Module 5: Aquatic Biochemistry & Marine Ecosystems

Ecological Biochemistry & Biodiversity

1. Ocean Acidification

The ocean has absorbed approximately 30% of anthropogenic CO\(_2\) emissions since the Industrial Revolution. This carbon uptake comes at a steep cost: the formation of carbonic acid, driving ocean pH downward in a process known as ocean acidification.

The Carbonate Buffering System

When CO\(_2\) dissolves in seawater, it undergoes a cascade of equilibrium reactions:

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

The equilibrium constants (at 25\(°\)C, S=35 psu):

\[ K_1 = \frac{[\text{H}^+][\text{HCO}_3^-]}{[\text{CO}_2]} \approx 1.4 \times 10^{-6} \quad (pK_1 \approx 5.85) \]

\[ K_2 = \frac{[\text{H}^+][\text{CO}_3^{2-}]}{[\text{HCO}_3^-]} \approx 1.2 \times 10^{-9} \quad (pK_2 \approx 8.92) \]

The Henderson-Hasselbalch equation for seawater pH:

\[ \text{pH} = pK_1 + \log\!\left(\frac{[\text{HCO}_3^-]}{[\text{CO}_2]}\right) \]

At current seawater pH ~8.1: \([\text{HCO}_3^-]\) dominates (~90%),\([\text{CO}_3^{2-}]\) ~9%, dissolved \([\text{CO}_2]\) ~1%

pH Change Since Pre-Industrial

Since pre-industrial times (CO\(_2\) ~280 ppm), surface ocean pH has dropped from ~8.2 to ~8.1. This 0.1 unit decrease may seem small, but because pH is logarithmic:

\[ \frac{[\text{H}^+]_{\text{now}}}{[\text{H}^+]_{\text{pre-industrial}}} = 10^{-(8.1 - 8.2)} = 10^{0.1} \approx 1.26 \]

This represents a ~26% increase in hydrogen ion concentration(often rounded to 30% when including spatial variability). Under RCP 8.5, pH could drop to ~7.7 by 2100, representing a 150% increase in H\(^+\) — unprecedented in the last 20 million years.

Effect on Calcification: Aragonite Saturation

The critical biological impact is on organisms that build calcium carbonate shells and skeletons (corals, pteropods, coccolithophores, foraminifera):

\[ \Omega_{\text{aragonite}} = \frac{[\text{Ca}^{2+}][\text{CO}_3^{2-}]}{K_{\text{sp}}} \]

When \(\Omega > 1\): seawater is supersaturated — CaCO\(_3\) precipitation is thermodynamically favorable
When \(\Omega < 1\): seawater is undersaturated — existing CaCO\(_3\) dissolves
Coral reef growth typically requires \(\Omega > 3\); at \(\Omega < 2\), reef erosion exceeds accretion

As CO\(_2\) increases, the reaction \(\text{CO}_2 + \text{H}_2\text{O} + \text{CO}_3^{2-} \rightarrow 2\text{HCO}_3^-\)consumes carbonate ions, directly reducing \(\Omega\). Since pre-industrial times, surface \(\Omega_{\text{aragonite}}\) has decreased from ~4.5 to ~3.5 globally.

Deriving the relationship between CO\(_2\) and \(\Omega\):

From the equilibrium expressions, we can show that the carbonate ion concentration scales inversely with dissolved CO\(_2\):

\[ [\text{CO}_3^{2-}] = \frac{K_1 K_2 \cdot [\text{CO}_2]}{[\text{H}^+]^2} = \frac{K_2 \cdot \text{DIC}}{1 + \frac{[\text{H}^+]}{K_2} + \frac{[\text{H}^+]^2}{K_1 K_2}} \]

Since increasing CO\(_2\) increases \([\text{H}^+]\) (lowers pH), the denominator grows and \([\text{CO}_3^{2-}]\) decreases, thus \(\Omega\) decreases. A doubling of atmospheric CO\(_2\) reduces surface \(\Omega\) by approximately 30%.

2. Coral Bleaching

Coral bleaching is the breakdown of the mutualistic symbiosis between reef-building corals and their endosymbiotic algae (Symbiodiniaceae, formerly zooxanthellae). These dinoflagellate algae provide up to 90% of the coral's energy through photosynthesis, and their pigments give corals their characteristic colors.

The Bleaching Mechanism

Under thermal stress (typically 1-2\(°\)C above the local summer maximum for extended periods), a cascade of biochemical events leads to symbiont expulsion:

Photosystem II damage: Excess temperature damages the D1 protein in PSII reaction centers, disrupting electron flow
ROS generation: Blocked electron transport leads to triplet chlorophyll formation, which reacts with O\(_2\) to produce reactive oxygen species (superoxide \(\text{O}_2^{\bullet-}\), singlet oxygen \(^1\text{O}_2\), hydrogen peroxide \(\text{H}_2\text{O}_2\)):\[ {}^3\text{Chl}^* + \text{O}_2 \rightarrow {}^1\text{O}_2 \quad (\text{singlet oxygen}) \]
Antioxidant overwhelm: When ROS production exceeds the detoxification capacity of superoxide dismutase (SOD), catalase, and ascorbate peroxidase, oxidative damage spreads to host tissues
Symbiont expulsion: The coral host initiates exocytosis, apoptosis, or in situ degradation of symbiont cells — resulting in the white “bleached” appearance

Thermal Threshold Model: Degree Heating Weeks

NOAA's Coral Reef Watch uses Degree Heating Weeks (DHW) to predict bleaching events. DHW accumulates thermal stress above the local bleaching threshold:

\[ \text{DHW} = \frac{1}{7} \sum_{\text{days}} \max(T_{\text{SST}} - T_{\text{threshold}}, 0) \]

where \(T_{\text{threshold}}\) is typically the maximum of the monthly mean (MMM) SST + 1\(°\)C, summed over the preceding 12 weeks.

The logistic bleaching probability model:

\[ P(\text{bleaching}) = \frac{1}{1 + \exp\!\left(-k(\text{DHW} - \text{DHW}_{\text{crit}})\right)} \]

\(\text{DHW}_{\text{crit}}\) = species-specific threshold (typically 4-8),\(k\) = sensitivity parameter

NOAA alert thresholds:

Bleaching Watch: DHW \(\geq\) 0, expected to exceed threshold
Alert Level 1 (DHW \(\geq\) 4): Significant bleaching likely
Alert Level 2 (DHW \(\geq\) 8): Severe bleaching and mortality likely

The 2014-2017 global bleaching event (the longest on record) affected 75% of the world's tropical reefs, with DHW values exceeding 16 in many regions.

