Module 7

Conservation Biochemistry

Biomarkers, eDNA, pollution biochemistry, bioremediation, and endocrine disruption

Conservation biology increasingly relies on biochemical tools to assess ecosystem health, detect hidden biodiversity, and remediate contaminated environments. From stress hormones in wildlife hair to environmental DNA in stream water, from mercury biomagnification through food webs to fungal enzymes that degrade petroleum, the molecular level reveals what traditional surveys cannot. This module explores how biochemistry serves as both diagnostic (detecting problems) and therapeutic (implementing solutions) in conservation.

7.1 Biomarkers of Ecosystem Health

A biomarker is any measurable biochemical, physiological, or morphological change in an organism that indicates exposure to or effects of environmental stressors. Unlike chemical monitoring of water or soil, biomarkers integrate cumulative exposure and reveal the biological significance of contamination.

Cortisol: The Stress Hormone

Cortisol, a glucocorticoid synthesized from cholesterol via the hypothalamic-pituitary-adrenal (HPA) axis, is elevated in chronically stressed wildlife. It can be measured non-invasively:

Hair/fur cortisol: Integrates HPA activity over weeks-months as cortisol is incorporated during hair growth. Elevated in bears near roads, wolves in fragmented habitat
Feather corticosterone: In birds, corticosterone (the avian equivalent) deposits in feathers during growth. Reflects breeding season stress
Fecal glucocorticoid metabolites: Non-invasive sampling of scat; metabolites reflect circulating levels with ~12–24 hr lag

The biosynthetic pathway: cholesterol is cleaved by CYP11A1 (cytochrome P450 side-chain cleavage enzyme) to pregnenolone, then sequentially hydroxylated by CYP17A1, CYP21A2, and CYP11B1 to produce cortisol. Chronic elevation suppresses immune function (via NF-\(\kappa\)B inhibition) and reproduction (via GnRH suppression).

Vitellogenin in Male Fish: Endocrine Disruption Marker

Vitellogenin (VTG) is an egg yolk precursor protein normally produced only by females under estrogen stimulation. Detection of VTG in male fish indicates exposure to estrogenic compounds in the water — a classic biomarker of endocrine disruption. Male trout downstream of sewage treatment plants can have VTG levels \(>10^6\)-fold higher than unexposed males.

Chlorophyll Fluorescence: Plant Stress Indicator

When photosystem II (PSII) absorbs light, the energy can follow three pathways: photochemistry, heat dissipation, or fluorescence re-emission. The ratio of variable to maximum fluorescence (\(F_v/F_m\)) measures PSII quantum yield:

PSII Maximum Quantum Yield

\[ \frac{F_v}{F_m} = \frac{F_m - F_0}{F_m} \]

where \(F_0\) is minimum fluorescence (all PSII reaction centers open, dark-adapted) and \(F_m\) is maximum fluorescence (all centers closed by a saturating pulse).

In healthy plants, \(F_v/F_m \approx 0.83\) (remarkably conserved across species). This value reflects the intrinsic efficiency of PSII photochemistry. The derivation:

\[ F_0 = \frac{k_f}{k_f + k_d + k_p} \cdot I_{\text{abs}} \]

\[ F_m = \frac{k_f}{k_f + k_d} \cdot I_{\text{abs}} \]

\[ \frac{F_v}{F_m} = 1 - \frac{F_0}{F_m} = 1 - \frac{k_f + k_d}{k_f + k_d + k_p} \cdot \frac{k_f + k_d}{k_f} \cdot \frac{k_f}{k_f + k_d + k_p} \]

Simplifying with \(k_f\) (fluorescence rate constant), \(k_d\)(thermal dissipation), and \(k_p\) (photochemistry):

\[ \frac{F_v}{F_m} = \frac{k_p}{k_f + k_d + k_p} \]

Stress (drought, heavy metals, ozone) damages the D1 protein of PSII, reducing \(k_p\)and causing \(F_v/F_m\) to decline. Values below 0.75 indicate significant photoinhibition; below 0.60 indicates severe stress.

7.2 Environmental DNA (eDNA)

Organisms constantly shed DNA into their environment — through skin cells, mucus, feces, urine, gametes, and decomposing tissue. This environmental DNA(eDNA) persists in water and soil for days to weeks and can be collected, amplified by PCR, and sequenced to identify species present without ever observing them directly.

eDNA Decay Kinetics

After an organism leaves an area, its eDNA degrades through nuclease activity, UV photolysis, microbial consumption, and hydrolysis. The decay follows first-order kinetics:

eDNA Decay Model

\[ C(t) = C_0 \cdot e^{-\lambda t} \]

where \(C_0\) is initial concentration, \(\lambda\) is the decay rate constant (d\(^{-1}\)), and the half-life is \(t_{1/2} = \ln 2 / \lambda\).

