Traits to stay, traits to move: a review of functional traits to assess sensitivity and adaptive capacity of temperate and boreal trees to climate change
Bibliographic record
Abstract
The integration of functional traits into vulnerability assessments is a promising approach to quantitatively capture differences in species sensitivity and adaptive capacity to climate change, allowing the refinement of tree species distribution models. In response to a clear need to identify traits that are responsive to climate change and applicable in a management context, we review the state of knowledge of the main mechanisms, and their associated traits, that underpin the ability of boreal and temperate tree species to persist and (or) shift their distribution in a changing climate. We aimed to determine whether current knowledge is sufficiently mature and available to be used effectively in vulnerability assessments. Marshalling recent conceptual advances and assessing data availability, our ultimate objective is to guide modellers and practitioners in finding and selecting sets of traits that can be used to capture differences in species’ ability to persist and migrate. While the physiological mechanisms that determine sensitivity to climate change are relatively well understood (e.g., drought-induced cavitation), many associated traits have not been systematically documented for North American trees and differences in methodology preclude their widespread integration into vulnerability assessments (e.g., xylem recovery capacity). In contrast, traits traditionally associated with the ability to migrate and withstand fire are generally well documented, but new key traits are emerging in the context of climate change that have not been as well characterized (e.g., age of optimum seed production). More generally, lack of knowledge surrounding the extent and patterns in intraspecific trait variation, as well as co-variation and interaction among traits, limit our ability to use this approach to assess tree adaptive capacity. We conclude by outlining research needs and potential strategies for the development of trait-based knowledge applicable in large-scale modelling efforts, sketching out important aspects of trait data organization that should be part of a coordinated effort by the forest science community.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".