3. Bioluminescence

Bioluminescence — the production of light by living organisms — has evolved independently at least 50 times across the tree of life. In the deep ocean (below 200 m), an estimated 76% of organisms are bioluminescent, making it the most common form of communication in the largest habitat on Earth.

The Core Chemistry

\[ \text{Luciferin} + \text{O}_2 \xrightarrow{\text{Luciferase}} \text{Oxyluciferin}^* \rightarrow \text{Oxyluciferin} + h\nu \]

The excited-state oxyluciferin (*) relaxes to ground state by emitting a photon

“Luciferin” and “luciferase” are generic terms — at least 11 chemically distinct luciferin/luciferase systems have been identified. The most well-characterized systems:

Firefly (Photinus pyralis)

Uses D-luciferin + ATP + O\(_2\) \(\rightarrow\) oxyluciferin + AMP + PPi + CO\(_2\) + h\(\nu\). Quantum yield \(\Phi = 0.88\) — the highest of any chemiluminescent reaction known. Emission at 562 nm (yellow-green). The ATP requirement makes it unique among bioluminescent systems.

Marine (Coelenterazine-based)

Coelenterazine is the most widespread marine luciferin, used by cnidarians, ctenophores, crustaceans, and fish. In Aequorea victoria, the photoprotein aequorin produces blue light (470 nm) that is then shifted to green (508 nm) by GFP (Green Fluorescent Protein) — the basis for the 2008 Nobel Prize in Chemistry.

Chemiluminescent Quantum Yield

The quantum yield is the product of three efficiencies:

\[ \Phi_{CL} = \Phi_C \cdot \Phi_{ES} \cdot \Phi_F \]

\(\Phi_C\) = chemical yield (fraction of substrate that reacts to form the excited product)
\(\Phi_{ES}\) = excitation yield (fraction of product molecules formed in the excited state)
\(\Phi_F\) = fluorescence yield (fraction of excited molecules that emit a photon vs non-radiative decay)

For firefly: \(\Phi_C \approx 1.0\), \(\Phi_{ES} \approx 1.0\),\(\Phi_F \approx 0.88\), giving overall \(\Phi_{CL} = 0.88\). For comparison, the best artificial chemiluminescent systems achieve \(\Phi \approx 0.05\text{-}0.15\).

Dinoflagellate Bioluminescence: Mechanically Triggered

Marine dinoflagellates (e.g., Noctiluca scintillans, Pyrocystis) produce the spectacular blue glow seen in breaking waves and boat wakes. Their bioluminescence is mechanically triggered:

The shear stress threshold model:

\[ L(t) = L_{\max} \cdot \Theta(\tau - \tau_{\text{crit}}) \cdot \exp\!\left(-\frac{t}{\tau_{\text{decay}}}\right) \]

where \(\Theta\) is the Heaviside step function, \(\tau\) is fluid shear stress,\(\tau_{\text{crit}} \approx 0.1\text{-}1\) dyn/cm\(^2\), and\(\tau_{\text{decay}} \approx 100\) ms. The mechanism involves:

Mechanical deformation activates stretch-activated Ca\(^{2+}\) channels in the cell membrane
Ca\(^{2+}\) influx triggers an action potential that propagates along the tonoplast membrane
H\(^+\) is released from vacuole into cytoplasmic “scintillons” (specialized organelles containing luciferin and luciferase)
pH drop activates dinoflagellate luciferase (optimal at pH 6, inactive at pH 8)
Flash duration ~100 ms, emitting \(\sim 10^8\) photons at 474 nm (blue)

The ecological function is debated: the “burglar alarm” hypothesis suggests that bioluminescence attracts predators of the dinoflagellate's grazers, providing indirect defense. The “startle response” hypothesis proposes that the flash directly startles and deters copepod grazers.

4. Extremophile Biochemistry

Extremophiles thrive in environments that would be lethal to most organisms. Their biochemical adaptations reveal the fundamental limits of molecular stability and have provided biotechnologically invaluable enzymes.

Thermophiles: Proteins That Thrive at 100\(°\)C

The most famous thermophilic enzyme is Taq polymerase from Thermus aquaticus (discovered in Yellowstone hot springs, \(T_{\text{opt}} = 72°\)C), which revolutionized biology by enabling PCR (polymerase chain reaction). Thermophilic protein stability comes from:

Increased ionic interactions (salt bridges): ~30% more than mesophilic homologs
Tighter hydrophobic packing: reduced internal cavities
Shorter surface loops: less conformational flexibility
Higher proline content: restricts backbone flexibility
Disulfide bonds in intracellular proteins (rare in mesophiles)

Halophiles: Compatible Solutes

Organisms in hypersaline environments (e.g., Dead Sea, salt evaporation ponds) face osmotic stress — water exits the cell down its concentration gradient. Two strategies evolved:

“Salt-in” Strategy

Haloarchaea (e.g., Halobacterium) accumulate KCl intracellularly to match external osmolality. All proteins must be adapted to function in ~4 M KCl — characterized by excess negative surface charges (acidic proteome, pI ~4.5).

“Compatible Solute” Strategy

Most halophilic bacteria synthesize organic osmolytes — betaine (glycine betaine), ectoine, trehalose, or proline — that balance osmotic pressure without disrupting protein function. These “compatible solutes” are preferentially excluded from protein surfaces, thermodynamically stabilizing the native state.

Membrane Fluidity vs Temperature: Lipid Phase Transition

All organisms must maintain membrane fluidity within a narrow range for proper function of membrane proteins, transport, and signaling. The lipid bilayer undergoes a phase transition from gel (ordered) to liquid-crystalline (disordered) state:

\[ \eta(T) = \eta_0 \cdot \exp\!\left(\frac{E_\eta}{RT}\right) \cdot \frac{1}{1 + \exp\!\left(\frac{\Delta H_m}{R}\left(\frac{1}{T} - \frac{1}{T_m}\right)\right)} \]

Membrane viscosity \(\eta\) as a function of temperature, combining Arrhenius behavior with a phase transition at \(T_m\)

Homeoviscous adaptation strategies:

Adaptation	Effect on \(T_m\)	Used by
Increase unsaturation (C=C bonds)	Lower \(T_m\)	Psychrophiles, plants in cold
Shorter acyl chains	Lower \(T_m\)	Cold-adapted bacteria
Branched chains (iso/anteiso)	Lower \(T_m\)	Psychrophilic Bacillus
Ether-linked lipids	Higher \(T_m\)	Thermophilic archaea
Tetraether monolayer membranes	Much higher \(T_m\)	Hyperthermophilic archaea

The desaturase response — a molecular thermostat:

When temperature drops, membrane-bound desaturases introduce double bonds into existing fatty acids:

\[ \text{R-CH}_2\text{-CH}_2\text{-R'} + \text{O}_2 + \text{NAD(P)H} \xrightarrow{\Delta\text{-desaturase}} \text{R-CH=CH-R'} + \text{H}_2\text{O} + \text{NAD(P)}^+ \]

The cis double bond introduces a ~30\(°\) kink, disrupting packing and lowering \(T_m\) by ~10-15\(°\)C per bond

5. Ocean Carbonate Chemistry Diagram

The following diagram illustrates the ocean carbonate buffering system, from atmospheric CO\(_2\) dissolution to aragonite saturation zones at depth:

6. Computational Simulations

The following simulations model: (1) ocean pH projections under three RCP scenarios; (2) coral bleaching probability as a function of degree heating weeks for species with different thermal sensitivities; (3) bioluminescence quantum yield comparison across taxa; and (4) aragonite saturation depth under different CO\(_2\) scenarios.

