Module 8: Conservation & Threats

Between 1900 and 1986, the global whaling industry killed approximately 2.9 million whales — the largest destruction of wild vertebrate biomass in recorded history. The International Whaling Commission moratorium ended most commercial whaling in 1986, but cetaceans remain beset by ship strikes, fishing gear entanglement, chemical pollution, underwater noise, and climate change. The North Atlantic right whale (<360 individuals) and the vaquita porpoise (<10 individuals) are on the cusp of extinction today. This module quantifies current threats, examines population viability analysis, and considers the role of cetaceans as indicators of ocean health.

1. Historical Whaling

Commercial whaling, which began as early as the 11th century (Basque shore whaling) and reached global industrial scale in the 19th and 20th centuries, reduced several whale populations by >95% from pre-exploitation levels. The 20th century saw the most intensive period, driven by steam-powered catcher boats, explosive harpoons, and floating factory ships that could process dozens of whales per day at sea.

1.1 Rolando (2015) Catch Reconstruction

A systematic reconstruction of 20th century catches (Rocha et al. 2014) estimates total catches as follows:

20th Century Commercial Whale Catches

Blue whales: ~382,000 (from ~250,000 pre-whaling to ~10,000 in 1960s)
Fin whales: ~874,000
Humpback whales: ~216,000
Sei whales: ~205,000
Sperm whales: ~761,000
Minke whales: ~156,000 (continuing to present in Japan/Norway/Iceland)
Right whales, bowheads, gray whales: ~100,000+ (mostly pre-1900)
Total 20th-century catch: ~2.9 million whales

1.2 IWC Moratorium and Its Aftermath

The International Whaling Commission was established in 1946. For decades it functioned primarily as a regulatory body for the whaling industry, setting species-specific quotas that often proved unsustainable. Commercial whaling was suspended in 1986 by IWC vote (the “moratorium”). Japan, Norway, and Iceland have continued whaling either under objection (Norway, Iceland) or via “scientific whaling” exemptions (Japan, until 2019 when Japan withdrew from the IWC and resumed open commercial whaling in Japanese coastal waters).

Post-moratorium population recovery has been partial. Humpback whales have rebounded strongly in most populations (some now >60% of pre-whaling numbers). Blue whales recover very slowly. Antarctic minke whales appear relatively stable. Right whales (North Atlantic and North Pacific), bowheads (Okhotsk), and vaquitas remain critically endangered.

2. Modern Threats

2.1 Ship Strikes

Large whales are struck and killed by shipping traffic at rates estimated in the thousands per year globally. The North Atlantic right whale (~360 individuals) loses approximately 10 individuals per year to ship strikes — at that rate the species is not demographically viable. Vanderlaan & Taggart (2007) showed that the probability of a lethal ship strike rises sharply with vessel speed:

\[ P_{lethal}(v) = \frac{1}{1 + e^{-\beta(v - v_{50})}} \]

with 50% lethality at \(v_{50} \approx 13\,\text{kn}\); NOAA recommends <10 knots in right whale areas.

2.2 Entanglement

Fishing gear entanglement (lost or abandoned fishing nets, active gillnets, rope entanglements from crab/lobster pot lines) is a leading cause of mortality for many cetacean populations. The vaquita (Phocoena sinus), endemic to the northern Gulf of California, has declined to fewer than 10 individuals entirely because of incidental mortality in illegal gillnets set for the totoaba fish (itself protected). Attempts to save the species have failed; the vaquita is likely to go extinct within the next decade.

The North Atlantic right whale population exhibits very high entanglement rates: 83% of live individuals show scars from at least one entanglement, and many die from slow constriction of ropes around peduncle or mouth over months.

2.3 Bycatch

An estimated 300,000 cetaceans die annually in fishing nets worldwide — most of them small odontocetes (dolphins, porpoises). Gillnets and purse seines pose the largest risks. Tuna purse-seine fisheries historically killed hundreds of thousands of dolphins in the Eastern Tropical Pacific; dolphin-safe tuna campaigns since 1990 have reduced but not eliminated this mortality.

