Estimating lichen biomass in forests and peatlands of northwestern Canada in a changing climate
Bibliographic record
Abstract
Climate warming in the North could lead to lichen decline within critical woodland caribou habitat. We used repeat measurements of sixty-nine plots over ten years (2007–2008 and 2017–2018) to assess lichen biomass changes under a warming climate along a latitudinal/climatic gradient in northwestern Canada. We compared lichen biomass on sensitive landscape features, including peat plateaux (permafrost-containing bogs), areas of permafrost thaw within the peat plateaux (collapse scars), and low-productivity upland forests occurring on mineral soils. Field-based measures of lichen cover and height were coupled with samples of lichen biomass to develop biomass prediction equations. The optimal model incorporated both cover and height, with landscape feature as a covariate. Although height significantly improved the equation fit, models were successfully developed with cover alone. Modeled lichen biomass differed significantly between landscape features, declining from peat plateau (502 g m−2) to upland forest (54.0 g m−2) and collapse scar (0.690 g m−2) environments. In the absence of permafrost collapse at any monitoring location, lichen biomass declined significantly over the ten years for peat plateaux (−75.6 g m−2) and upland forests (−17.5 g m−2). These results will be important for quantifying landscape-level lichen biomass changes under climate warming in boreal and subarctic environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".