Adaptation of plasticity to projected maximum temperatures and across climatically defined bioregions
Bibliographic record
Abstract
Resilience to environmental stressors due to climate warming is influenced by local adaptations, including plastic responses. The recent literature has focused on genomic signatures of climatic adaptation, but little is known about how plastic capacity may be influenced by biogeographic and evolutionary processes. We investigate phenotypic plasticity as a target of climatic selection, hypothesizing that lineages that evolved in warmer climates will exhibit greater plastic adaptive resilience to upper thermal stress. This was experimentally tested by comparing transcriptomic responses within and among temperate, subtropical, and desert ecotypes of Australian rainbowfish subjected to contemporary and projected summer temperatures. Critical thermal maxima were estimated, and ecological niches delineated using bioclimatic modeling. A comparative phylogenetic expression variance and evolution model was used to assess plastic and evolved changes in gene expression. Although 82% of all expressed genes were found in the three ecotypes, they shared expression patterns in only 5 out of 236 genes that responded to the climate change experiment. A total of 532 genes showed signals of adaptive (i.e., genetic-based) plasticity due to ecotype-specific directional selection, and 23 of those responded to projected summer temperatures. Network analyses demonstrated centrality of these genes in thermal response pathways. The greatest adaptive resilience to upper thermal stress was shown by the subtropical ecotype, followed by the desert and temperate ecotypes. Our findings indicate that vulnerability to climate change will be highly influenced by biogeographic factors, emphasizing the value of integrative assessments of climatic adaptive traits for accurate estimation of population and ecosystem responses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".