Combining acoustic telemetry and side-scan sonar to estimate abundance of endangered shortnose sturgeon in the Hudson River, New York
Bibliographic record
Abstract
The ability to estimate and monitor population size of the endangered shortnose sturgeon ( Acipenser brevirostrum) is critical for tracking species’ recovery. Yet, contemporary abundance estimates have not been completed for many shortnose sturgeon populations, largely owing to the difficulty in using traditional abundance estimators for sturgeons. Here, we estimate the adult shortnose sturgeon population size of the Hudson River, New York, by integrating data from two largely passive sampling methods—acoustic telemetry and side-scan sonar—into a Bayesian hierarchical model of abundance. We estimated the adult abundance to be 69 798 individuals (95% CI = 9207–185 666), making the Hudson River the largest extant shortnose sturgeon population. Despite this, the population remains vulnerable to localized disturbances, as over 40% of the population congregated in a small overwintering habitat that coincides with an area of high anthropogenic activity. Accordingly, recurrent demographic surveys could provide additional insights into the relative effects of anthropogenic and naturally stochastic processes shaping shortnose sturgeon demography. Our modeling framework provides a relatively low-cost alternative for future demographic monitoring of species of conservation concern.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".