retrospective analysis of a classical biological control ... 2019/naranj… · 2.3.2 | estimation...

J Appl Ecol 20181ndash12 wileyonlinelibrarycomjournaljpeemsp|emsp1

Received9February2018emsp |emsp Accepted3April2018DOI1011111365-266413163

R E S E A R C H A R T I C L E

Retrospective analysis of a classical biological control programme

Steven E Naranjo

ThisarticlepresentstheresultsofresearchonlyMentionofaproprietaryproductdoesnotconstituteendorsementorrecommendationforitsusebyUSDAUSDAisanequalopportunityemployer

USDA-ARSArid-LandAgriculturalResearchCenterMaricopaArizona

CorrespondenceStevenENaranjoUSDA-ARSArid-LandArid-LandAgriculturalResearchCenterMaricopaAZ85138Emailstevenaranjoarsusdagov

Funding informationUSDANationalInstituteofFoodandAgricultureWesternRegionIPMProgramCottonIncorporated

HandlingEditorBretElderd

Abstract1 Classicalbiologicalcontrolhasbeenakeytechnologyinthemanagementofinva-sivearthropodpestsgloballyforover120yearsyetrigorousquantitativeevalua-tionsofprogrammesuccessorfailurearerareHereIusedlifetableandmatrixmodel analyses and life table response experiments to quantitatively assess aclassicalbiologicalcontrolprogrammeforaninvasiveinsectpestinthewesternUnitedStates

2 LifetablesandmatrixmodelsweredevelopedforpopulationsofBemisia tabaci (sweetpotatowhitefly)oncottoninArizonabefore(1997ndash1999)andafter(2001ndash2010)thepermanentestablishmentoftwoexoticaphelinidparasitoidsAnalysestested multiple hypotheses relative to the expected outcome of a successfulprogramme

3 Marginalratesofparasitismratesofirreplaceablemortalityfromparasitismtotalgenerational mortality and finite population growth (λ) were unchanged for B tabacipopulationsbeforeandafterexoticparasitoidestablishmentProspectiveanalysesshowedthatpredationduringthefinalnymphalstadiumhadthegreatestinfluence on population growth rates regardless of parasitoid establishmentRetrospectiveLTREsshowedthatpredationandunknownmortalitycontributedmosttochangesinλafterparasitoidestablishment

4 Marginal parasitism actedweakly in a direct density dependence fashion afterparasitoid establishment and for all 14years combinedHowever this did nottranslateintoanassociationbetweenpestpopulationdensityandmarginalratesofparasitismforthe10-yearperiodfollowingestablishment

5 Synthesis and applicationsRarelyareclassicalbiologicalcontrolprogrammeout-comesassessedrigorouslyLifetablesmatrixmodelsandlifetableresponseex-perimentsshowedthatthedeclineinthepeststatusofBemisia tabaci(sweetpotatowhitefly)wasnotassociatedwiththeestablishmentoftwoexoticparasitoidspe-ciesinthecottonsystemInsteadnativearthropodpredatorsplayamajorroleinpest dynamics Further efforts to enhance conservation of the extant naturalenemycommunitywithfocusonincreasingmortalityinfinalstagenymphsandadultsmaybethemostefficientmeansofincreasingbiologicalcontrolservices

ThisisanopenaccessarticleunderthetermsoftheCreativeCommonsAttributionLicensewhichpermitsusedistributionandreproductioninanymediumprovidedtheoriginalworkisproperlycitedPublished2018ThisarticleisaUSGovernmentworkandisinthepublicdomainintheUSAJournalofAppliedEcologypublishedbyJohnWileyampSonsLtdonbehalfofBritishEcologicalSociety

2emsp |emsp emspenspJournal of Applied Ecology NARANJO

1emsp |emspINTRODUC TION

Classicalbiologicalcontrolhasbeenakeytechnologyintheman-agementof invasive arthropodpests globally forover120yearsThe goal is to provide long-lasting pest control by reestablishinguppertrophic level linksthroughthe introductionofnaturalene-miesfromthepestrsquosnativeregion (DeBach1964)Recentexam-inationofclassicalbiologicalcontrolprogrammes indicatesabouta33rateofsuccessinestablishingexoticagentsanda10rateofachievingsome levelof satisfactorycontrolof targeted insectpests (Cock etal 2016) Although a risky endeavour given thelow chances of success the few economic analyses conductedforinsecttargetsindicateamedianbenefittocostratioofaround801withmanyprogrammesprovidinglong-termpestsuppressionwithcontinuingeconomicbenefits(NaranjoEllsworthampFrisvold2015)Classicalbiological controlprogrammesare complexwithlongtimehorizonsandwithmanycriticalstepsrequiredtoachievesuccess(DeBach1964Hokkanen1985vanDriescheampHoddle2000) The final phase should involve evaluation of overall out-comesoftheprogrammeinecologicalsociologicalandeconomictermsastepthatisfrequentlyoverlookedduelargelytothelackofpersonnelandfinancialresourcesduringthelaterstagesoftheprogramme (McEvoy amp Coombs 1999 van Driesche amp Hoddle2000)

Lifetablesandassociatedmatrixmodelshavenotbeenwidelyappliedinclassicalbiologicalcontrolbuttheycanprovideimport-ant insight on the impact of introduced species on targeted pestpopulationsProspectiveapproacheshavebeenusefulforidentify-ing lifestagevulnerabilities suggestingpotentialagents toexploitthese vulnerabilities and predicting the impact on pest popula-tionsbyplannedorongoingbiologicalcontrolprogrammes(BarkerampAddison2006Davisetal2006MainesKnochelampSeastedt2013Mills2005SheaampKelly1998)Retrospectiveanalysescanidentify the contribution of specific vital rates and enhance ourunderstanding of factors contributing to the success or failure ofaprogramme (CattonLalondeBuckleyampdeClerck-Floate2016DauerMcEvoyampvanSickle2012McEvoyampCoombs1999)Lifetable response experiments (LTRE) are a retrospective approachwhereby changes in population growth due to an environmentalvariablecanbedecomposedintotheindividualcontributionsoftheunderlyingvitalrates(Caswell1996)LTREshavehadbroadappli-cation in investigating such areas as species conservation and ec-otoxicology (Caswell 2001) and are ideally suited to investigatingpestpopulationresponsestoclassicalbiologicalcontrolwherethe

environmentalchangeisrepresentedbytheadditionofnewnaturalenemyspeciesintoanexistingcommunity

Several interrelated hypotheses should be supported if in-troduced species have provided successful biological controlIntroductionsshouldsupplynewandhigherlevelsofmortalityandthismortalityshouldbeassociatedwithdeclinesinpestpopulationgrowthInadditionthenewsourceofmortalitymightdisplaysomelevelofregulatorydensitydependenceProspectiveandretrospec-tiveapproachesbasedonlifetablematrixmodelandLTREanalyseswereusedtoecologicallyevaluateandinformaclassicalbiologicalcontrolprogrammetargetingBemisia tabaciGennadius(MEAM1)aninsectpestofglobalsignificance

2emsp |emspMATERIAL S AND METHODS

21emsp|emspStudy system

The invasive B tabaci(sweetpotatowhitefly)isakeyeconomicpestof multiple annual crops in agricultural production regions in thesouthern tier of theUSA including cottonGossypium hirsutum L(OliveiraHenneberryampAnderson2001)Classicalbiologicalcon-trol was immediately considered a viable approach for pestman-agementduetopastsuccesswithotherwhiteflyspecies (BellowsBezark Paine Ball amp Gould 1992 Onillon 1990) and limitedparasitoiddiversity in affectedcrops (Hoelmer1996) Inpart theprogramme introduced about 30 populations of Eretmocerus spp and Encarsia spp nymphal parasitoids into the lowdesert agricul-tural production areas of California andArizona (GouldHoelmeramp Goolsby 2008) Eretmocerus sp from Ethiopia and the heter-onomoushyper-parasitoidEncarsia sophia (GiraultandDodd) fromPakistanbecamepermanentlyestablishedinArizonaaround2001largelydisplacingseveralnativeaphelinidspeciespreviouslyattack-ingthepest(NaranjoampLi2016)

