ECOLOGICAL RESTORATION OF DISTURBED LOW-ARCTIC UPLAND HEATH USING LOCALLY SOURCED, TRANSPLANTED VEGETATIVE TURFS
Bibliographic record
Abstract
Arctic environments have undergone ecological disturbances from industrial resource extraction for decades, yet knowledge of arctic plant-soil systems and effective means of ecological restoration is still largely unknown and understudied. To gain a better understanding of restoring arctic plant communities following mining disturbance, we examined whole turf transplants and shredded tundra material in maintaining vegetative community characteristics and soil nutrient concentrations two years post-transplantation onto disused gravel quarries. Community characteristics, and recovery of turf harvesting locations were determined through quadrat assessments, and soil nutrients were assessed through ion chromatography of soil samples. Additional turfs were harvested and transported to the University of Saskatchewan to investigate the effects of turf-adjacent fertilization on turf and substrate community characteristics, above and belowground biomass, and distance of vegetation expanding from the turf. Quadrat assessments were conducted to investigate community characteristics, and above and belowground biomass was harvested at specific distance increments from the turf. DNA metagenomics was used to identify the species responsible for expansion Overall, we found turf transplants were capable of surviving transplantation and extreme environmental conditions and transferred native species and vegetative cover to disturbed sites. The application of shredded tundra material may be effective at re-instating non-vascular communities over a large area, although requires greater protection from wind and water erosion. We found belowground expansion far exceeded aboveground and that graminoids were primarily responsible for this expansion. Fertilization of turf’s surroundings increases belowground biomass and the development of biological soil crusts on adjacent substrates, without impacting the development of vegetation within the turfs. We recommend that restoration practitioners seek to transplant forb and graminoid-dominated communities, as these communities will likely i) survive transplanting better than shrub-dominated communities, ii) stimulate development of organic layers and soil nutrient enrichment, iii) introduce common and critical nitrogen-fixing species, and iv) present the greatest likelihood of vegetative expansion. Further research is needed to optimize this technique in arctic environments; however, the results of this research indicate that turf transplants can maintain key plant-soil interactions allowing for the continued survival of arctic vegetative communities, along with expansion, and modification of their immediate surroundings within disturbed 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 | 0.000 | 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".