picante: Integrating Phylogenies and Ecology

Description

Functions for phylocom integration, community analyses, null-models, traits and evolution. Implements numerous ecophylogenetic approaches including measures of community phylogenetic and trait diversity, phylogenetic signal, estimation of trait values for unobserved taxa, null models for community and phylogeny randomizations, and utility functions for data input/output and phylogeny plotting. A full description of package functionality and methods are provided by Kembel et al. (2010) <doi:10.1093/bioinformatics/btq166>.

Details

Author(s)

Author: Steven W. Kembel <steve.kembel@gmail.com>, David D. Ackerly <dackerly@berkeley.edu>, Simon P. Blomberg <s.blomberg1@uq.edu.au>, Will K. Cornwell <cornwell@zoology.ubc.ca>, Peter D. Cowan <pdc@berkeley.edu>, Matthew R. Helmus <mrhelmus@wisc.edu>, Helene Morlon <morlon.helene@gmail.com>, Campbell O. Webb <cwebb@oeb.harvard.edu> Maintainer: Steven W. Kembel <steve.kembel@gmail.com>

Host-parasitoid food web data

Description

Data on the structure of a host-parasitoid food web from Ives & Godfray (2006). Includes information on phylogenetic covariances among 12 leaf-mining moth hosts and 27 species of parasitoid wasps.

Usage

data(IvesGodfray)

Format

A list with three elements:

• host Phylogenetic variance/covariance matrix for 12 leaf-mining moth hosts

• parasitoid Phylogenetic variance/covariance matrix for 27 species of parasitoid wasps

• interactions Matrix describing interactions between hosts and parasitoids

Source

Ives A.R. & Godfray H.C. (2006) Phylogenetic analysis of trophic associations. The American Naturalist, 168, E1-E14

See Also

pblm

K statistic of phylogenetic signal

Description

Calculates K statistic of phylogenetic signal

Usage

Kcalc(x, phy, checkdata=TRUE)

Arguments

Value

Author(s)

Simon Blomberg <s.blomberg1@uq.edu.au> and David Ackerly <dackerly@berkeley.edu>

References

Blomberg, S. P., and T. Garland, Jr. 2002. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. Journal of Evolutionary Biology 15:899-910.

Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57:717-745.

See Also

phylosignal

Examples

randtree <- rcoal(20)
randtraits <- rTraitCont(randtree)
Kcalc(randtraits[randtree$tip.label],randtree)

Color tip labels based on trait

Description

Plots a phylogeny with tip labels colored to indicate continuous or discrete trait values

Usage

  color.plot.phylo(phylo, df, trait, taxa.names,
                    num.breaks = ifelse(is.factor(df[,trait]),
                        length(levels(df[,trait])), 12),
                    col.names = rainbow(ifelse(length(num.breaks) > 1,
                        length(num.breaks) - 1, num.breaks)),
                    cut.labs = NULL,
                    leg.title = NULL,
                    main = trait,
                    leg.cex = 1,
                    tip.labs = NULL,
                    ...)
        

Arguments

Details

If if trait is a factor then each level of the factor is plotted with the corresponding col.names value (if length(num.breaks) > length(col.names) colors are recycled.) If trait is not a factor then it is assumed to be continuous and trait is evenly divided into num.breaks levels.

Value

The command is invoked for its side effect, a plot of the phylo with tips colored based on trait

Author(s)

Peter Cowan <pdc@berkeley.edu>

Calculates inter-community mean pairwise distance

Description

Calculates MPD (mean pairwise distance) separating taxa in two communities, a measure of phylogenetic beta diversity

Usage

comdist(comm, dis, abundance.weighted = FALSE)

Arguments

Details

This function calculates a measure of phylogenetic beta diversity: the expected phylogenetic distance separating two individuals or taxa drawn randomly from different communities.

Value

Distance object of MPD values separating each pair of communities.

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

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.

See Also

mpd, ses.mpd

Examples

data(phylocom)
comdist(phylocom$sample, cophenetic(phylocom$phylo), abundance.weighted=TRUE)

Calculates inter-community mean nearest taxon distance

Description

Calculates MNTD (mean nearest taxon distance) separating taxa in two communities, a measure of phylogenetic beta diversity

Usage

comdistnt(comm, dis, abundance.weighted = FALSE, exclude.conspecifics = FALSE)

Arguments

Details

This metric has also been referred to as MNND (mean nearest neighbour distance).

This function calculates a measure of phylogenetic beta diversity: the average phylogenetic distance to the most similar taxon or individual in the other community for taxa or individuals in two communities.

Value

Distance object of MNTD values separating each pair of communities.

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

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.

See Also

mntd, ses.mntd

Examples

data(phylocom)
comdistnt(phylocom$sample, cophenetic(phylocom$phylo), abundance.weighted=FALSE)

Correlations between species co-occurrence and phylogenetic distances

Description

Calculates measures of community phylogenetic structure (correlation between co-occurrence and phylogenetic distance) to patterns expected under various null models

Usage

comm.phylo.cor(samp, phylo, metric = c("cij", "checkerboard", "jaccard", "doij"),
    null.model = c("sample.taxa.labels", "pool.taxa.labels",
        "frequency", "richness", "independentswap","trialswap"), runs = 999, ...)

Arguments

Details

Currently implemented null models (arguments to null.model):

sample.taxa.labels

Shuffle phylogeny tip labels (only within set of taxa present in community data)

pool.taxa.labels

Shuffle phylogeny tip labels (across all taxa included in phylogenetic tree)

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency)

richness

Randomize community data matrix abundances within samples (maintains sample species richness)

independentswap

Randomize community data matrix maintaining species occurrence frequency and site richnessing using independent swap

trialswap

Randomize community data matrix maintaining species occurrence frequency and site richnessing using trial swap

Value

A list with elements:

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Cavender-Bares J., D.A. Ackerly, D. Baum and F.A. Bazzaz. 2004. Phylogenetic overdispersion in Floridian oak communities, American Naturalist, 163(6):823-843.

