Title: | Biogeographic Regionalization and Macroecology |
---|---|
Description: | Computational infrastructure for biogeography, community ecology, and biodiversity conservation (Daru et al. 2020) <doi:10.1111/2041-210X.13478>. It is based on the methods described in Daru et al. (2020) <doi:10.1038/s41467-020-15921-6>. The original conceptual work is described in Daru et al. (2017) <doi:10.1016/j.tree.2017.08.013> on patterns and processes of biogeographical regionalization. Additionally, the package contains fast and efficient functions to compute more standard conservation measures such as phylogenetic diversity, phylogenetic endemism, evolutionary distinctiveness and global endangerment, as well as compositional turnover (e.g., beta diversity). |
Authors: | Barnabas H. Daru [aut, cre, cph] , Piyal Karunarathne [aut] , Klaus Schliep [aut] , Xiaobei Zhao [ctb], Albin Sandelin [ctb], Luciano Pataro [ctb] |
Maintainer: | Barnabas H. Daru <[email protected]> |
License: | AGPL-3 |
Version: | 1.0.9 |
Built: | 2024-10-22 22:49:59 UTC |
Source: | https://github.com/darunabas/phyloregion |
This document describes the phyloregion
package for the R software.
phyloregion
is a computational infrastructure for biogeographic
regionalization
(the classification of geographical areas in terms of their biotas) and
spatial conservation in the R scientific computing environment. Previous
analyses of biogeographical regionalization were either focused on smaller
datasets or slower particularly when the number of species or geographic
scale is very large. With macroecological datasets of ever increasing size
and complexity, phyloregion
offers the possibility of handling and
executing large scale biogeographic regionalization efficiently and with
extreme speed. It also allows fast and efficient for analysis of more
standard conservation measures such as phylogenetic diversity, phylogenetic
endemism, evolutionary distinctiveness and global endangerment.
phyloregion
can run on any operating system (Mac, Linux, Windows or
even high performance computing cluster) with R 3.6.0 (or higher) installed.
phyloregion
The original implementation of phyloregion is described in:
Daru B.H., Karunarathne, P. & Schliep, K. (2020) phyloregion: R package for biogeographic regionalization and macroecology. Methods in Ecology and Evolution 11, 1483-1491.
It is based on the method described in:
Daru, B.H., Farooq, H., Antonelli, A. & Faurby, S. (2020) Endemism patterns are scale dependent. Nature Communications 11, 2115.
The original conceptual is described in:
Daru, B.H., Elliott, T.L., Park, D.S. & Davies, T.J. (2017) Understanding the processes underpinning patterns of phylogenetic regionalization. Trends in Ecology and Evolution 32: 845-860.
If you have any questions, suggestions or issues regarding the package, please add them to GitHub issues
phyloregion
is an open-source and free package hosted on
GitHub.
You will need to install the devtools
package. In R
, type:
if (!requireNamespace("devtools", quietly = TRUE))
install.packages("devtools")
Then:
devtools::install_github("darunabas/phyloregion")
Load the phyloregion package:
library(phyloregion)
Barnabas Daru thanks Texas A&M University-Corpus Christi for financial and logistic support.
Barnabas H. Daru, Piyal Karunarathne, Klaus Schliep
This dataset consists of a dated phylogeny of the woody plant species of southern Africa along with their geographical distributions. The dataset comes from a study that maps tree diversity hotspots in southern Africa (Daru et al. 2015). The study mapped five types of diversity hotspots including species richness (SR), phylogenetic diversity (PD), phylogenetic endemism (PE), species weighted endemism (CWE), and evolutionary distinctiveness and global endangerment (EDGE). The results revealed large spatial incongruence between biodiversity indices, resulting in unequal representation of PD, SR, PE, CWE and EDGE in hotspots and currently protected areas, suggesting that an integrative approach which considers multiple facets of biodiversity is needed to maximise the conservation of tree diversity in southern Africa. Specifically for this package, we arranged the dataset into four components: “comm”, “polys”, “phylo”, “mat”, “IUCN”.
comm: This is a sparse community composition matrix of each species presences/absences within 50 × 50 km grid cells. A sparse matrix is a matrix with a high proportion of zero entries (Duff 1977), of which only the non-zero entries are stored and used for downstream analysis.
polys: These are the grid cells covering the geographic extent of
study area. These can be created using the function fishnet
.
The polys object is of class SpatVector
and has a column labeled
“grids”, with the grid identities.
phylo: This corresponds to the phylogenetic tree which was estimated using Bayesian analysis of 1,400 species and 1,633 bp of chloroplast DNA sequences derived from a combination of matK and rbcLa, assuming an uncorrelated relaxed molecular clock model, using the program BEAST v.1.7.5 (Drummond & Rambaut, 2007). Branch lengths were calibrated in millions of years using a Bayesian MCMC approach by enforcing topological constraints assuming APG III backbone from Phylomatic v.3 (Webb & Donoghue, 2005) and 18 fossil calibration points from Bell et al. (2010).
mat: This is a distance matrix of phylogenetic beta diversity between all grid cells at the 50 × 50 km scale.
IUCN: This is a dataframe of IUCN conservation status of each woody
species (LC, NT, VU, EN, CR). This is useful for analysis of Evolutionary
Distinctiveness and Global Endangerment using the function
EDGE
.
Bell, C.D., Soltis, D.E., & Soltis, P.S. (2010). The age and diversification of the angiosperms re-revisited. American Journal of Botany 97, 1296–1303.
Daru, B.H., Van der Bank, M. & Davies, T.J. (2015) Spatial incongruence among hotspots and complementary areas of tree diversity in southern Africa. Diversity and Distributions 21, 769-780.
Drummond, A.J., & Rambaut, A. (2007). BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolutionary Biology 7, 214.
Duff, I.S. (1977). A survey of sparse matrix research. Proceedings of the IEEE 65, 500–535.
Webb, C.O., & Donoghue, M.J. (2005). Phylomatic: Tree assembly for applied phylogenetics. Molecular Ecology Notes 5, 181–183.
data(africa) names(africa) library(terra) library(ape) plot(africa$phylo)
data(africa) names(africa) library(terra) library(ape) plot(africa$phylo)
Add arc labels to plotted phylogeny
arc_labels(phy, tips, ...) ## Default S3 method: arc_labels( phy = NULL, tips, text, plot_singletons = TRUE, ln.offset = 1.02, lab.offset = 1.06, cex = 1, orientation = "horizontal", ... )
arc_labels(phy, tips, ...) ## Default S3 method: arc_labels( phy = NULL, tips, text, plot_singletons = TRUE, ln.offset = 1.02, lab.offset = 1.06, cex = 1, orientation = "horizontal", ... )
phy |
An object of class phylo. |
tips |
A character vector (or a list) with names of the tips that belong to the clade or group. If multiple groups are to be plotted, tips must be given in the form of a list. |
... |
Further arguments passed to or from other methods. |
text |
Desired clade label. |
plot_singletons |
Logical. If TRUE (default), adds arcs (and labels) to single tip lineages. If FALSE, no arc or labels will be plotted over that tip.. |
ln.offset |
Line offset (as a function of total tree height) |
lab.offset |
Label offset. |
cex |
Character expansion |
orientation |
Orientation of the text. Can be “vertical”, “horizontal”, or “curved”. |
NULL
old.par <- par(no.readonly = TRUE) require(ape) data(africa) par(mai=rep(0,4)) plot(africa$phylo, type = "fan", show.tip.label=FALSE, open.angle = 180, edge.width=0.5) y <- data.frame(species=africa$phylo$tip.label) y$genus <- gsub("_.*", "\\1", y$species) fx <- split(y, f=y$genus) suppressWarnings(invisible(lapply(fx, function(x) { y <- seq(from = 1.03, to = 1.09, by = ((1.09 - 1.03)/(length(fx) - 1))) z <- sample(y, 1, replace = FALSE, prob = NULL) if(nrow(x) > 10L) arc_labels(phy = africa$phylo, tips=x$species, text=as.character(unique(x$genus)), orientation = "curved", cex=0.5, lab.offset = z) }))) par(old.par)
old.par <- par(no.readonly = TRUE) require(ape) data(africa) par(mai=rep(0,4)) plot(africa$phylo, type = "fan", show.tip.label=FALSE, open.angle = 180, edge.width=0.5) y <- data.frame(species=africa$phylo$tip.label) y$genus <- gsub("_.*", "\\1", y$species) fx <- split(y, f=y$genus) suppressWarnings(invisible(lapply(fx, function(x) { y <- seq(from = 1.03, to = 1.09, by = ((1.09 - 1.03)/(length(fx) - 1))) z <- sample(y, 1, replace = FALSE, prob = NULL) if(nrow(x) > 10L) arc_labels(phy = africa$phylo, tips=x$species, text=as.character(unique(x$genus)), orientation = "curved", cex=0.5, lab.offset = z) }))) par(old.par)
Generates background sample as null for species distribution modeling and other things.
