West Mediterranean island butterflies provided with the package recluster

Description

This dataset represents occurrence data of butterfly species in 30 West-Mediterranean islands

Usage

data(dataisl)

Details

A data frame with 30 observations (islands) on 123 binary variables (species).

Author(s)

Leonardo Dapporto and Roger Vila

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Virtual island faunas provided with the package recluster

Description

This dataset represents a series of virtual faunas in different sites

Usage

data(datamod)

Details

A data frame with 9 observations (sites) on 31 binary variables (species).

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

A multiboot result obtained with the dataisl dataset.

Description

This dataset represents an output for a multiscale bootstrap composed of 30 scales (x1-x30).

Usage

data(dataisl)

Details

A data frame with 29 nodes (rows) and 30 different scales of bootstrap(columns). NAs values represent collapsed nodes

Author(s)

Leonardo Dapporto

Source

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Ordination methods for biodiversity patterns.

Description

The analysis of different aspects of biodiversity requires specific algorithms. For example, in regionalisation analyses, the high frequency of ties and zero values in dissimilarity matrices produced by Beta-diversity turnover produces hierarchical cluster dendrograms whose topology and bootstrap supports are affected by the order of rows in the original matrix. Moreover, visualisation of biogeographical regionalisation can be facilitated by a combination of hierarchical clustering and multi-dimensional scaling. The recluster package provides robust techniques to visualise and analyse pattern of biodiversity and to improve occurrence data for cryptic taxa. Other functions related to recluster (e.g. the biodecrypt family) are currently available in GitHub at https://github.com/leondap/recluster.

Details

Author(s)

Leonardo Dapporto, Matteo Ramazzotti, Simone Fattorini, Roger Vila, Gerard Talavera, Roger H.L. Dennis Maintainer: Leonardo Dapporto <leondap@gmail.com>

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Dapporto, L., Fattorini, S., Voda, R., Dinca, V., Vila, R. "Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity." J Biogeogr (2014), 41: 1639-1650.

Dapporto L., Voda R., Dinca V., Vila R. "Comparing population patterns for genetic and morphological markers with uneven sample sizes. An example for the butterfly Maniola jurtina" Methods Ecol Evol (2014), 5, 834-843.

Platania L., Menchetti M. Dinca V., Corbella C., Kay-Lavelle I., Vila R., Wiemers M., Schweiger O., Dapporto L. "Assigning occurrence data to cryptic taxa improves climatic niche assessments: biodecrypt, a new tool tested on European butterflies". Glocal Ecology and Biogeography (2020).

https://github.com/leondap/recluster

Examples

#load model data provided with the package 
## Not run: 
data(datamod)

#explore zero and tied values in the data set
simpdiss<- recluster.dist(datamod)
recluster.hist(simpdiss)

#create and view unbiased consensus tree (100
constree_full<-recluster.cons(datamod, tr=10, p=1)
plot(constree_full$cons,direction="downwards")

#compute and view node strength
recluster.node.strength(datamod, tr=10)

#create and view unbiased consensus tree (50
constree_half<-recluster.cons(datamod, tr=10, p=0.5)
plot(constree_half$cons, direction="downwards")

#the latter is the correct tree
tree<-constree_half$cons

#perform and view bootstrap on nodes
boot<-recluster.boot(tree, datamod, tr=10, p=0.5, boot=50)
recluster.plot(tree,boot)

#perform and view multiscale bootstrap on nodes
multiboot<- recluster.multi(tree, datamod, tr=10, boot=50, levels=2, step=1)
recluster.plot(tree,multiboot,low=1,high=2, direction="downwards")

#project and plot a bi-dimensional plot in the RGB colour space
sordiss<- recluster.dist(datamod,dist="sorensen")
points<-cmdscale(sordiss)
col<-recluster.col(points)
recluster.plot.col(col)

#inspect explained diversity for different cuts of a tree
tree<-recluster.cons(datamod, dist="sorensen",tr=10, p=0.5)
expl_div<-recluster.expl.diss(tree$cons,sordiss)
expl_div

#Select cut #4 and group data in RGB space
ncol<-recluster.group.col(col,expl_div$matrix[,4])

#Plot mean values for clusters
recluster.plot.col(ncol$aggr)

#Plot mean colours for sites in the geographic space
lat<-c(2,2,2,1,3,1,1,3,3)
long<-c(1,5,3,3,3,1,5,1,5)
recluster.plot.sites.col(long, lat, ncol$all,text=TRUE)