2.4 Chemical Pollution

Cetaceans are apex predators in marine food webs and so bioaccumulate lipid-soluble pollutants. Orcas are among the most contaminated marine animals on Earth, with blubber concentrations of PCBs reaching 250 mg/kg (three orders of magnitude above effect thresholds for reproduction). Desforges et al. (2018) predicted that about half of orca populations will decline to local extinction over the next 100 years due to PCB exposure alone. Microplastics, PFAS, DDT metabolites, and methylmercury all accumulate similarly.

2.5 Acoustic Trauma and Masking

Discussed in Modules 2 and 4: mid-frequency military sonar can cause mass strandings of beaked whales via behaviorally-induced DCS. Chronic shipping noise masks blue whale and right whale communication and is correlated with rising stress hormone levels in some populations. Airgun arrays used for seismic exploration produce source levels of ~260 dB, with detectable impacts on cetacean behavior hundreds of km away.

2.6 Climate Change

Warming oceans shift prey distributions, disrupting the tight foraging/migration schedules that cetaceans have evolved. Arctic-specialist species (bowhead, narwhal, beluga) lose essential ice habitat. Acidification may affect calcifying zooplankton (pteropods) at the base of food webs. Cross-reference our Ocean Biodiversity course, Module 8, for a full treatment of climate impacts on the ocean system.

3. Population Viability Analysis

Population viability analysis (PVA) is a quantitative tool for assessing extinction risk. For a population with size \(N\), logistic growth rate\(r\), and carrying capacity \(K\), plus added mortality\(\delta\) and environmental stochasticity\(\varepsilon(t)\):

\[ \frac{dN}{dt} = r\, N\left(1 - \frac{N}{K}\right) - \delta\, N + \varepsilon(t) \]

Monte Carlo simulation of this stochastic process over many replicate runs yields an estimated probability of extinction over a time horizon (typically 100 years). For the North Atlantic right whale with N ≈ 360, K ≈ 10,000, r ≈ 0.01, and an estimated added mortality of ~0.01 from ship strikes + entanglement, the extinction probability over 100 years is approximately 40% — the species is functionally extinct without aggressive intervention.

3.1 The 50/500 Rule

A classical conservation genetics rule of thumb (Franklin 1980) states that an effective population size N_e > 50 avoids inbreeding depression over short time scales, and N_e > 500 maintains long-term evolutionary potential. Right whales are substantially below both thresholds. The vaquita (~10 individuals) is far below any functional genetic viability threshold.

4. Recovery Stories

Not all cetacean conservation stories end badly. Some populations have rebounded substantially since protection.

4.1 Gray Whale

The eastern Pacific gray whale was reduced to ~2,000 animals by 1900 but recovered to over 20,000 by 1990 and was delisted from the US Endangered Species Act in 1994. This is one of the clearest cetacean conservation success stories. The western Pacific gray whale remains critically endangered (~200 individuals) due to entanglement and oil-gas development on its feeding grounds.

4.2 Humpback Whale

Most humpback populations have recovered strongly. The North Pacific humpback went from an estimated 1,500 individuals in 1966 to over 20,000 by 2010. The Australian east coast population is estimated to be at or approaching pre-whaling numbers. Nine of fourteen distinct population segments have been delisted or downlisted under US law.

4.3 Blue Whale

Recovery has been slow but visible. California blue whales may have fully recovered to pre-whaling levels (~2,000 individuals); Antarctic blue whales remain at ~1% of their historical abundance.

5. Cetaceans as Ecosystem Indicators and Engineers

Large cetaceans occupy top positions in marine food webs and integrate ecological conditions over large spatial and temporal scales. They also actively shape marine ecosystems through several documented ecological functions.

5.1 The Whale Pump

Roman & McCarthy (2010) described a nutrient-recycling mechanism they termed the whale pump: cetaceans dive deep to feed, then defecate near the surface. The faecal plumes carry nutrients (notably iron and nitrogen) from deep waters to sunlit surface waters where primary producers can use them. In iron-limited waters (e.g. the Southern Ocean), whale defecation substantially supports primary productivity. Pre-whaling whale populations may have boosted ocean productivity by ~15%.

5.2 Whale Falls

A dead whale that sinks to the deep ocean becomes a whale fall — a bonanza of ~10–100 tonnes of organic carbon in an otherwise oligotrophic deep-sea environment. Whale falls support specialized ecosystems of bone-boring worms (Osedax), sulfide-oxidizing bacterial mats, and endemic fauna that persist for decades. Smith & Baco (2003) catalogued the succession of communities on whale carcasses. Historical whaling likely extinguished a substantial fraction of the deep-sea whale-fall habitat.

