Title:	Evolutionary Diversity Metrics for Raster Data
Version:	2.2.0
Description:	Phylogenetic Diversity (PD, Faith 1992), Evolutionary Distinctiveness (ED, Isaac et al. 2007), Phylogenetic Endemism (PE, Rosauer et al. 2009; Laffan et al. 2016), and Weighted Endemism (WE, Laffan et al. 2016) for presence-absence raster. Faith, D. P. (1992) <doi:10.1016/0006-3207(92)91201-3> Isaac, N. J. et al. (2007) <doi:10.1371/journal.pone.0000296> Laffan, S. W. et al. (2016) <doi:10.1111/2041-210X.12513> Rosauer, D. et al. (2009) <doi:10.1111/j.1365-294X.2009.04311.x>.
License:	GPL (≥ 3)
URL:	https://CRAN.R-project.org/package=phyloraster, https://github.com/gabferreira/phyloraster, https:https://gabferreira.github.io/phyloraster/, https://doi.org/10.1111/ecog.06902
BugReports:	https://github.com/gabferreira/phyloraster/issues
Depends:	R (≥ 2.10)
Imports:	ape, methods, purrr, SESraster, stats, terra
Suggests:	knitr, maps, phylobase, rmarkdown, testthat (≥ 2.0.0)
VignetteBuilder:	knitr
Config/testthat/edition:	2
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-02-28 14:34:57 UTC; gabri
Author:	Gabriela Alves-Ferreira [aut, cre, cph], Flávio M. M. Mota [aut], Neander Marcel Heming [aut]
Maintainer:	Gabriela Alves-Ferreira <gabriela-alves77@hotmail.com>
Repository:	CRAN
Date/Publication:	2025-02-28 15:00:01 UTC

Classify Phylogenetic Endemism using rasters

Description

Use the results of rast.pe.ses() to identify centers of paleo-, neo-, super-, and mixed- endemism following the CANAPE scheme of Mishler et al., 2014.

Usage

.end.type(x)

Arguments

Value

SpatRaster

References

Mishler, B., Knerr, N., González-Orozco, C. et al. (2014) Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia. Nat Commun, 5: 4473. doi:10.1038/ncomms5473

Evaluate if the rasters generated in the function fits on available memory

Description

Tests if the amount of RAM required is available to process a SpatRaster

Usage

.fit.memory(x, n = 1)

Arguments

Value

logical

Calculate Evolutionary distinctiveness for each raster cell

Description

This function calculates evolutionary distinctiveness according to the fair-proportion index.

Usage

.rast.ed.B(x, edge.path, branch.length, n.descen, filename = "", ...)

Arguments

Value

SpatRaster

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

References

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

Calculate phylogenetic community metrics for raster data

Description

Calculate species richness, phylogenetic diversity, evolutionary distinctiveness, phylogenetic endemism and weighted endemism using rasters as input

Usage

.rast.geo.phylo(
  x,
  inv.R,
  edge.path,
  branch.length,
  n.descen,
  filename = "",
  ...
)

Arguments

Details

Community metrics calculated:

• Phylogenetic diversity (Faith 1992)

• Species Richness

• Evolutionary distinctiveness by fair-proportion (Isaac et al. 2007)

• Phylogenetic endemism (Rosauer et al. 2009)

• Weighted endemism (Crisp et al. 2001, Williams et al. 1994)

Value

SpatRaster with one layer for each metric

Author(s)

Neander Marcel Heming

References

Rosauer, D. A. N., Laffan, S. W., Crisp, M. D., Donnellan, S. C. and Cook, L. G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular ecology, 18(19), 4061-4072.

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

Williams, P.H., Humphries, C.J., Forey, P.L., Humphries, C.J. and VaneWright, R.I. (1994). Biodiversity, taxonomic relatedness, and endemism in conservation. In: Systematics and Conservation Evaluation (eds Forey PL, Humphries C.J., Vane-Wright RI), p. 438. Oxford University Press, Oxford.

Crisp, M., Laffan, S., Linder, H. and Monro, A. (2001). Endemism in the Australian flora. Journal of Biogeography, 28, 183–198.

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

Laffan, S. W., Rosauer, D. F., Di Virgilio, G., Miller, J. T., González‐Orozco, C. E., Knerr, N., ... & Mishler, B. D. (2016). Range‐weighted metrics of species and phylogenetic turnover can better resolve biogeographic transition zones. Methods in Ecology and Evolution, 7(5), 580-588.