Empirical measurements show:

Freshwater: \(t_{1/2} \approx 1\text{--}7\) days. UV exposure and warm temperature accelerate decay
Marine water: \(t_{1/2} \approx 0.5\text{--}7\) days. Salinity and dilution are additional factors
Soil: \(t_{1/2} \approx 7\text{--}120\) days. DNA binds to clay minerals, slowing degradation
Sediment: \(t_{1/2} \approx 14\text{--}300\) days. Anoxic conditions dramatically slow nuclease activity

The decay rate depends on temperature via the Arrhenius equation:

\[ \lambda(T) = \lambda_0 \cdot \exp\!\left(-\frac{E_a}{R}\left(\frac{1}{T} - \frac{1}{T_0}\right)\right) \]

where \(E_a \approx 50\text{--}80\) kJ/mol for DNA hydrolysis. At 25\(^\circ\)C, decay is roughly 2\(\times\) faster than at 15\(^\circ\)C.

Detection Probability: Multiple Sampling

A single water sample may fail to detect a rare species even when eDNA is present, due to low concentration and stochastic sampling. The probability of detecting a species with \(n\) independent samples, each with per-sample detection probability\(p\), is:

Multi-Sample Detection Probability

\[ P_{\text{detect}} = 1 - (1 - p)^n \]

For a species with per-sample \(p = 0.3\), taking \(n = 5\) samples gives\(P_{\text{detect}} = 1 - 0.7^5 = 0.832\), and \(n = 10\) gives 0.972.

The minimum number of samples needed to achieve detection probability \(P^*\):

\[ n \geq \frac{\ln(1 - P^*)}{\ln(1 - p)} \]

For \(P^* = 0.95\) and \(p = 0.2\): \(n \geq \ln(0.05)/\ln(0.8) = 13.4\), so 14 samples are needed.

Metabarcoding Pipeline

eDNA metabarcoding uses universal primers (e.g., 12S rRNA for vertebrates, COI for invertebrates, ITS for fungi, 16S for bacteria) to amplify DNA from all species simultaneously. The workflow: water filtration (0.22–0.45 \(\mu\)m) \(\to\) DNA extraction \(\to\) PCR amplification \(\to\) high-throughput sequencing \(\to\) bioinformatics (ASV/OTU clustering, taxonomic assignment against reference databases like BOLD or GenBank).

7.3 Pollution Biochemistry

Heavy Metal Toxicity Mechanisms

Heavy metals disrupt biochemistry primarily through ionic mimicry — toxic metals substitute for essential ones in enzyme active sites:

Cadmium (Cd\(^{2+}\)): Replaces Zn\(^{2+}\) in metalloenzymes (carbonic anhydrase, carboxypeptidase, alcohol dehydrogenase). Same charge, similar ionic radius (0.95 vs 0.74 \(\text{\AA}\)), but Cd cannot perform the catalytic function. Also binds thiol groups in metallothioneins.
Lead (Pb\(^{2+}\)): Replaces Ca\(^{2+}\) in calmodulin and bone. Inhibits \(\delta\)-aminolevulinic acid dehydratase (ALAD) in heme biosynthesis, causing anemia. Also inhibits NMDA receptors in neurons.
Arsenic (As): As(V) mimics phosphate, entering cells via phosphate transporters and uncoupling oxidative phosphorylation. As(III) binds vicinal thiol groups in lipoic acid, inhibiting pyruvate dehydrogenase.

Mercury Methylation and Biomagnification

Inorganic mercury (Hg\(^{2+}\)) is converted to methylmercury (CH₃Hg\(^+\)) by sulfate-reducing bacteria in anoxic sediments. The methylation reaction:

\[ \text{Hg}^{2+} + \text{CH}_3\text{-Co(III)-corrinoid} \to \text{CH}_3\text{Hg}^+ + \text{Co(I)-corrinoid} \]

The methyl group is transferred from methylcobalamin (vitamin B₁₂ derivative) to mercury via the hgcAB gene cluster in anaerobic microbes.

Methylmercury biomagnifies through food webs because it is lipophilic and binds cysteine residues in proteins, creating a long biological half-life (~70 days in fish). The bioconcentration factor (BCF) quantifies accumulation:

Bioconcentration Factor

\[ \text{BCF} = \frac{C_{\text{organism}}}{C_{\text{water}}} = \frac{k_{\text{uptake}}}{k_{\text{elimination}}} \]

where \(k_{\text{uptake}}\) and \(k_{\text{elimination}}\) are the first-order rate constants for absorption and excretion, respectively.

Biomagnification occurs when BCF increases with trophic level. For methylmercury, the trophic magnification factor (TMF) is approximately 3–10\(\times\) per trophic level. A simple model for concentration at trophic level \(n\):

\[ C_n = C_{\text{water}} \cdot \text{BCF}_1 \cdot \text{TMF}^{(n-1)} \]

With \(C_{\text{water}} = 1\) ng/L, BCF\(_1 = 10^4\) for phytoplankton, and TMF = 5: trophic level 4 (large predatory fish) reaches \(10^4 \times 5^3 = 1.25 \times 10^6\) ng/kg = 1.25 mg/kg, exceeding the 0.3 mg/kg FDA action level.

