Convergence of Terrestrial Dissolved Organic Matter Composition and the Role of Microbial Buffering in Aquatic Ecosystems
Bibliographic record
Abstract
Abstract Substantial changes in vegetation are expected as global climatic patterns shift, altering terrestrial sources of dissolved organic matter (DOM) entering rivers and streams. Since differences in the chemical composition of plant litters are reflected in the DOM that is leached, changes in riparian vegetation can directly influence the bioavailability of DOM to local aquatic microbial communities. We assessed the degradation dynamics and optical compositional changes of DOM from a variety of vascular plant leachates through microbial and coupled photochemical‐microbial degradation pathways. Initial decay rates ranged from 0.029 ± 0.011 day−1 (microbial, mixed wetland) to 0.73 ± 0.62 day−1 (photochemical‐microbial, mixed grasses), and all decay rates decreased to below 0.05 day−1 after 1 week, converging below 0.029 day−1 after 2 weeks. Overall, we found a decrease in leachate optical diversity under microbial and photochemical‐microbial degradations, corresponding to a decrease in degraded leachate sample dispersion using principal component analysis. We show that despite initial variability across DOM leachates, successive degradation promotes kinetic and optical convergence such that, in aquatic environments with long residence times, terrestrial DOM source and composition are much less important on exported DOM composition than historically thought. In these systems, DOM compositional convergence may act as a natural buffer to provide stability of aquatic DOM cycling in the face of future landscape changes.
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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 | 0.001 | 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) | 0.000 | 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".