Predicting the Current and Future Habitat Distribution for an Important Fruit Pest, Grapholita dimorpha Komai
Li Huang1,2, Shichao Zuo1, Yiqi Huo1
1College of Life Science and Agronomy, Zhoukou Normal University, Zhoukou 466001, China.
Abstract:
The Grapholita dimorpha is one of the significant borer pests that primarily damage plum, pear, and apple trees, often resulting in substantial economic losses in fruit production. However, the potential distribution range of this economically important pest remains poorly understood. In this study, we simulated an optimized maximum entropy (MaxEnt) model to predict the spatiotemporal distribution pattern of G. dimorpha and identified its underlying driving factors. The results indicate that suitable habitats, under current bioclimatic conditions, are mainly distributed in eastern China, northeastern China, Korea, and Japan, covering a total of 273.5 × 104 km2. The highly suitable habitats are primarily located in Korea and parts of central Japan, with a total area of 19.8 × 104 km2. In future projections, the suitable area is expected to increase by 17.74% to 62.10%, and the suitable habitats are predicted to shift northward overall. In particular, there are more highly suitable habitats for G. dimorpha in China and Japan compared to their predominance in Korea under current climatic conditions. The bio9 and bio18 contribute 51.9% and 20.7% to the modeling, respectively, indicating that the distribution of G. dimorpha may be shaped mainly by the mean temperature of the driest quarter and precipitation of the warmest quarter. In summary, the distribution range predicted, particularly for regions with highly suitable habitats, poses a high risk of G. dimorpha outbreaks, emphasizing the priority of pest monitoring and management. Furthermore, the key bioclimatic variables identified could also provide crucial reference for pest monitoring.
Related Concept Videos
Habitat Fragmentation
Conservation of Declining Populations
Maxwell-Boltzmann Distribution: Problem Solving
This distribution function f(v) is defined by saying that the expected number N (v1,v2) of particles with speeds between v1 and v2 is given by
End Point Prediction: Gran Plot
For potentiometric titration, the Gran plot is created by plotting...
Conservation of Small Populations
Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.


