Lichen Conservation in Heavily Managed Boreal Forests
Bibliographic record
Abstract
Lichens are an important component of the boreal forest, where they are long lived, tend to accumulate in older stands, and are a major food source for the threatened woodland caribou (Rangifer tarandus caribou). To be fully sustainable, silvicultural practices in the boreal forest must include the conservation of ecological integrity. Dominant forest management practices, however, have short-term negative effects on lichen diversity, particularly the application of herbicides. To better understand the long-term effects of forest management, we examined lichen regeneration in 35 mixed black spruce (Picea mariana) and jack pine (Pinus banksiana) forest stands across northern Ontario to determine recovery following logging and postharvest silvicultural practices. Our forest stands were 25-40 years old and had undergone 3 common sivilcultural treatments that included harvested and planted; harvested, planted, and treated with N-[phosphonomethyl] glycine (glyphosate); and harvested, planted, and treated with 2,4-dichlorophenoxyacetic acid (2,4-D). Forest stands with herbicide treatments had lower lichen biomass and higher beta and gamma diversity than planted stands that were not treated chemically or control stands. In northwestern Ontario, planted stands that were not treated chemically had significantly greater (p < 0.05) alpha diversity than stands treated with herbicides or control stands. Our results show that common silvicultural practices do not emulate natural disturbances caused by wildfires in the boreal forest for the lichen community. We suggest a reduction in the amount of chemical application be considered in areas where lichen biomass is likely to be high and where the recovery of woodland caribou is an objective.
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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 | 0.001 |
| 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".