Divergence and constraint in the thermal sensitivity of aquatic insect swimming performance
Bibliographic record
Abstract
Abstract Environmental temperature variation may play a significant role in the adaptive evolutionary divergence of ectotherm thermal performance curves (TPCs). However, divergence in TPCs may also be constrained due to various causes. Here, we measured TPCs for swimming velocity of temperate and tropical mayflies (Family: Baetidae) and their stonefly predators (Family: Perlidae) from different elevations. We predicted that differences in seasonal climatic regimes would drive divergence in TPCs between temperate and tropical species. Stable tropical temperatures should favor the evolution of “specialists” that perform well across a narrow range of temperatures. Seasonally, variable temperatures in temperate zones, however, should favor “generalists” that perform well across a broad range of temperatures. In phylogenetically paired comparisons of mayflies and stoneflies, swimming speed was generally unaffected by experimental temperature and did not differ among populations between latitudes, suggesting a maintenance of performance breadth across elevation and latitude. An exception was found between temperate and tropical mayflies at low elevation where climatic differences between latitudes are large. In addition, TPCs did not differ between mayflies and their stonefly predators, except at tropical low elevation. Our results indicate that divergence in TPCs may be constrained in aquatic insects except under the most different thermal regimes, perhaps because of trade-offs that reduce thermal sensitivity and increase performance breadth.
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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 | 0.000 |
| 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) | 0.001 | 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".