Species data

Author

Florencia Grattarola

Published

September 20, 2024

Data cleaning for presence-absence and presence-only records.

library(countrycode)
library(janitor)
library(patchwork)
library(knitr)
library(measurements)
library(lubridate)
library(terra)
library(sf)
sf::sf_use_s2(FALSE)
library(tidyverse)
equalareaCRS <-  '+proj=laea +lon_0=-73.125 +lat_0=0 +datum=WGS84 +units=m +no_defs'

latam <- st_read('data/latam.gpkg', layer = 'latam', quiet = T)
countries <- st_read('data/latam.gpkg', layer = 'countries', quiet = T)
latam_land <- st_read('data/latam.gpkg', layer = 'latam_land', quiet = T)
latam_raster <- rast('data/latam_raster.tif', lyrs='latam')
latam_countries <- rast('data/latam_raster.tif', lyrs='countries')
carnivores_list <- read_csv('data/carnivores_list.csv')

Data

Presence/absence data

Terms standardised:
- recordID
- species
- temporalSpan
- yearEnd
- decimalLatitude
- decimalLongitude
- area_m2
- countryCode
- stateProvince
- locality
- presence
- abundance
- effort
- effortUnit
- dataset

pointsAndSurveys_Literature <- read_csv('data/raw/OWN_literature_extraction.csv') 

data_PA_Ab_1 <- pointsAndSurveys_Literature %>% 
  janitor::clean_names('lower_camel') %>% 
  filter(speciesLatin %in% carnivores_list$sp) %>% 
  mutate(hemisphere_lat=ifelse(grepl('S', latitude), 'S', ifelse(grepl('N', latitude), 'N', NA))) %>%
  mutate(hemisphere_lon=ifelse(grepl('W', longitude), 'W', ifelse(grepl('E', longitude), 'E', NA))) %>%
  mutate(latitude = str_squish(str_replace_all(latitude, '°|’|S|W|E|N', ' '))) %>% 
  mutate(longitude = str_squish(str_replace_all(longitude, '°|’|S|W|E|N', ' '))) %>% 
  rowwise() %>%
  mutate(latitude = ifelse(latLonUnit=='deg', conv_unit(latitude, from = 'deg_dec_min', to = 'dec_deg'), latitude)) %>% 
  mutate(longitude = ifelse(latLonUnit=='deg', conv_unit(longitude, from = 'deg_dec_min', to = 'dec_deg'), longitude)) %>% 
  ungroup() %>% 
  mutate(latitude=ifelse(latLonUnit=='deg' & hemisphere_lat=='S', as.numeric(latitude)*-1, as.numeric(latitude))) %>% 
  mutate(longitude=ifelse(latLonUnit=='deg' & hemisphere_lon=='W', as.numeric(longitude)*-1, as.numeric(longitude))) %>% 
  mutate(dateStart=dmy(dateStart), dateEnd=dmy(dateEnd)) %>% 
  filter(!(year(dateEnd)>=2014 & year(dateStart)<2014)) %>% 
  mutate(area_m2=ifelse(areaUnit=='ha', area*10000, area*1e+6)) %>% 
  mutate(countryCode=countrycode::countrycode(countryCode, 
                                              origin = 'country.name', 
                                              destination = 'iso2c')) %>% 
  mutate(temporalSpan=ifelse(temporalSpan!='G', dateEnd-dateStart, temporalSpan)) %>% 
  mutate(effort=ifelse(effortUnit=='camera trap hours', effort/24, effort)) %>% 
  mutate(effortUnit='days') %>% 
  mutate(independentLocation=str_c(latitude,':',longitude)) %>% 
  mutate(independentYearSpan=str_c(year(dateStart),':',year(dateEnd))) %>% 
  mutate(dataset='pointsAndSurveys_Literature') %>% 
  select(recordID=recordId, date_start=dateStart, date_end=dateEnd,
         species=speciesLatin,
         temporalSpan,
         independentLocation,
         independentYearSpan,
         decimalLatitude=latitude,
         decimalLongitude=longitude, 
         area_m2=area,
         countryCode,
         stateProvince,
         locality,
         presence,
         abundance, # not all of the records have abundance data
         effort,
         effortUnit,
         dataset)
  