7.4 Bioremediation Pathways

Bioremediation uses living organisms or their enzymes to degrade, transform, or sequester environmental contaminants. Three major approaches:

Phytoremediation: Hyperaccumulator Plants

Approximately 700 plant species are known to hyperaccumulate heavy metals, concentrating them at levels 100–1000\(\times\) higher than normal plants:

Thlaspi caerulescens: Zn/Cd hyperaccumulator. HMA4 transporter (P-type ATPase) loads metals into xylem; vacuolar sequestration via phytochelatins
Pteris vittata: As hyperaccumulator (brake fern). Reduces As(V) to As(III) and stores in vacuoles complexed with thiol peptides
Alyssum bertolonii: Ni hyperaccumulator. Chelation by histidine and organic acids (citrate, malate) in xylem sap

Microbial Petroleum Degradation

Hydrocarbon-degrading bacteria (e.g., Alcanivorax borkumensis, Pseudomonas putida) oxidize alkanes using alkane hydroxylase (AlkB) and cytochrome P450 (CYP153):

\[ \text{R-CH}_3 + \text{O}_2 + \text{NAD(P)H} + \text{H}^+ \xrightarrow{\text{AlkB}} \text{R-CH}_2\text{OH} + \text{NAD(P)}^+ + \text{H}_2\text{O} \]

The terminal hydroxylation is followed by sequential oxidation to aldehyde, then carboxylic acid, which enters \(\beta\)-oxidation for energy production.

Mycoremediation: Fungal Laccase

White-rot fungi (e.g., Phanerochaete chrysosporium, Trametes versicolor) produce extracellular laccase and lignin peroxidase that oxidize a remarkable range of pollutants, including PAHs, PCBs, dioxins, and pesticides. These enzymes evolved to degrade lignin — the most recalcitrant natural polymer — and their non-specific radical mechanism allows them to attack structurally similar synthetic pollutants.

Biodegradation Kinetics: Monod Model

Pollutant degradation by microbial communities follows Monod kinetics, coupling substrate consumption to microbial growth:

Monod Biodegradation Model

\[ \frac{dS}{dt} = -\frac{\mu_{\max} \cdot S}{K_s + S} \cdot \frac{X}{Y} \]

\[ \frac{dX}{dt} = \frac{\mu_{\max} \cdot S}{K_s + S} \cdot X - b \cdot X \]

where \(S\) = substrate concentration, \(X\) = microbial biomass,\(\mu_{\max}\) = maximum specific growth rate, \(K_s\) = half-saturation constant,\(Y\) = yield coefficient (biomass/substrate), and \(b\) = endogenous decay rate.

At low substrate concentrations (\(S \ll K_s\)), the kinetics become pseudo-first-order:

\[ \frac{dS}{dt} \approx -\frac{\mu_{\max}}{K_s \cdot Y} \cdot X \cdot S = -k_1 \cdot S \]

where \(k_1 = \mu_{\max} X / (K_s Y)\) is the apparent first-order rate constant, giving exponential decay: \(S(t) = S_0 e^{-k_1 t}\).

7.5 Endocrine Disruptors

Endocrine-disrupting chemicals (EDCs) are exogenous substances that interfere with hormonal signaling, even at extremely low concentrations (parts per trillion). The most studied class are xenoestrogens — synthetic chemicals that mimic 17\(\beta\)-estradiol (E2) at the estrogen receptor (ER).

Major Xenoestrogens

Bisphenol A (BPA): Plasticizer in polycarbonate and epoxy resins. Binds ER\(\alpha\) with RBA ~0.01–0.1% relative to E2
Atrazine: Herbicide. Induces aromatase (CYP19) expression, converting testosterone to estrogen in male amphibians, causing feminization
Ethinyl estradiol (EE2): Synthetic estrogen in contraceptive pills, the most potent EDC in wastewater. RBA ~100% (equal to E2)
PCBs: Persistent organic pollutants. Some congeners are estrogenic, others anti-estrogenic. Bioaccumulate in adipose tissue
Nonylphenol: Surfactant degradation product. Binds ER with RBA ~0.01%. Ubiquitous in wastewater

Competitive Binding: IC50 and Relative Binding Affinity

The binding of a xenoestrogen \(X\) to the estrogen receptor competes with the natural ligand E2. In a competitive binding assay, increasing concentrations of \(X\)displace radiolabeled E2 from the receptor:

Competitive Binding Model

\[ \frac{[\text{ER} \cdot \text{E2}]}{[\text{ER}]_{\text{total}}} = \frac{1}{1 + \frac{[\text{X}]}{K_i}} \cdot \frac{[\text{E2}]}{K_d + [\text{E2}]} \]

The \(\text{IC}_{50}\) is the concentration of competitor \(X\) that displaces 50% of bound E2. The relative binding affinity (RBA) normalizes this to E2:

Relative Binding Affinity

\[ \text{RBA} = \frac{\text{IC}_{50}(\text{E2})}{\text{IC}_{50}(\text{X})} \times 100\% \]

E2 has RBA = 100% by definition. BPA has RBA \(\approx\) 0.01–0.1%, meaning it binds ER 1000–10,000\(\times\) more weakly than E2. However, environmental concentrations of BPA can be \(10^4\text{--}10^6\times\) higher than E2, partially compensating for weak affinity.

