Insights into response of seagrass (<i>Zostera marina</i>) to sulfide exposure at morphological, physiochemical and molecular levels in context of coastal eutrophication and warming
Bibliographic record
Abstract
Abstract Sulfide in sediment porewaters, is toxic to rooted macrophytes in both marine and freshwater environments. Current research on sulfide stress in seagrasses primarily focuses on morphological and physiological aspects, with little known about the molecular response and resistance mechanisms. This study first investigated the damage caused by sulfide to eelgrass (Zostera marina L.) using transcriptomic, metabolomic, and other physiological and biochemical indicators and explored the potential resistance of eelgrass at molecular level through laboratory simulated and in‐situ sulfide stress experiments. Comprehensive results showed that sulfide stress severely inhibited the growth, photosynthesis, and antioxidant enzyme activities of eelgrass. Importantly, transcriptome analysis revealed significant activation of pathways related to carbohydrate and sulfur metabolism. This activation served a dual purpose: providing an energy source for eelgrass stress response and achieving detoxification through accelerated sulfur metabolism—a potential resistance mechanism. The toxicity of sulfide increased with rising temperature as evidenced by a decrease in EC50. Results from recovery experiments indicated that when Fv/Fm reduced to about 0 under sulfide stress, the growth and photosynthesis of eelgrass recovered to normal level after timely removal of sulfide. However, prolonged exposure to sulfide resulted in failure to recover, leading ultimately to plant death. This study not only enhances our understanding of the molecular‐level impacts of sulfide on seagrasses but also provides guidance for the management and ecological restoration of seagrass meadows under sulfide stress.
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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".