Variable synchrony in insect outbreak cycling across a forest landscape gradient: multi-scale evidence from trembling aspen in Alberta
Bibliographic record
Abstract
Using multi-scale trembling aspen tree-ring width data from Alberta, we show that scaling has a profound influence on dendroecological inferencing. At all scales of sampling, there is a significant climatological signal whose strength is nevertheless superseded by the pervasive effect of insect herbivory. At the smallest spatial scale, 20 km × 20 km, we demonstrate a quasi-periodic pattern of sharp growth reductions and the existence of negative spatial correlations among successive outbreaks. At the intermediate spatial scale of 20 km × 80 km, we show that the period 1930–1963 was marked by extremely low correlations in aspen ring widths, with much higher correlations occurring both before and after, despite high variance in precipitation that might have caused ring widths to correlate under moisture limitation. At the largest spatial scale of the entire province, aspen ring widths in the boreal forest became decorrelated abruptly in 1916, after cycling synchronously for the previous 80 years. This occurred despite a warming climate that heightened moisture limitation. Something in the environment prevents some outbreak cycles from rising to maximum intensity and from spreading to maximum extent. This appears to be the key to better predictability and management of forest insects and forests at short and long time-scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".