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03_automated_conservation_Assessment.R
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# Automated conservation assessment
library(ConR)
library(tidyverse)
library(rredlist)
library(jsonlite)
# load and prepare data
dat <- read_csv("output/all_records.csv") %>%
filter(!is.na(decimalLongitude)) %>%
filter(!is.na(decimalLatitude))
dat_cl <- dat %>%
filter(summary)
## table of species per group
dat_cl %>%
select(taxon, species) %>%
distinct() %>%
group_by(taxon) %>%
count()
sp <- dat_cl %>%
select(taxon, species) %>%
distinct() %>%
filter(!taxon %in% c("Dipsadidae", "Iridaceae", "Tillandsia"))
sp <- split(sp, f = sp$taxon)
for(i in 1:length(sp)){
write_csv(sp[[i]], path = paste("output/species_lists/", names(sp)[i], ".csv", sep = ""))
}
# Automated assessment
## raw
rw_in <- dat%>%
dplyr::select(ddlat = decimalLatitude,
ddlon = decimalLongitude,
tax = species) %>%
distinct() %>%
filter(!is.na(tax))
rw_in <- rw_in %>% filter(ddlat >-80) # there is one erroneous record that makes the automated assessment crash
## filtered
cl_in <- dat_cl%>%
dplyr::select(ddlat = decimalLatitude,
ddlon = decimalLongitude,
tax = species) %>%
distinct()
# AUtomated assessment
## raw
# splist <- unique(rw_in$tax)
#
# for(i in 1:length(splist)){
# print(paste(i, length(splist), sep = "/"))
# sub <- rw_in %>% filter(tax == splist[i])
#
# out <- IUCN.eval(sub)
#
# if(i == 1){
# write_csv(out, "output/automated_assessment_raw.csv")
# }else{
# write_csv(out, "output/automated_assessment_raw.csv", append = TRUE)
# }
# }
#
# ## filtered
# splist <- unique(cl_in$tax)
#
# for(i in 1:length(splist)){
# print(paste(i, length(splist), sep = "/"))
# sub <- cl_in %>% filter(tax == splist[i])
#
# out <- IUCN.eval(sub)
#
# if(i == 1){
# write_csv(out, "output/automated_assessment_clean.csv")
# }else{
# write_csv(out, "output/automated_assessment_clean.csv", append = TRUE)
# }
# }
#
# IUCN assessment for the species
# iucn.key <- "01524b67f4972521acd1ded2d8b3858e7fedc7da5fd75b8bb2c5456ea18b01ba"
#
# sp.list <- rw_in$tax %>%
# as.character() %>%
# unique()
#
# #get conservation status from IUCN
# for(i in 1:length(sp.list)){
# print(i)
# pick <- jsonlite::fromJSON(rl_search_(sp.list[i], key = iucn.key))$result
#
# if(length(pick) >0){
# if(i == 1){
# write_csv(pick, "output/iucn_assessment.csv")
# }else{
# write_csv(pick, "output/iucn_assessment.csv", append = TRUE)
# }
# }
#
# Sys.sleep(1)
# }
# Load assessments
rw <- read_csv("output/automated_assessment_raw.csv") %>%
dplyr::select(taxa, EOO, AOO, Category_CriteriaB, Category_code)
names(rw) <- paste("raw", names(rw), sep = "_")
cl <- read_csv("output/automated_assessment_clean.csv")
names(cl) <- gsub("filtered_", "", names(cl))
cl <- dplyr::select(cl, taxa, EOO, AOO, Category_CriteriaB, Category_code)
names(cl) <- paste("flag", names(cl), sep = "_")
iucn <- read_csv("output/iucn_assessment.csv") %>%
dplyr::select(scientific_name, published_year, category, criteria, population_trend, aoo_km2, eoo_km2)