pointsAndSurveys_DataPaper <- 
  read_csv('data/raw/NEOTROPICAL_CARNIVORES_DATASET_2020-04.csv',
           guess_max = 90000) # Neotropical carnivores Data Paper

data_PA_Ab_2 <- 
  pointsAndSurveys_DataPaper %>% 
  # filter the records to include only the Neotropical Mammals
  filter(SPECIES %in% carnivores_list$sp) %>% 
  # filter only the records taken with camera traps -> assumption: these will reflect presence-absece data 
  filter(METHOD=='Camera trap') %>% 
  # filter records with no coordinates
  filter(!is.na(LAT_Y)) %>%
  # filter occurrences that don't have month and year when they were recorded
  filter(!is.na(COL_END_YR)&!is.na(COL_START_MO)&!is.na(COL_END_MO)&!is.na(COL_START_YR)) %>%
  # one area has a range instead of a value, I took the mean of the range as area
  mutate(AREA_HA=ifelse(AREA_HA=='700-1000', mean(c(700, 1000)), AREA_HA)) %>% 
  # filter records with no area of study recorded / or with sampling level area and precision recorded
  filter(!is.na(AREA_HA) | (is.na(AREA_HA) & !is.na(PRECISION_m) & SAMPLING_LEVEL=='AREA')) %>% 
  mutate(area_ha=as.numeric(AREA_HA),
         area_m2=ifelse(!is.na(AREA_HA), 
                          area_ha*10000, # 1 hectare are 10000 meters / will be used for buffers
                          PRECISION_m)) %>% 
  # to have the same values as the presence-only I kept country codes
  mutate(countryCode=countrycode::countrycode(COUNTRY, 
                                              origin = 'country.name', 
                                              destination = 'iso2c')) %>% 
  # since the dataset has no day, dates were assumed to start and end the first day of the month
  mutate(date_start=lubridate::dmy(str_c('1', COL_START_MO, COL_START_YR))) %>% 
  mutate(date_end=lubridate::dmy(str_c('1', COL_END_MO, COL_END_YR))) %>% 
  # the temporal span was calculated -> there are errors in some dates, and the temporal span is negative
  mutate(temporalSpan=ifelse(date_end-date_start<0, date_start-date_end, date_end-date_start)) %>% 
  filter(!(year(date_end)>=2014 & year(date_start)<2014)) %>% 
  # decimal places for some records corrected
  mutate(EFFORT=ifelse(grepl('\\.', EFFORT) & (temporalSpan>=31 | temporalSpan>=30), 
                       str_remove(EFFORT, '\\.' ), EFFORT)) %>% 
  # the sampling effort was standardized to days
  mutate(samplingEffort=ifelse(grepl('hours', EFFORT_UNIT, ignore.case = T), as.numeric(EFFORT)/24, 
                               ifelse(grepl('days', EFFORT_UNIT, ignore.case = T), as.numeric(EFFORT), NA))) %>% 
  # filter those without sampling effort (no data was provided and it cannot be derived)
  filter(!is.na(samplingEffort)) %>% 
  mutate(effortUnit='days') %>% 
  # calculate the number of camera traps needed 
  mutate(numCameras=floor(samplingEffort/temporalSpan)) %>% 
  # filter unreliable values 
  mutate(temporalSpan=ifelse(numCameras==Inf & temporalSpan==0 & samplingEffort>31, NA, 
                             ifelse(numCameras==Inf & temporalSpan==0 & samplingEffort<=31, 
                                    floor(as.numeric(EFFORT)), temporalSpan))) %>% 
  filter(!is.na(temporalSpan)) %>% 
  mutate(independentLocation=str_c(LAT_Y,':',LONG_X)) %>% 
  mutate(independentYearSpan=str_c(COL_START_YR,':',COL_END_YR)) %>% 
  dplyr::select(recordID=ID,
                date_start, date_end,
         species=SPECIES,
         temporalSpan,
         independentLocation,
         independentYearSpan,
         decimalLatitude=LAT_Y,
         decimalLongitude=LONG_X, 
         area_m2,
         countryCode,
         stateProvince=STATE,
         locality=SITE,
         presence=OCCUR,
         abundance=N_RECORDS, # not all of the records have abundance data
         effort=samplingEffort,
         effortUnit,
         dataset=DATASET)%>% 
  mutate(dataset='pointsAndSurveys_DataPaper') # in total 34,389 records