Sample random background points using a vector of probability weights.
backg(calib, spatkde, size = 10000, ...) randpoints(ras, size, prob = NULL, ...)
backg(calib, spatkde, size = 10000, ...) randpoints(ras, size, prob = NULL, ...)
calib |
A SpatRaster of the species calibration area. If a polygon, convert to SpatRaster using rasterize. |
spatkde |
A weighted or unweighted Gaussian Kernel Density estimate (KDE) for all input occurrence records. |
size |
An positive integer of the number of samples to generate. |
... |
further arguments passed to or from other methods. |
ras |
Input SpatRaster |
prob |
Vector of probability weights for obtaining the points sampled. |
A dataframe containing the generated background points.
A dataframe with sampled points.
Data are assumed to be presence / absence (0 / 1) and all values greater zero are assumed to reflect presence.
beta_core(x) beta_diss(x, index.family = "sorensen")
beta_core(x) beta_diss(x, index.family = "sorensen")
x |
an object of class Matrix, where rows are sites and columns are species. |
index.family |
family of dissimilarity indices, partial match of "sorensen" or "jaccard". |
beta_core
is helper function to compute the basic quantities needed
for computing the "sorensen" or "jaccard" index.
beta_core
returns an object of class beta_diss
like the
betapart.core
function. This object can be called by
beta.pair
or beta.multi
.
beta_diss
returns a list with three dissimilarity matrices. See
beta.pair
for details.
Klaus Schliep
betapart.core
, betapart
,
phylobeta
data(africa) x <- africa$comm bc <- beta_core(x) beta_sorensen <- beta_diss(x)
data(africa) x <- africa$comm bc <- beta_core(x) beta_sorensen <- beta_diss(x)
choropleth
discretizes the values of a quantity for mapping.
choropleth(x, k = 10, breaks = "quantile", min = NULL, max = NULL)
choropleth(x, k = 10, breaks = "quantile", min = NULL, max = NULL)
x |
Vector of values to discretize. |
k |
Numeric, the desired number of bins to discretize. |
breaks |
one of “equal”, “pretty”, “jenks”,
“quantile” or numeric vector with the actual breaks by
specifying the minimum ( |
min |
the minima of the lowest bound of the break. |
max |
the maxima of the upper bound of the break |
a vector with the discretized values.
Barnabas H. Daru [email protected]
coldspots
and hotspots
map areas or grid cells with lowest
or highest values, respectively, of a biodiversity metric e.g.
species richness, species endemism or degree of threat.
coldspots(x, y = NULL, prob = 2.5, ...) hotspots(x, y = NULL, prob = 2.5, ...) rast_hotspot(ras, ref = NULL, prob = 10)
coldspots(x, y = NULL, prob = 2.5, ...) hotspots(x, y = NULL, prob = 2.5, ...) rast_hotspot(ras, ref = NULL, prob = 10)
x |
a vector on which to compute hotspots or coldspots |
y |
a vector on which to compare x against |
prob |
The threshold quantile for representing the lowest
( |
... |
Further arguments passed to or from other methods. |
ras |
a SpatRaster on which to compute hotspots. |
ref |
a raster layer for reference. |
A vector of integers of 1s and 0s with 1 corresponding to the coldspots or hotspots
Barnabas H. Daru [email protected]
Myers, M., Mittermeier, R.A., Mittermeier, C.G., da Fonseca, G.A.B. & Kent, J. (2000) Biodiversity hotspots for conservation priorities. Nature 403: 853–858.
Ceballos, G. & Ehrlich, P.R. (2006) Global mammal distributions, biodiversity hotspots, and conservation. Proceedings of the National Academy of Sciences USA 103: 19374–19379.
Orme, C.D., Davies, R.G., Burgess, M., Eigenbrod, F., Pickup, N. et al. (2005) Global hotspots of species richness are not congruent with endemism or threat. Nature 436: 1016–1019.
Daru, B.H., Van der Bank, M. & Davies, T.J. (2015) Spatial incongruence among hotspots and complementary areas of tree diversity in southern Africa. Diversity and Distributions 21: 769-780.
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) Endm <- weighted_endemism(africa$comm) C <- coldspots(Endm, na.rm=TRUE) # coldspots H <- hotspots(Endm, na.rm=TRUE) # hotspots ## Merge endemism values to shapefile of grid cells. DF <- data.frame(grids=names(C), cold=C, hot=H) m <- merge(p, DF, by = "grids", all = TRUE) plot(p, border = "grey", col = "lightgrey", main = "Weighted Endemism Hotspots and Coldspots") plot(m[(m$cold == 1), ], col = "blue", add = TRUE, border = NA) plot(m[(m$hot == 1), ], col = "red", add = TRUE, border = NA) legend("bottomleft", fill = c("blue", "red", "yellow", "green"), legend = c("coldspots", "hotspots"), bty = "n", inset = .092)
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) Endm <- weighted_endemism(africa$comm) C <- coldspots(Endm, na.rm=TRUE) # coldspots H <- hotspots(Endm, na.rm=TRUE) # hotspots ## Merge endemism values to shapefile of grid cells. DF <- data.frame(grids=names(C), cold=C, hot=H) m <- merge(p, DF, by = "grids", all = TRUE) plot(p, border = "grey", col = "lightgrey", main = "Weighted Endemism Hotspots and Coldspots") plot(m[(m$cold == 1), ], col = "blue", add = TRUE, border = NA) plot(m[(m$hot == 1), ], col = "red", add = TRUE, border = NA) legend("bottomleft", fill = c("blue", "red", "yellow", "green"), legend = c("coldspots", "hotspots"), bty = "n", inset = .092)
This function collapses nodes and geographic ranges based on species' divergence times at various time depths.
collapse_range( x, tree, n, species = "species", grids = "grids", format = "wide" )
collapse_range( x, tree, n, species = "species", grids = "grids", format = "wide" )
x |
A community matrix or data frame. |
tree |
A phylogenetic tree. |
n |
Time depth to slice the phylogenetic tree (often in millions of years for dated trees). |
species |
If |
grids |
The column with the sites or grids if |
format |
Format of the community composition data: “long” or “wide” with species as columns and sites as rows. |
Two community data frames: the collapsed community data
and
original community data
Daru, B.H., Farooq, H., Antonelli, A. & Faurby, S. (2020) Endemism patterns are scale dependent. Nature Communications 11: 2115.
library(ape) tr1 <- read.tree(text ="(((a:2,(b:1,c:1):1):1,d:3):1,e:4);") com <- matrix(c(1,0,1,1,0,0, 1,0,0,1,1,0, 1,1,1,1,1,1, 1,0,1,1,0,1, 0,0,0,1,1,0), 6, 5, dimnames=list(paste0("g",1:6), tr1$tip.label)) collapse_range(com, tr1, n=1)
library(ape) tr1 <- read.tree(text ="(((a:2,(b:1,c:1):1):1,d:3):1,e:4);") com <- matrix(c(1,0,1,1,0,0, 1,0,0,1,1,0, 1,1,1,1,1,1, 1,0,1,1,0,1, 0,0,0,1,1,0), 6, 5, dimnames=list(paste0("g",1:6), tr1$tip.label)) collapse_range(com, tr1, n=1)
Generates a sparse community matrix as input for clustering regions based on the similairity of functional traits across species.
counts(x, trait, cut = NULL, phy = NULL, bin = 10, na.rm = FALSE)
counts(x, trait, cut = NULL, phy = NULL, bin = 10, na.rm = FALSE)
x |
A community data in long format with one column representing sites labeled “grids” and another column representing species labeled “species”. |
trait |
A data frame or matrix object with the first column labeled “species” containing the taxonomic groups to be evaluated whereas the remaining columns have the various functional traits. The variables must be a mix of numeric and categorical values. |
cut |
The slice time. |
phy |
is a dated phylogenetic tree with branch lengths stored as a phylo object (as in the ape package). |
bin |
The desired number of clusters or bins. |
na.rm |
Logical, whether NA values should be removed or not. |
Function returns a community data frame that captures the count of each species based on its cluster membership.
Gets the path of the current directory.
dirpath(path)
dirpath(path)
path |
Character vector of the directory path names. |
A character vector containing the name of the current directory.
