Pine caterpillar occurrence modeling using satellite spring phenology and meteorological variables
Bibliographic record
Abstract
Abstract Outbreaks of leaf-feeding Lepidopteran insects substantially weaken the quality of forest trees and strongly affect the ecosystem functions of plant photosynthesis and carbon uptake. The narrow phenological time window of leaf out about ten days, during which Lepidopteran larvae feed on high nutrient newly flushed leaves, may change the insect community and outbreak dynamics by determining the survival rate of larvae. The Chinese pine Caterpillar (Dendrolimus tabulaeformis Tsai et Liu) infestation of the northern Chinese pine (Pinus tabulaeformis) forest in China is a major concern, and accurately modeling the day of insect occurrence (DIO) in the spring remains challenging. With continuous in-situ observed insect activities of 20 plots and satellite and meteorological observations from 1983 to 2014, we found a strong synchronization (r = 0.54, p = 0.001) between the satellite-based vegetation spring phenology, i.e. the green-up day (GUD), and DIO of the pine caterpillar over time. We used partial least squares regression and ridge regression models, and identified that monthly preseason air temperature, wind speed, specific humidity, and downward radiation were key environmental cues that awakened the overwintering pine caterpillars. After removing the collinearity of multiple variables, we showed that the dimensionality reduction-based regression models substantially improved the accuracy of DIO modeling than commonly used models, such as interval and degree-day models. In particular, including GUD significantly enhanced the predictive strength of the models increasing the coefficient of determination (R 2) by 17.1% and consequently a decrease of 16.5% in the root mean square error. We further showed that evapotranspiration changed the environmental moisture content, which indirectly affected the activities of insects. Our results revealed a useful linkage between spring leaf development and insect occurrence, and therefore are of great importance for the large-scale monitoring of pest outbreaks with future remote sensing observations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".