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, general theoretical concepts of fitness are often hard to translate into quantities that can be measured in wild populations experiencing complex environmental, demographic, genetic, and selective variation. Although the “fittest” entities might be widely understood to be those that ultimately leave most descendants at some future time, such long-term legacies can rarely be measured, impeding evaluation of the degree to which tractable short-term metrics of individual fitness could potentially serve as useful direct proxies. One opportunity for conceptual and empirical convergence stems from the principle of individual reproductive value (Vi), here 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 realized pedigree of descendants. As Vi tightly predicts an individual's longer term genetic contribution, quantifying Vi provides a tractable route to quantifying what, to date, has been an abstract theoretical fitness concept. We used complete pedigree data from free-living song sparrows (Melospiza melodia) to demonstrate that individuals’ expected genetic contributions stabilize within an observed 20-year (i.e. approximately eight generation) time period, allowing estimation of individual Vi. Considerable among-individual variation in Vi was evident in both sexes. Standard metrics of individual lifetime fitness, comprising lifespan, lifetime reproductive success, and projected growth rate, typically explained less than half the variation. We thereby elucidate the degree to which fitness metrics observed on individuals concur with measures of longer term genetic contributions and consider the degree to which analyses of pedigree structure could provide useful complementary insights into evolutionary 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".