This function calculates EDGE by combining evolutionary distinctiveness (ED; i.e., phylogenetic isolation of a species) with global endangerment (GE) status as defined by the International Union for Conservation of Nature (IUCN).
EDGE(x, phy, Redlist = "Redlist", species = "species", ...)
EDGE(x, phy, Redlist = "Redlist", species = "species", ...)
x |
a data.frame |
phy |
a phylogenetic tree (object of class phylo). |
Redlist |
column in the data frame with the IUCN ranks: |
species |
data frame column specifying the taxon |
... |
Further arguments passed to or from other methods. |
EDGE is calculated as:
where ED represents the evolutionary distinctiveness score of each
species (function evol_distinct
), i.e. the degree of phylogenetic
isolation, and combining it with GE, global endangerment from IUCN
conservation threat categories. GE is calculated as the expected
probability of extinction over 100 years of each taxon in the phylogeny
(Redding & Mooers, 2006), scaled as follows: least concern = 0.001, near
threatened and conservation dependent = 0.01, vulnerable = 0.1,
endangered = 0.67, and critically endangered = 0.999.
Returns a dataframe of EDGE scores
Barnabas H. Daru
Redding, D.W., & Mooers, A.Ø. (2006) Incorporating evolutionary measures into conservation prioritization. Conservation Biology 20: 1670–1678.
Isaac, N.J., Turvey, S.T., Collen, B., Waterman, C. & Baillie, J.E. (2007) Mammals on the EDGE: conservation priorities based on threat and phylogeny. PLoS ONE 2: e296.
data(africa) y <- EDGE(x=africa$IUCN, phy=africa$phylo, Redlist="IUCN", species="Species")
data(africa) y <- EDGE(x=africa$IUCN, phy=africa$phylo, Redlist="IUCN", species="Species")
Calculates evolutionary distinctiveness measures for a suite of species by:
a) equal splits (Redding and Mooers 2006)
b) fair proportions (Isaac et al., 2007).
This a new implementation of the picante function evol.distinct
however allowing multifurcations and can be orders of magnitude faster.
evol_distinct( tree, type = c("equal.splits", "fair.proportion"), scale = FALSE, use.branch.lengths = TRUE, ... )
evol_distinct( tree, type = c("equal.splits", "fair.proportion"), scale = FALSE, use.branch.lengths = TRUE, ... )
tree |
an object of class |
type |
a) equal splits (Redding and Mooers 2006) or b) fair proportions (Isaac et al., 2007) |
scale |
The scale option refers to whether or not the phylogeny should be scaled to a depth of 1 or, in the case of an ultrametric tree, scaled such that branch lengths are relative. |
use.branch.lengths |
If use.branch.lengths=FALSE, then all branch lengths are changed to 1. |
... |
Further arguments passed to or from other methods. |
a named vector with species scores.
Klaus Schliep
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.
tree <- ape::rcoal(10) evol_distinct(tree) evol_distinct(tree, type = "fair.proportion")
tree <- ape::rcoal(10) evol_distinct(tree) evol_distinct(tree, type = "fair.proportion")
The fishnet
function creates a regular grid of locations covering
the study area at various grain sizes.
fishnet(mask, res = 0.5)
fishnet(mask, res = 0.5)
mask |
a vector polygon covering the boundary of the survey region. |
res |
the grain size of the grid cells in decimal degrees (default). |
A spatial vector polygon object of equal area grid cells covering the defined area.
Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling 190: 231-259.
d <- terra::vect(system.file("ex/nigeria.json", package="phyloregion")) f <- fishnet(d, res = 0.75)
d <- terra::vect(system.file("ex/nigeria.json", package="phyloregion")) f <- fishnet(d, res = 0.75)
Generates grade of membership, “admixture”, “topic” or “Latent Dirichlet Allocation” models, by representing sampling units as partial memberships in multiple groups. It can group regions based on phylogenetic information or functional traits.
fitgom( x, trait = NULL, cut = NULL, phy = NULL, bin = 10, na.rm = FALSE, K, shape = NULL, initopics = NULL, tol = 0.1, bf = TRUE, kill = 2, ord = TRUE, verb = 1, ... )
fitgom( x, trait = NULL, cut = NULL, phy = NULL, bin = 10, na.rm = FALSE, K, shape = NULL, initopics = NULL, tol = 0.1, bf = TRUE, kill = 2, ord = TRUE, verb = 1, ... )
x |
A community data in long format with one column representing sites labeled “grids” and another column representing species labeled “species”. |
trait |
A data frame or matrix object with the first column labeled “species” containing the taxonomic groups to be evaluated whereas the remaining columns have the various functional traits. The variables must be a mix of numeric and categorical values. |
cut |
The slice time for the phylogenetic tree. |
phy |
is a dated phylogenetic tree with branch lengths stored as a phylo object (as in the ape package). |
bin |
The desired number of clusters or bins. |
na.rm |
Logical, whether NA values should be removed or not. |
K |
The number of latent topics. If length(K)>1, topics will find the Bayes factor (vs a null single topic model) for each element and return parameter estimates for the highest probability K. |
shape |
Optional argument to specify the Dirichlet prior concentration parameter as shape for topic-phrase probabilities. Defaults to 1/(K*ncol(counts)). For fixed single K, this can also be a ncol(counts) by K matrix of unique shapes for each topic element. |
initopics |
Optional start-location for
|
tol |
An indicator for whether or not to calculate the Bayes factor for univariate K. If length(K)>1, this is ignored and Bayes factors are always calculated. |
bf |
An indicator for whether or not to calculate the Bayes factor for univariate K. If length(K)>1, this is ignored and Bayes factors are always calculated. |
kill |
For choosing from multiple K numbers of topics (evaluated in increasing order), the search will stop after kill consecutive drops in the corresponding Bayes factor. Specify kill=0 if you want Bayes factors for all elements of K. |
ord |
If |
verb |
A switch for controlling printed output. verb > 0 will print something, with the level of detail increasing with verb. |
... |
Further arguments passed to or from other methods. |
Mapping phylogenetic regions (phyloregions) involves successively slicing the phylogenetic tree at various time depths (e.g., from 1, 2, 3, 4, to 5 million years ago (Ma)), collapsing nodes and ranges that originated at each time depth, and generating a new community matrix based on the presence or absence of each lineage in a grid cell. A grade of membership model is then fitted to the reduced community matrix. To map functional trait regions (traitregions), the function uses k-means to cluster species based on their functional traits, often for mixed-type data including categorical and numeric functional traits. The ranges for each species in each resulting cluster are collapsed to generate a new community matrix based on the presence or absence of cluster representative in a grid cell. A grade of membership model is then fitted to the new reduced community matrix. Mapping bioregions for taxonomic diversity is based on fitting a grade of membership model directly to the original community matrix that is often represented with species in the columns and sites as rows.
An topics object list with entries
K
The number of latent topics estimated. If input
length(K)>1
, on output this is a single value corresponding to
the model with the highest Bayes factor.
theta
The ncol(counts)
by K matrix of estimated
topic-phrase probabilities.
omega
The nrow(counts)
by K matrix of estimated
document-topic weights.
BF
The log Bayes factor for each number of topics
in the input K, against a null single topic model.
D
Residual dispersion: for each element of K, estimated
dispersion parameter (which should be near one for the multinomial),
degrees of freedom, and p-value for a test of whether the true dispersion
is >1.
X
The input community matrix as a sparse matrix.
library(terra) data(africa) names(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) m <- fitgom(x=sparse2long(africa$comm), K=3) COLRS <- phyloregion:::hue(m$K) plot_pie(m$omega, pol = p, col=COLRS)
library(terra) data(africa) names(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) m <- fitgom(x=sparse2long(africa$comm), K=3) COLRS <- phyloregion:::hue(m$K) plot_pie(m$omega, pol = p, col=COLRS)
Computes turnover of functional diversity using k-prototypes clustering algorithm tailored for mixed-type functional traits (numeric and categorical) to generate an integer vector of cluster assignments. The ranges of each species in a cluster are collapsed to generate a new community matrix based on the presence or absence of cluster membership in a grid cell. A grade of membership model or beta diversity is then fitted to the new reduced community matrix for further analysis.
functional_beta( x, trait = NULL, bin = 10, na.rm = "no", quick_elbow = FALSE, abundance = FALSE, ... )
functional_beta( x, trait = NULL, bin = 10, na.rm = "no", quick_elbow = FALSE, abundance = FALSE, ... )
x |
A dataframe or sparse community matrix of species occurrences. |
trait |
A data frame with the first column labeled “species” containing the taxonomic groups to be evaluated whereas the remaining columns contain the various functional traits. The variables should be mixed-type combining numeric and categorical variables. |
bin |
The desired number of clusters or bins. If |
na.rm |
Logical, whether NA values should be removed prior to computation |
quick_elbow |
Quickly estimate the 'elbow' of a scree plot to determine the optimal number of clusters. |
abundance |
Logical, whether the reduced matrix should be returned as presence or absence of cluster representation or as abundances of cluster memberships |
... |
Further arguments passed to or from other methods. |
A list with three dissimilarity matrices capturing: (i) turnover (replacement), (ii) nestedness-resultant component, and (iii) total dissimilarity (i.e. the sum of both components).
