Imputation of Fisheries Reference Points for Endangered Data‐Poor Fishes, With Application to Rhino Rays
Bibliographic record
Abstract
ABSTRACT For data‐limited fish species, sustainable management frequently relies on biological metrics that are derived from life‐history trait data, as opposed to high‐resolution time series of catch and abundance. These biological metrics are used to assess a species' recovery potential at low population densities, as well as their extinction risk. However, for really data poor species, the life‐history traits required to derive these metrics are also often only partially known. Addressing this gap is essential for informing regulatory and conservation actions for vulnerable species and stocks lacking assessments. We developed a generalisable, phylogenetically informed framework for imputing missing life‐history traits across different taxa and applied it to 57 species within the order Rhinopristiformes (rhino rays), an evolutionarily distinct and highly threatened group with notably sparse life‐history data. We then used the imputed traits to derive four key management and conservation metrics: steepness of the Beverton–Holt stock–recruitment relationship, spawning potential ratio at maximum sustainable yield, maximum intrinsic population growth rate and generation length. We found strong correlations between mean life‐history traits and three management metrics. While uncertainty in management metrics remained high due to intraspecific variability, measurement error and limited data, using reconstructed traits reduced uncertainty compared to using surrogate trait data from other populations or congeneric species. We provide imputed trait values and corresponding management and conservation metrics alongside uncertainty bounds that should be recognised in any subsequent conservation assessments and management strategy evaluations. The proposed framework enables the generation of first‐order, evidence‐based management and conservation metrics for data‐limited taxa, thereby supporting more informed decision‐making for species without comprehensive species‐level 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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".