22emsp|emspStudy sites

Study sites were located on the asymp900ha University of ArizonaMaricopaAgriculturalCenterfarminMaricopaArizonaUSAThefarmgrowsamixofcropsrepresentativeofagriculturalproductionin central Arizona including agronomic (cotton wheat sorghum alfalfa)andvegetablecropsField-basedlifetabledataonimmatureB tabaciwere generated on insecticide-free cotton from1997 to2010Cottonvarietiesrepresentativeofcommercialproductionintheareawereplantedmid-Apriltoearly-Mayeachyearandgrown

Analysesdeployedhereshouldbemorewidelyappliedtoassessingandimprovingbiologicalcontrolgenerally

K E Y W O R D S

Bemisia tabaciclassicalbiologicalcontrolelasticitylifetableresponseexperimentmatrixmodelparasitismpopulationgrowthpredation

emspensp emsp | emsp3Journal of Applied EcologyNARANJO

using regional agronomic practices Between two and five life ta-blesweregeneratedeachyearforatotalof44lifetablesoverthe 14-yearperiod(seeSupportingInformationTableS1)

23emsp|emspLife table construction

LifetabledataweregeneratedusinganinsituobservationalmethodthattookadvantageofthesessilenatureoftheimmaturestagesofB tabaciBriefly foreach lifetable160ndash240 individualnewly-laideggs(1ndash4perleafoneleafperplant)wereestablishedfromexist-ingnaturalpopulationsoncottonleavesSeparately160ndash240indi-vidualnewly-settledfirst instarnymphs (1ndash4per leafone leafperplant)were similarly established Insectsweremarkedbydrawinga small circle around an individualwith a nontoxic pen Individualnymphswere thenobserved3timesperweekwithahand lens tode-termine the developmental stage and if dead the cause of deathObservationscontinueduntiltheinsectdiedoremergedasanadultLeaveswitheggswere returned to the laboratory forobservationabout10daysaftersetupbecausecausesofdeathweretoodifficultto discernwith a hand lensObservable causesof death includedinviabilitypredationanddislodgementforeggsandpredationpara-sitism dislodgement and unknown for nymphs (only fourth instarnymphsshowvisiblesignsofparasitism)Moredetailisprovidedin NaranjoandEllsworth(20052017)andinSupportingInformationAppendixS1

231emsp|emspEstimation of mortality rates

Marginal mortality rates were estimated using the approach ofElkintonBuonaccorsiBellows andvanDriesche (1992)Marginalrateswere needed because at least three simultaneousmortalityfactors operated on the egg stage and each of the four nymphalstages thus there was no temporal sequence of mortality fac-torsaffectinganystageandmortalityfromonefactormightmask theactionofanotherBasedonNaranjoandEllsworth(2005)theestimationofmarginalratessimplifiesto

where MBisthemarginaldeathratefromfactorBdBistheapparent(observed)rateofmortalityfromfactorB and dA isthesumofap-parentmortalitiesfromallotherrelevantcontemporaneousfactors

232emsp|emspEstimation of irreplaceable mortality rates

Irreplaceable mortality is that portion of total generational mor-talitythatwouldnotoccur ifagivenmortalityfactorwasmissingFollowing Carey (1989) irreplaceablemortalitywas estimated foreachmortalityfactoras

where Mkisthemarginalmortalityrateforfactork and jisthetotalnumberofmortalityfactorsThefirstproductincludesallmortality

factorswhilethesecondproductincludesallmortalityfactorsex-cepttheoneofinterestThismethodassumesnodensity-dependentcompensationinmortality

24emsp|emspMatrix models

Stage-structuredmatrixmodelsweredevelopedforeachofthe44lifetablesoverthe14-yearperiod(Caswell2001)Therequiredma-trixelements included (a)dailyprobabilitiesoftransitionfromonestageitothenext(Gi)givenasσiγiwhereσiisthedailystagesur-vivalrateandγithedailystagedevelopmentrate(b)thedailyprob-abilityofsurvivingandremaining in thestage (Pi)givenasσi(1minusγi)and(c)themeandailyrateofadultfertility (F femalesperfemale)(SupportingInformationTableS2andSupportingInformationFigureS1) Thedaily stage survival ratewas estimated as (1minusMi)

γiwhere

Mi is themarginalmortality rate for stage i This assumption of aconstant rate over time is reasonable given the short duration ofeachstageTheobservationintervalofthelifetableswastoolongtoaccuratelyestimatethedurationofeach immaturestageThustemperature-dependentratesofdevelopmentforeggsandnymphswereestimated fromWagner (1995)usingarchivedmeandailyairtemperatures from an on-siteweather station (AZMET 2016) forthe short period between cohort establishment and adult emer-gence(SupportingInformationAppendixS1)Foradultsurvivaltheconstantdailyratewasestimatedas1minus(1da)wheredaisadultlon-gevityTemperature-dependentadultfecundityandlongevitywereestimated indirectlyforeachgenerationusingthedataofWagner(1994 T L Wagner unpublished data Supporting InformationAppendixS1)Basedonthesummertemperaturesduringthisstudymeandailyairtemperatureswerecalculatedforthe10-dayintervalfollowing adult emergenceDaily rates of fertilitywere estimatedfromthequotientoflifetimetemperature-dependentfecundityandadultlongevityassuminga5050sexratio

25emsp|emspLife table and matrix model analyses

The exotic aphelinid parasitoids attacking B tabaci became per-manently established around 2001 (Naranjo amp Li 2016) Thuslife table studies conducted from 1997 to 1999 were consideredpre-establishment (control or reference treatment) and all the lifetablestudiesconductedfrom2001onwardwereconsideredpost-establishment(experimentaltreatment)nostudieswereconductedin 2000

251emsp|emspLife table analyses

Bootstrapped confidence intervalswere estimated formeanmar-ginal mortality irreplaceable mortality total mortality and finitepopulation growth (λ see below) for pre-establishment and post-establishment years (ie 1997ndash1999 and 2001ndash2010) FollowingLevinetal (1996) individualinsectswiththeirassociatedhistoriesofmortality growth and reproductionwithin each life tablewererandomlyresampledwithreplacementusingtheoriginalsamplesize

(1)MB=dB∕(

1minusdA

)

(2)(1minus

prodj

1[1minusMk])minus (1minus

prodjminus1

1[1minusMk])


ofeachlifetableForeachiterationmarginalandirreplaceableratesofmortality fromeach causewithin each stagewere reestimatedaswere rates of totalmortality and λ for the generation Life ta-bles used separate cohorts of eggs and nymphs thus resamplingwasdoneseparatelywithineachofthesestagestomimicthestudydesignConfidenceintervalswereestimatedasthe25thand975thpercentilesbasedon5000iterationsPermutation(two-sided)wasusedtotestthenullhypothesisthatdifferencesbetweenmeanpre-and post-establishment rateswere zero using resamplingwithoutreplacement but following the same life table resampling processabove with 5000 iterations Given the nonrandomized nature ofthetreatmentstestswereconductedusingabefore-aftercontrol-impact (BACI) approach (WiensampParker 1995) The test statisticwastheabsolutevalueofthedifferencebetweentreatmentmeansProbabilitylevelswereestimatedbycalculatingthenumberoftimespermutedmeandifferencesweregreaterthantheteststatisticTheerror rate formultiple testswascontrolledusinga falsediscoveryrate of 5 (Benjamini ampHochberg 1995) All analyseswere con-ductedwiththeresamplingandMonteCarloroutines inPopToolsV32(Hood2010)TheBACIapproachviolatestheprincipleofrand-omizationoftreatmentsTemperatureisoneobviousenvironmentalvariablethatislikelytodifferbetweenpre-andpost-establishmenttimeperiods Thus to test the impact of the experimental designflawthetemperaturesusedinall44lifetablesforestimatingratesofimmaturegrowthadultsurvivalandreproductionwereaveragedandeachmatrixmodelwasthenresolvedusingthesenewaveragerates A permutation test estimated if λ changed between treat-mentswith temperature effects removed and this was comparedwithobserveddifferencesintheoriginaldataset

