agent-based modeling of human-induced spread of invasive...
TRANSCRIPT
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©CopyrightJASSS
FrançoisRebaudo,VerónicaCrespo-Pérez,Jean-FrançoisSilvainandOlivierDangles(2011)
Agent-BasedModelingofHuman-InducedSpreadofInvasiveSpeciesinAgriculturalLandscapes:InsightsfromthePotatoMothinEcuador
JournalofArtificialSocietiesandSocialSimulation 14(3)7
Received:27-Oct-2010Accepted:07-May-2011Published:30-Jun-2011
Abstract
Agent-basedmodels(ABM)areidealtoolstodealwiththecomplexityofpestinvasionthroughoutagriculturalsocio-ecologicalsystems,yetveryfewstudieshaveappliedtheminsuchcontext.InthisworkwedevelopedanABMthatsimulatesinteractionsbetweenfarmersandaninvasiveinsectpestinanagriculturallandscapeofthetropicalAndes.Ourspecificaimsweretousethemodel1)toassesstheimportanceoffarmers'mobilityandpestcontrolknowledgeonpestexpansionand2)touseitasaneducationaltooltotrainfarmercommunitiesfacingpestrisks.Ourmodelcombinedanecologicalsub-model,simulatingpestpopulationdynamicsdrivenbyacellularautomatonincludingenvironmentalfactorsofthelandscape,withasocialmodelinwhichweincorporatedagents(farmers)potentiallytransportingandspreadingthepestthroughdisplacementsamongvillages.Resultsofmodelsimulationrevealedthatbothagents'movementsandknowledgehadasignificant,non-linear,impactoninvasionspread,confirmingpreviousworksondiseaseexpansionbyepidemiologists.However,heterogeneityinknowledgeamongagentshadaloweffectoninvasiondynamicsexceptathighlevelsofknowledge.EvaluationsofthetrainingsessionsusingABMsuggestthatfarmerswouldbeabletobettermanagetheircropafterourimplementation.Moreover,byprovidingfarmerswithevidencethatpestspropagatedthroughtheircommunitynotastheresultofisolateddecisionsbutratherastheresultofrepeatedinteractionsbetweenmultipleindividualsovertime,ourABMallowedintroducingthemwithsocialandpsychologicalissueswhichareusuallyneglectedinintegratedpestmanagementprograms.
Keywords:Socio-EcologicalSystems,Farmers,InvasivePest,LongDistanceDispersion,Teaching
Introduction
1.1 Agriculturalsystemsarecomposedbytwointerlinkedandinterdependentsubsystems,thesocialandtheecologicalsubsystems,whichco-evolveandinteractatvariouslevelsandscales(Liu2007).Asaconsequence,thesesystemsarecharacterizedbycomplexspatio-temporaldynamicsandculturalvariation(Papajorgji2009).Themanagementofagriculturalinvasivepestsliesattheheartofsuchacomplexityaspestpropagationdependsonbothenvironmentalfeatures(e.g.climate,landscapestructure)andfarmers'behaviors(e.g.man-inducedpestdispersion)(Epanchin-Niell2010).Theproblemswithdealingwithmultipleactors,nonlinearity,unpredictability,andtimelagsininvadedagriculturalsystemssuggestthatagent-basedmodels(ABM)mayhaveanimportantroletoplayinthesustainabledevelopmentoffarmers'practicestofacethoseemergentthreats(Berger2001).AlthoughABMhaveincreasinglybeenappliedtophysical,biological,medical,social,andeconomicproblems(Bagni2002;Bonabeau2002;Grimm2005a)ithasbeen,toourknowledge,disregardedbyinvasivepestmanagementtheoryandpractice.
1.2 Intrinsicdispersalcapacitiesofagriculturalinvasivepest(inparticularinsects)arerarelysufficienttomakethemmajorthreatsatalargespatialscale.Inmostcases,invasivepestexpansionisdependentonlong-distancedispersal(LDD)events,akeyprocessbywhichorganismscanbetransferredoverlargedistancesthroughpassivetransportationmechanisms(Liebold2008).Thestudyofthedynamicsofpestdispersioninagriculturallandscapeisthereforecomparabletothatofdiseasecontagion:asdiseases,pestsaretransmittedfromaninfectedperson(farmer)toanotherwhowaspreviously"healthy",throughdifferentbiological,socialandenvironmentalprocesses(Teweldemedhin2004;Dangles2010).Severalstudieshaveshownthatthedynamicsofinfectionspreadinvolvespositiveandnegativefeedbacks,timedelays,nonlinearities,stochasticevents,andindividualheterogeneity(Eubank2004;Bauer2009;Itakura2010).Twofactorshaverevealedparticularlyimportanttopredictdiseasedynamics:(1)thenumberofencountereventsbetweeninfectedandhealthyindividuals,whichmainlydependsonindividuals'mobility(Altizer2006),and(2)thecontaminationratebetweeninfectedandhealthyindividuals,whichdependsonheterogeneoussusceptibilitiesofindividualstobeinfected(Moreno2002;Xuan2009).Similarly,thespreadofinvasivepeststhroughouttheagriculturallandscapewoulddependon(1)movementsoffarmerscarryinginfestedplantsorseedsintonewareasand(2)farmer'sknowledgetodetectthepest(pestcontrolknowledge),thereforeavoidingbeinginfestedandimpedingthecontaminationofnewareas(Dangles2010).
1.3 Borrowingfromdiseasecontagionliterature(e.g.Gong2007;Yu2010),wedeveloped,usingNetLogo(Wilensky1999),anABMtosimulatethespreadofaninvasivepotatoinsectpestinanagriculturallandscapeofthetropicalAndes.Ourmodelcombinedanecologicalsub-model,simulatingpestpopulationdynamicsdrivenbyacellularautomatonincludingenvironmentalfactorsofthelandscape,withasocialmodelinwhichweincorporatedagents(farmers)potentiallytransportingandspreadingthepestthroughdisplacementsamongvillages.Wethenusedourmodelfortwopurposes.First,werantheABMunder10levelsofagents'(farmers)movementsamongvillagesand7levelsofheterogeneityinfarmer'spestcontrolknowledge.Wecomparedtheresultingdiffusiondynamicsonthespeedofpestspread,whichrepresentsarelevantmetricsforinvasivepestmanagementbylocalstakeholders(e.g.thetimeavailableforagricultureofficialstorespondtothethreat).Second,weusedourABMasaneducationtooltoincreasefarmerawarenessontheimportanceofhuman-relatedLDDeventsofthepestswhichfosteredtheinvasionsoftheirvalley(seeDangles2010).WhilewespecificallyfocusedonaninvasiveinsectpestinthetropicalAndesinthispaper,ourapproachtounderstandtheinfluenceoffarmers'movementsandpestcontrolknowledgeonpestdynamicsandtotransferitthrougheducationalprogramswouldbeapplicabletoamuchwidergeographicandspeciesrange.
Studysystem
2.1 Ourstudydealswiththepotatotubermoth(Teciasolanivora),aninvasivepestthathasspreadfromGuatemalaintoCentralAmerica,northernSouthAmericaandtheCanaryIslandsduringthepast30years(Puillandre2008).Thispestattackspotato(Solanumtuberosum)tubersinthefieldandinstorageandhasbecomeoneofthemostdamagingcroppestsintheNorthAndeanregion(Dangles2008).CommercialexchangesofpotatotubersatregionalandlocalscalesforbothseedingandconsumptionarethemaincausesfortherapidexpansionofthepestinallpartsoftheEcuadorianhighlands(2400-3500m.a.s.l).Theselandscapesarecharacterizedbyhighlyvariableenvironmentalandsocialconditionsduetosteepaltitudinalgradientsanddispersedhumansettlement,respectively.
Model
Overallstructureofthemodel
3.1 Thesocio-agronomicalframeworkofthemodelconsistsinthreekeyelements(Figure1):1)theagriculturallandscapecharacteristicsprovidedbyaGISenvironmentaldatabase(BiodiversityIndicatorsforNationalUse,MinisteriodelAmbienteEcuadorandEcoCiencia2005),2)theinsectpestpopulation,and3)thegroupsoffarmers.Pestdynamicsininteractionwithlandscapefeatures(e.g.landuse,climate)issimulatedthroughacellularautomaton(seethefollowingsub-section).Totransferthecellularautomatonintoanagent-basedsimulationmodelweincludedfarmersasagentsactingindividuallyuponpestdynamicsintheagriculturallandscape.PestsarethereforerepresentedasalayerinthecellularautomatonandfarmersasagentsintheABM.
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Figure1.Schematicrepresentationofthemodelstructure
Modelingpestdynamicsthroughcellularautomata
3.2 Thespatio-temporaldynamicsofpotatotubermothismodeledthroughasimplifiedversionofthecellularautomatondevelopedbyCrespo-Pérez(submitted).ThismodelwasdevelopedwiththeCORMASmodelingplatformandisdetailedinAppendix1.BrieflyitisbasedonbiologicalandecologicalrulesderivedfromfieldandlaboratoryexperimentaldataforT.solanivora'sphysiologicalresponsestoclimate.Mainprocessesincludemothsurvival(climatedependent),dispersalthroughdiffusionprocesses(densitydependent),andreproduction(climatedependent).Thismodelhasbeenvalidatedinastudyareaof20×20kmwithintheremotevalleyofSimiatugintheCentralEcuadorianAndes(seesection5)representedbyagridof1,600
cellswithacellsizeof0.25km2.
