Plant species (higher), threatened

The indicator "Plant species (higher), threatened" is a crucial metric that helps assess the conservation status of vascular plants worldwide. This indicator focuses on species that are at risk of extinction due to various environmental and anthropogenic pressures. Monitoring and understanding the threats to biodiversity, specifically plant species, is essential for effective conservation efforts.

The importance of tracking threatened plant species cannot be overstated. Plants play a vital role in maintaining ecological balance, supporting life forms by providing oxygen, food, and habitat. They also contribute to soil health, water purification, and carbon sequestration, which are essential for combating climate change. Furthermore, plant species form the basis of various industries, including pharmaceuticals, agriculture, and horticulture. As such, the decline of plant species threatens not only natural ecosystems but also human economic activities and health.

This indicator is interrelated with other biodiversity indicators, such as animal species threatened and overall ecosystem health. A decline in plant species often correlates with the decline in animal species, as many animals rely on plants for sustenance and shelter. Furthermore, the health of an ecosystem is often measured by the diversity of its plant life. Thus, monitoring threatened plant species provides insights into broader ecological trends and challenges.

Several factors impact the status of threatened plant species. Habitat loss due to urbanization, deforestation, and agricultural expansion is a significant driver of plant species decline. Climate change poses another considerable threat, as shifting temperature and precipitation patterns can disrupt plant growth and reproduction. Invasive species can outcompete native flora, leading to further reductions in plant diversity. Additionally, pollution and over-exploitation exacerbate the pressures on vulnerable plant species, pushing them closer to extinction.

To address the challenges posed to threatened plant species, several strategies and solutions can be employed. Conservation efforts need to focus on habitat preservation and restoration, ensuring that native ecosystems remain intact and can support a diverse array of plant species. Establishing protected areas and reserves can mitigate habitat loss and protect critical ecosystems from human encroachment. Furthermore, restoring degraded ecosystems can provide a second chance for declining plant species to recover.

Engaging local communities in conservation efforts is essential. By promoting sustainable land-use practices and raising awareness about the value of plant diversity, communities can play a crucial role in protecting endangered species. Educational programs can foster a culture of conservation, empowering individuals to take action against the loss of biodiversity.

Another key solution lies in legislation and policy. Governments must implement and enforce regulations that safeguard threatened plant species and their habitats. Research and monitoring are also critical; by understanding population trends and the factors leading to decline, conservationists can develop targeted strategies for protection and recovery.

Despite the importance of monitoring threatened plant species, there are flaws inherent in current methodologies. Data collection can be inconsistent, with many regions lacking comprehensive assessments of plant diversity. Additionally, some assessments prioritize charismatic or economically valuable species over less-known plants, leading to a skewed understanding of threats to biodiversity as a whole. Without reliable and consistent data, it becomes challenging to create effective conservation strategies.

Examining global data from 2018 reveals that the median number of threatened plant species was 18.0, which indicates a considerable level of concern regarding global plant diversity. Regions such as Ecuador and Madagascar exhibited astonishingly high numbers of threatened species, clocking in at 1859 and 1111, respectively. These figures point to the overwhelming pressures faced by biodiversity hotspots that are often under threat from factors like deforestation and land-use change. In fact, tropical regions, known for their rich vegetation, often struggle against rapid agricultural expansion and logging, driving their plant species toward the brink of extinction.

In contrast, areas like Andorra, Bahrain, Belgium, Estonia, and the Faroe Islands reported zero threatened plant species in 2018. While these figures may seem encouraging, they could also indicate a lack of research or awareness regarding plant biodiversity in these regions. It raises a critical question about the true nature of biodiversity in under-monitored areas; are they genuinely free of threats, or are there unrecognized declines that require attention?

The world average for threatened plant species was reported at 15735 species in 2017. This highlights a severe biodiversity crisis, where numerous plant species face various threats. The continuous tracking of these values over the years can serve to illustrate trends, painting a clearer picture of how global efforts in conservation are faring over time.

In conclusion, the indicator "Plant species (higher), threatened" serves as an essential benchmark for understanding and combating biodiversity loss. It underscores the intricate relationships among plant health, ecosystem stability, and human well-being. A concerted effort involving local communities, robust policies, and ongoing research is imperative to ensure these vital species do not vanish, thereby ensuring the planet's ecological integrity for future generations.