See Also

randomizeMatrix

Examples

data(phylocom)
comm.phylo.cor(phylocom$sample, phylocom$phylo, metric="cij",null.model="sample.taxa.labels")

Quantile regression slopes between species co-occurrence and phylogenetic distances

Description

Calculates measures of community phylogenetic structure (quantile regression between co-occurrence and phylogenetic distance) to patterns expected under various null models

Usage

comm.phylo.qr(samp, phylo, metric = c("cij", "checkerboard", "jaccard", "doij"),
    null.model = c("sample.taxa.labels", "pool.taxa.labels",
        "frequency", "richness", "independentswap","trialswap"), 
        quant = 0.75, runs = 999, show.plot = FALSE, ...)

Arguments

Details

This function fits a quantile regression of co-occurrence versus phylogenetic distances separating species, and compares observed patterns to the patterns expected under some null model. The quantile regressions are fit using the rq function from the quantreg package.

Currently implemented null models (arguments to null.model):

sample.taxa.labels

Shuffle phylogeny tip labels (only within set of taxa present in community data)

pool.taxa.labels

Shuffle phylogeny tip labels (across all taxa included in phylogenetic tree)

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency)

richness

Randomize community data matrix abundances within samples (maintains sample species richness)

independentswap

Randomize community data matrix maintaining species occurrence frequency and site richnessing using independent swap

trialswap

Randomize community data matrix maintaining species occurrence frequency and site richnessing using trial swap

Value

A list with elements:

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Cavender-Bares J., D.A. Ackerly, D. Baum and F.A. Bazzaz. 2004. Phylogenetic overdispersion in Floridian oak communities, American Naturalist, 163(6):823-843. Slingsby, J. A. and G. A. Verboom. 2006. Phylogenetic relatedness limits coexistence at fine spatial scales: evidence from the schoenoid sedges (Cyperaceae: Schoeneae) of the Cape Floristic Region, South Africa. The American Naturalist 168:14-27.

See Also

randomizeMatrix

Examples

data(phylocom)
comm.phylo.qr(phylocom$sample, phylocom$phylo, metric="cij",
  null.model="sample.taxa.labels", runs=99)

Table of correlations and P-values

Description

Table of correlations with associated P-values and df, can be used with regular or independent contrast data

Usage

cor.table(x, cor.method = c("pearson","spearman"),
    cor.type=c("standard","contrast")) 

Arguments

Value

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Garland, T., Jr., P. H. Harvey, and A. R. Ives. 1992. Procedures for the analysis of comparative data using phylogenetically independent contrasts. Systematic Biology 41:18-32.

Species' evolutionary distinctiveness

Description

Calculates evolutionary distinctiveness measures for a suite of species by: a) equal splits (Redding and Mooers 2006) b) fair proportions (Isaac et al., 2007). Returns a datafram with species identifiers and species scores.

Usage

evol.distinct(tree, type = c("equal.splits", "fair.proportion"),
    scale = FALSE, use.branch.lengths = TRUE)

Arguments

Note

This function will return a vector of evolutionary distinctivenss for every species in the given tree. If only a subset of values are needed there are two, concetually distinct options: either prune the tree first and then pass the tree in or subset the resulting vector. These two options will provide very different outputs.

Author(s)

Karen Magnuson-Ford, Will Cornwell, Arne Mooers, Mark Vellend

References

Redding, D.W. and Mooers, A.O. (2006). Incorporating evolutionary measures into conservation prioritisation. Conservation Biology, 20, 1670-1678.

Isaac, N.J.B., Turvey, S.T., Collen, B., Waterman, C. and Baillie, J.E.M. (2007). Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE, 2, e296.

Mark Vellend, William K. Cornwell, Karen Magnuson-Ford, and Arne Mooers. In press. Measuring phylogenetic biodiversity. In: Biological diversity: frontiers in measurement and assessment. Edited by Anne Magurran and Brian McGill.

Expected PD, PD Variance, and Edge Abundance Distribution of a phylogeny

Description

Calculates the expected phylogenetic diversity (Faith's PD) and variance of PD under binomial sampling with a fixed probability of each tip being sampled, and the Edge-length Abundance Distribution of a phylogeny.

Usage

expected.pd(phy)
variance.pd(phy, upper.bound=TRUE)
ead(phy)

Arguments

Details

The function expected.pd calculates the expected phylogenetic diversity (Faith's PD - total branch length) for all subsets of a phylogeny, based on an analytic solution for expected PD.

The function variance.pd additionally calculates the variance of expected PD for all subsets of a phylogeny, based on an analytic solution for expected PD. If argument upper.bound=TRUE, a fast solution for the upper bound of the variance is returned. Otherwise, the exact solution for the variance is returned. Note that the exact solution is much slower than the upper bound solution.

The function ead calculates the edge abundance distribution (EAD), the length of edges with different numbers of descendant tips.

Value

Author(s)

Steven Kembel <steve.kembel@gmail.com> and James O'Dwyer <jodwyer@santafe.edu>

References

J.P. O'Dwyer, S.W. Kembel, and J.L. Green. 2012. Phylogenetic Diversity Theory Sheds Light on the Structure of Microbial Communities. PLoS Comput Biol 8(12): e1002832.

See Also

pd

Examples

randtree <- rcoal(300)
randtree.pd.ub <- variance.pd(randtree, upper.bound=TRUE)
randtree.pd.exact <- variance.pd(randtree, upper.bound=FALSE)
plot(expected.pd(randtree), xlab="Number of tips",
    ylab="Phylogenetic diversity (PD)", type="l", log="xy")
lines(randtree.pd.exact$expected.pd+1.96*sqrt(randtree.pd.exact$variance.pd), lty=2)
lines(randtree.pd.exact$expected.pd-1.96*sqrt(randtree.pd.exact$variance.pd), lty=2)
lines(randtree.pd.ub$expected.pd+1.96*sqrt(randtree.pd.ub$variance.pd), lty=3)
lines(randtree.pd.ub$expected.pd-1.96*sqrt(randtree.pd.ub$variance.pd), lty=3)
legend("bottomright", lty=c(1,2,3), legend=c("Expected PD",
    "95 percent CI (exact)","95 percent CI (upper bound)"))

Match taxa in phylogeny and data

Description

These functions compare taxa present in phylogenies with community or trait data sets, pruning and sorting the two kinds of data to match one another for subsequent analysis.

