Pre-adult mortality: should we care about it, and what can we do about it?
Bibliographic record
Abstract
In a wide range of taxa, most individuals have zero fitness: they die before reproducing. In this Commentary, I first confirm that across taxa - from Drosophila to elephant seals to trees - pre-adult mortality is the norm, with ∼60-90% of offspring dying before reproduction. Two seemingly opposite, though not mutually exclusive, hypotheses explain who dies: (1) that this is simply due to stochastic events, a matter of chance or luck, or (2) that it involves selective disappearance, with the loss of low-quality individuals with specific phenotypic traits associated with low survival. I then review (a) what we know about (physiological) phenotypes early in development, at independence, (b) whether these might become fixed in early development, and (c) whether these traits are repeatable or labile during ontogeny, forming targets of selection determining fitness (cf. adult phenotype). I highlight four reasons to care about pre-adult mortality in current, experimental studies: (1) identifying the phenotypic traits (and physiology) determining life's winners and losers is a significant knowledge gap and worthy research goal; (2) it should matter if our study populations comprise a random sample of individuals (chance) or a 'biased' high-quality subset of individuals (selective disappearance); (3) we typically create conditions to minimize mortality in laboratory populations, but these are then totally different from natural populations (with high pre-reproductive mortality); and (4) if individuals that make it to reproduction are all high-quality individuals, the 'best of the best', this might explain the seeming absence of, or failure to detect, trade-offs and costs.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.015 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".