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Record W4391889000 · doi:10.1093/evlett/qrad070

Evolutionary adaptation to climate change

2024· article· en· W4391889000 on OpenAlexaff
Allan H. Edelsparre, Mark Fitzpatrick, Marjo Saastamoinen, Céline Teplitsky

Bibliographic record

VenueEvolution Letters · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicSpecies Distribution and Climate Change
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
FundersNovo Nordisk FondenCentre National de la Recherche ScientifiqueNovo NordiskJane ja Aatos Erkon Säätiö
KeywordsAdaptation (eye)Climate changeClimate change adaptationEvolutionary biologyEcologyBiologyNeuroscience

Abstract

fetched live from OpenAlex

When the notion of climate change emerged over 200 years ago, few speculated as to the impact of rising atmospheric temperatures on biological life. Tens of decades later, research clearly demonstrates that the impact of climate change on life on Earth is enormous, ongoing, and with foreseen effects lasting well into the next century. Responses to climate change have been widely documented. However, the breadth of phenotypic traits involved with evolutionary adaptation to climate change remains unclear. In addition, it is difficult to identify the genetic and/or epigenetic bases of phenotypes adaptive to climate change, in part because it often is not clear whether this change is plastic, genetic, or some combination of the two. Adaptive responses to climate-driven selection also interact with other processes driving genetic changes in general, including demography as well as selection driven by other factors. In this Special Issue, we explore the factors that will impact the overall outcome of climate change adaptation. Our contributions explain that traits involved in climate change adaptation include not only classic phenomena, such as range shifts and environmentally dependent sex determination, but also often overlooked phenomena such as social and sexual conflicts and the expression of stress hormones. We learn how climate-driven selection can be mediated via both natural and sexual selection, effectively influencing key fitness-related traits such as offspring growth and fertility as well as evolutionary potential. Finally, we explore the limits and opportunities for predicting adaptive responses to climate change. This contribution forms the basis of 10 actions that we believe will improve predictions of when and how organisms may adapt genetically to climate change. We anticipate that this Special Issue will inform novel investigations into how the effects of climate change unfold from phenotypes to genotypes, particularly as methodologies increasingly allow researchers to study selection in field experiments.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.004
Open science0.0010.005
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0120.002

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.

Opus teacher head0.029
GPT teacher head0.246
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2024
Admission routes1
Has abstractyes

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