Do linear clearings in boreal peatlands recover? Comparing taxonomic, phylogenetic, and functional plant diversity
Bibliographic record
Abstract
Land-use changes and anthropogenic disturbances are major threats to biodiversity, affecting ecosystem function and recovery. In boreal Alberta, Canada, petroleum development has led to extensive landscape fragmentation, notably through linear clearings created for seismic exploration that remove surface vegetation and microtopography. Despite partial recovery on forested seismic lines, peatland recovery is often arrested, impacting wildlife and carbon dynamics. Restoration efforts employ silviculture techniques to create microtopography and foster tree growth, but the efficacy of restoration treatments in restoring peatland plant diversity remains uncertain. We compared taxonomic, phylogenetic, and functional diversity of understory plant communities across treated and untreated seismic lines, alongside reference sites in treed bogs and fens. Treated lines generally had a twofold increase in plant height, leaf dry matter content, and foliar nitrogen and phosphorus contents compared to reference sites. In bogs, treated lines differed from reference conditions driven by increases in herbaceous taxa, while fens displayed disparities mainly in taxonomic and phylogenetic diversity. Differential responses of bogs and fens underline the necessity for tailored management strategies. Changes in plant diversity away from restoration targets have implications for ecosystem recovery, emphasizing the importance of long-term monitoring to evaluate the effectiveness of silviculture techniques in restoring boreal peatland plant communities.
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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".