Usage

match.phylo.comm(phy, comm)
match.phylo.data(phy, data)
match.comm.dist(comm, dis)

Arguments

Details

A common pitfall in comparative analyses in R is that taxa labels are assumed to match between phylogenetic and other data sets. These functions prune a phylogeny and community or trait data set to match one another, reporting taxa that are missing from one data set or the other.

Taxa names for phylogeny objects are taken from the phylogeny's tip labels. Taxa names for community data are taken from the column names. Taxa names for trait data are taken from the element names (vector) or row names (data.frame or matrix). Taxa names for distance data are taken from column/row names of the distance matrix/dist object.

If community data lack taxa names, the function will issue a warning and no result will be returned, since the community-phylogenetic analyses in picante require named taxa in the community data set.

If trait data or distance matrix lack names, a warning is issued and the data are assumed to be sorted in the same order as the phylogeny's tip labels or community's column labels.

These utility functions are used by several functions that assume taxa labels in phylogeny and data match, including Kcalc, phylosignal, and raoD.

Value

A list containing the following elements, pruned and sorted to match one another:

Author(s)

Steven Kembel <steve.kembel@gmail.com>

See Also

prune.missing, prune.sample

Examples

data(phylocom)
match.phylo.comm(phylocom$phylo, phylocom$sample)
match.phylo.data(phylocom$phylo, phylocom$traits[1:10,])

Convert community data matrix to Phylocom sample

Description

Converts a community data matrix to a Phylocom database-format community sample

Usage

matrix2sample(z)

Arguments

Value

Phylocom database-format community sample

Author(s)

Steven Kembel <steve.kembel@gmail.com> and Cam Webb <cwebb@oeb.harvard.edu>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

Examples

data(phylocom)
matrix2sample(phylocom$sample)

Mean nearest taxon distance

Description

Calculates MNTD (mean nearest taxon distance) for taxa in a community

Usage

mntd(samp, dis, abundance.weighted=FALSE)

Arguments

Details

This metric has also been referred to as MNND (mean nearest neighbour distance), and the function was named mnnd in picante versions < 0.7.

Value

Vector of MNTD values for each community.

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Webb, C., D. Ackerly, M. McPeek, and M. Donoghue. 2002. Phylogenies and community ecology. Annual Review of Ecology and Systematics 33:475-505.

See Also

ses.mntd

Examples

data(phylocom)
mntd(phylocom$sample, cophenetic(phylocom$phylo), abundance.weighted=TRUE)

Mean pairwise distance

Description

Calculates mean pairwise distance separating taxa in a community

Usage

mpd(samp, dis, abundance.weighted=FALSE)

Arguments

Value

Vector of MPD values for each community

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Webb, C., D. Ackerly, M. McPeek, and M. Donoghue. 2002. Phylogenies and community ecology. Annual Review of Ecology and Systematics 33:475-505.

See Also

ses.mpd

Examples

data(phylocom)
mpd(phylocom$sample, cophenetic(phylocom$phylo), abundance.weighted=TRUE)

Calculates phylogenetic signal for data.frame of traits

Description

Calculates phylogenetic signal for data.frame of traits. Traits may have missing values in which case the tree will be pruned prior to calculating phylogenetic signal for each trait.

Usage

multiPhylosignal(x, phy, checkdata=TRUE, ...)

Arguments

Value

Returns a data frame with phylogenetic signal results for each trait

Author(s)

Steven Kembel <steve.kembel@gmail.com>

Phylogenetic Bipartite Linear Model

Description

Fits a linear model to the association strengths of a bipartite data set with or without phylogenetic correlation among the interacting species

Usage

pblm(assocs, tree1=NULL, tree2=NULL, covars1=NULL, covars2=NULL, bootstrap=FALSE,
    nreps=10, maxit=10000, pstart=c(.5,.5))
pblmpredict(x, tree1.w.novel=NULL, tree2.w.novel=NULL, predict.originals=FALSE)

Arguments

Details

Fit a linear model with covariates using estimated generalized least squares to the association strengths between two sets of interacting species. Associations can be either binary or continuous. If phylogenies of the two sets of interacting species are supplied, two phyogenetic signal strength parameters (d1 and d2), one for each species set, based on an Ornstein-Uhlenbeck model of evolution with stabilizing selection are estimated. Values of d=1 indicate no stabilizing selection and correspond to the Brownian motion model of evolution; 0<d<1 represents stabilizing selection; d=0 depicts the absence of phylogenetic correlation (i.e., a star phylogeny); and d>1 corresponds to disruptive selection where phylogenetic signal is amplified. Confidence intervals for these and the other parameters can be estimated with bootstrapping.

The function pblmpredict predicts the associations of novel species following the methods given in appendix B of Ives and Godfray (2006).

Value

Note

Covariates that apply to both species sets (e.g., sampling site) should be supplied in the covariate matrix of the set with the most species.

Bootstrapping CIs is slow due to the function optim used to estimate the model parameters. See appendix A in Ives and Godfray (2006) for a discussion about this boostrapping procedure

If pblmpredict=TRUE the function does not first remove each species in turn when predicting the associations of the original species as is done in Ives and Godfray (2006).

Author(s)

Matthew Helmus mrhelmus@gmail.com

References

Ives A.R. & Godfray H.C. (2006) Phylogenetic analysis of trophic associations. The American Naturalist, 168, E1-E14

Blomberg S.P., Garland T.J. & Ives A.R. (2003) Testing for phylogenetic signal in comparative data: Behavioral traits are more labile. Evolution, 57, 717-745

Examples

#load example data from Ives & Godfray (2006)
data(IvesGodfray)

#net attack rate of parasitoid on host eq.4 in Ives and Godfray
A<-(-1*log(1-IvesGodfray$interactions[,-28]/t(IvesGodfray$interactions[28])))

# Make tips of the phylogenetic trees contemporaneous by extending tips
p<-dim(IvesGodfray$host)[1]
q<-dim(IvesGodfray$parasitoid)[1]
host.cov.scaled<-IvesGodfray$host
para.cov.scaled<-IvesGodfray$parasitoid
for (i in 1:p)
{
  host.cov.scaled[i,i]<-max(host.cov.scaled)
}
for (i in 1:q)
{
  para.cov.scaled[i,i]<-max(para.cov.scaled)
}