Calculate phylogenetic diversity for each raster cell

Description

Calculate the sum of the branch length for species present in raster data.

Usage

.rast.pd.B(x, edge.path, branch.length, filename = "", ...)

Arguments

Value

SpatRaster

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

References

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

Calculate phylogenetic endemism for a raster

Description

Calculate phylogenetic endemism using rasters as input and output.

Usage

.rast.pe.B(
  x,
  inv.R,
  branch.length,
  branch.length.alt,
  metric = c("pe", "pe.alt", "rpe", "all")[1],
  filename = "",
  overwrite = TRUE,
  ...
)

Arguments

Details

Metrics available are:

• pe: Phylogenetic endemism (Rosauer et al., 2009)

• pe.alt: Alternate Phylogenetic endemism (Mishler et al., 2014)

• rpe: Relative Phylogenetic endemism (Mishler et al., 2014)

• all: Calculate all available metrics Alternate phylogenetic endemism (PE.alt, Mishler et al., 2014) is calculated using an alternate phylogeny with non-zero branch lengths converted to a constant value (here we use 1) and rescaled so the sum of all branch lengths is 1. Relative phylogenetic endemism (RPE, Mishler et al., 2014) is the ratio of phylogenetic endemism (PE, Rosauer et al., 2009) measured on the original tree versus PE measured on a alternate tree (PE.alt).

Value

SpatRaster

Author(s)

Gabriela Alves-Ferreira and Neander Heming

References

Mishler, B. D., Knerr, N., González-Orozco, C. E., Thornhill, A. H., Laffan, S. W. and Miller, J. T. 2014. Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia. – Nat. Commun. 5: 4473.

Rosauer, D. A. N., Laffan, S. W., Crisp, M. D., Donnellan, S. C., & Cook, L. G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular ecology, 18(19), 4061-4072.

Calculate weighted endemism for each raster cell

Description

Calculate the sum of the inverse of the range size for species present in each raster cell.

Usage

.rast.we.B(x, inv.R, filename = "", ...)

Arguments

Value

SpatRaster

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

References

Williams, P.H., Humphries, C.J., Forey, P.L., Humphries, C.J., VaneWright, R.I. (1994). Biodiversity, taxonomic relatedness, and endemism in conservation. In: Systematics and Conservation Evaluation (eds Forey PL, Humphries CJ, Vane-Wright RI), p. 438. Oxford University Press, Oxford.

Crisp, M., Laffan, S., Linder, H., Monro, A. (2001). Endemism in the Australian flora. Journal of Biogeography, 28, 183–198.

Check for missing arguments in function call

Description

Check for missing arguments using function call and a provided vector with argument names to check

Usage

arg.check(
  call,
  arguments = c("LR", "inv.R", "branch.length", "n.descen", "tree")
)

Arguments

Value

logical

Author(s)

Neander Marcel Heming

Examples

geop <- function(x, tree, ...){
                f4 <- arg.check(match.call(),
                                c("LR", "inv.R",
                                "branch.length", "n.descen"))
                f1 <- arg.check(match.call(),
                                c("tree"))
                c(f1, f4)
                }
geop(1, 1)
geop(1)
geop(1, LR=1)

Classify Phylogenetic Endemism using rasters

Description

Use the results of rast.pe.ses() to identify centers of paleo-, neo-, super-, and mixed- endemism following the CANAPE scheme of Mishler et al., 2014.

Usage

canape.rast(
  pe.obs.p.upper,
  pe.alt.obs.p.upper,
  rpe.obs.p.upper,
  rpe.obs.p.lower,
  filename = NULL,
  overwrite = FALSE
)

Arguments

Value

SpatRaster

See Also

rast.pe.ses, SESraster

Examples

library(SESraster)
library(terra)
library(phyloraster)
x <- rast(system.file("extdata", "rast.presab.tif", package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
data <- phylo.pres(x, tree)
ses <- rast.pe.ses(x = data$x, data$tree,
                aleats = 5, metric = "all")
# CANAPE
canape <- canape.rast(ses$p.upper.PE, ses$p.upper.PE.alt,
                   ses$p.upper.RPE, ses$p.lower.RPE)
unique(canape)
plot(canape)

Presence-absence of 33 Australian tree frogs (Rosauer 2017)

Description

A dataset containing presence-absence of 33 Australian tree frogs. This dataset also provide coordinates x and y for each site.

Usage

dataR

Format

A matrix with 2891 rows and 35 columns.

