Module 8

Climate Change Impacts on Ocean Biodiversity

Warming, acidification, deoxygenation, compound stressors, deep-sea mining, and conservation

The ocean is absorbing the vast majority of anthropogenic heat and a substantial fraction of CO₂ emissions, buffering the atmosphere but at enormous cost to marine ecosystems. The “triple threat” of warming, acidification, and deoxygenation is reshaping ocean biodiversity from surface to abyss, with synergistic effects worse than the sum of individual stressors. This module quantifies the depth-specific impacts using metabolic theory, carbonate chemistry, and climate velocity frameworks. For broader climate dynamics see Climate Science; for terrestrial impacts see Climate & Biodiversity.

8.1 Ocean Warming

The ocean has absorbed 93% of the excess heat trapped by greenhouse gases since the 1970s — equivalent to approximately 436 × 10²¹ J (436 ZJ) over 1971–2020. Sea surface temperature (SST) has increased by ~0.88°C since 1900, with the rate of warming accelerating: 0.11°C/decade since 1971 vs 0.06°C/decade over the full 20th century.

Metabolic Theory of Ecology (MTE)

Warming impacts marine organisms through fundamental metabolic scaling. The MTE predicts that metabolic rate scales with both body mass and temperature:

\( B = B_0 \cdot M^{3/4} \cdot e^{-E_a/(k_B T)} \)

where \( B \) is metabolic rate, \( M \) is body mass, \( E_a \approx 0.65 \) eV is the activation energy for aerobic metabolism, \( k_B \) is Boltzmann's constant, and \( T \) is absolute temperature. A 1°C warming increases metabolic rate by ~10% for a typical ectotherm.

The critical consequence: warming simultaneously increases oxygen demand (higher metabolic rate) while decreasing oxygen supply (lower O₂ solubility, increased stratification reduces ventilation). This creates a metabolic squeeze:

\( \text{Metabolic Index} \;\Phi = \frac{p\text{O}_2 \cdot A_0 \cdot e^{-E_0/(k_B T)}}{\alpha_0 \cdot M^{\delta} \cdot e^{-E_d/(k_B T)}} \)

where \( \Phi > 1 \) indicates sufficient O₂ supply relative to metabolic demand, and \( \Phi < 1 \) indicates metabolic limitation. Deutsch et al. (2015) showed that species' geographic range limits closely correspond to \( \Phi = 1 \)contours, meaning warming will compress habitable ranges toward the poles and toward the surface.

Thermal Habitat Compression

For cold-water species (e.g., Atlantic cod, Gadus morhua), warming compresses their thermal habitat from both sides: surface waters become too warm, while deeper cold waters may lack sufficient oxygen or food. The thermal tolerance window narrows with depth, creating a thermal squeeze that reduces viable habitat volume faster than surface temperature change alone would suggest.

8.2 Ocean Acidification

Since pre-industrial times, ocean surface pH has declined from ~8.2 to ~8.1 — a 30% increase in hydrogen ion concentration (pH is logarithmic). The ocean has absorbed approximately 30% of anthropogenic CO₂ emissions, driving the following equilibrium reaction:

\( \text{CO}_2 + \text{H}_2\text{O} \rightleftharpoons \text{H}_2\text{CO}_3 \rightleftharpoons \text{H}^+ + \text{HCO}_3^- \rightleftharpoons 2\text{H}^+ + \text{CO}_3^{2-} \)

The net effect: CO₂ addition increases [H⁺] and [HCO₃⁻] while decreasing [CO₃²⁻]. Since organisms need CO₃²⁻ to build CaCO₃ shells and skeletons, acidification fundamentally undermines calcification.

Depth-Specific Impacts

Surface (0–200 m): Calcifiers (corals, pteropods, coccolithophores, foraminifera) directly affected. \( \Omega_{\text{arag}} \) declining from 3.5 to projected 2.0 by 2100 (SSP5-8.5). Pteropod shell dissolution already observed in Southern Ocean surface waters.
Mesopelagic (200–1000 m): Naturally lower \( \Omega \) here (1.5–2.5). Acidification pushes many mesopelagic waters below \( \Omega = 1 \) for aragonite, threatening deep-dwelling pteropods and foraminifera that form the basis of deep-water food webs.
Deep ocean (1000+ m): The aragonite saturation horizon (ASH) — the depth below which aragonite dissolves — is shoaling from ~2500 m to a projected ~1500 m by 2100. In the Southern Ocean, the ASH may reach the surface.