#Use recluster.region procedure on island butterflies
data(dataisl)
simpson<-recluster.dist(dataisl)
turn_cl<-recluster.region(simpson,tr=10,rettree=TRUE)
turn_cl

#Select solution with three cluster and plot the tree.
plot(turn_cl$tree[[2]])
turn_cl$grouping

#Perform a procrustes with uneven sample size
#Create and plot a target matrix
ex1 <-rbind(c(1,5),c(5,5),c(3,4),c(3,6))
plot(ex1,col=c(1:4),pch=19,xlim=c(0,6),ylim=c(0,6),cex=2)
#Create and plot a matrix to be rotated. Only the points 1-4 are shared
ex2<-rbind(c(3,1),c(3,3),c(2.5,2),c(3.5,2),c(3,4))
plot(ex2,col=c(1:5),pch=19,xlim=c(0,6),ylim=c(0,6),cex=2)

#Perform the procrustes on points 1-4
#Apply the transformation to point 5 of ex2 and plot the matrices
procr1<-recluster.procrustes(ex1,ex2,num=4)
plot(procr1$X,col=c(1:4),pch=19,xlim=c(-4,4),ylim=c(-4,4),cex=2)
plot(procr1$Yrot,col=c(1:5),pch=19,xlim=c(-4,4),ylim=c(-4,4),cex=2)

## End(Not run)

Bootstrap nodes of consensus trees

Description

Given an initial tree and a data matrix, this function computes bootstrap for nodes. Each tree used for bootstrap is constructed by re-sampling the row order several times and by applying a consensus rule as done by recluster.cons. The number of sampled columns (species) can be varied.

Usage

recluster.boot(tree, mat, phylo = NULL, tr = 100, p = 0.5, 
dist = "simpson", method = "average", boot = 1000, level = 1)

Arguments

Details

Computation can be time consuming due to the high number of trees required for analysis. It is suggested to assess the degree of row bias by recluster.hist and recluster.node.strength to optimize the number of required consensus trees before starting the analysis.

Value

A vector indicating the percentage of bootstrap trees replicating each original node.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

data(datamod)
tree<-recluster.cons(datamod,tr=10)
boot<-recluster.boot(tree$cons,tr=5,boot=50,datamod)
recluster.plot(tree$cons,boot,direction="downwards")

data(treemod)
tree<-recluster.cons(datamod,treemod, dist="phylosort", tr=10)
boot<-recluster.boot(tree$cons, datamod, treemod,tr=5,boot=50)
recluster.plot(tree$cons,boot,direction="downwards")

An internal workaround for the new ape package

Description

This function solve a label problem producing unreliable low values of bootstrap

Usage

recluster.check(tree, tip)

Arguments

Value

An oject of the class phylo

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Projecting a two dimensional plot in RGB space

Description

This function projects a two dimensional matrix into a RGB space with red, green, yellow and blue at its four corners. RGB combination for each case corresponding to its position in this space is provided together with new coordinates.

Usage

recluster.col(mat,st=TRUE,rot=TRUE)

Arguments

Value

A matrix with the first two columns representing the coordinates and the third, fourth and fifth representing the red, green and blue components, respectively.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Kreft H., Jetz, W. "A framework for delineating biogeographic regions based on species distributions" J Biogeogr (2010),37: 2029-2053.

Dapporto, L., Fattorini, S., Voda, R., Dinca, V., Vila, R. "Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity." J Biogeogr (2014), 41: 1639-1650.

Examples

data(datamod)
sordiss<- recluster.dist(datamod,dist="sorensen")
points<-cmdscale(sordiss)
col<-recluster.col(points)
col

Consensus tree among re-sampled trees

Description

This function creates a series of trees by resampling the order of sites in the original dissimilarity matrix. Then, it computes a consensus among them. The resulting tree is independent of the original row order.

Usage

recluster.cons(mat, phylo = NULL, tr = 100, p = 0.5, 
dist = "simpson", method = "average", blenghts=TRUE, select=FALSE)

Arguments

Details

According to the primitive "consensus" function from the "ape" package, p must range between 0.5 and 1. Select = TRUE can allow lowering polytomies by removing trees with topology showing particularly low correlation with the distance matrix. Row names are required.

