Surviving the depths: metazoan resilience in sulphidic aquaria environments
Bibliographic record
Abstract
Hydrogen sulphide (H2S) is widely acknowledged as a potent respiratory toxin for eukaryotic cells. However, macrofauna have been observed thriving in environments with elevated H2S concentrations. Here we report on a saltwater aquarium hosting a community of invertebrates and inhabited by an epibenthic microbial mat. The aquarium was left undisturbed for the duration of the COVID-19 pandemic stay-at-home order, leading to the development of high concentrations of H2S. Remarkably, the invertebrate community did not collapse. This success offers valuable insights into how invertebrates respond to physiochemical stressors at both individual and community levels. We also observed persistent disequilibrium between H2S and oxygen (O2), exhibiting out-of-phase periodic cycles driven by a simulated solar cycle. During daylight, photosynthetic O2 production increased, resulting in more active behaviour from the metazoan community. Conversely, H2S production peaked during the dark cycle, causing a moribund animal community. Additionally, over time, overall community diversity in the tank decreased, while macrofaunal abundance appeared largely unaffected. Polychaete worms and cnidarians demonstrated resilience to the high-sulphide conditions for the entire duration of the experiment, whereas others experienced gradual declines in abundance until they perished. These findings challenge conventional expectations of eukaryotic tolerance to H2S and underscore the significance of behavioural adaptations in withstanding high-sulphide environments. Our findings provide insights into how primitive metazoans may have survived in sulphidic to euxinic Ediacaran seas.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".