Lysocline & CCD

Saturation depth depends on pressure and temperature:

\( K_{sp}(z) = K_{sp}^0 \cdot \exp\!\left(\frac{-\Delta V \cdot P(z)}{RT}\right) \)

where \( \Delta V \) is the molar volume change of dissolution (<0 for CaCO₃, meaning solubility increases with pressure), and \( P(z) \) is pressure at depth \( z \). The lysocline is the depth where dissolution rate markedly increases; the CCD (calcite compensation depth) is where dissolution equals supply of CaCO₃ from above. Both are shoaling as ocean CO₂ increases.

8.3 Ocean Deoxygenation

The global ocean has lost approximately 2% of its dissolved oxygen since 1960, with oxygen minimum zones (OMZs) expanding by 3–8% over the same period. Open-ocean oxygen loss has been observed at all depth levels, with the most severe losses in the upper 1000 m of tropical and subtropical regions.

Drivers of O₂ Loss

Attribution of oxygen decline:

\( \Delta[\text{O}_2] = \underbrace{\Delta[\text{O}_2]_{\text{sol}}}_{15\%} + \underbrace{\Delta[\text{O}_2]_{\text{vent}}}_{50\%} + \underbrace{\Delta[\text{O}_2]_{\text{bio}}}_{35\%} \)

Three mechanisms drive deoxygenation: (1) Reduced solubility: warmer water holds less dissolved O₂ (\( \partial[\text{O}_2]/\partial T \approx -6 \) μmol/kg per °C); (2) Increased stratification: warming strengthens the pycnocline, reducing ventilation of subsurface waters; (3) Increased biological O₂ demand: higher temperatures accelerate microbial respiration.

Depth-Zone Effects

Epipelagic (0–200 m): Moderate O₂ decline (~1–3%), partially compensated by air-sea exchange. Main impact: reduced O₂ at thermocline depths compresses habitat for large pelagic fish (tuna, billfish).
Mesopelagic (200–1000 m): Most severely affected. OMZ cores expanding vertically (shoaling upper boundary by 5–10 m/decade) and horizontally. Hypoxia threshold (<60 μmol/kg) expanding.
Bathypelagic & deep (1000+ m): Small absolute O₂ changes but significant for deep-sea organisms adapted to near-threshold conditions. Reduced ventilation of bottom waters in semi-enclosed basins (e.g., Japan Sea, Baltic).

8.4 Climate Velocity by Depth Zone

Climate velocity quantifies how fast organisms must move to track their thermal niche as ocean temperatures shift. It is defined as:

\( v_{\text{clim}} = \frac{\partial T / \partial t}{|\nabla_{\text{spatial}} T|} \)

where the numerator is the rate of local warming (°C/yr) and the denominator is the spatial temperature gradient (°C/km). Where gradients are steep (e.g., frontal zones), organisms need to move less far; where gradients are shallow (e.g., tropical surface ocean), climate velocity is very high.

Depth-Specific Climate Velocities

Surface (0–200 m): Lateral climate velocity ~4.2 km/yr (global mean), up to 20 km/yr in tropical regions. Many pelagic species can track this, but sessile organisms (corals, kelp) and slow-dispersers cannot.
Mesopelagic (200–1000 m): ~1.5 km/yr laterally. Warming signal attenuated and delayed. Temperature changes are slower but organisms are less mobile.
Deep ocean (1000+ m): ~0.1 km/yr laterally (slow deep circulation). But warming is projected to accelerate as warm water masses penetrate deeper. Deep-sea species with extremely slow metabolism, low fecundity, and limited dispersal capacity are exceptionally vulnerable.
Vertical velocity: 5–10 m/decade downward. Isotherms are deepening as the ocean warms from above. Deep-water organisms have no “deeper refuge” to retreat to.