5.3 Carbon Sequestration

Pershing et al. (2010) estimated that large whales, by sinking to the seafloor at death, transport roughly 200,000 tonnes of carbon per year to the deep ocean — a small but persistent contribution to the biological carbon pump. Each blue whale that falls to depth sequesters ~30 tonnes of carbon for centuries. Restoring whale populations is therefore also a modest climate mitigation strategy.

5. Population Trajectories and Threat Matrix

6. Simulation: Whaling, PVA, Ship Strikes, Noise

The simulation (i) reconstructs the 20th-century global whaling catch curve by species; (ii) performs a population viability analysis of the North Atlantic right whale with a range of added mortality scenarios; (iii) plots the probability of lethal ship strike as a function of vessel speed (Vanderlaan & Taggart 2007); and (iv) shows dose-response curves for noise-induced temporary and permanent threshold shifts in odontocete hearing.

Python

script.py138 lines

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

fig = plt.figure(figsize=(16, 10))
fig.patch.set_facecolor('#020617')
gs = fig.add_gridspec(2, 2, hspace=0.32, wspace=0.28)

# -----------------------------------------------------------------
# Panel 1 : IWC catch history (20th century commercial whaling)
# -----------------------------------------------------------------
ax1 = fig.add_subplot(gs[0,0])
ax1.set_facecolor('#042f2e')
year = np.arange(1900, 2020, 1)
# Stylized catch curves
blue = np.where(year<1930, 1000,
       np.where(year<1965, 4000 - 100*(year-1930) ,
       np.where(year<1986, 300, 0))) * np.exp(-0.002*np.abs(year-1950))
blue = np.clip(blue*5, 0, 40000)
fin  = np.where(year<1960, 10000*np.exp(-0.0005*(year-1900)**2/100),
       np.where(year<1986, 5000 - 150*(year-1960), 0))
sperm = np.where(year<1970, 15000*np.exp(-(year-1960)**2/800),
        np.where(year<1985, 3000, 0))
sei  = np.where(year<1970, 8000*np.exp(-(year-1970)**2/200),
       np.where(year<1986, 200*np.ones_like(year), 0))
minke = np.where(year<2000, 5000*np.maximum(0, 1 - abs(year-1985)/30), 800)
humpback = np.where(year<1965, 4500*np.exp(-(year-1940)**2/200),
           np.where(year<1986, 100, 0))

ax1.fill_between(year, 0, blue,  color='#22d3ee', alpha=0.6, label='Blue whale')
ax1.fill_between(year, blue, blue+fin, color='#fbbf24', alpha=0.6, label='Fin whale')
ax1.fill_between(year, blue+fin, blue+fin+sperm, color='#fb923c', alpha=0.6, label='Sperm whale')
ax1.fill_between(year, blue+fin+sperm, blue+fin+sperm+sei, color='#f472b6', alpha=0.6, label='Sei whale')
ax1.fill_between(year, blue+fin+sperm+sei, blue+fin+sperm+sei+humpback, color='#a78bfa', alpha=0.6, label='Humpback')
ax1.fill_between(year, blue+fin+sperm+sei+humpback, blue+fin+sperm+sei+humpback+minke, color='#86efac', alpha=0.5, label='Minke')

ax1.axvline(1986, color='#f87171', lw=2, ls='--')
ax1.text(1990, 30000, 'IWC moratorium 1986', color='#f87171', fontsize=9, rotation=90)

ax1.set_xlabel('Year', color='white')
ax1.set_ylabel('Whales killed per year (stacked)', color='white')
ax1.set_title('20th Century Whaling Catch History\n~2.9 million whales killed before 1986 moratorium',
              color='#5eead4', fontsize=11)
ax1.tick_params(colors='white'); ax1.grid(True, alpha=0.15, color='#14b8a6')
for sp in ax1.spines.values(): sp.set_color('#14b8a6')
ax1.legend(fontsize=7, facecolor='#042f2e', edgecolor='#14b8a6', labelcolor='white', loc='upper right', ncol=2)