The relationship to the Cheng-Prusoff equation for deriving \(K_i\) from \(\text{IC}_{50}\):

\[ K_i = \frac{\text{IC}_{50}}{1 + \frac{[\text{E2}]}{K_d}} \]

where \(K_d\) is the dissociation constant for E2-ER binding (~0.1–1 nM).

7.6 Mercury Biomagnification Pyramid

The diagram below illustrates how methylmercury concentration increases approximately 10-fold at each trophic level, from dissolved mercury in water through phytoplankton, zooplankton, small fish, large predatory fish, and finally to apex predators like bald eagles.

7.7 Computational Simulations

Four simulations exploring conservation biochemistry: (1) eDNA decay in different environments, (2) bioconcentration factors across taxa, (3) Monod-model biodegradation kinetics, and (4) mercury biomagnification through a food web.

Conservation Biochemistry: eDNA, BCF, Biodegradation & Biomagnification

Python

script.py163 lines

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

np.random.seed(42)
fig, axes = plt.subplots(2, 2, figsize=(14, 11))

# ====== Panel 1: eDNA Decay in Different Environments ======
ax = axes[0, 0]
t = np.linspace(0, 60, 200)  # days

environments = {
    'Warm stream (25\u00b0C)': {'lambda': 0.7, 'color': '#ef4444'},
    'Cool lake (15\u00b0C)': {'lambda': 0.2, 'color': '#f59e0b'},
    'Marine (20\u00b0C)': {'lambda': 0.35, 'color': '#3b82f6'},
    'Soil': {'lambda': 0.05, 'color': '#a78bfa'},
    'Sediment': {'lambda': 0.015, 'color': '#2dd4bf'},
}

C0 = 1.0  # normalized initial concentration
for env, params in environments.items():
    C = C0 * np.exp(-params['lambda'] * t)
    half_life = np.log(2) / params['lambda']
    ax.plot(t, C, color=params['color'], linewidth=2.5,
            label=f"{env} (t\u00bd={half_life:.1f}d)")

# Detection threshold
ax.axhline(y=0.01, color='#f97316', linewidth=1, linestyle='--', alpha=0.7)
ax.text(45, 0.015, 'Detection threshold', color='#f97316', fontsize=10)

ax.set_xlabel('Time (days)', fontsize=12, color='#99f6e4')
ax.set_ylabel('Relative eDNA Concentration', fontsize=12, color='#99f6e4')
ax.set_title('eDNA Decay Kinetics', fontsize=14, fontweight='bold', color='#5eead4')
ax.set_yscale('log')
ax.set_ylim(0.001, 2)
ax.legend(fontsize=8.5, facecolor='#0a0a0a', edgecolor='#5eead4',
          labelcolor='#d1d5db', loc='upper right')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4')

# ====== Panel 2: BCF Across Species ======
ax = axes[0, 1]
organisms = ['Algae', 'Daphnia', 'Snail', 'Minnow', 'Trout', 'Pike', 'Osprey']
bcf_hg = [1e4, 5e4, 8e4, 2e5, 5e5, 1e6, 3e6]
bcf_pcb = [5e3, 2e4, 3e4, 1e5, 3e5, 8e5, 2e6]
bcf_ddt = [8e3, 3e4, 5e4, 1.5e5, 4e5, 9e5, 2.5e6]

x = np.arange(len(organisms))
width = 0.25
ax.bar(x - width, bcf_hg, width, color='#ef4444', label='MeHg', edgecolor='#0a0a0a')
ax.bar(x, bcf_pcb, width, color='#3b82f6', label='PCBs', edgecolor='#0a0a0a')
ax.bar(x + width, bcf_ddt, width, color='#2dd4bf', label='DDT', edgecolor='#0a0a0a')

ax.set_xticks(x)
ax.set_xticklabels(organisms, rotation=30, ha='right')
ax.set_yscale('log')
ax.set_xlabel('Organism', fontsize=12, color='#99f6e4')
ax.set_ylabel('Bioconcentration Factor', fontsize=12, color='#99f6e4')
ax.set_title('BCF Across Trophic Levels', fontsize=14, fontweight='bold', color='#5eead4')
ax.legend(fontsize=10, facecolor='#0a0a0a', edgecolor='#5eead4', labelcolor='#d1d5db')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4', axis='y')

# ====== Panel 3: Monod Biodegradation Kinetics ======
ax = axes[1, 0]
# Solve Monod equations numerically
dt_sim = 0.01  # days
t_total = 30
n_steps = int(t_total / dt_sim)