Modelinghuman-relatedpestdispersionthroughtheagent-basedmodel
3.3 TheABMaimsatsimulatingtheinfluenceoffarmersonthespatio-temporaldynamicsofthepotatomoth.Inthisparticularmodel,farmersareconsideredaspotentialagentsforpestLDD,forexamplewhentheycarryinfestedpotatosacksfromlocalmarketstotheirhome(otherinteractionswiththepest,suchascontrolbypesticide,arenotincludedinthismodel).TheirefficiencyasLDDagentsdependsontheirpestcontrolknowledge:thehighertheirknowledge,thelowertheprobabilitytheygettheirfieldinfestedafterpotatosackstransport(seebelow).
Agentprocessoverviewandscheduling
3.4 Agentprocessoverviewandschedulingarepresentedinfigure2.Agentsmovearoundonagridofcellswhoselevelofpestinfestationismodeledbythecellularautomaton(seeAppendix1).Duringeachmovementwithinasingletimeframeagentsturn"infested"(i.e.theirpotatocropsareinfestedbythemoth)orremain"non-infested"dependingontheirpestcontrolknowledgeandthepestinfestationinagivencell.Eachtimeframeisequaltoonemothgeneration(i.e.about2months)duringwhichagentscanmoveseveraltimesdependingontheirtraveldecisions.Agentswithhigherpestcontrolknowledge(e.g.knowinghowtorecognizemothdamagewhentheybuypotatosacksatthemarket)havealowerprobabilityofbecominginfested.Then,agentsmovefromonevillagetoanothertobuyand/orsellpotatoes.Agents'movementsfollowagravitymodel(Rodrigue2009),wheretheattractivenessofavillageicomparedtoavillagejisafunctionofbothpopulationsizeandcost-distancebetweenthem.Villageinfestationoccurswhenaninfestedagentmovestoanon-infestedvillage(carryinginfestedpotatosackswhichwillbeusedaspotatoseedsandtherebyinfestneighboringfields).Agentinfestationoccurswhenanon-infestedagentmovestoaninfestedvillage(buyinginfestedpotatoseedsacks),dependingonhispestcontrolknowledge(higherpestcontrolknowledgeleadstolowerprobabilityofbuyinginfestedsacks).
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Figure2.Agents'processesloopshowinghowfarmersinfluencepestinfestationspread.Thisloopisexecutedvarioustimesdependingonfarmers'travelingdecisionsduringeachtimeframe.
Designconcepts
3.5 Agentscansensethepestinfestationofthecellsbuttheydonotusethisinformationfortheirtravelingdecision.Instead,agentssensevillagepopulationsizeanddistancebetweenvillagessothattheyareabletoperceivetherelativecost/benefitofgoingtoeachvillagetosell/buytheircrop:(1)itislessexpensivetotraveltocloservillagesand(2)morepopulatedvillagesprovidehighercommercialopportunities.Asaresult,timeneededtoreachacompletepestinfestationintheareaemergesfromacombinationofpurelybiologicalpestdispersion,agents'movementsfromvillagetovillageandagent'spestcontrolknowledge.Amodelexampleisavailableonlineathttp://www.openabm.org.
Testingtheeffectofagents'movementandpestcontrolknowledgeonpestspreaddynamics
Effectofagents'movements
4.1 WeexaminedwithourABMhowthenumberofagents'movementspergenerationwouldimpactpestinvasiondynamics.Aswewereinterestedintheearlyphasesofinvasions,whichrepresentarelevantmetricsforinvasivepestmanagementbylocalstakeholders,weusedthetimeneededtoreach5%ofinfestedcellsasanoutcomevariable.
4.2 Wefoundthatincreasingfrom1to10thenumberofagents'movementsinthelandscapehadanegativeexponentialeffectonthespreadoftheinvasivepest(Figure3andanimationinAppendix2).Invasionspeedwasparticularlyincreasedupto4movementsandthentendedtostabilize.Asexpected,theeffectofagents'movementoninvasionspeedwasintensifiedbythenumberofagentslocatedonthelandscape,butonceagainthiseffectwasnotlinear:insectpestdynamicswasspeededupwhenaddingupto10agentsbutremainedroughlyunchangedforthe10followingones.Foranintermediatescenario(4movements,10agents),thespeedofinvasionwastwicefasterthatofapurelybiologicalspread(i.e.throughinsect'sdispersioncapabilitiesalone).Weareawarethatthespatialconfigurationofoursociallandscape(seethefrequencyofinfestedfarmermovementsinFigure4)likelyinfluencedourresults.Furtherstudiesincludingrandomlygeneratedsociallandscapescouldhelptoquantifythiseffectonagents'movementsandsubsequentpestinfestationdynamics.
Figure3.Influenceofagents'movements(perpestgeneration)onpestinfestationdynamicsfordifferentagentdensities(n=2to20).Thedashedlinerepresentstimeneededtoreach5%ofinfestedcellswithoutagents(purely"biological"spread).
4.3 Ourresultshighlighttheimportanceofinsectpestpassivetransportationbyhumanswhichallowsinvasivepeststomakelong-distancedispersaljumps.Eventhoughseveralauthorshaveacknowledgedthesignificanceofthistypeofdispersalforspeciesspread,(e.g.,Bossenbroek2001;Suarez2001)itsinclusioninmodelsstillposesdifficultiesformodelers(Pitt2009).Mostdispersalmodelsarebasedonempiricallymeasuredratesofpestdispersal,whileinthecaseofLDDeventsitwouldbemoreusefultomodelhumanbehaviorstobetterunderstandpestinvasiondynamics.Inthiscontext,ABMofferaninterestingyetpoorlyusedmethod,tobeappliedtothevastfieldofbiologicalinvasions(seeLuo2010andVinatier2009foroneoftherarestudyonexoticspeciesusingABM,althoughintheircase,agentsaretheinvasivespecies).ResultsofourABMsimulationsfurtherrevealednonlinearprocessesbetweenfarmers'behavior(e.g.movement)anddensitiesandpestspread,asalreadyshownfordiseaseexpansionbyepidemiologicmodels(e.g.Gong2007).Thissuggeststhatagoodunderstandingofsocialnetworkstructureswouldbeakeysteptobetterpredictpestinvasionspeedinhumandominatedlandscapes.Inthiscontext,ecologistswouldgaininfollowingthepathtracedbyepidemiologistswithABMtobetterunderstandthedynamicsofinvasivepests.
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Figure4.FrequencyofvisitsofinfestedagentsforeachvillageandmapoftheSimiatugvalleywithagents'movementsandvillageslocation.
Effectofagent'sheterogeneityinpestcontrolknowledge
4.4 WethenexploredwithourABMhowagents'pestcontrolknowledge(rankedfrom0to100)wouldimpactpestpropagationdynamics.Aspestcontrolknowledgewasusuallyvariableamongfarmers(Dangles2010),wewereinterestedinexaminingtheinfluenceofheterogeneouslevelsamongagentsonpestspreaddynamics.Toachievethisgoal,wetested7levelsofheterogeneity(standarddeviation=0,5,10,15,20,25,30)around10meanvaluesofpestcontrolknowledge(mean=0to100).Foreachsimulation,agents'pestcontrolknowledgelevelswererandomlychosenfromaNormaldistribution,N(mean,standarddeviation).
4.5 Oursimulationsrevealedthatagents'pestcontrolknowledgehadasignificanteffectonpestinvasiondynamics(Figure5andanimationinAppendix2).Inallsimulations,loweragents'pestcontrolknowledgeledtohigherinvasionspeed,almosttwicefasterthanintrinsicpestdispersionspreadforhighestinfectivityvalues.Agents'movementhadaworseningeffect,withfasterinvasionoccurringforhigheragent'smobility.Agents'heterogeneityinpestcontrolknowledgehadaweakeffectonpestdynamics,especiallyforhighagents'mobility(6and4).However,heterogeneityinknowledgedidintroducesomesochasticityininvasiondynamicswhenagentsseldommoved.
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Figure5.Influenceofagents'pestcontrolknowledge(means)andheterogeneity(standarddeviation=0to30%)onpestinfestationdynamicsforthreefrequenciesofmovements(2,4,and6).
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Thedashedlinerepresentstimeneededtoreach5%ofinfestedcellswithoutagents(purely"biological"spread).
