Warming undermines emergence success in a threatened alpine stonefly: a multi-trait perspective on vulnerability to climate change
Bibliographic record
Abstract
Species vulnerability to global warming is often assessed using short-term metrics such as the critical thermal maximum (CTmax), which represents an organism’s ability to survive extreme heat. However, an understanding of the long-term effects of sub-lethal warming is an essential link to fitness in the wild, and these effects are not adequately captured by metrics like CTmax. 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. 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 how this short-term tolerance relates to chronic, long-term warming has 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 well below the CTmax values measured for its nymphs. Although warmer temperatures maximized growth rates, they appear 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 CTmax as a measure of thermal sensitivity, while highlighting the power and complexity of multi-trait approaches to assessing climate vulnerability.
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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".