Modeling long-term tree growth curves in response to warming climate: test cases from a subtropical mountain forest and a tropical rainforest in Mexico
Bibliographic record
Abstract
The Earth’s temperature has increased 0.6 °C over the last 100 years, and further climate change is predicted to potentially raise it by 3.5 °C over the next century. More than half of the global annual net primary production of biomass is estimated to occur in the tropics, especially tropical evergreen forest. In temperate forests, increasing temperature may extend the non-frost growing season, and thus increase the CO2sequestration rate, but some authors have also suggested a negative impact of warming in tropical forests from decreased photosynthetic activity. Using the PL model (Ricker and del Río 2004), we forecast growth of two Mexican tree species after climate warming. The model predicts the high-mountain species Pinus hartwegii Lindl. to decrease its expected relative growth throughout its lifetime by 10.6% as a consequence of a 0.6 °C temperature increase; in contrast, the tropical rainforest species Diospyros digyna Jacq. is predicted to increase its expected relative growth throughout its lifetime by 25.4%. The key factor appears to be the expected relationship between temperature and precipitation, rather than temperature alone. While one cannot expect a universal response across sites, some standing tropical rainforests such as those at Los Tuxtlas in Mexico may constitute a carbon sink in a changing 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".