8.5 Compound Stressors: The Triple Threat

Warming, acidification, and deoxygenation do not act independently. Their combined effect is often synergistic — worse than the sum of individual stressors:

Multi-stressor dose-response model:

\( S_{\text{combined}} = S_T + S_{\text{pH}} + S_{\text{O}_2} + \beta_{T,\text{pH}} \cdot S_T \cdot S_{\text{pH}} + \beta_{T,\text{O}_2} \cdot S_T \cdot S_{\text{O}_2} + \beta_{\text{pH},\text{O}_2} \cdot S_{\text{pH}} \cdot S_{\text{O}_2} \)

where \( S_T, S_{\text{pH}}, S_{\text{O}_2} \) are individual stressor effects (0 = no impact, 1 = lethal) and the \( \beta \) terms capture interactions. When \( \beta > 0 \): synergistic (combined effect exceeds sum). Meta-analyses show \( \beta_{T,\text{pH}} \approx 0.2\text{--}0.5 \) for calcifying organisms and \( \beta_{T,\text{O}_2} \approx 0.3\text{--}0.8 \) for ectotherms.

The mechanism behind synergy: warming increases O₂ demand while reducing supply; lower pH forces organisms to expend more energy on acid-base regulation, further increasing O₂ demand; hypoxia reduces scope for energy-expensive processes like calcification against acidification. Each stressor amplifies vulnerability to the others.

Aerobic scope under compound stress:

\( \text{AS} = \text{MMR}(T, p\text{O}_2) - \text{SMR}(T, \text{pH}) \)

Maximum metabolic rate (MMR) is limited by O₂ supply, while standard metabolic rate (SMR) increases with warming and acid-base regulation costs. The aerobic scope (AS) collapses from both sides under compound stress, reducing scope for growth, reproduction, and activity to zero — the “oxygen and capacity limited thermal tolerance” (OCLTT) framework of Pörtner & Farrell (2008).

8.6 Deep-Sea Mining

Three primary mineral resources drive commercial interest in the deep sea:

Polymetallic nodules: Mn-Fe concretions (4–10 cm) on abyssal plains (4000–6000 m), rich in Mn, Ni, Co, Cu. Clarion-Clipperton Zone (CCZ) alone contains ~21 billion tonnes. Growth rate: 1–10 mm per million years.
Cobalt-rich ferromanganese crusts: Encrust seamounts at 800–2500 m depth. Up to 25 cm thick, growing at 1–5 mm/Myr. Rich in Co, Te, rare earth elements.
Seafloor massive sulfides (SMS): Formed at hydrothermal vents (1500–5000 m). Rich in Cu, Zn, Au, Ag. Smallest deposits but highest metal grades.

Ecological Impacts

The ecological consequences of deep-sea mining are severe and effectively permanent:

Habitat destruction: Collector vehicles remove the upper 5–15 cm of sediment, destroying all benthic organisms. Recolonization experiments (DISCOL, IOM BIE) show <50% faunal recovery after 26 years.
Sediment plumes: Mining generates plumes extending 100+ km, smothering filter feeders, clogging respiratory surfaces, and blocking light for benthic phototrophs. Modeling suggests plumes at 10–100 mg/L persist for weeks.
Noise & light pollution: Mining equipment produces low-frequency sound (<1 kHz) at 150–180 dB re 1 μPa, potentially disrupting deep-sea species that have evolved in near-total silence.
Recovery timescale: Given nodule growth rates of mm/Myr, functional recovery of abyssal habitats requires >1000 years to millions of years. This is effectively irreversible on human timescales.

Ecosystem Service Valuation

Cost-benefit framework:

\( \text{Net value} = \sum_{t=0}^{T} \frac{R_{\text{mineral}}(t) - C_{\text{extract}}(t) - D_{\text{eco}}(t)}{(1+r)^t} \)

where \( R_{\text{mineral}} \) is mineral revenue, \( C_{\text{extract}} \) is extraction cost, \( D_{\text{eco}} \) is ecosystem damage (carbon storage loss, biodiversity loss, fisheries impact), and \( r \) is the discount rate. When ecosystem services are properly valued (including deep-sea carbon sequestration of ~0.4 Gt C yr⁻¹ in abyssal sediments), the cost-benefit analysis frequently becomes negative.