Python

script.py211 lines

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

# ══════════════════════════════════════════════════════════════════════
# Panel 1: Ocean pH Projection under RCP scenarios
# ══════════════════════════════════════════════════════════════════════
years = np.arange(1750, 2101)

# CO2 concentrations (approximate, ppm)
def co2_rcp(year, scenario='rcp85'):
    if scenario == 'rcp26':
        if year < 2020: return 280 + (420 - 280) * (year - 1750) / (2020 - 1750)
        elif year < 2050: return 420 + (440 - 420) * (year - 2020) / 30
        else: return 440 - (440 - 400) * (year - 2050) / 50
    elif scenario == 'rcp45':
        if year < 2020: return 280 + (420 - 280) * (year - 1750) / (2020 - 1750)
        elif year < 2060: return 420 + (540 - 420) * (year - 2020) / 40
        else: return 540 + (540 - 530) * np.exp(-0.02 * (year - 2060))
    else:  # rcp85
        if year < 2020: return 280 + (420 - 280) * (year - 1750) / (2020 - 1750)
        else: return 420 + (year - 2020) * 5 + 0.03 * (year - 2020)**2

# Simplified pH model: pH ≈ -log10(K1 * pCO2 / [HCO3-])
# Empirical: pH ≈ 8.35 - 0.0015 * (CO2 - 280)
def ocean_ph(co2):
    return 8.35 - 0.0015 * (co2 - 280)

scenarios = {'RCP 2.6': 'rcp26', 'RCP 4.5': 'rcp45', 'RCP 8.5': 'rcp85'}
colors_rcp = {'RCP 2.6': '#2dd4bf', 'RCP 4.5': '#fbbf24', 'RCP 8.5': '#f87171'}

# ══════════════════════════════════════════════════════════════════════
# Panel 2: Coral Bleaching — Degree Heating Weeks Model
# ══════════════════════════════════════════════════════════════════════
DHW = np.linspace(0, 20, 200)  # degree heating weeks

def bleaching_probability(dhw, dhw_crit=4, k=0.8):
    """Logistic bleaching probability model"""
    return 1 / (1 + np.exp(-k * (dhw - dhw_crit)))

# Different coral species sensitivities
corals = {
    'Acropora (sensitive)': {'dhw_crit': 3, 'k': 1.2, 'color': '#f87171'},
    'Pocillopora (moderate)': {'dhw_crit': 5, 'k': 0.8, 'color': '#fbbf24'},
    'Porites (resistant)': {'dhw_crit': 8, 'k': 0.6, 'color': '#2dd4bf'},
}

# ══════════════════════════════════════════════════════════════════════
# Panel 3: Bioluminescence Quantum Yield Comparison
# ══════════════════════════════════════════════════════════════════════
organisms_bio = [
    'Firefly\n(Photinus)', 'Click beetle\n(Pyrophorus)', 'Jellyfish\n(Aequorea)',
    'Dinoflagellate\n(Noctiluca)', 'Anglerfish\n(Melanocetus)', 'Copepod\n(Metridia)',
    'Squid\n(Watasenia)', 'Bacterial\n(Vibrio)'
]
quantum_yields = [0.88, 0.61, 0.23, 0.12, 0.15, 0.08, 0.30, 0.05]
wavelengths = [562, 546, 508, 474, 480, 460, 462, 490]  # peak emission (nm)

# ══════════════════════════════════════════════════════════════════════
# Panel 4: Carbonate Saturation Depth vs CO2
# ══════════════════════════════════════════════════════════════════════
depth = np.linspace(0, 5000, 500)  # ocean depth (m)

# Aragonite saturation state decreases with depth (pressure increases Ksp)
def omega_aragonite(depth_m, surface_omega=3.5, co2_factor=1.0):
    """Aragonite saturation state vs depth"""
    # Ksp increases with pressure (depth)
    # [Ca2+] roughly constant, [CO3^2-] decreases with depth
    co3_surface = 240 * co2_factor  # umol/kg (decreases with higher CO2)
    co3 = co3_surface * np.exp(-depth_m / 3000)
    Ksp = 6.65e-7 * np.exp(depth_m * 0.00013)  # pressure effect
    Ca = 10.3e-3  # mol/kg (roughly constant)
    omega = Ca * co3 * 1e-6 / Ksp
    return omega

co2_scenarios_depth = {
    'Pre-industrial\n(280 ppm)': {'factor': 1.2, 'color': '#2dd4bf'},
    'Current\n(420 ppm)': {'factor': 1.0, 'color': '#fbbf24'},
    'Year 2100 RCP4.5\n(540 ppm)': {'factor': 0.8, 'color': '#f87171'},
    'Year 2100 RCP8.5\n(940 ppm)': {'factor': 0.5, 'color': '#dc2626'},
}

# ══════════════════════════════════════════════════════════════════════
# Plotting
# ══════════════════════════════════════════════════════════════════════
fig, axes = plt.subplots(2, 2, figsize=(15, 12))
fig.patch.set_facecolor('#0a0a1a')

# Plot 1: Ocean pH projection
ax1 = axes[0, 0]
for name, scen in scenarios.items():
    co2_vals = np.array([co2_rcp(y, scen) for y in years])
    ph_vals = ocean_ph(co2_vals)
    ax1.plot(years, ph_vals, color=colors_rcp[name], linewidth=2.5, label=name)

ax1.axhline(8.1, color='gray', linewidth=0.8, linestyle=':', alpha=0.5)
ax1.text(1760, 8.11, 'Current pH ≈ 8.1', color='gray', fontsize=9)
ax1.axhline(7.7, color='#f87171', linewidth=0.8, linestyle=':', alpha=0.5)
ax1.text(1760, 7.71, 'Critical threshold (pH 7.7)', color='#f87171', fontsize=9)
ax1.fill_between(years, 7.6, 7.7, alpha=0.05, color='#f87171')
ax1.set_xlabel('Year', color='white', fontsize=11)
ax1.set_ylabel('Surface Ocean pH', color='white', fontsize=11)
ax1.set_title('Ocean pH Under RCP Scenarios\n(30% H⁺ increase since pre-industrial)', color='white', fontsize=12, fontweight='bold')
ax1.legend(fontsize=10, facecolor='#1a1a2e', edgecolor='#2dd4bf', labelcolor='white')
ax1.set_facecolor('#0a0a1a')
ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.15, color='#2dd4bf')
ax1.set_ylim(7.5, 8.4)
for sp in ax1.spines.values(): sp.set_color('#2dd4bf')

