Boreal tree growth exhibits decadal‐scale ecological memory to drought and insect defoliation, but no negative response to their interaction
Bibliographic record
Abstract
Abstract Interactions between drought and insect defoliation may dramatically alter forest function under novel climate and disturbance regimes, but remain poorly understood. We empirically tested two important hypotheses regarding tree responses to drought and insect defoliation: (a) trees exhibit delayed, persistent, and cumulative growth responses to these stressors; (b) physiological feedbacks in tree responses to these stressors exacerbate their impacts on tree growth. These hypotheses remain largely untested at a landscape scale, yet are critical for predicting forest function under novel future conditions, given the connection between tree growth and demographic processes such as mortality and regeneration. We developed a Bayesian hierarchical model to quantify the ecological memory of tree growth to past water deficits and insect defoliation events, derive antecedent variables reflecting the persistent and cumulative effects of these stressors on current growth, and test for their interactive effects. The model was applied to extensive tree growth, weather, and defoliation survey data from western and eastern regions of the Canadian boreal forest impacted by recent drought and defoliation events and characterized by contrasting tree compositions, climates, and insect defoliators. Results revealed persistent negative tree growth responses to past water (all trees) and defoliation (host trees) stress lasting 3–6 and 10–12 years, respectively, depending on study region. Accounting for the ecological memory of tree growth to water and defoliation stress allowed for detection of interactions not previously demonstrated. Contrary to expectations, we found evidence for positive interactions among non‐host trees likely due to reduced water stress following defoliation events. Regional differences in ecological memory to water stress highlight the role of climate in shaping forest responses to drought. Synthesis . Study results suggest negative feedbacks in tree responses to drought and insect attack may be weaker than predicted for defoliator‐dominated boreal forest systems. Instead, insect defoliation may offset the impacts of water deficit on boreal tree growth by reducing transpirational water demand. This offset mimics increased resistance to drought following forest thinning and may lessen growth and mortality losses due to increased aridity and more severe insect damage forecast for the boreal forest under global change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".