4.6 Asreportedbyepidemiologistsfordiseasespread(e.g.,Newman2002),ourresultsshowedthatagents'pestcontrolknowledgehadanimportantimpactonthedynamicsofpestinvasionspread.Thissuggeststhatfarmers'pestcontrolknowledgewouldbeakey,yetpoorlystudied,variabletotakeintoaccountformodelingpestinvasionsinagriculturallandscapes.Lessexpectedly,wefoundthatheterogeneityofknowledgeamongagentshadarelativelyweakeffectonpestdynamics,especiallyforhighmobilitylevelsofagents.Thiscontrastwithepidemiologicalmodelswhichhavegenerallyshownthatheterogeneouspopulationsenhancethespreadofinfectionsaswellasmakethemhardertoeradicate(forareviewseeAnderson1992).Onepotentialexplanationisthatthelimitednumberofvillagesusedinourstudyandtheabsenceofspatialclustersfavorinfestationmixtureamongagentsandrapidlysmoothupitsimpactoninvasionspreaddynamics.However,ourresultsshowedthatwhenallagentsare"healthy"(pestcontrolknowledge=100),anyadditionofagentswithlowerlevelsofknowledgewillconsiderablyspeeduppestdynamics(especiallyathighlevelsofmovements),therebyconfirmingpredictionsofdiseasespreadtheory.
Teachingwiththemodel
5.1 Inasecondstep,weusedourABMasaneducationaltooltoteachfarmersaboutpotentialinvasionrisksresultingfromindividualbehaviors.TeachingactivitieswererealizedinFebruary2009attheAgricultureandTechnologyCollegeoftheSimiatugvalleyinthecentralEcuadorianAndes.Thisparishiscomprisedofroughly45kichwacommunitieslivingbetween2800mand4250mofaltitude,thatsharesimilarcharacteristicsintermsofsocialorganization,dateofestablishment,andagriculturalpractices.Currently,about25,000people,mainlysubsistenceandmarket-orientedfarmers,liveintheSimiatugparish.Themainagriculturalproductsarepasture,cereals(barley),legumes(favabean)andpotatoesaswellascattleandsheep(seemoredetailsinDangles2010).Althoughtheremotenessofthevalleyprotectsitagainstmothinvasion,increasingcommercialexchangesfromandtoSimiatugarecurrentlyincreasingtheriskofmothintroduction.Localfarmerswerethereforeinterestedinlearningaboutpotentialrisksassociatedwiththepestandhowtocontroltheirspreadinthevalley.
Modelintroductiontothefarmers
5.2 Introductionofthemodelsandvariablerepresentationtothefarmershasbeenalongprocessthatbeganwiththeeducationalprogramsetupin2007(Dangles2010,seethetimelineofthegroundworkinFigure6).
Figure6.Timelineofthegroundworkpriortotheteachingsession
5.3 Forthisprogram,weheldanegotiationsessiontoinsurethatteachingwasdrivenbyfarmers'interestsfollowedbyatrainingsessiononintegratedpestmanagementandonparticipatorymonitoringofpotatomothinthevalley.Afterthedataanalysissession,farmershadacquiredaratherclearconnectionbetweenpestabundanceandairtemperature,villagesizeandremoteness(seeDangles2010,foradetaileddescriptionofthesessionswithfarmers).Thisinitialprocessallowedustointroduceourmodelinasecondstepandtouseitasateachingtool.Farmerswereyoung(17to25yearsold)andshowedinnateinterestin"playing"withthecomputersandseeingsimulations(anInternetcaféjustopenedinSimiatugtheyearbeforestartingtheABMteachingsession).Themodelwaspresentedasawaytobetterunderstandaresultthatfarmersthemselveshadfound:theimportanceofLDDinmothdispersion(seeDangles2010).
Modelparameterization
5.4 Forteachingpurposes,farmerswereseparatedintotwo,"blue"and"red"teams;havingtwoteamsthatcompeteforminimizationofpestpresenceinthevalleystimulatedenthusiasmamongfarmers.Eachmemberoftheteamwasaskedtofillaquestionnaireincluding20items,10onbasicissues(biologyandecologyofthepest)and10onappliedissues(pestmanagement).Afacilitatorhelpedtheplayerstofillinthesequestionnaires.Basedonfilledquestionnaires,webuilta"pestcontrolknowledgeindex"foreachfarmer,whichcorrespondedtothepercentofquestionsansweredcorrectly.Farmerswerealsoaskedtoanswerquestionsabouttheirtravelbehaviorinthevalley(destinationandfrequencies).Villages'locationsandpopulationsizesweredefinedbyfarmersusingmaps(seefigure7).Environmentaldatasuchastemperatureorprecipitationwereupdatedusingrealvaluesintheconsideredarea(DanglesandCarpio,unpublisheddataprovidedwiththemodelintheopenabm.orgwebsite).
Figure7.Teachingwithanagent-basedmodelinanagriculturalvalleyofEcuador
Playingandlearningwiththeagent-basedmodel
5.5 Onceinputdatawerecollectedandsetup(Table1),weranthemodelandregisteredthespreadofthepestthroughoutthevalley.Inallsimulations,agentsarerandomlylocatedatthebeginningoftherun.
Table1:Parametersandsimulationresultsofthegamingsessionwithfarmers
Parameters Parametersvaluesusedforthegaming Parametersvaluesattheendofthegaming
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session sessionParameterizationNumberoffarmers(agents) 10 10Numberofagents'movementspertimeframe(pestgenerations)
6 3
Pestcontrolknowledge(followingaNormaldistribution~N(mean,sd))
<n(0.4;0.1) <n(0.8;0.1)
ResultsTimeneededforcompleteinfestation(pestgeneration) 39 45
5.6 Ourmodeloutputcoulddistinguishbetween1)cellsinfestedduetoLDDeventsmadebytheblueteam,2)cellsinfestedbyredteamLDDand3)cellsinfestedbyinsect'sowndispersalcapabilities(seehttp://www.openabm.org;see"pestdispersion"byinnomip).EachteamwasthereforeabletovisualizeitsrelativeimpactonmothdispersionthroughouttheSimiatugvalleythroughthemaincolorofaspatialinterfacerepresentingthelandscape.Theywerefurtherinvitedto"play"withthesimulationinterfacebychangingLDDandthepestcontrolknowledgevaluesandtoseetheconsequencesintermsofmothspreadthroughouttheirvalley.Asynthesisoftheprocessesinvolvedintheteachingsession(includingrequiredtime)isgiveninTable2.
Table2:Processesandtimerequiredforteachingandlearning
Gamingsessionprocess Mainactivities TimespentIntroduction Overallpresentationofallactors 1hourComputerpresentation Presentationofcomputersimulationutility 30minutesModeladoption:buildingcommunitymap(villagesandpopulations)
Presentationofthespatialrepresentationofthemodel 30minutes
Modelinputvariables(interviews) Modelparameterization 1hourModeloutputvariables Runningthemodelwiththetwoteams,resultpresentationanddiscussion 1hourPlayingsession1:farmermovementsandpestinfestationspread
Farmerteamsmodifyagents'movementsandvisualizeconsequencesonpestspreading
30minutes
Playingsession2:farmerknowledgeandpestinfestationspread
Farmerteamsmodifyagents'pestcontrolknowledgeandvisualizeconsequencesonpestspreading
30minutes
Conclusionandevaluation Generaldiscussionwithfarmersandinterviews 1hour
Modeladoption
5.7 Becauseparticipantswereyoungfarmerswehadnoproblemrelatedtopotentialtechnical,cultural,knowledgeorattitudebarriers.Oneofthemaindifficultiesrelatedtomodeladoptionturnedouttobethespatialrepresentationoffarmer'svillages,whichwaspartiallysolvedbybuildingwiththemadigitalmapoftheirvalley.Anotherdifficultywasthatfarmershadahardtimeinassociatinggridcellcolorswiththepresenceofmoths.Unfortunately,wecouldnotfixthisproblemduringtheteachingsessionandthiswasprobablyoneofthemaindrawbacksofourapproach.However,sincethisdate,weimprovedthesimulationtointegratethedrawingofmothsspreadingonthecellularautomatagridinasimplemodelaimedatimprovingitsadoption(seehttp://www.openabm.orgsee"pestdispersionversion1"byinnomip).
Benefitsofmodel-basedteachingtofarmers
5.8 Attheendofthesessionwere-evaluatedparticipantpestcontrolknowledgeonbasicandpracticalmothcontrolissueswiththesame20-itemindicatorsquestionnaire(seeabove).Themeanpestcontrolknowledge(percentofquestionsansweredcorrectly)increasedfrom40±10(basic)and40±20(practical)atthebeginningofthesessionto80±10(basic),and80±10(practical)attheendofthesession,suggestingthatfarmerswouldbeabletobettermanagepestrisksaftertheteachingsessions.Asawhole,oureducationalprogram(2007-2009)indeedenhancedlocalawarenessabouttheneedtocontrolthepestsbeforetheybecametoonumerousandcoveredthewholelandscape.ThemainspecificmanagementdecisiontakenbyfarmerswasapromisetosystematicallycheckformothinfestationwhenbuyingpotatotubersintheSimiatugmarketbeforetransportationtotheircommunity(seealsoDangles2010).Althoughfarmersvouchedformodel'sattractivenessandusefulnesstolearnaboutpestproblems,itremainedhardtoquantifyknowledgeenhancementspecificallyduetotheABMasopposedtothatduetotherestoftheeducationalparticipatoryprogram.However,webelievethattheuseofABMandcomputerssignificantlycomplementedoureducationalprogramonpestmanagementinthevalleyasithadaclearconsequenceinenhancingyoungfarmers'interestinagriculturalissues.TheCollegeofSimiatugindeedsufferedfromanincreasinglackofinterestfromstudentsofagriculturedisciplinesinfavoroftechnical/computationalones.OurprogramshowedyoungfarmersthatbothdisciplinescouldbemergedandthattheycouldfindthroughtheInternet(http://www.innomip.ird.fr)computationaltoolstoincreasetheirknowledgeonpestmanagement.OurstudyisapreliminaryapproachintheuseofABMforpestmanagementissues.Furthereffortsshouldbedonetooptimizemodeladoptionprocesssuchastheearlyidentificationofgapsinfarmers'knowledge(Wilson2009),theconsiderationofpeak-laborperiods(White2005)orthesocialnetworkoflearners(Boahene1999).
