Sulfide toxicity may offset projected Ocean Acidification seagrass gains
Bibliographic record
Abstract
Sulfide Toxicity and Ocean Acidification in Seagrasses Authors: Neta Soto1, André Pellerin2, Gidon Winters3,4, Nurit Weber5, Avner Gross1+, Gilad Antler6,7+* 1The Department of Environment, Geoinformatics and Urban planning Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel 2Institut des sciences de la mer, Université du Québec à Rimouski and GEOTOP, Rimouski, Québec, Canada 3The Dead Sea and Arava Science Center, Masada National Park, Mount Masada 8698000, Israel 4Eilat Campus, Ben-Gurion University of the Negev, Hatmarim Blv, Eilat 8855630, Israel 5GEOMAR Helmholtz Centre for Ocean Research Kiel, Wischhofstr. 1-3, D-24148, Kiel, Germany. 6Department of Earth and Environmental Sciences, Ben-Gurion University of the Negev, Beersheba, Israel 7The Interuniversity Institute for Marine Sciences in Eilat, Eilat, Israel + Co–senior authorship *Corresponding author (giladantler@gmail.com) Data Description: Experimental Overview: The data were collected as part of an experimental study aimed at assessing the impact of varying sulfide concentrations and pH levels on the photosynthetic performance of the tropical seagrass Halophila stipulacea. Seagrass samples were exposed to treatment solutions with different pH levels (8.1, 7.9, 7.6) and sulfide concentrations (0, 500, 1000 µM). Incubations were conducted in sealed glass vials at 23 ± 0.5°C under dark conditions to simulate nighttime hypoxia. Photosynthesis rates were calculated based on dissolved oxygen concentration changes over a fixed incubation period. The data provide insights into how ocean acidification may alter sulfide toxicity, potentially offsetting the anticipated growth benefits of increased CO₂ availability in seagrass meadows. This dataset includes experimental data and model output related to the study examining the effects of varying pH and sulfide concentrations on the photosynthetic performance of the tropical seagrass Halophila stipulacea. The experiments were conducted using controlled sulfide exposure treatments under different pH conditions to assess potential implications of ocean acidification on seagrass ecosystems. Data Files: Calculated rates.xlsx - Contains data on net photosynthesis rates for Halophila stipulacea under varying pH and sulfide concentrations. The dataset includes the following columns: pH: pH levels (8.1, 7.9, 7.6) Sulfide (uM): Sulfide concentration (0, 500, 1000 µM) Incubation Time (min): Duration of the incubation Leaf Area (cm²): Surface area of the seagrass leaf Delta O2 (µmol/L): Change in oxygen concentration Photosynthesis Rate (µmol O2 m⁻² s⁻¹): Calculated net photosynthesis rate Methodology: Seagrass plants were collected from the Gulf of Aqaba and exposed to treatment solutions with pH levels of 8.1, 7.9, and 7.6, and sulfide concentrations of 0, 500, and 1000 µM. Incubations were conducted in sealed glass vials with filtered seawater at 23 ± 0.5°C under dark conditions to simulate nighttime hypoxic conditions. Photosynthetic performance was assessed by measuring dissolved oxygen concentration before and after the incubation period using a FireSting®-O₂ optical oxygen sensor. Further methodological details are available in the manuscript (Soto et al., 2025) and Supplementary Online Information (SOI.pdf). Data Processing: Photosynthesis rates were calculated using the formula: Photosynthesis Rate = (Delta O2 *Volum/ Incubation Time) × (Leaf Area) Citation: Please cite the following when using this dataset: Soto, N., Pellerin, A., Winters, G., Weber, N., Gross, A., & Antler, G. (submitted). Sulfide toxicity may offset projected ocean acidification seagrass gains. Limnology and Oceanography Letters. Funding: This research was supported by Israeli Science Foundation (#2010/24). Data Availability: Data are available under the CC-BY 4.0 license. Contact: For further inquiries, please contact Gilad Antler at giladantler@gmail.com.
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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 | 0.001 |
| 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 | 0.001 | 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".