Bark beetles as microclimate engineers – thermal characteristics of infested spruce trees at the canopy surface and below the canopy
Bibliographic record
Abstract
Over recent decades, Spruce bark beetle outbreaks have expanded and intensified across Europe, driven by a warming climate and more frequent drought events. While research has largely focused on early detection and vulnerability prediction, little is known about the consequences of beetle infestations on forest microclimates. Bark beetle attacks are expected to alter forest microclimates due to changes in canopy cover, albedo, wind patterns, and evapotranspiration. We explored the effect of bark beetle attacks on summer forest microclimate using two approaches. Firstly, we measured understory microclimate at 2-m height in 31 Swedish forest stands using small temperature loggers. Along a gradient of attack severity, represented by increasing proportions of attacked spruces, maximum summer day-time temperatures increased by up to 2°C, with this warming effect being moderated by the presence of deciduous broadleaf trees. Surprisingly, night-time minimum temperatures were not affected by bark beetle attacks. Secondly, we mapped canopy surface temperature over one part of the study area using multispectral and thermal drone imaging, contrasting canopy temperatures of living and dead trees. We observed that dead trees were generally warmer than living trees, by an average of 2.6°C on a sunny day and 0.7°C on a cloudy day. Our study documented changes in thermal regimes in both the understory and overstory after bark beetle attacks, indicating that climate-change related disturbances are fuelling rapid increases in microclimate warming. However, even dead forest stands may function as thermal buffers for understory vegetation, as we found minimum temperatures being unaffected by bark beetles. Finally, our results suggest that increasing the proportion of deciduous trees can decrease the risk of bark-beetle induced microclimate warming. The insights gained can guide forest succession and regeneration management after disturbances, contributing to critical decisions on conservation areas and salvage logging strategies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".