# Parameters
mu_max = 0.5   # per day
Ks = 50.0      # mg/L
Y = 0.4        # biomass yield
b = 0.05       # decay rate

# Initial conditions for different scenarios
scenarios = [
    {'S0': 200, 'X0': 5, 'label': 'High substrate (200 mg/L)', 'color': '#2dd4bf'},
    {'S0': 100, 'X0': 5, 'label': 'Medium substrate (100 mg/L)', 'color': '#f59e0b'},
    {'S0': 50, 'X0': 5, 'label': 'Low substrate (50 mg/L)', 'color': '#a78bfa'},
]

for sc in scenarios:
    S = np.zeros(n_steps)
    X = np.zeros(n_steps)
    t_arr = np.zeros(n_steps)
    S[0] = sc['S0']
    X[0] = sc['X0']

for i in range(1, n_steps):
        mu = mu_max * S[i-1] / (Ks + S[i-1])
        dS = -(mu / Y) * X[i-1] * dt_sim
        dX = (mu - b) * X[i-1] * dt_sim
        S[i] = max(0, S[i-1] + dS)
        X[i] = max(0, X[i-1] + dX)
        t_arr[i] = i * dt_sim

ax.plot(t_arr, S, color=sc['color'], linewidth=2.5, label=sc['label'])
    ax.plot(t_arr, X, color=sc['color'], linewidth=1.5, linestyle='--', alpha=0.6)

ax.set_xlabel('Time (days)', fontsize=12, color='#99f6e4')
ax.set_ylabel('Concentration (mg/L)', fontsize=12, color='#99f6e4')
ax.set_title('Monod Biodegradation (solid=S, dashed=X)', fontsize=13, fontweight='bold', color='#5eead4')
ax.legend(fontsize=9, facecolor='#0a0a0a', edgecolor='#5eead4', labelcolor='#d1d5db')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4')

# ====== Panel 4: Hg Biomagnification Through Food Web ======
ax = axes[1, 1]
trophic_levels = ['Water', 'Phytopl.', 'Zoopl.', 'Sm. Fish', 'Lg. Fish', 'Eagle']
hg_conc = [0.001, 0.01, 0.05, 0.2, 1.0, 10.0]  # ug/g or ug/L
tl_numbers = [0, 1, 2, 3, 4, 5]

colors_bar = ['#164e63', '#115e59', '#134e4a', '#0f766e', '#0d9488', '#14b8a6']
bars = ax.bar(trophic_levels, hg_conc, color=colors_bar, edgecolor='#5eead4', linewidth=1)

# FDA limit line
ax.axhline(y=0.3, color='#ef4444', linewidth=2, linestyle='--', alpha=0.8)
ax.text(4.3, 0.4, 'FDA action\nlevel 0.3 \u00b5g/g', color='#ef4444', fontsize=10,
        ha='center')

ax.set_yscale('log')
ax.set_xlabel('Trophic Level', fontsize=12, color='#99f6e4')
ax.set_ylabel('[MeHg] (\u00b5g/g or \u00b5g/L)', fontsize=12, color='#99f6e4')
ax.set_title('Mercury Biomagnification', fontsize=14, fontweight='bold', color='#5eead4')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4', axis='y')

# Add concentration labels on bars
for bar, conc in zip(bars, hg_conc):
    ax.text(bar.get_x() + bar.get_width()/2, conc * 1.5,
            f'{conc}', ha='center', va='bottom', color='#f97316',
            fontsize=10, fontweight='bold')

fig.patch.set_facecolor('#0a0a0a')
plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight',
            facecolor='#0a0a0a', edgecolor='none')
plt.close()
print("Conservation biochemistry simulations complete: eDNA decay, BCF, biodegradation, biomagnification.")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

7.8 Microplastics & Biochemical Interactions

Microplastics (MPs, <5 mm) and nanoplastics(<1 \(\mu\)m) are now ubiquitous environmental contaminants, detected in every ocean basin, in Arctic sea ice, in soils, in drinking water, and in human blood. Beyond their physical effects (gut obstruction, false satiation), MPs interact with the biochemical environment in ways that amplify their toxicity.

The Trojan Horse Effect: Pollutant Sorption

Microplastic surfaces adsorb hydrophobic organic pollutants (HOPs) from the surrounding water, concentrating them by orders of magnitude. The partition coefficient between the plastic surface and water is:

Sorption Partition Coefficient

\[ K_p = \frac{C_{\text{sorbed}}}{C_{\text{water}}} = K_{ow} \times f_{oc} \]

where \(K_{ow}\) is the octanol-water partition coefficient of the pollutant (a measure of hydrophobicity), and \(f_{oc}\) is the organic carbon fraction of the plastic surface (including biofilm). For polyethylene MPs adsorbing pyrene (\(\log K_{ow} = 5.2\)): \(K_p \approx 10^4\text{--}10^5\) L/kg.

The sorption process follows two isotherm models depending on the concentration range:

Langmuir Isotherm (surface saturation)

\[ q_e = \frac{q_{\max} \cdot K_L \cdot C_e}{1 + K_L \cdot C_e} \]

where \(q_e\) is the amount adsorbed at equilibrium, \(q_{\max}\) is the maximum adsorption capacity, \(K_L\) is the Langmuir constant, and \(C_e\)is the equilibrium aqueous concentration.

Freundlich Isotherm (heterogeneous surfaces)

\[ q_e = K_F \cdot C_e^{1/n} \]

where \(K_F\) is the Freundlich constant and \(1/n\) is the heterogeneity factor. For MPs, \(n \approx 1.2\text{--}2.5\), indicating favorable adsorption.

When organisms ingest these pollutant-laden MPs, the change from external seawater (\(C_{\text{water}}\)) to the gut environment (acidic pH, bile salts, warm temperature) promotes pollutant desorption directly into gut tissues — the Trojan horse effect. This delivers concentrated doses of PAHs, PCBs, and pesticides that would otherwise remain at sub-toxic aqueous concentrations.