Value

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

#Faunistic beta diversity
data(datamod,treemod)
tree<-recluster.cons(datamod,tr=10)
plot(tree$cons,direction="downwards")

#Phylogenetic beta diversity
tree_p<-recluster.cons(datamod,treemod,dist="phylosort",tr=10)
plot(tree_p$cons, direction="downwards")

Compute a dissimilarity matrix using a battery of beta-diversity indices

Description

This function computes dissimilarity matrices based on the two most popular partitions of faunistic and phylogenitic beta-diversity. In particular Jaccard = beta3 + richness (Carvalho et al. 2012), Jaccard = Jturnover + Jnestedness (Baselga, 2012) and Sorensen = Simpson + nestedness (Baselga 2010) for faunistic indexes and Unifrac = Unifrac_turn + Unifrac_PD and PhyloSor = PhyloSor_turn + Phylosor_PD (Leprieur et al. 2012). Any other binary index can be included in brackets by using the syntax of designdist function of the vegan package.

Usage

recluster.dist(mat, phylo=NULL, dist="simpson")

Arguments

Details

Syntax for binary indices in vegdist: J, number of common species; A and B, number of species exclusive of the first and of the second site.

Value

An object of class dist (see vegan:designdist for further details)

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Baselga A. "Partitioning the turnover and nestedness components of beta diversity." Global Ecol Biogeogr (2010), 19: 134-143.

Carvalho J. C., Cardoso P., Gomes P. "Determining the relative roles of species replacement and species richness differences in generating beta-diversity patterns." Global Ecol Biogeogr (2012), 21: 760-771.

Leprieur F., Albouy C., De Bortoli J., Cowman P.F., Bellwood D.R., Mouillot D. "Quantifying Phylogenetic Beta Diversity: Distinguishing between 'True' Turnover of Lineages and Phylogenetic Diversity Gradients." Plos One (2012), 7

Computes the dissimilarity contained in a distance matrix which is explained by a clustering solution.

Description

This function computes the fraction of the distances contained in a dissimilarity matrix which is explained by a clustering solution of the elements. The value is obtained by computing the sum of all the dissimilarity values among elements belonging to different clusters and divided by the sum of all the cells of the original dissimilarity matrix.

Usage

recluster.expl(mat, clust)

Arguments

Value

A number ranging between 0 and 1 indicating the fraction of explained dissimilarity.

Author(s)

Leonardo Dapporto

References

Holt, B.G. et al "An Update of Wallace's Zoogeographic Regions of the World." Science, 339:74-78.

Examples

data(datamod)
sor_tree<- recluster.cons(datamod, dist="sorensen")
sor_diss <- recluster.dist (datamod, dist="sorensen")
expl_diss <- recluster.expl.diss (sor_tree$cons,sor_diss)
expl_diss 

Cuts a phylogenetic tree to a given series of nodes and computes explained dissimilarity.

Description

This function cuts a phylogenetic tree at any given series of nodes, provides membership for each element in the series of resulting clusters and computes the fraction of dissimilarity explained by each cut.

Usage

recluster.expl.diss(tree, dist, maxcl=NULL, mincl=NULL, expld=TRUE) 

Arguments

Details

When polytomic nodes are involved in a cut the number of clusters at that cut will increase more than one unit. Holt at al. (2013) identified levels of explained dissimilarity to be used as a reliable threshold to assess a tree cut. When cases are highly numerous maxcl can be set in order to avoid a very long computation

Value

Author(s)

Leonardo Dapporto

References

Dapporto L., Ciolli G., Dennis R.L.H., Fox R., Shreeve, T.G. "A new procedure for extrapolating turnover regionalization at mid?small spatial scales, tested on B ritish butterflies." Methods in Ecology and Evolution (2015), 6:1287-1297.

Examples

data(datamod)
sor_tree<- recluster.cons(datamod, dist="sorensen")
sor_diss <- recluster.dist (datamod, dist="sorensen")
expl_diss <- recluster.expl.diss (sor_tree$cons,sor_diss)
expl_diss 

Compute some indexes of genetic differentiation

Description

This function computes some indexes of genetic differentiation based on a distance matrix and on a vector for populations.

Usage

recluster.fst(dist,vect,setzero=F,setnazero=F)

Arguments

Details

There has been a large dabate around FST like indexes. Two main indexes are culcalated by this function: the absolute differentiation (Dst) and the standardized differentiation (Gst) (Nei, 1987) .Dst is calculated as: Dst = Ht - Hs where Ht represents the average distances among all the specimens in the sample, and Hs is the average of the intra-area (or intra-sub-area) distances. Thus, Dst represents the average genetic differentiation among areas in p-distance units. Gst is a standardized index defined as: Gst = Dst/Ht representing the fraction of the total genetic differentiation encompassed by the differentiation among areas (Nei, 1987). This index ranges from negative values to 1 (complete differentiation). Negative values in Gst and Dst (intra-area differentiation higher than inter-area differentiation) can have different subtle meanings, but are most often generated as bias due to relatively small sample sizes; usually they are set to zero (Meirmans & Hedrick, 2011) and we applied this solution. In the species showing no mutations in the sample, Gst returns a NA value (while Dst equals to zero). These cases can be also set to zero The use of Dst and Gst has been debated as a measure of population diversification for extremely variable markers (as micro-satellites) as it tends to underestimate differentiation among populations and to strongly depend on intra-population variability (Jost, 2008; Whitlock, 2011). D and G-st indices are less affected by high values of Hs

Value

Author(s)

Leonardo Dapporto

References

Jost L. "GST and its relatives do not measure differentiation." Mol Ecol (2008), 17:4015-4026.