# Generate 0 (zeros) for locations where the species hasn't been recorded
# this can very probably be done in a more efficient way, but this one works!
data_PA_Ab_0 <- bind_rows(data_PA_Ab_1, data_PA_Ab_2) %>% 
  pivot_wider(names_from = species,
              values_from = c(presence, abundance, effort),
              values_fill = c(list(presence=0), 
                              list(abundance=0),
                              list(effort=0))) %>%
  pivot_longer(cols=starts_with(c('presence_', 'abundance_', 'effort_')),
               names_to=c('metric', 'species'),
               names_sep='_') %>% 
  pivot_wider(names_from = metric,
              values_from = value,
              values_fill = c(list(value=0))) %>% 
  distinct(species, independentYearSpan, independentLocation, presence, .keep_all = T) %>%
  group_by(species, independentYearSpan, independentLocation) %>% 
  mutate(presence=sum(presence, na.rm = T),
         abundance=sum(abundance, na.rm = T),
         effort=max(effort, na.rm = T)) %>% 
  distinct(species, independentYearSpan, independentLocation, .keep_all = T) %>% 
  ungroup() %>% group_by(independentLocation, independentYearSpan) %>% 
  mutate(effort=max(effort, na.rm = T)) %>% 
  ungroup()

Check the data

Code 
data_PA_Ab_0 %>% 
  mutate(period=ifelse(year(date_end)<2014, 'time1', 'time2')) %>% 
  filter(species %in% c('Herpailurus yagouaroundi', 'Leopardus pardalis', 
                        'Cerdocyon thous', 'Chrysocyon brachyurus', 
                        'Eira barbara', 'Leopardus wiedii', 
                        'Nasua nasua', 'Pteronura brasiliensis')) %>% 
  filter(presence==1) %>% 
  group_by(species, period) %>% count() %>% 
  pivot_wider(names_from = species, values_from = n, values_fill = 0) %>% 
  janitor::adorn_totals() %>% kable()
period Cerdocyon thous Chrysocyon brachyurus Eira barbara Herpailurus yagouaroundi Leopardus pardalis Leopardus wiedii Nasua nasua Pteronura brasiliensis
time1 886 174 819 290 1584 393 878 15
time2 1106 212 826 312 1379 327 1029 6
Total 1992 386 1645 602 2963 720 1907 21
Code 
bind_rows(data_PA_Ab_1, data_PA_Ab_2) %>% 
  mutate(period=ifelse(year(date_end)<2014, 'time1', 'time2')) %>%
  filter(species %in% c('Herpailurus yagouaroundi', 'Leopardus pardalis', 
                        'Cerdocyon thous', 'Chrysocyon brachyurus', 
                        'Eira barbara', 'Leopardus wiedii', 
                        'Nasua nasua', 'Pteronura brasiliensis')) %>% 
  filter(presence==1) %>% 
  sf::st_as_sf(coords=c('decimalLongitude', 'decimalLatitude')) %>% 
  sf::st_set_crs(4326) %>% 
  ggplot() + 
  geom_sf(data = latam, fill='white', size=0.2) +
  geom_sf(aes(col=year(date_end))) + 
  facet_grid(species~period) +
  scale_color_distiller(palette = 'Greens', direction = 1) + 
  labs(col='Year') +
  theme_bw()

Presence-only data

Terms standardised:
- recordID,
- species,
- year,
- eventDate,
- decimalLatitude,
- decimalLongitude,
- countryCode, - stateProvince,
- locality,
- dataset