                    
# Install missing packages
import sys
import subprocess

def install(package):
subprocess.check_call([sys.executable, "-m", "pip", "install", package])

# Required packages
for package in ['wbdata', 'country_converter']:
try:
__import__(package)
except ImportError:
install(package)

# Import libraries
import wbdata
import country_converter as coco
from datetime import datetime

# Define World Bank indicator code
dataset_code = 'EN.HPT.THRD.NO'

# Download data from World Bank API
data = wbdata.get_dataframe({dataset_code: 'value'},
date=(datetime(1960, 1, 1), datetime.today()),
parse_dates=True,
keep_levels=True).reset_index()

# Extract year
data['year'] = data['date'].dt.year

# Convert country names to ISO codes using country_converter
cc = coco.CountryConverter()
data['iso2c'] = cc.convert(names=data['country'], to='ISO2', not_found=None)
data['iso3c'] = cc.convert(names=data['country'], to='ISO3', not_found=None)

# Filter out rows where ISO codes could not be matched — likely not real countries
data = data[data['iso2c'].notna() & data['iso3c'].notna()]

# Sort for calculation
data = data.sort_values(['iso3c', 'year'])

# Calculate YoY absolute and percent change
data['value_change'] = data.groupby('iso3c')['value'].diff()
data['value_change_percent'] = data.groupby('iso3c')['value'].pct_change() * 100

# Calculate ranks (higher GDP per capita = better rank)
data['rank'] = data.groupby('year')['value'].rank(ascending=False, method='dense')

# Calculate rank change from previous year
data['rank_change'] = data.groupby('iso3c')['rank'].diff()

# Select desired columns
final_df = data[['country', 'iso2c', 'iso3c', 'year', 'value',
'value_change', 'value_change_percent', 'rank', 'rank_change']].copy()

# Optional: Add labels as metadata (could be useful for export or UI)
column_labels = {
'country': 'Country name',
'iso2c': 'ISO 2-letter country code',
'iso3c': 'ISO 3-letter country code',
'year': 'Year',
'value': 'GDP per capita (current US$)',
'value_change': 'Year-over-Year change in value',
'value_change_percent': 'Year-over-Year percent change in value',
'rank': 'Country rank by GDP per capita (higher = richer)',
'rank_change': 'Change in rank from previous year'
}

# Display first few rows
print(final_df.head(10))

# Optional: Save to CSV
#final_df.to_csv("gdp_per_capita_cleaned.csv", index=False)
                    
                

                    
# Check and install required packages
required_packages <- c("WDI", "countrycode", "dplyr")

for (pkg in required_packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
  }
}

# Load the necessary libraries
library(WDI)
library(dplyr)
library(countrycode)

# Define the dataset code (World Bank indicator code)
dataset_code <- 'EN.HPT.THRD.NO'

# Download data using WDI package
dat <- WDI(indicator = dataset_code)

# Filter only countries using 'is_country' from countrycode
# This uses iso2c to identify whether the entry is a recognized country
dat <- dat %>%
  filter(countrycode(iso2c, origin = 'iso2c', destination = 'country.name', warn = FALSE) %in%
           countrycode::codelist$country.name.en)

# Ensure dataset is ordered by country and year
dat <- dat %>%
  arrange(iso3c, year)

# Rename the dataset_code column to "value" for easier manipulation
dat <- dat %>%
  rename(value = !!dataset_code)

# Calculate year-over-year (YoY) change and percentage change
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(
    value_change = value - lag(value),                              # Absolute change from previous year
    value_change_percent = 100 * (value - lag(value)) / lag(value) # Percent change from previous year
  ) %>%
  ungroup()

# Calculate rank by year (higher value => higher rank)
dat <- dat %>%
  group_by(year) %>%
  mutate(rank = dense_rank(desc(value))) %>% # Rank countries by descending value
  ungroup()

# Calculate rank change (positive = moved up, negative = moved down)
dat <- dat %>%
  group_by(iso3c) %>%
  mutate(rank_change = rank - lag(rank)) %>% # Change in rank compared to previous year
  ungroup()

# Select and reorder final columns
final_data <- dat %>%
  select(
    country,
    iso2c,
    iso3c,
    year,
    value,
    value_change,
    value_change_percent,
    rank,
    rank_change
  )

# Add labels (variable descriptions)
attr(final_data$country, "label") <- "Country name"
attr(final_data$iso2c, "label") <- "ISO 2-letter country code"
attr(final_data$iso3c, "label") <- "ISO 3-letter country code"
attr(final_data$year, "label") <- "Year"
attr(final_data$value, "label") <- "GDP per capita (current US$)"
attr(final_data$value_change, "label") <- "Year-over-Year change in value"
attr(final_data$value_change_percent, "label") <- "Year-over-Year percent change in value"
attr(final_data$rank, "label") <- "Country rank by GDP per capita (higher = richer)"
attr(final_data$rank_change, "label") <- "Change in rank from previous year"

# Print the first few rows of the final dataset
print(head(final_data, 10))