Meirmans P. G., Hedrick P. W. "Assessing population structure: FST and related measures: Invited Technical Reviwev." Mol Ecol Res (2011), 11: 5-18.

Nei M. Molecular evolutionary genetics (1987), Columbia University Press.

Whitlock M.C. "G'ST and D do not replace FST." Mol Ecol (2011), 20: 1083-1091.

Examples

datavirtual<-data.frame(replicate(10,sample(0:1,30,rep=TRUE)))
dist<-recluster.dist(datavirtual)
population<-c(rep(1,20),rep(2,20),rep(3,20))
recluster.fst(dist,population)

Compute pairwise indexes of genetic differentiation among populations

Description

This function computes pairwise indexes of genetic differentiation among populations based on a distance matrix and on a vector for populations.

Usage

recluster.fst.pair(dist,vect,setzero=F,setnazero=F)

Arguments

Details

The formulas used for pairwise calculations between i and j populations are Dstij = Htij - Hsij Gstij = Dstij/Ht Dij = (Dstij/(1-Hsij))*2 G'stij = Gstij/((1-Hsij)/(1+Hsij)) see also recluster.fst for a discussion of indexes

Value

Author(s)

Leonardo Dapporto

References

Jost L. "GST and its relatives do not measure differentiation." Mol Ecol (2008), 17:4015-4026.

Meirmans P. G., Hedrick P. W. "Assessing population structure: FST and related measures: Invited Technical Reviwev." Mol Ecol Res (2011), 11: 5-18.

Nei M. Molecular evolutionary genetics (1987), Columbia University Press.

Whitlock M.C. "G'ST and D do not replace FST." Mol Ecol (2011), 20: 1083-1091.

Examples

datavirtual<-data.frame(replicate(20,sample(0:1,60,rep=TRUE)))
dist<-recluster.dist(datavirtual)
population<-c(rep(1,20),rep(2,20),rep(3,20))
recluster.fst.pair(dist,population)

Computes mean coordinate values and RGB colours.

Description

This function computes barycenters and their RGB colours for cases belonging to the same group from an original RGB colour matrix obtained by recluster.col.

Usage

recluster.group.col(mat,member)

Arguments

Value

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Kreft H., Jetz, W. "A framework for delineating biogeographic regions based on species distributions" J Biogeogr (2010),37: 2029-2053.

Dapporto, L., Fattorini, S., Voda, R., Dinca, V., Vila, R. "Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity." J Biogeogr (2014), 41: 1639-1650.

Examples

data(datamod)
sordiss<- recluster.dist(datamod,dist="sorensen")
points<-cmdscale(sordiss)
col<-recluster.col(points)
group<-c(1,2,3,3,3,1,2,1,2)
ncol<-recluster.group.col(col,group)
recluster.plot.col(ncol$aggr)

Histogram of dissimilarity with tied and zero values

Description

This function creates a histogram with the values of a dissimilarity matrix where the number of cells with zero value are explicitely showed in the first bar. Moreover, it provides the percentage of cells having equal values in the matrix.

Usage

recluster.hist(x)

Arguments

Value

An histogram with supplementary information. The first bar only shows the zero values.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

data(datamod)
simpdiss<- recluster.dist(datamod)
recluster.hist(simpdiss)

Evaluating solutions in multiscale bootstrap

Description

This function helps to understand different behaviours of node supports in multiscale bootstrap by i) plotting trends of support values in different bootstrap scales, ii) identifying the boostrap scale with highest diversification between two groups of nodes and iii) identifying nodes into two classes according to the best bootstrap level identified in (ii) and ploting their mean support values.

