Potential for use of passive acoustic monitoring of piranhas in the Pacaya–Samiria National Reserve in Peru
Bibliographic record
Abstract
Abstract Passive acoustic monitoring ( PAM ) can be an effective tool in the identification of fishes and mapping of their temporal and spatial distribution patterns and thereby aids in ecosystem management in remote locations. However, to date measurements of the acoustic properties of piranha have been primarily made in aquaria on captive specimens obtained through the aquarium trade. Data on the sound production of wild piranha taken under natural field conditions would enhance PAM applications. Piranha captured as part of routine annual monitoring by Operation Wallacea within the Pacaya–Samiria National Reserve in Peru were auditioned for sound production prior to release. Auditioning was done by gently holding a fish underwater in the river near a suspended hydrophone, thus recordings included the piranha sound as well as natural ambient sound. Seventy‐nine per cent of the 129 auditioned piranha, including Pygocentrus nattereri , Serrasalmus maculatus , Serrasalmus cf. sanchezi and an unidentified Serrasalmus spp. complex, produced sounds consisting of 2 to 23 barks in a sequential series. Sound production by S. maculatus and S. cf. sanchezi are reported for the first time. Bark characteristics exhibited high variation within bark series of individual fish. Piranha could not be distinguished by single variables but did exhibit significant multivariate differences. The relationship of several variables to fish size also differed significantly among species. Measurements of within‐fish variation and bark series pattern attributes were found to be useful for discrimination of sounds among piranha species. We demonstrate that closely related species of piranha can be distinguished by their sounds under natural acoustic conditions based on multivariate analyses, suggesting that passive acoustic monitoring ( PAM ) can be an effective tool for ecosystem management in the Amazon. More broadly, our study also suggests (a) the need to report more detailed statistical descriptions of fish sounds, including measures of within‐fish variation, (b) the importance of describing characteristics of sound series produced by fishes in addition to those of individual sound types and (c) the need to deposit museum voucher specimens to anchor specific sounds to specific individuals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".