Warming undermines emergence success in a threatened alpine stonefly: A multi‐trait perspective on vulnerability to climate change
Bibliographic record
Abstract
Abstract Vulnerability to warming is often assessed using short‐term metrics such as the critical thermal maximum (CT MAX ), which represents an organism's ability to survive extreme heat. However, the long‐term effects of sub‐lethal warming are an essential link to fitness in the wild, and these effects are not adequately captured by metrics like CT MAX . The meltwater stonefly, Lednia tumana , is endemic to high‐elevation streams of Glacier National Park, MT, USA, and has long been considered acutely vulnerable to climate‐change‐associated stream warming. As a result, in 2019, it was listed as Threatened under the U.S. Endangered Species Act. This presumed vulnerability to warming was challenged by a recent study showing that nymphs can withstand short‐term exposure to temperatures as high as ~27°C. But whether they also tolerate exposure to chronic, long‐term warming remained unclear. By measuring fitness‐related traits at several ecologically relevant temperatures over several weeks, we show that L. tumana cannot complete its life‐cycle at temperatures only a few degrees above what some populations currently experience. The temperature at which growth rate was maximized appears to have a detrimental impact on other key traits (survival, emergence success and wing development), thus extending our understanding of L. tumana 's vulnerability to climate change. Our results call into question the use of CT MAX as a sole metric of thermal sensitivity for a species, while highlighting the power and complexity of multi‐trait approaches to assessing vulnerability. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".