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.
.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!
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.
data(laja)
data <- comparative.comm(invert.tree, river.sites)
.psv(data)
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