Boreal Tree Growth May Be More Tolerant to Warming Than Previously Considered: Implications for Future Modelling Studies
Bibliographic record
Abstract
ABSTRACT Aim Most process‐based forest simulation models used to project the impacts of climate change on forest dynamics rely on thermal transfer functions (TTF) that describe the relationship between temperature and growth. However, these functions have faced criticism, undermining model forecasts and highlighting the need for more robust TTF based on large empirical datasets. In this study, we modelled growth response to growing degree‐days (GDD) of common tree species in eastern North America using an unprecedented dataset of over 1 million tree records from 29,809 permanent sample plots. Location Canada and the United States. Time Period 1958–2018. Major Taxa Studied Tree species. Methods We used quantile regression analysis to build TTF by modelling tree growth response to GDD for 16 tree species using a widely distributed data across North America. The newly fitted TTF were then used to project near‐term (2041–2070 time period) growth responses to climate warming and were compared with TTF currently applied in published modelling studies. Results Our results support the assumption of a parabolic growth response curve to GDD, but challenge the assumption of optimal growth occurring at the centre of the species' climatic range (as assumed by the climatic envelope approach commonly used to develop TTF). Compared to our empirically derived TTF, the TTF used in four well‐established, published forest simulation models tend to overestimate the negative impact of climate warming on the growth of cold‐adapted, boreal tree species, while underestimating it for some temperate species. Main Conclusions Our results indicate that the selected published forest models often underestimate the optimum temperature under which maximum tree growth occurs for cold‐adapted, boreal species, suggesting greater resilience to climate change than previously forecasted. We recommend the application of this empirical approach to other tree species and integrating these more realistic parameters into existing modelling frameworks to improve ecological forecasting.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".