Divergent transcriptional responses to low temperature among populations of alpine and lowland species of <scp>N</scp>ew <scp>Z</scp>ealand stick insects (<i><scp>M</scp>icrarchus</i>)
Bibliographic record
Abstract
In widespread and genetically structured populations, temperature variation may lead to among-population differentiation of thermal biology. The New Zealand stick insect genus Micrarchus contains four species that inhabit different thermal environments, two of which are geographically widespread. RNA-Seq and quantitative PCR were used to investigate the transcriptional responses to cold shock among lowland and alpine species to identify cold-responsive transcripts that differ between the species and to determine whether there is intraspecific geographical variation in gene expression. We also used mitochondrial DNA, nuclear 28S ribosomal DNA and transcriptome-wide SNPs to determine phylogeographic structure and the potential for differences in genetic backgrounds to contribute to variation in gene expression. RNA-Seq identified 2160 unigenes that were differentially expressed as a result of low-temperature exposure across three populations from two species (M. hystriculeus and M. nov. sp. 2), with a majority (68% ± 20%) being population specific. This extensive geographical variation is consistent across years and is likely a result of background genetic differences among populations caused by genetic drift and possibly local adaptation. Responses to cold shock shared among alpine M. nov. sp. 2 populations included the enrichment of cuticular structure-associated transcripts, suggesting that cuticle modification may have accompanied colonization of low-temperature alpine environments and the development of a more cold-hardy phenotype.
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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 | 0.000 |
| 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 | 0.000 | 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".