# scale covariance matrices (this reduces numerical problems caused by
# determinants going to infinity or zero)
  host.cov.scaled<-host.cov.scaled/(det(as.matrix(host.cov.scaled))^(1/p))
  para.cov.scaled<-para.cov.scaled/(det(as.matrix(para.cov.scaled))^(1/q))

pblm.A <- pblm(sqrt(A),tree1=host.cov.scaled,tree2=para.cov.scaled)
pblm.A$signal.strength    #compare to Ives and Godfray (2006) Table 1 Line 1
pblm.A$MSE

Phylogenetic Community Dissimilarity

Description

Pairwise dissimilarity in phylogenetic community composition that is partitioned into a nonphylogenetic and a phylogenetic component.

Usage

    pcd(comm, tree, PSVmncd=NULL, PSVpool=NULL, reps=10^4)

Arguments

Details

Phylogenetic community dissimilarity (PCD) is the pairwise differences between communities derived by asking how much of the variance among species in the values of a hypothetical nonselected trait in one community can be predicted by the known trait values of species in another community. PCD is partitioned into a nonphylogenetic component that reflects shared species between communities (PCDc) and a phylogenetic component that reflects the evolutionary relationships among nonshared species (PCDp). In order to compare communities that vary in species richness, the metric is standardized under the assumption that the species in communities are selected at random from the species pool. The analyses here define the species pool as the list of all species in the set of communities in comm, but the species pool can be defined under any hypothesis of community assembly either by manipulating the code or inputting a user defined PSVmncd and PSVpool.

Value

The function returns a list with items:

Note

The sampling procedure used to standardize PCD and produce PSVmncd and PSVpool can be slow.

Author(s)

Anthony Ives <arives@wisc.edu> and Matthew Helmus <mrhelmus@gmail.com>

References

Ives A.R. & Helmus M.R. (2010). Phylogenetic metrics of community similarity. The American Naturalist, 176, E128-E142.

See Also

psv, phylosor, unifrac

Examples

data(phylocom)
pcd(phylocom$sample, phylocom$phylo)

Calculate Faith's Phylogenetic Diversity

Description

Calculate the sum of the total phylogenetic branch length for one or multiple samples.

Usage

pd(samp, tree, include.root=TRUE)

Arguments

Value

Returns a dataframe of the PD and species richness (SR) values for all samples

Warning

If the root is to be included in all calculations (include.root=TRUE), the PD of all samples will include the branch length connecting taxa in those samples and the root node of the supplied tree. The root of the supplied tree may not be spanned by any taxa in the sample. If you want the root of your tree to correspond to the most recent ancestor of the taxa actually present in your sample, you should prune the tree before running pd:

prunedTree <- prune.sample(sample,tree)

Note

The data sets need not be species-community data sets but may be any sample data set with an associated phylogeny. PD is not statistically independent of species richness, it positively correlates with species richness across samples. The function ses.pd compares observed PD to the values expected under various randomizations and allows a way to standardize for unequal richness across samples.

If the root is to be included in all calculations of PD (include.root=TRUE), the tree must be rooted. Single-species samples will be assigned a PD value equal to the distance from the root to the present.

If the root is not included in all calculations by default (include.root=FALSE), the tree need not rooted, but in the case of single-species samples the PD will be equal to NA and a warning will be issued.

Author(s)

Matthew Helmus mrhelmus@gmail.com, Jonathan Davies davies@nceas.ucsb.edu, Steven Kembel steve.kembel@gmail.com

References

Faith D.P. (1992) Conservation evaluation and phylogenetic diversity. Biological Conservation, 61, 1-10.

See Also

psr, ses.pd

Examples

data(phylocom)
pd(phylocom$sample, phylocom$phylo)

Phylogenetic estimation of traits for unobserved taxa

Description

Uses phylogenetic ancestral state reconstruction to estimate trait values for unobserved taxa.

Usage

phyEstimate(phy, trait, method="pic", ...)
phyEstimateDisc(phy, trait, best.state=TRUE, cutoff=0.5, ...)

Arguments

Details

These functions use phylogenetic ancestral state estimation to infer trait values for novel taxa on a phylogenetic tree, for continuous (phyEstimate) and discrete (phyEstimateDisc) traits.

The required input is a phylogenetic tree object plus a vector or data.frame containing estimated trait values for a subset of the taxa in the phylogenetic tree. Trait values for taxa that are present in the tree but not the trait data will be estimated using ancestral state estimation (Garland and Ives 2000). Briefly, for each taxon present in the tree but not the trait data, the phylogeny is rerooted at the most recent common ancestor of the novel taxon and the rest of the phylogeny, and the trait value of the novel taxon is estimated from the reconstructed trait value at the root of the rerooted phylogeny.

For phyEstimateDisc, the state with the highest support will be reported if argument best.state=TRUE. If the best-supported state's support is less than the specified cutoff, no best state is reported and a NA value will be returned.

Value

phyEstimate produces a data frame with columns:

phyEstimateDisc produces a data frame with columns:

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

T. Garland Jr., and A.R. Ives. 2000. Using the past to predict the present: confidence intervals for regression equations in phylogenetic comparative methods. American Naturalist 155:346364.

S.W. Kembel, M. Wu, J.A. Eisen, and J.L. Green. 2012. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLoS Computational Biology 8(10):e1002743.

Examples

#generate random phylogeny
randtree <- rcoal(50)
#simulate trait evolution for a subset of taxa on phylogeny
randtraits <- sample(rTraitCont(randtree, sigma=10, root.value=100), 40)
#estimate trait values for "missing" taxa using PIC method
phyEstimate(randtree, randtraits, method="pic")

Phylocom default data

Description

Phylogeny, community and trait data from the Phylocom 4.0 distribution

Usage

data(phylocom)

Format

A list with three elements:

• phylo Phylogenetic tree (an object of class phylo)

• sample Community data (a data.frame with samples in rows and species in columns

• traits Trait data (a data.frame with species in rows and traits in columns

Source

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

Measure phylogenetic signal

Description

Calculates K statistic of phylogenetic signal as well as P-value based on variance of phylogenetically independent contrasts relative to tip shuffling randomization.