Usage

recluster.identify.nodes(mat, low=TRUE)

Arguments

Details

This function recognizes nodes showing different trends of support in multiscale bootstrap. In the analysis of turnover in biogeography some nodes may show a substantial increase in support in a multiscale bootstrap. Areas connected by these nodes may host a few species responsible for turnover, but the biogeographic pattern with respect is clear. Other nodes may show a slow (or no) increase in support. In this case, the links among areas can be considered as uncertain. Partitioning Around Medioids is used to identify two classes of nodes at each level, then the bootstrap scale showing the best diversification in two classes is identified by silhouette scores weighted by differences in mean values between classes. If "low" is set to TRUE the function favours low scales.

Value

A plot with bootstrap supports and their means (diamonds) for the best combination of two groups of nodes (black and red).

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

data(multiboot)
recluster.identify.nodes(multiboot)

Identifies a line in a configuration and computes its intercept and angular coefficient

Description

This function identifies a line in a configuration based on different criteria and produces its slope and intercept values. I can be used together with recluster.rotate to rotate a configuration based on a custom line.

Usage

recluster.line(mat,type="maxd",X1=NULL,X2=NULL)

Arguments

Value

Author(s)

Leonardo Dapporto

References

Dapporto L., Voda R., Dinca V., Vila R. "Comparing population patterns for genetic and morphological markers with uneven sample sizes. An example for the butterfly Maniola jurtina" Methods Ecol Evol (2014), 5, 834-843.

Examples

data(dataisl)
#Compute bidimensional representation for islands
pcoa<-cmdscale(recluster.dist(dataisl))
#Compute the line
lin<-recluster.line(pcoa)

Multiscale bootstrap based on a consensus tree

Description

Given an initial tree and a data matrix, this function computes bootstrap for nodes as done by recluster.boot. Different levels of bootstrap can be computed by varying the proportions of species sampled from the original matrix.

Usage

recluster.multi(tree, mat, phylo = NULL, tr = 100, p = 0.5, 
dist = "simpson", method = "average", boot = 1000, levels = 2, step = 1)

Arguments

Details

Computation can be time consuming. It is suggested to assess the degree of row bias by recluster.hist and recluster.node.strength to optimize the number of consensus trees before starting the analysis.

Value

A matrix indicating the percentage of bootstrap trees replicating each node for each level.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

data(datamod)
tree<-recluster.cons(datamod,tr=10)
multiboot<-recluster.multi(tree$cons,tr=10,boot=50,datamod,levels=2,step=1)
recluster.plot(tree$cons,multiboot,1,2,direction="downwards")

Evaluating order row bias in a cluster

Description

This function helps to understand the magnitude of row bias by computing a first tree with the original order of areas. Then it creates a default series of six trees by recluster.cons with increasing consensus rule from 50

Usage

recluster.node.strength(mat, phylo = NULL, dist = "simpson", 
nodelab.cex=0.8, tr = 100, levels=6, method = "average", ...)

Arguments

Details

It has to be noted that values obtained by this function are not bootstrap supports for nodes but a crude indication of the magnitude of the row bias. Nodes with low value in this analysis can have strong bootstrap support and vice versa. This preliminary analysis can avoid that the use of a strict consensus (100

Value

A cluster with percentages of recurrence over different consensus runs for each node.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

data(datamod)
recluster.node.strength(datamod, tr=10)

A plotter for recluster bootstrapped objects

Description

This function produces plots for recluster trees and assignes single or pairs of support values belonging to single or multiscale analyses.

Usage

recluster.plot(tree, data, low = 1, high = 0, id=NULL, 
nodelab.cex=0.8, direction="downwards",...)

Arguments

Details

This function allows to print on a tree, one or two labels for bootstrap values and optimize their layout. This is done with the nodelabels ape function, by specifying the adj parameters in the appropriate way.

Value

A plot representing the tree with pairs of bootstrap values, below (usually x1 BP above) and high, above.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Examples

data(datamod)
tree<-recluster.cons(datamod, tr=10)
boot<-recluster.boot(tree$cons,datamod, tr=10, boot=50)
recluster.plot(tree$cons,boot,direction="downwards")

Plotting data in RGB space

Description

This function plots a matrix obtained by recluster.col in the RGB space.

Usage

recluster.plot.col(mat,cext=0.3,cex=1,cex.axis=0.7,cex.lab=0.8,pch=16,text=TRUE,
add=F,xlim=NULL,ylim=NULL,ylab="Axis 2",xlab="Axis 1",...)

Arguments

Value

A colour plot.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Kreft H., Jetz, W. 2010. "A framework for delineating biogeographic regions based on species distributions" J Biogeogr (2010),37: 2029-2053.

Dapporto, L., Fattorini, S., Voda, R., Dinca, V., Vila, R. "Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity." J Biogeogr (2014), 41: 1639-1650.

