An ensemble learning approach to map the genetic connectivity of the parasitoid Stethynium empoasca (Hymenoptera: Mymaridae) and identify the key influencing environmental and landscape factors - Sun, Linyang; Li, Jinyu; Chen, Jie; Chen, Wei; Yue, Zhen; Shi, Jingya; Huang, Huoshui; You, Minsheng; You, Shijun (Frontiers in Ecology and Evolution, 2022)
The effect of landscape patterns and environmental factors on the population structure and genetic diversity of organisms is well documented. However, this phenomenon is still unclear in the case of Mymaridae parasitoids. Despite recent advances in machine learning methods for landscape genetics, ensemble learning still needs to be investigated further. Here, we evaluated the performance of different boosting algorithms, and analyzed the effects of landscape and environmental factors on the genetic variations in the tea green leafhopper parasitoid Stethynium empoasca (Hymenoptera: Mymaridae) using different ensemble learning methods. The S. empoasca populations showed a distinct pattern of isolation by distance, and temperature, perception, and forest were identified as the dominant factors affecting the genetic divergence of S. empoasca populations. Notably, compared to previous machine learning studies, our model showed an unprecedented accuracy (r = 0.87) for the prediction of genetic differentiation. These findings not only demonstrated how the landscape shaped S. empoasca genetics, but also provided an important basis to develop conservation strategies for this biocontrol agent. In a broader sense, this study demonstrated the importance and efficiency of using ensemble learning in the field of landscape genetics.