Using these functions, you can calculate any of the phylogenetic
metrics within pez, using comparative.comm
objects. While you can call each individually, using the
pez.shape, pez.evenness,
pez.dispersion, and pez.dissimilarity
wrapper functions (and the more flexible
generic.metrics and null model functions) are probably
your best bet. Note that *all of these functions* take a common
first parameter: a comparative.comm object. There are
additional parameters that can be passed, which are described
below.
.colless(x, ...).hed(x, ...)
.eed(x, na.rm = TRUE, ...)
.psv(x, ...)
.psr(x, ...)
.mpd(x, dist = NULL, abundance.weighted = FALSE, ...)
.vpd(x, dist = NULL, abundance.weighted = FALSE, ...)
.vntd(x, dist = NULL, abundance.weighted = FALSE, ...)
.pd(x, include.root = TRUE, abundance.weighted = FALSE, ...)
.mntd(x, dist = NULL, abundance.weighted = FALSE, ...)
.gamma(x, ...)
.taxon(x, dist = NULL, abundance.weighted = FALSE, ...)
.eigen.sum(x, dist = NULL, which.eigen = 1, ...)
.dist.fd(x, method = "phy", abundance.weighted = FALSE, ...)
.sqrt.phy(x)
.phylo.entropy(x, ...)
.aed(x, ...)
.haed(x, ...)
.simpson.phylogenetic(x)
.iac(x, na.rm = TRUE, ...)
.pae(x, na.rm = TRUE, ...)
.scheiner(x, q = 0, abundance.weighted = FALSE, ...)
.pse(x, ...)
.rao(x, ...)
.lambda(x, ...)
.delta(x, ...)
.kappa(x, ...)
.eaed(x, ...)
.unifrac(x, ...)
.pcd(x, permute = 1000, ...)
.comdist(x, dist = NULL, abundance.weighted = FALSE, ...)
.phylosor(x, dist = NULL, abundance.weighted = FALSE, ...)
.d(x, permute = 1000, ...)
.ses.mpd(x, dist = NULL, null.model = "taxa.labels",
abundance.weighted = FALSE, permute = 1000, ...)
.ses.mntd(x, dist = NULL, null.model = "taxa.labels",
abundance.weighted = FALSE, permute = 1000, ...)
.ses.vpd(x, dist = NULL, null.model = "taxa.labels",
abundance.weighted = FALSE, permute = 1000, ...)
.ses.vntd(x, dist = NULL, null.model = "taxa.labels",
abundance.weighted = FALSE, permute = 1000, ...)
.ses.mipd(x, dist = NULL, null.model = "taxa.labels",
abundance.weighted = FALSE, permute = 1000, ...)
.ses.innd(x, dist = NULL, null.model = "taxa.labels",
abundance.weighted = FALSE, permute = 1000, ...)
.mipd(x, dist = NULL, abundance.weighted = FALSE, ...)
.innd(x, dist = NULL, abundance.weighted = FALSE, ...)
.innd(x, dist = NULL, abundance.weighted = FALSE, ...)
.pe(x, ...)
.bed(x, ...)
comparative.comm object
ignored
remove NAs in calculations (altering this can obscure errors that are meaningful; I would advise leaving alone)
distance matrix for use with calculations; could be
generated from traits, a square-root-transformed distance matrix
(see .sqrt.phy for creating a
comparative.comm object with a square-root
transformed phylogeny). Default: NULL (--> calculate distance
matrix from phylogeny)
whether to include species' abundances in
metric calculation, often dictating whether you're calculating a
pez.shape or pez.evenness
metric. Default: FALSE
include root in PD calculations (default is
TRUE, as in picante, but within pez.shape I specify
FALSE
which phylo-eigenvector to be used for PVR metric
whether to calculate using phylogeny ("phy"; default) or trait data ("traits")
the q parameter for .scheiner; default 0.0001
number of permutations of null randomisations
(mostly only applies to dispersion
metrics)
one of "taxa.labels", "richness", "frequency",
"sample.pool", "phylogeny.pool", "independentswap", or
"independentswap". These correspond to the null models available in
picante; only d does not use these null models
.pd returns two metrics: Faith's PD (which does not take
into account abundance) and Faith's PD corrected for species
richness or total abundance (depending on
abundance.weighted). I am almost certain that I got the idea
for this from somewhere, but I can't find the reference: if you
published on this before 2012, please get in touch with me.
.scheiner has a different formula for the case where
q is equal to 1 (check the code if interested). The nature
of its definition means that values very close to, but not exactly
equal to, 1 may be extremely large or extremely small. This is a
feature, not a bug, and an inherent aspect of its definition. Check
the formula in the code for more information!
colless Colless D.H. (1982). Review of
phylogenetics: the theory and practice of phylogenetic
systematics. Systematic Zoology, 31, 100-104.
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.
PSV,PSR,PSE 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.
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,aed,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.
scheiner Scheiner, S.M. (20120). A metric of
biodiversity that integrates abundance, phylogeny, and function.
Oikos, 121, 1191-1202.
rao Webb C.O. (2000). Exploring the phylogenetic
structure of ecological communities: An example for rain forest
trees. American Naturalist, 156, 145-155.
lambda,delta,kappa Mark Pagel (1999) Inferring
the historical patterns of biological evolution. Nature 6756(401):
877--884.
unifrac Lozupone C.A. & Knight
R. (2005). UniFrac: a new phylogenetic method for comparing
microbial communities. Applied and Environmental Microbiology, 71,
8228-8235.
pcd Ives A.R. & Helmus M.R. (2010). Phylogenetic
metrics of community similarity. The American Naturalist, 176,
E128-E142.
comdist C.O. Webb, D.D. Ackerly, and
S.W. Kembel. 2008. Phylocom: software for the analysis of
phylogenetic community structure and trait
evolution. Bioinformatics 18:2098-2100.
phylosor Bryant J.A., Lamanna C., Morlon H.,
Kerkhoff A.J., Enquist B.J. & Green J.L. (2008). Microbes on
mountainsides: Contrasting elevational patterns of bacterial and
plant diversity. Proceedings of the National Academy of Sciences of
the United States of America, 105, 11505-11511.
d Fritz S.A. & Purvis A. (2010). Selectivity in
Mammalian Extinction Risk and Threat Types: a New Measure of
Phylogenetic Signal Strength in Binary Traits. Conservation
Biology, 24, 1042-1051.
sesmpd,sesmntd Webb C.O. (2000). Exploring the
phylogenetic structure of ecological communities: An example for
rain forest trees. American Naturalist, 156, 145-155.
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.
PE Rosauer, D. A. N., Laffan, S. W., Crisp,
M. D., Donnellan, S. C., & Cook, L. G. (2009). Phylogenetic
endemism: a new approach for identifying geographical
concentrations of evolutionary history. Molecular Ecology,
18(19), 4061-4072.
BED Cadotte, M. W., & Jonathan Davies,
T. (2010). Rarest of the rare: advances in combining
evolutionary distinctiveness and scarcity to inform
conservation at biogeographical scales. Diversity and
Distributions, 16(3), 376-385.
# NOT RUN {
data(laja)
data <- comparative.comm(invert.tree, river.sites)
.psv(data)
# }
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