Examples

data(datamod)
sordiss<- recluster.dist(datamod,dist="sorensen")
points<-cmdscale(sordiss)
col<-recluster.col(points)
recluster.plot.col(col)

Plot the values of the cells of a matrix in grey scale

Description

This function plots the values of the cells of a matrix in grey scale.

Usage

recluster.plot.matrix(mat)

Arguments

Value

A plot of cell values.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto, L., Fattorini, S., Voda, R., Dinca, V., Vila, R. "Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity." J Biogeogr (2014), 41: 1639-1650.

Examples

data(datamod)
simpdiss<- recluster.dist(datamod)
recluster.plot.matrix(simpdiss)

Plotting pies with RGB colours on a custom coordinate space

Description

This function groups cases based on a space grid in a user defined set of coordinates (usually longitude and latitude) and plot them in pies using RGB colours. The function can either use an output from recluster.col function or compute colours based on any distance matrix where the cases are in the same order as in the latitude and longitude data.

Usage

recluster.plot.pie(long, lat, mat=NULL, distance=NULL, loc=NULL, areas=NULL, square=2,
                   map=NULL,add=FALSE,minsize=NULL,proportional=T,xlim=NULL,ylim=NULL,
                   main=NULL,xlab=NULL,ylab=NULL,...)

Arguments

Value

A colour plot.

Author(s)

Leonardo Dapporto

References

Hernandez Roldan J.L., Dapporto L., Dinca V, Vicente J.C., Hornett E.A., Sichova J., Lukhtanov V.L., Talavera G. & Vila, R. Integrative analyses unveil speciation linked to host plant shift in Spialia butterflies. Molecular Ecology (2016) 25: 4267-4284.

Examples

# create a virtual dataset and a corresponding distance matrix
lat<-runif(50,min=20,max=40)
long<-runif(50,min=20,max=40)
datavirtual<-data.frame(replicate(20,sample(0:1,50,rep=TRUE)))
dist<-recluster.dist(datavirtual)

# Make a plot using a custom distance
recluster.plot.pie(long,lat,distance=dist,xlab="Longitude",ylab="Latitude")

# Make a plot using a recluster.col matrix
colours<-recluster.col(cmdscale(dist))
recluster.plot.pie(long,lat,mat=colours,xlab="Longitude",ylab="Latitude")

# Make points of equal size
recluster.plot.pie(long,lat,mat=colours,xlab="Longitude", proportional=FALSE,
ylab="Latitude")

# Reduce the grid
recluster.plot.pie(long,lat,distance=dist,square=1, xlab="Longitude",ylab="Latitude")

# Reduce the size of the plots
recluster.plot.pie(long,lat,distance=dist,xlab="Longitude",ylab="Latitude", minsize=0.5)
# Use a custom colour matrix
pcoa<-cmdscale(dist)
colour<-recluster.col(pcoa)
recluster.plot.col(colour)
recluster.plot.pie(long,lat,mat=colour,xlab="Longitude",ylab="Latitude")

# Include an additional factor for separating dots in groups(e.g. two continents)
continent<-rep(1,50)
continent[which(long>25)]<-2
recluster.plot.pie(long,lat,distance=dist,xlab="Longitude",ylab="Latitude", 
areas=continent)

Plotting RGB dots on a custom coordinate space

Description

This function plots the RGB dots belonging to a matrix obtained by recluster.col on a user defined set of coordinates (usually longitude and latitude) for original sites.

Usage

recluster.plot.sites.col (long, lat, mat, cext = 0.3, cex = 1, cex.axis = 0.7, 
cex.lab = 0.8, text = FALSE, pch=21, add = FALSE,...) 

Arguments

Value

A colour plot.

Author(s)

Leonardo Dapporto and Matteo Ramazzotti

References

Dapporto, L., Fattorini, S., Vod?, R., Dinc?, V., Vila, R. "Biogeography of western Mediterranean butterflies: combining turnover and nestedness components of faunal dissimilarity." J Biogeogr (2014), 41: 1639-1650.

Examples

data(datamod)
sordiss<- recluster.dist(datamod, dist="sorensen")
lat<-c(2,2,2,1,3,1,1,3,3)
long<-c(1,5,3,3,3,1,5,1,5)
points<-cmdscale(sordiss)
col<-recluster.col(points)
recluster.plot.sites.col(long, lat, col,text=TRUE)

Computes a procrustes analysis between two matrices even if only a subset of cases are shared.

