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一种基于机器学习的方法,用于预测生物防治入侵欧亚水蕹草减少的成功率。

A machine-learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction.

机构信息

Department of Mathematics, Clarkson University, Potsdam, New York, USA.

Department of Biology, Clarkson University, Potsdam, New York, USA.

出版信息

Ecol Appl. 2022 Sep;32(6):e2625. doi: 10.1002/eap.2625. Epub 2022 Jun 2.

Abstract

Myriophyllum spicatum, more commonly known as Eurasian watermilfoil (EWM), is one of the most invasive aquatic plants in North America, causing negative ecological and economic impacts in ecosystems where it proliferates. Many control strategies have been developed and implemented to mitigate EWM growth and spread, although the results are mixed and there is no consensus on lake-specific strategies. Here, we describe the development of a predictive model using a support vector technique, that predicts the success of biological pest control using Euhrychiopsis lecontei (the milfoil weevil), a milfoil specialist, to reduce EWM in lakes. Such a model is informed by lake characteristics (limnological and landscape) and augmentation strategies. To develop our predictive model, we performed a metadata analysis from 133 published peer-reviewed literature and professional reports of milfoil weevil augmentation field experiments that contained information on lake characteristics. The predictive model's algorithm uses a support vector machine (SMV) to learn patterns among lake characteristics, along with the recorded augmentation strategy and the reported success of each study, where success is a measure of EWM change over a season and is recorded in a variety of ways (e.g., EWM biomass change, EWM percent change, EWM visual change, etc.,). Overall, the model results suggests that shallower lakes, more frequent weevil augmentations, and larger weevil overwintering habitat are the most important predictors for EWM reduction success by weevil augmentation. Although watermilfoil weevil augmentation is a promising mitigation strategy, it may not work for all lakes. However, in terms of suggesting weevil augmentation, our model is a valuable tool for lake stakeholders and resource managers, who can use it to determine whether milfoil weevil augmentation, which can be very costly due to the difficulties in finding and raising milfoil weevils, will be a useful and sustainable approach to control EWM in their lake community.

摘要

穗状狐尾藻,通常被称为欧亚水鳖(EWM),是北美最具入侵性的水生植物之一,在其繁殖的生态系统中造成了负面的生态和经济影响。已经开发并实施了许多控制策略来减轻 EWM 的生长和传播,尽管结果参差不齐,并且对于特定湖泊的策略没有共识。在这里,我们使用支持向量技术描述了一个预测模型的开发,该模型使用欧亚水鳖象鼻虫(一种水鳖专家)来预测生物害虫控制的成功,以减少湖泊中的 EWM。这种模型是通过湖泊特征(湖泊学和景观)和增强策略来告知的。为了开发我们的预测模型,我们对 133 篇已发表的同行评议文献和关于欧亚水鳖象鼻虫增强现场实验的专业报告进行了元数据分析,这些文献和报告包含有关湖泊特征的信息。预测模型的算法使用支持向量机(SMV)来学习湖泊特征之间的模式,以及记录的增强策略和每个研究报告的成功,成功是衡量一个季节内 EWM 变化的指标,并以多种方式记录(例如,EWM 生物量变化、EWM 百分比变化、EWM 视觉变化等)。总体而言,该模型结果表明,较浅的湖泊、更频繁的象鼻虫增强以及更大的象鼻虫越冬栖息地是通过象鼻虫增强减少 EWM 的最重要预测因素。尽管水鳖象鼻虫增强是一种很有前途的缓解策略,但它可能不适用于所有湖泊。然而,就建议象鼻虫增强而言,我们的模型是湖泊利益相关者和资源管理者的宝贵工具,他们可以使用该模型来确定象鼻虫增强是否是控制其湖泊社区中 EWM 的有用和可持续方法,因为象鼻虫增强由于寻找和饲养象鼻虫的困难而非常昂贵。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8b24/9539498/dffc682c96dc/EAP-32-e2625-g001.jpg

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