Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach

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dc.contributor.author Holloway, Paul
dc.contributor.author Kudenko, Daniel
dc.contributor.author Bell, James R.
dc.date.accessioned 2018-03-16T15:10:00Z
dc.date.available 2018-03-16T15:10:00Z
dc.date.issued 2018-02-19
dc.identifier.citation Holloway, P., Kudenko, D. and Bell, J. R. (2018) 'Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach', Ecological Indicators, 88, pp. 512-521. doi: 10.1016/j.ecolind.2017.10.032 en
dc.identifier.volume 88 en
dc.identifier.startpage 512 en
dc.identifier.endpage 521 en
dc.identifier.issn 1470-160X
dc.identifier.uri http://hdl.handle.net/10468/5630
dc.identifier.doi 10.1016/j.ecolind.2017.10.032
dc.description.abstract Insect pests now pose a greater threat to crop production given the recent emergence of insecticide resistance, the removal of effective compounds from the market (e.g. neonicotinoids) and the changing climate that promotes successful overwintering and earlier migration of pests. As surveillance tools, predictive models are important to mitigate against pest outbreaks. Currently they provide decision support on species emergence, distribution, and migration patterns and their use effectively gives growers more time to take strategic crop interventions such as delayed sowing or targeted insecticide use. Existing techniques may have met their optimal usefulness, particularly in complex systems and changing climates. Machine learning (ML) arguably is an advance over current capabilities because it has the potential to efficiently identify the most informative time-windows whilst simultaneously improving species predictions. In doing so, ML is likely to advance the length of any integrated pest management opportunity when growers can intervene. As an example, we studied the migration of 51 species of aphids, which include some of the most economically important pests worldwide. We used a combination of entropy and C5.0 boosted decision trees to identify the most informative time windows to link meteorological variables to aphid migration patterns across the UK. Decision trees significantly improved the accuracy of first flight prediction by 20% compared to general additive models; further, meteorological variables that were selected by entropy significantly improved the accuracy by a further 3–5% compared to expert derived variables. Coarser (e.g. monthly) weather variables resulted in similar accuracies to finer (e.g. daily) variables but the most accurate model included multiple temporal resolutions with different period lengths. This combined resolution model alone highlights the ability of machine learning to accurately predict complex relationships between species and their meteorological drivers, largely beyond the experience of experts in the field. Finally, we identified the potential of these models to predict long-term first flight patterns in which machine learning attained equally high predictive ability as shorter-term forecasts. Whilst machine learning is a statistical advance, it is not necessarily a panacea: experts will be needed to underpin results with a mechanistic understanding, thus avoiding spurious relationships. The results of this study should provide researchers with an automated methodology to derive and select the most appropriate environmental variables when predicting ecological phenomena, while simultaneously improving the accuracy of such models. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S1470160X17306696
dc.rights © 2017 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Entropy en
dc.subject Scale en
dc.subject Weather en
dc.subject Decision trees en
dc.subject General Additive Models (GAM) en
dc.subject First Flight en
dc.title Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Paul Holloway, Geography, University College Cork, Cork, Ireland. +353-21-490-3000 Email: paul.holloway@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 24 months after publication by request of the publisher. en
dc.check.date 2020-02-19
dc.date.updated 2018-03-16T14:57:40Z
dc.description.version Accepted Version en
dc.internal.rssid 427500642
dc.contributor.funder Biotechnology and Biological Sciences Research Council en
dc.contributor.funder Innovate UK en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Ecological Indicators en
dc.internal.copyrightchecked No !!CORA!! en
dc.internal.licenseacceptance Yes en
dc.internal.IRISemailaddress paul.holloway@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/RCUK/BBSRC/BB/M006980/1/GB/14TSB_DataExpl Crowd-Sourced Prediction of Plant Pest and Disease Occurrence using Mobile Apps/ en


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© 2017 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2017 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license.
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