A suite of models to support the quantitative assessment of spread in pest risk analysis
Pest Risk Analyses (PRAs) are conducted worldwide to decide whether and how exotic plant pests should be regulated to prevent invasion. There is an increasing demand for science-based risk mapping in PRA. Spread plays a key role in determining the potential distribution of pests, but there is no sui...
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creator | Robinet, Christelle Kehlenbeck, Hella Kriticos, Darren J Baker, Richard H A Battisti, Andrea Brunel, Sarah Dupin, Maxime Eyre, Dominic Faccoli, Massimo Ilieva, Zhenya Kenis, Marc Knight, Jon Reynaud, Philippe Yart, Annie van der Werf, Wopke |
description | Pest Risk Analyses (PRAs) are conducted worldwide to decide whether and how exotic plant pests should be regulated to prevent invasion. There is an increasing demand for science-based risk mapping in PRA. Spread plays a key role in determining the potential distribution of pests, but there is no suitable spread modelling tool available for pest risk analysts. Existing models are species specific, biologically and technically complex, and data hungry. Here we present a set of four simple and generic spread models that can be parameterised with limited data. Simulations with these models generate maps of the potential expansion of an invasive species at continental scale. The models have one to three biological parameters. They differ in whether they treat spatial processes implicitly or explicitly, and in whether they consider pest density or pest presence/absence only. The four models represent four complementary perspectives on the process of invasion and, because they have different initial conditions, they can be considered as alternative scenarios. All models take into account habitat distribution and climate. We present an application of each of the four models to the western corn rootworm, Diabrotica virgifera virgifera, using historic data on its spread in Europe. Further tests as proof of concept were conducted with a broad range of taxa (insects, nematodes, plants, and plant pathogens). Pest risk analysts, the intended model users, found the model outputs to be generally credible and useful. The estimation of parameters from data requires insights into population dynamics theory, and this requires guidance. If used appropriately, these generic spread models provide a transparent and objective tool for evaluating the potential spread of pests in PRAs. Further work is needed to validate models, build familiarity in the user community and create a database of species parameters to help realize their potential in PRA practice. |
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There is an increasing demand for science-based risk mapping in PRA. Spread plays a key role in determining the potential distribution of pests, but there is no suitable spread modelling tool available for pest risk analysts. Existing models are species specific, biologically and technically complex, and data hungry. Here we present a set of four simple and generic spread models that can be parameterised with limited data. Simulations with these models generate maps of the potential expansion of an invasive species at continental scale. The models have one to three biological parameters. They differ in whether they treat spatial processes implicitly or explicitly, and in whether they consider pest density or pest presence/absence only. The four models represent four complementary perspectives on the process of invasion and, because they have different initial conditions, they can be considered as alternative scenarios. All models take into account habitat distribution and climate. We present an application of each of the four models to the western corn rootworm, Diabrotica virgifera virgifera, using historic data on its spread in Europe. Further tests as proof of concept were conducted with a broad range of taxa (insects, nematodes, plants, and plant pathogens). Pest risk analysts, the intended model users, found the model outputs to be generally credible and useful. The estimation of parameters from data requires insights into population dynamics theory, and this requires guidance. If used appropriately, these generic spread models provide a transparent and objective tool for evaluating the potential spread of pests in PRAs. Further work is needed to validate models, build familiarity in the user community and create a database of species parameters to help realize their potential in PRA practice.