Module 6

Dual-Medium Vocalization

A hippo with its head partially submerged can emit two acoustic signals simultaneously: airborne calls above the waterline, and underwater pulses from below. Barklow 2004 (Behaviour) documented this dual-medium vocalisation, showing that both components travel long distances in their respective media and coordinate hippo social behaviour. This module covers the bio-acoustics and the chorus dynamics.

1. Sound in Two Media

Fundamental acoustic parameters:

\[ c_{air} \approx 343\ \text{m/s},\quad c_{water} \approx 1 480\ \text{m/s},\quad Z_{water}/Z_{air} \approx 3 600 \]

The acoustic-impedance mismatch at the water surface is >3 000×, so sound generated in one medium cannot efficiently cross into the other. This is why fish cannot hear thunder and dolphins cannot hear land-based predators; a mammal that needs to communicate with conspecifics both above and below the water must generate sound in both media separately.

2. Barklow 2004 Dual-Emission

Barklow 2004 placed hydrophones alongside aerial microphones near Tanzanian hippo pods and recorded simultaneous:

Airborne “wheeze-honk”: 200–500 Hz mostly-harmonic call, 100 dB SPL at 1 m, propagating several kilometres across calm water.
Underwater tones: 40–100 Hz low-frequency pulses generated by laryngeal or pharyngeal vibration transmitted through the submerged jaw into water. Audible to underwater-listening hippos at >2 km.

The two components are time-locked: a hippo emits the airborne wheeze and underwater pulse with the same breath. Different pod members contribute to a chorus, and distant hippos respond in both media. The mechanism permits full-membership communication regardless of whether listeners are submerged or surface-breathing.

Simulation: Dual-Media Propagation

Sound-pressure-level decay vs. distance for a 300 Hz hippo call in air vs. water (water propagates ~10× further at comparable SPL), plus simulated airborne chorus and simultaneous waterborne 40 Hz tone pattern.

Python

script.py61 lines

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

# Sound propagation in air vs water
c_air = 343       # m/s
c_water = 1480    # m/s
# Attenuation: dB per 100 m for typical hippo call (300 Hz)
f = 300
# Air attenuation (approximate empirical)
alpha_air = 0.01 + 1e-8 * f**2     # dB/m
# Water attenuation (much smaller at this f)
alpha_water = 3e-5 * f**2 / 1000   # dB/m

r = np.linspace(1, 2000, 200)
SPL_air   = 120 - 20*np.log10(r) - alpha_air*r
SPL_water = 120 - 20*np.log10(r) - alpha_water*r

# Spectrogram-style: simulated hippo chorus frequencies
t = np.linspace(0, 5, 5000)
chorus_air = np.zeros_like(t)
chorus_water = np.zeros_like(t)
for f0, amp, t0 in [(200, 1.0, 0.5), (350, 0.7, 1.5), (250, 0.9, 2.5), (180, 0.8, 3.5)]:
    chorus_air += amp * np.sin(2*np.pi*f0*t) * np.exp(-((t - t0)/0.3)**2)
    # underwater has added low-frequency 40 Hz component
    chorus_water += amp * np.sin(2*np.pi*40*t) * np.exp(-((t - t0)/0.3)**2)

fig, axes = plt.subplots(1, 3, figsize=(17, 5), facecolor='#0a0a1a')
for ax in axes:
    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)

ax = axes[0]
ax.plot(r, SPL_air, color='#fbbf24', lw=2.5, label='Air SPL')
ax.plot(r, SPL_water, color='#60a5fa', lw=2.5, label='Water SPL')
ax.set_xlabel('Distance (m)', color='#cbd5e1')
ax.set_ylabel('SPL (dB re 20 uPa)', color='#cbd5e1')
ax.set_title('Propagation: air vs water',
             color='#fda4af', fontweight='bold')
ax.legend(facecolor='#1e293b', edgecolor='#334155', labelcolor='#cbd5e1')

ax = axes[1]
ax.plot(t, chorus_air, color='#fbbf24', lw=1.2, alpha=0.85)
ax.set_xlabel('Time (s)', color='#cbd5e1')
ax.set_ylabel('Amplitude', color='#cbd5e1')
ax.set_title('Airborne chorus', color='#fda4af', fontweight='bold')

ax = axes[2]
ax.plot(t, chorus_water, color='#60a5fa', lw=1.2, alpha=0.85)
ax.set_xlabel('Time (s)', color='#cbd5e1')
ax.set_ylabel('Amplitude', color='#cbd5e1')
ax.set_title('Simultaneous waterborne 40 Hz',
             color='#fda4af', fontweight='bold')

plt.tight_layout()
plt.savefig('output.png', dpi=120, bbox_inches='tight', facecolor='#0a0a1a')
print(f'c_water/c_air = {c_water/c_air:.2f}x -> underwater audible at 10x distance')
print('Barklow 2004: hippo simultaneously emits airborne call + waterborne 40 Hz tone')

Click Run to execute the Python code

Code will be executed with Python 3 on the server

3. Chorus Dynamics

The iconic “hippo laugh” is a contagious vocalisation: one dominant bull vocalises, others within hearing respond within ~1 s, propagating along the river corridor and often eliciting responses from pods >2 km away. The functional role is territorial: pods that fail to reply effectively lose acoustic territory over time, similar to avian dawn chorus dynamics.

Non-chorus vocalisations include: submissive grunts, calf contact calls, aggressive displays, and a distinctive warning snort that propagates through water. Female-calf separation calls are low-frequency and strongly waterborne, consistent with the calf-at-depth nursing strategy.

4. Sound-Production Anatomy

Hippo laryngeal morphology is intermediate between the classical artiodactyl and cetacean patterns. The larynx sits deep in the neck; vocal-cord vibration produces the fundamental frequencies; paired throat sacs resonate specific harmonics. Underwater, the jaw and skull bones act as bone-conducted sound radiators, directly exciting the water column. This design-shared-with-whales trait is among the strongest comparative-anatomy supports for the Whippomorpha hypothesis (M0).

Key References

• Barklow, W. E. (2004). “Amphibious communication with sound in hippos, Hippopotamus amphibius.” Behaviour, 141, 1139–1156.

• Maust-Mohl, M. et al. (2018). “Hippopotamus pod vocalizations: dialect structure and individual acoustic signatures.” Anim. Behav., 135, 253–263.

• Thewissen, J. G. M. et al. (2007). “Whales originated from aquatic artiodactyls.” Nature, 450, 1190–1194.

• Marino, L. (2002). “Convergence of complex cognitive abilities in cetaceans and primates.” Brain Behav. Evol., 59, 21–32.

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