Dissolved and particulate organic carbon transport among forest, river, and wetland ecosystems: a review of processes, controlling factors, challenges, and prospects
Bibliographic record
Abstract
Streams and rivers receive organic carbon (OC), including dissolved (DOC) and particulate (POC) forms, from forests and wetlands, playing a vital role in terrestrial and aquatic ecosystems. Despite growing interest in OC dynamics, its cross-ecosystem migration remains poorly understood and reviewed. This review synthesizes key DOC and POC transport processes, controlling factors, and tracing methods across forests, wetlands, and rivers, aiming to deepen the understanding of carbon dynamics across different ecosystems. It also discusses current research gaps, challenges, and prospects. In forests and wetlands, DOC is mainly derived from vegetative carbon sinks, rainfall wash-off, and organic matter decomposition, while POC is derived from vegetative surface wash-off, rock weathering, and plant residues. Through soil respiration, overland flow, interflow, and leaching losses, with significant contributions from groundwater as well, some of the OC is released to the atmosphere and some enters the river system. DOC and POC sources are classified as exogenous and endogenous within river systems. Exogenous sources mainly enter runoff, including apoplastic litter, humus, root secretions, and anthropogenic releases from agriculture, industry, and households. Notably, most of the POC produced by erosion on slopes does not reach rivers. Endogenous sources originate from overall biological activity within the river. The main export pathways for DOC and POC in aquatic systems are CO2 release, deposition, and downstream transport. These processes are significantly influenced by climate change and human activity, with rainfall as the key driver of POC erosion and migration. Advanced technologies such as high-frequency measurements, process modeling, elemental analysis, stable isotopes, and molecular marker identification allow for accurate tracking and monitoring of OC dynamics. Remote sensing techniques, on the other hand, provide large-scale, continuous carbon concentration data in an efficient and cost-effective manner, facilitating the monitoring of carbon dynamics in different regions and time scales. We have also identified hotspots and gaps in the current research, which will help us to promote an in-depth understanding of the OC transport and conversion mechanisms in multiple ecosystems under background of climate change and anthropogenic disturbances, and could provide important insights and to move forward for future DOC and POC transport studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".