# Plot 2: Coral bleaching probability
ax2 = axes[0, 1]
for name, params in corals.items():
    P = bleaching_probability(DHW, params['dhw_crit'], params['k'])
    ax2.plot(DHW, P * 100, color=params['color'], linewidth=2.5, label=name)

ax2.axhline(50, color='gray', linewidth=0.8, linestyle=':', alpha=0.5)
ax2.text(15, 52, '50% threshold', color='gray', fontsize=9)
ax2.axvline(4, color='#a78bfa', linewidth=1, linestyle='--', alpha=0.5)
ax2.text(4.2, 10, 'NOAA Alert 1\n(DHW=4)', color='#a78bfa', fontsize=9)
ax2.axvline(8, color='#a78bfa', linewidth=1, linestyle='--', alpha=0.5)
ax2.text(8.2, 10, 'Alert 2\n(DHW=8)', color='#a78bfa', fontsize=9)
ax2.set_xlabel('Degree Heating Weeks (DHW = Σ(T - T_threshold) × weeks)', color='white', fontsize=11)
ax2.set_ylabel('Bleaching Probability (%)', color='white', fontsize=11)
ax2.set_title('Coral Bleaching Probability\nP = 1/(1 + exp(-k(DHW - DHW_crit)))', color='white', fontsize=12, fontweight='bold')
ax2.legend(fontsize=10, facecolor='#1a1a2e', edgecolor='#2dd4bf', labelcolor='white')
ax2.set_facecolor('#0a0a1a')
ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.15, color='#2dd4bf')
for sp in ax2.spines.values(): sp.set_color('#2dd4bf')

# Plot 3: Bioluminescence quantum yields
ax3 = axes[1, 0]
# Color bars by wavelength (blue → green → yellow)
bar_colors = plt.cm.cool(np.array(wavelengths) / 600)
bars = ax3.barh(organisms_bio, quantum_yields, color=bar_colors, alpha=0.85,
                edgecolor='white', linewidth=0.8, height=0.7)
for bar, qy, wl in zip(bars, quantum_yields, wavelengths):
    ax3.text(bar.get_width() + 0.01, bar.get_y() + bar.get_height()/2,
             f'Φ={qy:.2f} ({wl}nm)', va='center', color='white', fontsize=9)
ax3.set_xlabel('Quantum Yield (Φ = photons / molecules)', color='white', fontsize=11)
ax3.set_title('Bioluminescence Quantum Yields\n(bar color ≈ emission wavelength)', color='white', fontsize=12, fontweight='bold')
ax3.set_xlim(0, 1.1)
ax3.set_facecolor('#0a0a1a')
ax3.tick_params(colors='white')
ax3.grid(True, alpha=0.15, color='#2dd4bf', axis='x')
for sp in ax3.spines.values(): sp.set_color('#2dd4bf')

# Plot 4: Carbonate saturation depth
ax4 = axes[1, 1]
for name, params in co2_scenarios_depth.items():
    omega = omega_aragonite(depth, co2_factor=params['factor'])
    name_clean = name.replace('\n', ' ')
    ax4.plot(omega, depth, color=params['color'], linewidth=2.5, label=name_clean)

ax4.axvline(1, color='white', linewidth=2, linestyle='--', alpha=0.7)
ax4.text(1.1, 200, 'Ω=1 (saturation\nhorizon)', color='white', fontsize=10)
ax4.fill_betweenx(depth, 0, 1, alpha=0.08, color='#f87171')
ax4.text(0.3, 3500, 'Undersaturated\n(shells dissolve)', color='#f87171', fontsize=10, ha='center')
ax4.text(2.5, 500, 'Supersaturated\n(shells stable)', color='#2dd4bf', fontsize=10, ha='center')
ax4.set_xlabel('Aragonite Saturation State (Ω)', color='white', fontsize=11)
ax4.set_ylabel('Ocean Depth (m)', color='white', fontsize=11)
ax4.set_title('Aragonite Saturation Horizon\nvs CO₂ Scenario', color='white', fontsize=12, fontweight='bold')
ax4.legend(fontsize=8, facecolor='#1a1a2e', edgecolor='#2dd4bf', labelcolor='white', loc='lower right')
ax4.invert_yaxis()
ax4.set_facecolor('#0a0a1a')
ax4.tick_params(colors='white')
ax4.grid(True, alpha=0.15, color='#2dd4bf')
for sp in ax4.spines.values(): sp.set_color('#2dd4bf')

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0a1a')

# ── Numerical Output ──
print("=== Aquatic & Marine Biochemistry Simulations ===\n")

print("--- Ocean Acidification ---")
for name, scen in scenarios.items():
    co2_2100 = co2_rcp(2100, scen)
    ph_2100 = ocean_ph(co2_2100)
    print(f"{name}: CO2(2100) = {co2_2100:.0f} ppm, pH(2100) = {ph_2100:.2f}")
print(f"Pre-industrial pH: {ocean_ph(280):.2f}")
print(f"Current pH: {ocean_ph(420):.2f}")
h_increase = 10**(-(ocean_ph(420))) / 10**(-(ocean_ph(280)))
print(f"H+ increase since pre-industrial: {h_increase:.0f}%\n")

print("--- Coral Bleaching Thresholds ---")
for name, params in corals.items():
    dhw_50 = params['dhw_crit']  # 50% bleaching at DHW_crit
    print(f"{name}: DHW_crit = {dhw_50}, 50% bleaching at DHW ≈ {dhw_50}")

print("\n--- Bioluminescence Records ---")
max_idx = np.argmax(quantum_yields)
print(f"Highest quantum yield: {organisms_bio[max_idx].replace(chr(10), ' ')} (Φ = {quantum_yields[max_idx]})")
print(f"76% of deep-sea organisms are bioluminescent")
print(f"Firefly luciferin-luciferase: most efficient chemiluminescent system known\n")

print("--- Aragonite Saturation ---")
for name, params in co2_scenarios_depth.items():
    omega = omega_aragonite(depth, co2_factor=params['factor'])
    sat_idx = np.where(omega < 1)[0]
    if len(sat_idx) > 0:
        sat_depth = depth[sat_idx[0]]
        name_clean = name.replace('\n', ' ')
        print(f"{name_clean}: saturation horizon at {sat_depth:.0f} m")
    else:
        name_clean = name.replace('\n', ' ')
        print(f"{name_clean}: supersaturated throughout")
print("\nRising CO2 shoals the saturation horizon, threatening deep-water corals and pteropods.")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

7. The Biological Pump & Carbon Sequestration

The biological pump is the suite of processes by which photosynthetically fixed carbon is exported from the surface ocean to the deep sea, effectively sequestering carbon away from the atmosphere for centuries to millennia. It removes approximately 11 Gt C/yr from the surface ocean — comparable to anthropogenic CO\(_2\) emissions.