5.9 AnotherachievementofABMwasthat,byprovidingfarmerswithevidencethatpestspropagatedthroughtheircommunitynotastheresultofisolateddecisionsbyindividualsbutratherastheresultofrepeatedinteractionsbetweenmultipleindividualsovertime,ourABMpointedatkeypsychologicalandsocialissues,highlyrelevantforefficientmanagementofinvasivepests(Peshin2008).ABMmaythereforebeapowerfultooltoadvancetheapplicationofsocialpsychologytheorybystakeholdersinruralcommunities( Smith2007)andtochangeindividualattitudes(Jacobson2006).Thissuggeststhatnewapproachesinpestmanagementextensionpracticesshouldincludetopicssuchasgroupdecisionmaking,intergrouprelation,commitment,andpersuasionwhichdealdirectlywithhowotherfarmersinfluenceone'sthoughtsandactions(Mason2007;Urbig2008).Byexamininggroup-andpopulation-levelconsequencesoninvasionprocess,agent-basedmodelingmaythereforerevealsasapowerfulpedagogicalapproachtochangebehaviorsacrosslargepopulations,alonglastingissueinpestmanagementoutreachprogramsworldwide(Feder2004).
Conclusion5.10 Weshowedinthisstudythatagent-basedmodelingmaybeapowerfultooltosimulateinvasivepestspreadinhumandominatedlandscapes.Oursimulationsfurtherrevealedthatboth
farmers'movementsandpestcontrolknowledgecouldsignificantlyimpactinvasionspeedandshouldbeconsideredaskeyvariablestobetterpredictpestinvasiondynamicsinagriculturallandscapes.RegardingtheuseofABMaseducationaltools,wefoundthatnewtechnologies(computers)increasedtheinterestofyoungfarmersinlearningabouthowtobetterfacepestproblems.AlthoughwewouldneedtodesignproperstudiestobetterunderstandthespecificwaysABMfosterslearningprocesses,theintroductionofABMintolearningenvironmentslocatedinremoteplacesmaypromisetoimproveeducationoffarmers,especiallyyoungones.Forexample,ABMcanbeintegratedintointeractivewebsitesorburnedonaCDandbeavailabletofarmercommunitiesinwhichtechnologyaccessincreasesrapidlythankstogovernmentalinitiatives.Inviewofthegrowingthreatmadebyemergentinsectpestsworldwide,especiallyinremoteandpoorlocalities,furthereffortstoincludecost-efficientABMintointegratedpestmanagementprogramsmayrepresentapromisinglineofresearchandapplications.
Appendix1:DescriptionofthecellularautomatonusedtosimulatethepestusingtheODDprotocol
A1.1 ThemodeldescriptionfollowstheODDprotocolfordescribingindividual-andagent-basedmodels(Polhilletal.2008;Grimmetal.2006;Grimm&Railsback2005b)andcellularautomaton(Grimmetal.2006,appendixAp136-147).Notethatinthecaseofcellularautomaton,someofthedesignconceptsoftheODDprotocoldonotapply.ThemodelwasdevelopedusingCORMAS(CIRAD,France,http://cormas.cirad.fr)basedontheVisualWorksprogrammingenvironment(CincomSmalltalk,http://www.cincomsmalltalk.com).
OVERVIEW
Purpose
A2.1 TheSimPolillamodelwasdevelopedtodescribetheinvasionanddiffusionofthepotatotubermoths(PTM)(Teciasolanivora,PhthorimaeaoperculellaandSymmetrischematangolias,Gelechiidae,Lepidoptera),tinymothsthatinvadedtheagriculturallandscapeoftheNorthAndeanregioninthelastdecades.Thelarvaeofthesemothsareseriouspestsofpotatoes,oneofthemainfoodcropsoftheregion.Asecondobjectiveofthemodelwastomakepredictionandgeneratemapsofinvasionriskforlocalfarmercommunities.ThemodelwasdevelopedandvalidatedinapilotregionofcentralEcuadorbutwasbuilttobeapplicabletoamuchwidergeographicrangeinNorthAndes.
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Statevariablesandscales
A2.2 ThemodelisbasedonbiologicalandecologicalrulesderivedfromfieldandlaboratoryexperimentaldataforthethreePTMspecies(Dangles&Carpio2008;Danglesetal.2008;Roux&Baumgärtner1997).TheSimiatugvalley,usedasapilotregiontobuildthemodel,islocatedintheprovinceofBolívar,inthecentralhighlandsofEcuador.Wefocusedonastudyareaof40×
40kmrepresentedbyagridof6,400cellswithacellsizeof0.25km2.Eachcelliischaracterizedbyitsqualityofhabitatnii.e.thequantityoffoodresourcesavailableforthemothlarvae.Weconsiderthatniwasfixedto0or1dependingonthelanduse(i.e.cropsorotherusessuchaswoodsorhighlands).Eachcellisalsocharacterizedbyarangeoftemperaturevalues(meanTmoyi,maximumTmaxiandminimumTminiin°C),amonthlyamountofprecipitationPi;j(inmm),andameanelevationαi(m.a.s.l.).
Table1:FullsetofstatevariablesinSimPolilla
Nameofvariable Units
Habitat Qualityofhabitatofcelli ni
Temperature Averagemeantemperatureover30yearspercelli Tmoyi ºC
Averageminimumtemperatureover30yearspercelli Tmini ºC
Averagemaximumtemperatureover30yearspercelli Tmaxi ºC
Precipitation Averageprecipitationamountover30yearspercelliandpermonthj Pi;j mm
Elevation Elevationonthestudyzonepercelli αi m
PTMspecies Levelofinfestationofjuvenilesdensityofspeciekpercelli(k=1,2,3;T.solanivora,P.operculella,S.tangolias,respectively)
Jk;i Number
Levelofinfestationofadultsdensityofspeciekpercelli(k=1,2,3;T.solanivora,P.operculella,S.tangolias,respectively)
Ak;i Number
Levelofinfestationofgravidfemalesdensityofspeciekpercelli(k=1,2,3;T.solanivora,P.operculella,S.tangolias,respectively)
Gk;i Number
Distancecoveredbyamoth Distancecoveredbyamoth d Meters
A2.3 Thehigher-levelentitiesarebasedonthenumberofgravidfemalesofthethreePTMspecies.EachtimesteprepresentsonePTMgenerationbasedonT.solanivoralifecycleduration(i.e.about3monthsat15°C).AnadjustmentismadeonthetwootherspeciessothateachstepcorrespondstoonePTMgeneration.The500×500mscaleforcellswaschosenforfittingthelevelofprecisionwehaveconcerningPTMdispersion,basedonavailableknowledgeonmothdispersion(Cameronetal.2002b).Temperatures,precipitationsandelevationshavea1per1kmresolution.Insideasquareoffourcells,theseparametershavethesamevalue.
Processoverviewandscheduling
A2.4 Inthissection,webrieflydescribetheprocessesandschedulingofourmodel.Detailsaregiveninthesubmodelssection.Eachprocessispresentedaccordingtoitssequenceproceedingandintheorderatwhichstatevariablesareupdated.EachtimestepisoneT.solanivorageneration.
Table2:ProcessesofSimPolillamodel
Process SubmodelsStatevariablesupdate StatevariablesupdateStochastictemperature StochastictemperatureStochasticrainfall StochasticrainfallPTMmortality Crudemortality
TemperaturedependentmortalityPrecipitationdependentmortality
PTMdispersal NeighbourhooddispersalPTMreproduction Matingrate
SexratioTemperaturedependentfecundity
Process:statevariablesupdate
A2.5 Eachstatevariablecorrespondingtorealdata(AlmanaqueElectrónicodeEcuadorbyAlianzaJatunSacha-CDC,digitisedbyDINAREN,2003;Hijmansetal.2005),isimportedfromindividualfiles(oneperlayer),sothatSimPolillamaybeeasilyadaptedtootherregions.
Process:stochastictemperature
A2.6 Meantemperatureistransformedaccordingtoastochasticfactor(Box&Muller1958).
Process:stochasticrainfall
A2.7 Twoconsecutivemonthlyprecipitationsarerandomlychosenduringagivenstep.
Process:PTMmortality
A2.8 PTMpopulationisupdatedaccordingtocrude,temperatureandprecipitationmortality.
Process:PTMdispersal
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A2.9 PTMdispersethroughtheterritoryfromonecelltoanotherbydiffusion.
