Estimating population abundance at a site in the open ocean: combining information from conventional and telemetry tags with application to gray triggerfish (<i>Balistes capriscus</i>)
Bibliographic record
Abstract
Estimates of animal abundance are widely used to support conservation and resource management. For populations in open systems, abundance estimates from tagging data can be highly uncertain or biased. Here, we develop a novel approach to estimate abundance of an open population by pairing two models, each utilizing distinct tagging data. Using data from telemetry tags, we infer movement rates to and from the study site with a Markovian model allowing for an environmental effect. Then, using data from conventional passive tags, we apply a Lincoln–Petersen abundance estimator modified to account for mortality and movement. After developing the model within a Bayesian framework, we demonstrate its application to data on gray triggerfish (Balistes capriscus) tagged in the Atlantic Ocean off North Carolina, USA. For this open population, we estimate site abundance to be ∼1000 fish (∼2000 fish·km–2) and additionally find evidence for an effect of hurricanes on movement. The general approach may be useful for fisheries, wildlife, and other ecological studies utilizing multiple tag types, particularly for estimating abundance of an open population.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".