# The data download and cleaning can be found in the GBIF_download.R script
occurrencePoints_GBIF <- read_csv('data/raw/0240776-230224095556074_CLEAN.csv', 
                                  guess_max = 130000) # GBIF data (cleaned)

data_PO_1 <- occurrencePoints_GBIF %>% 
  # filter only presences
  filter(occurrenceStatus == 'PRESENT') %>% 
  # rename the species yaguarundi and filter our species of interest
  mutate(species=ifelse(species=='Puma yagouaroundi', 'Herpailurus yagouaroundi', species)) %>% 
  filter(species %in% c('Herpailurus yagouaroundi', 'Leopardus pardalis', 
                        'Cerdocyon thous', 'Chrysocyon brachyurus', 
                        'Eira barbara', 'Leopardus wiedii', 
                        'Nasua nasua', 'Pteronura brasiliensis')) %>% 
  # filter records from unrealistic or unwanted) locations: USA, Denmark, Belgium and France
  filter(!countryCode %in% c('US', 'DK', 'BE', 'FR')) %>%
  # filter occurrences that don't have the year when they were recorded
  filter(!is.na(year) & year>=2000 & year<=2020) %>%
  # transform eventdate to class datetime
  mutate(eventDate=lubridate::ymd_hms(eventDate)) %>% 
  # remove duplicates according to species, date, latitude and longitude
  group_by(species, eventDate, decimalLatitude, decimalLongitude) %>% 
  slice_head(n=1) %>% ungroup() %>% 
  # select columns of interest
  dplyr::select(recordID=gbifID, 
         species, 
         year, eventDate,  
         decimalLatitude,
         decimalLongitude, 
         countryCode,
         stateProvince,
         locality) %>% 
  mutate(dataset='occurrencePoints_GBIF') 


data_PO_2 <- pointsAndSurveys_DataPaper %>%
    filter(SPECIES %in% c('Herpailurus yagouaroundi', 'Leopardus pardalis',
                          'Cerdocyon thous', 'Chrysocyon brachyurus', 
                          'Eira barbara', 'Leopardus wiedii', 
                          'Nasua nasua', 'Pteronura brasiliensis')) %>% 
    filter(!is.na(COL_END_YR)&!is.na(COL_START_MO)&!is.na(COL_END_MO)&!is.na(COL_START_YR)) %>%
    filter(DATA_TYPE=='Count data') %>% 
    filter(METHOD %in% c('Opportunistic', 'Line transect', 'Active searching', 'Roadkill', 'Museum')) %>% 
    mutate(countryCode=countrycode::countrycode(COUNTRY, 
                                                origin = 'country.name', 
                                                destination = 'iso2c')) %>% 
    mutate(date_start=lubridate::dmy(str_c('1', COL_START_MO, COL_START_YR))) %>% 
    mutate(date_end=lubridate::dmy(str_c('1', COL_END_MO, COL_END_YR))) %>% 
    filter(date_start==date_end) %>% mutate(year=year(date_start)) %>%  
    filter(!is.na(date_end) & year(date_start)>=2000 & year(date_end)<=2020) %>% 
    group_by(SPECIES, date_start, LAT_Y, LONG_X) %>% 
    slice_head(n=1) %>% ungroup() %>% 
    dplyr::select(recordID=ID, 
                  species=SPECIES, 
                  year, eventDate=date_start,  
                  decimalLatitude=LAT_Y,
                  decimalLongitude=LONG_X,
                  countryCode,
                  stateProvince=STATE,
                  locality=SITE) %>% 
    mutate(dataset='occurrencePoints_NeotropCarnivores') 


data_PO_0 <- bind_rows(data_PO_1, data_PO_2) %>% 
  filter(species %in% c('Herpailurus yagouaroundi', 'Leopardus pardalis',
                        'Cerdocyon thous', 'Chrysocyon brachyurus', 
                        'Eira barbara', 'Leopardus wiedii', 
                        'Nasua nasua', 'Pteronura brasiliensis'))

