Quantifying the known unknowns: estimating maximum intrinsic rate of population increase in the face of uncertainty
Bibliographic record
Abstract
Abstract Sensitivity to overfishing is often estimated using simple models that depend upon life history parameters, especially for species lacking detailed biological information. Yet, there has been little exploration of how uncertainty in life history parameters can influence demographic parameter estimates and therefore fisheries management options. We estimate the maximum intrinsic rate of population increase (rmax) for ten coastal carcharhiniform shark populations using an unstructured life history model that explicitly accounts for uncertainty in life history parameters. We evaluate how the two directly estimated parameters, age at maturity αmat and annual reproductive output b, most influenced rmax estimates. Uncertainty in age at maturity values was low, but resulted in moderate uncertainty in rmax estimates. The model was sensitive to uncertainty in annual reproductive output for the least fecund species with fewer than 5 female offspring per year, which is not unusual for large elasmobranchs, marine mammals, and seabirds. Managers and policy makers should be careful to restrict mortality on species with very low annual reproductive output <2 females per year. We recommend elasmobranch biologists to measure frequency distributions of litter sizes (rather than just a range) as well as improving estimates of natural mortality of data-poor elasmobranchs.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".