Business as usual: macrolichen community response to the resilience of spruce–fir forests to beetle disturbance in northwestern Colorado
Bibliographic record
Abstract
Over the past few decades, spruce beetles (Dendroctonus rufipennis Kirby) have drastically altered spruce–fir forests in western North America. Past research has shown that forest conditions (i.e., canopy openings, amounts of coarse woody material) change after spruce beetle disturbance, but little work has studied the impacts of these changes on the resident biotic communities. Even less research has investigated how spruce beetle disturbance affects lichen communities, which we anticipated as benefiting them due to canopy opening and increased woody material availability. We studied macrolichen community structure, including abundance and community composition, and habitat characteristics in areas with significant spruce beetle damage, as first detected between 1996 and 2017 in northwestern Colorado. We found few habitat differences among beetle disturbance classes that reflected varying time since infestation, and only the most recently affected plots (2012–2017) had significantly different lichen community structure relative to other sampled areas. We observed that the spruce–fir forests within our study exhibited ecological resilience to beetle outbreaks, and we did not measure differences in the amount of canopy openings or amounts of coarse woody material. Subsequently, the lack of differential lichen community response was not surprising.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.119 | 0.021 |
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".