For index.family="sorensen" the three matrices are:
beta.sim
A distance object, dissimilarity matrix accounting
for spatial turnover (replacement), measured as Simpson pair-wise
dissimilarity.
beta.sne
dist
object, dissimilarity matrix accounting for
nestedness-resultant dissimilarity, measured as the nestedness-fraction
of Sorensen pair-wise dissimilarity
beta.sor
dist
object, dissimilarity matrix accounting
for total dissimilarity, measured as Sorensen pair-wise dissimilarity
(a monotonic transformation of beta diversity)
For index.family="jaccard" the three matrices are:
beta.jtu
A distance object, dissimilarity matrix accounting
for spatial turnover, measured as the turnover-fraction of Jaccard
pair-wise dissimilarity
beta.jne
dist
object, dissimilarity matrix accounting
for nestedness-resultant dissimilarity, measured as the
nestedness-fraction of Jaccard pair-wise dissimilarity
beta.jac
dist
object, dissimilarity matrix accounting
for beta diversity, measured as Jaccard pair-wise dissimilarity (a
monotonic transformation of beta diversity)
Szepannek, G. (2018) clustMixType: User-friendly clustering of mixed-type data in R. The R Journal, 10: 200-208.
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) fb <- functional_beta(x=africa$comm, trait = africa$trait) p <- phyloregion(fb[[1]], pol = p) plot(p)
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) fb <- functional_beta(x=africa$comm, trait = africa$trait) p <- phyloregion(fb[[1]], pol = p) plot(p)
get_clades
returns the tips that descend from a given node or time
depth on a dated phylogenetic tree.
get_clades(tree, cut = NULL, k = NULL)
get_clades(tree, cut = NULL, k = NULL)
tree |
is a dated phylogenetic tree with branch lengths stored
as a phylo object (as in the |
cut |
the slice time |
k |
number of slices |
A list of descendants
Schliep, K.P. (2010) phangorn: phylogenetic analysis in R. Bioinformatics 27: 592–593.
require(ape) data(bird.orders) plot(bird.orders) axisPhylo(side = 1) abline(v=28-23) # the root is here at 28 get_clades(bird.orders, 23)
require(ape) data(bird.orders) plot(bird.orders) axisPhylo(side = 1) abline(v=28-23) # the root is here at 28 get_clades(bird.orders, 23)
A function to generate colors in Hue-Chroma-Luminance colour scheme for mapping phyloregions.
hexcols(x)
hexcols(x)
x |
An object of class |
A range of discrete colors differentiating between phyloregions in terms of their shared relationships.
Barnabas H. Daru [email protected]
library(vegan) data(dune) c1 <- metaMDS(dune, trace = 0) hexcols(c1) plot(c1$points, pch = 21, cex = 7, bg = hexcols(c1), las = 1)
library(vegan) data(dune) c1 <- metaMDS(dune, trace = 0) hexcols(c1) plot(c1$points, pch = 21, cex = 7, bg = hexcols(c1), las = 1)
This function applies a KL-divergence approach to a list of indicator species in phyloregions.
indicators( theta, top_indicators = 5, method = c("poisson", "bernoulli"), options = c("min", "max"), shared = FALSE )
indicators( theta, top_indicators = 5, method = c("poisson", "bernoulli"), options = c("min", "max"), shared = FALSE )
theta |
A matrix or data.frame of cluster probability distributions from a topics modeling. |
top_indicators |
Integer to obtain the top driving species in clusters. |
method |
The model assumption for KL divergence measurement. Available choices are "poisson" (default) and "bernoulli". |
options |
Option "min" selects species that maximize the minimum KL divergence of a phyloregion vs all other phyloregions. Option "max" selects species that maximize the maximum KL divergence of a phyloregion against all other phyloregions. |
shared |
Logical if TRUE, lists top species driving patterns in more than one phyloregion. |
A list of top indicator species and their indicator values
data(africa) indsp <- indicators(africa$theta, top_indicators = 5, options = "max", method = "poisson")
data(africa) indsp <- indicators(africa$theta, top_indicators = 5, options = "max", method = "poisson")
These functions convert a community data to compressed sparse matrix, dense matrix and long format (e.g. species records).
long2sparse(x, grids = "grids", species = "species") sparse2long(x) dense2sparse(x) sparse2dense(x) long2dense(x) dense2long(x)
long2sparse(x, grids = "grids", species = "species") sparse2long(x) dense2sparse(x) sparse2dense(x) long2dense(x) dense2long(x)
x |
A community data which one wants to transform |
grids |
column name of the column containing grid cells |
species |
column name of the column containing the species / taxa names |
A compressed sparse community matrix of sites by species
data(africa) africa$comm[1:5, 1:20] long <- sparse2long(africa$comm) long[1:5, ] sparse <- long2sparse(long) all.equal(africa$comm, sparse) dense_comm <- matrix(c(1,0,1,1,0,0, 1,0,0,1,1,0, 1,1,1,1,1,1, 0,0,1,1,0,1), 6, 4, dimnames=list(paste0("g",1:6), paste0("sp", 1:4))) dense_comm sparse_comm <- dense2sparse(dense_comm) sparse_comm sparse2long(sparse_comm)
data(africa) africa$comm[1:5, 1:20] long <- sparse2long(africa$comm) long[1:5, ] sparse <- long2sparse(long) all.equal(africa$comm, sparse) dense_comm <- matrix(c(1,0,1,1,0,0, 1,0,0,1,1,0, 1,1,1,1,1,1, 0,0,1,1,0,1), 6, 4, dimnames=list(paste0("g",1:6), paste0("sp", 1:4))) dense_comm sparse_comm <- dense2sparse(dense_comm) sparse_comm sparse2long(sparse_comm)
map_trait
add species trait values to species distribution
in geographic space.
map_trait(x, trait, FUN = sum, pol = NULL, ...)
map_trait(x, trait, FUN = sum, pol = NULL, ...)
x |
A community data object - a vector (with names matching trait data) or a data.frame or matrix (with column names matching names in trait data) |
trait |
A data.frame of species traits with a column of species names matching species names in the community data, and another column with the trait values. |
FUN |
The function used to aggregate species trait values
in geographic space. By default, if |
pol |
a vector polygon of grid cells. |
... |
Further arguments passed to or from other methods. |
A data frame of species traits by site.
Barnabas H. Daru [email protected]
data(africa) library(terra) p <- vect(system.file("ex/sa.json", package = "phyloregion")) x <- EDGE(africa$IUCN, africa$phylo, Redlist = "IUCN", species = "Species") y <- map_trait(africa$comm, x, FUN = sd, pol = p) plot(y, "traits", col = hcl.colors(n=20, palette = "Blue-Red 3", rev=FALSE))
data(africa) library(terra) p <- vect(system.file("ex/sa.json", package = "phyloregion")) x <- EDGE(africa$IUCN, africa$phylo, Redlist = "IUCN", species = "Species") y <- map_trait(africa$comm, x, FUN = sd, pol = p) plot(y, "traits", col = hcl.colors(n=20, palette = "Blue-Red 3", rev=FALSE))
match_phylo_comm compares taxa (species, labels, tips) present in a phylogeny with a community matrix. Pruning, sorting and trying to add missing species on genus level if possible to match in subsequent analysis.
match_phylo_comm(phy, comm, delete_empty_rows = TRUE)
match_phylo_comm(phy, comm, delete_empty_rows = TRUE)
phy |
A phylogeny |
comm |
A (sparse) community data matrix |
delete_empty_rows |
delete rows with no observation |
Based on the function of the same name in picante but allows sparse matrices and with taxa addition.
