A New Approach for Tracking Vegetation Change after Restoration: A Case Study with Peatlands
Bibliographic record
Abstract
Developing objective tools for tracking progress of restored sites is of general concern. Here, we present an innovative approach based on principal response curves (PRC) and species classification according to their preferential habitats to monitor changes in community composition. Following large‐scale restoration of a cut‐over peatland, vegetation was surveyed biannually over 8 years. We evaluated whether the establishing plant communities fell within the range of natural variation. We used both general diversity curves and PRC applied on plant species grouped by preferred habitat to compare restored sites and unrestored sites to a reference ecosystem. After 8 years, diversity and richness differed between the sites, withForestandRuderalspecies more prominent in unrestored sites, andPeatland,Forest, andWetlandspecies dominant in restored sites. The PRC revealed that the restored site became rapidly dominated by typical peatland plants, the main drivers of temporal changes beingSphagnum rubellum,Pohlia nutans, andMylia anomala. Some differences remained between the restored and the undisturbed species pools: the former had more herbaceous species associated with wetlands such asCalamagrostis canadensisandTypha latifoliaand the latter had more forested species likeKalmia angustifoliathroughout the study. PRC revealed to be an efficient tool identifying species driving changes at the community level after restoration. In our case study, examining PRC scores after classifying species according to their preferred habitat allowed to illustrate objectively how restoration promotes target species (associated to peatlands) and how lack of intervention benefits ruderal species.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".