Process:PTMreproduction
A2.10 PTMpopulationsareupdatedaccordingtobiologicalrules(matingrate,sexratio,fecundity).AcorrectionismadeoverfitnessonthetwootherPTMspeciestoadjusttimestepbasedonT.solanivoralifecycle.
DESIGNCONCEPTS
A3.1 InSimpolillamodel,moths'implicitobjectiveistoinfesttheconsideredlandscapebymaximizingdispersalspeed.Emergentkeyresultsarelevelofinfestationineachcellandinfestationspeed.Interspecificinteractionsarenottakenintoaccountinthismodelandastochasticfactorovertemperatureandrainfallsareincludedmimicactualclimaticvariation.
DETAILS
Initializationandinput
A4.1 TheenvironmentisbasedontheSimiatugagriculturalregion(Bolivar,centralpartofEcuadorianAndes),withtemperature,precipitationandelevationfromavailabledatawitha1km2
resolution(Hijmansetal.2005).QualityofhabitatisbasedonGISinformationaboutlandusewitha0.25km2resolution(AlmanaqueElectrónicodeEcuadorbyAlianzaJatunSacha-CDC,digitisedbyDINAREN,2003).Thecellularautomatonisa4-connexsquareshapedgrid,withclosedboundariesasweareconsideringanexistinggeographicallocation.Atthebeginningofeachsimulation,PTMinoculumsareplacedintheSimiatugvillageandspreadisobservedandrecordedforeachspecies.
Submodels
A4.2 Inthissectionwedescribethesubmodelsgivenintable2.
ClimaticdriverofPTMdynamic
Temperature
A4.3 Inordertofeedthemodelwithrealclimatevariables,wechosetointroduceastochasticfactorTstochasticinthemodel(seealsoSikderetal.2006)thatallowedustoobtainbymultiplicationastochastictemperatureincelliTStoi .
A4.4 WeusethepolarformoftheBox-Mullertransformation(Box&Muller1958),togenerateaGaussianrandomnumber,basedonclimaticdatafromtheregion(seeDanglesetal.2008,appendixA).RandomnumberusedisbasedonrandomprocedureinVisualWoks(VisualWorks®NonCommercial,7.5ofApril16,2007.Copyright©1999-2007CincomSystems,Inc.AllRightsReserved.).
(1)
Thestochastictemperaturereplacesaveragetemperatureinallequationsbellow.
Precipitation
A4.5 Asfortemperature,wechoosetointroduceastochasticfactortoobtainastochasticprecipitationPStoi.Usingarandomnumberjfrom1to12(VisualWorks®NonCommercial,7.5ofApril16,2007.Copyright©1999-2007CincomSystems,Inc.AllRightsReserved.),wetaketheaverageofthemonthlyamountofprecipitationPi;jcorrespondingtotherandomnumberandthefollowing.
(2)
PTMlifedynamics
A4.6 Dataondevelopmentandsurvivalforimmaturestages(eggs,larva,andpupa)andonfecundityforadultswereacquiredfromtwosources.FirstweusedpublisheddatafromlaboratoryexperimentsperformedintheAndeanregion(Notzetal1995;Danglesetal.2008).Secondweuseddataobtainedwithinthelast8yearsattheEntomologyLaboratoryatthePUCE(Pollet,Barragan&Padilla,unpublisheddata).Forthesetwosources,onlydataacquiredunderconstanttemperatures(±2°C)wereconsidered.Inallstudies,relativehumidityrangedfrom60to90%,valuesaboveanyphysiologicalstress.
Crudemortality
A4.7 Overallforceofmortalityamongapopulationisthesumofcrudecause-specificforces.Hereweconsiderinnatemortality(λi),dispersalrelatedmortality(λd)andpredation(λe)(seeRoux1993;Rouxetal.1997)forP.operculella.
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(3)
A4.8 Innatemortalityisnottakenintoaccountusingequation(3),becauseatemperaturedependentparameterfitsbettertorealitythanλi(seebellow).
A4.9 WearealsoconsideringseparatelysurvivalratewithpredationSPredationbybirds,spiders,antsandotherspredatorsfortheadultstagefollowingequation(4)(Tanhuanpääetal.2003).Levelofpredation_iscalesfrom0to10(ie10thelower,0thehigherlevel),inordertosimulatedifferentscenarios,fromtheorytoreality.
(4)
Temperaturedependentmortality
A4.10 Temperatureisthemostbasiccontrollerinpoikilothermicorganisms(Zaslavskietal.1988).SurvivalrateunderlaboratoriesconditionshasbeenstudiedforthethreePTMspecies,usingatemperaturedependentkineticmodel.
A4.11 WeusedthefollowingequationtocalculatethesurvivalrateSDforeachstageateachtemperatureforwhichdatawereavailable:
(5)
withSTthetotalsurvivalatthegivenstage,expressedasaproportion,andDDthedaystodevelopment.FollowingRoux(1993),weappliedtheSharpeandDeMichelmodel(eq.5)tothesurvival-responsetotemperatureasinequation6:
(6)
witha,b,c,d,e,andftheequationparameterstobeestimated.
Table3:Parametervaluesofthekineticmodel(equation7)describingthestagespecificsurvivalrateSD(T)responseofthethreeinvasivePTMspecies(T.solanivora,P.operculella,andS.tangolias)toconstanttemperatures.NotethattemperatureisgiveninKelvindegrees.
Species Instar a b c d e fS.tangolias Egg 0,834 10,94 -234000 282,4 616600 304,1
Larva 0,694 -236,3 -420300 283.1 1551000 305,6Pupa 0,882 39,93 -992700 282,9 1110000 304,7
P.operculella Egg 0,917 50 -200000 283.3 400000 310.1Larva 0,950 -150 -400000 284.4 900000 310.0Pupa 0,960 50 -800000 283.1 700000 312.2
T.solanivora Egg 0,822 -758,5 -212100 281,9 405200 303,8Larva 0,758 -180,2 -475700 282,7 1298000 301,5Pupa 0,900 -73,72 -1263000 286,5 1095000 306,3
A4.12 Fortemperatureshigherthan13°C,P.operculellaimmaturestagesshowedhighersurvivalrates(0.9-1.0)andtolerancetohightemperatures(upto37°C)thanthetwootherspecies.BothT.solanivoraandS.tangoliashadaslightlybettertolerancetolowtemperaturesthanP.operculella.
Precipitationdependentmortality
A4.13 Precipitationsplayaminorbutsignificantroleinmothsurvivalrate(Wakisakaetal.1989;Koborietal.2003).BecauseeachinsectstageisconcernedandbecausenostudieshavebeenconducedonPTM,weuseacorrectingfactoronsurvivalrate.ThisrateisdependentontheamountofprecipitationsinmmovertwomonthsrandomlychosenontheGISdatabase(SICAGRO).Thecorrectingfactorisadjustedfromhypotheticalrelationshipbaseduponavailableknowledge.
Neighborhooddispersal
A4.14 WeconsiderthatthefractionofPTMleavingacellisdependentonadultpopulationdensityandqualityofhabitatniwithinthecell(seealsoBendoretal.2006;BendorandMetcalf2006b;Eizaguirreetal.2004).PTMdonothaveaperceptionoftheenvironmentsituatedinaneighborhoodcell.AccordingtoBendoretal.,weassumethatemigrationrate(ye),followsans-shapedcurve,whichlevelsoutasitapproachesthemaximumdensity(carryingcapacity).DensityisafractionofK,carryingcapacity(0<density<K).
(7)
A4.15 WeassumedthatPTMcantravelupto200mawayfromtheiroriginduringageneration(larvaecanhardlymoveto1mandadults'lifetimeisveryshort).TheprobabilityofaPTMtocoveradefineddistance(yd)isadecreasingfunctionofemigrationrate.ThisfunctionmaycertainlyoverestimatePTMdispersalbutwepreferoverestimationthanbelowestimation(Cameronetal.2002a;2002b).
(8)
A4.16 Asourunitcellis0.25km2,eachmigratingPTM,dependingonitspositiononthesquare,andondistancecoveredd,hasaprobability(yeR)ofcrossingthecellboarder.WeassumethatPTMmoveinsidethecelleitherhorizontallyorvertically.ThisassumptionmaycertainlyoverestimatePTMdispersalbutwepreferoverestimationthanbelowestimation(Cameronetal.2002a;2002b).
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(9)
Reproduction
Matingrate
A4.17 Matingrateiscorrelatedwithage,sexratioandweighofindividuals,butalsowithdistancefromoneindividualtoanother(Makeeetal.2001;Cameronetal.2004).Nospecificstudieshavebeenmadeonmatingrateundernaturalconditions,andlaboratorymeasurementsmayfrequentlyrepresentanoverestimationofthenaturalsituationbecauselaboratoryfemaleshavelittleopportunitytoavoidmating(Reinhardtetal.2007).However,asourcellsare500mlong,andthankstofieldobservation,weknowthatpheromonesworksatleastona200mradius,andweassumethatwithinacell,matingrateisequaltoonenomatterthedensity.