A list containing the following elements, pruned and sorted to match one another:
phy |
A phylogeny object of class phylo |
comm |
A (sparse) community data matrix |
data(africa) tree <- africa$phylo x <- africa$comm subphy <- match_phylo_comm(tree, x)$phy submat <- match_phylo_comm(tree, x)$com
data(africa) tree <- africa$phylo x <- africa$comm subphy <- match_phylo_comm(tree, x)$phy submat <- match_phylo_comm(tree, x)$com
This function generates the mean pairwise distance matrix from a set many pairwise distance matrices. Note: all matrices should be of the same dimension.
mean_dist(files, trace = 1, ...)
mean_dist(files, trace = 1, ...)
files |
list of pairwise distance matrices stored as CSVs or .rds with the same dimensions. |
trace |
Trace the function; trace = 2 or higher will be more voluminous. |
... |
Further arguments passed to or from other methods. |
average distance matrix
Label phylogenetic nodes using pie
nodepie( pie, radius = 2, pie_control = list(), legend = FALSE, col = hcl.colors(5), ... )
nodepie( pie, radius = 2, pie_control = list(), legend = FALSE, col = hcl.colors(5), ... )
pie |
Estimates from ancestral character reconstruction |
radius |
Radius of the pie |
pie_control |
The list of control parameters to be passed into the add.pie function. |
legend |
Logical, whether to add a legend or not. |
col |
List of colors for the pies. |
... |
Further arguments passed to or from other methods. |
Returns no value, just add color pies on phylogenetic nodes!
This function divides the hierarchical dendrogram into meaningful clusters ("phyloregions"), based on the ‘elbow’ or ‘knee’ of an evaluation graph that corresponds to the point of optimal curvature.
optimal_phyloregion(x, method = "average", k = 20)
optimal_phyloregion(x, method = "average", k = 20)
x |
a numeric matrix, data frame or “dist” object. |
method |
the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of “ward.D”, “ward.D2”, “single”, “complete”, “average” (= UPGMA), “mcquitty” (= WPGMA), “median” (= WPGMC) or “centroid” (= UPGMC). |
k |
numeric, the upper bound of the number of clusters to compute. DEFAULT: 20 or the number of observations (if less than 20). |
a list containing the following as returned from the GMD package (Zhao et al. 2011):
k
: optimal number of clusters (bioregions)
totbss
: total between-cluster sum-of-square
tss
: total sum of squares of the data
ev
: explained variance given k
Salvador, S. & Chan, P. (2004) Determining the number of clusters/segments in hierarchical clustering/segmentation algorithms. Proceedings of the Sixteenth IEEE International Conference on Tools with Artificial Intelligence, pp. 576–584. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey, USA.
Zhao, X., Valen, E., Parker, B.J. & Sandelin, A. (2011) Systematic clustering of transcription start site landscapes. PLoS ONE 6: e23409.
data(africa) tree <- africa$phylo bc <- beta_diss(africa$comm) (d <- optimal_phyloregion(bc[[1]], k=15)) plot(d$df$k, d$df$ev, ylab = "Explained variances", xlab = "Number of clusters") lines(d$df$k[order(d$df$k)], d$df$ev[order(d$df$k)], pch = 1) points(d$optimal$k, d$optimal$ev, pch = 21, bg = "red", cex = 3) points(d$optimal$k, d$optimal$ev, pch = 21, bg = "red", type = "h")
data(africa) tree <- africa$phylo bc <- beta_diss(africa$comm) (d <- optimal_phyloregion(bc[[1]], k=15)) plot(d$df$k, d$df$ev, ylab = "Explained variances", xlab = "Number of clusters") lines(d$df$k[order(d$df$k)], d$df$ev[order(d$df$k)], pch = 1) points(d$optimal$k, d$optimal$ev, pch = 21, bg = "red", cex = 3) points(d$optimal$k, d$optimal$ev, pch = 21, bg = "red", type = "h")
PD
calculates Faith's (1992) phylogenetic diversity and relative
phylogenetic diversity.
PD(x, phy) RPD(x, phy)
PD(x, phy) RPD(x, phy)
x |
a community matrix, i.e. an object of class matrix or Matrix or an object of class phyloseq. |
phy |
a phylogenetic tree (object of class phylo). |
a vector with the PD for all samples.
Faith, D.P. (1992) Conservation evaluation and phylogenetic diversity. Biological Conservation 61: 1–10.
read.community read.tree phylobeta_core
library(ape) library(Matrix) tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) PD(com, tree) # Relative phylogenetic diversity RPD(com, tree)
library(ape) library(Matrix) tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) PD(com, tree) # Relative phylogenetic diversity RPD(com, tree)
This function computes the standard effect size of PD by correcting for changes in species richness. The novelty of this function is its ability to utilize sparse community matrix making it possible to efficiently randomize very large community matrices spanning thousands of taxa and sites.
PD_ses( x, phy, model = c("tipshuffle", "rowwise", "colwise"), reps = 10, metric = "pd", ... )
PD_ses( x, phy, model = c("tipshuffle", "rowwise", "colwise"), reps = 10, metric = "pd", ... )
x |
a (sparse) community matrix, i.e. an object of class matrix or Matrix. |
phy |
a phylogenetic tree (object of class phylo). |
model |
The null model for separating patterns from processes and for contrasting against alternative hypotheses. Available null models include:
|
reps |
Number of replications. |
metric |
The phylodiversity measure to compute. |
... |
Further arguments passed to or from other methods. |
A data frame of results for each community or grid cell
grids: Site identity
richness: Number of taxa in community
pd_obs: Observed PD in community
pd_rand.mean: Mean PD in null communities
pd_rand.sd: Standard deviation of PD in null communities
pd_obs.rank: Rank of observed PD vs. null communities
pd_obs.z: Standardized effect size of PD vs. null communities
pvalue: P-value (quantile) of observed PD vs. null communities
reps: Number of replicates
p_obs_c_lower: Number of times observed value < random value
p_obs_c_upper: Number of times observed value > random value
p_obs_p_lower: Percentage of times observed value < random value
p_obs_p_upper: Percentage of times observed value > random value
p_obs_q: Number of the non-NA random values used for comparison
Proches, S., Wilson, J.R.U. & Cowling, R.M. (2006) How much evolutionary history in a 10 x 10m plot? Proceedings of Royal Society B 273: 1143-1148.
library(ape) library(Matrix) tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) PD_ses(com, tree, model="rowwise")
library(ape) library(Matrix) tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) PD_ses(com, tree, model="rowwise")
Calculates phylogenetic endemism (sum of 'unique' branch lengths) of multiple ecological samples.
phylo_endemism(x, phy, weighted = TRUE)
phylo_endemism(x, phy, weighted = TRUE)
x |
is the community data given as a data.frame or matrix with species/OTUs as columns and samples/sites as rows (like in the vegan package). Columns are labeled with the names of the species/OTUs. Rows are labelled with the names of the samples/sites. Data can be either abundance or incidence (0/1). Column labels must match tip labels in the phylogenetic tree exactly! |
phy |
a (rooted) phylogenetic tree (phylo) with branch lengths |
weighted |
is a logical indicating whether weighted endemism (default) or strict endemism should be calculated. |
Takes a community data table and a (rooted) phylogenetic tree (with branch lengths) and calculates either strict or weighted endemism in Phylogenetic Diversity (PD). Strict endemism equates to the total amount of branch length found only in the sample/s and is described by Faith et al. (2004) as PD-endemism. Weighted endemism calculates the "spatial uniqueness" of each branch in the tree by taking the reciprocal of its range, multiplying by branch length and summing for all branch lengths present at a sample/site. Range is calculated simply as the total number of samples/sites at which the branch is present. This latter approach is described by Rosauer et al. (2009) as Phylogenetic endemism.
phylo_endemism
returns a vector of phylogenetic endemism for
each sample or site.
Faith, D.P., Reid, C.A.M. & Hunter, J. (2004) Integrating phylogenetic diversity, complementarity, and endemism for conservation assessment. Conservation Biology 18(1): 255-261.
Rosauer, D., Laffan, S.W., Crisp, M.D., Donnellan, C. & Cook, L.G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular Ecology 18(19): 4061-4072.
Daru, B.H., Farooq, H., Antonelli, A. & Faurby, S. (2020) Endemism patterns are scale dependent. Nature Communications 11 : 2115.
data(africa) pe <- phylo_endemism(africa$comm, africa$phylo) plot(density(pe))
data(africa) pe <- phylo_endemism(africa$comm, africa$phylo) plot(density(pe))
phylobeta_core
computes efficiently for large community matrices and
trees the necessary quantities used by the betapart package to compute
pairwise and multiple-site phylogenetic dissimilarities.
phylobeta_core(x, phy) phylobeta(x, phy, index.family = "sorensen")
phylobeta_core(x, phy) phylobeta(x, phy, index.family = "sorensen")
x |
an object of class Matrix, matrix or phyloseq |
phy |
a phylogenetic tree (object of class phylo) |
index.family |
family of dissimilarity indices, partial match of "sorensen" or "jaccard". |
phylobeta_core
returns an object of class "phylo.betapart",
see phylo.betapart.core
for details. This object can be called
by phylo.beta.pair
or phylo.beta.multi
.
phylobeta
returns a list with three phylogenetic dissimilarity
matrices. See phylo.beta.pair
for details.
