Using a Trait-Based Approach to Compare Tree Species Sensitivity to Climate Change Stressors in Eastern Canada and Inform Adaptation Practices
Bibliographic record
Abstract
Despite recent advances in understanding tree species sensitivities to climate change, ecological knowledge on different species remains scattered across disparate sources, precluding their inclusion in vulnerability assessments. Information on potential sensitivities is needed to identify tree species that require consideration, inform changes to current silvicultural practices and prioritize management actions. A trait-based approach was used to overcome some of the challenges involved in assessing sensitivity, providing a common framework to facilitate data integration and species comparisons. Focusing on 26 abundant tree species from eastern Canada, we developed a series of trait-based indices that capture a species’ ability to cope with three key climate change stressors—increased drought events, shifts in climatically suitable habitat, increased fire intensity and frequency. Ten indices were developed by breaking down species’ response to a stressor into its strategies, mechanisms and traits. Species-specific sensitivities varied across climate stressors but also among the various ways a species can cope with a given stressor. Of the 26 species assessed, Tsuga canadensis (L.) Carrière and Abies balsamea (L.) Mill are classified as the most sensitive species across all indices while Acer rubrum L. and Populus spp. are the least sensitive. Information was found for 95% of the trait-species combinations but the quality of available data varies between indices and species. Notably, some traits related to individual-level sensitivity to drought were poorly documented as well as deciduous species found within the temperate biome. We also discuss how our indices compare with other published indices, using drought sensitivity as an example. Finally, we discuss how the information captured by these indices can be used to inform vulnerability assessments and the development of adaptation measures for species with different management requirements under climate change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".