Source

Rosauer, 2017. Available on: https://github.com/DanRosauer/phylospatial/tree/master/PhyloEndemism_in_R/Tree%20Frog%20Data

Delta of Diversity Metrics

Description

Calculates the difference of rasterized diversity metrics (richness, phylogenetic endemism, phylogenetic diversity, weighted endemism, evolutionary distinctiveness) between time periods.

Usage

delta.grid(r1, r2, filename = NULL, cores = 1, ...)

Arguments

Details

The two input rasters (r1 and r2) must have the same extent and resolution.

Value

SpatRaster

Examples

# data
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))

# metric SR richness
riq.pres <- rast.sr(x)
# imagine we lost some species in the future
riq.fut <- rast.sr(x[[c(1:15)]])
dg <- delta.grid(riq.pres, riq.fut)
terra::plot(dg)

Transform a data.frame to raster

Description

The function transforms a data.frame or a matrix of presence- absence in a raster of distribution.

Usage

df2rast(x, CRS = "+proj=longlat +datum=WGS84", ...)

Arguments

Value

SpatRaster

Examples

dat <- phyloraster::load.data.rosauer()
df2rast(dat$presab, crs = "+proj=longlat +datum=WGS84 +ellps=WGS84
+towgs84=0,0,0")

Calculate phylogenetic community metrics for raster data

Description

Calculate species richness, phylogenetic diversity, evolutionary distinctiveness, phylogenetic endemism and weighted endemism using rasters as input.

Usage

geo.phylo(
  x,
  tree,
  inv.R,
  edge.path,
  branch.length,
  n.descen,
  full_tree_metr = TRUE,
  filename = "",
  ...
)

Arguments

Details

Community metrics calculated:

• Phylogenetic diversity (Faith 1992)

• Species Richness

• Evolutionary distinctiveness by fair-proportion (Isaac et al. 2007)

• Phylogenetic endemism (Rosauer et al. 2009)

• Weighted endemism (Crisp et al. 2001, Williams et al. 1994)

Value

SpatRaster with one layer for each metric

Author(s)

Neander Marcel Heming

References

Rosauer, D. A. N., Laffan, S. W., Crisp, M. D., Donnellan, S. C. and Cook, L. G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular ecology, 18(19), 4061-4072.

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

Williams, P.H., Humphries, C.J., Forey, P.L., Humphries, C.J. and VaneWright, R.I. (1994). Biodiversity, taxonomic relatedness, and endemism in conservation. In: Systematics and Conservation Evaluation (eds Forey PL, Humphries C.J., Vane-Wright RI), p. 438. Oxford University Press, Oxford.

Crisp, M., Laffan, S., Linder, H. and Monro, A. (2001). Endemism in the Australian flora. Journal of Biogeography, 28, 183–198.

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

Laffan, S. W., Rosauer, D. F., Di Virgilio, G., Miller, J. T., González‐Orozco, C. E., Knerr, N., ... & Mishler, B. D. (2016). Range‐weighted metrics of species and phylogenetic turnover can better resolve biogeographic transition zones. Methods in Ecology and Evolution, 7(5), 580-588.

See Also

phylo.pres, inv.range, rast.ed, rast.pd, rast.we, rast.pe, rast.sr, geo.phylo.ses,

Examples

library(terra)
library(phyloraster)
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))[[1:10]]
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
data <- phylo.pres(x, tree)
inv.R <- inv.range(data$x)
t <- geo.phylo(data$x, inv.R = inv.R, edge.path = data$edge.path,
branch.length = data$branch.length, n.descen = data$n.descendants)
terra::plot(t)

Calculate phylogenetic community metrics and their standardized effect sizes for raster data

Description

Calculates the standardized effect size for phylogenetic community metrics. See Details for more information.

Usage

geo.phylo.ses(
  x,
  tree,
  inv.R,
  edge.path,
  branch.length,
  n.descen,
  full_tree_metr = TRUE,
  spat_alg = "bootspat_str",
  spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL),
  aleats = 10,
  cores = 1,
  filename = "",
  ...
)

Arguments

Details

The dependency ‘SESraster’ is used to calculate the null models. This package currently implements six algorithms to randomize binary species distribution with several levels of constraints: SIM1, SIM2, SIM3, SIM5, SIM6 and SIM9 (sensu Gotelli 2000). The methods implemented in ‘SESraster’ are based on how species (originally rows) and sites (originally columns) are treated (i.e. fixed, equiprobable, or proportional sums) (Gotelli 2000). By default, the ‘phyloraster’ uses the function bootspat_ str() from the ‘SESraster’ package to conduct the randomizations, but the user is free to choose any of the other methods mentioned above through the spat_alg argument in the *.ses() functions of the ‘phyloraster’ package. The bootspat_str() is equivalent to the SIM5 (proportional-fixed) method of Gotelli (2000), which partially relaxes the spatial structure of species distributions, but keeps the spatial structure of the observed richness pattern across cells.

