Impacts of a regional multiyear insect defoliation event on growing‐season runoff ratios and instantaneous streamflow characteristics
Bibliographic record
Abstract
Abstract Repeated moderate severity forest disturbances can cause short‐ and long‐term shifts in ecosystem processes. Prior work has found that stand‐replacing disturbances (e.g., clear‐cutting) increase streamflow in temperate forests, but streamflow responses to repeated moderate severity disturbances are more equivocal. This study examined a moderate disturbance caused by an unexpected population irruption of the invasive insect Lymantria dispar (common name: gypsy moth) in 2015–2017. This irruption resulted in defoliation that was locally severe in some areas but spatially heterogeneous at a regional scale. L . dispar larvae consume leaves during the summer growing season, which effectively reduces tree leaf area and associated evapotranspiration. Our regional approach in Southern New England, USA, used data from 83 US Geological Survey (USGS) stream gages to assess whether changes in growing‐season watershed runoff ratios and instantaneous streamflow characteristics during the 2015–2017 L . dispar irruption were associated with satellite‐derived metrics of changes in forest condition (i.e., defoliation), compared to a 20‐year baseline streamflow period. We found a small, linear increase in growing‐season runoff ratio anomalies that was associated with defoliation intensity. The association between defoliation intensity and runoff ratio anomalies was magnified in less anthropogenically impacted reference watersheds. We also found that defoliation intensity was associated with larger volumes of high and medium instantaneous streamflows compared to baseline mean flow conditions. This study provided important insights into the impacts of moderate disturbance on ecohydrology in mesic temperate forests with a unique methodological approach that assessed the impacts of spatially heterogeneous and repeated moderate disturbance at a regional scale.
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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 | 0.000 | 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) | 0.000 | 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".