Quantifying carbon stocks and functional diversity of roadside ecosystems – A case study in Quebec, Canada
Bibliographic record
Abstract
The present study aimed to evaluate the capacity for carbon storage of ecosystems located on areas bordering paved or travelled roads, using the southern part of Quebec (Canada) as a case study. About 59 roadsides covering a variety of visually contrasting vegetation assemblages (from herbaceous covers to forest stands) and soil conditions were inventoried to quantify the carbon stocks of above- and belowground pools, including living and dead biomass and soils. The functional diversity and identity of overstory and understory vegetation were analyzed to explore their links with carbon storage. The highest total ecosystem carbon content was observed in sites dominated by trees and shrubs, with an average of 282.16 Mg ha¯¹. Functional diversity mediated by functional dispersion (FDIS) of both overstory and understory vegetation was a significant predictor of aboveground carbon stocks on roadsides. FDIS of overstory vegetation (trees and shrubs) and functional richness (FRIC) of understory vegetation were also significant predictors of the total carbon storage of ecosystems. In contrast, we found a negative relationship between overstory functional identity mediated by community-weighted means of leaf nitrogen content (CWM-N) and total ecosystem carbon storage (including aboveground and soil). Our results suggest that roadside ecosystems can be important carbon sinks, and species with varied functional traits could promote carbon storage in these sites.
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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 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".