Genetic variance in fitness indicates rapid contemporary adaptive evolution in wild animals
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: ObservationalConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.617
- Threshold uncertainty score
- 0.249
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
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.
- Teacher spread
- 0.202 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
The rate of adaptive evolution, the contribution of selection to genetic changes that increase mean fitness, is determined by the additive genetic variance in individual relative fitness. To date, there are few robust estimates of this parameter for natural populations, and it is therefore unclear whether adaptive evolution can play a meaningful role in short-term population dynamics. We developed and applied quantitative genetic methods to long-term datasets from 19 wild bird and mammal populations and found that, while estimates vary between populations, additive genetic variance in relative fitness is often substantial and, on average, twice that of previous estimates. We show that these rates of contemporary adaptive evolution can affect population dynamics and hence that natural selection has the potential to partly mitigate effects of current environmental change.
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.
The record
- Venue
- Science
- Topic
- Animal Behavior and Reproduction
- Field
- Agricultural and Biological Sciences
- Canadian institutions
- Université de SherbrookeUniversity of AlbertaUniversity of British Columbia
- Funders
- Leibniz-GemeinschaftResearch EnglandNatural Sciences and Engineering Research Council of CanadaMax-Planck-Institut für demografische ForschungResearch School of Biology, Australian National UniversityUppsala UniversitetUniversität ZürichCentre National de la Recherche ScientifiqueVetenskapsrådetMAVA FoundationUniversity of PretoriaKoninklijke Nederlandse Akademie van WetenschappenNederlands Instituut voor EcologieSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Center for Research ResourcesAustralian GovernmentAgence Nationale de la RechercheRoyal Society Te ApārangiNational Science FoundationUniversity of AberdeenSvenska Forskningsrådet FormasUniversity of St AndrewsUniversity of ExeterUniversity of AlbertaNational Computational InfrastructureNational Cancer InstituteLeakey FoundationBiotechnology and Biological Sciences Research CouncilUniversité de SherbrookeEmory UniversityNatural Environment Research CouncilLeibniz-Institut für Zoo- und WildtierforschungNorges ForskningsrådUniversity of OxfordNorges Teknisk-Naturvitenskapelige UniversitetDirectorate for Biological SciencesNational Geographic SocietyNational Institutes of HealthMuséum National d'Histoire NaturelleSight Research UKUniversité de MontpellierIllinois State University
- Keywords
- Selection (genetic algorithm)BiologyNatural selectionVariance (accounting)PopulationAdaptive evolutionEvolutionary biologyGenetic FitnessGenetic driftQuantitative geneticsGenetic variabilityGenetic variationBiological evolutionGeneticsDemographyComputer scienceMachine learningGeneGenotype
- Has abstract in OpenAlex
- yes