Increasing spruce budworm defoliation increases catchment discharge in conifer forests
Bibliographic record
Abstract
Forest insect outbreaks cause significant reductions in the forest canopy through defoliation and tree mortality that modify the storage and flow of water, altering catchment runoff and stream discharge patterns. Despite a growing understanding of the impacts of insect outbreaks on the hydrology of broadleaf forests, little is known about these impacts to catchment hydrology in northern conifer-dominated forests. We measured the effects of cumulative defoliation by spruce budworm (Choristoneura fumiferana) on stream discharge and runoff in 12 experimental catchments (6.33–9.85 km2) across the central Gaspé Peninsula in eastern Québec, Canada over a three-year period (2019–2021). Six catchments were aerially treated with BtK (Bacillus thuringiensis kurstaki) insecticide to suppress the outbreak and six catchments were left untreated, leading to a defoliation gradient across the study sites. Stage-discharge relationships were established between June and October from 2019 to 2021. Stream volumetric discharge (r = 0.71, p < 0.01, t(34) = 5.85), runoff (r = 0.55, p < 0.01, t(34) = 3.81) and runoff ratios (r = 0.67, p < 0.01, t(33) = 5.19) were all strongly positively correlated with cumulative defoliation intensity, likely through reducing available water storage in the catchment and therefore enhancing runoff generation. Seasonally, volumetric discharge, runoff, and runoff ratios were more strongly correlated with defoliation in the summer than autumn months, likely because available catchment storage was more limited following the freshet. Overall, we found that insect defoliation impacts forested catchment hydrology similar to other landscape disturbances, and such consequences should be considered in forest management and the control of forest insect outbreaks.
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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 | 0.001 |
| 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) | 0.002 | 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".