The dependency ‘SESraster’ is used to calculate the null models. This package currently implements six algorithms to randomize binary species distribution with several levels of constraints: SIM1, SIM2, SIM3, SIM5, SIM6 and SIM9 (sensu Gotelli 2000). The methods implemented in ‘SESraster’ are based on how species (originally rows) and sites (originally columns) are treated (i.e. fixed, equiprobable, or proportional sums) (Gotelli 2000). By default, the ‘phyloraster’ uses the function bootspat_ str() from the ‘SESraster’ package to conduct the randomizations, but the user is free to choose any of the other methods mentioned above through the spat_alg argument in the *.ses() functions of the ‘phyloraster’ package. The bootspat_str() is equivalent to the SIM5 (proportional-fixed) method of Gotelli (2000), which partially relaxes the spatial structure of species distributions, but keeps the spatial structure of the observed richness pattern across cells.

Value

SpatRaster. The function returns the observed value of the metric, the mean of the simulations calculated over n times, the standard deviation of the simulations, the standardized effect size (SES) for the metric, and the p-values.

Author(s)

Neander Marcel Heming

References

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

Heming, N. M., Mota, F. M. M. and Alves-Ferreira, G. 2023. SESraster: raster randomization for null hypothesis testing. https://CRAN.R-project.org/package=SESraster.

See Also

phylo.pres, inv.range, geo.phylo, rast.ed.ses, rast.pd.ses, rast.we.ses, rast.pe.ses, bootspat_str, bootspat_naive, bootspat_ff, SESraster

Examples

library(terra)
library(phyloraster)
require("SESraster")
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
tses <- geo.phylo.ses(x = x,
                     tree = tree,
                      spat_alg = "bootspat_str",
                      spat_alg_args = list(rprob = NULL,
                                           rich = NULL,
                                           fr_prob = NULL),
                      aleats = 2)
terra::plot(tses)

Calculate the inverse of range size

Description

Get range size in square kilometers for all cells that are not NA, the inverse of range size and the inverse of range size multiplied by branch length for multiple species using a raster of presence-absence.

Usage

inv.range(x, filename = "", overwrite = FALSE, ...)

Arguments

Value

SpatRaster and numeric

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

Examples

# calculating the inverse of range size
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
inv.range(x[[5]])

Load an example dataset with presence-absence data of 33 tree frogs and a phylogenetic tree for this species

Description

This function load a phylogenetic tree, a raster and a data.frame with presence-absence of 33 Australian tree frogs from Rosauer (2017). We also provide distribution shapefiles for ten species according to the IUCN.

Usage

load.data.rosauer()

Value

data.frame, SpatRaster, SpatVector and phylo

Source

Rosauer, 2017. Available on: Github

IUCN. 2022. The IUCN Red List of Threatened Species (spatial data). Version 2022-1. IUCN

Prepare rasters and phylogenetic tree to run community metrics

Description

Reorder a stack of rasters of species distribution to match the order of the tips of the tree, and get branch length and number of descendants for each species to calculate diversity metrics using phyloraster::geo.phylo(). The branch length and the number of descendants can be calculated based on the full tree or the raster based tree subset. The names must be the same in the phylogenetic tree and in the raster for the same species. For example, if you have the name "Leptodactylus_latrans" in the raster and "Leptodactylus latrans" in the tree, the function will not work. The same goes for uppercase and lowercase letters.

Usage

phylo.pres(x, tree, full_tree_metr = TRUE, ...)

Arguments

Value

Returns a list containing a SpatRaster reordered according to the order that the species appear in the phylogenetic tree, a subtree containing only the species that are in the stack of rasters and finally two named numerical vectors containing the branch length and the number of descendants of each species.

Author(s)

Neander Marcel Heming and Gabriela Alves Ferreira

Examples

library(phyloraster)
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
phylo.pres(x[[1:3]], tree, full_tree_metr = TRUE)

# using the prunned tree
phylo.pres(x[[1:3]], tree, full_tree_metr = FALSE)

Calculate range size for a set of species using a raster as input

Description

This function calculate range size in square meters (by default) for all cells that are not NA. The size of the cells is constant in degrees but not in square meters, which was considered in the method applied to calculate the area.

