Parallel evolution of senescence in annual fishes in response to extrinsic mortality
Bibliographic record
Abstract
BACKGROUND: Early evolutionary theories of aging predict that populations which experience low extrinsic mortality evolve a retarded onset of senescence. Experimental support for this theory in vertebrates is scarce, in part for the difficulty of quantifying extrinsic mortality and its condition- and density-dependent components that -when considered- can lead to predictions markedly different to those of the "classical" theories. Here, we study annual fish of the genus Nothobranchius whose maximum lifespan is dictated by the duration of the water bodies they inhabit. Different populations of annual fish do not experience different strengths of extrinsic mortality throughout their life span, but are subject to differential timing (and predictability) of a sudden habitat cessation. In this respect, our study allows testing how aging evolves in natural environments when populations vary in the prospect of survival, but condition-dependent survival has a limited effect. We use 10 Nothobranchius populations from seasonal pools that differ in their duration to test how this parameter affects longevity and aging in two independent clades of these annual fishes. RESULTS: We found that replicated populations from a dry region showed markedly shorter captive lifespan than populations from a humid region. Shorter lifespan correlated with accelerated accumulation of lipofuscin (an established age marker) in both clades. Analysis of wild individuals confirmed that fish from drier habitats accumulate lipofuscin faster also under natural conditions. This indicates faster physiological deterioration in shorter-lived populations. CONCLUSIONS: Our data provide a strong quantitative example of how extrinsic mortality can shape evolution of senescence in a vertebrate clade. Nothobranchius is emerging as a genomic model species. The characterization of pairs of closely related species with different longevities should provide a powerful paradigm for the identification of genetic variations responsible for evolution of senescence in natural populations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".