| title | author | date | output | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Species data download |
Florencia Grattarola |
2022-09-08 |
|
# spatial libraries
library(rnaturalearth)
library(CoordinateCleaner)
library(rgbif)
library(taxize)
library(lubridate)
library(redlistr)
library(sf)
library(tidyverse)Neotropical Carnivores
# From the AMS Database (American Mammalogical Society)
MDD <- read_csv('data/MDD_v1.4_6533species.csv')
# Function to get species taxon keys
speciesList_TAXON_KEY <- function(speciesList) {
species_TAXON_KEY <- data.frame(sp=character(),
TAXON_KEY = numeric(),
stringsAsFactors=FALSE)
for(sp in speciesList){
TAXON_KEY <- get_gbifid_(sp) %>% bind_rows()
if(length(TAXON_KEY)!=0){
KEY <- TAXON_KEY %>%
filter(matchtype == 'EXACT' & status == 'ACCEPTED') %>%
pull(usagekey)
species_TAXON_KEY_i <- data.frame(sp=sp,
TAXON_KEY = ifelse(any(!is.na(KEY)), KEY, NA),
stringsAsFactors=FALSE)
}
else {
species_TAXON_KEY_i <- data.frame(sp=sp,
TAXON_KEY = NA,
stringsAsFactors=FALSE)
}
species_TAXON_KEY <- rbind(species_TAXON_KEY, species_TAXON_KEY_i)
}
return(species_TAXON_KEY)
}
List_MDD <- MDD %>% filter(order=='CARNIVORA' &
(grepl('Neotropic', biogeographicRealm) |
grepl('Mexico', countryDistribution)) &
extinct==0 &
domestic==0 &
family != 'OTARIIDAE' &
family != 'PHOCIDAE') %>%
mutate(sp=str_replace(sciName, '_', ' ')) %>% pull(sp)
carnivora_sp_TAXON_KEY <- speciesList_TAXON_KEY(List_MDD)
# Save species list
MDD %>% filter(order=='CARNIVORA' &
(grepl('Neotropic', biogeographicRealm) |
grepl('Mexico', countryDistribution)) &
extinct==0 &
domestic==0 &
family != 'OTARIIDAE' &
family != 'PHOCIDAE') %>%
mutate(sp=str_replace(sciName, '_', ' ')) %>%
full_join(., carnivora_sp_TAXON_KEY, by='sp') %>%
select(sciName, TAXON_KEY, sp, order, family, genus,
specificEpithet, authoritySpeciesAuthor,
biogeographicRealm, countryDistribution, iucnStatus) %>%
rename(TAXON_KEY_GBIF= TAXON_KEY) %>%
write_excel_csv('data/MDD_Carnivora_List.csv')# Credentials from gbif.org
GBIF_USER <- '' # your gbif.org username
GBIF_PWD <- '' # your gbif.org password
GBIF_EMAIL <- '' # your email
# Use species list
speciesList_MDD <- read_csv('data/MDD_Carnivora_List.csv')
# Download and Cleaning Functions
species_GBIF_download <- function(gbif_taxon_keys){
occ_download(pred_in('taxonKey', gbif_taxon_keys),
pred('hasCoordinate',TRUE),
pred('hasGeospatialIssue', FALSE),
pred_not(pred('basisOfRecord', 'FOSSIL_SPECIMEN')),
pred_or(
pred_not(pred('establishmentMeans','MANAGED')),
pred_not(pred_notnull('establishmentMeans'))
),
pred_or(
pred_not(pred('establishmentMeans','INTRODUCED')),
pred_not(pred_notnull('establishmentMeans'))
),
pred_or(
pred_not(pred('establishmentMeans','INVASIVE')),
pred_not(pred_notnull('establishmentMeans'))
),
pred_or(
pred_not(pred('establishmentMeans','NATURALISED')),
pred_not(pred_notnull('establishmentMeans'))
),
format='SIMPLE_CSV',
user=GBIF_USER,pwd=GBIF_PWD,email=GBIF_EMAIL)
}
clean_GBIF_download <- function(download_toClean){
df <- download_toClean %>%
filter(occurrenceStatus == 'PRESENT') %>%
filter(!basisOfRecord %in% c("FOSSIL_SPECIMEN","LIVING_SPECIMEN")) %>%
filter(!establishmentMeans %in% c("MANAGED", "INTRODUCED", "INVASIVE", "NATURALISED")) %>%
mutate(len_precision=nchar(word(as.character(decimalLatitude), 2, sep = fixed('.')))) %>%
filter(coordinatePrecision < 0.001 | len_precision>4) %>%
filter(coordinateUncertaintyInMeters < 25000 | (year>2000&is.na(coordinateUncertaintyInMeters))) %>%
filter(!coordinateUncertaintyInMeters %in% c(301,3036,999,9999)) %>%
filter(!decimalLatitude == 0 | !decimalLongitude == 0) %>%
as.data.frame() %>%
mutate(decimallatitude=decimalLatitude, decimallongitude=decimalLongitude) %>%
cc_cen(buffer = 2000, test='country', verbose=T) %>% # remove country centroids within 2km
cc_cap(buffer = 1000) %>% # remove capitals centroids within 2km
cc_inst(buffer = 2000) %>% # remove zoo and herbaria within 2km
cc_sea() %>%
distinct(decimalLatitude,decimalLongitude,speciesKey,datasetKey,eventDate, .keep_all = TRUE)
return(as_tibble(df))
}gbif_taxon_keys <- speciesList_MDD %>% filter(!is.na(TAXON_KEY_GBIF)) %>%
pull(TAXON_KEY_GBIF)
#####################################################
### Run 07/10/2021
### Download data from GBIF
species_GBIF_download(gbif_taxon_keys)
# <<gbif download>>
# Username: ***
# E-mail: ***
# Format: SIMPLE_CSV
# Download key: 0046613-210914110416597# Read data
data_GBIF_toClean <- read_tsv('data/0046613-210914110416597.csv',
quote="",
guess_max = 500000)
data_GBIF_Carnivora <- clean_GBIF_download(data_GBIF_toClean)## OGR data source with driver: ESRI Shapefile
## Source: "/private/var/folders/_l/ypqbr8pj7wn9wkh9lpxqv0480000gn/T/RtmpAiOt92", layer: "ne_110m_land"
## with 127 features
## It has 3 fields
write_excel_csv(data_GBIF_Carnivora, 'data/0046613-210914110416597_CLEAN.csv') # Basemaps
world <- rnaturalearth::ne_countries(scale = 'large', returnclass = 'sf')
bbox_Latam_unprojected <- c(xmin=-118.40137, ymin=-55.89170, xmax=-34.80547, ymax= 32.71533)
Latam_unprojected <- world %>% st_crop(bbox_Latam_unprojected)
### Check data: yaguarundí records from 2000 onwards
data_GBIF_Carnivora %>%
mutate(species=ifelse(species=='Puma yagouaroundi', 'Herpailurus yagouaroundi', species)) %>%
filter(countryCode!='US') %>%
filter(!is.na(year) & year>=2000) %>%
filter(species=='Herpailurus yagouaroundi') %>%
as.data.frame() %>%
sf::st_as_sf(coords=c('decimalLongitude', 'decimalLatitude')) %>%
sf::st_set_crs(4326) %>%
ggplot() +
geom_sf(data = Latam_unprojected, fill='white', size=0.2) +
geom_sf(aes(col=year)) +
scale_color_distiller(palette = 'Greens', direction = 1) +
labs(col='Year') +
theme_bw() 