Machine learning ensemble model prediction of northward shift in potato cyst nematodes(Globodera rostochiensis and G.pallida)distribution under climate change conditions  

作　　者：Yitong He Guanjin Wang Yonglin Ren Shan Gao Dong Chu Simon J.McKirdy 

机构地区：[1]Shandong Engineering Research Centre for Environment-friendly Agricultural Pest Management,College of Plant Health andMedicine,Qingdao Agricultural University,Qingdao 266109,China [2]Harry Butler Institute,Murdoch University,Murdoch,WA 6150,Australia [3]School of Information Technology,Murdoch University,Perth,WA 6150,Australia [4]Hebei Dahaituo National Nature Reserve Management Centre,Chicheng 075500,China

出　　处：《Journal of Integrative Agriculture》2024年第10期3576-3591,共16页农业科学学报（英文版）

基　　金：funded by the National Key R&D Program of China(2021YFD1400200);the Taishan Scholar Constructive Engineering Foundation of Shandong,China(tstp20221135)。

摘　　要：Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning(ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles(ESA)and multi-algorithm ensembles(EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20%of the total land surface(18%under current conditions).This research alerts policymakers and practitioners to the risk of PCNs’incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.

关 键 词：invasive species distribution future climates homogeneous climate predictors single-algorithm ensembles multi-algorithm ensembles artificial neural network 

分 类 号：S435.32[农业科学—农业昆虫与害虫防治]