Description

This function computes a procrustes analysis (as done by the vegan procrustes function) but it also allows including a subset of cases shared between the two matrices and some unshared cases. The shared cases must be listed first and in the same order in the two matrices. Moreover, the number of shared cases must be indicated. The function applies a procrustes analysis by scaling, mirroring ad rotating the second matrix to minimizing its dissimilarity from the first on the basis of shared cases. Then, the same transformation is applied to the unshared cases of the second matrix. Finally, it allows including the matrices of coordinates for variables as obtained, for example, by PCA.

Usage

recluster.procrustes(X, Y, Yv=FALSE, num=nrow(X), scale = TRUE, ...)

Arguments

Details

recluster.procrustes uses the vegan function procrustes to rotate a configuration (Y) to maximum similarity with another target matrix configuration (X) on the basis of a series of shared objects (rows). These objects must be in the same order in the two X and Y matrices. In case of additional cases (rows) in both the X and Y matrices, the same transformation is applied to the case of the Y matrices which are not shared with X. Moreover, the same transformation can be applied to an additional Yv matrix likely representing the coordinates of variables as obtained for example by PCA or other ordination methods. The functions returns an object of the class "procrustes" as implemented in vegan.

Value

Author(s)

Leonardo Dapporto

References

Dapporto L., Voda R., Dinca V., Vila R. "Comparing population patterns for genetic and morphological markers with uneven sample sizes. An example for the butterfly Maniola jurtina" Methods Ecol Evol (2014), 5, 834-843.

Examples

#Create and plot a target matrix
ex1 <-rbind(c(1,5),c(5,5),c(3,4),c(3,6))
plot(ex1,col=c(1:4),pch=19,xlim=c(0,6),ylim=c(0,6),cex=2)
#Create and plot a matrix to be rotated. Only the points 1-4 are shared
ex2<-rbind(c(3,1),c(3,3),c(2.5,2),c(3.5,2),c(3,4))
plot(ex2,col=c(1:5),pch=19,xlim=c(0,6),ylim=c(0,6),cex=2)

#Perform the procrustes and plot the matrices
procr1<-recluster.procrustes(ex1,ex2,num=4)
plot(procr1$X,col=c(1:4),pch=19,xlim=c(-4,4),ylim=c(-4,4),cex=2)
plot(procr1$Yrot,col=c(1:5),pch=19,xlim=c(-4,4),ylim=c(-4,4),cex=2)

A new clustering method based on continuous consensus.

Description

This function is specifically designed to allow regionalization analysis when occurrence of zero and tied values are particularly numerous. This often occurs when using turnover indices at small or mid-spatial scales where huge barriers do not occur. The function requires as an input a matrix having areas in rows and species occurrence (1,0) in columns. It also allows for inclusion of a phylogenetic tree to compute phylogenetic beta-diversity. The indices to be used are those allowed by recluster.dist, but custom indices can be introduced (see recluster.dist). Alternatively a dissimilarity metrix obtained with any function can be provided. The function requires the input of a custom number of trees (n=50 by default) and of an interval of mincl-maxcl values (by default 2-3) indicating the number of regions to be identified. Clustering methods implemented by hclust are allowed as well as Partition Around Medoids and DIANA. The ward.2D default method is generally the best performing method, but ward.D, complete clustering, Partition Around Medoids and DIANA can also perform well. The function produces n trees by randomly re-ordering the original row order. Then, the trees are cut to different nodes (from the mincl-1th to the maxcl-1th node), thus producing an increasing number of clusters. Then, the function compares clustering solutions among the same cuts in different re-sampled trees. This produces a dissimilarity matrix among cells represented by the times each pair of areas is located in different clusters over the different solutions extracted for different trees for the same cut. This dissimilarity is standardised by the number of re-sampled trees to produce values ranging from 0 (for pairs of cells always belonging to the same cluster) to 1 (pairs never belonging to the same cluster). A final hierarchical clustering is applied generating an interval of maxcl-mincl. Since the number of clusters requested by the user interval cannot precisely match the mean number of clusters obtained by the tree cuts, the clustering solution for each k value is obtained by selecting the dissimilarity matrix obtained from the nearest mean number of clustering solutions.

Usage

recluster.region (mat,tr=50,dist="simpson",method="ward.D2",phylo=NULL, mincl=2,maxcl=3,
rettree=FALSE,retmat=FALSE,retmemb=FALSE)

Arguments

Details

Like other evaluators for goodness of clustering solutions, the funtion provides silhouette values and the explained dissimilarity. The explained dissimilarity (sensu Holt et al. 2013) is represented by the ratio between sums of mean dissimilarities among members of different clusters and the sum of all dissimilarities of the matrix. This value clearly tends to 1 when all areas are considered as independent groups. Silhouette width measures the strength of any partition of objects from a dissimilarity matrix by comparing the minimum distance between each cell and the most similar cell belonging to any other cluster and the mean distance between that cell and the others belonging to the same cluster (see silhouette function in the cluster package). Silhouette values range between -1 and +1, with a negative value suggesting that most cells are probably located in an incorrect cluster.

