Challenges in assessing adaptive genetic diversity: overview of methods and empirical illustrations
Bibliographic record
Abstract
INTRODUCTION Since its inception, the concept of ‘evolutionarily significant unit’ (ESU) has had several theoretical definitions differing mainly on the emphasis given to neutral versus adaptive genetic diversity (Ryder 1986; Waples 1991; Moritz 1994; Crandall et al . 2000; reviewed in Fraser and Bernatchez 2001). The ‘neutral’ definition highlights a genetic background shaped over a long-term evolutionary time scale by evolutionary forces such as genetic drift and migration (Moritz 2002). In contrast, the ‘adaptive’ definition underlines the existing, adaptively significant phenotypic variation resulting from the ongoing action of natural selection (McKay and Latta 2002; van Tienderen et al . 2002). Thus, neutral and adaptive diversities depict distinct temporal realms (Fig. 6.1) and should be assessed separately and differently (Bowen 1999; Moritz 2002). The neutral component of genetic diversity was the first to be investigated, coinciding with the breakthrough in the development of molecular markers and new tools and concepts in phylogeography and population genetics. A wide range of methods is currently available to measure neutral genetic variability, which has resulted in crucial answers to several key conservation issues (see examples in Frankham et al . 2002). In contrast, characterizing adaptive genetic variation remains challenging (van Tienderen et al . 2002; Vasemägi and Primmer 2005), requiring the identification of a small number of adaptive loci scattered throughout the genome (Black et al . 2001). Moreover, molecular markers are generally assumed to be neutral and they have been shown to be poor indicators of adaptive genetic diversity (Merilä and Crnokrak 2001; Reed and Frankham 2001; McKay and Latta 2002). Given this background, we review here different strategies that can be adopted to reveal genetic polymorphisms with an adaptive role. The goal of this chapter is not to present an exhaustive and detailed list of existing methods, but rather to highlight the underlying principles of those we believe to be the most useful and/or readily applicable, as well as their advantages and drawbacks. Several examples relevant to conservation are also presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.038 | 0.065 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".