RRgeo (version 0.0.5)

ENphylo_prediction: Project the ENFA and ENphylo models into new geographical space and time interval

Description

The function projects species marginality and specialization factors in different geographical areas and timescales. The function is able to convert marginality and specialization factors in habitat suitability values by using the Mahalanobis distances method.

Usage

ENphylo_prediction(object, newdata,
 convert.to.suitability=FALSE,output.dir,proj_name="outputs")

Value

The function stores all the results in a number of nested subfolders all contained in the "ENphylo_prediction" folder created in

output.dir. This contains a subfolder for each individual species in

object, in which a subfolder named according to proj_name

contains all the outputs. Specifically, the function saves the predictions for marginality and specificity (more than one depending on the number of significant axes selected by ENphylo_modeling) in the new geographical areas along with the suitability and binarized maps. The latter are calculated by using the three different predefined thresholds: MaxSensSpec (i.e. maximize TSS), SensSpec (i.e. equalize sensitivity and specificity) and 10th percentile of predicted probability.

Arguments

object

a list of ENFA and ENphylo models. Each element of the list must be named using the names of the modelled species.

newdata

a SpatRaster object including explanatory variables onto which ENFA or ENphylo models are to be projected. The list of variables must match the list used to model the species.

convert.to.suitability

logical. If TRUE, ENphylo_prediction projects ENFA or ENphylo model predictions in different geographical areas and timescales.

output.dir

the file path wherein ENphylo_prediction creates an "ENphylo_prediction" folder to store prediction outputs for each species.

proj_name

name of the subfolder created within the individual species folders to contain the ENphylo_prediction outputs.

Author

Alessandro Mondanaro, Mirko Di Febbraro, Silvia Castiglione, Carmela Serio, Marina Melchionna, Pasquale Raia

Details

If convert.to.suitability is set as TRUE, ENphylo_prediction uses the function mahasuhab from the adehabitatHS R package (Calenge, 2006) to compute the habitat suitability map of the species over a given area. The conversion of Mahalanobis distances into probabilities follows the chi-squared distribution. Specifically, we set the degree of freedom equal to n rather than n-1 following Etherington (2019). To convert habitat suitability values into binary presence/absence values, ENphylo_prediction relies on three different thresholding methods available in the function optimal.thresholds (Freeman & Moisen, 2008).

References

Calenge, C. (2006) The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecological Modelling, 197, 516-519.

Etherington, T. R. (2019). Mahalanobis distances and ecological niche modelling: correcting a chi-squared probability error. PeerJ, 7, e6678.

Freeman, E. A. & Moisen, G. (2008). PresenceAbsence: An R Package for Presence-Absence Model Analysis. Journal of Statistical Software, 23(11):1-31.

Mondanaro, A., Di Febbraro, M., Castiglione, S., Melchionna, M., Serio, C., Girardi, G., Blefiore, A.M., & Raia, P. (2023). ENphylo: A new method to model the distribution of extremely rare species. Methods in Ecology and Evolution, 14: 911-922. doi:10.1111/2041-210X.14066

See Also

vignette("ENphylo", package = "RRgeo")

getENphylo_results; ENphylo vignette

Examples

# \donttest{
library(ape)
library(terra)
library(sf)
library(RRgeo)

newwd<-tempdir()
# newwd<-"YOUR_DIRECTORY"

latesturl<-RRgeo:::get_latest_version("12734585")
curl::curl_download(url = paste0(latesturl,"/files/dat.Rda?download=1"),
                    destfile = file.path(newwd,"dat.Rda"), quiet = FALSE)
load(file.path(newwd,"dat.Rda"))
read.tree(system.file("exdata/Eucopdata_tree.txt", package="RRgeo"))->tree
tree$tip.label<-gsub("_"," ",tree$tip.label)
curl::curl_download(paste0(latesturl,"/files/X35kya.tif?download=1"),
                    destfile = file.path(newwd,"X35kya.tif"), quiet = FALSE)
rast(file.path(newwd,"X35kya.tif"))->map35
project(map35,st_crs(dat[[1]])$proj4string,res = 50000)->map

ENphylo_modeling(input_data=dat[c(1,11)],
                 tree=tree,
                 input_mask=map[[1]],
                 obs_col="OBS",
                 time_col="age",
                 min_occ_enfa=15,
                 boot_test_perc=20,
                 boot_reps=10,
                 swap.args=list(nsim=5,si=0.2,si2=0.2),
                 eval.args=list(eval_metric_for_imputation="AUC",
                                eval_threshold=0.7,
                                output_options="best"),
                 clust=NULL,
                 output.dir=newwd)


getENphylo_results(input.dir =newwd,
                   mods="all",
                   species_name=names(dat)[c(1,11)])->mod


library(rnaturalearth)
ne_countries(returnclass = "sf")->globalmap
subset(globalmap,continent=="North America")->ame_map

map35[[c("bio1","bio4","bio11","bio19")]]->newmap
crop(newmap,ext(ame_map))->newmap
project(newmap,st_crs(dat[[1]])$proj4string,res = 50000)->newmap

ENphylo_prediction(object = mod,
                   newdata = newmap,
                   convert.to.suitability = TRUE,
                   output.dir=newwd,
                   proj_name="proj_example")
# }