Anticipated impacts in habitat of diagnostic species of potential natural vegetations due to climate change at the ecotone between temperate and boreal forests
Bibliographic record
Abstract
• Potential natural vegetations are largely used in ecosystem management. • Habitat of diagnostic coniferous and boreal hardwoods species at the ecotone temperate-boreal forest will decline. • Climate change could favor the emergence of new potential natural vegetations. • Updating potential natural vegetations will be needed for management to rely on accurate information. Potential natural vegetations are crucial for forest research, management, and monitoring, especially considering their evolution amidst climate change. In Quebec (Canada), these vegetations were defined in the 2000s but haven't been updated since then. Originally, stability in their composition and dynamics relied on unchanged climate, soil characteristics, and disturbance regimes. However, in the southwestern part of Quebec forests, we have enhanced the description of potential natural vegetations using diagnostic species, based on their indicator values and relative abundance. This improvement prompts a reevaluation of these vegetations, particularly at the transition between temperate and boreal forests, considering climate change. Our study, using tree habitat suitability models, reveals that even under a moderately warming scenario (2041–2070 RCP 4.5 W m −2 ), diagnostic coniferous and boreal hardwood species face habitat suitability declines but the current classification remains adequate. However, a more severe warming scenario (2071–2100 RCP 8.5 W m −2 ) results in significant habitat unsuitability for these diagnostic species, questioning the relevance of the current classification at the ecotone. Given the crucial tool of potential natural vegetations in forest management, updating their classification becomes imperative to guide forestry practices' adaptation to climate change.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".