Effective/census population size ratio estimation: a compendium and appraisal
Bibliographic record
Abstract
With an ecological-evolutionary perspective increasingly applied toward the conservation and management of endangered or exploited species, the genetic estimation of effective population size (N(e)) has proliferated. Based on a comprehensive analysis of empirical literature from the past two decades, we asked: (i) how often do studies link N(e) to the adult census population size (N)? (ii) To what extent is N(e) correctly linked to N? (iii) How readily is uncertainty accounted for in both N(e) and N when quantifying N(e)/N ratios? and (iv) how frequently and to what degree might errors in the estimation of N(e) or N affect inferences of N(e)/N ratios? We found that only 20% of available N(e) estimates (508 of 2617; 233 studies) explicitly attempted to link N(e) and N; of these, only 31% (160 of 508) correctly linked N(e) and N. Moreover, only 7% (41 of 508) of N(e)/N ratios (correctly linked or not) reported confidence intervals for both N(e) and N; for those cases where confidence intervals were reported for N(e) only, 31% of N(e)/N ratios overlapped with 1, of which more than half also reached below N(e)/N = 0.01. Uncertainty in N(e)/N ratios thus sometimes spanned at least two orders of magnitude. We conclude that the estimation of N(e)/N ratios in natural populations could be significantly improved, discuss several options for doing so, and briefly outline some future research directions.
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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 | 0.017 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.023 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".