Sexratio
A4.18 AmongPTMpopulation,sexratiohasbeenstudiedandis1:1(Saour1999).Afterdispersal,theremainingadultpopulation,combinedwiththematingrateandthesexratiogivethegravidfemalespopulation.
PTMfecundity
A4.19 Althoughenergy-partitioningmodelshavebeendevelopedtoexplaintheshapeofinsectfecundityasafunctionofaging(Kindlmannetal.2001),wearenotawareofanymechanisticmodelsthatdescribesinsectfecundityasafunctionoftemperature.Severalprobabilisticnon-linearmodelstofitinsectfecundityacrosstemperaturehavebeenproposedintheliterature(Roux1993;Lactinetal.1995;KimandLee2003;Bonatoetal.2007),butnoneofthemgaveussignificantresultswithourdata(r<0.35,Fstat<2.01).Wethereforeusedweightedleastsquare(WLS)regressiontofindthebestmodelthatfitsourfecunditydataacrosstemperature.WLSregressionisparticularlyefficienttohandleregressionsituationsinwhichthedatapointsareofvaryingquality,i.e.thestandarddeviationoftherandomerrorsinthedatamaybenotconstantacrossalllevelsoftheexplanatoryvariables,whichcouldbethecase.Forthethreetubermothspecies,thebestfitwasobtainedwiththeWeibulldistributionfunction,whichhaslongbeenrecognizedasusefulfunctiontomodelinsectdevelopment(Wagneretal.1984).
A4.20 TheeffectoftemperatureuponfecunditywaswelldescribedbytheWeibulldistributionfunctions(r2=0.75,0.83,and0.91forT.solanivora,S.tangoliasandP.operculella,respectively).ResultsshowedmarkeddifferencesamongPTMspecies,bothintermsoftotalnumbersofeggslaidperfemalesandoptimalfecunditytemperature:thehighestfecunditywasfoundinT.solanivora,withabout300eggs/femaleat19°C,followedbyS.tangolias(220eggs/femaleat15°C)andP.operculella(140eggsat23°C).
Appendix2:Animations
A5.1 Thefollowinganimationsillustratesimulationsinwhichblueandredfigurinesrepresentsagents,andblueandredlinksagents'movementsfromvillagetovillage.Thenumberinthetoprightcornercorrespondstothenumberoftimeframeandthebackgroundcolortothepestinfestation(black:nopestinfestation;green:pestinfestationduetopurelybiologicaldiffusion;redandblue:pestinfestationduetoaninfestedagentmovement).Attheendofeachanimatedsimulation,theareatotherightremainsuninfected.Thisareacorrespondstohigherelevationswherethepestcannotsurvive.
FigureA-2.Animatedsimulationsshowingtheeffectofagents'movementsonthepestspreadwith2movementspertimeframeand6movementspertimeframe.Thepestinfestationisquickerwhenagentsmovemore.
FigureA-3.Simulationsshowingtheeffectofagents'pestcontrolknowledgewithoutheterogeneityonthepestspreadwithameanpestcontrolknowledgeof0and100.Whenthepestcontrolknowledgeishigh,thepestcanonlydispersethroughdiffusion(i.e.veryslowly),comparedtoasimulationwhenpestcontrolknowledgeislow,wheretheagents'behaviorsleadtoafull
infestationbylongdistancedispersaleventsfromvillagetovillage.
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FigureA-4.Animatedsimulationofthegamesession.ParametersarepresentedinTable1.Thesimulationrantoreachfullinfestationofthelandscapesuitableforthepest.Integratingrealdistributionofpestcontrolknowledge(Normaldistribution),weobservedthatalmostallthelandscapeisinfestedduetolongdistancedispersalevents.Itrevealedtheimportancetofocuson
pestcontrolknowledgereinforcementtoreducetheincidenceofthepestatthelandscapelevel.
Acknowledgements
Wethanktwoanonymousreviewersfortheirhelpfulcommentsonapreviousversionofthiswork.WearegratefultoClaireNicklin,fromtheMcKnightFoundation,forthelinguisticrevisionofthemanuscript.WealsothankCarlosCarpioandMarioHererrafortheirtechnicalsupportandallfarmersinvolvedinthisprocess.ThisstudywaspartoftheresearchconductedwithintheprojectInnovativeApproachesforintegratedPestManagementinchangingAndes(C09-031)fundedbytheMcKnightFoundation.VCPreceivedthefinancialsupportoftheDirectionSoutienetFormationoftheIRDthroughaPh.D.grant(2009-2011).
References
ALTIZER,S,Dobson,A,Hosseini,Petal(2006).Seasonalityandthedynamicsofinfectiousdiseases.EcologyLetters:9:467-84.[doi:10.1111/j.1461-0248.2005.00879.x]
ANDERSON,RMandMay,RM(1992).InfectiousdiseasesinhumansOxfordUniversityPress,Oxford.
BAGNI,R,Berchi,RandCariello,P(2002).Acomparisonofsimulationmodelsappliedtoepidemics.JournalofArtificialSocietiesandSocialSimulation5(3)5http://jasss.soc.surrey.ac.uk/5/3/5.html.
BAUER,AL,Beauchemin,CAAandPerelson,AS(2009).Agent-basedmodelingofhost-pathogensystems:Thesuccessesandchallenges.InformationSciences179:1379-1389.[doi:10.1016/j.ins.2008.11.012]
BENDORT.K,MetcalfS.S.,FontenotL.E.,SangunettB.andHannonB.(2006)ModelingthespreadoftheEmeraldAshBorer.Ecologicalmodelling4328.[doi:10.1016/j.ecolmodel.2006.03.003]
BENDORT.K.,MetcalfS.S.(2006)Thespatialdynamicsofinvasivespeciesspread.SystemDynamicsReview22(1)27-50.[doi:10.1002/sdr.328]
BERGER,T.(2001).Agent-basedspatialmodelsappliedtoagriculture:asimulationtoolfortechnologydiffusion,resourceusechangesandpolicyanalysis.AgriculturalEconomics25245-260.[doi:10.1111/j.1574-0862.2001.tb00205.x]
BOAHENE,K.andSnijders,T.A.B.(1999).AnIntegratedSocioeconomicAnalysisofInnovationAdoption:TheCaseofHybridCocoainGhana.JournalofPolicyModeling21,167-184.[doi:10.1016/S0161-8938(97)00070-7]
BONABEAU,E,(2002).Agent-basedmodeling:methodsandtechniquesforsimulatinghumansystems,ProcNatlAcadSci99:7280-7.[doi:10.1073/pnas.082080899]
BONATOO,LuretteA,VidalC,FarguesJ(2007)Modellingtemperature-dependantbionomicsofBemisiatabaci(Q-biotype).PhysiolEntomol32:50-55[doi:10.1111/j.1365-3032.2006.00540.x]
BOSSENBROEK,JM,Kraft,CEandNekola,JC(2001).Predictionoflong-distancedispersalusinggravitymodels:Zebramusselinvasionofinlandlakes.EcologicalApplications11:1778-88.[doi:10.1890/1051-0761(2001)011[1778:POLDDU]2.0.CO;2]
BOX,G.E.P,M.E.Muller(1958).Anoteonthegenerationofrandomnormaldeviates,AnnalsMath.Stat,V.29:610-1.[doi:10.1214/aoms/1177706645]
CAMERONP.J.,Walker,G.P.;Penny,G.M.;Wigley,P.J(2002a).MovementofPotatoTuberworm(Lepidoptera:Gelechiidae)withinandBetweenCrops,andSomeComparisonswithDiamondbackMoth(Lepidoptera:Plutellidae).Environ.Entomol.31(1):65-75.[doi:10.1603/0046-225X-31.1.65]
CAMERONP.J.,Walker,G.P.;Penny,G.M.;Wigley,P.J(2002b).Movementofpotatomothestimatedbymarkrecaptureexperiments.55thConferenceProceedingsofTheNewZealandPlantProtectionSocietyIncorporated.
CAMERONP.J.,A.R.Wallace,V.V.Madhusudhan,P.J.Wigley,M.S.Qureshi&G.P.Walker,(2004).Matingfrequencyindispersingpotatotubermoth,Phthorimaeaoperculella,anditsinfluenceonthedesignofrefugiatomanageresistanceinBttransgeniccrops.EntomologiaExperimentalisetApplicata,115(2)323-32.[doi:10.1111/j.1570-7458.2005.00256.x]
CRESPO-PEREZ,V.,Rebaudo,F.,Silvain,J.F.,andDangles,O.(submitted).Modelinginvasivespeciesspreadincomplexlandscapes:thecaseofpotatomothinEcuador.SubmittedtoLandscapeEcology.[doi:10.1007/s10980-011-9649-4]
DANGLES,O.&Carpio,F.C.,(2008).Cuandoloscientíficosylascomunidadesandinasunensusesfuerzosparalucharencontradeunaplagainvasora.NuestraCiencia10:23-25
DANGLES,O.,Carpio,C.,Barragan,A.R.,Zeddam,J.L.andSilvain,J.F.(2008).TemperatureasakeydriverofecologicalsortingamonginvasivepestspeciesinthetropicalAndes.EcologicalApplications18:1795-1809.[doi:10.1890/07-1638.1]
DANGLES,O.,Carpio,F.C.,Villares,M.,Yumisaca,F.,Liger,B.,Rebaudo,F.andSilvain,J.F.(2010).Community-basedparticipatoryresearchhelpsfarmersandscientiststomanageinvasivepestsintheEcuadorianAndes.Ambio:AJournaloftheHumanEnvironment,39:325-335.