How the Biological Pump Works

Surface photosynthesis: Phytoplankton fix CO\(_2\) in the euphotic zone (0-200 m), producing ~50 Gt C/yr of marine NPP
Particle formation: Dead cells, fecal pellets, and aggregates (“marine snow”) form sinking particles
Export flux: ~20% of surface production sinks below the euphotic zone (~10 Gt C/yr)
Remineralization: Heterotrophic bacteria decompose sinking particles, releasing CO\(_2\) at depth
Deep burial: Only ~0.2 Gt C/yr reaches the seafloor sediments for long-term storage

The Martin Curve: Particle Flux vs Depth

Martin et al. (1987) described the attenuation of sinking particle flux with depth using a power-law function that has become the standard parameterization of the biological pump:

\[ F(z) = F_0 \cdot \left(\frac{z}{z_0}\right)^{-b} \]

where \(F_0\) = export flux at reference depth \(z_0 = 100\) m, and \(b \approx 0.86\) (the Martin exponent)

Deriving the transfer efficiency: the fraction of export flux that reaches depth \(z\):

\[ T_{\text{eff}}(z) = \frac{F(z)}{F_0} = \left(\frac{z}{100}\right)^{-0.86} \]

Example transfer efficiencies:

At 500 m: \(T_{\text{eff}} = (500/100)^{-0.86} = 0.24\) — 24% of export flux remains
At 1000 m: \(T_{\text{eff}} = (1000/100)^{-0.86} = 0.14\) — 14% remains
At 4000 m (deep sea): \(T_{\text{eff}} = (4000/100)^{-0.86} = 0.04\) — only 4% reaches the abyss

The Ballast Hypothesis

Armstrong et al. (2002) proposed that mineral ballast — CaCO\(_3\) (calcite, aragonite) and biogenic silica (opal from diatom frustules) — protects associated organic matter from microbial degradation during sinking:

\[ F_{\text{org}}(z) = r_{\text{CaCO}_3} \cdot F_{\text{CaCO}_3}(z) + r_{\text{opal}} \cdot F_{\text{opal}}(z) + F_{\text{free}} \cdot \left(\frac{z}{z_0}\right)^{-b_{\text{free}}} \]

where \(r_{\text{CaCO}_3} \approx 0.07\) g C / g CaCO\(_3\) and\(r_{\text{opal}} \approx 0.03\) g C / g opal are the carrying capacities, and \(F_{\text{free}}\) is the unballasted organic flux (which attenuates faster,\(b_{\text{free}} \approx 1.5\text{-}2\)).

Regions dominated by coccolithophores (CaCO\(_3\) producers) export more carbon to the deep than diatom-dominated regions, despite diatoms having higher surface productivity. This has implications for climate: ocean acidification may reduce CaCO\(_3\) ballast and weaken the biological pump.

8. Harmful Algal Blooms (HABs)

Harmful algal blooms are explosive growths of toxin-producing phytoplankton that pose serious threats to aquatic ecosystems, human health, and economies. Climate change and eutrophication are increasing their frequency, duration, and geographic range.

Major Cyanotoxins

Microcystin

A cyclic heptapeptide hepatotoxin produced by Microcystis, Anabaena, and Planktothrix. Mechanism: inhibits protein phosphatases PP1 and PP2A by binding to the catalytic subunit, causing hyperphosphorylation of cytoskeletal proteins \(\rightarrow\) liver cell death. LD\(_{50}\) ~50 \(\mu\)g/kg (mouse, i.p.).

Saxitoxin

A tricyclic bis-guanidinium neurotoxin causing paralytic shellfish poisoning (PSP). Produced by dinoflagellates (Alexandrium) and cyanobacteria. Mechanism: blocks voltage-gated Na\(^+\) channels by binding site 1, preventing action potential propagation. One of the most potent toxins known (LD\(_{50}\) ~10 \(\mu\)g/kg).

Domoic Acid

A tricarboxylic amino acid causing amnesic shellfish poisoning (ASP). Produced by diatoms (Pseudo-nitzschia). Mechanism: structural analog of glutamate — agonist of AMPA/kainate receptors, causing excitotoxic neuronal death especially in the hippocampus\(\rightarrow\) memory loss. Also causes seizures and death at high doses.

Bloom Dynamics: Monod + Droop Model

HAB dynamics can be modeled using the Droop (cell quota) model with luxury nutrient uptake — cells store excess nutrients internally, allowing growth to continue even after external nutrients are depleted:

\[ \frac{dB}{dt} = \mu_{\max} \cdot \left(1 - \frac{Q_{\min}}{Q}\right) \cdot B - \text{loss} \cdot B \]

The full system of equations:

\[ \frac{dB}{dt} = \mu_{\max}\!\left(1 - \frac{Q_{\min}}{Q}\right) B - (m + g) \cdot B \]

\[ \frac{dS}{dt} = -V_{\max} \cdot \frac{S}{K_s + S} \cdot B + I_{\text{loading}} \]

\[ \frac{dQ}{dt} = V_{\max} \cdot \frac{S}{K_s + S} - \mu_{\max}\!\left(1 - \frac{Q_{\min}}{Q}\right) \cdot Q \]

where \(m\) = mortality rate, \(g\) = grazing rate, \(Q\) = internal nutrient quota (mol nutrient / cell), \(Q_{\min}\) = minimum subsistence quota,\(I_{\text{loading}}\) = external nutrient loading rate.

