Three decades of vegetation changes in peatlands isolated in an agricultural landscape
Bibliographic record
Abstract
Abstract Questions What vegetation changes occurred over three decades on two large temperate peatlands (1115 ha) isolated in an agricultural landscape and affected by a human‐ignited fire? Location Southwest Québec, Canada. Methods In 2012, we revisited 103 plots first sampled in 1984–85. Changes in species composition were evaluated using the Sørensen dissimilarity index, in species frequency with Chi‐square goodness‐of‐fit tests, and in species cover using one‐sample t‐tests. Tree encroachment was evaluated using aerial photographs and satellite imagery. We used linear discriminant analyses (LDA) and ANOVA to evaluate the impact of tree encroachment on species composition. Results We found a floristic dissimilarity of 35% between 1984 and 1985 and 2012. Most species whose frequency and mean cover increased were peatland species, while most species with lower frequency and mean cover in 2012 were non‐peatland species. The total area occupied by forest increased from 26% to 51%, an overall gain of 280 ha of forest. The species composition of old and new forests as well as of open sectors was highly distinct, as shown by the LDA that correctly assigned 97% of the sampling plots to these groups. Non‐peatland species were 15 and five times more abundant than peatland species in old and new forests than open habitats, respectively. Conclusions Gradual drying of the peatland margins due to drainage of the surrounding catchment, as well as post‐fire succession are likely the main drivers of the changes observed. Overall, our study showed that peatlands isolated in an anthropogenic landscape are dynamic ecosystems where vegetation communities can experience substantial changes in a short time frame. The broader implication is that peatland conservation in highly modified landscapes must be linked to restoration.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".