names(iucn) <- paste("iucn", names(iucn), sep = "_")
tax <- read_csv("output/all_records.csv") %>%
dplyr::select(taxon, species) %>%
distinct()
## join the three datasets and save as the full data for the supplementary
dat <- rw %>%
left_join(cl, by = c("raw_taxa" = "flag_taxa")) %>%
left_join(iucn, by = c("raw_taxa" = "iucn_scientific_name")) %>%
left_join(tax, by = c("raw_taxa" = "species"))
sp_count <- dat %>%
group_by(taxon) %>%
mutate(threatened = iucn_category %in% c("EX", "CR", "EN", "VU")) %>%
mutate(eval = iucn_category %in% c("EX", "CR", "EN", "VU", "NT", "LC")) %>%
mutate(spnum = !is.na(iucn_category)) %>%
summarize(iucn_spnum = sum(spnum),
iucn_frac_eval = round(mean(eval, na.rm = TRUE) * 100, 1),
iucn_frac_threat = round(mean(threatened, na.rm = TRUE) * 100, 1),
raw_spnum = sum(!is.na(raw_Category_CriteriaB)),
flag_spnum = sum(!is.na(flag_Category_CriteriaB)))
dat <- dat %>%
mutate(flag_EOO = ifelse(is.na(flag_EOO), flag_AOO, flag_EOO)) %>%
mutate(raw_EOO = ifelse(is.na(raw_EOO), raw_AOO, raw_EOO)) %>%
mutate(EOO_change = ((flag_EOO - raw_EOO) / raw_EOO *100))%>%
mutate(AOO_change = ((flag_AOO - raw_AOO) / raw_AOO *100)) %>%
mutate(raw_status = ifelse(raw_Category_CriteriaB == "LC or NT", "not_threatened", "possibly_threatened"))%>%
mutate(flag_status = ifelse(flag_Category_CriteriaB == "LC or NT", "not_threatened", "possibly_threatened"))
# remove potential duplicates
dat <- dat %>%
distinct()
write_csv(dat, "output/supplement_full_results_conservation_assessment.csv")
iucn <- dat %>%
filter(!iucn_category %in% c("DD", "EX", "NA")) %>%
filter(!is.na(iucn_category)) %>%
mutate(iucn_category = ifelse(iucn_category %in% c("LC", "NT"), "LC or NT", iucn_category)) %>%
mutate(iucn_status = ifelse(iucn_category == "LC or NT", "not_threatened", "possibly_threatened")) %>%
mutate(raw_iucn_agreement = raw_status == iucn_status) %>%
mutate(flag_iucn_agreement = flag_status == iucn_status) %>%
group_by(taxon) %>%
summarize(raw_iucn_match = round(mean(raw_iucn_agreement, na.rm = TRUE) * 100, 1),
flag_iucn_match = round(mean(flag_iucn_agreement, na.rm = TRUE) * 100, 1)
)
## select the relevant columns for the table in the paper
out <- dat %>%
select(taxon,
iucn_category,
raw_Category_CriteriaB,
raw_status,
flag_Category_CriteriaB,
flag_status,
EOO_change,
AOO_change) %>%
group_by(taxon) %>%
summarize(
raw_frac_threat = round(mean(raw_status == "possibly_threatened") * 100, 1),
flag_frac_threat = round(mean(flag_status == "possibly_threatened", na.rm = TRUE) * 100, 1),
flag_median_EOO_change = round(mean(EOO_change, na.rm = TRUE), 1),
flag_median_AOO_change = round(mean(AOO_change, na.rm = TRUE), 1)) %>%
left_join(iucn) %>%
full_join(sp_count) %>%
select(taxon,
iucn_spnum,
iucn_frac_eval,
iucn_frac_threat,
raw_spnum,
raw_frac_threat,
raw_iucn_match,
flag_spnum,
flag_frac_threat,
flag_iucn_match,
flag_median_EOO_change,
flag_median_AOO_change)
write_csv(out, "output/table_results_conservation_assessment.csv")