EIZAGUIRREM,LópezC.andAlbajesR.(2004).DispersalcapacityintheMediterraneancornborer,Sesamianonagrioides.EntomologiaExperimentalisetApplicata113:25-34.[doi:10.1111/j.0013-8703.2004.00201.x]
EPANCHIN-NIELL,R.S.,Hufford,M.B.,Aslan,C.E.,Sexton,J.P.,Port,J.D.andWaring,T.M.(2010).Controllinginvasivespeciesincomplexsociallandscapes.FrontEcolEnviron8:210-216.[doi:10.1890/090029]
EUBANK,S.,Guclu,H.,Kumar,V.S.A.,Marathe,M.V.,Srinivasan,A.,Toroczkai,Z.andWang,N.(2004).Modellingdiseaseoutbreaksinrealisticurbansocialnetworks.Nature429:180-184.[doi:10.1038/nature02541]
FEDER,G.,Murgai,R.,andQuizon,J.(2004).Theacquisitionanddiffusionofknowledge:Thecaseofpestmanagementtraininginfarmerfieldschools,Indonesia.JournalofAgriculturalEconomics55:217-239.
GONG,X.andRenbin,X.(2007).ResearchonMulti-AgentSimulationofEpidemicNewsSpreadCharacteristics.JournalofArtificialSocietiesandSocialSimulation10(3)1
http://jasss.soc.surrey.ac.uk/14/3/7.html 12 08/10/2015
http://dx.doi.org/10.1111/j.1461-0248.2005.00879.xhttp://jasss.soc.surrey.ac.uk/5/3/5.htmlhttp://dx.doi.org/10.1016/j.ins.2008.11.012http://dx.doi.org/10.1016/j.ecolmodel.2006.03.003http://dx.doi.org/10.1002/sdr.328http://dx.doi.org/10.1111/j.1574-0862.2001.tb00205.xhttp://dx.doi.org/10.1016/S0161-8938(97)00070-7http://dx.doi.org/10.1073/pnas.082080899http://dx.doi.org/10.1111/j.1365-3032.2006.00540.xhttp://dx.doi.org/10.1890/1051-0761(2001)011[1778:POLDDU]2.0.CO;2http://dx.doi.org/10.1214/aoms/1177706645http://dx.doi.org/10.1603/0046-225X-31.1.65http://dx.doi.org/10.1111/j.1570-7458.2005.00256.xhttp://dx.doi.org/10.1007/s10980-011-9649-4http://dx.doi.org/10.1890/07-1638.1http://dx.doi.org/10.1111/j.0013-8703.2004.00201.xhttp://dx.doi.org/10.1890/090029http://dx.doi.org/10.1038/nature02541
-
http://jasss.soc.surrey.ac.uk/10/3/1.html.
GRIMM,V.etal.(2005a).Pattern-orientedmodelingofagent-basedcomplexsystems:lessonsfromecology.Science310:987-991.[doi:10.1126/science.1116681]
GRIMMV.andRailsbackS.F.(2005b).Individual-basedModelingandEcology.PrincetonUniversityPress,Princeton,NJ.[doi:10.1515/9781400850624]
GRIMMV.,BergerU.,BastiansenF.,EliassenS.,GinotV.,GiskeJ.,Goss-CustardJ.,GrandT.,HeinzS.K.,HuseG.,HuthA.,JepsenJ.U.,JørgensenC.,MooijW.M.,MüllerB.,Pe'erG.,PiouC.,RailsbackS.F.,RobbinsA.M.,RobbinsM.M.,RossmanithE.,RügerN.,StrandE.,SouissiS.,StillmanR.A.,VabøR.,VisserU.andDeAngelisD.L.(2006).Astandardprotocolfordescribingindividual-basedandagent-basedmodels.EcologicalModelling198(1-2),115-126.[doi:10.1016/j.ecolmodel.2006.04.023]
HIJMANSR.J.,CameronS.E.,ParraJ.L.,JonesP.G.andJarvisA.(2005).VeryHighResolutionInterpolatedClimateSurfacesForGlobalLandAreas,Int.J.Climatol.25:1965-78[doi:10.1002/joc.1276]
ITAKURA,J,Kurosaki,M,Itakuraa,Y,Maekawab,S,Asahinaa,Y,Izumi,NandEnomotoN(2010).Reproducibilityandusabilityofchronicvirusinfectionmodelusingagent-basedsimulation;comparingwithamathematicalmodel.Biosystems99:70-78.[doi:10.1016/j.biosystems.2009.09.001]
JACOBSON,SK,McDuff,MDDandMonroe,MC(2006).Conservationeducationandoutreachtechniques.NewYork:OxfordUniversityPress.[doi:10.1093/acprof:oso/9780198567714.001.0001]
KIM,D-S.andJ-H.Lee.(2003).OvipositionmodelofoverwinteredadultTetranychusurticae(Acarina:Tetranychidae)andmitephenologyonthegroundcoverinappleorchards.Exp.Appl.Acarol.31:191-208.[doi:10.1023/B:APPA.0000010385.00864.28]
KINDLMANN,P.,A.F.G.Dixon,andI.Dostálková.(2001).Roleofageing1091andtemperatureinshapingreactionnormsandfecundityfunctionsininsects.JournalofEvolutionaryBiology14:835-840.[doi:10.1046/j.1420-9101.2001.00323.x]
KOBORIY,AmanoH,(2003).Effectofrainfallonapopulationofthediamondbackmoth,Plutellaxylostella(Lepidoptera:Plutellidae).Appl.Entomol.Zool.38(2):249-253.[doi:10.1303/aez.2003.249]
LACTIN,D.J.,N.J.Holliday,D.L.Johnson,andR.Craigen.(1995).Improvedratemodeloftemperature-dependentdevelopmentbyarthropods. Environ.Entomol13:868-72.[doi:10.1093/ee/24.1.68]
LIEBHOLD,AMandTobin,PC(2008).Populationecologyofinsectinvasionsandtheirmanagement.AnnualReviewofEntomology,53:387-408.[doi:10.1146/annurev.ento.52.110405.091401]
LIU,Jetal.(2007).ComplexityofCoupledHumanandNaturalSystems.Science317:1513-1516.[doi:10.1126/science.1144004]
LUO,M,OpaluchJ.J.(2010).Analyzetherisksofbiologicalinvasion:Anagentbasedsimulationmodelforintroducingnon-nativeoystersinChesapeakeBay,USA.StochEnvironResRiskAssess,25(3)377-388.[doi:10.1007/s00477-010-0375-2]
MAKEEH.,etSaourG.(2001).FactorsInfluencingMatingSuccess,MatingFrequency,andFecundityinPhthorimaeaoperculella(Lepidoptera:Gelechiidae).Environ.Entomol.30(1):31-36.[doi:10.1603/0046-225X-30.1.31]
MASON,WA,Conrey,FR,andSmith,ER(2007).Situatingsocialinfluenceprocesses:Dynamic,multidirectionalflowsofinfluencewithinsocialnetworks.PersonalityandSocialPsychologyReview,11(3),279-300.[doi:10.1177/1088868307301032]
MORENO,Y,Pastor-Satorras,RandVespignaniA(2002).Epidemicoutbreaksincomplexheterogeneousnetworks,Eur.Phys.J.B26:521-529.[doi:10.1140/epjb/e20020122]
NEWMAN,M.E.J.(2002).Spreadofepidemicdiseaseonnetworks,Phys.Rev.E66(1)016128-1:11.
NOTZ,A.(1995)Influenciadelatemperaturasobrela1138biologíadeTeciasolanivora(Povolny)criadasentubérculosdepapaSolanumtuberosumL.BoletínEntomológicoVenezolano11:49-54.
PAPAJORGJI,PJandPardalosPM(2009).AdvancesinModelingAgriculturalSystems,SpringerPublishingCompany,NY.
PESHIN,RandDhawan,AK(2008).Integratedpestmanagement:disseminationandimpact,Springer
PITT,JPW,Worner,SPandSuarez,AV(2009).PredictingArgentineantspreadovertheheterogeneouslandscapeusingaspatiallyexplicitstochasticmodel.EcologicalApplications19:1176-1186.[doi:10.1890/08-1777.1]
POLHILLJ.G,ParkerD,BrownDandGrimmV(2008).UsingtheODDProtocolforDescribingThreeAgent-BasedSocialSimulationModelsofLand-UseChange.JournalofArtificialSocietiesandSocialSimulation11(2)3http://jasss.soc.surrey.ac.uk/11/2/3.html
PUILLANDRE,N,Dupas,S,Dangles,O,Zeddam,JL,Capdevielle-Dulac,C,Barbin,K,Torres-Leguizamon,MandSilvain,JF(2008).Geneticbottleneckininvasivespecies:thepotatotubermothaddstothelist.BiologicalInvasions10:319-333.[doi:10.1007/s10530-007-9132-y]
REINHARDTK.,KöhlerG.,WebbS.,andChildD.(2007).Fieldmatingrateoffemalemeadowgrasshoppers,Chorthippusparallelus,estimatedfromspermcounts.EcologicalEntomology,32(6)637-642.[doi:10.1111/j.1365-2311.2007.00923.x]
RODRIGUE,JP,Comtois,CandSlack,B(2009).TheGeographyofTransportSystems.Routledge
ROUXO.(1993).PopulationecologyofpotatoestubermothPhthorimaeaoperculella(Zeller)(Lepidoptera:Gelechiidae)anddesignofanintegratedpestmanagementprograminTunisia.ThesisETHNo10120,Zurich.