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0043366</identifier><identifier>PMID: 23056174</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Agriculture ; Algorithms ; Animal behavior ; Animal biology ; Animals ; asterisk ; Biology ; Climate ; Climate models ; Coleoptera - physiology ; Computer Science ; Computer Simulation ; Corn ; Data processing ; Diabrotica ; Diabrotica virgifera virgifera ; economic-impact ; Ecosystem ; Eichhornia crassipes ; Europe ; Familiarity ; Food ; Geography ; Habitat ; Health risks ; Host-Parasite Interactions ; Initial conditions ; Insect Control - methods ; Insect Control - statistics & numerical data ; Insects ; Introduced species ; invasions ; Invasive species ; Life Sciences ; long-distance dispersal ; Mapping ; Models, Biological ; Natural resources ; Nematoda ; Nematodes ; Parameter estimation ; Pathogens ; Pests ; pitch canker ; Plants (botany) ; Plants - parasitology ; Population biology ; Population Dynamics ; population expansion ; Risk analysis ; Risk Assessment ; Spatial distribution ; Taxa ; Vegetal Biology ; western corn-rootworm ; Zea mays - parasitology</subject><ispartof>PloS one, 2012-10, Vol.7 (10), p.e43366-e43366</ispartof><rights>COPYRIGHT 2012 Public Library of Science</rights><rights>Robinet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><rights>2012 Robinet et al 2012 Robinet et al</rights><rights>Wageningen University & Research</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c810t-1362a212b8e8bdfdf76e28b17088825257384fee0a52991033553ba8950c78713</citedby><orcidid>0000-0002-9355-0516 ; 0000-0002-4933-4656 ; 0000-0002-0602-8595 ; 0000-0002-5506-4699</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467266/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467266/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23056174$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.inrae.fr/hal-02648833$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Robinet, Christelle</creatorcontrib><creatorcontrib>Kehlenbeck, Hella</creatorcontrib><creatorcontrib>Kriticos, Darren J</creatorcontrib><creatorcontrib>Baker, Richard H A</creatorcontrib><creatorcontrib>Battisti, Andrea</creatorcontrib><creatorcontrib>Brunel, Sarah</creatorcontrib><creatorcontrib>Dupin, Maxime</creatorcontrib><creatorcontrib>Eyre, Dominic</creatorcontrib><creatorcontrib>Faccoli, Massimo</creatorcontrib><creatorcontrib>Ilieva, Zhenya</creatorcontrib><creatorcontrib>Kenis, Marc</creatorcontrib><creatorcontrib>Knight, Jon</creatorcontrib><creatorcontrib>Reynaud, Philippe</creatorcontrib><creatorcontrib>Yart, Annie</creatorcontrib><creatorcontrib>van der Werf, Wopke</creatorcontrib><title>A suite of models to support the quantitative assessment of spread in pest risk analysis</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Pest Risk Analyses (PRAs) are conducted worldwide to decide whether and how exotic plant pests should be regulated to prevent invasion. There is an increasing demand for science-based risk mapping in PRA. Spread plays a key role in determining the potential distribution of pests, but there is no suitable spread modelling tool available for pest risk analysts. Existing models are species specific, biologically and technically complex, and data hungry. Here we present a set of four simple and generic spread models that can be parameterised with limited data. Simulations with these models generate maps of the potential expansion of an invasive species at continental scale. The models have one to three biological parameters. They differ in whether they treat spatial processes implicitly or explicitly, and in whether they consider pest density or pest presence/absence only. The four models represent four complementary perspectives on the process of invasion and, because they have different initial conditions, they can be considered as alternative scenarios. All models take into account habitat distribution and climate. We present an application of each of the four models to the western corn rootworm, Diabrotica virgifera virgifera, using historic data on its spread in Europe. Further tests as proof of concept were conducted with a broad range of taxa (insects, nematodes, plants, and plant pathogens). Pest risk analysts, the intended model users, found the model outputs to be generally credible and useful. The estimation of parameters from data requires insights into population dynamics theory, and this requires guidance. If used appropriately, these generic spread models provide a transparent and objective tool for evaluating the potential spread of pests in PRAs. Further work is needed to validate models, build familiarity in the user community and create a database of species parameters to help realize their potential in PRA practice.</description><subject>Agriculture</subject><subject>Algorithms</subject><subject>Animal behavior</subject><subject>Animal biology</subject><subject>Animals</subject><subject>asterisk</subject><subject>Biology</subject><subject>Climate</subject><subject>Climate models</subject><subject>Coleoptera - physiology</subject><subject>Computer Science</subject><subject>Computer Simulation</subject><subject>Corn</subject><subject>Data processing</subject><subject>Diabrotica</subject><subject>Diabrotica virgifera virgifera</subject><subject>economic-impact</subject><subject>Ecosystem</subject><subject>Eichhornia crassipes</subject><subject>Europe</subject><subject>Familiarity</subject><subject>Food</subject><subject>Geography</subject><subject>Habitat</subject><subject>Health risks</subject><subject>Host-Parasite Interactions</subject><subject>Initial conditions</subject><subject>Insect Control - methods</subject><subject>Insect Control - statistics & numerical data</subject><subject>Insects</subject><subject>Introduced species</subject><subject>invasions</subject><subject>Invasive species</subject><subject>Life