The molecular arsenal of the key coastal bioturbator <i>Hediste diversicolor</i> faced with changing oceans
Bibliographic record
Abstract
Abstract The importance of infaunal bioturbators for the functioning of marine ecosystems cannot be overstated. Inhabitants of estuarine and coastal habitats are expected to show resilience to fluctuations in seawater temperature and pH, which adds complexity to our understanding of the effects of global change drivers. Further, stress responses may be propagated through chemical cues within and across species, which may amplify the costs of life and alter species interactions. Research into the molecular mechanisms underlying this resilience has been limited by a lack of annotated genomes and associated molecular tools. In this study, we present the first chromosome-level, annotated draft genome of the marine ragworm Hediste diversicolor , specifically mapping genes important for chemical communication, sensing and pH homeostasis. Using these resources, we then evaluate the transcriptomic and behavioural responses of two distinct populations — one field-sampled from Portugal (Ria Formosa) and one laboratory-acclimated and -bred from the United Kingdom (Humber) — to changes in seawater pH, temperature, and odour cues from a low pH-stressed predator. Both populations displayed adaptive responses to future oceanic conditions, with targeted acid-base regulation in the Ria Formosa population experiment, and broader changes in metabolism and growth genes in the Humber population experiment. Chemical cues from stressed fish predators induced genes related to Schreckstoff biosynthesis in ragworms. Additionally, under future ocean conditions including increased temperature, the Humber population exhibited signs of cellular stress and damage. Our findings using the new annotated genome offer novel insights into the molecular arsenal of acid-base regulation which aids in predicting the impacts of an increasingly acidified and unstable ocean, and to transfer this knowledge to investigate these mechanisms in species with less tolerance.
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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".