Building on the Mediterranean monk seal vocal repertoire: Foundations for long-term passive acoustic monitoring
Bibliographic record
Abstract
With a population comprising <1000 individuals, the Mediterranean monk seal (Monachus monachus) is among the world’s rarest marine mammals. Monitoring this elusive species is challenging due to its population size, inaccessible haul-out sites, and wide historical range, which limit many traditional survey methods. Passive acoustic monitoring (PAM) offers a promising, low-impact approach to tracking range-wide population trends and assessing anthropogenic impacts. To support the development of long-term PAM for this species, we characterized the underwater vocal repertoire from recordings in the Inner Ionian Sea Archipelago, Greece. Ten call types were identified, five of which were also documented in underwater video of monk seals vocalizing. Eventual variety and social network analyses of vocal bouts revealed patterns of structure and complexity and identified three frequently co-occurring call types likely central to monk seal communication. Building on these findings, we are developing automated detection tools using PAMGuard and a novel multi-step framework to identify these calls in large datasets. Initial results show monk seal vocal activity year-round and reveal seasonal variation in calling rates. This work provides a foundation for scalable PAM of Mediterranean monk seals, offering tools to better understand vocal behavior, habitat use, and to support conservation of this rare species.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".