Usage

range_size(x, cellSz, unit = "m", ...)

Arguments

Value

vector

Author(s)

Gabriela Alves Ferreira and Neander Marcel Heming

Examples

x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
range_size(x[[1:2]], cellSz <- terra::cellSize(x))

Calculate Evolutionary distinctiveness for raster data

Description

This function calculates evolutionary distinctiveness according to the fair-proportion index. The values represents the mean ED for species presents in each raster cell.

Usage

rast.ed(
  x,
  tree,
  edge.path,
  branch.length,
  n.descen,
  full_tree_metr = TRUE,
  filename = "",
  ...
)

Arguments

Value

SpatRaster

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

References

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

Examples

library(terra)
library(phyloraster)
x <- rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
# phylogenetic tree
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
data <- phylo.pres(x[[1:3]], tree)
ed <- rast.ed(data$x, edge.path = data$edge.path,
              branch.length = data$branch.length,
              n.descen = data$n.descen)
plot(ed)

Standardized effect size for Evolutionary distinctiveness

Description

Calculates the standardized effect size for evolutionary distinctiveness. See Details for more information.

Usage

rast.ed.ses(
  x,
  tree,
  edge.path,
  branch.length,
  n.descen,
  full_tree_metr = TRUE,
  spat_alg = "bootspat_str",
  spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL),
  aleats = 10,
  filename = "",
  ...
)

Arguments

Details

The dependency ‘SESraster’ is used to calculate the null models. This package currently implements six algorithms to randomize binary species distribution with several levels of constraints: SIM1, SIM2, SIM3, SIM5, SIM6 and SIM9 (sensu Gotelli 2000). The methods implemented in ‘SESraster’ are based on how species (originally rows) and sites (originally columns) are treated (i.e. fixed, equiprobable, or proportional sums) (Gotelli 2000). By default, the ‘phyloraster’ uses the function bootspat_ str() from the ‘SESraster’ package to conduct the randomizations, but the user is free to choose any of the other methods mentioned above through the spat_alg argument in the *.ses() functions of the ‘phyloraster’ package. The bootspat_str() is equivalent to the SIM5 (proportional-fixed) method of Gotelli (2000), which partially relaxes the spatial structure of species distributions, but keeps the spatial structure of the observed richness pattern across cells.

Value

SpatRaster. The function returns the observed value of the metric, the mean of the simulations calculated over n times, the standard deviation of the simulations, the standardized effect size (SES) for the metric, and the p-values.

Author(s)

Neander M. Heming and Gabriela Alves-Ferreira

References

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

Heming, N. M., Mota, F. M. M. and Alves-Ferreira, G. 2023. SESraster: raster randomization for null hypothesis testing. https://CRAN.R-project.org/package=SESraster.

See Also

phylo.pres, inv.range, geo.phylo.ses, rast.ed.ses, rast.pd.ses, rast.we.ses, rast.pe.ses, bootspat_str, bootspat_naive, bootspat_ff, SESraster

Examples

library(phyloraster)
library(SESraster)
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
t <- rast.ed.ses(x[[1:10]], tree, aleats = 3)
terra::plot(t)

Calculate phylogenetic diversity for raster data

Description

Calculate the sum of the branch length for species present in each cell of the raster.

Usage

rast.pd(
  x,
  tree,
  edge.path,
  branch.length,
  full_tree_metr = TRUE,
  filename = "",
  ...
)

Arguments

Value

SpatRaster

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

References

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

Examples

library(terra)
library(phyloraster)
x <- rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
data <- phylo.pres(x[[1:3]], tree)
pd <- rast.pd(data$x, data$tree)
plot(pd)

Standardized effect size for Phylogenetic diversity

Description

Calculates the standardized effect size for phylogenetic diversity. See Details for more information.

Usage

rast.pd.ses(
  x,
  tree,
  edge.path,
  branch.length,
  full_tree_metr = TRUE,
  spat_alg = "bootspat_str",
  spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL),
  aleats = 10,
  filename = "",
  ...
)

Arguments

Details

The dependency ‘SESraster’ is used to calculate the null models. This package currently implements six algorithms to randomize binary species distribution with several levels of constraints: SIM1, SIM2, SIM3, SIM5, SIM6 and SIM9 (sensu Gotelli 2000). The methods implemented in ‘SESraster’ are based on how species (originally rows) and sites (originally columns) are treated (i.e. fixed, equiprobable, or proportional sums) (Gotelli 2000). By default, the ‘phyloraster’ uses the function bootspat_ str() from the ‘SESraster’ package to conduct the randomizations, but the user is free to choose any of the other methods mentioned above through the spat_alg argument in the *.ses() functions of the ‘phyloraster’ package. The bootspat_str() is equivalent to the SIM5 (proportional-fixed) method of Gotelli (2000), which partially relaxes the spatial structure of species distributions, but keeps the spatial structure of the observed richness pattern across cells.

