Unravelling Hydrogen Isotope Fractionation in Marine Macroalgae: Insights from Macrocystis pyrifera
Bibliographic record
Abstract
Marine ecosystems play a critical role in global photosynthetic carbon fixation, with approximately 5.36 Pg C exported annually via the biological pump. Macroalgae alone sequester around 200 million tons of CO₂ annually, though these estimations are largely based on indirect calculations. Hydrogen isotope (δ²H) analyses offer a promising avenue to refine such estimates while advancing our understanding of macroalgal carbon and energy metabolism.Stable isotope studies have been instrumental in ecological and biogeochemical research, yet the application of δ²H analyses to marine algae remains limited. Most prior studies have focused on salinity-driven δ²H variations in algae, overlooking the potential of δ²H to reveal key biochemical processes. Recent findings suggest that δ²H values of organic molecules are significantly influenced by biosynthetic fractionation (²H-εbio), governed by the interplay between photosynthetic (²H-ελ) and post-photosynthetic (²H-εΗ) processes. This metabolic signal, previously observed in terrestrial plants, is strongly modulated by the photosynthetic carbohydrate supply rate, impacting δ²H variability in organic compounds.The giant kelp Macrocystis pyrifera provides an ideal model system to investigate these processes in marine environments. Unlike terrestrial plants, M. pyrifera offers a simplified isotopic system due to: (i) access to water with stable δ²H values, (ii) exclusion of evaporative ²H-fractionation, and (iii) a primitive vascular system that minimizes isotopic exchange across its structure. These unique features allow us to isolate and examine the variability of ²H-ελ under different light conditions, shedding light on the metabolic processes underlying δ²H variability in marine photoautotrophs.This study highlights the potential of δ²H analyses to bridge the gap between isotopic and biochemical research in marine systems. By focusing on M. pyrifera, we aim to provide critical insights into the drivers of δ²H variability and their broader implications for understanding marine carbon dynamics and the role of macroalgae in global biogeochemical cycles. This work lays the groundwork for advancing isotopic methodologies and applying them to ecological and palaeoenvironmental studies in marine ecosystems.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".