pez.evenness {pez}

Calculate (phylogenetic) evenness: examine assemblage composition and abundance

Description

As described in Pearse et al. (2014), an evenness metric is one the examines the phylogenetic structure of species present in each assemblage, taking into account their abundances. For completeness, options are provided to calculate these metrics using species traits.

Usage

pez.evenness(
  data,
  sqrt.phy = FALSE,
  traitgram = NULL,
  traitgram.p = 2,
  ext.dist = NULL,
  quick = TRUE,
  q = 1
)

Arguments

data

comparative.comm object

sqrt.phy

If TRUE (default is FALSE) your phylogenetic distance matrix will be square-rooted; specifying TRUE will force the square-root transformation on phylogenetic distance matrices (in the spirit of Leitten and Cornwell, 2014). See ‘details’ for details about different metric calculations when a distance matrix is used.

traitgram

If not NULL (default), a number to be passed to funct.phylo.dist (phyloWeight; the ‘a’ parameter), causing analysis on a distance matrix reflecting both traits and phylogeny (0–>only phylogeny, 1–> only traits; see funct.phylo.dist). If a vector of numbers is given, pez.eveness iterates across them and returns a data.frame with coefficients from each iteration. See ‘details’ for details about different metric calculations when a distance matrix is used.

traitgram.p

A value for ‘p’ to be used in conjunction with traitgram when calling funct.phylo.dist.

ext.dist

Supply an external species-level distance matrix for use in calculations. See ‘details’ for comments on the use of distance matrices in different metric calculations.

quick

Only calculate metrics which are quick to calculate (default: TRUE); setting to FALSE will also calculate fd.dist and the Pagel transformations (lambda, delta, kappa).

q

value for q in scheiner (default 1)

Details

Most of these metrics do not involve comparison with some kind of evolutionary-derived expectation for phylogenetic shape. Those that do, however, such as PSE, make no sense unless applied to a phylogenetic distance matrix - their null expectation *requires* it. Using square-rooted distance matrices, or distance matrices that incorporate trait information, can be an excellent thing to do, but (for the above reasons), pez won't give you an answer for metrics for which WDP thinks it makes no sense. pae, iac, haead & eaed can (...up to you whether you should!...) be used with a square-rooted distance matrix, but the results *will always be wrong* if you do not have an ultrametric tree (branch lengths proportional to time) and you will be warned about this. WDP strongly feels you should only be using ultrametric phylogenies in any case, but code to fix this bug is welcome.

Value

phy.structure list object of metric values. Use coefs to extract a summary metric table, or examine each individual metric (which gives more details for each) by calling print on the output (i.e., type output in the example below).

Note

As mentioned above, dist.fd is calculated using a phylogenetic distance matrix if no trait data are available, or if you specify sqrt.phy. It is not calculated by default because it generates warning messsages (which WDP is loathe to suppress) which are related to the general tendency for a low rank of phylogenetic distance matrices. Much ink has been written about this, and in par this problem is why the eigen.sum measure came to be suggested.

Some of these metrics can cause (inconsequential) warnings if given assemblages with only one species/individual in them, and return NA/NaN values depending on the metric. I consider these ‘features’, not bugs.

Some of the metrics in this wrapper are also in pez.shape; such metrics can be calculated using species' abundances (making them evenness) metrics or simply using presence/absence of species (making them shape metrics).

Author(s)

M.R. Helmus, Will Pearse

References

Pearse W.D., Purvis A., Cavender-Bares J. & Helmus M.R. (2014). Metrics and Models of Community Phylogenetics. In: Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Springer Berlin Heidelberg, pp. 451-464.

pse Helmus M.R., Bland T.J., Williams C.K. & Ives A.R. (2007). Phylogenetic measures of biodiversity. American Naturalist, 169, E68-E83.

Pearse W.D., Purvis A., Cavender-Bares J. & Helmus M.R. (2014). Metrics and Models of Community Phylogenetics. In: Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology. Springer Berlin Heidelberg, pp. 451-464.

pse Helmus M.R., Bland T.J., Williams C.K. & Ives A.R. (2007). Phylogenetic measures of biodiversity. American Naturalist, 169, E68-E83.

rao Webb C.O. (2000). Exploring the phylogenetic structure of ecological communities: An example for rain forest trees. American Naturalist, 156, 145-155.

taxon Clarke K.R. & Warwick R.M. (1998). A taxonomic distinctness index and its statistical properties. J. Appl. Ecol., 35, 523-531.

entropy Allen B., Kon M. & Bar-Yam Y. (2009). A New Phylogenetic Diversity Measure Generalizing the Shannon Index and Its Application to Phyllostomid Bats. The American Naturalist, 174, 236-243.

pae,iac,haed,eaed Cadotte M.W., Davies T.J., Regetz J., Kembel S.W., Cleland E. & Oakley T.H. (2010). Phylogenetic diversity metrics for ecological communities: integrating species richness, abundance and evolutionary history. Ecology Letters, 13, 96-105.

lambda,delta,kappa Mark Pagel (1999) Inferring the historical patterns of biological evolution. Nature 6756(401): 877–884.

innd,mipd Ness J.H., Rollinson E.J. & Whitney K.D. (2011). Phylogenetic distance can predict susceptibility to attack by natural enemies. Oikos, 120, 1327-1334.

scheiner Scheiner, S.M. (20120). A metric of biodiversity that integrates abundance, phylogeny, and function. Oikos, 121, 1191-1202.

See Also

pez.shape pez.dispersion pez.dissimilarity

Examples

data(laja)
data <- comparative.comm(invert.tree, river.sites, invert.traits)
pez.evenness(data)