Temporaldensitydependenceinmortalityfactorswastestedbyregressingthek-value(=minusln[1minusM]whereM isthemarginalrate)ofeachmortalityfactorwithineachstage(eggornymph)onthenatu-rallogdensityofB tabaciatthebeginningofthestageDensitiesofB tabacismall(firstandsecondinstar)andlargenymphs(thirdandfourthinstar)perleafdiskandadultsperleafwereestimatedeachweekfromlateJunetolateSeptember(NaranjoampFlint19941995)Because sampling pooled small (first and second instar) nymphsand large (thirdand fourth instar)nymphsk-valuesweresummedaccordingly for thesepoolednymphalstagesDelayeddensityde-pendencewastestedbyusing insectdensities fromthebeginningofthepriorgeneration(c21daysprior)Testingforspatialdensitydependencewasnotpossibleduetothewayinwhichlifetableco-hortswereestablishedandthelackofplant-specificsamplingforB tabaciThestatisticalerrorrateformultiplesimpleregressionswascontrolledusingafalsediscoveryrateof5

252emsp|emspProspective matrix model analyses

Matrix models were developed for each life table (44 total) andwereanalysedtoestimateλandtheelasticityofλwithrespecttovital ratesElasticitymeasures theproportional change inpopula-tiongrowthinresponsetoaproportionalchangeinavitalrateforagivenlifestageTheelasticitiesofλtodecomposedstagesurvival

ratesgivenasthesumoftheelasticitiesofthePi and Gimatrixele-mentsforagivenstageanddecomposedgrowthratesestimatedastheproductofσiγiλandthedifferenceoftheGi and Pisensitivities(Caswell2001p233)wereestimatedPermutationtestsexaminedeffectsofparasitoidintroductiononelasticitiesofλtoPiGiσi and γi usingtheresamplingmethodsdetailedaboveAgainerrorratesformultipletestswerecontrolledusingafalsediscoveryrateof5andanalyseswereconductedusingPopToolsV32(Hood2010)

ElasticityanalysesarebasedonlookingatresponsesinλtoverysmallchangesinunderlyingvitalratesbutmaynotaccuratelypredictresponsestolargerchangesHorovitzSchemskeandCaswell(1997)suggestedthatitmaybeinstructivetovarymatrixparametersandthensolveforλdirectlyThisapproachwasusedtoexaminethecontributionofspecificmortalityfactorstopopulationgrowthThemortalityasso-ciatedwitheachfactor(overallstages)eachstage(overallfactors)oreachfactorwithineachstagewasremovedandthematrixmodelwasthenresolvedforλThepercentchangeinλwasusedasameasureofrelativecontributionAnalyseswerecomparedtotraditionallifetablekeyfactoranalysis(SupportingInformationAppendixS2)

253emsp|emspRetrospective matrix model analyses

ALTREapproachwasusedtoassessthecontributionoflowerlevelvitalratesxtochangesinpopulationgrowthduetoparasitoides-tablishmentFollowingCaswell(1996)theeffectonλisgivengener-ally by

where the superscriptsm and r refer to treatment and reference(control) respectivelyaij are theelementsof theprojectionmatri-cespartλpartaij isthesensitivityofλ toaijandpartaijpartxk isthesensitivityofaij toxkbothevaluatedon themeanmatrix ([A

m+Ar]2)Herex representslowerlevelvitalratesofgrowth(γi)andsurvival(σi)wheresurvival in turn isdecomposed intoeffects frommultiplespecificmortalityfactorsksuchaspredationparasitismanddislodgement(seeSupporting InformationAppendixS3fordetail)Tocontrol fornonadditivitythemeanmatriceswereestimatedbyaveragingthein-dividualratesoftheproductmakingupeachmatrixelementaij (Table S2CoochRockwellampBrault2001)Bootstrapconfidenceintervalswereestimatedasbeforeforeachdecomposedvitalratecontribu-tionPermutationtestedifcontributionsdifferedfromzerowithmul-tipleerrorratescontrolledusingafalsediscoveryrateof5

26emsp|emspParasitism and host association

PearsonrsquoscorrelationwasusedtoevaluatetheassociationbetweentheseasonalannualabundanceoflargenymphsoradultsofB tabaci and annualmeanmarginal parasitismover the post-establishmentperiod (2001ndash2010)Thesehoststageswerechosenbecausetheyrepresentthefocusofcurrentpestmanagementstrategiesforthispestandbestrepresentoverallpestdynamictrajectories(NaranjoampEllsworth2009)

(3)mminus

rasympΣijΣk(xmkminusxr

k)

aij

aij

xk|(Am+Ar

)∕2


3emsp |emspRESULTS


Ratesofmarginalmortalitybyfactorpooledoverall immaturede-velopmentalstagesvariedoverthe14yearsofthestudybutcon-sistent patterns emerged (Figure1) Rates of marginal predationand dislodgementwere consistently the greatest sources ofmor-talitybut theseratesdidnotdifferstatistically frompre- topost-establishment of parasitoids after accounting for multiple tests(pgt003)Likewisemarginalratesofparasitismdidnotdifferasa

result of parasitoid establishment (p =045) The highest rates ofmortalitywereconsistentlyobserved in the fourthnymphal stage(069overallyears) followedbytheeggstage (040)andthentheremainingnymphalstages(020ndash021datanotshown)

Adifferentpatternwasobservedforratesofirreplaceablemor-talityorthemortalitythatwouldbelostifagivenmortalityfactorwasabsent(Figure2)Irreplaceablemortalityfrompredationsignifi-cantly increased after exotic parasitoid establishment (p =0001)Irreplaceablemortalityfromunknowncausesdeclinedsignificantlywith establishment (p =0003 not shown) Irreplaceablemortality

F IGURE 1emspMeanmarginalratesofBemisia tabacimortalitybyfactorpooledacrossallimmaturelifestagesforeachyear(a)andaveragedovertimeperiodsbefore(1997ndash1999)andafter(2001ndash2010)theestablishmentofexoticparasitoids(b)Thelinein(a)emphasizesratesofmarginalparasitismovertimeErrorbarsarebootstrapped95confidenceintervals

F IGURE 2emspMeanmarginalratesofBemisia tabaciirreplaceablemortalitybymortalityfactorspooledacrossallimmaturelifestagesforeachyear(a)andaveragedovertimeperiodsbefore(1997ndash1999)andafter(2001ndash2010)theestablishmentofexoticparasitoids(b)Errorbarsarebootstrapped95confidenceintervalsasterisksdenotesignificantdifferencesbasedonpermutationtests


fromparasitism increasedslightlyand that fromdislodgementde-clinedslightlybutneithersignificantly(p gt012)

Ratesoftotalimmaturemortalitydidnotchangerelativetotheestablishment of exotic parasitoids (p =048 Figure3) consistentwith the lackofchanges in factor-orstage-specificmarginalmor-talityratesovertime

Evidenceof temporal density dependence in any stage-specificmortalityassociatedwithnaturalenemieswasweak(Table1)Priorto

establishment therewas an inversedensity-dependent relationshipbetweeneggdensityanddislodgementandasimilarinverserelation-shipfordislodgementofsmallnymphsoverall14yearsTherewasdi-rectdensitydependenceforratesofparasitism(associatedwithlargenymphsorallnymphs)followingparasitoidestablishmentandforallyearscombinedFinallytherewasdelayeddirectdensitydependenceforparasitismbasedonallyearsInallcasestheslopesoftherelation-shipsweresmallsuggestingweakresponsestohostdensity

F IGURE 3emspMeanratesoftotalimmaturemortalityandfiniteratesofincrease(λ)forBemisia tabaciforeachyear(a)andaveragedovertimeperiodsbefore(1997ndash1999)andafter(2001ndash2010)theestablishmentofexoticparasitoids(b)Errorbarsarebootstrapped95confidenceintervals

TABLE 1emspTemporaldensitydependenceofnaturalenemy-inducedmortalityofBemisia tabacioncottonbeforeandaftertheestablishmentofexoticaphelinidparasitoidsMaricopaArizonaUSA

StageFactor

Within generation Lag- 1 generation

1997ndash1999 2001ndash2010 All years 1997ndash1999 2001ndash2010 All yearsSlope (P) Slope (P) Slope (P) Slope (P) Slope (P) Slope (P)

Egg

Predation minus0011(070) minus0015(032) minus0017(027) 0005(086) minus0030(025) minus0014(047)

Dislodgement minus0073 (001) minus0021(049) minus0043(003) 0007(074) minus0062(016) 0030(025)

