Designer Niches Promote Seedling Survival in Forest Restoration: A 7-Year Study of Whitebark Pine (Pinus albicaulis) Seedlings in Waterton Lakes National Park
Bibliographic record
Abstract
Designer niches in which environmental variables are controlled are useful in forest restoration to enhance survival of planted tree seedlings. Here, we evaluate particular manipulated habitats, on site variables, and pre-seedling conditions hypothesized to improve the survival rate of whitebark pine (Pinus albicaulis) seedlings out-planted in Waterton Lakes National Park. The tree species is in peril due to blister rust and mountain pine beetle infestations in its range; and is a restoration priority in Waterton Lakes because populations in the park are highly infected with blister rust (up to 90%). At Summit Lake, 21 plots were set up and half of each was terra-torched; 1000 seedlings were planted in clusters of three, under four conditions: on burned areas in burned beargrass mats, in burned areas where beargrass mats were not present, in unburned areas where beargrass was present, and in unburned areas without beargrass. This study reports data for the seventh year after planting, and overall, survival was 53% for individual seedlings and at least one seedling survived in 60.8% of clusters. Planting in burned areas increased cluster survival (by 34.3%, p ˂ 0.0001) and planting near microsites increased cluster survival (by 19.3%, p ˂ 0.0001); the type of microsite did not make a difference. Planting in beargrass mats decreased survival, but not significantly (8.9%, p = 0.11) and this was true for burns, not unburned areas. Inoculation with native ectomycorrhizal fungi did not enhance survival most likely because controls on lightly terra-torched and unburned areas had access to local native fungi. This is the first study to report statistics on the planting of seedlings in clusters; the results need to be compared with studies where seedlings are planted individually.
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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 | 0.000 |
| 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".