#' @title Sample representativeness score estimation (In-situ conservation indicators).
#' @name srs_insitu
#' @description Performs an estimation of sample representativeness score for in-situ gap analysis (SRSin) using Khoury et al., (2019) methodology
#' This function uses counts from herbarium an germplasm accessions and calculate the SRS in-situ score as:
#' \deqn{SRSin = Number of germplasm accessions in protected areas / Number of herbarium accessions in protected areas}
#'
#' @param species A name species compiled using '_' to call occurrences files from Workspace/parameter/occurrences folder
#' @param Workspace A forder where the pipeline will be executed
#' @param run_version The version of the analysis used (e.g 'v1')
#'
#' @return It returns a data frame file saved at gap_analysis folder with eight fields:
#'
#' \tabular{lcc}{
#' ID \tab Species name \cr
#' NTOTAL \tab Number of records available for a given species in protected areas \cr
#' NTOTAL_COORDS \tab Number of records with geographical coordinates available for a given species in protected areas \cr
#' NG \tab Number of germplasm accessions available for a given species in protected areas \cr
#' NG_COORDS \tab Number of germplasm accessions with geographical coordinates Savailable for a given species in protected areas \cr
#' NH \tab Number of herbarium accessions available for a given specie in protected areas s \cr
#' NH_COORDS \tab Number of herbarium accessions with geographical coordinates Savailable for a given species in protected areas \cr
#' SRS \tab SRSin score calculated from a CSV file summarizing the number of records for a given species in protected areas \cr
#' }
#'
#' @examples srs_insitu('Cucurbita_digitata',Workspace,'v1')
#'
#' Workspace <- 'E:/CIAT/workspace/Workspace_test/workspace'
#' run_version <- 'v1'
#' species_list <- c('Cucurbita_cordata',
#' 'Cucurbita_digitata',
#' 'Cucurbita_foetidissima',
#' 'Cucurbita_palmata')
#'
#' run_version <-'v1'
#
#' lapply(1:length(species_list),function(i){
#' species <- species_list[[i]]
#' x <- srs_insitu(species,Workspace,run_version)
#' print(paste0(species,' DONE!'))
#' })
#'
#'@references
#'
#'Ramírez-Villegas, J., Khoury, C., Jarvis, A., Debouck, D. G., & Guarino, L. (2010).
#'A Gap Analysis Methodology for Collecting Crop Genepools: A Case Study with Phaseolus Beans.
#'PLOS ONE, 5(10), e13497. Retrieved from https://doi.org/10.1371/journal.pone.0013497
#'
#' Khoury, C. K., Amariles, D., Soto, J. S., Diaz, M. V., Sotelo, S., Sosa, C. C., … Jarvis, A. (2019).
#' Comprehensiveness of conservation of useful wild plants: An operational indicator for biodiversity
#' and sustainable development targets. Ecological Indicators. https://doi.org/10.1016/j.ecolind.2018.11.016
#'
#' @export
###
# Calculate the proportion of points that fall within a protected areas. Insitu SRS
# 20191002
# carver.dan1@gmail.com
###
srs_insitu <- function(species_list,occurrenceData, raster_list){
#importFrom("methods", "as")
#importFrom("stats", "complete.cases", "filter", "median")
#importFrom("utils", "data", "memory.limit", "read.csv", "write.csv")
# Load in protect areas
proArea <- raster(system.file("data/protectedArea/wdpa_reclass.tif",
package = "gapAnalysisR"))
# create an empty dataframe
df <- data.frame(matrix(ncol = 2, nrow = length(species_list)))
colnames(df) <- c("species", "SRSin")
for(i in 1:length(species_list)){
# pull the sdm to mask for
for(j in 1:length(raster_list)){
if(grepl(j, i, ignore.case = TRUE)){
sdm <- raster_list[[j]]
}
}
# restrict protect areas those that are present in the model threshold
##**double check about this step with jullian/chrys/colin**
proArea1 <- raster::crop(x = proArea,y = sdm)
sdm[sdm == 0]<-NA
proAreaSpecies <- sdm * proArea1
# filter by specific species
occData1 <- occurrenceData %>%
dplyr::filter(taxon == species_list[i])
totalNum <- nrow(occData1)
# extract values to all points
sp::coordinates(occData1) <- ~longitude+latitude
sp::proj4string(occData1) <- CRS("+proj=longlat +datum=WGS84")
protectPoints <- sum(!is.na(raster::extract(x = proArea1,y = occData1)))
#define SRS
if(protectPoints >= 0 ){
srsInsitu <- 100 *(protectPoints/totalNum)
}else{
srsInsitu <- 0
}
# add values to empty df
df$species[i] <- as.character(species_list[i])
df$SRSin[i] <- srsInsitu
}
return(df)
}
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