Standard histological techniques systematically under‐estimate the size fish start spawning
Bibliographic record
Abstract
Abstract Beverton & Holt's (1957) functional definition of maturity in fish (L50), as being the size class in which 50% of individuals begin producing gametes in some proportion to body weight, is widely used in assessment models to estimate spawning stock biomass, and manage the minimum size of capture. Standardized histological techniques for estimating L50 apply physiological definitions that identify cellular or hormonal markers indicating individuals are capable of producing gametes. Few studies have examined how those phases of gonadal development correlate with reproductive behaviour or reproductive output. We compare histological estimates of L50 for 10 species of reef fish from Palau, with the size composition of catches, surveys and spawning aggregations, and interpret those comparisons with simulation modelling. Our study shows that the histological L50 estimates coincide with the size species begin ontogenetically shifting between juvenile and adult habitat, and with the size the smallest individuals join spawning aggregations, but are ~15% smaller than the length at which we infer 50% of individuals begin joining spawning aggregations. This highlights a mismatch between the functional definition of L50 assumed for assessment and management, and the physiological definitions developed for histological studies, which identify the beginning of a sub‐adult adolescent phase, rather than the end, when functional adulthood starts. This definitional mismatch could be causing stock assessments to systematically over‐estimate the reproductive output of fisheries. By considering fish behaviour, we hope to better align estimates of physiological maturation with the estimation of functional reproductive potential required for assessment and management.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".