Smallnymph

Predation 0002(091) 0003(080) 0003(071) minus0015(047) 0012(048) 00159(020)

Dislodgement minus0084(005) minus0015(031) minus0047 (0006) minus0008(059) minus0047(010) minus0028(012)

Largenymph

Parasitism 0009(078) 0082 (0002) 0048 (001) 0034(044) 0106(0016) 0078 (001)

Predation 0016(068) 0008(084) 0039(018) 0090(009) minus0021(073) 0061(021)

Dislodgement 0015(060) 0012(060) 0006(071) 0028(049) 0029(034) 0021(036)

Allnymph

Parasitism 0012(069) 0065 (001) 0041(003) 0028(053) 0102(002) 0075(002)

n 14 31 45 11 23 34

Note k-valueofstage-specificmortalityfactorregressedonlnofBemisia tabaci lifestagedensityValuesinboldtext indicateaslopesignificantly differentfromzeroMultipletestscorrectedoverobservationswithinatimeperiodwiththefalsediscoveryrateof5


32emsp|emspMatrix model analyses

Finiteratesofincreaseλdidnotchangewithparasitoidestablish-ment(p =070Figure3)Finitepopulationgrowthaveraged1085beforeestablishment1090afterand1089overall14yearsindi-catinggrowingpopulationsTherewerenodifferencesinλpre-andpost-establishment when using the same mean temperatures forestimatingimmaturegrowthandadultsurvivalandfecundityinallmatrices(p =016)


Elasticitiesofλ tomatrixelementsPi and Gi and decomposed sur-vival (σi)andgrowth(γi)rateswerestrongestforthefourthnymphalstage (Figure4) Elasticities remained largely unchanged with theestablishmentofexoticparasitoids (pgt014)butsomesmallelas-ticitiesassociatedwith1stndash3rdnymphalsurvivaldidvarybetweentreatments(p lt0008)Elasticityofλtoadultsurvivalwascompara-tivelyhighbutadult longevitywasestimatedfromlaboratorydataandmay not accurately reflect themortality dynamics of this lifestageinthefield(Figure4)

Analyses examining the effect of removing mortalities asso-ciated with specific life stages or factors on population growthshowedthatmortalityduringthefourthstadiumpredationoverallstagesandpredationduring the fourthnymphal stadiumwereas-sociatedwiththelargestreductionsinpopulationgrowth(Table2)Theseresultswereconsistentwithmoretraditionalkeyfactoranal-yses(SupportingInformationTableS4)Theestablishmentofexoticparasitoidsdidnotalterthesepatterns


PopulationgrowthrateincreasedslightlyintheyearsfollowingexoticparasitoidestablishmentasnotedaboveandLTREanalysesenabledassessmentofthecontributionsofeachdecomposedvitalratetothischange(Figure5)Consistentwithanincreaseinλthecontributionsofmanyof the individual sourcesofmortalitywerepositive albeitonlythecontributionfromunknowncausesduringthethirdnymphalstadiumwassignificantlygreaterthanzero(p =0001Figure5a)Incontrastpredationduringthefourthstadiumcontributednegatively(p =0001)consistentwiththehigherlevelofirreplaceablemortalitysuppliedbythisfactoroverallstages(seeFigure2)Thepatternsofpositive contributions to the change in λ are further demonstratedwhenthedifferencesaresummedoverfactororstage(Figure5cd)ComparedwiththeactualchangeinλbetweentreatmentstheLTREanalysisaccountedfor9933ofthedifference

33emsp|emspChanging host density and parasitism

A common approach to assessing the impact of biological controlintroductions is toexaminethe longer termtrends inpestdensityin relation to the activity of the introduced agent(s) Populationdensities ofB tabaci large nymphs and adults andmarginal rates

F IGURE 4emspElasticityofλtoPi(probabilityofsurvivingandremaininginstagei)F(dailyrateoffemalesperfemale)σi(stagesurvivalprobabilities)andγi(stagegrowthprobabilities)overtimeElasticityofλtoGi(probabilityoftransitionfromstageitoi+1)wasnotplottedbecauseitwassmallandinvariableamonglifestagesNotescalechangesiny-axisN1ndashN4nymphalstages1ndash4


ofparasitismvariedconsiderablyoverthe10yearssinceestablish-mentoftheexotics (Figure6)However therewasnorelationshipbetweenhostdensity andmarginal parasitism rate (largenymphsSpearmanrsquos=003 p=093 n=10 adults Spearmanrsquos=014p=070n=10)sinceestablishment

4emsp |emspDISCUSSION

Lifetableresponseexperimentsandanalysesoftheassociatedun-derlyinglifetablesandmatrixmodelsrepresentarobustapproachforquantifyinghowchangesinfactorsaffectingvitalratesultimatelycontributetopopulationgrowthordeclineTheadditionofanexoticnaturalenemytoanecosystemrepresentsaclearchange the im-pactsofwhichshouldbemeasurableintermsofalteredvitalratesandanassociateddeclineinthetargetpestpopulation(Cattonetal2016Dauer etal 2012McEvoyampCoombs 1999)Whileobser-vationsshowingcorrelationbetweenincreasedapparentparasitismanddeclineofpestdensitymaysupportaconclusionofpotentiallysuccessful biological control (egBellowsetal 1992DeBarroampCoombs2009PickettKeavenyampRose2013)matrixmodelsandLTREsprovide for amore robustquantitative andmechanistic as-sessmentSuchanapproachenablesanunderstandingofthefactorscontributingtopestpopulationdeclineandstrengthensourabilitytobuildonsuchsuccesses(orfailures)inthefutureRecentlyCocketal(2016)notedthatjustover600classicalbiologicalcontrolpro-grammesforinsectpestshaveresultedinatleastsatisfactorycon-trolof the targetbutGreatheadandGreathead (1992) suggestedthatreportingisnotalwaysaccurateanditisunclearhowmanyofthesehavereceivedcarefulquantitativeassessmentWhilenotsim-pleorinexpensivelifetablematrixmodelandLTREanalysesshould

bemore broadly applied to the assessment of classical and otherapproachestobiologicalpestcontrol

The classical biological control programme for B tabaci re-sultedinthesuccessfulestablishmentoftwoexoticparasitoidsthatlargely or entirely replaced their native parasitoid counterparts inthecottonsystemby2004(NaranjoampLi2016)butdidnotappearto change anyof the factors contributing topest population sup-pression Specifically analyses showed that introductions (a) didnotresultinincreasedmarginalratesofparasitism(b)didnotsup-plynovel sourcesofmortality absentbefore introductionsand (c)ultimatelydidnot result in lower ratesofpestpopulationgrowthAdditionally mortality during the fourth nymphal stadium espe-ciallythatprovidedbypredationcontributedthemosttoreductionsinpopulationgrowth and thispatternwasnotalteredbyparasit-oid establishment Therewas evidenceof temporal direct densitydependenceinparasitismratesfollowingintroductionsanddelayeddensitydependenceoverall14yearsbutitisunclearwhatrolethisplayedinpestpopulationdynamicsasitdidnottranslateintoare-lationshipbetweenincreasingparasitismanddecliningpestdensityat the study siteGenerally theprevalence and impactofdensitydependenceininsectpopulationshasbeendebated(HassellLattoamp May 1989 Stiling 1988) Overall the outcome here supportsthehypothesis thatclassicalbiological isunsuccessful ifmaximumparasitismratesfailtoexceed33ndash36(HawkinsampCornell1994)Parasitismexceededthisthresholdinonlyasingleyearineachofthepre-andpost-establishmentperiodswithparasitismaveraging20sinceestablishmentand189overthe14-yearstudy

PopulationgrowthwaspositivebothbeforeandafterparasitoidestablishmentEvensoregionalpopulationofB tabacihavegreatlydeclinedfromtheinitialinvasionashavetheassociatedapplicationofinsecticides(NaranjoampEllsworth2009)Ishowherethatthese

Factorstage

Matrix model analysisa

Pre (97ndash99) Post (01ndash10) All years

Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567

Factor Inviable 0857 0484 0594

Dislodgement 3208 2669 2828

Parasitism 0379 0586 0524

Predation 3394 4076 3874

Unknown 1812 0883 1157

N4factor Dislodgement 0458 0385 0406


Predation 0955 1720 1494

Unknown 0739 0576 0624

Note aMeanpercentchangeinλwhenmortalityfromtheindicatedstageorfactorwasremovedfromthematrixmodeln=13pre-establishment31post-establishment