Eutrophication Thresholds

Vollenweider (1968) established critical nutrient loading thresholds for lake eutrophication:

Oligotrophic: Total P < 10 \(\mu\)g/L, Total N < 350 \(\mu\)g/L — clear, well-oxygenated
Mesotrophic: Total P = 10-30 \(\mu\)g/L — transitional
Eutrophic: Total P > 30 \(\mu\)g/L, Total N > 650 \(\mu\)g/L — algal blooms, hypoxia likely
Hypereutrophic: Total P > 100 \(\mu\)g/L — persistent blooms, fish kills, dead zones

The typical bloom sequence: nutrient pulse \(\rightarrow\) exponential growth\(\rightarrow\) nutrient depletion \(\rightarrow\) bloom crash \(\rightarrow\)massive decomposition \(\rightarrow\) hypoxia (\([\text{O}_2] < 2\) mg/L)\(\rightarrow\) dead zone.

9. Deep-Sea Chemosynthesis

The discovery of hydrothermal vent ecosystems in 1977 revolutionized our understanding of the energy sources that can sustain life. At depths of 2,000-4,000 m, far below the photic zone, entire ecosystems thrive on chemosynthesis — the oxidation of reduced inorganic compounds as energy sources for carbon fixation.

Hydrothermal Vent Chemosynthesis

Superheated water (up to 400\(°\)C) emerges from the seafloor carrying dissolved H\(_2\)S, H\(_2\), CH\(_4\), Fe\(^{2+}\), and Mn\(^{2+}\). Chemolithoautotrophic bacteria and archaea oxidize these compounds:

Sulfide oxidation (primary energy source at most vents):

\[ \text{H}_2\text{S} + 2\text{O}_2 \rightarrow \text{SO}_4^{2-} + 2\text{H}^+ \quad \Delta G°' = -798 \text{ kJ/mol} \]

Hydrogen oxidation (Knallgas reaction):

\[ \text{H}_2 + \frac{1}{2}\text{O}_2 \rightarrow \text{H}_2\text{O} \quad \Delta G°' = -237 \text{ kJ/mol} \]

Iron oxidation:

\[ 4\text{Fe}^{2+} + \text{O}_2 + 4\text{H}^+ \rightarrow 4\text{Fe}^{3+} + 2\text{H}_2\text{O} \quad \Delta G°' = -66 \text{ kJ/mol (per Fe)} \]

Riftia tubeworms: A Chemosynthetic Symbiosis

The giant tubeworm Riftia pachyptila (up to 2 m long) has no mouth, gut, or anus. Instead, it houses billions of Gammaproteobacteria endosymbionts in a specialized organ called the trophosome, which can constitute 50% of the worm's body mass.

The worm provides:

H\(_2\)S delivery via specialized hemoglobin (contains zinc-binding site for sulfide, preventing toxicity)
O\(_2\) delivery via a separate hemoglobin with unusually high affinity
CO\(_2\) supply for bacterial carbon fixation (via CBB cycle)

The bacteria fix CO\(_2\) using the Calvin-Benson-Bassham cycle, providing all organic carbon to the worm. Growth rates are extraordinary: up to 85 cm/year — making Riftiaone of the fastest-growing marine invertebrates.

Cold Seeps: Anaerobic Methane Oxidation

Cold seeps release methane from the seafloor without the extreme temperatures of vents. Anaerobic oxidation of methane (AOM) is performed by ANME archaea(anaerobic methanotrophic archaea) in consortium with sulfate-reducing bacteria:

\[ \text{CH}_4 + \text{SO}_4^{2-} \rightarrow \text{HCO}_3^- + \text{HS}^- + \text{H}_2\text{O} \quad \Delta G°' = -16 \text{ kJ/mol} \]

The extremely small \(\Delta G°'\) makes this reaction barely thermodynamically feasible — ANME archaea essentially run reverse methanogenesis. They consume ~90% of the methane released from seafloor sediments (~80 Tg CH\(_4\)/yr), acting as a critical filter preventing catastrophic methane release to the ocean and atmosphere.

Energy Yield Comparison with Photosynthesis

Comparing energy sources for autotrophy:

Energy Source	\(\Delta G°'\) (kJ/mol)	ATP equivalents
H\(_2\)S + 2O\(_2\)	-798	~26
H\(_2\) + \(\frac{1}{2}\)O\(_2\)	-237	~8
Photosynthesis (8 photons)	~1360 (light input)	~26-34
CH\(_4\) + SO\(_4^{2-}\) (AOM)	-16	~0.5

Sulfide oxidation provides comparable energy to photosynthesis per reaction. However, photosynthesis has an essentially unlimited energy supply (sunlight), while chemosynthesis is limited by the flux of reduced compounds from geological processes.

10. Advanced Simulations: Biological Pump, HABs & Chemosynthesis

The following simulations model: (1) the Martin curve for particle flux attenuation with depth including ballast effects; (2) HAB bloom dynamics showing a nutrient pulse triggering bloom growth, nutrient depletion, and crash; (3) chemosynthetic energy yield comparison across different electron donors.

Python

script.py245 lines

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

# ══════════════════════════════════════════════════════════════════════
# Panel 1: Martin Curve — Biological Pump Particle Flux vs Depth
# ══════════════════════════════════════════════════════════════════════
z = np.linspace(100, 5000, 500)  # depth (m)
z0 = 100  # reference depth

# Martin curve with different b values
b_values = {
    'Martin (b=0.86)': {'b': 0.86, 'color': '#2dd4bf', 'ls': '-'},
    'Oligotrophic (b=0.60)': {'b': 0.60, 'color': '#60a5fa', 'ls': '--'},
    'High-latitude (b=1.30)': {'b': 1.30, 'color': '#f87171', 'ls': '--'},
    'Ballast-enhanced (b=0.50)': {'b': 0.50, 'color': '#fbbf24', 'ls': ':'},
}

F0 = 100  # export flux at 100m (mg C/m²/day)

# ══════════════════════════════════════════════════════════════════════
# Panel 2: HAB Bloom Dynamics (Nutrient Pulse → Bloom → Crash)
# ══════════════════════════════════════════════════════════════════════
dt = 0.01
t_max = 60  # days
t = np.arange(0, t_max, dt)
n = len(t)

B = np.zeros(n)    # Bloom biomass (cells/mL)
S = np.zeros(n)    # External nutrient (μmol P/L)
Q = np.zeros(n)    # Cell quota (fmol P/cell)
O2 = np.zeros(n)   # Dissolved oxygen (mg/L)

B[0] = 100       # initial cells
S[0] = 0.5       # background nutrient
Q[0] = 20e-3     # initial quota (fmol/cell)
O2[0] = 8.0      # saturated O2

mu_max = 1.8     # max growth rate (day⁻¹)
Q_min = 2e-3     # minimum quota (fmol/cell)
V_max = 50e-3    # max uptake (fmol P/cell/day)
K_s = 0.3        # half-saturation (μmol/L)
m = 0.1          # mortality (day⁻¹)
g = 0.05         # grazing (day⁻¹)