8.7 Conservation: Depth-Integrated Protection

The 30×30 target (Kunming-Montreal Global Biodiversity Framework, 2022) aims to protect 30% of ocean area by 2030. Currently, only ~8% of the ocean is in MPAs, and <3% is fully or highly protected. Critically, most MPAs protect only surface waters, ignoring the three-dimensional nature of ocean ecosystems.

Vertical Connectivity

Protecting surface waters alone is insufficient because of deep vertical connectivity:

Biological pump: Surface productivity fuels deep-sea communities via sinking particles. Disrupting surface ecosystems starves the deep.
Diel vertical migration: Mesopelagic organisms traverse 400–800 m daily, connecting surface and deep food webs. A surface-only MPA leaves half their habitat unprotected.
Ontogenetic migration: Many species use different depth zones at different life stages (larvae at surface, adults at depth). Protection at one depth is meaningless if another critical habitat is degraded.

Depth-integrated conservation effectiveness:

\( E_{\text{MPA}} = \int_0^{z_{\max}} w(z) \cdot p(z) \cdot c(z) \, dz \)

where \( w(z) \) is the biodiversity weighting at depth \( z \), \( p(z) \) is the protection level (0–1), and \( c(z) \) is the connectivity to surface. Maximizing \( E_{\text{MPA}} \) requires three-dimensional marine spatial planning that accounts for vertical linkages.

Climate Impact Cascade by Depth

Computational Models

Five models: (1) depth-specific warming projections, (2) aragonite saturation horizon shift, (3) OMZ expansion, (4) multi-stressor synergy, and (5) climate velocity by depth zone.

Python

script.py216 lines

import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from matplotlib.patches import FancyArrowPatch

fig = plt.figure(figsize=(16, 13))
fig.patch.set_facecolor('#0a0a0a')
fig.suptitle('Climate Change Impacts on Ocean Biodiversity', fontsize=18, color='#7dd3fc', fontweight='bold', y=0.98)

# Create grid: 3 rows, 2 columns (last row spans full width)
ax1 = fig.add_subplot(3, 2, 1)
ax2 = fig.add_subplot(3, 2, 2)
ax3 = fig.add_subplot(3, 2, 3)
ax4 = fig.add_subplot(3, 2, 4)
ax5 = fig.add_subplot(3, 1, 3)

# ========== Panel 1: Depth-Specific Warming ==========
depths = np.array([0, 50, 100, 200, 500, 1000, 2000, 3000, 4000, 5000])

# Warming by 2100 under different SSPs
warming_ssp126 = np.array([0.8, 0.7, 0.5, 0.3, 0.15, 0.08, 0.03, 0.02, 0.01, 0.01])
warming_ssp245 = np.array([1.5, 1.3, 1.0, 0.6, 0.3, 0.15, 0.06, 0.04, 0.02, 0.01])
warming_ssp585 = np.array([3.5, 3.0, 2.2, 1.4, 0.7, 0.35, 0.12, 0.06, 0.03, 0.02])

ax1.plot(warming_ssp126, depths, color='#22c55e', linewidth=2.5, marker='o', markersize=4, label='SSP1-2.6')
ax1.plot(warming_ssp245, depths, color='#f59e0b', linewidth=2.5, marker='s', markersize=4, label='SSP2-4.5')
ax1.plot(warming_ssp585, depths, color='#ef4444', linewidth=2.5, marker='^', markersize=4, label='SSP5-8.5')

ax1.set_xlabel('ΔT by 2100 (°C)', fontsize=12, color='white')
ax1.set_ylabel('Depth (m)', fontsize=12, color='white')
ax1.set_title('Depth-Specific Warming Projections', fontsize=14, color='#7dd3fc', fontweight='bold')
ax1.invert_yaxis()
ax1.set_facecolor('#0a0a0a')
ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.15, color='white')
ax1.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#3b82f6', labelcolor='white')

