Local controls on tree seedling growth following mounding on peatland seismic lines in Brazeau County and Lac La Biche, Alberta
Bibliographic record
Abstract
Seismic lines in boreal peatlands are struggling to restore native canopy level vegetation. Mounding is a common restoration method that provides an advantageous growing environment for native tree seedlings. Although many mounding methodologies exist, it remains unclear how each unique mound type changes microsite conditions and influences seedling growth. This study compares five unique mounding and/or planting methods, and the influence of fertilization on black spruce and tamarack seedlings two years post-planting. This study was conducted the summer of 2021 in Alberta and observed seedling, mound microsite and seismic line variables for over 1500 samples. Data compared between mounding methods using ANOVA and characteristics supporting seedling growth were isolated using linear mixed effects models. The results of this study suggest that planted tree seedling survival and growth is heavily correlated with seismic line width, mound height, mound soil moisture and the diversity and density of surrounding vegetation. Comparing between mounding treatments, results indicate that traditional or 'Inverse' mounds are affected by heavy soil subsidence and have higher soil moisture content than any other treatment; they do not provide ideal habitat for black spruce seedlings. Non-traditional mounds like, Rip and Lift, Hummock Transfer and Inline, each had favourable microsite characteristics and support seedling growth. Regardless, Unmounded planting is a viable restoration technique if the seismic line has appropriate microtopographical variability and planting is targeted on the highest microsites. Tamarack seedlings had higher rates of growth than black spruce, but both species benefitted from fertilization with slow-release NPK (nitrogen-phosphorus-potassium) prills. Fertilizer was effective on all mounding treatments except Rip and Lift. The differences in seedling growth and ideal microsite characteristics identified in this study can be used to inform restoration planning. An effective landscape restoration plan can be tailored to existing seismic line characteristics and can build ideal mound microsites to support tree growth and the regrowth of forest canopies.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".