Forest ecotone response to climate change: sensitivity to temperature response functional forms
Bibliographic record
Abstract
Past simulation studies using a variety of models have generally agreed that climatic warming could have adverse effects on forests, including large-scale diebacks in some regions and drastic range shrinkages of many species. These effects should be most evident at biome transition zones. Other studies have pointed out, however, that past models have used a parabolic temperature response function that is based on geographic range limits rather than functional responses or data and that this parabolic model could exaggerate dieback effects. A new model is proposed for growing degree-days temperature response, which is asymptotic rather than parabolic. In this new model, tree height growth rate increases and then levels off with increasing growing degree-days. Species from more southern regions have a higher asymptote. It is shown that this model can be derived from the integration of a parabolic growth response to temperature over a year-long sinusoidal temperature regime. The SORTIE forest simulation model was modified to incorporate this response function. An ecotonal region was simulated under a warming scenario. The traditional parabolic temperature response model produced a wide zone of dieback following warming. In contrast, the new asymptotic response function produced no dieback and a stable ecotone that migrated north at <100 m/100 years.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".