A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes
Bibliographic record
Abstract
Abstract Technological and methodological advances have facilitated the use of genetic data to infer census population size (N c ) in natural populations, particularly where traditional mark‐and‐recapture is challenging. The effective number of breeders (N b ) describes how many adults effectively contribute to a cohort and is often correlated with N c . Predicting N c from N b or vice versa in species with overlapping generations has important implications for conservation by permitting (i) estimation of the more difficult to quantify variable and (ii) inferences of N b /N c relationships in related species lacking data. We quantitatively synthesized N b /N c relationships in three salmonid fishes where sufficient data have recently accumulated. Mixed‐effects models were analysed in which each variable was included as a dependent variable or predictor term (N b from N c and vice versa). Species‐dependent N b /N c slope estimates were significantly positive in two of three species. Variation in species slopes was likely due to varying life histories and reinforce caution when inferring N b /N c from taxonomically related species. Models provided maximum probable estimates for N b and N c for two species. However, study, population and year effects explained substantial amounts of variation (39%–57%). Consequently, prediction intervals were wide and included or were close to zero for all population sizes and species; model predictive utility was limited. Cost‐benefit trade‐offs when estimating N b and/or N c were also discussed using a real‐world system example. Our findings based on salmonids suggest that no short cuts currently exist when estimating population size and researchers should focus on quantifying the variable of interest or be aware of caveats when inferring the desired variable because of cost or logistics. We caution that the salmonid species examined share life‐history traits that may obscure relationships between N b and N c . Sufficient data on other taxa were unavailable; additional research examining N b /N c relationships in species with potentially relevant life‐history trait differences (e.g., differing survival curves) is needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".