Module 1

Arctic Ice Ecology & Hunting

A polar bear’s entire energy budget depends on sea ice. Without a stable ice platform, the bear cannot reach ringed seals (Pusa hispida), its primary prey and essentially the only Arctic food source with sufficient caloric density. This module covers the two principal hunting strategies — still-hunting at breathing holes and stalk-ambush at haul-out lairs — and the sensory and energetic economy that supports them.

1. The Ringed Seal Ecosystem

Ringed seals maintain several breathing holes across landfast and pack ice, scratched open with claws and forelimb crampons. In winter, pupping lairs are dug into drift snow over breathing holes. A single ringed seal weighs 50–110 kg, of which 30–50% is blubber — an enormous energy packet (~2–4 × 10⁵kcal).

Polar bears have co-evolved with ringed seals to the point that the bear’s olfactory range (30+ km by field trials; Stirling 2011) and patience (hours to days at single breathing holes) are specifically calibrated to seal lair biology. Other Arctic prey — bearded seals, beluga, walrus calves, geese — are supplementary; ringed seal provides the caloric backbone.

2. Still-Hunt & Stalk-Ambush

Stirling 1988 classified the two dominant strategies:

Still-hunt: motionless vigil at a breathing hole or lair, often for >60 minutes. Low metabolic cost, high per-encounter capture probability (~25%). The dominant strategy in spring.
Stalk-ambush: slow approach against the wind toward seals on the ice edge, terminated with a rush. Higher caloric cost, lower per-encounter success (~12%) but allows engagement with prey unreachable by still-hunt.

Stirling 1988 estimated still-hunts deliver ~75% of all kills at less than 40% of the caloric cost. Expected inter-kill interval in good spring ice is 4–7 days; a single successful ringed-seal kill provides 10–20 days of maintenance energy for an adult male.

Simulation: Hunting Energetics

Metabolic rate trajectories over a 10-hour hunt for still-hunt vs. stalk-ambush, and expected per-day capture rates assuming typical encounter counts and success probabilities.

Python

script.py42 lines

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

# Still-hunt energetics vs stalk-ambush over a 10-hour day
t = np.linspace(0, 10, 600)

# Still-hunt: near-zero metabolic cost while waiting, pulse on strike
still = np.full_like(t, 1.05)    # BMR multiple
for ts in [2.3, 5.8, 8.1]:
    still += 4.0 * np.exp(-((t - ts) / 0.08) ** 2)

# Active stalk: higher sustained cost
stalk = 2.3 + 0.5 * np.sin(2*np.pi*t/1.3)

# Success probability per encounter
P_success_still = 0.25
P_success_stalk = 0.12
encounters = np.array([3, 7])  # per 10 h
expected_kills = encounters * np.array([P_success_still, P_success_stalk])

fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5), facecolor='#0a0a1a')
for ax in (ax1, ax2):
    ax.set_facecolor('#111827'); ax.tick_params(colors='#cbd5e1')
    for s in ax.spines.values(): s.set_color('#334155')
    ax.grid(True, color='#334155', alpha=0.3)

ax1.plot(t, still, color='#38bdf8', lw=2.4, label='Still-hunt')
ax1.plot(t, stalk, color='#f87171', lw=2.4, label='Stalk-ambush')
ax1.set_xlabel('Hour', color='#cbd5e1'); ax1.set_ylabel('Metabolic rate (x BMR)', color='#cbd5e1')
ax1.set_title('Two hunting strategies', color='#bae6fd', fontweight='bold')
ax1.legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1')

ax2.bar(['Still-hunt', 'Stalk-ambush'], expected_kills,
        color=['#38bdf8', '#f87171'], edgecolor='#bae6fd')
ax2.set_ylabel('Expected kills / 10 h', color='#cbd5e1')
ax2.set_title('Expected prey capture rate', color='#bae6fd', fontweight='bold')

plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a0a1a')
print('Stirling 1988: still-hunt yields ~75% of kills at <40% of caloric cost')
print('One ringed seal = 10-20 days maintenance energy')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

3. GPS Telemetry & Movement Ecology

Satellite-collar data (Durner 2009–2017, Pagano 2018 Science) show individual polar bears range across 100 000–500 000 km² per year, with movement strongly tied to drifting ice edges. Walking speeds average ~3 km h^-1 but 50+ km per day is common during ice-edge tracking. Activity budgets are now known with hour-scale resolution, enabling precise metabolic reconstructions of wild individuals.

Key References

• Stirling, I. (1988). Polar Bears. University of Michigan Press.

• Pagano, A. M. et al. (2018). “High-energy, high-fat lifestyle challenges an Arctic apex predator, the polar bear.” Science, 359, 568–572.

• Durner, G. M. et al. (2009). “Predicting 21st-century polar bear habitat distribution from global climate models.” Ecol. Monogr., 79, 25–58.

• Stirling, I. & Derocher, A. E. (2012). “Effects of climate warming on polar bears: a review of the evidence.” Glob. Change Biol., 18, 2694–2706.

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