Individuals’ expected genetic contributions to future generations, reproductive value, and short-term metrics of fitness in free-living song sparrows ( <i>Melospiza melodia</i> )
Bibliographic record
Abstract
Abstract Appropriately defining and enumerating ‘fitness’ is fundamental to explaining and predicting evolutionary dynamics. Yet theoretical concepts of fitness are often hard to translate into quantities that can be quantified in wild populations experiencing complex environmental, demographic, genetic and selective variation. While the ‘fittest’ entities might be widely understood to be those that ultimately leave most descendants at some future time, such long-term legacies are hard to measure, impeding evaluation of how well more tractable short-term metrics of individual fitness directly predict longer-term outcomes. One opportunity for conceptual and empirical convergence stems from the principle of individual reproductive value ( V i ), defined as the number of copies of each of an individual’s alleles that is expected to be present in future generations given the individual’s realised pedigree of descendants. Since V i tightly predicts an individual’s longer-term genetic contribution, quantifying V i provides a tractable route to quantifying what, to date, has been an abstract fitness concept. We used complete pedigree data from free-living song sparrows ( Melospiza melodia ) to demonstrate that individuals’ expected genetic contributions stabilise within an observed 20-year time period, allowing individual V i to be evaluated. Considerable among-individual variation in V i was evident in both sexes. However, standard short-term metrics of individual fitness, comprising lifespan, lifetime reproductive success and projected growth rate, typically explained less than half the variation. Given these results, we discuss what evolutionary inferences can and cannot be directly drawn from short-term versus longer-term fitness metrics observed on individuals, and highlight that analyses of pedigree structure may provide useful complementary insights into evolutionary processes and outcomes.
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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 | 0.001 | 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 | 0.000 | 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) | 0.000 | 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".