Nanoplastic Membrane Disruption

Nanoplastics (<1 \(\mu\)m) can interact directly with cell membranes due to their comparable size to lipid bilayer thickness (~5 nm). Molecular dynamics simulations show that nanoplastics partition into the hydrophobic core of lipid bilayers, causing:

Increased membrane fluidity: disruption of lipid packing order
Ion channel dysfunction: altered membrane potential and Ca\(^{2+}\) signaling
ROS generation: oxidative stress from surface radical formation
Lysosomal destabilization: release of hydrolytic enzymes into cytoplasm

MPs have now been documented in over 1,500 marine species across all trophic levels, from zooplankton to baleen whales. The estimated global ocean burden is 75–199 million metric tonnes, with 8–12 million tonnes added annually. Trophic transfer of MPs through food webs means that apex predators accumulate both the plastics and their sorbed pollutant payloads.

7.9 PFAS: Forever Chemicals

Per- and polyfluoroalkyl substances (PFAS) are a class of >12,000 synthetic chemicals characterized by chains of carbon atoms with fluorine substitution. The C-F bond, with a bond dissociation energy of 485 kJ/mol, is the strongest single bond in organic chemistry. This extraordinary stability is both their industrial virtue (non-stick coatings, firefighting foam, water-resistant textiles) and their environmental curse: PFAS are essentially indestructible under natural conditions, with environmental half-lives exceeding 1,000 years.

Why the C-F Bond Is Indestructible

Comparison of carbon-halogen bond strengths illustrates why PFAS persist:

C-F: 485 kJ/mol — no known enzyme can cleave this bond efficiently
C-Cl: 339 kJ/mol — dehalogenase enzymes exist (evolved for natural organochlorines)
C-Br: 276 kJ/mol — readily biodegraded
C-I: 240 kJ/mol — highly labile

Fluorine’s extreme electronegativity (3.98 Pauling) creates a dense electron shield around the carbon backbone, repelling nucleophilic attack by water, hydroxyl radicals, and enzymatic catalysts. No microbial degradation pathway for perfluorinated chains has been convincingly demonstrated in the environment.

PFAS Bioaccumulation: Protein Binding

Unlike lipophilic pollutants (PCBs, DDT) that accumulate in adipose tissue, PFAS are both hydrophobic and oleophobic (oil-repelling). Instead, they bioaccumulate by binding to proteins, particularly serum albumin and liver fatty acid binding protein (L-FABP):

PFOS-Albumin Binding

\[ K_d \approx 10^{-5} \text{ M} \quad \text{(PFOS-serum albumin)} \]

The perfluorooctane tail inserts into the hydrophobic binding pocket of albumin (the same site that binds fatty acids), while the sulfonate head group forms electrostatic contacts with lysine residues. This is a moderate-affinity interaction, but because albumin is the most abundant plasma protein (~40 g/L), the total binding capacity is enormous.

Pharmacokinetic Model for PFAS Accumulation

The body burden of PFAS follows a one-compartment pharmacokinetic model:

PFAS Accumulation Kinetics

\[ \frac{dC}{dt} = \text{intake} - k_{\text{elim}} \cdot C \]

where \(C\) is serum concentration, intake is the daily dose (ng/kg/day from food, water, and dust), and \(k_{\text{elim}}\) is the first-order elimination rate constant.

At steady state (\(dC/dt = 0\)):

Steady-State PFAS Concentration

\[ C_{ss} = \frac{\text{intake}}{k_{\text{elim}}} \]

The elimination half-life \(t_{1/2} = \ln 2 / k_{\text{elim}}\) is extraordinarily long for PFAS: \(\sim\)3.8 years for PFOS and \(\sim\)5.4 years for PFOA in humans. This means even low daily intake produces high steady-state concentrations.

Why is elimination so slow? PFAS are efficiently filtered by the kidney glomerulus but then reabsorbed in the proximal tubule via organic anion transporters (OAT1, OAT3) that normally recover valuable endogenous acids. The renal reabsorption rate \(k_{\text{reabs}} \approx 0.99 \times k_{\text{filtration}}\), meaning ~99% of filtered PFAS is returned to the blood. This evolutionary trap arises because PFAS structurally mimic endogenous substrates of these transporters.

7.10 Antibiotic Resistance in the Environment

Antibiotic resistance is not solely a clinical problem — the environment is both the origin and the reservoir of resistance genes. Soil bacteria have produced antibiotics for billions of years, and their neighbors evolved resistance genes (the “resistome”) long before clinical antibiotic use. The crisis arises because anthropogenic antibiotic discharge into waterways, soils, and livestock operations massively amplifies the selection pressure for resistance.

