Water physicochemical properties shape the distribution of submerged macrophytes: implications for wetland restoration in Songnen Plain
Bibliographic record
Abstract
Submerged macrophytes play a pivotal role in maintaining the clear-water state and enhancing biodiversity in wetland ecosystems. However, the environmental mechanisms underlying their assemblage and biomass patterns remain poorly resolved in continental alkaline wetlands. Here, we conducted a 27 hydrochemical parameters analysis and dissected its relations with the distribution of submerged macrophytes in Momoge wetland of the Songnen Plain, Northeastern China. The results revealed that rock weathering and evaporation-crystallization processes jointly regulated the baseline alkalinity and salinity of the water, thereby determining 62.5% species of submerged macrophytes capable of utilizing HCO 3 - as an alternative carbon source. In contrast, nutrient inputs and wind-induced resuspension caused fluctuations in physicochemical conditions between light (50 < TLI ≤ 60) and moderate (60 < TLI ≤ 70) eutrophic states, resulting in Potamogeton pectinatus , Najas marina , and Chara sp iralis thriving in nutrient-rich, low-transparency waters, whereas Utricularia aurea and Ceratophyllum demersum favored clearer and less nutrient-enriched conditions. These findings highlight a two-tiered environmental control over submerged macrophytes in boreal wetlands, whereby geochemical processes shape species assemblages, and nutrient dynamics and physical disturbance drive biomass allocation. We propose a restoration strategy that combines species configuration and pilot selection, prioritizing HCO 3 - -utilizing pioneer species in degraded zones to gradually re-establish submerged macrophytes and ecosystem functions.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".