# Depth zone shading
ax1.axhspan(0, 200, alpha=0.05, color='#ef4444')
ax1.axhspan(200, 1000, alpha=0.05, color='#f59e0b')
ax1.axhspan(1000, 4000, alpha=0.05, color='#22c55e')
ax1.axhspan(4000, 5000, alpha=0.05, color='#3b82f6')

# ========== Panel 2: ASH Shift ==========
years = np.arange(1800, 2105)

# Aragonite saturation horizon depth over time
ash_preindustrial = 2500  # meters
ash_ssp126 = ash_preindustrial - np.maximum(0, 3.0 * (years - 1850)) * np.where(years < 2050, 1, 0.7)
ash_ssp245 = ash_preindustrial - np.maximum(0, 5.0 * (years - 1850))
ash_ssp585 = ash_preindustrial - np.maximum(0, 8.0 * (years - 1850))

ash_ssp126 = np.maximum(ash_ssp126, 500)
ash_ssp245 = np.maximum(ash_ssp245, 300)
ash_ssp585 = np.maximum(ash_ssp585, 0)

ax2.plot(years, ash_ssp126, color='#22c55e', linewidth=2.5, label='SSP1-2.6')
ax2.plot(years, ash_ssp245, color='#f59e0b', linewidth=2.5, label='SSP2-4.5')
ax2.plot(years, ash_ssp585, color='#ef4444', linewidth=2.5, label='SSP5-8.5')

ax2.invert_yaxis()
ax2.axhline(2500, color='#6b7280', linewidth=1, linestyle=':', alpha=0.5)
ax2.text(1810, 2450, 'Pre-industrial ASH', fontsize=9, color='#9ca3af')

# Mark key depths
ax2.axhline(1000, color='#f472b6', linewidth=1, linestyle='--', alpha=0.5)
ax2.text(1810, 950, 'Mesopelagic base (1000m)', fontsize=8, color='#f472b6')

ax2.set_xlabel('Year', fontsize=12, color='white')
ax2.set_ylabel('ASH depth (m)', fontsize=12, color='white')
ax2.set_title('Aragonite Saturation Horizon Shoaling', fontsize=14, color='#7dd3fc', fontweight='bold')
ax2.set_facecolor('#0a0a0a')
ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.15, color='white')
ax2.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#3b82f6', labelcolor='white')
ax2.set_ylim(3000, 0)

# ========== Panel 3: OMZ Expansion ==========
# Vertical profile of O2 showing OMZ expansion
depth_o2 = np.linspace(0, 4000, 500)

# O2 profiles (umol/kg)
o2_preindustrial = 250 * np.exp(-depth_o2/300) + 30 + 150 * (1 - np.exp(-depth_o2/2000))
# OMZ: minimum around 400-800m
omz_dip = 120 * np.exp(-((depth_o2 - 600)/200)**2)
o2_preindustrial = o2_preindustrial - omz_dip
o2_preindustrial = np.maximum(o2_preindustrial, 20)

# Current (2020)
o2_current = o2_preindustrial * 0.98 - 8 * np.exp(-((depth_o2 - 600)/300)**2)
o2_current = np.maximum(o2_current, 10)

# 2100 SSP5-8.5
o2_2100 = o2_preindustrial * 0.94 - 20 * np.exp(-((depth_o2 - 600)/350)**2)
o2_2100 = np.maximum(o2_2100, 5)

ax3.plot(o2_preindustrial, depth_o2, color='#22c55e', linewidth=2, label='Pre-industrial')
ax3.plot(o2_current, depth_o2, color='#f59e0b', linewidth=2, label='Current (2020)')
ax3.plot(o2_2100, depth_o2, color='#ef4444', linewidth=2, label='2100 (SSP5-8.5)')

# Hypoxia threshold
ax3.axvline(60, color='#f472b6', linewidth=1.5, linestyle='--', alpha=0.7)
ax3.text(62, 200, 'Hypoxia threshold\n(60 μmol/kg)', fontsize=8, color='#f472b6')