Horizontal Gene Transfer Mechanisms

Resistance genes spread between bacterial species via three mechanisms, each with distinct biochemical machinery:

Conjugation: Direct cell-to-cell transfer of plasmids via a pilus (type IV secretion system). Typical transfer rate:\(\beta_{\text{conj}} \approx 10^{-9}\text{--}10^{-7}\) mL/(cell\(\cdot\)hr). The dominant mechanism for spreading resistance in gut microbiomes and wastewater.
Transformation: Uptake of free DNA from lysed cells. Requires natural competence (com genes). Rate depends on DNA concentration and fragment size. Important in biofilms and sediments where eDNA accumulates.
Transduction: Bacteriophage-mediated transfer. Phages accidentally package host DNA (including resistance genes on mobile genetic elements) and inject it into new hosts. Transduction rate: \(\sim 10^{-8}\) per phage infection.

SIR-Like Model for Resistance Spread

The spread of antibiotic resistance through a bacterial population can be modeled analogously to infectious disease transmission. Let \(S\) = susceptible bacteria,\(R\) = resistant bacteria:

Resistance Spread Dynamics

\[ \frac{dR}{dt} = \beta \cdot S \cdot R - \mu_R \cdot R + \nu_R \cdot R \]

\[ \frac{dS}{dt} = \nu_S \cdot S - \beta \cdot S \cdot R - \mu_S \cdot S \]

where \(\beta\) is the horizontal gene transfer rate (conjugation + transformation + transduction), \(\mu\) is the death rate, and \(\nu\) is the growth rate. Under antibiotic selection: \(\nu_R > \nu_S\) and \(\mu_R < \mu_S\)(resistant bacteria grow faster and die slower when antibiotics are present).

The basic reproduction number for resistance spread:

\[ R_0 = \frac{\beta \cdot S_0}{\mu_R - \nu_R} \]

Resistance spreads when \(R_0 > 1\). In antibiotic-contaminated environments,\(\mu_S\) increases (susceptible bacteria are killed), effectively increasing the relative fitness advantage of resistant strains and lowering the threshold for spread.

Sub-Inhibitory Selection: The MSC Concept

A critical finding is that resistance can be selected at antibiotic concentrations far below the minimum inhibitory concentration (MIC). The minimum selective concentration (MSC) is the lowest concentration that gives a fitness advantage to resistant over susceptible bacteria:

Minimum Selective Concentration

\[ \text{MSC} \ll \text{MIC} \quad \text{(typically MSC} \approx \text{MIC}/100\text{--MIC}/10\text{)} \]

For ciprofloxacin: MIC \(\approx\) 1 \(\mu\)g/mL, but MSC \(\approx\) 0.01\(\mu\)g/mL. Environmental concentrations in hospital wastewater, agricultural runoff, and downstream rivers routinely exceed MSC even when well below MIC, creating a massive “selection window” for resistance evolution.

This has profound implications: even heavily diluted antibiotic residues in waterways can drive resistance evolution. Wastewater treatment plants, aquaculture facilities, and livestock operations are hotspots where sub-inhibitory antibiotic concentrations combine with high bacterial densities and horizontal gene transfer to create ideal conditions for resistance amplification and dissemination.

7.11 Advanced Simulations: Microplastics, PFAS & Resistance

Three simulations exploring emerging conservation biochemistry topics: (1) microplastic pollutant sorption isotherms comparing Langmuir and Freundlich models, (2) PFAS bioaccumulation over a human lifespan under different exposure scenarios, and (3) antibiotic resistance spread dynamics in a river system.

Microplastic Sorption, PFAS Bioaccumulation & Antibiotic Resistance Spread

Python

script.py151 lines

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

np.random.seed(42)
fig, axes = plt.subplots(1, 3, figsize=(18, 5.5))

# ====== Panel 1: Microplastic Sorption Isotherms ======
ax = axes[0]

Ce = np.linspace(0.001, 50, 500)  # equilibrium aqueous concentration (ug/L)

# Langmuir parameters for different pollutants on PE microplastic
pollutants = {
    'Pyrene (PAH)': {'qmax': 800, 'KL': 0.5, 'KF': 200, 'n': 1.8, 'color': '#ef4444'},
    'PCB-153': {'qmax': 500, 'KL': 0.3, 'KF': 120, 'n': 1.5, 'color': '#f59e0b'},
    'DDT': {'qmax': 600, 'KL': 0.4, 'KF': 160, 'n': 1.6, 'color': '#a78bfa'},
    'Phenanthrene': {'qmax': 350, 'KL': 0.2, 'KF': 80, 'n': 1.4, 'color': '#3b82f6'},
}

for name, p in pollutants.items():
    # Langmuir
    q_langmuir = p['qmax'] * p['KL'] * Ce / (1 + p['KL'] * Ce)
    # Freundlich
    q_freundlich = p['KF'] * Ce**(1/p['n'])

ax.plot(Ce, q_langmuir, color=p['color'], linewidth=2.5, label=f"{name}")
    ax.plot(Ce, q_freundlich, color=p['color'], linewidth=1.5, linestyle='--', alpha=0.6)

ax.set_xlabel('Aqueous concentration, Ce (\u00b5g/L)', fontsize=11, color='#99f6e4')
ax.set_ylabel('Sorbed amount, qe (\u00b5g/g MP)', fontsize=11, color='#99f6e4')
ax.set_title('MP Sorption Isotherms\n(solid=Langmuir, dashed=Freundlich)', fontsize=12,
             fontweight='bold', color='#5eead4')
ax.legend(fontsize=8.5, facecolor='#0a0a0a', edgecolor='#5eead4', labelcolor='#d1d5db')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4')