Sciences</subject><subject>long-distance dispersal</subject><subject>Mapping</subject><subject>Models, Biological</subject><subject>Natural resources</subject><subject>Nematoda</subject><subject>Nematodes</subject><subject>Parameter estimation</subject><subject>Pathogens</subject><subject>Pests</subject><subject>pitch canker</subject><subject>Plants (botany)</subject><subject>Plants - parasitology</subject><subject>Population biology</subject><subject>Population Dynamics</subject><subject>population expansion</subject><subject>Risk analysis</subject><subject>Risk Assessment</subject><subject>Spatial distribution</subject><subject>Taxa</subject><subject>Vegetal 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one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Robinet, Christelle</au><au>Kehlenbeck, Hella</au><au>Kriticos, Darren J</au><au>Baker, Richard H A</au><au>Battisti, Andrea</au><au>Brunel, Sarah</au><au>Dupin, Maxime</au><au>Eyre, Dominic</au><au>Faccoli, Massimo</au><au>Ilieva, Zhenya</au><au>Kenis, Marc</au><au>Knight, Jon</au><au>Reynaud, Philippe</au><au>Yart, Annie</au><au>van der Werf, Wopke</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A suite of models to support the quantitative assessment of spread in pest risk analysis</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2012-10-09</date><risdate>2012</risdate><volume>7</volume><issue>10</issue><spage>e43366</spage><epage>e43366</epage><pages>e43366-e43366</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Pest Risk Analyses (PRAs) are conducted worldwide to decide whether and how exotic plant pests should be regulated to prevent invasion. There is an increasing demand for science-based risk mapping in PRA. Spread plays a key role in determining the potential distribution of pests, but there is no suitable spread modelling tool available for pest risk analysts. Existing models are species specific, biologically and technically complex, and data hungry. Here we present a set of four simple and generic spread models that can be parameterised with limited data. Simulations with these models generate maps of the potential expansion of an invasive species at continental scale. The models have one to three biological parameters. They differ in whether they treat spatial processes implicitly or explicitly, and in whether they consider pest density or pest presence/absence only. The four models represent four complementary perspectives on the process of invasion and, because they have different initial conditions, they can be considered as alternative scenarios. All models take into account habitat distribution and climate. We present an application of each of the four models to the western corn rootworm, Diabrotica virgifera virgifera, using historic data on its spread in Europe. Further tests as proof of concept were conducted with a broad range of taxa (insects, nematodes, plants, and plant pathogens). Pest risk analysts, the intended model users, found the model outputs to be generally credible and useful. The estimation of parameters from data requires insights into population dynamics theory, and this requires guidance. If used appropriately, these generic spread models provide a transparent and objective tool for evaluating the potential spread of pests in PRAs. Further work is needed to validate models, build familiarity in the user community and create a database of species parameters to help realize their potential in PRA practice.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23056174</pmid><doi>10.1371/journal.pone.0043366</doi><tpages>e43366</tpages><orcidid>https://orcid.org/0000-0002-9355-0516</orcidid><orcidid>https://orcid.org/0000-0002-4933-4656</orcidid><orcidid>https://orcid.org/0000-0002-0602-8595</orcidid><orcidid>https://orcid.org/0000-0002-5506-4699</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1932-6203 |
ispartof | PloS one, 2012-10, Vol.7 (10), p.e43366-e43366 |
issn | 1932-6203 1932-6203 |
language | eng |
recordid | cdi_plos_journals_1326551780 |
source | Public Library of Science (PLoS) Journals Open Access; MEDLINE; DOAJ Directory of Open Access Journals; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
subjects | Agriculture Algorithms Animal behavior Animal biology Animals asterisk Biology Climate Climate models Coleoptera - physiology Computer Science Computer Simulation Corn Data processing Diabrotica Diabrotica virgifera virgifera economic-impact Ecosystem Eichhornia crassipes Europe Familiarity Food Geography Habitat Health risks Host-Parasite Interactions Initial conditions Insect Control - methods Insect Control - statistics & numerical data Insects Introduced species invasions Invasive species Life Sciences long-distance dispersal Mapping Models, Biological Natural resources Nematoda Nematodes Parameter estimation Pathogens Pests pitch canker Plants (botany) Plants - parasitology Population biology Population Dynamics population expansion Risk analysis Risk Assessment Spatial distribution Taxa Vegetal Biology western corn-rootworm Zea mays - parasitology |
title | A suite of models to support the quantitative assessment of spread in pest risk analysis |
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