Value

SpatRaster. The function returns the observed value of the metric, the mean of the simulations calculated over n times, the standard deviation of the simulations, the standardized effect size (SES) for the metric, and the p-values.

Author(s)

Gabriela Alves-Ferreira and Neander Heming

References

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

Heming, N. M., Mota, F. M. M. and Alves-Ferreira, G. 2023. SESraster: raster randomization for null hypothesis testing. https://CRAN.R-project.org/package=SESraster.

See Also

phylo.pres, inv.range, geo.phylo.ses, rast.ed.ses, rast.pd.ses, rast.we.ses, rast.pe.ses, bootspat_str, bootspat_naive, bootspat_ff, SESraster

Examples

library(terra)
library(phyloraster)
library(SESraster)
x <- rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
data <- phylo.pres(x[[1:10]], tree)
t <- rast.pd.ses(data$x, edge.path = data$edge.path,
                branch.length = data$branch.length, aleats = 3)
plot(t)

Calculate phylogenetic endemism for raster data

Description

Calculate the sum of the inverse of the range size multiplied by the branch length for the species present in raster data.

Usage

rast.pe(
  x,
  tree,
  inv.R,
  branch.length,
  full_tree_metr = TRUE,
  filename = "",
  ...
)

Arguments

Value

SpatRaster

Author(s)

Gabriela Alves-Ferreira and Neander Marcel Heming

References

Laffan, S. W., Rosauer, D. F., Di Virgilio, G., Miller, J. T., González‐Orozco, C. E., Knerr, N., ... & Mishler, B. D. (2016). Range‐weighted metrics of species and phylogenetic turnover can better resolve biogeographic transition zones. Methods in Ecology and Evolution, 7(5), 580-588.

Rosauer, D. A. N., Laffan, S. W., Crisp, M. D., Donnellan, S. C. and Cook, L. G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular ecology, 18(19), 4061-4072.

Examples

library(terra)
library(phyloraster)
x <- rast(system.file("extdata", "rast.presab.tif",
package = "phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package = "phyloraster"))
pe <- rast.pe(x = x[[1:3]], tree)
plot(pe)

Standardized effect size for Phylogenetic endemism

Description

Calculates the standardized effect size for phylogenetic endemism. See Details for more information.

Usage

rast.pe.ses(
  x,
  tree,
  branch.length,
  branch.length.alt,
  inv.R,
  full_tree_metr = TRUE,
  spat_alg = "bootspat_str",
  spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL),
  metric = c("pe", "pe.alt", "rpe", "all")[4],
  aleats = 10,
  cores = 1,
  filename = "",
  overwrite = TRUE,
  ...
)

Arguments

Details

The dependency ‘SESraster’ is used to calculate the null models. This package currently implements six algorithms to randomize binary species distribution with several levels of constraints: SIM1, SIM2, SIM3, SIM5, SIM6 and SIM9 (sensu Gotelli 2000). The methods implemented in ‘SESraster’ are based on how species (originally rows) and sites (originally columns) are treated (i.e. fixed, equiprobable, or proportional sums) (Gotelli 2000). By default, the ‘phyloraster’ uses the function bootspat_ str() from the ‘SESraster’ package to conduct the randomizations, but the user is free to choose any of the other methods mentioned above through the spat_alg argument in the *.ses() functions of the ‘phyloraster’ package. The bootspat_str() is equivalent to the SIM5 (proportional-fixed) method of Gotelli (2000), which partially relaxes the spatial structure of species distributions, but keeps the spatial structure of the observed richness pattern across cells. Biodiversity metrics available are:

• pe: Phylogenetic endemism (Rosauer et al., 2009)

• pe.alt: Alternate Phylogenetic endemism (Mishler et al., 2014)

• rpe: Relative Phylogenetic endemism (Mishler et al., 2014)

• all: Calculate all available metrics Alternate phylogenetic endemism (PE.alt, Mishler et al., 2014) is calculated using an alternate phylogeny with non-zero branch lengths converted to a constant value (here we use 1) and rescaled so the sum of all branch lengths is 1. Relative phylogenetic endemism (RPE, Mishler et al., 2014) is the ratio of phylogenetic endemism (PE, Rosauer et al., 2009) measured on the original tree versus PE measured on a alternate tree (PE.alt).

