Reintroducing Vascular and Non-Vascular Plants to Disturbed Arctic Sites: Investigating Turfs and Turf Fragments
Bibliographic record
Abstract
ABSTRACT Disturbed low-Arctic environments provide many challenges for ecological restoration, from harsh climates and remote locations to limited knowledge on plant establishment and successional pathways within tundra ecosystems. Due to limited commercially available materials for restoration of native low-Arctic plant communities, transplantation may provide an effective technique for revegetation in these difficult-to-restore environments. In this study, whole-turfs and shredded turfs were harvested from undisturbed upland-heath tundra near Rankin Inlet, Canada, and transplanted onto nearby disturbed gravel quarries to investigate species survivability and development of upland-heath vegetative communities. Two years following transplantation, turfs were found to maintain 85% of the initial vegetative cover and 91% of the initial species richness, with expansion up to 8 cm into the surrounding substrate, and production of seeds and spores. Although shredded turfs were unable to significantly establish vascular species, evidence suggests a shredded turf may establish non-vascular plant cover over a larger area than intact turfs, if given greater protection from environmental stressors. Our results demonstrate that whole-turfs are resistant to harvesting and transplantation stresses, flooding, drought, and poor soil conditions, and are an effective means of species transfer promoting development of vegetative cover on disturbed substrates. High species survivability indicates that turfs have the potential to provide disturbed areas with a wide array of native species, critical for the development of sustainable and self-organizing assemblages of native vegetation.
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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".