9.5 Marine Conservation
Marine conservation aims to protect ocean ecosystems and biodiversity while allowing sustainable use. Facing threats from overfishing, pollution, climate change, and habitat destruction, conservation efforts are critical.
Major Threats
Overfishing
34% of stocks overfished. Bycatch. Illegal fishing. Food web disruption.
Climate Change
Warming, acidification, deoxygenation. Coral bleaching. Species range shifts.
Pollution
Plastics, chemicals, nutrients, noise. Dead zones. Microplastic contamination.
Habitat Loss
Coastal development, dredging, bottom trawling. Mangrove/coral/seagrass loss.
Marine Protected Areas (MPAs)
~8%
Of ocean in MPAs
~3%
Fully protected
30%
2030 target
MPAs range from multiple-use areas to strict no-take reserves. Well-enforced MPAs show increased fish biomass, biodiversity, and resilience.
International Frameworks
UNCLOS
UN Convention on Law of the Sea. Defines EEZs, high seas governance.
BBNJ Treaty (2023)
High Seas Treaty. Enables MPAs in international waters. Historic agreement.
SDG 14: Life Below Water
UN Sustainable Development Goal. Conservation and sustainable use of oceans.
Python: MPA Effectiveness
#!/usr/bin/env python3
"""conservation.py - MPA effectiveness analysis"""
import numpy as np
import matplotlib.pyplot as plt
def biomass_recovery(t, r=0.1, K=1, B0=0.2):
"""
Logistic recovery of fish biomass after MPA establishment
"""
return K / (1 + ((K - B0) / B0) * np.exp(-r * t))
def spillover_effect(distance, max_effect=0.5, decay_km=5):
"""
Spillover benefit outside MPA boundaries
"""
return max_effect * np.exp(-distance / decay_km)
# Recovery inside MPA
years = np.linspace(0, 50, 100)
biomass = biomass_recovery(years)
# Spillover outside
distance = np.linspace(0, 20, 50)
spillover = spillover_effect(distance)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
ax1.plot(years, biomass, 'g-', lw=2)
ax1.axhline(0.2, color='r', linestyle='--', label='Pre-MPA')
ax1.axhline(1.0, color='b', linestyle='--', label='Carrying capacity')
ax1.set_xlabel('Years after MPA establishment')
ax1.set_ylabel('Relative Biomass')
ax1.set_title('Fish Biomass Recovery in MPA')
ax1.legend()
ax1.grid(True, alpha=0.3)
ax2.plot(distance, spillover, 'b-', lw=2)
ax2.fill_between(distance, 0, spillover, alpha=0.3)
ax2.set_xlabel('Distance from MPA (km)')
ax2.set_ylabel('Relative Biomass Increase')
ax2.set_title('Spillover Effect')
ax2.grid(True, alpha=0.3)
plt.tight_layout()
print("MPA effectiveness (meta-analysis results):")
print(" Fish biomass: +446% inside MPAs")
print(" Fish density: +166%")
print(" Species richness: +21%")
print(" Organism size: +28%")