ROUXO,BaumgärtnerJ,(1997).Evaluationofmortalityfactorsandriskanalysisforthedesignofanintegratedpestmanagementsystem.Ecologicalmodeling109:61-75.
SAOURG.(1999).Susceptibilityofpotatoplantsgrownfromtubersirradiatedwithstimulationdosesofgammairradiationtopotatotubermoth,PhthorimaeaoperculellaZeller(Lep.,Gelechiidae).JournalofAppliedEntomology,23(3)159-164.[doi:10.1046/j.1439-0418.1999.00335.x]
SIKDERI.U.Sikder,Mal-SarkarS,MalT.K.(2006).Knowledge-BasedRiskAssessmentUnderUncertaintyforSpeciesInvasion.RiskAnalysis26(1)239-252.[doi:10.1111/j.1539-6924.2006.00714.x]
TANHUANPÄÄM,Ruohomaki,K.,Kaitaniemi,P.(2003)InfluenceofadultandeggpredationonthereproductivesuccessofEpirritaautumnata(Lepidoptera:Geometridae).Oikos102:263-272.[doi:10.1034/j.1600-0706.2003.12546.x]
SMITH,ERandConrey,FR(2007).Agent-basedmodeling:Anewapproachfortheory-buildinginsocialpsychology.PersonalityandSocialPsychologyReview,11:87-104.[doi:10.1177/1088868306294789]
SUAREZ,AV,Holway,DAandCase,TJ(2001).Patternsofspreadinbiologicalinvasionsdominatedbylong-distancejumpdispersal:InsightsfromArgentineants.ProcNatlAcadSci,98:1095-1100.[doi:10.1073/pnas.98.3.1095]
TEWELDEMEDHIN,E,Marwala,TandMueller,C(2004).Agent-basedmodelling:acasestudyinHIVepidemic.Proceedingsofthe4thInternationalConferenceonHybridIntelligentSystems(HIS'04);Washington,DC,USA.IEEEComputerSociety.pp.154-159.[doi:10.1109/ICHIS.2004.16]
URBIG,D,Lorenz,JandHerzberg,H(2008).OpinionDynamics:theEffectoftheNumberofPeersMetatOnce.JournalofArtificialSocietiesandSocialSimulation11(2)4http://jasss.soc.surrey.ac.uk/11/2/4.html.
VINATIER,F,Tixier,P,LePage,C,Duyck,PFandLescourret,F(2009).COSMOS,aspatiallyexplicitmodeltosimulatetheepidemiologyofCosmopolitessordidusinbananafields.EcologicalModelling220:2244-2254.[doi:10.1016/j.ecolmodel.2009.06.023]
WAGNERT.L.,WuH.,SharpeP.J.H.,SchoolfieldR.M.,CoulsonR.N.(1984).ModelingInsectDevelopmentRates:aLiteratureReviewandApplicationofaBiophysicalModel.Ann.Entomol.Soc.Amer.77:208-225.[doi:10.1093/aesa/77.2.208]
WAKISAKAS.,TsukudaRandNakasujiF(1989).EffectsofNaturalEnemies,Rainfall,Temperature,HostPlantsonSurvivalandReproductionofDiamondbackMoth.OtsukaChemicalCo.,Ltd.,NarutoResearchCenter,NarutoTokushima772,JapanandLaboratoryofAppliedEntomology,FacultyofAgriculture,OkayamaUniversity,Tsushimanaka,Okayama700,Japan.
http://jasss.soc.surrey.ac.uk/14/3/7.html 13 08/10/2015
http://jasss.soc.surrey.ac.uk/10/3/1.htmlhttp://dx.doi.org/10.1126/science.1116681http://dx.doi.org/10.1515/9781400850624http://dx.doi.org/10.1016/j.ecolmodel.2006.04.023http://dx.doi.org/10.1002/joc.1276http://dx.doi.org/10.1016/j.biosystems.2009.09.001http://dx.doi.org/10.1093/acprof:oso/9780198567714.001.0001http://dx.doi.org/10.1023/B:APPA.0000010385.00864.28http://dx.doi.org/10.1046/j.1420-9101.2001.00323.xhttp://dx.doi.org/10.1303/aez.2003.249http://dx.doi.org/10.1093/ee/24.1.68http://dx.doi.org/10.1146/annurev.ento.52.110405.091401http://dx.doi.org/10.1126/science.1144004http://dx.doi.org/10.1007/s00477-010-0375-2http://dx.doi.org/10.1603/0046-225X-30.1.31http://dx.doi.org/10.1177/1088868307301032http://dx.doi.org/10.1140/epjb/e20020122http://dx.doi.org/10.1890/08-1777.1http://jasss.soc.surrey.ac.uk/11/2/3.htmlhttp://dx.doi.org/10.1007/s10530-007-9132-yhttp://dx.doi.org/10.1111/j.1365-2311.2007.00923.xhttp://dx.doi.org/10.1046/j.1439-0418.1999.00335.xhttp://dx.doi.org/10.1111/j.1539-6924.2006.00714.xhttp://dx.doi.org/10.1034/j.1600-0706.2003.12546.xhttp://dx.doi.org/10.1177/1088868306294789http://dx.doi.org/10.1073/pnas.98.3.1095http://dx.doi.org/10.1109/ICHIS.2004.16http://jasss.soc.surrey.ac.uk/11/2/4.htmlhttp://dx.doi.org/10.1016/j.ecolmodel.2009.06.023http://dx.doi.org/10.1093/aesa/77.2.208
-
WHITE,DS,Labarta,RAandLeguia,EJ(2005).Technologyadoptionbyresource-poorfarmers:consideringtheimplicationsofpeak-seasonlabourcosts.AgriculturalSystems85:183-201.[doi:10.1016/j.agsy.2004.07.018]
WILENSKY,U(1999).NetLogo.http://ccl.northwestern.edu/netlogo.CenterforConnectedLearningandComputer-BasedModeling.NorthwesternUniversity,Evanston,IL.
WILSON,RSandHooker,N(2009).Targetingthefarmerdecisionmakingprocess:Apathwaytoincreasedadoptionofintegratedweedmanagement.CropProtection28:756-764.[doi:10.1016/j.cropro.2009.05.013]
XUAN,H,Xu,LandLi,L(2009).ACA-basedepidemicmodelforHIV/AIDStransmissionwithheterogeneity.AnnalsofOperationResearch168:81-99.[doi:10.1007/s10479-008-0369-3]
YU,B,Wang,J,McGowan,M,Vaidyanathan,GandYounger,K(2010).Gryphon:AHybridAgent-BasedModelingandSimulationPlatformforInfectiousDiseases.AdvancesinSocialComputing6007/2010:199-207.
ZASLAVSKIV.A.(1988)Insectdevelopment:photoperiodicandtemperaturecontrol.Springer,Berlin.
http://jasss.soc.surrey.ac.uk/14/3/7.html 14 08/10/2015
http://dx.doi.org/10.1016/j.agsy.2004.07.018http://ccl.northwestern.edu/netlogohttp://dx.doi.org/10.1016/j.cropro.2009.05.013http://dx.doi.org/10.1007/s10479-008-0369-3
AbstractIntroductionStudy systemModelOverall structure of the modelModeling pest dynamics through cellular automataModeling human-related pest dispersion through the agent-based modelAgent process overview and schedulingDesign concepts
Testing the effect of agents' movement and pest control knowledge on pest spread dynamicsEffect of agents' movementsEffect of agent's heterogeneity in pest control knowledge
Teaching with the modelModel introduction to the farmersModel parameterizationPlaying and learning with the agent-based modelModel adoptionBenefits of model-based teaching to farmers
ConclusionAppendix 1: Description of the cellular automaton used to simulate the pest using the ODD protocolOVERVIEWPurposeState variables and scalesProcess overview and schedulingProcess: state variables updateProcess: stochastic temperatureProcess: stochastic rainfallProcess: PTM mortalityProcess: PTM dispersalProcess: PTM reproduction
DESIGN CONCEPTSDETAILSInitialization and inputSubmodelsClimatic driver of PTM dynamicTemperaturePrecipitation
PTM life dynamicsCrude mortalityTemperature dependent mortalityPrecipitation dependent mortalityNeighborhood dispersalReproductionMating rateSex ratioPTM fecundity
Appendix 2: AnimationsAcknowledgementsReferences