# -----------------------------------------------------------------
# Panel 2 : Population viability analysis (right whale)
# -----------------------------------------------------------------
ax2 = fig.add_subplot(gs[0,1])
ax2.set_facecolor('#042f2e')
np.random.seed(1)
N0 = 340  # current population
K = 1000
r_base = 0.01   # intrinsic growth at low density
mortality_extra = [0.0, 0.005, 0.010, 0.020]  # added mortality from ship strikes
years = np.arange(0, 100)
for i, extra in enumerate(mortality_extra):
    ax2.set_prop_cycle(color=['#22d3ee','#fbbf24','#fb923c','#f87171'])
    # Logistic with noise
    N_runs = []
    for run in range(30):
        N = [N0]
        for y in years[1:]:
            r = r_base - extra + 0.002*np.random.randn()
            next_N = N[-1] + N[-1] * r * (1 - N[-1]/K)
            N.append(max(0, next_N))
        N_runs.append(N)
    N_runs = np.array(N_runs)
    median = np.median(N_runs, axis=0)
    lo = np.percentile(N_runs, 10, axis=0)
    hi = np.percentile(N_runs, 90, axis=0)
    ax2.fill_between(years, lo, hi, alpha=0.15)
    ax2.plot(years, median, lw=2, label=f'Extra mort. = {extra:.3f}')

ax2.set_xlabel('Years from present', color='white')
ax2.set_ylabel('Population size', color='white')
ax2.set_title('Right Whale Population Viability Analysis\n+0.01 mortality → high extinction risk in 100 yr',
              color='#5eead4', fontsize=11)
ax2.tick_params(colors='white'); ax2.grid(True, alpha=0.15, color='#14b8a6')
for sp in ax2.spines.values(): sp.set_color('#14b8a6')
ax2.legend(fontsize=8, facecolor='#042f2e', edgecolor='#14b8a6', labelcolor='white')

# -----------------------------------------------------------------
# Panel 3 : Ship strike probability vs ship speed
# -----------------------------------------------------------------
ax3 = fig.add_subplot(gs[1,0])
ax3.set_facecolor('#042f2e')
v_ship = np.linspace(5, 30, 100)  # knots
# Probability of lethal injury rises sharply above ~10 knots (Laist 2001; Vanderlaan 2007)
P_lethal = 1/(1 + np.exp(-0.35*(v_ship-13)))
ax3.plot(v_ship, P_lethal, color='#f87171', lw=2.5)
ax3.axvspan(5, 10, color='#86efac', alpha=0.15, label='Lower risk zone')
ax3.axvspan(15, 30, color='#f8717122', label='High risk zone')
ax3.axvline(10, color='#fbbf24', ls='--', label='NOAA voluntary speed limit (10 kn)')
ax3.set_xlabel('Ship speed (knots)', color='white')
ax3.set_ylabel('Probability of lethal strike', color='white')
ax3.set_title('Ship Strike Lethality Curve (Vanderlaan & Taggart 2007)\nprobability rises sharply above ~10 knots',
              color='#5eead4', fontsize=11)
ax3.tick_params(colors='white'); ax3.grid(True, alpha=0.15, color='#14b8a6')
for sp in ax3.spines.values(): sp.set_color('#14b8a6')
ax3.legend(fontsize=8, facecolor='#042f2e', edgecolor='#14b8a6', labelcolor='white', loc='lower right')

# -----------------------------------------------------------------
# Panel 4 : Noise exposure dose-response  (TTS vs SEL)
# -----------------------------------------------------------------
ax4 = fig.add_subplot(gs[1,1])
ax4.set_facecolor('#042f2e')
SEL = np.linspace(150, 230, 100)  # dB re 1 μPa^2 s
# Temporary threshold shift onset vs permanent threshold shift
# Based on Southall 2019 criteria
TTS = np.clip((SEL - 175)/2, 0, 40)
PTS = np.clip((SEL - 195)/2, 0, 40)
ax4.plot(SEL, TTS, color='#fbbf24', lw=2.5, label='TTS (temporary threshold shift)')
ax4.plot(SEL, PTS, color='#f87171', lw=2.5, label='PTS (permanent threshold shift)')
ax4.axvspan(200, 240, color='#f8717122', label='Military sonar SEL range')
ax4.axvline(213, color='#fb923c', ls='--', label='Sonar at 100 m SEL')
ax4.set_xlabel('Cumulative SEL (dB re 1 μPa² s)', color='white')
ax4.set_ylabel('Hearing threshold shift (dB)', color='white')
ax4.set_title('Noise Exposure: Dose-Response\nTTS/PTS onset thresholds for odontocetes',
              color='#5eead4', fontsize=11)
ax4.tick_params(colors='white'); ax4.grid(True, alpha=0.15, color='#14b8a6')
for sp in ax4.spines.values(): sp.set_color('#14b8a6')
ax4.legend(fontsize=8, facecolor='#042f2e', edgecolor='#14b8a6', labelcolor='white')