TABLE 2emspContributionofmortalitybystageandfactortofinitepopulationgrowth(λ)forBemisia tabaciincottonbeforeandaftertheestablishmentofexoticparasitoidsMaricopaArizonaUSAThemostinfluentialstagesorfactorsarenotedinboldtype


F IGURE 5emspContributionofdecomposedvitalratestochangesinλrelativetotheestablishmentofexoticparasitoids(a)Allstagesandmortalityfactors(b)stagegrowthrates(c)mortalitycontributionssummedoverallstagesand(d)stagecontributionssummedoverallmortalityfactorsAsterisksdenotesignificantcontributionsbasedonpermutationtestsN1ndashN4nymphalstages1ndash4Inv=inviableDis=dislodgementPred=predationPara=parasitismUnk=unknownSurv=adultlongevityFec=fecundity

F IGURE 6emspAssociationbetweenseasonal mean Bemisia tabacidensitiesandmeanmarginalratesofparasitismincotton1997ndash2010ErrorbarsareSEMn=2ndash5lifetablesperyearn=7ndash15datesperyearforhostdensity


patterns were not the result of improved biological control fromthetwointroducedparasitoidsInsteadmultipletacticsassociatedwithimprovedintegratedpestmanagementstrategiesforallpestsinmajorhostcropssuchascottonandvariousvegetables(NaranjoampEllsworth2009PalumboHorowitzampPrabhaker2001)haveaf-fected thisoutcome Incottononekey tactichasbeenconserva-tionanduseofgeneralistarthropodpredatorsthroughadherencetoeconomicthresholdsanduseofselectiveinsecticidesThegenerallyincreasinglevelsofpredationevenaftertheestablishmentofexoticparasitoidspointtothecriticalroleofpredationinthepopulationdynamicsofB tabaciThesepatternsalsosuggestthattheoverallagroecosystemhasbecomemorefavourabletoecosystemserviceslikebiologicalcontrol

GreatheadandGreathead (1992) suggested thatmanyclassicalbiological control failures go unreported andmany factors can in-fluencesuccessorfailure(Hokkanen1985vanDriescheampHoddle2000)Severalelementsmayhavecontributedtothelackofefficacyofthisclassicalbiologicalcontrolprogrammeincludingpoorparasit-oiddispersal(HaglerJacksonHenneberryampGould2002)coupledwiththeephemeralnatureofannualcropsthatrequirerecolonizationeachseasonand thepolyphagousbiologyof thepest (KennedyampStorer2000)Therelativelypoorrecordofsuccessofclassicalbio-logicalcontrolinannualcomparedwithperennialcropsfurthersup-ports thischallenge (DeBach1964HawkinsMills JervisampPrice1999) Incontrast thegeneralistpredatorcommunityappearswelladaptedtoexploitingephemeralpreyresources(NaranjoEllsworthampCańas2009)Theintroductionoftheautoparasitoid(E sophia)alsomayhaveplayeda rolebut this speciesbecamedominantonlyby2006(NaranjoampLi2016)andthereisnoclearchangeinlevelsofparasitismfrombeforethistimeLifetablesmaynothaveadequatelyquantified host-feeding and thus additional mortality imposed byeitherthenativeorexoticparasitoidsThisbehaviouriscommoninaphelinidparasitoidsattackingawiderangeofinsectspeciesinclud-ingthosehere (ArdehdeJongampvanLenteren2005ZangampLiu2008)Lifetableobservationsfailedtodetectanyobviousevidenceof host-feeding on B tabaci nymphs (Naranjo amp Ellsworth 2017)although this mortality could have on occasion been incorrectlycapturedintheunknowncategoryNonethelessevenifexoticpara-sitoidsengagedmorereadilyinhost-feedingthantheirnativecoun-terpartsthisdidnottranslateintoreducedpopulationgrowthratesFinallystudiesherewereintensivelyfocusedoveralongdurationinasinglecropinasingleregionbutwerenotextensiveintermsofex-aminingotherkeyhostcropsandorothergeographicregionsforthispolyphagous pest Stronger biological control in other crops couldhavecascadedintobettercontrolincottonbyreducingimmigration(seeSupportingInformationAppendixS4forfurtherdiscussion)

Prospective matrix model analyses point to potential avenuesof exploration for improving biological control ofB tabaci in thissystemForexampleelasticitywasgreatestforsurvivalduringthefourthnymphalinstarandtheadultstageThisformervulnerabilityisalreadybeingexploitedbynativegeneralistarthropodpredators(NaranjoampEllsworth2005)andcouldperhapsbeenhancedbeyondselective insecticides by active habitatmanagement to encourage

even higher populations of predators Interestingly although pro-spectiveanalyseswereneverappliedbeforethelaunchoftheB ta-baciclassicalbiologicalcontrolprogrammea prioriapplicationofmyanalyseswouldlikelyhavepointedtoaphelinidparasitoidsasusefulagentsTheymayattackearlierinstarnymphsbutaffecttheirmor-talityduringthefourthstadiumUnfortunatelythemortalitycausedbytheexoticspeciessimplyreplacedthatofthenativespeciesTheadult stage representsanothervulnerability thathasnotbeenex-plicitly exploited Arthropod predators and entomopathogens canaffect adults (Faria amp Wraight 2001 Hagler Jackson Isaacs ampMachtley2004)andfurtherexaminationandenhancementofthesemortalityforceslaterinthelifecycleappearwarranted

Parasitoids aremost often the key factor in introductions forcontrol of exotic pests on native or exotic crops (Hawkins etal1999)Howeverinthissystempredatorsremainedtheldquokeyrdquofactorevenafterparasitoidestablishmentbasedonbothlifetableandma-trixmodelanalysesTraditionalkeyfactoranalysis(VarleyGradwellampHassell 1973) hasbeen criticized as not being reflectiveof thepatternsofpopulationchangeandtheunderlyingcauses(Royama1996)Elasticityanalysisremovalofmortalityforcesinmatrixmodelanalysesandkeyfactoranalysisallpointedtomortalityduringthefourthnymphal stadium (specificallypredation)asmost influentialtotherateofpopulationgrowthThissuggeststhattraditionalkeyfactoranalysismayberevealinginsomesituations

Ideallydeploymentof lifetablesandmatrixmodelsforclassicalbiologicalcontrolshouldbeconsideredinadvancesothatproperbe-fore and after treatments canbeestablished In some instances itmaybepossibletoimplementtreatmentsspatiallybeforetheexoticnaturalenemiesbecomewidelyestablishedWithonlytemporalsep-arationbetweentreatmentsanotherlimitationwasthenatureofthestatisticalanalysesthatarepossibleInthisstudytherewerenoran-domizedreplicatesofeachtreatment(beforeorafterestablishment)Thislimitationiscommonintheassessmentofclassicalbiologicalcon-trol(vanDriescheampHoddle2000)andinotherecologicalprocessesthathavechangedorbeendisturbedbydisasters (WiensampParker1995)TheBACIapproachcannotprovideunbiasedstatisticaltestsofeffectsHoweverifthebreadthofthemetricsexaminedislargeandortheeffectsizesareeitherverysmallorverylargethenthestatisti-caltestsarelessimportantrelativetotheobviousecologicaleffects

InconclusionlifetablesmatrixmodelsandLRTEsenablerobustquantitativeassessmentofbiologicalcontrolprogrammesandpro-vide important insight into pest demographicswithin the contextofexistingmortalityforcesTheirapplicationconfirmsandextendspreviousworkinshowingthatimmaturestagesofB tabaci are con-sistentlysubjecttohighlevelsofnaturalmortalityincotton(NaranjoampEllsworth2005)withmuchofitduetotheactivityofgeneralistarthropodpredatorsBiologicalcontrolplaysakeyroleintheman-agementofthispestbuttheclassicalbiologicalcontrolprogrammedidnotappeartoimprovetheseecosystemservicesTheseanalyti-calapproachesshouldbemorebroadlyappliedinbiologicalcontrolboth prospectively to bettermatch agentswith pest demograph-ics and retrospectively to delineate mechanisms responsible for programmesuccessorfailure