# Fill OMZ region for 2100
ax3.fill_betweenx(depth_o2, 0, 60, where=o2_2100 < 60, alpha=0.1, color='#ef4444')

ax3.set_xlabel('Dissolved O₂ (μmol/kg)', fontsize=12, color='white')
ax3.set_ylabel('Depth (m)', fontsize=12, color='white')
ax3.set_title('OMZ Expansion: O₂ Profiles', fontsize=14, color='#7dd3fc', fontweight='bold')
ax3.invert_yaxis()
ax3.set_facecolor('#0a0a0a')
ax3.tick_params(colors='white')
ax3.grid(True, alpha=0.15, color='white')
ax3.legend(fontsize=9, facecolor='#1a1a1a', edgecolor='#3b82f6', labelcolor='white')
ax3.set_xlim(0, 280)

# ========== Panel 4: Multi-Stressor Synergy ==========
# 2D heatmap: warming x acidification, showing combined stress with O2 decline
T_anomaly = np.linspace(0, 4, 100)  # degrees warming
pH_decline = np.linspace(0, 0.4, 100)  # pH units decrease

T_grid, pH_grid = np.meshgrid(T_anomaly, pH_decline)

# Individual stressor effects (0-1 scale)
S_T = T_grid / 4.0  # linear for simplicity
S_pH = (pH_grid / 0.4) ** 1.2  # slightly nonlinear
S_O2 = 0.3 * T_grid / 4.0  # O2 decline correlates with warming

# Synergistic combined effect
beta_T_pH = 0.4
beta_T_O2 = 0.5
S_combined = S_T + S_pH + S_O2 + beta_T_pH * S_T * S_pH + beta_T_O2 * S_T * S_O2

im = ax4.pcolormesh(T_anomaly, pH_decline, S_combined, cmap='YlOrRd', shading='auto')
cbar = plt.colorbar(im, ax=ax4, label='Combined stress index')
cbar.set_label('Combined stress index', color='white', fontsize=10)
cbar.ax.tick_params(colors='white')

# Contour lines
contours = ax4.contour(T_anomaly, pH_decline, S_combined, levels=[0.5, 1.0, 1.5, 2.0],
                       colors='white', linewidths=1, linestyles='--')
ax4.clabel(contours, fontsize=8, colors='white', fmt='%.1f')

# Mark current and projected conditions
ax4.plot(0.88, 0.1, 'o', color='#22c55e', markersize=10, markeredgecolor='white', markeredgewidth=2, zorder=5)
ax4.text(1.0, 0.12, 'Current', fontsize=10, color='#22c55e', fontweight='bold')

ax4.plot(2.5, 0.25, 's', color='#f59e0b', markersize=10, markeredgecolor='white', markeredgewidth=2, zorder=5)
ax4.text(2.65, 0.27, 'SSP2-4.5\n(2100)', fontsize=9, color='#f59e0b')

ax4.plot(3.5, 0.35, '^', color='#ef4444', markersize=10, markeredgecolor='white', markeredgewidth=2, zorder=5)
ax4.text(3.1, 0.37, 'SSP5-8.5\n(2100)', fontsize=9, color='#ef4444')

ax4.set_xlabel('Warming (ΔT, °C)', fontsize=12, color='white')
ax4.set_ylabel('Acidification (ΔpH)', fontsize=12, color='white')
ax4.set_title('Multi-Stressor Synergy Map', fontsize=14, color='#7dd3fc', fontweight='bold')
ax4.set_facecolor('#0a0a0a')
ax4.tick_params(colors='white')

# ========== Panel 5: Climate Velocity by Depth ==========
depth_zones = ['Surface\n(0-200m)', 'Mesopelagic\n(200-1000m)', 'Bathypelagic\n(1-4 km)',
               'Abyssal\n(4-6 km)', 'Hadal\n(6-11 km)']
lat_velocity = [4.2, 1.5, 0.4, 0.1, 0.02]  # km/yr
vert_velocity = [10.0, 5.0, 2.0, 0.5, 0.1]  # m/decade
organism_speed = [50, 5, 0.5, 0.05, 0.01]  # km/yr (typical dispersal ability)

x_pos = np.arange(len(depth_zones))
width = 0.25

bars1 = ax5.bar(x_pos - width, lat_velocity, width, color='#ef4444', alpha=0.8, label='Climate velocity (km/yr)')
bars2 = ax5.bar(x_pos, organism_speed, width, color='#22c55e', alpha=0.8, label='Organism dispersal (km/yr)')