Usage

phylosignal(x, phy, reps = 999, checkdata=TRUE, ...)

Arguments

Value

Data frame with columns:

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Blomberg, S. P., and T. Garland, Jr. 2002. Tempo and mode in evolution: phylogenetic inertia, adaptation and comparative methods. Journal of Evolutionary Biology 15:899-910.

Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution 57:717-745.

See Also

Kcalc

Examples

randtree <- rcoal(20)
randtraits <- rTraitCont(randtree)
phylosignal(randtraits[randtree$tip.label],randtree)

Phylogenetic index of beta-diversity PhyloSor

Description

Fraction of branch-length shared between two communities

Usage

phylosor(samp, tree)    

Arguments

Value

A distance object of the PhyloSor index of similarity between communities, the fraction of PD (branch-length) shared between two samples

Warning

The phylosor of all samples will include the branch length connecting taxa in those samples and the root of the supplied tree. The root of the supplied tree may not be spanned by any taxa in the sample. If you want the root of your tree to correspond to the most recent ancestor of the taxa actually present in your sample, you should prune the tree before running phylosor:

prunedTree <- prune.sample(sample,tree)

Note

The root of the supplied tree is included in calculations of PhyloSor. The supplied tree must be rooted. Single-species samples will be assigned a PD value equal to the distance from the root to the present.

Author(s)

Helene Morlon <morlon.helene@gmail.com> and Steven Kembel <steve.kembel@gmail.com>

References

Bryant, J.B., 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 105 Supplement 1: 11505-11511

See Also

phylosor.rnd, pd

Examples

data(phylocom)
phylosor(phylocom$sample, phylocom$phylo)

Null PhyloSor values of phylogenetic beta-diversity

Description

PhyloSor values obtained by randomization for different choices of null models

Usage

phylosor.rnd(samp,tree, cstSor=TRUE, null.model=c("taxa.labels",
    "frequency","richness","independentswap","trialswap"),
    runs=999, iterations=1000)

Arguments

Details

Currently implemented null models (arguments to null.model):

taxa.labels

Shuffle community data matrix labels. Maintains species richness in each community and species shared between communities. Should be used with cstSor=TRUE

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency). Does not maintain species richness in communities nor species shared between communities. Can only be used with cstSor=FALSE

richness

With cstSor=TRUE: For each pair of community, maintains species richness in each community and species shared between communities. Sample in the species pool with equal probability; With cstSor=FALSE: Maintains species richness in each community, does not maintain species shared between communities. Sample in the species pool with equal probability

independentswap

Randomize community data matrix with the independent swap algorithm (Gotelli 2000) maintaining species occurrence frequency and sample species richness. Can only be used with cstSor=FALSE

trialswap

Randomize community data matrix with the trial-swap algorithm (Miklos & Podani 2004) maintaining species occurrence frequency and sample species richness. Can only be used with cstSor=FALSE

Value

A list of length the number of runs. Each element of the list is a distance matrix containing the PhyloSor values of phylogenetic beta-diversity obtained by randomization

Author(s)

Helene Morlon <morlon.helene@gmail.com> and Steven Kembel <steve.kembel@gmail.com>

References

Bryant, J.B., 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 105 Supplement 1: 11505-11511

See Also

phylosor, randomizeMatrix

Examples

data(phylocom)
phylosor.rnd(phylocom$sample,phylocom$phylo,cstSor=TRUE,null.model="richness",runs=5)

Permutations to Test for Phylogenetic Signal in Community Composition

Description

Randomize sample/community data matrices to create null distributions of given metrics

Usage

phylostruct(samp, tree, env=NULL, metric=c("psv","psr","pse","psc","sppregs"),
    null.model=c("frequency", "richness","independentswap","trialswap"),
    runs=100, it=1000, alpha=0.05, fam="binomial")

Arguments

Details

The function creates null distributions for the psd set of metrics and for the correlations of sppregs from observed community data sets.

Value

Author(s)

Matthew Helmus mrhelmus@gmail.com

References

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

Helmus M.R., Savage K., Diebel M.W., Maxted J.T. & Ives A.R. (2007b) Separating the determinants of phylogenetic community structure. Ecology Letters, 10, 917-925

Gotelli N.J. (2000) Null model analysis of species co-occurrence patterns. Ecology, 81, 2606-2621

See Also

psd ,sppregs, randomizeMatrix

Prune tree to match community data or trait data

Description

Prune a phylogenetic tree to include only species present in a community data set or with non-missing trait data

Usage

prune.sample(samp, phylo)
prune.missing(x, phylo)

Arguments

Value

Returns a pruned phylo object

Author(s)

Steven Kembel <steve.kembel@gmail.com>

Phylogenetic Species Diversity Metrics

Description

Calculate the bounded phylogenetic biodiversity metrics: phylogenetic species variability, richness, evenness and clustering for one or multiple samples.

Usage

psv(samp,tree,compute.var=TRUE,scale.vcv=TRUE)
psr(samp,tree,compute.var=TRUE,scale.vcv=TRUE)
pse(samp,tree,scale.vcv=TRUE)
psc(samp,tree,scale.vcv=TRUE)
psd(samp,tree,compute.var=TRUE,scale.vcv=TRUE)
psv.spp(samp,tree)

Arguments

Details

Phylogenetic species variability (PSV) quantifies how phylogenetic relatedness decreases the variance of a hypothetical unselected/neutral trait shared by all species in a community. The expected value of PSV is statistically independent of species richness, is one when all species in a sample are unrelated (i.e., a star phylogeny) and approaches zero as species become more related. PSV is directly related to mean phylogenetic distance, except except calculated on a scaled phylogenetic covariance matrix. The expected variance around PSV for any sample of a particular species richness can be approximated. To address how individual species contribute to the mean PSV of a data set, the function psv.spp gives signed proportions of the total deviation from the mean PSV that occurs when all species are removed from the data set one at a time. The absolute values of these “species effects” tend to positively correlate with species prevalence.

