Mosaics of climatic stress across species' ranges: tradeoffs cause adaptive evolution to limits of climatic tolerance
Bibliographic record
Abstract
reveal mechanisms generating these stresses: geographic mosaics of natural selection, acting on tradeoffs between climate adaptation and fitness traits, cause some range-central populations to evolve to limits of climatic tolerance, while others remain resilient. In one ecotype, selection for predator avoidance drives evolution to limits of thermal tolerance. In a second ecotype, the endangered Bay Checkerspot, selection on fecundity drives evolution to the climate-sensitive limit of ability to complete development within the lifespans of ephemeral hosts, causing routinely high mortality from insect-host phenological asynchrony. The tradeoff between maternal fecundity and offspring mortality generated similar values of fitness on different dates, partly explaining why fecundity varied by more than an order of magnitude. Evolutionary response to the tradeoff rendered climatic variability the main driver of Bay Checkerspot dynamics, and increases in this variability, associated with climate change, were a key factor behind permanent extinction of a protected metapopulation. Finally, we discuss implications for conservation planning of our finding that adaptive evolution can reduce population-level resilience to climate change and generate geographic mosaics of climatic stress. This article is part of the theme issue 'Species' ranges in the face of changing environments (Part II)'.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".