Fish species, threatened

The indicator of threatened fish species serves as a crucial metric in understanding the health of aquatic ecosystems worldwide. As biodiversity within oceans, rivers, and lakes declines, the status of fish species becomes an alarming barometer for environmental changes. In 2018, the median value of threatened fish species was recorded at 29.0, indicating a significant level of concern for the preservation of aquatic life.

This metric is not just a reflection of fish populations but highlights the interconnected web of ecological relationships. Fish species play vital roles in marine and freshwater ecosystems, serving as both predators and prey, maintaining the balance of food webs. Their decline impacts not only the aquatic environments they inhabit but also the communities that depend on them for sustenance, recreation, and cultural significance. Additionally, threatened fish populations often signal broader environmental issues, such as habitat loss, pollution, and climate change, which can be tied to other marine and terrestrial biodiversity indicators.

The relationships between threatened fish species and other environmental indicators are significant. Factors like water quality, habitat diversity, and overall biodiversity health are interrelated—degraded habitats often correlate with declining fish populations. Furthermore, indicators of human impact, such as overfishing, industrial pollution, and urbanization, can illuminate the pressures placed upon fish habitats. For instance, areas with high industrial activity often reflect higher rates of fish species at risk, as toxic runoff can devastate aquatic life.

Various factors contribute to the predicament faced by threatened fish species. Overfishing is a primary concern, propelled by rising global demand for seafood, leading to unsustainable fishing practices that push fish populations to their limits. Additionally, habitat destruction from coastal development and pollution dramatically impacts breeding grounds and essential ecosystems such as mangroves and coral reefs. Climate change, through ocean acidification and rising sea temperatures, compounds these threats, altering migration patterns and reproductive behaviors of fish species.

The significance of addressing the threats faced by fish species cannot be overstated. This is where effective strategies and solutions come into play. Strengthening regulatory measures concerning fishing quotas, implementing sustainable practices, and promoting aquaculture are pivotal. Protected marine areas where fishing is restricted enable ecosystems to recover and enhance biodiversity, fostering natural resilience. Community involvement in sustainable practices also creates a sense of stewardship that encourages conservation efforts.

International collaborations among nations are critical, especially for migratory fish species that traverse national boundaries. Joint conservation agreements can help manage fish populations more effectively, ensuring healthy ecosystems are maintained for future generations. Education and awareness-raising campaigns can empower local communities, fostering an understanding of the importance of preserving fish species and their habitats.

Despite these strategies, several flaws exist in current approaches to balancing fish conservation with human needs. Economic pressures often prioritize short-term gains over long-term sustainability, leading to compromised efforts. Implementation of regulations can be hindered by insufficient resources for enforcement, creating loopholes for illegal fishing and resource exploitation. Additionally, while marine protected areas are beneficial, they require careful planning and management to maximize their efficacy; poorly designed protected areas may not adequately protect critical habitats.

The data highlights the stark contrast in the status of threatened fish species across different regions. The latest figures from 2017 indicate a staggering count of 8233 threatened fish species globally. The stark discrepancies can be observed when reviewing the top five locations with the highest threatened species counts: the United States leads with 251 threatened fish species, followed closely by India at 227, Mexico at 181, Tanzania at 179, and Indonesia at 166. These figures underline the urgent environmental pressures certain regions face, potentially tied to their economic activities and environmental policies.

On the contrary, countries like Andorra, Liechtenstein, Paraguay, San Marino, and South Sudan reported zero threatened fish species. These lower figures suggest more stable aquatic ecosystems possibly due to less industrial activity or better-managed environmental policies. However, it’s essential to approach this data critically; a lack of reported threatened species does not necessarily equate to environmental health. It raises questions about monitoring capabilities, ecological richness, and whether these countries accurately reflect their biodiversity status.

Ultimately, addressing the alarming status of threatened fish species is a multifaceted challenge that requires a cohesive effort spanning community initiatives, government regulations, and international cooperation. As global citizens, there is a shared responsibility to advocate for sustainable practices that protect our oceans and freshwater ecosystems, ensuring a future where diverse fish populations can 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.FSH.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.FSH.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))