Phylogenetic species richness (PSR) is the number of species in a sample multiplied by PSV. It can be considered the species richness of a sample after discounting by species relatedness. The value is maximum at the species richness of the sample, and decreases towards zero as relatedness increases. The expected variance around PSR for any sample of a particular species richness can be approximated.

Phylogenetic species evenness (PSE) is the metric PSV modified to incorporate relative species abundances. The maximum attainable value of PSE (i.e., 1) occurs only if species abundances are equal and species phylogeny is a star. PSE essentially grafts each individual of a species onto the tip of the phylogeny of its species with branch lengths of zero.

Phylogenetic species clustering (PSC) is a metric of the branch tip clustering of species across a sample's phylogeny. As PSC increases to 1, species are less related to one another the tips of the phylogeny. PSC is directly related to mean nearest neighbor distance.

Value

Returns a dataframe of the respective phylogenetic species diversity metric values

Note

These metrics are bounded either between zero and one (PSV, PSE, PSC) or zero and species richness (PSR); but the metrics asymptotically approach zero as relatedness increases. Zero can be assigned to communities with less than two species, but conclusions drawn from assigning communities zero values need be carefully explored for any data set. The data sets need not be species-community data sets but may be any sample data set with an associated phylogeny.

Author(s)

Matthew Helmus mrhelmus@gmail.com

References

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

See Also

mpd ,mnnd, specaccum.psr

Examples

data(phylocom)
psd(phylocom$sample, phylocom$phylo)

Null models for community data matrix randomization

Description

Various null models for randomizing community data matrices

Usage

randomizeMatrix(samp, null.model = c("frequency", "richness",
    "independentswap", "trialswap"), iterations = 1000)    

Arguments

Details

Currently implemented null models (arguments to null.model):

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency)

richness

Randomize community data matrix abundances within samples (maintains sample species richness)

independentswap

Randomize community data matrix with the independent swap algorithm (Gotelli 2000) maintaining species occurrence frequency and sample species richness

trialswap

Randomize community data matrix with the trial-swap algorithm (Miklos & Podani 2004) maintaining species occurrence frequency and sample species richness

Value

Randomized community data matrix

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Gotelli, N.J. 2000. Null model analysis of species co-occurrence patterns. Ecology 81: 2606-2621

Miklos I. & Podani J. 2004. Randomization of presence-absence matrices: Comments and new algorithms. Ecology 85: 86-92.

Examples

data(phylocom)
randomizeMatrix(phylocom$sample, null.model="richness")

Rao's quadratic entropy

Description

Calculates Rao's quadratic entropy, a measure of within- and among-community diversity taking species dissimilarities into account

Usage

  raoD(comm, phy=NULL)

Arguments

Details

Rao's quadratic entropy (Rao 1982) is a measure of diversity in ecological communities that can optionally take species differences (e.g. phylogenetic dissimilarity) into account. This method is conceptually similar to analyses of genetic diversity among populations (Nei 1973), but instead of diversity of alleles among populations, it measures diversity of species among communities.

If no phylogeny is supplied, Dkk is equivalent to Simpson's diversity (probability that two individuals drawn from a community are from different taxa), Dkl is a beta-diversity equivalent of Simpson's diversity (probability that individuals drawn from each of two communities belong to different taxa), and H is Dkl standardized to account for within-community diversity.

If an ultrametric phylogeny is supplied, Dkk is equivalent to the mean pairwise phylogenetic distance (distance to MRCA) between two individuals drawn from a community, Dkl is the mean pairwise phylogenetic distance between individuals drawn from each of two communities, and H is Dkl standardized to account for within-community diversity.

D[kl] = sum(t[ij] * x[ki] * x[lj])

where x[ki] is the relative abundance of taxon i in community k and t[ij] is a matrix of weights for all pairs of taxa i,j. Without a phylogeny, when i=j, t[ij]=0, otherwise t[ij]=1. With a phylogeny, t[ij] is the phylogenetic distance to MRCA for taxa i,j.

H[kl] = D[kl] - (D[kk] + D[ll])/2

Alpha, beta and total measure the average diversity within, among, and across all communities based on Dkk and H values taking variation in number of individuals per community into account. A Fst-like measure is calculated by dividing beta by the total diversity across all samples.

Value

A list of results

Warning

Alpha, beta, and total diversity components and Fst should not be interpreted as a measure of relative differentiation among versus within communities. See Jost (2007) for a detailed description of this problem. Hardy and Jost (2008) suggest Fst can be interpreted as 'local species identity excess' or 'local phylogenetic similarity excess' rather than as a measure of among-community differentiation.

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Hardy, O.J., and Jost. L. 2008. Interpreting and estimating measures of community phylogenetic structuring. J. Ecol. 96:849-852.

Jost, L. 2007. Partitioning diversity into independent alpha and beta components. Ecology 88: 24272439.

Nei, M. 1973. Analysis of gene diversity in sub-divided populations. Proceedings of the National Academy of Sciences of the USA 70:3321-3323.

Rao, C.R. 1982. Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology 21:2443.

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

See Also

mpd, comdist

Examples

data(phylocom)
raoD(phylocom$sample)
raoD(phylocom$sample, phylocom$phylo)

Read Phylocom sample

Description

Reads a Phylocom sample file and converts to a community data matrix

Usage

readsample(filename = "")

Arguments

Value

Community data matrix

Author(s)

Steven Kembel <skembel> and Cam Webb <cwebb@oeb.harvard.edu>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

Convert Phylocom sample to community data matrix

Description

Convert a Phylocom database-format sample to community data matrix.

Usage

sample2matrix(x)

Arguments

Author(s)

Steven Kembel <steve.kembel@gmail.com> and Cam Webb <cwebb@oeb.harvard.edu>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

Standardized effect size of MNTD

Description

Standardized effect size of mean nearest taxon distances in communities. When used with a phylogenetic distance matrix, equivalent to -1 times the Nearest Taxon Index (NTI).