# ====== Panel 2: PFAS Bioaccumulation Over Lifespan ======
ax = axes[1]

# One-compartment PK model: dC/dt = intake - k_elim * C
years = np.arange(0, 81)
dt_yr = 0.01
t_sim = np.arange(0, 80, dt_yr)

scenarios = {
    'High exposure\n(contaminated water)': {'intake': 50, 'color': '#ef4444'},
    'Moderate exposure\n(average diet)': {'intake': 10, 'color': '#f59e0b'},
    'Low exposure\n(filtered water)': {'intake': 2, 'color': '#2dd4bf'},
}

# PFOS elimination half-life: 3.8 years
t_half = 3.8  # years
k_elim = np.log(2) / t_half

for label, sc in scenarios.items():
    C = np.zeros(len(t_sim))
    intake = sc['intake']  # ng/kg/day -> convert to ng/mL/year approx

for i in range(1, len(t_sim)):
        dC = (intake - k_elim * C[i-1]) * dt_yr
        C[i] = max(0, C[i-1] + dC)

ax.plot(t_sim, C, color=sc['color'], linewidth=2.5, label=label)

# Mark steady state
    C_ss = intake / k_elim
    ax.axhline(y=C_ss, color=sc['color'], linewidth=1, linestyle=':', alpha=0.4)

# Health advisory level
ax.axhline(y=20, color='#ef4444', linewidth=2, linestyle='--', alpha=0.7)
ax.text(5, 22, 'EPA health advisory (20 ng/mL)', color='#ef4444', fontsize=9)

ax.set_xlabel('Age (years)', fontsize=11, color='#99f6e4')
ax.set_ylabel('Serum PFOS (ng/mL)', fontsize=11, color='#99f6e4')
ax.set_title('PFAS Bioaccumulation\n(PFOS, t\u00bd = 3.8 yr)', fontsize=12,
             fontweight='bold', color='#5eead4')
ax.legend(fontsize=8, facecolor='#0a0a0a', edgecolor='#5eead4', labelcolor='#d1d5db',
          loc='center right')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4')

# ====== Panel 3: Antibiotic Resistance Spread ======
ax = axes[2]

# SIR-like model for resistance in a river bacterial community
dt_h = 0.1  # hours
t_total_h = 500  # hours
t_arr = np.arange(0, t_total_h, dt_h)

# Different antibiotic concentration scenarios
ab_scenarios = {
    'No antibiotics': {'mu_S': 0.1, 'mu_R': 0.08, 'beta': 1e-9, 'color': '#2dd4bf'},
    'Sub-MIC (MSC level)': {'mu_S': 0.09, 'mu_R': 0.10, 'beta': 5e-9, 'color': '#f59e0b'},
    'Near MIC': {'mu_S': 0.02, 'mu_R': 0.09, 'beta': 1e-8, 'color': '#ef4444'},
}

for label, p in ab_scenarios.items():
    S = np.zeros(len(t_arr))
    R = np.zeros(len(t_arr))
    S[0] = 1e8   # initial susceptible (cells/mL)
    R[0] = 1e4   # initial resistant (rare)
    K_cap = 1e9  # carrying capacity

death_rate = 0.02

for i in range(1, len(t_arr)):
        total = S[i-1] + R[i-1]
        growth_factor = (1 - total / K_cap)

dS = (p['mu_S'] * S[i-1] * growth_factor - p['beta'] * S[i-1] * R[i-1] - death_rate * S[i-1]) * dt_h
        dR = (p['mu_R'] * R[i-1] * growth_factor + p['beta'] * S[i-1] * R[i-1] - death_rate * R[i-1]) * dt_h

S[i] = max(0, S[i-1] + dS)
        R[i] = max(0, R[i-1] + dR)

frac_R = R / (S + R + 1)  # fraction resistant
    ax.plot(t_arr, frac_R * 100, color=p['color'], linewidth=2.5, label=label)

ax.set_xlabel('Time (hours)', fontsize=11, color='#99f6e4')
ax.set_ylabel('Resistant fraction (%)', fontsize=11, color='#99f6e4')
ax.set_title('Antibiotic Resistance Spread\nin River Bacterial Community', fontsize=12,
             fontweight='bold', color='#5eead4')
ax.legend(fontsize=9, facecolor='#0a0a0a', edgecolor='#5eead4', labelcolor='#d1d5db')
ax.set_facecolor('#0a0a0a')
ax.tick_params(colors='#94a3b8')
ax.spines['bottom'].set_color('#5eead4')
ax.spines['left'].set_color('#5eead4')
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.grid(True, alpha=0.15, color='#5eead4')
ax.set_ylim(0, 105)

fig.patch.set_facecolor('#0a0a0a')
plt.tight_layout()
plt.savefig('output.png', dpi=150, bbox_inches='tight',
            facecolor='#0a0a0a', edgecolor='none')
plt.close()
print("Microplastic sorption, PFAS bioaccumulation, and antibiotic resistance simulations complete.")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

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M6. Biodiversity-Stability M8. Climate Change & Adaptation