(dune.env)
data(dune)

diversityresult(dune, y=NULL, index="Shannon", method="each site", 
    sortit=TRUE, digits=5)
diversityresult(dune, y=dune.env, factor="Management", level="NM", 
    index="Shannon", method="each site", 
    sortit=TRUE, digits=5)
diversityresult(dune, y=NULL, index="Shannon", method="pooled", digits=5)
diversityresult(dune, y=dune.env, factor="Management", level="NM", 
    index="Shannon", method="pooled", digits=5)
diversityresult(dune, y=NULL, index="Shannon", method="mean", 
    digits=5)
diversityresult(dune, y=NULL, index="Shannon", method="sd", 
    digits=5)
diversityresult(dune, y=NULL, index="Shannon", method="jackknife", 
    digits=5)
diversityresult(dune, y=dune.env, factor="Management", level="NM", 
    index="Shannon", method="jackknife", digits=5)

diversitycomp(dune, y=dune.env, factor1="Moisture", index="Shannon",
    method="pooled", sortit=TRUE)
diversitycomp(dune, y=dune.env, factor1="Moisture", index="Shannon",
    method="mean", sortit=TRUE)
diversitycomp(dune, y=dune.env, factor1="Management", index="Shannon",
    method="jackknife", sortit=TRUE)

diversitycomp(dune, y=dune.env, factor1="Management", factor2="Moisture", 
    index="Shannon", method="pooled", digits=6)
diversitycomp(dune, y=dune.env, factor1="Management", factor2="Moisture", 
    index="Shannon", method="mean", digits=6)

}