Interspecific Responses to Fire in a Mixed Forest Reveal Differences in Seasonal Growth
Bibliographic record
Abstract
Despite recurring episodes of fire exacerbated by climate change, post-fire dynamics in trees remain to be fully understood. In a mixed forest in northern Mexico that experiences frequent fires, we aimed to determine how tree growth responds to surface fire by examining earlywood (EW) and latewood (LW) responsiveness, as well as their connection with canopy activity, using UAV-acquired NDVI data. We compared EW and LW growth from mini cores of burned and unburned trees (n = 100) across four species, correlating this with NDVI data from 33 UAV monthly flights at the individual tree level from 2021 to 2023. Our results identified Quercus durifolia Seemen as the species that presented the highest growth following exposure to surface fire. Arbutus arizonica (A. Gray) Sarg. was the species most affected by fire in terms of EW production immediately after burning but showed benefits in subsequent summers. Juniperus deppeana Steud. demonstrated adaptive plasticity by responding more quickly to fire, with notable growth in EW. Pinus engelmannii Carrière responded in 2023, and its NDVI was associated to the least extent with seasonal growth. Thus, there is an evident seasonal response in trees subjected to low-intensity fire, which can act to shape the stand habitat. However, there is a divergence in response between broadleaf and evergreen species that could be attributed to fire-adaptive traits and hydraulic strategies. Although combining the tree-ring data with the NDVI served to improve our understanding of the effects of fire, further research is required.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".