Mammal species, threatened

The indicator of threatened mammal species serves as a vital measure of biodiversity and ecosystem health. It reflects the number of mammal species at risk of extinction, offering a snapshot of the ecological challenges faced by various regions around the world. Understanding this indicator is crucial not only for conservation efforts but also for promoting a balanced relationship between human activities and wildlife protection.

The importance of tracking threatened mammal species cannot be overstated. Mammals play integral roles in ecosystems, including pollination, seed dispersal, nutrient cycling, and predator-prey dynamics. When mammal species teeter on the brink of extinction, entire ecosystems can become unbalanced, leading to cascading effects on other species—be they flora or fauna. A decline in mammal populations can signify broader environmental issues, including habitat destruction, climate change, and pollution. Thus, the status of mammal species serves as an indicator of the overall health of biodiversity and the effectiveness of conservation strategies.

This indicator is closely related to several other biodiversity metrics, such as the number of threatened bird species, amphibians, and plants. Trends seen in the mammal population can mirror those in other taxonomic groups, allowing scientists and policymakers to assess the overall direction of biodiversity loss. For instance, if mammal species are being threatened due to habitat loss, it is likely that other species relying on the same ecosystem face similar perils. Monitoring these relationships also aids in prioritizing conservation efforts across taxonomic groups, as many environmental and anthropogenic factors—like deforestation or climate change—impact a variety of organisms simultaneously.

Several factors contribute to the decline of mammal species, including habitat destruction, climate change, poaching, and the introduction of invasive species. Habitat destruction, primarily through deforestation and urbanization, leads to the fragmentation and loss of natural habitats, making it difficult for mammals to find food, reproduce, and maintain healthy populations. Climate change brings about altering weather patterns, impacting breeding cycles, migration routes, and the availability of resources. Furthermore, poaching for bushmeat or illegal wildlife trade can reduce population numbers exponentially, while invasive species can outcompete native mammals for resources.

Strategies to combat the declining trend in threatened mammal species must be multifaceted and comprehensive. Conservation planning needs to prioritize the protection of critical habitats through the establishment of wildlife reserves and national parks. Creating corridors that connect isolated populations can facilitate gene flow and increase resilience against environmental changes. Additionally, implementing anti-poaching initiatives, investing in community-based conservation programs, and raising awareness through education can build local stewardship of wildlife. Engaging various stakeholders—from government entities and NGOs to local communities—ensures a holistic approach to managing and protecting mammalian diversity.

Solutions also involve national and international cooperation. The Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) and the Convention on Biological Diversity (CBD) play pivotal roles in fostering cooperation between countries to ensure that conservation policies are not only developed but effectively implemented. Coordinated efforts can also enhance research on threatened species, track population trends, and inform best practices in conservation management.

Despite these strategies, flaws exist in effectively addressing the threats to mammal species. Limited funding often hampers conservation efforts, and gaps in data can lead to misinformed priorities or ineffective policies. Moreover, political instability or lack of governance in certain regions may obstruct conservation initiatives. The issue of balancing economic development with wildlife protection often leads to conflicts of interest, where short-term economic gains overshadow long-term ecological sustainability.

As of the latest year on record, 2018, the median value of threatened mammal species reached 10.0, highlighting varying degrees of risk across different regions. The top five areas severely affected by this trend were Indonesia, Madagascar, Mexico, India, and Brazil, with threatening figures pointing to the urgent need for targeted conservation in these biodiversity hotspots. Indonesia, with a staggering 191.0 threatened species, underscores the extreme challenges posed by habitat destruction, primarily driven by deforestation for palm oil plantations and logging. Madagascar's unique biodiversity is also under pressure, as its endemic species face habitat loss and environmental threats. Mexico and Brazil, both rich in mammalian diversity, confront similar issues exacerbated by urban expansion and agricultural practices.

Conversely, areas such as French Polynesia, the Isle of Man, Liechtenstein, Luxembourg, and Macao SAR China reported a zero count for threatened mammal species. While this might seem positive on the surface, it mirrors a lack of sufficient mammalian diversity to begin with. These regions may have limited species richness overall, which, while maintaining a status quo, could lead to vulnerabilities if climate change or other factors start to impact their ecosystems.

Reflecting on global values, a total of 3,434 reported threatened mammal species in 2017 indicates an urgent need for conservation focused on these captive species within the various ecosystems. Continuous monitoring, policy adjustments, and innovative conservation solutions are essential to mitigate the threats faced by these mammals. Through collective actions and an increasingly conscientious global community, we can work towards a future where mammal species—and, by proxy, our ecosystems—thrive.

                    
# 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.MAM.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.MAM.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))