Tree vulnerability to climate change: improving exposure‐based assessments using traits as indicators of sensitivity
Bibliographic record
Abstract
Abstract Projected changes in climate conditions vary widely across Canada's 350 M ha of forests, and so does the capacity of forest species to cope with these changes (sensitivity). Development and prioritization of adaptation strategies for sustainable forest management will depend on integrated assessments of relative stand vulnerability. We developed species‐specific indices of sensitivity to (1) drought‐induced mortality and (2) migration failure, based on traits for 22 of the most abundant tree species in Canada. By combining this information with stand composition data and spatially explicit climate change projections, we were able to map Canadian forest vulnerability to drought and migration failure. Our maps show forest vulnerability changing rapidly under a high carbon emission scenario ( RCP 8.5) between short‐ (2011–2040), medium‐ (2041–2070), and long‐term projections (2071–2100). Several zones of special concern emerged based on the biomass involved, stand sensitivity, and vulnerability trends across time. Boreal forests in the central regions of Alberta and Saskatchewan appeared most vulnerable to drought‐induced mortality in the mid to long term. In the short term, distance to suitable habitat is projected to shift quickly along latitudinal gradients, particularly in Central Canada, while zones of vulnerability to migration failure appeared across the Rockies region in the long term as suitable conditions disappear from mountainous areas. This spatial assessment of vulnerability, which integrates species‐specific sensitivity, highlights important regional contrasts between vulnerability to drought (from high exposure, high proportion of sensitive species, or both) and to migration failure. By affecting either species’ ability to persist in place or to migrate, different climate change impacts can yield distinct biotic responses, with important implications for regional climate change adaptation strategies. Multi‐faceted vulnerability assessments, integrating both exposure and sensitivity indices specific to expected impacts of climate change, have the potential to provide crucial information to managers. We discuss some of these implications, explore the current limitations of our approach, and suggest a path forward.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".