Usage

ses.mntd(samp, dis, null.model = c("taxa.labels", "richness", "frequency",
    "sample.pool", "phylogeny.pool", "independentswap", "trialswap"),
    abundance.weighted=FALSE, runs = 999, iterations = 1000)

Arguments

Details

The metric used by this function has also been referred to as MNND (mean nearest neighbour distance), and the function was named ses.mnnd in picante versions < 0.7.

Currently implemented null models (arguments to null.model):

taxa.labels

Shuffle distance matrix labels (across all taxa included in distance matrix)

richness

Randomize community data matrix abundances within samples (maintains sample species richness)

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency)

sample.pool

Randomize community data matrix by drawing species from pool of species occurring in at least one community (sample pool) with equal probability

phylogeny.pool

Randomize community data matrix by drawing species from pool of species occurring in the distance matrix (phylogeny pool) with equal probability

independentswap

Randomize community data matrix with the independent swap algorithm (Gotelli 2000) maintaining species occurrence frequency and sample species richness

trialswap

Randomize community data matrix with the trial-swap algorithm (Miklos & Podani 2004) maintaining species occurrence frequency and sample species richness

Value

A data frame of results for each community

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

See Also

mntd, randomizeMatrix

Examples

data(phylocom)
ses.mntd(phylocom$sample, cophenetic(phylocom$phylo),null.model="taxa.labels")

Standardized effect size of MPD

Description

Standardized effect size of mean pairwise distances in communities. When used with a phylogenetic distance matrix, equivalent to -1 times the Nearest Relative Index (NRI).

Usage

ses.mpd(samp, dis, null.model = c("taxa.labels", "richness", "frequency", "sample.pool",
            "phylogeny.pool", "independentswap", "trialswap"),
            abundance.weighted = FALSE, runs = 999, iterations = 1000)

Arguments

Details

Currently implemented null models (arguments to null.model):

taxa.labels

Shuffle distance matrix labels (across all taxa included in distance matrix)

richness

Randomize community data matrix abundances within samples (maintains sample species richness)

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency)

sample.pool

Randomize community data matrix by drawing species from pool of species occurring in at least one community (sample pool) with equal probability

phylogeny.pool

Randomize community data matrix by drawing species from pool of species occurring in the distance matrix (phylogeny pool) with equal probability

independentswap

Randomize community data matrix with the independent swap algorithm (Gotelli 2000) maintaining species occurrence frequency and sample species richness

trialswap

Randomize community data matrix with the trial-swap algorithm (Miklos & Podani 2004) maintaining species occurrence frequency and sample species richness

Value

A data frame of results for each community

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

See Also

mpd, randomizeMatrix

Examples

data(phylocom)
ses.mpd(phylocom$sample, cophenetic(phylocom$phylo),null.model="taxa.labels")

Standardized effect size of PD

Description

Standardized effect size of phylogenetic diversity (Faith's PD) in communities.

Usage

ses.pd(samp, tree, null.model = c("taxa.labels", "richness", "frequency",
    "sample.pool", "phylogeny.pool", "independentswap", "trialswap"),
    runs = 999, iterations = 1000, include.root=TRUE)

Arguments

Details

Currently implemented null models (arguments to null.model):

taxa.labels

Shuffle taxa labels across tips of phylogeny (across all taxa included in phylogeny)

richness

Randomize community data matrix abundances within samples (maintains sample species richness)

frequency

Randomize community data matrix abundances within species (maintains species occurence frequency)

sample.pool

Randomize community data matrix by drawing species from pool of species occurring in at least one community (sample pool) with equal probability

phylogeny.pool

Randomize community data matrix by drawing species from pool of species occurring in the phylogeny (phylogeny pool) with equal probability

independentswap

Randomize community data matrix with the independent swap algorithm (Gotelli 2000) maintaining species occurrence frequency and sample species richness

trialswap

Randomize community data matrix with the trial-swap algorithm (Miklos & Podani 2004) maintaining species occurrence frequency and sample species richness

Value

A data frame of results for each community

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

Proches, S., Wilson, J.R.U. and Cowling, R.M. 2006. How much evolutionary history in a 10 x 10m plot? Proceedings of Royal Society of London B, Biological Sciences 273:1143-1148.

See Also

pd, randomizeMatrix

Examples

data(phylocom)
ses.pd(phylocom$sample, phylocom$phylo, null.model="taxa.labels", runs=99)

Phylogenetic Species Richness Sample-Based Rarefaction Curve

Description

Finds a sample-based rarefaction curve for phylogentic species richness for a set of samples.

Usage

specaccum.psr(samp, tree, permutations = 100, method = "random", ...)

Arguments

Value

The function returns an object of class "specaccum" with items:

Author(s)

Matthew Helmus mrhelmus@gmail.com based on the vegan package specaccum function by Roeland Kindt and Jari Oksanen.

References

Gotelli N.J. & Colwell R.K. (2001) Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4, 379-391

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

See Also

psr, specaccum

Examples

data(phylocom)
accum.sr<-specaccum(phylocom$sample, permutations = 100, method = "random")
plot(accum.sr, col="blue")
points(accum.sr$sites, accum.sr$richness, pch=19, col="blue")

accum.psr<-specaccum.psr(phylocom$sample, phylocom$phylo, permutations = 100, method = "random")
plot(accum.psr, add=TRUE, col = "red")
points(accum.psr$sites, accum.psr$richness, pch=19, col="red")

legend(5,5,legend=c("SR","PSR"),pch=c(19,19),col=c("blue","red"))

Species co-occurrence distances

Description

Compute interspecific distances based on patterns of species co-occurrence in communities.

Usage

species.dist(x, metric = c("cij", "jaccard", "checkerboard", "doij"))

Arguments

Details

Currently implemented co-occurrence measures (arguments to metric):

cij

Schoener's index of co-occurrence

jaccard

Jaccard index of co-occurrence

checkerboard

Checkerboard index of co-occurrence

doij

DOij index of co-occurrence

Value

A dist object with co-occurrences among all species pairs

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Hardy, O.J. 2008. Testing the spatial phylogenetic structure of local communities: statistical performances of different null models and test statistics on a locally neutral community. Journal of Ecology 96:914-926.

