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

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 gr...

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Autores principales: White, Diana T., Antoniou, Thibaud M., Martin, Jonathan M., Kmetz, William, Twiss, Michael R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons, Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539498/
https://www.ncbi.nlm.nih.gov/pubmed/35397182
http://dx.doi.org/10.1002/eap.2625
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author White, Diana T.
Antoniou, Thibaud M.
Martin, Jonathan M.
Kmetz, William
Twiss, Michael R.
author_facet White, Diana T.
Antoniou, Thibaud M.
Martin, Jonathan M.
Kmetz, William
Twiss, Michael R.
author_sort White, Diana T.
collection PubMed
description 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.
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spelling pubmed-95394982022-10-14 A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction White, Diana T. Antoniou, Thibaud M. Martin, Jonathan M. Kmetz, William Twiss, Michael R. Ecol Appl Articles 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. John Wiley & Sons, Inc. 2022-06-02 2022-09 /pmc/articles/PMC9539498/ /pubmed/35397182 http://dx.doi.org/10.1002/eap.2625 Text en © 2022 The Authors. Ecological Applications published by Wiley Periodicals LLC on behalf of The Ecological Society of America. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Articles
White, Diana T.
Antoniou, Thibaud M.
Martin, Jonathan M.
Kmetz, William
Twiss, Michael R.
A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
title A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
title_full A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
title_fullStr A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
title_full_unstemmed A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
title_short A machine‐learning approach to predict success of a biocontrol for invasive Eurasian watermilfoil reduction
title_sort machine‐learning approach to predict success of a biocontrol for invasive eurasian watermilfoil reduction
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539498/
https://www.ncbi.nlm.nih.gov/pubmed/35397182
http://dx.doi.org/10.1002/eap.2625
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