Klaus Schliep
read.community
, phylo.betapart.core
,
beta_core
library(ape) tree <- read.tree(text = "((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) com pbc <- phylobeta_core(com, tree) pb <- phylobeta(com, tree)
library(ape) tree <- read.tree(text = "((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) com pbc <- phylobeta_core(com, tree) pb <- phylobeta(com, tree)
This function computes the standard effect size of phylogenetic beta diversity by correcting for changes in species beta diversity. The novelty of this function is its ability to utilize sparse community matrix making it possible to efficiently randomize very large community matrices spanning thousands of taxa and sites.
phylobeta_ses( x, phy, index.family = "simpson", model = c("tipshuffle", "rowwise", "colwise"), reps = 1000, ... )
phylobeta_ses( x, phy, index.family = "simpson", model = c("tipshuffle", "rowwise", "colwise"), reps = 1000, ... )
x |
a (sparse) community matrix, i.e., an object of class matrix or Matrix. |
phy |
a phylogenetic tree (object of class phylo). |
index.family |
the family of dissimilarity indices including “simpson”, “sorensen” and “jaccard”. |
model |
The null model for separating patterns from processes and for contrasting against alternative hypotheses. Available null models include:
|
reps |
Number of replications. |
... |
Further arguments passed to or from other methods. |
A data frame of results for each community or grid cell
phylobeta_obs: Observed phylobeta in community
phylobeta_rand_mean: Mean phylobeta in null communities
phylobeta_rand_sd: Standard deviation of phylobeta in null communities
phylobeta_obs_z: Standardized effect size of phylobeta vs.
null communities
reps: Number of replicates
Proches, S., Wilson, J.R.U. & Cowling, R.M. (2006) How much evolutionary history in a 10 x 10m plot? Proceedings of Royal Society B 273: 1143-1148.
library(ape) library(Matrix) tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) phylobeta_ses(com, tree, model="rowwise")
library(ape) library(Matrix) tree <- read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) phylobeta_ses(com, tree, model="rowwise")
phylobuilder creates a subtree with largest overlap from a species list. If species in the species list are not already in the tip label, species will be added at the most recent common ancestor at the genus or family level when possible.
phylobuilder(species, tree, extract = TRUE)
phylobuilder(species, tree, extract = TRUE)
species |
A vector or matrix containing a species list |
tree |
a phylogenetic tree (object of class phylo) |
extract |
extract the species in the list after trying to add missing labels to the tree. If FALSE phylobuilder adds only the taxa in the list. |
phylobuilder
returns a phylogenetic tree, i.e. an object of
class phylo
.
add.tips
, label2table
,
stripLabel
library(ape) txt <- "(((((Panthera_leo,Panthera_pardus), Panthera_onca),(Panthera_uncia, (Panthera_tigris_altaica, Panthera_tigris_amoyensis)))Panthera)Felidae, (((((((Canis_lupus,Canis_lupus_familiaris),Canis_latrans),Canis_anthus), Canis_aureus),Lycaon_pictus),(Canis_adustus,Canis_mesomelas))Canis) Canidae)Carnivora;" txt <- gsub("[[:space:]]", "", txt) cats_and_dogs <- read.tree(text=txt) plot(cats_and_dogs, node.depth=2, direction="downwards") nodelabels(cats_and_dogs$node.label, frame="none", adj = c(0.5, 0)) tree <- drop.tip(cats_and_dogs, c("Panthera_uncia", "Lycaon_pictus"), collapse.singles=FALSE) dogs <- c("Canis_lupus", "Canis_lupus_familiaris", "Canis_latrans", "Canis_anthus", "Canis_aureus", "Lycaon_pictus", "Canis_adustus", "Canis_mesomelas") # try to extract tree with all 'dogs' t1 <- phylobuilder(dogs, tree) plot(t1, direction="downwards") attr(t1, "species_list") # providing extra information ("Family", "Order", ...) can help sp <- data.frame(Order = c("Carnivora", "Carnivora", "Carnivora"), Family = c("Felidae", "Canidae", "Canidae"), Genus = c("Panthera", "Lycaon", "Vulpes"), Species = c("uncia", "pictus", "vulpes"), Common_name = c("Snow leopard", "Africa wild dog", "Red fox")) sp # Now we just add some species t2 <- phylobuilder(sp, tree, extract=FALSE) plot(t2, direction="downwards") attr(t2, "species_list")
library(ape) txt <- "(((((Panthera_leo,Panthera_pardus), Panthera_onca),(Panthera_uncia, (Panthera_tigris_altaica, Panthera_tigris_amoyensis)))Panthera)Felidae, (((((((Canis_lupus,Canis_lupus_familiaris),Canis_latrans),Canis_anthus), Canis_aureus),Lycaon_pictus),(Canis_adustus,Canis_mesomelas))Canis) Canidae)Carnivora;" txt <- gsub("[[:space:]]", "", txt) cats_and_dogs <- read.tree(text=txt) plot(cats_and_dogs, node.depth=2, direction="downwards") nodelabels(cats_and_dogs$node.label, frame="none", adj = c(0.5, 0)) tree <- drop.tip(cats_and_dogs, c("Panthera_uncia", "Lycaon_pictus"), collapse.singles=FALSE) dogs <- c("Canis_lupus", "Canis_lupus_familiaris", "Canis_latrans", "Canis_anthus", "Canis_aureus", "Lycaon_pictus", "Canis_adustus", "Canis_mesomelas") # try to extract tree with all 'dogs' t1 <- phylobuilder(dogs, tree) plot(t1, direction="downwards") attr(t1, "species_list") # providing extra information ("Family", "Order", ...) can help sp <- data.frame(Order = c("Carnivora", "Carnivora", "Carnivora"), Family = c("Felidae", "Canidae", "Canidae"), Genus = c("Panthera", "Lycaon", "Vulpes"), Species = c("uncia", "pictus", "vulpes"), Common_name = c("Snow leopard", "Africa wild dog", "Red fox")) sp # Now we just add some species t2 <- phylobuilder(sp, tree, extract=FALSE) plot(t2, direction="downwards") attr(t2, "species_list")
This function estimates evolutionary distinctiveness of each phyloregion by computing the mean value of phylogenetic beta diversity between a focal phyloregion and all other phyloregions in the study area.
phyloregion(x, k = 10, method = "average", pol = NULL, ...) infomap(x, pol = NULL, ...)
phyloregion(x, k = 10, method = "average", pol = NULL, ...) infomap(x, pol = NULL, ...)
x |
A distance matrix |
k |
The desired number of phyloregions, often as determined by
|
method |
the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of “ward.D”, “ward.D2”, “single”, “complete”, “average” (= UPGMA), “mcquitty” (= WPGMA), “median” (= WPGMC) or “centroid” (= UPGMC). |
pol |
a vector polygon of grid cells or spatial points. |
... |
Further arguments passed to or from other methods. |
An object of class phyloregion
containing
a data frame membership with columns grids and cluster
k the number of clusters and additionally there can be an shape file and other objects. This representation may still change.
Barnabas H. Daru [email protected]
Daru, B.H., Van der Bank, M., Maurin, O., Yessoufou, K., Schaefer, H., Slingsby, J.A. & Davies, T.J. (2016) A novel phylogenetic regionalization of the phytogeographic zones of southern Africa reveals their hidden evolutionary affinities. Journal of Biogeography 43: 155-166.
Daru, B.H., Elliott, T.L., Park, D.S. & Davies, T.J. (2017) Understanding the processes underpinning patterns of phylogenetic regionalization. Trends in Ecology and Evolution 32: 845-860.
