Natural regeneration potential and dynamics in boreal lichen woodlands of eastern Canada following soil scarification
Bibliographic record
Abstract
Boreal lichen woodlands (LWs) are stable low tree-density zones of the Canadian boreal forest whose afforestation has been proposed as a way to create new C sinks and thus mitigate climate change. Planting operations in these remote areas are however costly and time-consuming, and may not be necessary when soil scarification is followed by dense natural regeneration. In the present study, we assessed the natural regeneration potential and dynamics in six boreal LWs of Québec, Canada, 11 years after soil scarification. The number, size (height and stem diameter) and age of seedlings were measured in 2-4 sampling plots per site (18 plots in total). Our data show that scarification operations produced on average 1,400 m 2 ha –1 of exposed mineral soil (scarification intensity of 14%) with, however, a large within-site variability. The natural regeneration was mainly composed of black spruce seedlings (> 95%), averaged ∼12,000 seedlings ha –1 across the six sites and significantly varied among sites, mostly due to the variation in scarification intensity. Seedling density averaged ∼9 seedlings m –2 of exposed mineral soil and increased with seed tree mean diameter at breast height (DBH) ( R 2 = 0.51; P < 0.05) but not with the density of seed trees, revealing the importance of old and large seed trees in natural regeneration success. Together, scarification intensity and the DBH of remaining seed trees explained ∼60% of the variation in natural regeneration density across the 18 sampled plots. The rate of establishment of seedlings was generally high – with on average 60% of the carrying capacity of the substrate being reached within three years following scarification – and increased with seed tree mean DBH ( R 2 = 0.77; P < 0.05). However, the growth rate of seedlings was very low. Eleven years after scarification, 60% of the seedlings were < 15 cm and the height of 10-yr-old seedlings averaged 27.5 cm. Thus, even though seedling establishment was successful, the biomass accumulated by the natural regeneration was negligible in the span of a decade. Therefore, the implementation of afforestation following scarification appears to be necessary to create significant C sinks in the midterm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".