ACKNOWLEDG EMENTS

I thankMarkHoddleandNickMillsandanonymousreviewersforhelpfulcommentsonearlierdraftsSpecialthankstoPeterEllsworthJonathon Diehl and Peter Asiimwe for their contributions to thepublishedlifetableworkandtothemanytechnicalsupportpeoplePartialsupportprovidedbytheUSDA-NIFAWesternRegionUSAIPMProgramandCottonIncorporated

DATA ACCE SSIBILIT Y

DataavailableviatheAgDataCommons (USDANationalAgriculturalLibrary)httpsdoiorg1015482usdaadc1373297(Naranjo2018)

ORCID

Steven E Naranjo httporcidorg0000-0001-8643-5600

R E FE R E N C E S

ArdehMJdeJongPWampvanLenterenJC (2005)SelectionofBemisianymphalstagesforovipositionorfeedingandhost-handlingtimes of arrhenotokous and thelytokous Eretmocerus mundus and arrhenotokous E eremicus BioControl 50 449ndash463 httpsdoiorg101007s10526-004-3071-7

AZMET(2016)TheArizonameteorologicalnetworkhttpagarizonaeduazmet

Barker G M amp Addison P J (2006) Early impact of endoparasit-oid Microctonus hyperodae (Hymenoptera Braconidae) after itsestablishment inListronotus bonariensis (ColeopteraCurculionidae)populations of northern New Zealand pastures Journal of Economic Entomology 99 273ndash287 httpsdoiorg101093jee 992273

BellowsTSBezarkLGPaineTDBallJCampGouldJR(1992)Biological controlof ashwhiteflyA success inprogressCalifornia Agriculture4624ndash28

BenjaminiYampHochbergY(1995)ControllingthefalsediscoveryrateApracticalandpowerfulapproachtomultipletestingJournal of the Royal Statistical Society B57289ndash300

CareyJR(1989)ThemultipledecrementlifetableAunifyingframe-workforcause-of-deathanalysisinecologyOecologia78131ndash137httpsdoiorg101007BF00377208

Caswell H (1996) Analysis of life table response experi-ments II Alternative parameterizations for size- and stage-structured models Ecological Modelling 88 73ndash82 httpsdoiorg1010160304-3800(95)00070-4

CaswellH (2001)Matrix population models2ndedSunderlandMASinauerAssociatesIncPublishers

CattonHALalondeRGBuckleyYMampdeClerck-FloateRA(2016) Biocontrol insect impacts population growth of its targetplantspeciesbutnotanincidentallyusednontargetEcosphere7(5)e01280httpsdoiorg101002ecs21280

CockMMurphySKairoMThompsonEMurphyRampFrancisA(2016) Trends in the classical biological control of insect pests byinsectsAnupdateoftheBIOCATdatabaseBioControl61349ndash363httpsdoiorg101007s10526-016-9726-3

CoochERockwellRFampBraultS(2001)Retrospectiveanalysisofdemographic responses to environmental change A lesser snowgooseexampleEcological Monographs71377ndash400httpsdoiorg1018900012-9615(2001)071[0377RAODRT]20CO2

DauerJTMcEvoyPBampvanSickleJ(2012)Controllingaplantin-vaderbytargeteddisruptionofitslifecycleJournal of Applied Ecology49322ndash330httpsdoiorg101111j1365-2664201202117x

DavisAS LandisDANuzzoVBlosseyBGerberEampHinzHL (2006) Demographic models inform selection of biocontrolagents for garlic mustard (Alliaria petiolata) Ecological Applications16 2399ndash2410 httpsdoiorg1018901051-0761(2006)016[2399 DMISOB]20CO2

De Barro P J amp Coombs M T (2009) Post-release evaluation ofEretmocerus hayati Zolnerowich and Rose in Australia Bulletin of Entomological Research 99 193ndash206 httpsdoiorg101017S0007485308006445

DeBachP(Ed)(1964)Biological control of insect pests and weedsNewYorkNYReinholdPublishing

Elkinton JSBuonaccorsi JPBellowsTSampvanDriescheRG(1992)Marginal attack rate k-values and density dependence inthe analysis of contemporaneousmortality factorsResearches on Population Ecology3429ndash44httpsdoiorg101007BF02513520

FariaM ampWraight S P (2001) Biological control ofBemisia tabaci with fungi Crop Protection 20 767ndash778 httpsdoiorg101016S0261-2194(01)00110-7

GouldJHoelmerKampGoolsbyJ(Eds)(2008)Classical biological con-trol of Bemisia tabaci in the United States A review of interagency re-search and implementationDordrechtTheNetherlands-HeidelbergGermany-LondonUK-NewYorkNYSpringer

GreatheadDJampGreatheadAH(1992)Biologicalcontrolofinsectpests by insect parasitoids and predators The BIOCAT databaseBiocontrol News and Information1361Nndash68N

Hagler J R Jackson C G Henneberry T J amp Gould J R (2002)Parasitoidmark-release-recapturetechniquesndashIIDevelopmentandapplicationofaproteinmarkingtechniqueforEretmocerusspppar-asitoidsofBemisia argentifolii Biocontrol Science and Technology12661ndash675httpsdoiorg1010800958315021000039851

HaglerJRJacksonCGIsaacsRampMachtleySA(2004)Foragingbehaviorandpreyinteractionsbyaguildofpredatorsonvariouslife-stages ofBemisia tabaci Journal of Insect Science4 1 httpsdoiorg1016730310043101

HassellMLatto JampMayR (1989)Seeing thewoodfor the treesDetectingdensitydependencefromexistinglifetablestudiesJournal of Animal Ecology58883ndash892httpsdoiorg1023075130

Hawkins B A amp Cornell H V (1994) Maximum parasitism ratesand successful biological control Science 266 1886 httpsdoiorg101126science26651921886

HawkinsBAMillsNJJervisMAampPricePW(1999)Isthebio-logicalcontrolofinsectsanaturalphenomenonOikos86493ndash506httpsdoiorg1023073546654

HoelmerKA(1996)WhiteflyparasitoidsCantheycontrolfieldpop-ulationsofBemisia InDGerlingampDMayer (Eds)Bemisia 1995 Taxonomy biology damage control and management (pp 451ndash476)AndoverHantsUKIntercept

Hokkanen H M T (1985) Success in classical biological con-trol Critical Reviews in Plant Sciences 3 35ndash72 httpsdoiorg10108007352688509382203

HoodGM(2010)PopToolsversion325Availablefromhttpwwwpoptoolsorg

HorovitzC SchemskeDWampCaswellH (1997) The relative ldquoim-portancerdquo of life-history stages to population growth Prospectiveand retrospective analyses In S Tuljapurkar amp H Caswell (Eds)Structured-population models in marine terrestrial and freshwater sys-tems (pp 247ndash271)NewYorkNYChapman andHall httpsdoiorg101007978-1-4615-5973-3

KennedyGCampStorerNP (2000)Lifesystemsofpolyphagousar-thropod pests in temporally unstable cropping systems Annual Review of Entomology 45 467ndash493 httpsdoiorg101146 annurevento451467


Levin LCaswellH Bridges TDibaccoCCabreraDampPlaiaG(1996) Demographic responses of estuarine polychaetes to pol-lutants Life table response experimentsEcological Applications61295ndash1313httpsdoiorg1023072269608

MainesAKnochelDampSeastedtT(2013)Biologicalcontrolandpre-cipitationeffectsonspottedknapweed(Centaurea stoebe)Empiricalandmodeling resultsEcosphere4(7) 80 httpsdoiorg101890ES13-000941

McEvoy P B amp Coombs EM (1999) Biological control of plant in-vaders Regional patterns field experiments and structured pop-ulation models Ecological Applications 9 387ndash401 httpsdoiorg1018901051-0761(1999)009[0387BCOPIR]20CO2

MillsNJ (2005)SelectingeffectiveparasitoidsforbiologicalcontrolintroductionsCodlingmoth as a case studyBiological Control34274ndash282httpsdoiorg101016jbiocontrol200502012

Naranjo S E (2018) Data from Retrospective analysis of a classical biologicalcontrolprogrammeUSDANationalAgriculturalLibraryAgDataCommonshttpsdoiorg1015482usdaadc1373297

