Assessing the size spectra of marine fish communities with hydroacoustics: examining the challenges of abundant schools, diverse assemblages, and variable orientations
Bibliographic record
Abstract
Assessing marine fish community size spectra with hydroacoustics is challenging, as communities are diverse, schooling and swim-bladder-less fishes are common, and fish orientation is variable. We developed an approach to examine these challenges and applied it to data from 51 optic–acoustic surveys of fishes at petroleum platforms throughout the US Gulf of Mexico. When in situ target strength (TS; dB re 1 m2) distributions were used to calculate the density (and subsequently abundance) of schooling fishes, fish lengths and size spectra slopes were significantly smaller than in simulated communities and fish community censuses at platforms (i.e., reference datasets). However, acoustic slopes were comparable to reference slopes when simulated TS values (based on species composition) were used to calculate schooling fish abundance. These findings held regardless of whether specific or general models were used to convert TS to length. Fish orientation was not a useful predictor of TS or slope, but may explain why in situ TS measurements from small groups of fishes around schools were unsuitable for abundance calculations. By examining the challenges associated with assessing size spectra with acoustics, this study aids progress towards using acoustic size spectra metrics for ecological inferences.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".