See Also

vegdist

Regressions to Separate Phylogenetic Attraction and Repulsion

Description

Fit regressions on species abundance or presence/absence across communities and calculate phylogenetic correlations

Usage

sppregs(samp, env, tree=NULL, fam="gaussian")
sppregs.plot(sppreg, rows=c(1,3), cex.mag=1, x.label="phylogenetic correlations", 
    y.label=c("occurrence correlations w/ env", "occurrence correlations wo/ env",
    "change in correlations"))

Arguments

Details

For each species in samp, the function fits regressions of species presence/absence or abundances on the environmental variables supplied in env; and calculates the (n^2-n)/2 pairwise species correlations between the residuals of these fits and pairwise species phylogenetic correlations. The residuals can be thought of as the presence/absence of species across sites/communities after accounting for how species respond to environmental variation across sites. Each set of coefficients can be tested for phylogenetic signal with, for example, the function phylosignal.

The function sppregs.plot produces a set of three plots of the correlations of pairwise species phylogenetic correlations versus: the observed pairwise correlations of species across communities, the residual correlations, and the pairwise differences between (i.e., the change in species co-occurrence once the environmental variables are taken into account). The significance of these correlations can be tested via permutation with the function phylostruct.

Value

Note

The function requires the library brglm to perform logistic regressions

Author(s)

Matthew Helmus mrhelmus@gmail.com

References

Helmus M.R., Savage K., Diebel M.W., Maxted J.T. & Ives A.R. (2007) Separating the determinants of phylogenetic community structure. Ecology Letters, 10, 917-925

See Also

phylostruct, phylosignal

Taxonomic distinctiveness sensu Vane-Wright or May

Description

Taxic diversity: Vane-Wright et al., 1991 and May 1990 which accounts for polytomies by counting the number of branches descending from each node that lies on the path from a spp tip to the root (not just counting the number of nodes).

Usage

tax.distinctiveness(tree, type = c("Vane-Wright", "May"))

Arguments

Author(s)

Karen Magnuson-Ford, Will Cornwell, Arne Mooers, Mark Vellend

References

Vane-Wright, R.I., Humphries, C.J. and Williams, P.H. (1991). What to protect? - Systematics and the agony of choice. Biological Conservation, 55, 235-254.

May, R.M. (1990). Taxonomy as destiny. Nature, 347, 129-130.

Mark Vellend, William K. Cornwell, Karen Magnuson-Ford, and Arne Mooers. In press. Measuring phylogenetic biodiversity In: Biological diversity: frontiers in measurement and assessment. Edited by Anne Magurran and Brian McGill.

Draw phylogeny with nodes at trait positions

Description

Draws a phylogeny where x position of nodes and tips corresponds to value of a continuous trait variable, and y position corresponds to node depth (i.e. age).

Usage

traitgram(x, phy, xaxt = 's', underscore = FALSE, show.names = TRUE,
            show.xaxis.values = TRUE, method = c('ML','pic'), ...)

Arguments

Value

Plots a traitgram, no values returned.

Author(s)

David Ackerly <dackerly@berkeley.edu>

References

Ackerly, D. D. 2009. Conservatism and diversification of plant functional traits: Evolutionary rates versus phylogenetic signal. Proceedings of the National Academy of Sciences USA 106:19699-19706. doi: 10.1073/pnas.0901635106.

Evans, M. E. K., S. A. Smith, R. S. Flynn, and M. J. Donoghue. 2009. Climate, Niche Evolution, and Diversification of the "bird-cage" Evening Primroses (Oenothera, Sections Anogra and Kleinia). American Naturalist 173:225-240.

Examples

randtree <- rcoal(20)
randtraits <- rTraitCont(randtree)
traitgram(randtraits,randtree)
traitgram(randtraits,randtree,method='pic')

Unweighted UniFrac distance between communities

Description

Calculates unweighted UniFrac, a phylogenetic beta diversity metric of the the unique (non-shared) fraction of total phylogenetic diversity (branch-length) between two communities.

Usage

unifrac(comm, tree)    

Arguments

Value

A dist object of the unweighted UniFrac distances between communities (the unique (non-shared) fraction of total phylogenetic diversity (branch-length) between two communities).

Warning

The UniFrac distance between samples will include the branch length connecting taxa in those samples and the root of the supplied tree. The root of the supplied tree may not be spanned by any taxa in the sample. If you want the root of your tree to correspond to the most recent ancestor of the taxa actually present in your samples, you should prune the tree before running unifrac: prunedTree <- prune.sample(sample,tree)

Note

The supplied tree must be rooted. Single-species samples will be assigned a PD value equal to the distance from the root to the present.

Author(s)

Steven Kembel <steve.kembel@gmail.com>

References

Lozupone, C., Hamady, M., and Knight, R. 2006. UniFrac - an online tool for comparing microbial community diversity in a phylogenetic context. BMC Bioinformatics 7:371.

See Also

pd

Examples

data(phylocom)
unifrac(phylocom$sample, phylocom$phylo)

Picante utility functions

Description

Picante utility functions for tree and data manipulation

Usage

df2vec(x, colID=1)
internal2tips(phy, int.node, return.names = FALSE)
node.age(phy)
pic.variance(x, phy, scaled = TRUE)
sortColumns(x)
sortRows(x)
taxaShuffle(x)
tipShuffle(phy)

Arguments

Details

Various utility functions for manipulating trees, data, etc.

Value

Author(s)

Steven Kembel <steve.kembel@gmail.com>, Peter Cowan <pdc@berkeley.edu>, David Ackerly <dackerly@berkeley.edu>

Write a Phylocom community sample file

Description

Write a community data matrix to a Phylocom community sample file

Usage

writesample(community, filename = "")

Arguments

Author(s)

Steven Kembel <steve.kembel@gmail.com> and Cam Webb <cwebb@oeb.harvard.edu>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.

Write a Phylocom traits formatted file

Description

Write a Phylocom traits formatted file

Usage

writetraits(trt, file = "", bin = NULL, sigd = 3)

Arguments

Author(s)

David Ackerly <dackerly@berkeley.edu> and Steven Kembel <steve.kembel@gmail.com>

References

Webb, C.O., Ackerly, D.D., and Kembel, S.W. 2008. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Version 4.0.1. http://www.phylodiversity.net/phylocom/.