Check the data

Code 
bind_rows(data_PO_1, data_PO_2) %>% 
  mutate(period=ifelse(year(eventDate)<2014, 'time1', 'time2')) %>% 
  filter(species %in% c('Herpailurus yagouaroundi', 'Leopardus pardalis',
                        'Cerdocyon thous', 'Chrysocyon brachyurus', 
                        'Eira barbara', 'Leopardus wiedii', 
                        'Nasua nasua', 'Pteronura brasiliensis')) %>% 
  group_by(species, period) %>% count() %>% 
  pivot_wider(names_from = species, values_from = n, values_fill = 0) %>% 
  janitor::adorn_totals()%>% kable()
period Cerdocyon thous Chrysocyon brachyurus Eira barbara Herpailurus yagouaroundi Leopardus pardalis Leopardus wiedii Nasua nasua Pteronura brasiliensis
time1 1112 335 294 216 378 106 469 103
time2 1891 140 1446 588 2212 448 1515 96
Total 3003 475 1740 804 2590 554 1984 199
Code 
bind_rows(data_PO_1, data_PO_2) %>% 
  mutate(period=ifelse(year(eventDate)<2014, 'time1', 'time2')) %>% 
  sf::st_as_sf(coords=c('decimalLongitude', 'decimalLatitude')) %>% 
  sf::st_set_crs(4326) %>% 
  ggplot() + 
  geom_sf(data = latam, fill='white', size=0.2) +
  geom_sf(aes(col=year)) + 
  facet_grid(species~period) +
  scale_color_distiller(palette = 'Greens', direction = 1) + 
  labs(col='Year') +
  theme_bw()

Calculate the ratio of the global PO effort between time1 and time2

globalEffort <- occurrencePoints_GBIF %>% 
  filter(occurrenceStatus == 'PRESENT') %>% 
  filter(!countryCode %in% c('US', 'DK', 'BE', 'FR')) %>%
  mutate(eventDate=lubridate::ymd_hms(eventDate), year=year(eventDate)) %>% 
  filter(!is.na(year) & year>=2000 & year<=2020) %>%
  mutate(species=ifelse(species=='Puma yagouaroundi', 'Herpailurus yagouaroundi', species)) %>% 
  # select columns of interest
  dplyr::select(recordID=gbifID, 
         species, year, eventDate,  
         decimalLatitude,
         decimalLongitude, 
         countryCode) %>% 
  mutate(period=ifelse(year<2014, 'time1', 'time2')) 

globalEffort_sf <- globalEffort %>% 
  sf::st_as_sf(coords=c('decimalLongitude', 'decimalLatitude')) %>% 
  sf::st_set_crs(4326) %>% st_transform(crs=equalareaCRS)

globalEffort_time <- st_join(latam, globalEffort_sf, left=TRUE) %>% 
  group_by(gridID, period) %>% 
  summarise(effort=n(), .groups = 'drop') %>% st_drop_geometry()

globalEffort_time %>% select(-gridID) %>% ungroup() %>%  
  janitor::adorn_totals()%>% kable()
period effort
time1 2056
time2 7571
Total 9627 
globalEffort_value <- globalEffort_time %>% filter(period=='time1') %>% pull(effort) / 
  globalEffort_time %>% filter(period=='time2') %>% pull(effort) * 100

ggplot(globalEffort_sf) +
    geom_sf(data = latam, fill='white', size=0.2) +
    geom_sf(aes(col=species, alpha=0.5), show.legend = F) + 
    facet_grid(~period) +
    scale_color_brewer(palette = 'Set3') + 
    labs(col='Year', title= paste0('Records increase ', 
                                   round(globalEffort_value, 1), '% from time1 to time2' )) +
    theme_bw()

Save files

# R object species data
saveRDS(data_PO_0, 'data/data_PO.Rds')
saveRDS(data_PA_Ab_0, 'data/data_PA.Rds')

# csv species data
write_csv(data_PO_0, 'data/data_PO.csv')
write_csv(data_PA_Ab_0, 'data/data_PA.csv')