Combining Hydroacoustics and <scp>eDNA</scp> to Estimate Species‐Specific Biomass in a Pelagic Fish Community
Bibliographic record
Abstract
ABSTRACT Hydroacoustic surveys and eDNA monitoring are rapidly evolving technologies with significant applications for monitoring fish populations. Hydroacoustic technology is capable of enumerating size classes; however, species identification often relies on time‐consuming, costly, and lethal supplementary sampling methods. Environmental DNA (eDNA) detection is a nonlethal alternative for ground‐truthing hydroacoustic surveys; however, on its own, it does not provide estimates of fish size or stock biomass. We tested the utility of paired hydroacoustic and eDNA surveys by replicating samples over a 12‐h period along the depth gradient of pelagic lake habitat where the fish community exhibits diel vertical migration. Generally, we found that (1) the detection and proportion of target species estimated by eDNA was similar to those found in historical gill‐netting across depth strata, (2) eDNA‐apportioned hydroacoustic data agreed with expected diel patterns in species vertical distributions, and (3) with some exceptions, eDNA‐apportioned hydroacoustic estimates of biomass were strongly correlated with expected species biomass. Some species yielded unrealistically high concentrations in the deepest samples, suggesting that benthic accumulation of eDNA can result in inflated biomass estimates near the lake bottom. Combining eDNA and hydroacoustics as complementary noninvasive assessment tools provides a simplified species apportioning protocol for future fish populations and community assessments.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".