Climate warming weakens local adaptation
Bibliographic record
Abstract
Abstract Anthropogenic climate change is generating mismatches between the environmental conditions that populations historically experienced and those in which they reside. Understanding how climate change affects population performance is a critical scientific challenge. We combine a quantitative synthesis of field transplant experiments with a novel statistical approach based in evolutionary theory to quantify the effects of temperature and precipitation variability on population performance. We find that species’ average performance is affected by both temperature and precipitation, but populations show signs of local adaptation to temperature only. Contemporary responses to temperature are strongly shaped by the local climates under which populations evolved, resulting in performance declines when temperatures deviate from historic conditions. Adaptation to other local environmental factors is strong, but temperature deviations as small as 2°C erode the advantage that these non-climatic adaptations historically gave populations in their home sites. One sentence summary Climate change is pulling the thermal rug out from under populations, reducing average performance and eroding their historical home-site advantage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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; both teacher heads agree on what is shown here.
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".