气候变化条件下马铃薯胞囊线虫(Globodera rostochiensis 和 G. pallida)分布北移的机器学习集合模型预测

IF 4.6 1区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY Journal of Integrative Agriculture Pub Date : 2024-08-05 DOI:10.1016/j.jia.2024.08.001
Yitong He, Guanjin Wang, Yonglin Ren, Shan Gao, Dong Chu, Simon J. McKirdy
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引用次数: 0

摘要

马铃薯胞囊线虫(PCNs)是马铃薯生产的一个重大威胁,在许多国家都造成了重大损失。预测 PCN 物种的未来分布对实施有效的生物安全战略至关重要,特别是考虑到气候变化对害虫物种入侵和分布的影响。机器学习(ML),特别是集合模型,由于能够基于复杂的数据集进行学习和预测,已成为预测物种分布的有力工具。因此,本研究利用先进的机器学习技术来预测气候变化条件下 PCN 物种的分布,为入侵风险评估提供初始要素。我们首先利用全球气候模型生成同质气候预测因子,以减少预测因子之间的差异。然后,利用五个机器学习模型建立了两组集合,即单算法集合(ESA)和多算法集合(EMA),并比较了它们的性能。在这项研究中,EMA 的性能并不总是优于 ESA,而人工神经网络的 ESA 性能最高,同时具有成本效益。预测结果表明,多氯化萘的分布范围将北移,热带地区减少,北纬地区增加。不过,适宜区域的总面积不会发生重大变化,占陆地总面积的 16-20%(当前条件下为 18%)。这项研究提醒政策制定者和从业人员注意多氯化萘侵入新区域的风险。此外,这一多极化过程还能跟踪各种物种的分布变化,为制定长期的生物安全控制计划提供科学依据。
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Machine learning ensemble model prediction of northward shift in potato cyst nematodes (Globodera rostochiensis and G. pallida) distribution under climate change conditions
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.
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来源期刊
Journal of Integrative Agriculture
Journal of Integrative Agriculture AGRICULTURE, MULTIDISCIPLINARY-
CiteScore
7.90
自引率
4.20%
发文量
4817
审稿时长
3-6 weeks
期刊介绍: Journal of Integrative Agriculture publishes manuscripts in the categories of Commentary, Review, Research Article, Letter and Short Communication, focusing on the core subjects: Crop Genetics & Breeding, Germplasm Resources, Physiology, Biochemistry, Cultivation, Tillage, Plant Protection, Animal Science, Veterinary Science, Soil and Fertilization, Irrigation, Plant Nutrition, Agro-Environment & Ecology, Bio-material and Bio-energy, Food Science, Agricultural Economics and Management, Agricultural Information Science.
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