Comparison of genetic effective population size estimates in species across a large range of life-history strategies
Bibliographic record
Abstract
Population genetic diversity (GD) is essential for long-term adaptive potential of wild species and is shaped by life-history traits (LHTs) and microevolutionary processes. International conservation policy has endorsed the protection of GD with the adoption of a Headline Indicator in the 2022 Kunming-Montreal Global Biodiversity Framework: “the proportion of populations with an effective population size Ne above 500”. To shed light on the drivers of population Ne and improve GD indicator estimation from genetic data, we developed a categorization framework and estimated Ne for a selected set of 25 DNA datasets with rich metadata. The species represented with these datasets harbour contrasted LHTs strategies that relate to lifespan (axis 1), the spread of reproductive stages throughout lifespan (axis 2), and, additionally, to spatial genetic structure, dispersal capacity or population history (axis 3). For each species, we compared Ne estimates obtained from two methods, based on the linkage disequilibrium in populations, or based on a Bayesian method including various genetic summary statistics as prior information. We also considered technical data features such as the type of molecular marker, and the number and quality of SNPs. Our results show a wide range of Ne estimates within some species (including populations with Ne below the 500 threshold and others above it), and also among species within the same categories of LHTs. We discuss challenges for obtaining reliable DNA-based Ne estimates for GD indicators in species with contrasted life-history trait variation, life cycle and evolutionary scenarios. By exploring these challenges, we aim to contribute to developing best practices for GD indicator estimation for nature managers.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 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".