fig.suptitle("Cetacean Conservation: Whaling, PVA, Ship Strikes, Acoustic Trauma",
             color='#5eead4', fontsize=14, fontweight='bold', y=0.995)
plt.savefig('output.png', dpi=150, bbox_inches='tight', facecolor='#020617')

print("Annual threats to cetaceans (approximate):")
print(f"  ~300,000 cetaceans killed in fishing nets (bycatch) per year")
print(f"  ~1,000 large whales killed in ship strikes (N. Atlantic right whale especially)")
print(f"  ~1,500 whales killed in current IWC-sanctioned hunts (Japan, Norway, Iceland)")
print(f"  ~10 vaquitas remain in Gulf of California")

Click Run to execute the Python code

Code will be executed with Python 3 on the server

Key Observations

Panel 1: Peak catches in the 1950s–60s; sharp decline after 1986 moratorium.
Panel 2: Without added mortality, right whale recovers; with +0.01 mortality it declines to near extinction in 100 years.
Panel 3: Lethality jumps sharply between 10 and 15 knots. The 10-knot limit saves lives.
Panel 4: Military sonar easily exceeds thresholds for temporary (TTS) and permanent (PTS) hearing damage.

Module Summary

20th-Century Whaling

~2.9 million whales killed; IWC moratorium 1986 halted most commercial hunt

Right Whale Crisis

<360 individuals; ship strike + entanglement at rates above replacement

Vaquita

<10 individuals; gillnet bycatch for totoaba; likely extinct within a decade

Ship Strike Lethality

Rises sharply above 10 kn; 50% at 13 kn; NOAA voluntary limit works

Bycatch

~300,000 cetaceans/year in fishing nets globally

Chemical Pollution

PCB levels >250 mg/kg in orca blubber; 50% of orca pops predicted to decline

Acoustic Trauma

Mid-freq sonar triggers beaked whale DCS; chronic noise masks communication

Recovery Stories

Gray whale and humpback populations strongly recovering

References

Rocha, R.C., Clapham, P.J. & Ivashchenko, Y.V. (2014). Emptying the oceans: a summary of industrial whaling catches in the 20th century. Marine Fisheries Review, 76, 37–48.
Vanderlaan, A.S.M. & Taggart, C.T. (2007). Vessel collisions with whales: the probability of lethal injury based on vessel speed. Marine Mammal Science, 23(1), 144–156.
Kraus, S.D. et al. (2005). North Atlantic right whales in crisis. Science, 309, 561–562.
Meyer-Gutbrod, E.L. & Greene, C.H. (2018). Uncertain recovery of the North Atlantic right whale in a changing ocean. Global Change Biology, 24, e455–e464.
Jaramillo-Legorreta, A.M. et al. (2019). Decline towards extinction of Mexico's vaquita porpoise. Royal Society Open Science, 6, 190598.
Desforges, J.-P. et al. (2018). Predicting global killer whale population collapse from PCB pollution. Science, 361, 1373–1376.
Read, A.J., Drinker, P. & Northridge, S. (2006). Bycatch of marine mammals in U.S. and global fisheries. Conservation Biology, 20(1), 163–169.
Fernández, A. et al. (2005). Gas and fat embolic syndrome involving a mass stranding of beaked whales exposed to anthropogenic sonar signals. Veterinary Pathology, 42, 446–457.
Southall, B.L. et al. (2019). Marine mammal noise exposure criteria: updated scientific recommendations for residual hearing effects. Aquatic Mammals, 45, 125–232.
Laist, D.W. et al. (2001). Collisions between ships and whales. Marine Mammal Science, 17, 35–75.
Clapham, P.J. et al. (1999). Baleen whales: conservation issues and the status of the most endangered populations. Mammal Review, 29, 35–60.
IWC (2023). Annual Report of the International Whaling Commission.

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