Data from: Mechanisms of thermal adaptation and evolutionary potential of conspecific populations to changing environments
Bibliographic record
Abstract
AbstractHeterogeneous and ever-changing thermal environments drive the evolution of populations and species, especially when extreme conditions increase selection pressure for traits influencing fitness. However, projections of biological diversity under scenarios of climate change rarely consider evolutionary adaptive potential of natural species. In this study, we tested for mechanistic evidence of evolutionary thermal adaptation among ecologically divergent redband trout populations (Oncorhynchus mykiss gairdneri) in cardiorespiratory function, cellular response and genomic variation. In a common garden environment, fish from an extreme desert climate had significantly higher critical thermal maximum (p<0.05) and broader optimum thermal window for aerobic scope (>3°C) than fish from cooler montane climate. In addition, the desert population had the highest maximum heart rate during warming (20% greater than montane populations), indicating improved capacity to deliver oxygen to internal tissues. In response to acute heat stress, distinct sets of cardiac genes were induced among ecotypes, which helps to explain the differences in cardiorespiratory function. Candidate genomic markers and genes underlying these physiological adaptations were also pinpointed, such as genes involved in stress response and metabolic activity (hsp40, ldh-b and camkk2). These markers were developed into a multi-variate model that not only accurately predicted critical thermal maxima, but also evolutionary limit of thermal adaptation in these specific redband trout populations relative to the expected limit for the species. This study demonstrates mechanisms and limitations of an aquatic species to evolve under changing environments that can be incorporated into advanced models to predict ecological consequences of climate change for natural organisms.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".