# Nutrient pulse at day 5
nutrient_pulse_time = 5
nutrient_pulse_amount = 15  # μmol P/L

for i in range(n - 1):
    # Nutrient pulse
    if abs(t[i] - nutrient_pulse_time) < dt:
        S[i] += nutrient_pulse_amount

# Droop growth
    mu = mu_max * max(0, (1 - Q_min / max(Q[i], Q_min * 1.001)))
    V = V_max * S[i] / (K_s + S[i])

dB = (mu - m - g) * B[i]
    dS = -V * B[i] * 1e-6   # convert fmol to μmol
    dQ = V - mu * Q[i]

# O2: production by photosynthesis, consumption by respiration + decomposition
    o2_prod = 0.001 * mu * B[i]
    o2_cons = 0.0005 * m * B[i]  # dead cells consume O2
    o2_reaer = 0.2 * (8.0 - O2[i])  # reaeration
    dO2 = o2_prod - o2_cons + o2_reaer

B[i+1] = max(0, B[i] + dt * dB)
    S[i+1] = max(0, S[i] + dt * dS)
    Q[i+1] = max(Q_min * 1.001, Q[i] + dt * dQ)
    O2[i+1] = max(0, min(12, O2[i] + dt * dO2))

if B[i+1] > 1e7: B[i+1] = 1e7

# ══════════════════════════════════════════════════════════════════════
# Panel 3: Chemosynthetic Energy Yields Comparison
# ══════════════════════════════════════════════════════════════════════
reactions = [
    'H₂S + 2O₂\n→ SO₄²⁻',
    'H₂ + ½O₂\n→ H₂O',
    'NH₄⁺ + 1.5O₂\n→ NO₂⁻',
    'Fe²⁺ + ¼O₂\n→ Fe³⁺',
    'CH₄ + 2O₂\n→ CO₂',
    'CH₄ + SO₄²⁻\n→ HCO₃⁻ (AOM)',
    'S⁰ + 1.5O₂\n→ SO₄²⁻',
    'Mn²⁺ + ½O₂\n→ MnO₂',
]
dG_values = [-798, -237, -275, -66, -818, -16, -507, -163]
ecosystems = ['Vents', 'Vents', 'Nitrifiers', 'Iron seeps', 'Seeps',
              'Cold seeps', 'Vents', 'Sediments']

# ══════════════════════════════════════════════════════════════════════
# Panel 4: Martin Curve — Cumulative Carbon Sequestered
# ══════════════════════════════════════════════════════════════════════
z_fine = np.linspace(100, 5000, 1000)
# Carbon remineralized at each depth = -dF/dz
# F(z) = F0 * (z/z0)^(-b) → dF/dz = -F0*b/z0 * (z/z0)^(-b-1)
b_martin = 0.86
F_martin = F0 * (z_fine / z0) ** (-b_martin)

# Sequestration timescale increases with depth
# Simple model: water age ≈ depth/1000 * 500 years (very rough)
water_age = z_fine / 1000 * 500  # years

# Carbon sequestered = flux reaching depth × residence time
C_sequestered = F_martin * water_age / 1000  # relative units

# Plot 1: Martin curve
ax1 = axes[0, 0]
for name, params in b_values.items():
    F = F0 * (z / z0) ** (-params['b'])
    ax1.plot(F, z, color=params['color'], linewidth=2.5, linestyle=params['ls'], label=name)

ax1.axhline(200, color='#fbbf24', linewidth=1, linestyle=':', alpha=0.5)
ax1.text(70, 220, 'Euphotic zone base', color='#fbbf24', fontsize=9)
ax1.axhline(1000, color='gray', linewidth=1, linestyle=':', alpha=0.3)
ax1.text(70, 1050, 'Mesopelagic/Bathypelagic', color='gray', fontsize=9)

ax1.set_xlabel('Particle Flux (mg C/m²/day)', color='white', fontsize=11)
ax1.set_ylabel('Depth (m)', color='white', fontsize=11)
ax1.set_title('Martin Curve: Biological Pump\nF(z) = F₀(z/z₀)^(-b)', color='white', fontsize=12, fontweight='bold')
ax1.legend(fontsize=9, facecolor='#1a1a2e', edgecolor='#2dd4bf', labelcolor='white', loc='lower right')
ax1.invert_yaxis()
ax1.set_facecolor('#0a0a1a')
ax1.tick_params(colors='white')
ax1.grid(True, alpha=0.15, color='#2dd4bf')
for sp in ax1.spines.values(): sp.set_color('#2dd4bf')

# Plot 2: HAB bloom dynamics
ax2 = axes[0, 1]
color_B = '#f87171'
color_S = '#2dd4bf'
color_O2 = '#60a5fa'

ax2.fill_between(t, 0, B/1000, alpha=0.2, color=color_B)
ln1 = ax2.plot(t, B/1000, color=color_B, linewidth=2.5, label='Bloom biomass (×10³ cells/mL)')
ax2.set_ylabel('Biomass (×10³ cells/mL)', color=color_B, fontsize=11)

ax2b = ax2.twinx()
ln2 = ax2b.plot(t, S, color=color_S, linewidth=2, linestyle='--', label='P nutrient (μmol/L)')
ln3 = ax2b.plot(t, O2, color=color_O2, linewidth=2, linestyle=':', label='Dissolved O₂ (mg/L)')
ax2b.axhline(2, color=color_O2, linewidth=1, linestyle=':', alpha=0.3)
ax2b.text(40, 2.3, 'Hypoxia threshold', color=color_O2, fontsize=9)
ax2b.set_ylabel('Nutrient / O₂', color='white', fontsize=11)
ax2b.tick_params(colors='white')
for sp in ax2b.spines.values(): sp.set_color('#2dd4bf')

# Add pulse annotation
ax2.annotate('Nutrient\npulse', xy=(5, S[int(5/dt)]/1000*0.1), xytext=(10, B.max()/1000*0.8),
            color='#fbbf24', fontsize=10, fontweight='bold',
            arrowprops=dict(arrowstyle='->', color='#fbbf24'))

lns = ln1 + ln2 + ln3
labs = [l.get_label() for l in lns]
ax2.legend(lns, labs, fontsize=8, facecolor='#1a1a2e', edgecolor='#2dd4bf', labelcolor='white', loc='upper right')
ax2.set_xlabel('Time (days)', color='white', fontsize=11)
ax2.set_title('HAB Bloom Dynamics: Nutrient Pulse → Bloom → Crash\n(Droop model with luxury uptake)', color='white', fontsize=12, fontweight='bold')
ax2.set_facecolor('#0a0a1a')
ax2.tick_params(colors='white')
ax2.grid(True, alpha=0.15, color='#2dd4bf')
for sp in ax2.spines.values(): sp.set_color('#2dd4bf')

