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

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

x: SpatRaster. A SpatRaster containing presence-absence data (0 or 1) for a set of species. The layers (species) will be sorted according to the tree order. See the phylo.pres function.
tree: phylo. A dated tree.
inv.R: SpatRaster. Inverse of range size. See inv.range
edge.path: matrix. Matrix representing the paths through the tree from root to each tip. See phylo.pres
branch.length: numeric. A Named numeric vector of branch length for each species. See phylo.pres
n.descen: numeric. A Named numeric vector of number of descendants for each branch. See phylo.pres
full_tree_metr: logical. Whether edge.path, branch length and number of descendants should be calculated with the full (TRUE) or the prunned tree (FALSE). The default is TRUE.
spat_alg: A function with the algorithm implementing the desired randomization method. It must work with SpatRaster objects. See examples. Example of functions that work are: bootspat_naive, bootspat_str, bootspat_ff.
spat_alg_args: List of arguments passed to the randomization method chosen in 'spat_alg'. See bootspat_naive, bootspat_str, bootspat_ff
aleats: positive integer. A positive integer indicating how many times the calculation should be repeated.
cores: positive integer. If cores > 1, a 'parallel' package cluster with that many cores is created and used. You can also supply a cluster object. Ignored for functions that are implemented by terra in C++ (see under fun)
filename: character. Output filename
...: additional arguments passed for terra::app

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.

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.

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.

Author

Neander Marcel Heming

Examples

# \donttest{
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)

# }