Daru, B.H., Holt, B.G., Lessard, J.P., Yessoufou, K. & Davies, T.J. (2017) Phylogenetic regionalization of marine plants reveals close evolutionary affinities among disjunct temperate assemblages. Biological Conservation 213: 351-356.
evol_distinct
, optimal_phyloregion
,
evol.distinct
for a different approach.
library(ape) tree <- read.tree(text = "((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) pbc <- phylobeta(com, tree) # phyloregion(pbc[[1]], k = 3)
library(ape) tree <- read.tree(text = "((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) pbc <- phylobeta(com, tree) # phyloregion(pbc[[1]], k = 3)
Visualize biogeographic patterns using pie charts
plot_pie( omega, pol, radius = 0.55, col = hcl.colors(5), pie_control = list(), legend = FALSE, legend_pie = FALSE, ... )
plot_pie( omega, pol, radius = 0.55, col = hcl.colors(5), pie_control = list(), legend = FALSE, legend_pie = FALSE, ... )
omega |
a matrix of phyloregion of probabilities of each species |
pol |
a vector polygon of grid cells with a column labeled “grids”. |
radius |
Radius of the pie legend to be displayed |
col |
List of colors for the pies. |
pie_control |
The list of control parameters to be passed into the add.pie function. |
legend |
Logical, whether to plot a legend or not. |
legend_pie |
Legend for the pie plots. |
... |
Further arguments passed to or from other methods. |
Returns no value, just map color pies in geographic space!
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) K <- ncol(africa$omega) CLRS <- hcl.colors(K) plot_pie(africa$omega, pol = p, col=CLRS)
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) K <- ncol(africa$omega) CLRS <- hcl.colors(K) plot_pie(africa$omega, pol = p, col=CLRS)
Visualize biogeographic patterns
## S3 method for class 'phyloregion' plot(x, pol = NULL, palette = "NMDS", col = NULL, label = FALSE, ...) plot_NMDS(x, ...) text_NMDS(x, ...)
## S3 method for class 'phyloregion' plot(x, pol = NULL, palette = "NMDS", col = NULL, label = FALSE, ...) plot_NMDS(x, ...) text_NMDS(x, ...)
x |
an object of class phyloregion from |
pol |
a polygon shapefile of grid cells. |
palette |
name of the palette to generate colors from. The default,
“NMDS”, allows display of phyloregions in multidimensional
scaling color space matching the color vision of the human visual
system. The name is matched to the list of available color palettes from
the |
col |
vector of colors of length equal to the number of phyloregions. |
label |
Logical, whether to print cluster names or not |
... |
arguments passed among methods. |
No return value, called for plotting.
library(terra) data(africa) tree <- africa$phylo x <- africa$comm p <- vect(system.file("ex/sa.json", package = "phyloregion")) subphy <- match_phylo_comm(tree, x)$phy submat <- match_phylo_comm(tree, x)$com pbc <- phylobeta(submat, subphy) y <- phyloregion(pbc[[1]], pol=p) plot_NMDS(y, cex=6) text_NMDS(y, cex=2) plot(y, cex=1, palette="NMDS") plot(y, cex=1)
library(terra) data(africa) tree <- africa$phylo x <- africa$comm p <- vect(system.file("ex/sa.json", package = "phyloregion")) subphy <- match_phylo_comm(tree, x)$phy submat <- match_phylo_comm(tree, x)$com pbc <- phylobeta(submat, subphy) y <- phyloregion(pbc[[1]], pol=p) plot_NMDS(y, cex=6) text_NMDS(y, cex=2) plot(y, cex=1, palette="NMDS") plot(y, cex=1)
This function visualizes a sparse matrix using vertical bands corresponding to presence or absence of a species in an area.
## S3 method for class 'sparse' plot(x, col = c("red", "yellow"), lwd = 1, ...)
## S3 method for class 'sparse' plot(x, col = c("red", "yellow"), lwd = 1, ...)
x |
A matrix |
col |
A vector of colors to represent presence or absence of a species |
lwd |
Line width |
... |
Further arguments passed to or from other methods. |
Returns no value, just plot sparse matrix
This function generates random species distributions in geographic space as extent of occurrence range polygons based on convex hulls of random points.
random_species(n, species, pol, ...)
random_species(n, species, pol, ...)
n |
vector of one or more elements to choose from, or a positive integer. |
species |
the desired number of species. |
pol |
the vector polygon of the study area for determining the species distributions |
... |
Further arguments passed to or from other methods. |
A vector polygon of species' extent of occurrence ranges.
Barnabas H. Daru [email protected]
This function standardizes values of a raster layer for mapping.
rast_quantile(ras, ref)
rast_quantile(ras, ref)
ras |
an input raster layer. |
ref |
a raster layer for reference. |
A raster layer that has been standardized and ready for mapping
The functions points2comm
, polys2comm
, rast2comm
provide convenient interfaces to convert raw distribution data often
available as point records, polygons and raster layers,
respectively, to a community composition data frame at varying spatial grains
and extents for downstream analyses.
rast2comm(files) polys2comm(dat, res = 0.25, pol.grids = NULL, ...) points2comm(dat, res = 0.25, pol.grids = NULL, ...)
rast2comm(files) polys2comm(dat, res = 0.25, pol.grids = NULL, ...) points2comm(dat, res = 0.25, pol.grids = NULL, ...)
files |
list of SpatRaster layer objects with the same spatial extent and resolution. |
dat |
layers of merged maps corresponding to species polygons for
|
res |
the grain size of the grid cells in decimal degrees (default). |
pol.grids |
if specified, the vector polygon of grid cells with a column labeled “grids”. |
... |
Further arguments passed to or from other methods. |
Each of these functions generate a list of two objects as follows:
comm_dat: (sparse) community matrix
map: vector or raster of grid cells with the values per cell for mapping.
mapproject
for conversion of
latitude and longitude into projected coordinates system.
long2sparse
for conversion of community data.
fdir <- system.file("NGAplants", package="phyloregion") files <- file.path(fdir, dir(fdir)) ras <- rast2comm(files) # Note, this function generates # a list of two objects head(ras[[1]]) require(terra) s <- vect(system.file("ex/nigeria.json", package="phyloregion")) sp <- random_species(100, species=5, pol=s) pol <- polys2comm(dat = sp) head(pol[[1]]) library(terra) s <- vect(system.file("ex/nigeria.json", package="phyloregion")) set.seed(1) m <- as.data.frame(spatSample(s, 1000, method = "random"), geom = "XY")[-1] names(m) <- c("lon", "lat") species <- paste0("sp", sample(1:100)) m$taxon <- sample(species, size = nrow(m), replace = TRUE) pt <- points2comm(dat = m, res = 0.5) # This generates a list of two objects head(pt[[1]])
fdir <- system.file("NGAplants", package="phyloregion") files <- file.path(fdir, dir(fdir)) ras <- rast2comm(files) # Note, this function generates # a list of two objects head(ras[[1]]) require(terra) s <- vect(system.file("ex/nigeria.json", package="phyloregion")) sp <- random_species(100, species=5, pol=s) pol <- polys2comm(dat = sp) head(pol[[1]]) library(terra) s <- vect(system.file("ex/nigeria.json", package="phyloregion")) set.seed(1) m <- as.data.frame(spatSample(s, 1000, method = "random"), geom = "XY")[-1] names(m) <- c("lon", "lat") species <- paste0("sp", sample(1:100)) m$taxon <- sample(species, size = nrow(m), replace = TRUE) pt <- points2comm(dat = m, res = 0.5) # This generates a list of two objects head(pt[[1]])
read.community
reads in file containing occurrence data and returns a
sparse matrix.
read.community(file, grids = "grids", species = "species", ...)
read.community(file, grids = "grids", species = "species", ...)
file |
A file name. |
grids |
Column name of the column containing grid cells. |
species |
Column name of the column containing the species / taxa names. |
... |
further arguments passed to or from other methods. |
read.community
returns a sparse matrix (an object of class
"dgCMatrix").
df <- data.frame(grids=paste0("g", c(1,1,2,3,3)), species = paste0("sp", c(1,3,2,1,4))) df tmp <- tempfile() write.csv(df, tmp) (M <- read.community(tmp) ) sparse2long(M) unlink(tmp)
df <- data.frame(grids=paste0("g", c(1,1,2,3,3)), species = paste0("sp", c(1,3,2,1,4))) df tmp <- tempfile() write.csv(df, tmp) (M <- read.community(tmp) ) sparse2long(M) unlink(tmp)
This function computes species distribution models using
two modelling algorithms: generalized linear models,
and maximum entropy (only if rJava
is available).