Value

Author(s)

Leonardo Dapporto

References

Dapporto L. et al. A new procedure for extrapolating turnover regionalization at mid-small spatial scales, tested on British butterflies. Methods Ecol Evol (2015), 6, 1287-1297

Examples

data(dataisl)
simpson<-recluster.dist(dataisl)
turn_cl<-recluster.region(simpson,tr=10,rettree=TRUE)
#plot the three for three clusters
plot(turn_cl$tree[[2]])
#inspect cluster membership
turn_cl$grouping

Rotates a bidimensional configuration according to a line

Description

This function rotates the points of a configuration to a new configuration where a line identified by its intercept and its angular coefficient is rotated to become horizontal. The function can also flip or centre a configuration

Usage

recluster.rotate(table,m=FALSE,q=FALSE,flip="none",centre=TRUE)

Arguments

Value

Author(s)

Leonardo Dapporto

References

Dapporto L., Voda R., Dinca V., Vila R. "Comparing population patterns for genetic and morphological markers with uneven sample sizes. An example for the butterfly Maniola jurtina" Methods Ecol Evol (2014), 5, 834-843.

Examples

data(dataisl)
#Compute bidimensional representation for islands
pcoa<-cmdscale(recluster.dist(dataisl))
plot (pcoa)
#Compute the line
lin<-recluster.line(pcoa)
transf<-recluster.rotate(pcoa,m=lin$m,q=lin$q)
plot(transf)

Test variation lost by a bidimensional configuration when the coordinates of the elements are reduced to the configuration of the barycentres of a given series of groups.

Description

This function evaluates the amount of variation maintained by a bidimensional configuration after the elements are reduced to the barycentres according to a grouping variable. If elements of different groups are randomly scattered in the configuration, almost all barycentres are expected to attain a rather central position with respect to the original elements, which would result in a small mean distance between barycentres. Conversely, if the elements of different groups are strictly clustered in the representation, the distances among barycentres are expected to be similar to the distances among original elements.

Usage

recluster.test.dist(mat1,mat2,member,perm=1000,elev=2)

Arguments

Details

The function produces a ratio between the mean squared pairwise distance for all elements and the mean squared pairwise distance for barycentres. This ratio is calculated for the overall configuration and for the two axes separately. The function also provides a test for the significance of the variation preserved by barycentres by creating a custom number of matrices (1000 by default) by randomly sampling the original vector defining groups. Then it computes the frequency of mean squared distance ratios in random configurations higher than the observed ratio.

Value

Author(s)

Leonardo Dapporto

References

Dapporto L., Voda R., Dinca V., Vila R. "Comparing population patterns for genetic and morphological markers with uneven sample sizes. An example for the butterfly Maniola jurtina" Methods Ecol Evol (2014), 5, 834-843.

Examples

data(dataisl)
#Define groups of islands
memb<-c(2,3,5,7,5,3,1,1,2,5,1,3,1,1,5,2,2,1,2,4,1,3,1,5,2,1,7,6,1,1,1) 
#Compute bidimensional representation for elements
pcoa<-cmdscale(recluster.dist(dataisl))
bar<-aggregate(pcoa~memb,FUN="mean")[,2:3]
# test if the variation has been significantly lost
recluster.test.dist(pcoa,bar,memb,perm=100)

Phylogenetic tree for the butterfly species included in dataisl dataset

Description

This phylogenetic tree has been created based on known phylogeny of butterflies at family and subfamily level and on COI sequences at genus and species level. Branch lenghts have been calculated by Graphen method

Usage

data(treemod)

Details

A phylogenetic tree of butterfly species occurring on Western Mediterranean islands.

Author(s)

Gerard Talavera and Roger Vila

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.

Hypothetical phylogenetic tree for the virtual island faunas provided with the package recluster

Description

This phylogenetic tree has been created from the datamod dataset representing a series of virtual faunas in different sites

Usage

data(treemod)

Details

A phylogenetic tree of 31 species taken from 9 sites.

Author(s)

Gerard Talavera

References

Dapporto L., Ramazzotti M., Fattorini S., Talavera G., Vila R., Dennis R. "recluster: an unbiased clustering procedure for beta-diversity turnover" Ecography (2013), 36:1070-1075.