Image credit to Florent Lavergne

Plants in Danger

The data this week comes from the International Union for Conservation of Nature (IUCN) Red list of Threatened Species (Version 2020-1) and was scrapped and prepared by Florent Lavergne for his fantastic and unique infographic.

Here is what Florent says about the rationale of this project:

Just like animals, plants are going through an important biodiversity crisis. Many species from isolated areas are facing extinction due to human activities. Using distribution data from the International Union for Conservation of Nature (IUCN), I designed these network maps to inform on an important yet underrepresented topic.

In total, 500 plant species are considered extinct as of 2020. 19.6% of those were endemic to Madagascar, 12.8% to Hawaiian islands.

Note that simply joining the threats and actions datasets together is not fully appropriate as the row alignment of threats and actions doesn’t correspond. You can do dplyr::left_join() without any problem, but again be warned that you shouldn’t make any decisions based off threat + action occuring or not occuring in the same observation.

Further reading:

  • You can find more details on threatened species, summary statistics, articles, and more on the different Red List categories on the IUCN main page.

  • This study published in Science in 2019 provides some general information about extinction risks of plants in general and some analyses and visualization about the African flora at risk.

  • The IUCN itself shared a blog post on the extinction risk of European endemic trees.

Credit: Florent Lavergne and Cédric Scherer

Get the data here

# Get the Data

# Read in with tidytuesdayR package 
# Install from CRAN via: install.packages("tidytuesdayR")
# This loads the readme and all the datasets for the week of interest

# Either ISO-8601 date or year/week works!

tuesdata <- tidytuesdayR::tt_load('2020-08-18')
tuesdata <- tidytuesdayR::tt_load(2020, week = 34)

plants <- tuesdata$plants

# Or read in the data manually

plants <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-08-18/plants.csv')
actions <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-08-18/actions.csv')
threats <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2020/2020-08-18/threats.csv')

Data Dictionary

plants.csv

variable class description
binomial_name character Species name (Genus + species)
country character Country of origin
continent character Continent of origin
group character Taxonomic group
year_last_seen character Period species was last seen
threat_AA double Threat: Agriculture & Aquaculture
threat_BRU double Threat: Biological Resource Use
threat_RCD double Threat: Commercial Development
threat_ISGD double Threat: Invasive Species
threat_EPM double Threat: Energy Production & Mining
threat_CC double Threat: Climate Change
threat_HID double Threat: Human Intrusions
threat_P double Threat: Pollution
threat_TS double Threat: Transportation Corridor
threat_NSM double Threat: Natural System Modifications
threat_GE double Threat: Geological Events
threat_NA double Threat unknown
action_LWP double Current action: Land & Water Protection
action_SM double Current action: Species Management
action_LP double Current action: Law & Policy
action_RM double Current action: Research & Monitoring
action_EA double Current action: Education & Awareness
action_NA double Current action unknown
red_list_category character IUCN Red List category

threats.csv

variable class description
binomial_name character Species name (Genus + species)
country character Country of origin
continent character Continent of origin
group character Taxonomic group
year_last_seen character Period species was last seen
red_list_category character IUCN Red List category
threat_type character Type of threat
threatened double Binary 0 or 1 (not threatened by this), and 1 (threatened)

actions.csv

variable class description
binomial_name character Species name (Genus + species)
country character Country of origin
continent character Continent of origin
group character Taxonomic group
year_last_seen character Period species was last seen
red_list_category character IUCN Red List category
action_type character Type of action
action_taken double Binary 0 (Action not taken) or 1 (Action Taken)

Cleaning Script

library(tidyverse)
library(tidytext)

plants_wide <- read_csv("https://raw.githubusercontent.com/Z3tt/TidyTuesday/master/data/raw_plants/plants_extinct_wide.csv")

plants_wide %>% 
  write_csv(here::here("2020", "2020-08-18", "plants.csv"))

threats <- plants_wide %>% 
  select(-contains("action")) %>% 
  pivot_longer(cols = contains("threat"), names_to = "threat_type", 
               values_to = "threatened", names_prefix = "threat_") %>% 
  mutate(threat_type = case_when(
    threat_type == "AA" ~ "Agriculture & Aquaculture",
    threat_type == "BRU" ~ "Biological Resource Use",
    threat_type == "RCD" ~ "Commercial Development",
    threat_type == "ISGD" ~ "Invasive Species",
    threat_type == "EPM" ~ "Energy Production & Mining",
    threat_type == "CC" ~ "Climate Change",
    threat_type == "HID" ~ "Human Intrusions",
    threat_type == "P" ~ "Pollution",
    threat_type == "TS" ~ "Transportation Corridor",
    threat_type == "NSM" ~ "Natural System Modifications",
    threat_type == "GE" ~ "Geological Events",
    threat_type == "NA" ~ "Unknown",
    TRUE ~ NA_character_
  )) 

threats %>% 
  write_csv(here::here("2020", "2020-08-18", "threats.csv"))

threat_filtered <- threats %>% 
  filter(threatened == 1) 

threat_filtered %>% 
  janitor::tabyl(threat_type, threatened)

actions <- plants_wide %>% 
      select(-contains("threat")) %>% 
      pivot_longer(cols = contains("action"), names_to = "action_type", 
                   values_to = "action_taken", names_prefix = "action_") %>% 
      mutate(action_type = case_when(
        action_type == "LWP" ~ "Land & Water Protection",
        action_type == "SM" ~ "Species Management",
        action_type == "LP" ~ "Law & Policy",
        action_type == "RM" ~ "Research & Monitoring",
        action_type == "EA" ~ "Education & Awareness",
        action_type == "NA" ~ "Unknown",
        TRUE ~ NA_character_
      )) 

actions %>% 
  write_csv(here::here("2020", "2020-08-18", "actions.csv"))

action_filtered <- actions %>% 
  filter(action_taken == 1) 

action_filtered %>% 
  janitor::tabyl(action_type, action_taken)

threat_filtered %>% 
  count(continent, group, threat_type) %>% 
  ggplot(aes(y = tidytext::reorder_within(threat_type, n, continent), x = n, fill = group)) +
  geom_col() +
  tidytext::scale_y_reordered() +
  facet_wrap(~continent, scales = "free_y", ncol = 1)