Probing the fin whale song evolution

Paul Best, Ricard Marxer, Sébastien Paris, Hervé Glotin

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Paul Best, Ricard Marxer, Sébastien Paris, Hervé Glotin. Probing the fin whale song evolution. inauguration du LSPM, Feb 2023, Marseille (FRANCE), France. hal-05009850

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Distributed under a Creative Commons Attribution 4.0 International License
# Probing the fin whale song evolution

Paul Best, Ricard Marxer, Sébastien Paris, Hervé Glotin

DYNI, Laboratoire d'Informatique des Systèmes, Université de Toulon

paul.best@univ-tln.fr

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## Introduction

The fin whale (*Balaenoptera physalus*) is the second largest animal on earth, roaming all oceans including the Mediterranean Sea. Their songs are rhythmic sequences composed of pulse like vocalisations around 20Hz. Each fin whale population has its own song dialect which defines a pulse frequency and a rhythmic pattern (inter-pulse intervals). Automatic detection systems applied on long term recordings of the Ligurian Sea have allowed to characterise the Mediterranean fin whale song, as well as its evolution through the last 20 years.

<figure><img src="image_4.png" /><figcaption>Pulse frequencies decrease each winter (-0.1Hz/month, $R^2 = 0.73$).</figcaption></figure>

<figure><img src="image_5.png" /><figcaption>IPI increase across years ($R^2 > 0.86$)</figcaption></figure>

## Deep Learning-based detection and song analysis

### 1. Using a CNN to detect 20Hz pulses :

Long term passive acoustic recordings from the Ligurian Sea allowed to gather numerous fin whale songs through the years. For their automated analysis, we trained a CNN to detect 20Hz pulses on spectrograms. This 3 layer lightweight CNN achieves 0.99 of AUC when tested on antennas unseen in training.

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### 2. Song analysis:

Given the CNN detections, we extracted the timestamp and center frequency of each pulse. Pulse frequencies allowed to classify them by type (A and B, with A the higher pitched one). Grouping close pulse together in sequences allowed to estimate Inter-Pulse Intervals (IPI) which are characteristic of each bi-gram.

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### 3. Summary of fin whale song trends worldwide:

<table><thead><tr><th rowspan="2">Study</th><th rowspan="2">Location</th><th colspan="2">Inter-annual</th><th colspan="2">Intra-annual</th></tr><tr><th>Frequency</th><th>IPI</th><th>Frequency</th><th>IPI</th></tr></thead><tbody><tr><td>Weirathmueller et al. 2017</td><td>N.E. Pacific</td><td>-0.17 Hz/yr</td><td>0.5-0.9 sec/yr</td><td>-</td><td>-</td></tr><tr><td>Oleson et al. 2014</td><td>N. Pacific</td><td>-</td><td>-</td><td>-</td><td>+7.5 sec</td></tr><tr><td>Leroy et al. 2018</td><td>Indian</td><td>-0.21 Hz/yr</td><td>-</td><td>~ -0.1 Hz/mth</td><td>-</td></tr><tr><td>Helble et al. 2020</td><td>N. Pacific</td><td>-</td><td>0.6-1.3 sec/yr</td><td>-</td><td>-</td></tr><tr><td>morano2012seasonal</td><td>N.W. Atlantic</td><td>-</td><td>* 0.5 sec/yr</td><td>-</td><td>+5.5 sec</td></tr><tr><td>Watkins et al. 1987</td><td>N.W. Atlantic</td><td>-</td><td>-</td><td>-</td><td>+6 sec</td></tr><tr><td>Širović et al. 2017</td><td>Gulf of California</td><td>-</td><td>~ 1 sec/yr</td><td>-</td><td>~ +8 sec</td></tr><tr><td>Furumaki et al. 2021</td><td>Chukchi sea</td><td>-</td><td>~ 0.5 sec/yr</td><td>-</td><td>~ +1 sec</td></tr><tr><td>Wood and Širović 2022</td><td>W. Antarctic</td><td>-0.2 Hz/yr</td><td>0.1 sec/yr</td><td>-</td><td>-</td></tr><tr><td>Ours</td><td>W. Mediterranean</td><td>-</td><td>0.1 sec/yr</td><td>-0.1 Hz/mth</td><td>-</td></tr></tbody></table>

## Singing depth and water temperature

- A decrease of vocalisation frequencies is observed worldwide in 4 mysticete species

- The observation of seasonal trends has led the hypothesis of a temperature related phenomenon

- A preferred singing depth could be motivated by the whale's buoyancy, which is related to water temperature and body condition

- The global sea surface temperature has been increasing linearly since 1960

- Singing depth could impact vocalisation frequencies (optimal propagation or pressure on the vocal apparatus)

<figure><img src="image_8.png" /><figcaption>Correlation matrix of a diving & singing pygmy blue whale *Davenport et al. 2022*</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Inter and intra annual decrease of Antarctic blue whales *Gavrilov et al. 2012*</figcaption></figure>

## Conclusions and Future Work

Blue whales, pygmy blue whales, bowhead whales and fin whales demonstrate **long term variations in their vocalisation frequencies**. Numerous **hypothesis are being tested** such as a post whaling increase in population density or body size, increasing ambient noise levels and water characteristic evolution (warming, acidification), with **no current consensus**.

The **KM3Net acoustic recorders** could contribute to probing this phenomenon, by jointly estimating **vocalisation frequency and calling depth** across seasons and years.

Additionally, alongside the **Bombyx 2 network**, the KM3Net observatory could allow to alert on **cetacean presence in real time** and protect them from **fatal ship strikes**.

## References

Davenport et al. "Pygmy Blue Whale Diving Behaviour Reflects Song Structure." Journal of Marine Science and Engineering (2022)

Gavrilov et al. "Steady inter and intra-annual decrease in the vocalization frequency of Antarctic blue whales." JASA (2012)

McDonald et al. "Worldwide decline in tonal frequencies of blue whale songs." Endangered species research (2009)

Best et al. "Temporal evolution of the Mediterranean fin whale song." Scientific reports (2022)