Note: this is an experimental function, and may change in the future.
sdm( x, layers = NULL, pol = NULL, thin = TRUE, thin.size = 500, algorithm = "all", size = 50, width = 50000, mask = FALSE, predictors, background = NULL )
sdm( x, layers = NULL, pol = NULL, thin = TRUE, thin.size = 500, algorithm = "all", size = 50, width = 50000, mask = FALSE, predictors, background = NULL )
x |
A dataframe containing the species occurrences and geographic coordinates. Column 1 labeled as "species", column 2 "lon", column 3 "lat". |
layers |
A |
pol |
A vector polygon specifying the calibration area or boundary to
account for a more realistic dispersal capacity and ecological limitation
of a species. If |
thin |
Whether to spatially thin occurrences |
thin.size |
The size of the thin occurrences. |
algorithm |
Character. The choice of algorithm to run the species distribution model. For now, the available algorithms include:
|
size |
Minimum number of points required to successfully run a species distribution model especially for species with few occurrences. |
width |
Width of buffer in meter if x is in longitude/latitude CRS. |
mask |
logical. Should |
predictors |
If predicting to new time points, the climate layers for the time points. |
background |
A dataframe of background points, specifying 2 columns with long lat or x and y as nulls for species distribution modeling, often using a vector of probability weights. |
A list with the following objects:
ensemble_raster
The ensembled raster that predicts
the potential species distribution based on the algorithms selected.
data
The dataframe of occurrences used to implement the model.
polygon
Map polygons of the predicted distributions
analogous to extent-of-occurrence range polygon.
indiv_models
Raster layers for the separate models that
predict the potential species distribution.
Zurell, D., Franklin, J., König, C., Bouchet, P.J., Dormann, C.F., Elith, J., Fandos, G., Feng, X., Guillera‐Arroita, G., Guisan, A., Lahoz‐Monfort, J.J., Leitão, P.J., Park, D.S., Peterson, A.T., Rapacciuolo, G., Schmatz, D.R., Schröder, B., Serra‐Diaz, J.M., Thuiller, W., Yates, K.L., Zimmermann, N.E. and Merow, C. (2020), A standard protocol for reporting species distribution models. Ecography, 43: 1261-1277.
# get predictor variables library(predicts) f <- system.file("ex/bio.tif", package="predicts") preds <- rast(f) #plot(preds) # get species occurrences b <- file.path(system.file(package="predicts"), "ex/bradypus.csv") d <- read.csv(b) # fit ensemble model for four algorithms # m <- sdm(d, layers = preds, predictors = preds, algorithm = "all") # plot(m$ensemble_raster) # plot(m$polygon, add=TRUE)
# get predictor variables library(predicts) f <- system.file("ex/bio.tif", package="predicts") preds <- rast(f) #plot(preds) # get species occurrences b <- file.path(system.file(package="predicts"), "ex/bradypus.csv") d <- read.csv(b) # fit ensemble model for four algorithms # m <- sdm(d, layers = preds, predictors = preds, algorithm = "all") # plot(m$ensemble_raster) # plot(m$polygon, add=TRUE)
This function contrasts different hierarchical clustering algorithms on the phylogenetic beta diversity matrix for degree of data distortion using Sokal & Rohlf’s (1962) cophenetic correlation coefficient.
select_linkage(x)
select_linkage(x)
x |
a numeric matrix, data frame or "dist" object. |
A numeric value corresponding to the good clustering algorithm for the distance matrix
If plot = TRUE, a barplot of cophenetic correlation for all the clustering algorithms is drawn.
Sokal, R.R. & Rohlf, F.J. (1962) The comparison of dendrograms by objective methods. Taxon 11: 33–40.
data(africa) tree <- africa$phylo bc <- beta_diss(africa$comm) y <- select_linkage(bc[[1]]) barplot(y, horiz = TRUE, las = 1)
data(africa) tree <- africa$phylo bc <- beta_diss(africa$comm) y <- select_linkage(bc[[1]]) barplot(y, horiz = TRUE, las = 1)
The selectbylocation
function selects features based on
their location relative to features in another layer.
selectbylocation(x, y)
selectbylocation(x, y)
x |
source layer of the class SpatVect |
y |
Target layer or mask extent to subset from. |
A spatial polygons or spatial points object pruned to the extent of the target layer.
library(terra) d <- vect(system.file("ex/nigeria.json", package="phyloregion")) e <- ext(d) set.seed(1) m <- data.frame(lon = runif(1000, e[1], e[2]), lat = runif(1000, e[3], e[4]), sites = seq(1000)) m <- vect(m) z <- selectbylocation(m, d) plot(d) points(m, col = "blue", pch = "+") points(z, col = "red", pch = "+")
library(terra) d <- vect(system.file("ex/nigeria.json", package="phyloregion")) e <- ext(d) set.seed(1) m <- data.frame(lon = runif(1000, e[1], e[2]), lat = runif(1000, e[3], e[4]), sites = seq(1000)) m <- vect(m) z <- selectbylocation(m, d) plot(d) points(m, col = "blue", pch = "+") points(z, col = "red", pch = "+")
This function slices a dated phylogenetic tree at successive time depths back in time by collapsing younger phylogenetic branches into older ones to infer the origins of species assemblages.
timeslice(phy, n = 0.2, collapse = FALSE, ...)
timeslice(phy, n = 0.2, collapse = FALSE, ...)
phy |
A dated phylogenetic tree as an object of class “phylo”. |
n |
Time depth to slice the phylogenetic tree (often in millions of years for dated trees). |
collapse |
Logical, collapse internal edges with zero edge length. |
... |
arguments passed among methods. |
A tree with the phylogenetic structure removed at the specified time depth
Barnabas H. Daru [email protected]
Daru, B.H., van der Bank, M. & Davies, T.J. (2018) Unravelling the evolutionary origins of biogeographic assemblages. Diversity and Distributions 24: 313–324.
library(ape) set.seed(1) tree <- rcoal(50) x <- timeslice(tree, .5) old.par <- par(no.readonly = TRUE) par(mfrow = c(1, 2)) plot(tree) axisPhylo() plot(x) axisPhylo() par(old.par)
library(ape) set.seed(1) tree <- rcoal(50) x <- timeslice(tree, .5) old.par <- par(no.readonly = TRUE) par(mfrow = c(1, 2)) plot(tree) axisPhylo() plot(x) axisPhylo() par(old.par)
This function randomly samples a subset of trees from a posterior distribution of trees derived from multiple runs of MrBayes.
tree_sampler(wd, n = 100, pattern = ".tre", ...)
tree_sampler(wd, n = 100, pattern = ".tre", ...)
wd |
A path to the working directory with the distributions of multiple phylogenetic trees. |
n |
The desired number of subsets of trees. This defaults to 100. |
pattern |
An optional regular expression specifying the file extension. |
... |
arguments passed among methods. |
An object of class "multiPhylo" with the subset of trees.
Dominic Bennett & Harith Farooq [email protected]
unifrac
calculates the unweighted UniFrac distance between
communities.
unifrac(x, phy)
unifrac(x, phy)
x |
a community matrix, i.e. an object of class matrix or Matrix, or an object of class phyloseq. |
phy |
a phylogenetic tree (object of class phylo). |
a dist object.
Lozupone C, Knight R. (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Appl Environ Microbiol. 71 (12):8228–35. BMC Bioinformatics 7:371.
tree <- ape::read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- Matrix::sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) unifrac(com, tree)
tree <- ape::read.tree(text ="((t1:1,t2:1)N2:1,(t3:1,t4:1)N3:1)N1;") com <- Matrix::sparseMatrix(c(1,3,4,1,4,5,1,2,3,4,5,6,3,4,6), c(1,1,1,2,2,2,3,3,3,3,3,3,4,4,4),x=1, dimnames = list(paste0("g", 1:6), tree$tip.label)) unifrac(com, tree)
weighted_endemism
is species richness inversely weighted
by species ranges.
weighted_endemism(x)
weighted_endemism(x)
x |
A (sparse) community matrix. |
A data frame of species traits by site.
Crisp, M.D., Laffan, S., Linder, H.P. & Monro, A. (2001) Endemism in the Australian flora. Journal of Biogeography 28: 183–198.
Daru, B.H., Farooq, H., Antonelli, A. & Faurby, S. (2020) Endemism patterns are scale dependent. Nature Communications 11 : 2115.
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) Endm <- weighted_endemism(africa$comm) m <- merge(p, data.frame(grids=names(Endm), WE=Endm), by="grids") m <- m[!is.na(m$WE),] plot(m, "WE", col = hcl.colors(20), type="continuous")
library(terra) data(africa) p <- vect(system.file("ex/sa.json", package = "phyloregion")) Endm <- weighted_endemism(africa$comm) m <- merge(p, data.frame(grids=names(Endm), WE=Endm), by="grids") m <- m[!is.na(m$WE),] plot(m, "WE", col = hcl.colors(20), type="continuous")