Examining boreal forest resilience to temperature variability using bryophytes: forest type matters
Bibliographic record
Abstract
Abstract The capacity of individual species to tolerate environmental changes is a major driver of ecosystem resilience. This subject has been a hot topic in boreal forests due to the greater effect of climate change anticipated at northern latitudes. However, to date it has been mainly examined for trees, with comparatively little emphasis on other species groups. In this study, bryophytes were sampled in the boreal forest of Québec (Canada) using a stratified sampling design allowing us to separate the effect of dominant canopy composition (two types: coniferous and mixedwood) and of the spatial climatic gradient (relatively warm and relatively cold sites) on bryophyte community structure (species richness and composition). The results indicated that bryophyte composition was different between coniferous and mixedwood stands. The impacts of spatial variation in climate were more visible in mixedwood than coniferous stands, indicating an interaction between stand type and climate. Specifically, bryophytes from mixedwood stands, and particularly specialist communities associated with deadwood and live tree bases, were climate‐sensitive and represent a potential target group to investigate the effects of climate change. In contrast, bryophyte communities from coniferous stands are stand structure‐sensitive, with little response to climate, presumably partly because coniferous canopies buffer climatic variations. Since bryophyte community sensitivity to regional variations in climatic conditions depends on dominant canopy characteristics and microhabitat availability, we suggest that adapted forest management practices could play a role in attenuating climate change impacts on these organisms.
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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 | 0.000 | 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".