Initiation of microtopography in re‐vegetated cutover peatlands: evolution of plant species composition
Bibliographic record
Abstract
Abstract Questions How has plant species composition changed following initiation of microstructures in re‐vegetated cutover peatlands? How many years are required for plant species composition of re‐vegetated cutover peatlands to resemble natural boreal bogs? Location Newly formedSphagnumcarpets on restored, cutover peatlands (inCanada) or re‐vegetated spontaneously after site abandonment (inEstonia) and on undisturbed natural bogs nearby. Methods Plant frequencies (point intercept method) and abundances (vegetation quadrats) were assessed along linear transects. At each assessment point, moss surface height was measured relative to a local reference point (lowest point on a given transect) to associate frequencies or abundances to a position in the gradient of microtopography.PCAs (separately forCanada andEstonia) were conducted to follow evolution of plant species frequency in the gradient of microtopography in re‐vegetated sites and similarity with those of natural peatlands. InCanada, regressions were also performed to estimate relationships between moss surface height and vascular plant cover (ericaceous shrubs andCyperaceae) as well as time required for vascular plant cover to become similar to that of natural bogs. Results Species composition was still dissimilar to microstructures of natural bogs 10 yr post‐restoration and 70 yr post‐abandonment; however, some trends were observed in re‐vegetated peatlands. The greatest differences were for ericaceous species (two‐ to three‐fold less abundant in re‐vegetated peatlands), dominantCyperaceae, and relative proportions ofSphagnum. In addition, hummock formation was closely related to dense (>50%) ericaceous cover. Conclusions All species tolerant to abiotic conditions prevailing in re‐vegetated sites contributed to initiation of microtopography, although some species were found in atypical positions within the gradient of microtopography. Random events and establishment priority seemed initially to be more important in temporal evolution of microstructures than plant interactions. However, ecological restoration could effectively reduced time needed for species occurrences to approach those in natural peatlands, relative to time required for recovery of spontaneously re‐vegetated peat extraction 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".