Lichen it: Optimizing post-fire caribou lichen transplantation and assessment
Bibliographic record
Abstract
Caribou lichen species are common in mid- to late-seral boreal forests, serving as an important winter diet staple for boreal woodland caribou. However, as climate change causes boreal wildfires to increase in size, severity, and frequency, available tracts of mature forest are decreasing, diminishing the extent of mature caribou lichen stands. Caribou lichens reproduce vegetatively, so bigger and more severe fires could exacerbate their dispersal limitations, potentially extending the timeline for caribou lichen stand recovery beyond 80 years. Trials have shown that “transplanting” caribou lichen fragments into disturbed areas can lead to the establishment of caribou lichen mats; as such, caribou lichen transplantation (CLT) could accelerate the recovery of caribou lichen stands by compensating for their dispersal limitations. However, much remains ambiguous about the ideal transplantation locations in burned forests, as well as the best way to assess whether CLT has been successful. In my thesis, I evaluated the health and retention of caribou lichen fragments two years post-transplantation at 50 burned plots in the Dehcho region of Northwest Territories, Canada. My thesis had three objectives: 1) to identify the macro- and microenvironmental factors influencing CLT success, including the stand and fire history, the abiotic environment, resource competition, and interspecies associations; 2) to determine if three commonly used CLT success measures (fragment retention, chlorophyll fluorescence, and vigour) respond similarly to environmental conditions, and 3) to develop an accessible dichotomous key that resource management practitioners can use to identify optimal CLT locations in burns. My results showed that CLT is more successful in sunny, dry, and conifer-dominated burns and in burned peat bogs. They also point to vegetative species that were indicative of CLT success, such as Vaccinium vitis-ideae and Rhododendron groenlandicum. My results also showed that fragment retention, chlorophyll fluorescence, and vigour – three common measures of CLT success – were not strongly correlated, indicating that these methods capture different aspects of success and should not be used interchangeably. My results will help CLT practitioners identify optimal CLT locations in burns and to assess the outcomes effectively, improving CLT efficiency and offering new possibilities for boreal woodland caribou conservation efforts.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".