Effects of road dust on vegetation composition and surface chemistry of three ombrotrophic peatlands in eastern Canada
Bibliographic record
Abstract
Dust deposition can fertilize nutrient-limited peatlands and affect their plant assemblages and ecosystem functions, but the effects of local road dust on peatlands have seldom been studied. Here, we investigate the responses of vegetation composition and surface chemistry (vegetation, surface peat, and groundwater chemistry) at three ombrotrophic peatlands: Rivière-au-Tonnerre (RT), Chemin de l'Anse-de-la-Grande-Pointe (GP), and Kegaska (KEG) in eastern Québec, Canada, to varying levels of road dust deposition with differential chemical composition. At each site, a transect aligned with the dominant wind direction, starting at and perpendicular to the roadside, was sampled at increasing intervals up to 250 m away from the gravel (unpaved) road. We find that the ash content of surface peat is highest closest to the roads at all three sites and decreases exponentially with increasing distance up to 200 m away from the roads. At KEG, with the highest amount of, and the most phosphorus (P) rich, road dust deposition, the ash content is highest among the three sites, decreasing most rapidly with distance away from the road. The stoichiometric mass ratios of carbon (C), nitrogen (N) and P, i.e., C:N, C:P, and N:P, in foliar tissues and surface peat increase significantly with distance away from the road at KEG, and mosses are found to be more responsive in terms of foliar chemistry than shrubs to the road dust deposition. A higher concentration of total dissolved phosphorus at KEG is found closer to the road. At GP, having a moderate amount of road dust deposition, significant increases in C:N, C:P, and N:P ratios with distance are found only in surface peat but not in foliar tissues. Due to the presence of carbonate minerals in the road dust of GP, the calcium (Ca) concentration and pH value of groundwater closer to the road are higher, leading to an obvious loss of vegetation coverage, especially mosses, and a consequent increase in NO3− leaching. In contrast, at RT, having the lowest amount of road dust deposition, only a few clear variation patterns in surface chemistry are found along the sampling transect. Overall, our results suggest that dust from gravel roads can be an important localized source of nutrients for adjacent peatlands and influence their vegetation composition and surface chemistry, depending on the amount of road dust and its chemical composition, especially its contents in Ca and P.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".