Naranjo S EampEllsworthPC (2005)Mortality dynamics andpop-ulation regulation in Bemisia tabaci Entomologia Experimentalis et Applicata11693ndash108httpsdoiorg101111(ISSN)1570-7458

Naranjo S E amp Ellsworth P C (2009) Fifty years of the integratedcontrol concept Moving the model and implementation forwardin Arizona Pest Management Science 65 1267ndash1286 httpsdoiorg101002ps1861

NaranjoSEampEllsworthPC(2017)MethodologyfordevelopinglifetablesforsessileinsectsinthefieldusingthewhiteflyBemisia tabaciin cottonasamodel system Journal of Observed Experiments129e56150httpsdoiorg10379156150

NaranjoSEEllsworthPCampCańasL (2009)Mortalityandpop-ulations dynamics of Bemisia tabaci within a multi-crop systemInPGMasonDRGillespieampCDVincent(Eds)Proceedings of the third international symposium on biological control of arthropods Christchurch New Zealand (pp202ndash207)MorgantownWVUSDAForestServicePublFHTET-2008-06

NaranjoSEEllsworthPCampFrisvoldGB(2015)Economicvalueofbiologicalcontrolinintegratedpestmanagementofmanagedplantsystems Annual Review of Entomology 60 621ndash645 httpsdoiorg101146annurev-ento-010814-021005

NaranjoSEampFlintHM (1994)SpatialdistributionofpreimaginalBemisia tabaci (Homoptera Aleyrodidae) in cotton and develop-ment of fixed-precision sequential sampling plans Environmental Entomology23254ndash266httpsdoiorg101093ee232254

NaranjoSEampFlintHM(1995)SpatialdistributionofadultBemisia tabaci (Homoptera Aleyrodidae) in cotton and development andvalidation of fixed-precision sampling plans for estimating popula-tion density Environmental Entomology 24 261ndash270 httpsdoiorg101093ee242261

NaranjoSampLiS(2016)Longtermdynamicsofaphelinidparasitoidsattacking Bemisia tabaci Biological Control 93 56ndash64 httpsdoiorg101016jbiocontrol201511010

Oliveira M R V Henneberry T J amp Anderson P (2001) Historycurrent status and collaborative research projects for Bemisia tabaci Crop Protection 20 709ndash723 httpsdoiorg101016S0261-2194(01)00108-9

OnillonJC(1990)TheuseofnaturalenemiesforthebiologicalcontrolofwhitefliesInDGerling(Ed)Whiteflies Their bionomics pest sta-tus and management(pp287ndash313)AndoverHantsUKIntercept

PalumboJCHorowitzARampPrabhakerN(2001)Insecticidalcon-trolandresistancemanagementforBemisia tabaci Crop Protection20739ndash765httpsdoiorg101016S0261-2194(01)00117-X

PickettCKeavenyDampRoseM (2013) Spreadandnon-targetef-fectsofEretmocerus mundus importedintoCaliforniaforcontrolofBemisia tabaci 2002-2011Biological Control65 6ndash13 httpsdoiorg101016jbiocontrol201301008

RoyamaT(1996)AfundamentalprobleminkeyfactoranalysisEcology7787ndash93httpsdoiorg1023072265658

SheaKampKellyD (1998)Estimatingbiocontrolagent impactwithma-trixmodelsCarduus nutansinNewZealandEcological Applications8824ndash832 httpsdoiorg1018901051-0761(1998)008[0824EBAI WM]20CO2

Stiling P (1988) Density-dependent processes and key factors in in-sectpopulationsJournal of Animal Ecology57581ndash593httpsdoiorg1023074926

van Driesche R G amp Hoddle M S (2000) Classical arthropod bi-ological controlMeasuring success step by step InGGurramp SWratten (Eds) Biological control Measures of success (pp 39ndash75)DordrechtTheNetherlandsKluwerAcademicPublishershttpsdoiorg101007978-94-011-4014-0

VarleyGCGradwellGRampHassellMP (1973) Insect population ecology An analytical approachLondonUKBlackwell

Wagner T L (1994) Temperature-dependent reproduction of sweet-potato whitefly Bemisia tabaci (Homoptera Aleyrodidae) In D JHerberampDARichter(Eds)Proceedings beltwide cotton conferences (pp885ndash886)MemphisTNNationalCottonCouncil

Wagner T L (1995) Temperature-dependent development mortal-ity and adult size of sweetpotatowhitefly biotypeB (HomopteraAleyrodidae) on cotton Environmental Entomology24 1179ndash1188httpsdoiorg101093ee2451179

Wiens J amp Parker K (1995) Analyzing the effects of accidental en-vironmental impacts Approaches and assumptions Ecological Applications51069ndash1083httpsdoiorg1023072269355

ZangLSampLiuTX(2008)Host-feedingofthreeparasitoidspecieson Bemisia tabacibiotypeBandimplicationsforwhiteflybiologicalcontrolEntomologia Experimentalis et Applicata12755ndash63httpsdoiorg101111j1570-7458200800667x

SUPPORTING INFORMATION

Additional supporting information may be found online in theSupportingInformationsectionattheendofthearticle

How to cite this articleNaranjoSERetrospectiveanalysisofaclassicalbiologicalcontrolprogrammeJ Appl Ecol 2018001ndash12 httpsdoiorg1011111365-266413163

Appendix S1 Life table set-up identification of mortality factors and temperature-dependent development and reproduction LIFE TABLE SET-UP

Life table data were generated using an in situ observational method that took advantage of the sessile nature of the immature stages of B tabaci This approach avoided practical issues associated with the broadly overlapping generations of this multivoltine insect Groups of newly-laid eggs were established from existing natural populations on the undersides of cotton leaves These eggs were characterized by their creamy-white color and their location on leaves near the terminal growth of the cotton plant With the aid of an 8X Peakreg loupe (Adorama New York New York USA) a non-toxic ultra-fine-point black permanent marker (Sanford Illinois USA) was used to draw a small circle around individual eggs or small clusters of no more than 4ndash5 eggs A small numbered tag was then placed around the petiole of the leaf and flagging tape was tied around the mainstem of the plant to facilitate relocation No more than 5ndash7 eggs were marked on a single leaf and the main leaf sectors defined by the major leaf veins were used to help track each egg in the study cohort Only a single leaf was used per plant and plants were evenly spaced along the central 2ndash3 rows of each study plot The total number of plants used per plot varied depending on insect density and ranged from 20ndash40 Forty to sixty eggs were marked in each plot for a total of 160ndash240 eggs on 80-160 plants per life table over four replicate plots

A similar procedure was used to locate and mark the position of newly settled 1st instar nymphs with the exception that marked circles never contained more than one nymph Crawlers were readily distinguished from settled 1st instars as the latter have a more translucent color and are flush with the leaf surface Typically crawlers settle in a few minutes to 1-2 h after eclosion To verify that settled nymphs and not mobile crawlers were marked leaves were re-examined 1-2 h later and re-marked as needed Forty to sixty nymphs were marked per plot on 20-40 plants (total of 160ndash240 nymphs on 80-160 plants per cohort) Cohorts were established on a single day during morning hours and eggs and 1st instar nymphs were marked on different plants Between 2ndash6 individual life tables were established each year of the study and generally between late-June and late September These dates cover the breadth of the insectrsquos occurrence on cotton in central Arizona (Naranjo amp Ellsworth 2009) Using this in situ observational approach each life table represented a single generation of this multivoltine insect which facilitated direct estimation of mortality rates due to various causes

