Data from: A critical assessment of estimating census population size from genetic population size (or vice versa) in three fishes
Bibliographic record
Abstract
Technological and methodological advances have facilitated the use of genetic data to infer census population size (Nc) in natural populations, particularly where traditional mark-and-recapture is challenging. The effective number of breeders (Nb) describes how many adults effectively contribute to a cohort and is often correlated with Nc. Predicting Nc from Nb 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 Nb/Nc relationships in related species lacking data. We quantitatively synthesized Nb/Nc relationships in three salmonid fishes where sufficient data has recently accumulated. Mixed-effects models were analyzed in which each variable was included as a dependent variable or predictor term (Nb from Nc and vice versa). Species-dependent Nb/Nc slope estimates were significantly positive in two of three species; variation in species slopes were likely due to varying life histories and reinforce caution when inferring Nb/Nc from taxonomically-related species. Models provided maximum probable estimates for Nb and Nc 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 Nb and/or Nc 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; 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 Nb and Nc. Sufficient data on other taxa were unavailable; additional research examining Nb/Nc relationships in species with potentially relevant life-history trait differences (e.g. differing survival curves) are needed.
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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 | 0.015 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".