Value

SpatRaster. The function returns the observed value of the metric, the mean of the simulations calculated over n times, the standard deviation of the simulations, the standardized effect size (SES) for the metric, and the p-values.

Author(s)

Gabriela Alves-Ferreira and Neander Heming

References

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

Heming, N. M., Mota, F. M. M. and Alves-Ferreira, G. 2023. SESraster: raster randomization for null hypothesis testing. https://CRAN.R-project.org/package=SESraster.

Mishler, B. D., Knerr, N., González-Orozco, C. E., Thornhill, A. H., Laffan, S. W. and Miller, J. T. 2014. Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia. – Nat. Commun. 5: 4473.

Rosauer, D. A. N., Laffan, S. W., Crisp, M. D., Donnellan, S. C., & Cook, L. G. (2009). Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history. Molecular ecology, 18(19), 4061-4072.

See Also

phylo.pres, inv.range, geo.phylo.ses, rast.ed.ses, rast.pd.ses, rast.we.ses, rast.pe.ses, bootspat_str, bootspat_naive, bootspat_ff, SESraster

Examples

library(terra)
library(phyloraster)
library(SESraster)
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
data <- phylo.pres(x[[1:3]], tree)
t <- rast.pe.ses(x = data$x, data$tree, aleats = 99, metric = "all")
plot(t)

Calculate species richness for raster data

Description

Calculate the species richness for raster data.

Usage

rast.sr(x, filename = "", ...)

Arguments

Value

SpatRaster

Author(s)

Gabriela Alves Ferreira and Neander Marcel Heming

Examples

x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
rse <- phyloraster::rast.sr(x)
terra::plot(rse)

Calculate weighted endemism for raster data

Description

Calculate the weighted endemism for species present in raster data.

Usage

rast.we(x, inv.R, filename = "", ...)

Arguments

Value

SpatRaster

Author(s)

Neander Marcel Heming and Gabriela Alves Ferreira

References

Laffan, S. W., Rosauer, D. F., Di Virgilio, G., Miller, J. T., González‐Orozco, C. E., Knerr, N., ... & Mishler, B. D. (2016). Range‐weighted metrics of species and phylogenetic turnover can better resolve biogeographic transition zones. Methods in Ecology and Evolution, 7(5), 580-588.

Williams, P.H., Humphries, C.J., Forey, P.L., Humphries, C.J., VaneWright, R.I. (1994). Biodiversity, taxonomic relatedness, and endemism in conservation. In: Systematics and Conservation Evaluation (eds Forey PL, Humphries CJ, Vane-Wright RI), p. 438. Oxford University Press, Oxford.

Crisp, M., Laffan, S., Linder, H., Monro, A. (2001). Endemism in the Australian flora. Journal of Biogeography, 28, 183–198.

Examples

library(terra)
library(phyloraster)
x <- rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
inv.R <- inv.range(x)
we <- rast.we(x, inv.R)
plot(we)

Calculate weighted endemism standardized for species richness

Description

Calculates the standardized effect size for weighted endemism. See Details for more information.

Usage

rast.we.ses(
  x,
  inv.R,
  spat_alg = "bootspat_str",
  spat_alg_args = list(rprob = NULL, rich = NULL, fr_prob = NULL),
  aleats = 10,
  filename = "",
  ...
)

Arguments

Details

The dependency ‘SESraster’ is used to calculate the null models. This package currently implements six algorithms to randomize binary species distribution with several levels of constraints: SIM1, SIM2, SIM3, SIM5, SIM6 and SIM9 (sensu Gotelli 2000). The methods implemented in ‘SESraster’ are based on how species (originally rows) and sites (originally columns) are treated (i.e. fixed, equiprobable, or proportional sums) (Gotelli 2000). By default, the ‘phyloraster’ uses the function bootspat_ str() from the ‘SESraster’ package to conduct the randomizations, but the user is free to choose any of the other methods mentioned above through the spat_alg argument in the *.ses() functions of the ‘phyloraster’ package. The bootspat_str() is equivalent to the SIM5 (proportional-fixed) method of Gotelli (2000), which partially relaxes the spatial structure of species distributions, but keeps the spatial structure of the observed richness pattern across cells.

