Related Experiment Video
Updated: Mar 25, 2026
07:44
An Experimental Study on Colorado Potato Beetle Hibernation Under Natural Conditions
Published on: November 17, 2023
13.5K
Interactions among weather and landscape affect Colorado potato beetle population dynamics
Abigail L Cohen1, Benjamin Bradford2, Russell Groves2
1Michigan State University, Department of Entomology, East Lansing, Michigan, United States of America.
Plos One
|March 23, 2026
Summary
Understanding Colorado Potato Beetle (Leptinotarsa decimlineata) population dynamics requires considering both heat accumulation and environmental factors like precipitation and soil temperature during diapause. Potato acreage also influences abundance trends.
Area of Science:
- Ecology
- Entomology
- Agricultural Science
Background:
- Population dynamics are influenced by life history traits and environmental conditions.
- Predicting crop pest populations often focuses on growing season heat accumulation, neglecting pre-diapause and diapause weather impacts.
Purpose of the Study:
- To investigate the influence of seasonal weather and landscape variables on Colorado Potato Beetle (Leptinotarsa decimlineata) abundance.
- To identify key factors predicting pest population dynamics across different life stages.
Main Methods:
- Utilized 16 years of Colorado Potato Beetle abundance data alongside landscape and daily weather data.
- Employed boosted-tree multiclass models for abundance prediction and general additive mixed models to analyze temporal interactions.
Main Results:
- Cumulative degree days were the most significant predictor of Colorado Potato Beetle abundance.
- Precipitation, air saturation, soil temperature during diapause, and potato acreage also significantly influenced population trends.
Conclusions:
- While heat accumulation is primary, other environmental factors during diapause and landscape features like potato acreage are crucial for predicting Colorado Potato Beetle population dynamics.
- A comprehensive approach integrating temporal and non-temporal variables improves pest forecasting accuracy.
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