# Plot 3: Chemosynthetic energy yields
ax3 = axes[1, 0]
colors_chem = ['#2dd4bf', '#60a5fa', '#a78bfa', '#fbbf24', '#f87171', '#dc2626', '#34d399', '#818cf8']
y_pos = np.arange(len(reactions))
bars = ax3.barh(y_pos, [-dg for dg in dG_values], color=colors_chem, alpha=0.85,
                edgecolor='white', linewidth=0.8, height=0.7)
ax3.set_yticks(y_pos)
ax3.set_yticklabels([r.replace('\n', ' ') for r in reactions], fontsize=9)
for bar, dg, eco in zip(bars, dG_values, ecosystems):
    ax3.text(bar.get_width() + 10, bar.get_y() + bar.get_height()/2,
             f'{-dg} kJ/mol ({eco})', va='center', color='white', fontsize=9)

ax3.set_xlabel('-ΔG°\' (kJ/mol)', color='white', fontsize=11)
ax3.set_title('Chemosynthetic Energy Yields\n(Electron Donor Comparison)', color='white', fontsize=12, fontweight='bold')
ax3.set_facecolor('#0a0a1a')
ax3.tick_params(colors='white')
ax3.grid(True, alpha=0.15, color='#2dd4bf', axis='x')
for sp in ax3.spines.values(): sp.set_color('#2dd4bf')

# Plot 4: Carbon sequestration efficiency vs depth
ax4 = axes[1, 1]
transfer_eff = (z_fine / z0) ** (-b_martin) * 100
ax4.plot(transfer_eff, z_fine, color='#2dd4bf', linewidth=3, label='Transfer efficiency (%)')

# Shade remineralization zones
ax4.fill_betweenx(z_fine, 0, transfer_eff, alpha=0.1, color='#2dd4bf')

# Mark key depths
key_depths = [200, 500, 1000, 2000, 4000]
for d in key_depths:
    eff = (d / z0) ** (-b_martin) * 100
    ax4.plot(eff, d, 'o', color='#fbbf24', markersize=8, markeredgecolor='white', markeredgewidth=1.5, zorder=5)
    ax4.annotate(f'{eff:.1f}%', xy=(eff, d), xytext=(eff + 5, d),
                color='#fbbf24', fontsize=10, fontweight='bold')

ax4.set_xlabel('Transfer Efficiency (% of export flux)', color='white', fontsize=11)
ax4.set_ylabel('Depth (m)', color='white', fontsize=11)
ax4.set_title('Biological Pump Transfer Efficiency\n(% of surface export reaching each depth)', color='white', fontsize=12, fontweight='bold')
ax4.legend(fontsize=10, facecolor='#1a1a2e', edgecolor='#2dd4bf', labelcolor='white')
ax4.invert_yaxis()
ax4.set_facecolor('#0a0a1a')
ax4.tick_params(colors='white')
ax4.grid(True, alpha=0.15, color='#2dd4bf')
for sp in ax4.spines.values(): sp.set_color('#2dd4bf')
ax4.set_xlim(0, 105)

plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#0a0a1a')

# ── Numerical Output ──
print("=== Advanced Aquatic & Marine Simulations ===\n")

print("--- Biological Pump (Martin Curve) ---")
for name, params in b_values.items():
    F_1000 = F0 * (1000/z0) ** (-params['b'])
    F_4000 = F0 * (4000/z0) ** (-params['b'])
    print(f"{name}: F(1000m) = {F_1000:.1f}, F(4000m) = {F_4000:.1f} mg C/m²/day")
print(f"Standard Martin: {(1000/100)**(-0.86)*100:.1f}% reaches 1000m, {(4000/100)**(-0.86)*100:.1f}% reaches 4000m\n")

print("--- HAB Bloom Dynamics ---")
peak_idx = np.argmax(B)
print(f"Nutrient pulse: {nutrient_pulse_amount} μmol P/L at day {nutrient_pulse_time}")
print(f"Peak bloom: {B[peak_idx]/1000:.0f} × 10³ cells/mL at day {t[peak_idx]:.1f}")
print(f"Nutrient depleted to {S[peak_idx]:.3f} μmol/L at peak")
min_o2_idx = np.argmin(O2)
print(f"Minimum O₂: {O2[min_o2_idx]:.1f} mg/L at day {t[min_o2_idx]:.1f}\n")

print("--- Chemosynthetic Energy Yields ---")
for rxn, dg, eco in zip(reactions, dG_values, ecosystems):
    rxn_clean = rxn.replace('\n', ' ')
    atp_equiv = abs(dg) / 30.5
    print(f"{rxn_clean}: ΔG°' = {dg} kJ/mol (~{atp_equiv:.1f} ATP equiv.) [{eco}]")
print(f"\nSulfide oxidation (-798 kJ/mol) is comparable to photosynthesis energy input")
print(f"AOM (-16 kJ/mol) is at the thermodynamic limit of life")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

References

Doney, S.C. et al. (2009). Ocean acidification: the other CO2 problem. Annual Review of Marine Science, 1, 169-192.
Hoegh-Guldberg, O. et al. (2007). Coral reefs under rapid climate change and ocean acidification. Science, 318(5857), 1737-1742.
Hughes, T.P. et al. (2018). Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science, 359(6371), 80-83.
Haddock, S.H.D. et al. (2010). Bioluminescence in the sea. Annual Review of Marine Science, 2, 443-493.
Shimomura, O. (2006). Bioluminescence: Chemical Principles and Methods. World Scientific Publishing.
Rothschild, L.J. & Mancinelli, R.L. (2001). Life in extreme environments. Nature, 409(6823), 1092-1101.
Zeebe, R.E. & Wolf-Gladrow, D. (2001). CO2 in Seawater: Equilibrium, Kinetics, Isotopes. Elsevier.
Feely, R.A. et al. (2004). Impact of anthropogenic CO2 on the CaCO3 system in the oceans. Science, 305(5682), 362-366.
Martin, J.H. et al. (1987). VERTEX: carbon cycling in the northeast Pacific. Deep-Sea Research, 34(2), 267-285.
Armstrong, R.A. et al. (2002). A new, mechanistic model for organic carbon fluxes in the ocean based on the quantitative association of POC with ballast minerals. Deep-Sea Research II, 49(1-3), 219-236.
Carmichael, W.W. (2001). Health effects of toxin-producing cyanobacteria. Human and Ecological Risk Assessment, 7(5), 1393-1407.
Van Dover, C.L. (2000). The Ecology of Deep-Sea Hydrothermal Vents. Princeton University Press.

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