Value

SpatRaster. The function returns the observed value of the metric, the mean of the simulations calculated over n times, the standard deviation of the simulations, the standardized effect size (SES) for the metric, and the p-values.

Author(s)

Gabriela Alves-Ferreira and Neander Heming

References

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

Heming, N. M., Mota, F. M. M. and Alves-Ferreira, G. 2023. SESraster: raster randomization for null hypothesis testing. https://CRAN.R-project.org/package=SESraster.

See Also

phylo.pres, inv.range, geo.phylo.ses, rast.ed.ses, rast.pd.ses, rast.we.ses, rast.pe.ses, bootspat_str, bootspat_naive, bootspat_ff, SESraster

Examples

library(terra)
library(SESraster)
x <- terra::rast(system.file("extdata", "rast.presab.tif",
package="phyloraster"))
t <- rast.we.ses(x[[1:10]], aleats = 3)
plot(t)

Rasterize shapefile

Description

The function will rasterize the shapefile using the parameters of y, a SpatRaster. When the argument y is provided, the resolution parameter is ignored. When the argument ymask is TRUE, y is used as a mask for x.

Usage

shp2rast(
  x,
  y = NULL,
  sps.col,
  ymask = FALSE,
  background = NA,
  touches = TRUE,
  resolution,
  values = 1,
  filename = NULL,
  ...
)

Arguments

Value

SpatRaster

Examples

library(terra)

shp <- terra::vect(system.file("extdata", "shps_iucn_spps_rosauer.shp",
                              package="phyloraster"))

# create a polygon to use as mask with an extent
e <- terra::ext(113, 123, -43.64, -33.90)
p <- terra::as.polygons(e, crs="")
coun.crop <- terra::crop(p, terra::ext(shp))
coun.rast <- terra::rasterize(coun.crop,
terra::rast(terra::ext(shp), resolution = 0.5))

plot(coun.rast, col = "green")

# rasterizing with the mask of the polygon
shp.t <- shp2rast(shp, y = coun.rast, sps.col = "BINOMIAL",
ymask = TRUE, background = 0)
plot(shp.t, col = c("grey", "green"))

# rasterizing without using mask
shp.t2 <- shp2rast(shp, sps.col = "BINOMIAL", ymask = FALSE,
background = NA, resolution = 0.1)
plot(shp.t2[[9]], col = c("grey", "green"))

Calculate Evolutionary distinctiveness for each species

Description

This function calculates evolutionary distinctiveness according to the fair-proportion index for each species.

Usage

species.ed(tree)

Arguments

Value

data.frame

Author(s)

Neander Marcel Heming and Gabriela Alves-Ferreira

References

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

Examples

library(phyloraster)
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))
plot(tree)
ed <- species.ed(tree)
ed

Compute species tip length

Description

Computation of species tip length using a phylogeny.

Usage

species.tip.length(tree = NULL, edge.info = NULL, ...)

Arguments

Details

Calculates tip lengths for all species in a phylogeny

Value

returns a numeric vector giving the length of species branch.

Author(s)

Neander M. Heming

Examples

library(phyloraster)
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))

species.tip.length(tree)

library(ape)
set.seed(1)
tree <- rtree(n=40)

plot(tree)

species.tip.length(tree)

edge.info <- tip.root.path(tree)

species.tip.length(edge.info = edge.info)

Compute tree edge lengths and node paths from root to each tip

Description

Computation of tree edge lengths and node paths from root to each tip to calculate PD for a entire phylogeny (= sum of all edge or branch lengths)

Usage

tip.root.path(tree)

Arguments

Details

Based on the algorithm FastXtreePhylo of Peter D. Wilson

Value

returns a list with two components: matrix H1 representing the paths through the tree from root to each tip, and edge.length a numeric vector giving the length of each branch in the tree. Some matrix algebra and a summation of the resulting vector gives the whole-tree PD value.

Author(s)

Peter Wilson

Examples

library(phyloraster)
tree <- ape::read.tree(system.file("extdata", "tree.nex",
package="phyloraster"))

fxtp <- tip.root.path(tree)
H1 <- fxtp$H1
edge.length <- fxtp$edge.length
# PD for the whole community
pres <- rep(1, nrow(H1))
sum((crossprod(H1, pres)>0) * edge.length)

# PD for a random subset of the community
pres <- sample(c(1, 0), nrow(H1), TRUE)
sum((crossprod(H1, pres)>0) * edge.length)