As described in Pearse et al. (2014), a shape metric is one the examines the phylogenetic structure of species present in each assemblage, ignoring abundances entirely. For completeness, options are provided to calculate these metrics using species traits.
pez.shape(
data,
sqrt.phy = FALSE,
traitgram = NULL,
traitgram.p = 2,
ext.dist = NULL,
which.eigen = 1,
quick = TRUE,
q = 1e-04
)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).
Some of the metrics in this wrapper are also in
pez.evenness; such metrics can be calculated using
species' abundances (making them evenness) metrics or simply
using presence/absence of species (making them shape
metrics).
comparative.comm object
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.
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.shape 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.
A value for `p' to be used in conjunction with
traitgram when calling funct.phylo.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.
The eigen vector to calculate for the PhyloEigen
metric (eigen.sum)
Only calculate metrics which are quick to calculate
(default: TRUE); setting to FALSE will also calculate
fd.dist.
value for q in scheiner (default 0.0001)
M.R. Helmus, Will Pearse
Most of these metrics do not involve comparison with some kind of
evolutionary-derived expectation for phylogenetic shape. Those that
do, however, such as PSV, 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. pd, eed & hed 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.
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.
PSV,PSR Helmus M.R., Bland T.J., Williams C.K. &
Ives A.R. (2007). Phylogenetic measures of biodiversity. American
Naturalist, 169, E68-E83.
PD Faith D.P. (1992). Conservation evaluation
and phylogenetic diversity. Biological Conservation, 61, 1-10.
gamma Pybus O.G. & Harvey P.H. (2000) Testing
macro-evolutionary models using incomplete molecular
phylogenies. _Proceedings of the Royal Society of London. Series
B. Biological Sciences 267: 2267--2272.
taxon Clarke K.R. & Warwick R.M. (1998). A
taxonomic distinctness index and its statistical
properties. J. Appl. Ecol., 35, 523-531.
eigen.sum Diniz-Filho J.A.F., Cianciaruso M.V.,
Rangel T.F. & Bini L.M. (2011). Eigenvector estimation of
phylogenetic and functional diversity. Functional Ecology, 25,
735-744.
eed,hed (i.e., Eed, Hed) 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.
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.
pez.evenness pez.dispersion
pez.dissimilarity
data(laja)
data <- comparative.comm(invert.tree, river.sites, invert.traits)
(output<-pez.shape(data))
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