Linking population genetics and tree height growth models to predict impacts of climate change on forest production
Bibliographic record
Abstract
Abstract Changes to forest growth models used widely in global change research and sustainable forest management are needed to account for expected climate change impacts. We provide a new approach that dynamically merges height–age functions prevalent in forest growth models with transfer functions prevalent in population adaptation research to better represent changes to forest productivity as climates gradually change. Our simulations with data from an extensive provenance test of lodgepole pine (Pinus contorta) inBritishColumbia,Canada, suggest that climate change will reduce production in lodgepole pine forests established today by at least 7–13% at the end of this century – considerably less than most predictions based solely on transfer or response functions, which do not integrate impacts as climate gradually changes. This work illustrates the need for forest productivity models to consider the changing climate in which a population is growing relative to the static climate of its origin. It also demonstrates the value of long‐term provenance trials in assessing the dynamic impact of climate change on forest productivity, and serves as an example of how provenance trials may be exploited in other forest productivity models or other research fields to assess plant responses to climate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".