Climate drivers of bark beetle outbreak dynamics in Norway spruce forests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bark beetles are among the most devastating biotic agents affecting forests globally and several species are expected to be favored by climate change. Given the potential interactions of insect outbreaks with other biotic and abiotic disturbances, and the potentially strong impact of changing disturbance regimes on forest resources, investigating climatic drivers of destructive bark beetle outbreaks is of paramount importance. We analyzed 17 time‐series of the amount of wood damaged by Ips typographus , the most destructive pest of Norway spruce forests, collected across 8 European countries in the last three decades. We aimed to quantify the relative importance of key climate drivers in explaining timber loss dynamics, also testing for possible synergistic effects. Local outbreaks shared the same drivers, including increasing summer rainfall deficit and warm temperatures. Large availability of storm‐felled trees in the previous year was also strongly related to an increase in timber loss, likely by providing an alternative source of breeding material. We did not find any positive synergy among outbreak drivers. On the contrary, the occurrence of large storms reduced the positive effect of warming temperatures and rainfall deficit. The large surplus of breeding material likely boosted I. typographus population size above the density threshold required to colonize and kill healthy trees irrespective of other climate triggers. Importantly, we found strong negative density dependence in I. typographus that may provide a mechanism for population decline after population eruptions. Generality in the effects of complex climatic events across different geographical areas suggests that the large‐scale drivers can be used as early warning indicators of increasing local outbreak probability.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it