Reliable growth estimation from mark–recapture tagging data in elasmobranchs
Bibliographic record
Abstract
The somatic growth of individuals governs many aspects of a species’ life history and is an important parameter in the assessment of populations. Population growth parameters are typically derived by relating the length of individuals to their age, with ages commonly estimated from growth bands formed in calcified structures such as the vertebrae or dorsal fin spines. However, routinely utilized vertebrae aging methods may not be reliable for many elasmobranchs (sharks, rays and skates), motivating alternative approaches. This study evaluates the performance of seven techniques that estimate von Bertalanffy growth parameters from mark-recapture tagging data. Evaluation of the performance was done by applying each estimation technique to: 1) simulated error-free mark-recapture tagging data and comparing the estimated versus known simulated growth parameters; 2) simulated mark-recapture data considering individual growth variability, measurement error, different length-at-capture distributions, as well as different sample sizes and comparing the estimated versus known simulated growth parameters; and 3) mark-recapture data of 14 North Atlantic elasmobranch stocks and discussing the estimated growth parameters with respect to biological plausibility and conventional length-at-age data. All investigated estimation techniques returned the known simulated growth parameters when the data is without error. When errors are introduced in the simulation, Bayesian implementations of Fabens' (BFa) and Francis’ (BFr) methods were found to be most reliable. For the observed mark-recapture data only BFa gave biologically plausible results for all 14 elasmobranch stocks. Overall, the results suggest that BFa is a reliable alternative to conventional length-at-age methods for estimating growth parameters, especially in data-limited situations which commonly occur with elasmobranchs. The only prior information needed is limited expert knowledge on maximum length in the population or stock in question. A user guide is provided to facilitate application of the method.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 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".