Parallelism in eco-morphology and gene expression despite variable evolutionary and genomic backgrounds in a Holarctic fish
Bibliographic record
Abstract
Understanding the extent to which ecological divergence is repeatable is essential for predicting responses of biodiversity to environmental change. Here we test the predictability of evolution, from genotype to phenotype, by studying parallel evolution in a salmonid fish, Arctic charr (Salvelinus alpinus), across eleven replicate sympatric ecotype pairs (benthivorous-planktivorous and planktivorous-piscivorous) and two evolutionary lineages. We found considerable variability in eco-morphological divergence, with several traits related to foraging (eye diameter, pectoral fin length) being highly parallel even across lineages. This suggests repeated and predictable adaptation to environment. Consistent with ancestral genetic variation, hundreds of loci were associated with ecotype divergence within lineages of which eight were shared across lineages. This shared genetic variation was maintained despite variation in evolutionary histories, ranging from postglacial divergence in sympatry (ca. 10-15kya) to pre-glacial divergence (ca. 20-40kya) with postglacial secondary contact. Transcriptome-wide gene expression (44,102 genes) was highly parallel across replicates, involved biological processes characteristic of ecotype morphology and physiology, and revealed parallelism at the level of regulatory networks. This expression divergence was not only plastic but in part genetically controlled by parallel cis-eQTL. Lastly, we found that the magnitude of phenotypic divergence was largely correlated with the genetic differentiation and gene expression divergence. In contrast, the direction of phenotypic change was mostly determined by the interplay of adaptive genetic variation, gene expression, and ecosystem size. Ecosystem size further explained variation in putatively adaptive, ecotype-associated genomic patterns within and across lineages, highlighting the role of environmental variation and stochasticity in parallel evolution. Together, our findings demonstrate the parallel evolution of eco-morphology and gene expression within and across evolutionary lineages, which is controlled by the interplay of environmental stochasticity and evolutionary contingencies, largely overcoming variable evolutionary histories and genomic backgrounds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".