# Add "can track?" indicators
for i in range(len(depth_zones)):
    ratio = organism_speed[i] / lat_velocity[i] if lat_velocity[i] > 0 else float('inf')
    if ratio > 2:
        symbol = '✓'
        color = '#22c55e'
    elif ratio > 0.5:
        symbol = '~'
        color = '#f59e0b'
    else:
        symbol = '✗'
        color = '#ef4444'
    ax5.text(i, max(lat_velocity[i], organism_speed[i]) * 1.3, symbol,
             ha='center', fontsize=16, color=color, fontweight='bold')

# Vertical velocity on secondary axis
ax5_twin = ax5.twinx()
ax5_twin.bar(x_pos + width, vert_velocity, width, color='#60a5fa', alpha=0.6, label='Vertical shift (m/decade)')
ax5_twin.set_ylabel('Vertical shift (m/decade)', fontsize=11, color='#60a5fa')
ax5_twin.tick_params(colors='#60a5fa')

ax5.set_xlabel('Depth Zone', fontsize=12, color='white')
ax5.set_ylabel('Velocity (km/yr)', fontsize=12, color='white')
ax5.set_title('Climate Velocity vs. Organism Dispersal by Depth (✓ can track, ✗ cannot)', fontsize=14, color='#7dd3fc', fontweight='bold')
ax5.set_xticks(x_pos)
ax5.set_xticklabels(depth_zones, fontsize=9)
ax5.set_yscale('log')
ax5_twin.set_yscale('log')
ax5.set_facecolor('#0a0a0a')
ax5.tick_params(colors='white')
ax5.grid(True, alpha=0.15, color='white', axis='y')

lines1, labels1 = ax5.get_legend_handles_labels()
lines2, labels2 = ax5_twin.get_legend_handles_labels()
ax5.legend(lines1 + lines2, labels1 + labels2, fontsize=9, facecolor='#1a1a1a',
           edgecolor='#3b82f6', labelcolor='white', loc='upper right')

plt.tight_layout(rect=[0, 0, 1, 0.96])
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0a0a')
plt.close()
print("Climate change impact models generated successfully.")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

References

Cheng, L. et al. (2022). Another record: ocean warming continues through 2021 despite La Niña conditions. Advances in Atmospheric Sciences, 39, 373–385.
Deutsch, C., Ferrel, A., Seibel, B., Pörtner, H.-O. & Huey, R.B. (2015). Climate change tightens a metabolic constraint on marine habitats. Science, 348(6239), 1132–1135.
Doney, S.C., Fabry, V.J., Feely, R.A. & Kleypas, J.A. (2009). Ocean acidification: the other CO₂ problem. Annual Review of Marine Science, 1, 169–192.
Breitburg, D. et al. (2018). Declining oxygen in the global ocean and coastal waters. Science, 359(6371), eaam7240.
Burrows, M.T. et al. (2011). The pace of shifting climate in marine and terrestrial ecosystems. Science, 334(6056), 652–655.
Pörtner, H.-O. & Farrell, A.P. (2008). Physiology and climate change. Science, 322(5902), 690–692.
Levin, L.A. et al. (2020). Challenges to the sustainability of deep-seabed mining. Nature Sustainability, 3, 784–794.
Jones, D.O.B. et al. (2017). Biological responses to disturbance from simulated deep-sea polymetallic nodule mining. PLoS ONE, 12(2), e0171750.
Sala, E. et al. (2021). Protecting the global ocean for biodiversity, food and climate. Nature, 592, 397–402.
IPCC (2019). Special Report on the Ocean and Cryosphere in a Changing Climate. Cambridge University Press.
Kroeker, K.J. et al. (2013). Impacts of ocean acidification on marine organisms: quantifying sensitivities and interaction with warming. Global Change Biology, 19(6), 1884–1896.
Brito-Morales, I. et al. (2020). Climate velocity reveals increasing exposure of deep-ocean biodiversity to future warming. Nature Climate Change, 10, 576–581.

M7. Polar Ocean Life Course Overview