Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological networks offers a promising approach to under...

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Autores principales:	Hannah J Kotula, Guadalupe Peralta, Carol M Frost, Jacqui H Todd, Jason M Tylianakis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/58b5f1ba8bfc4ec4930378af99d9e11b
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spelling	oai:doaj.org-article:58b5f1ba8bfc4ec4930378af99d9e11b2021-12-02T20:11:10ZPredicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.1932-620310.1371/journal.pone.0252448https://doaj.org/article/58b5f1ba8bfc4ec4930378af99d9e11b2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252448https://doaj.org/toc/1932-6203Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological networks offers a promising approach to understanding the community-wide impacts of biocontrol agents (via direct and indirect interactions). Independently, species traits and phylogenies have been shown to successfully predict species interactions and network structure (alleviating the need to collect quantitative interaction data), but whether these approaches can be combined to predict indirect impacts of natural enemies remains untested. Whether predictions of interactions (i.e. direct effects) can be made equally well for generalists vs. specialists, abundant vs. less abundant species, and across different habitat types is also untested for consumer-prey interactions. Here, we used two machine-learning techniques (random forest and k-nearest neighbour; KNN) to test whether we could accurately predict empirically-observed quantitative host-parasitoid networks using trait and phylogenetic information. Then, we tested whether the accuracy of machine-learning-predicted interactions depended on the generality or abundance of the interacting partners, or on the source (habitat type) of the training data. Finally, we used these predicted networks to generate predictions of indirect effects via shared natural enemies (i.e. apparent competition), and tested these predictions against empirically observed indirect effects between hosts. We found that random-forest models predicted host-parasitoid pairwise interactions (which could be used to predict attack of non-target host species) more successfully than KNN. This predictive ability depended on the generality of the interacting partners for KNN models, and depended on species' abundances for both random-forest and KNN models, but did not depend on the source (habitat type) of data used to train the models. Further, although our machine-learning informed methods could significantly predict indirect effects, the explanatory power of our machine-learning models for indirect interactions was reasonably low. Combining machine-learning and network approaches provides a starting point for reducing risk in biocontrol introductions, and could be applied more generally to predicting species interactions such as impacts of invasive species.Hannah J KotulaGuadalupe PeraltaCarol M FrostJacqui H ToddJason M TylianakisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 6, p e0252448 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hannah J Kotula Guadalupe Peralta Carol M Frost Jacqui H Todd Jason M Tylianakis Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
description	Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological networks offers a promising approach to understanding the community-wide impacts of biocontrol agents (via direct and indirect interactions). Independently, species traits and phylogenies have been shown to successfully predict species interactions and network structure (alleviating the need to collect quantitative interaction data), but whether these approaches can be combined to predict indirect impacts of natural enemies remains untested. Whether predictions of interactions (i.e. direct effects) can be made equally well for generalists vs. specialists, abundant vs. less abundant species, and across different habitat types is also untested for consumer-prey interactions. Here, we used two machine-learning techniques (random forest and k-nearest neighbour; KNN) to test whether we could accurately predict empirically-observed quantitative host-parasitoid networks using trait and phylogenetic information. Then, we tested whether the accuracy of machine-learning-predicted interactions depended on the generality or abundance of the interacting partners, or on the source (habitat type) of the training data. Finally, we used these predicted networks to generate predictions of indirect effects via shared natural enemies (i.e. apparent competition), and tested these predictions against empirically observed indirect effects between hosts. We found that random-forest models predicted host-parasitoid pairwise interactions (which could be used to predict attack of non-target host species) more successfully than KNN. This predictive ability depended on the generality of the interacting partners for KNN models, and depended on species' abundances for both random-forest and KNN models, but did not depend on the source (habitat type) of data used to train the models. Further, although our machine-learning informed methods could significantly predict indirect effects, the explanatory power of our machine-learning models for indirect interactions was reasonably low. Combining machine-learning and network approaches provides a starting point for reducing risk in biocontrol introductions, and could be applied more generally to predicting species interactions such as impacts of invasive species.
format	article
author	Hannah J Kotula Guadalupe Peralta Carol M Frost Jacqui H Todd Jason M Tylianakis
author_facet	Hannah J Kotula Guadalupe Peralta Carol M Frost Jacqui H Todd Jason M Tylianakis
author_sort	Hannah J Kotula
title	Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
title_short	Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
title_full	Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
title_fullStr	Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
title_full_unstemmed	Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
title_sort	predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/58b5f1ba8bfc4ec4930378af99d9e11b
work_keys_str_mv	AT hannahjkotula predictingdirectandindirectnontargetimpactsofbiocontrolagentsusingmachinelearningapproaches AT guadalupeperalta predictingdirectandindirectnontargetimpactsofbiocontrolagentsusingmachinelearningapproaches AT carolmfrost predictingdirectandindirectnontargetimpactsofbiocontrolagentsusingmachinelearningapproaches AT jacquihtodd predictingdirectandindirectnontargetimpactsofbiocontrolagentsusingmachinelearningapproaches AT jasonmtylianakis predictingdirectandindirectnontargetimpactsofbiocontrolagentsusingmachinelearningapproaches
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Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

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