New tree‐level temperature response curves document sensitivity of tree growth to high temperatures across a <scp>US</scp>‐wide climatic gradient
Bibliographic record
Abstract
Temperature is a key climate indicator, whose distribution is expected to shift right in a warming world. However, the high-temperature tolerance of trees is less widely understood than their drought tolerance, especially when it comes to sub-lethal impacts of temperature on tree growth. I use a large data set of annual tree ring widths, combined with a flexible degree day model, to estimate the relationship between temperature and tree radial growth. I find that tree radial growth responds non-linearly to temperature across many ecoregions of the United States: across temperate and/or dry ecoregions, spring-summer temperature increases are beneficial or mostly neutral for tree growth up to around 25-30°C in humid climates and 10-15°C in dry climates, beyond which temperature increases suppress growth. Thirty additional degree days above the optimal temperature breakpoint lead to an average decrease in tree ring width of around 1%-5%, depending on ecoregions, seasons, and inclusion or exclusion of temperature-mediated drought impacts. High temperatures have legacy effects across a 5-year horizon in dry ecoregions, but none in the temperate-humid South-East or among temperature-sensitive trees. I find limited evidence that trees acclimatize to high temperatures within their lifetime: local variation in early exposure to high temperatures, which stems from local variation in the timing of tree birth, does not significantly impact the response to high temperatures, although temperature-sensitive trees acquire some heightened sensitivity from early exposure. I also find some evidence that trees adapt to high temperatures in the long run: across humid ecoregions of the United States, high temperatures are 40% less harmful to tree growth, where their average incidence is one standard deviation above average. Overall, these results highlight the strength of a new methodology which, applied to representative tree ring data, could contribute to predicting forest carbon uptake potential and composition under global change.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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)
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 teacher head, 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".