Inference of selection gradients using performance measures as fitness proxies
Bibliographic record
Abstract
Summary Selection coefficients, i.e. selection differentials and gradients, are useful for quantifying selection and for making comparisons across traits and organisms, because they appear in known equations for relating selection and genetic variation to one another and to evolutionary change. However, selection coefficients can only be estimated in organisms where traits and fitness (components) can be measured. This is probably a major contributor to taxonomic biases of selection studies. Aspects of organismal performance, i.e. quantities that are likely to be positively related to fitness components, such as body size, are sometimes used as proxies for fitness, i.e. used in place of fitness components in regression‐based selection analysis. To date, little theory exists to inform empirical studies about whether such procedures may yield selection coefficients with known relationships to genetic variation and evolution. We show that the conditions under which performance measures can be used as proxies for fitness are very limited. Such analyses require that the regression of fitness on the proxy is linear and goes through the origin. We illustrate how fitness proxies may be used in conjunction with information about the performance–fitness relationship, and clarify how this is different from substituting fitness proxies for fitness components in selection analyses. We apply proxy‐based and fitness component‐based selection analysis to a system where traits, a performance measure (size; similar to proxies that are commonly used in place of fitness), and a more proximate fitness measure, are all available on the same set of individuals. We find that proxy‐based selection gradients are poorly reflective of selection gradients estimated using fitness components, even when proxy–fitness relationships are quite strong and reasonably linear. We discuss the implications for proxy‐based selection analysis. We emphasise that measures of organismal performance, such as size, may in many cases provide useful information that can contribute to quantitative inferences about natural selection, and their use could allow quantitative inference about selection to be conducted in a wider range of taxa. However, such inferences require quantitative analysis of both trait–performance and performance–fitness relationships, rather than substitution of performance for measures of fitness or fitness components.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".