IDENTIFICATION OF MORTALITY FACTORS

After establishing cohorts each nymph was examined every 2ndash3 days (three times per week) in the field with the aid of a 15X Peakreg loupe (Adorama New York New York USA) until that individual died or emerged as an adult Eggs were too difficult to examine in the field and so all leaves containing eggs were collected after 8-10 days and returned to the laboratory for examination under a dissecting microscope Under typical summer conditions in central Arizona eggs complete development in about 5 days and so any relevant field mortality would have already occurred by the time they were collected During each observation the instar of each live nymph and the instar and cause of death of each dead nymph were recorded Mortality was categorized into one of five causes dislodgment predation parasitism inviability (eggs only) and unknown Dislodgement was the result of abiotic factors such as wind and rain and chewing predation The stage of dislodged nymphs was estimated by the average stage of other dead

nymphs on the same observation date Predation was mortality primarily due to sucking predators that had evacuated the contents of the whitefly body leaving a deflated and transparent nymphal cuticle or egg chorion on the leaf (see Naranjo amp Ellsworth 2017) Very infrequently chewing predation could be seen in partially intact cadavers Aphelinid parasitoids can successfully attack all nymphal stages of B tabaci (Foltyn amp Gerling 1985 Headrick Bellows amp Perring 1995 Liu amp Stansly 1996) but parasitism is only visible in 4th instar nymphs Parasitoid presence was characterized by the displacement of the whiteflyrsquos bacteriosomes or by live parasitoid larvae or pupae within the host There are no known egg parasitoids of B tabaci Inviable eggs were simply those that failed to eclose after 10 days When all marked individuals on a leaf either had died or emerged the leaf was collected and returned to the laboratory to verify the cause of death (with a dissecting microscope) for those cadavers remaining on the leaf Mortality during the brief period between crawler emergence and settling was not explicitly measured The duration between eclosion and settling averages only a few hours (Price amp Taborsky 1992 Simmons 2002) and field studies have shown that crawler mortality on cotton is negligible (Naranjo 2007)

TEMPERATURE-DEPENDENT DEVELOPMENT AND REPRODUCTION The temperature-dependent rates of development for eggs and nymphs were estimated from Wagner (1995) He used the 6 and 4 parameter Sharpe and DeMichele (1977) models for eggs and nymphs (1st instar to adult) respectively given as

$ amp()+-

[0 1(333456+ amp)]

9-[ 1(+ amp6+ amp)]-[ 1(+ amp6+ amp)] eqn S1 $ amp

()+-[0 1(333456+ amp)]

9-[ 1(+ amp6+ amp)] eqn S2 where temperature T is measured in degrees Kelvin R is the university gas constant (1987) and RHO25 HA HL TL HH and TH are fitted biophysical parameters The duration of each nymphal stadium was then estimated from the proportion of time spent in each of the four nymphal stages (Bethke Paine amp Nuessly 1991 Powell amp Bellows 1992) Temperatures were estimated from archived mean daily air temperatures from an on-site weather station (AZMET 2016) for the short period (2-3 week) between cohort establishment and adult emergence for each life table Parameter Egg Nymph RHO25 02043 00977 HA 21176 25232 HL -84284 TL 2862 HH 42173 61409 TH 3042 3017 R2 099 098 Temperature-dependent lifetime fecundity (eggs per female) and adult longevity was derived from published and unpublished data by Wagner (1994) A bounded (15 ndash 40˚C) 3rd degree polynomial regression predicted lifetime fecundity as a function of temperature t in ˚C (fecundity = 9599t ndash 362t2 + 004t3 ndash 6552 R2 = 036) Adult longevity was predicted by regressing ln longevity on

temperature (ln[longevity] = 441 ndash 0073t R2 = 068) Mean daily air temperatures were calculated for the 10-day interval following adult emergence using the archived weather data noted above for each life table References AZMET (2016) The Arizona meteorological network URL httpagarizonaeduazmet Bethke JA Paine TD amp Nuessly GS (1991) Comparative biology morphometrics and

development of two populations of Bemisia tabaci (HomopteraAleyrodidae) on cotton and poinsettia Annals of the Entomological Society of America 84 407-411

Foltyn S amp Gerling D (1985) The parasitoids of the aleyrodid Bemisia tabaci in Israel development host preference and discrimination of the aphelinid wasp Eretmocerus mundus Entomologia Experimentalis et Applicata 38 255-260

Headrick DH Bellows TS amp Perring TM (1995) Behaviors of female Eretmocerus sp nr californicus (Hymenoptera Aphelinidae) attacking Bemisia argentifolii (Homoptera Aleyrodidae) on sweet potato Environmental Entomology 24 412-422

Liu TX amp Stansly PA (1996) Oviposition development and survivorship of Encarsia pergandiella (Hymenoptera Aphelinidae) in four instars of Bemisia argentifolii (Homoptera Aleyrodidae) Annals of the Entomological Society of America 89 96-102

Naranjo SE (2007) Survival and movement of Bemisia tabaci (Homoptera aleyrodidae) crawlers on cotton Southwestern Entomologist 32 17-23

Naranjo SE amp Ellsworth PC (2009) Fifty years of the integrated control concept moving the model and implementation forward in Arizona Pest Management Science 65 1267-1286

Naranjo SE amp Ellsworth PC (2017) Methodology for developing life tables for sessile insects in the field using the whitefly Bemisia tabaci in cotton as a model system Journal of Observed Experiments e56150 doi10379156150

Powell DA amp Bellows TS Jr (1992) Preimaginal development and survival of Bemisia tabaci on cotton and cucumber Environmental Entomology 21 359-363

Price JF amp Taborsky D (1992) Movement of immature Bemisia tabaci (Homoptera Aleyrodidae) on poinsettia leaves Florida Entomologist 75 151-153

Simmons AM (2002) Settling of crawlers of Bemisia tabaci (Homoptera Aleyrodidae) on five vegetable hosts Annals of the Entomological Society of America 95 464-468

Table S1 Summary of cotton life table study sites 1997ndash2010 Maricopa Arizona USA

Year Cultivar Study area

(ha)

Total

exp area

(ha)

No

generations

Associated

study

1997 DP NuCOTN33B 024 19 4 dagger



2001 DP NuCOTN33B5415 068 14 4 Dagger



2004 DP 449BR 038 08 2 Current study

2005 DP 449BR 030 ndash 052 53 4 Current study


2007 DP 164B2RF 052 37 2 Current study

2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para

Deltapine 5415 is the non-Bt isoline of NuCOTN33B dagger Naranjo SE amp Ellsworth PC (2005) Mortality dynamics and population regulation in

Bemisia tabaci Entomologia Experimentalis et Applicata 116 93-108 Dagger Naranjo SE (2005) Long-term assessment of the effects of transgenic Bt cotton on the

function of the natural enemy community Environmental Entomology 34 1211-1223 para Asiimwe P Ellsworth PC amp Naranjo SE (2016) Natural enemy impacts on Bemisia tabaci

(MEAM1) dominate plant quality effects in the cotton system Ecological Entomology 41 642-652

Table S2 General projection matrix for the stage-structured B tabaci population model where si is daily stage-specific survival rate gi is daily stage-specific rate of growth to the next stage and F is the mean daily rate of adult fertility (females per female)

Egg N1 N2 N3 N4 Adult

Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0

Adult 0 0 0 0 sN4(g4) sa

Where N1-N4 are nymphal instars 1-4 and

se = sinviable(e)sdislodged(e)spredation(e)

sN1 = sdislodged(N1)spredation(N1)sunknown(N1)

sN2 = sdislodged(N2)spredation(N2)sunknown(N2) sN3 = sdislodged(N3)spredation(N3)sunknown(N3)

sN4 = sdislodged(N4)sparasitism(N4)spredation(N4)sunknown(N4)

Figure S1 Life cycle diagram for projection matrix depicted in Table S2

N1 AdultEgg N2 N3 N4

se(ge)

se(1-ge)

F

sN1(1-gN1) sN2(1-gN2) sN3(1-gN3) sN4(1-gN4) sa

sN1(gN1) sN2(gN2) sN3(gN3) sN4(gN4)

Appendix S2 Key factor analysis

Introduction

Key factor analysis (Varley Gradwell amp Hassell 1973) is frequently applied in life table studies as a means of understanding what mortality factor andor stage contribute the most to variability in total population survival The approach has been criticized because it is thought to lack the ability to reflect patterns of population change and the underlying causes of this change (Royama 1996 Sibly and Smith 1998) A key factor analysis was conducted here to compare patterns with elasticity analysis and with matrix manipulations where mortality associated with particular factors and life stages were removed from the model Materials and Methods

Key factor analysis was conducted using the regression approach of Podoler amp Rogers (1975) where individual stage or factor mortality k-values (k=-ln[1-M] where M is marginal mortality) are regressed on total K (generational mortality) The largest positive regression coefficient (slope) identifies the key factor Analyses identified the key stage across all factors the key factor across all stages and the key factor within the 4th nymphal stage Additional focus on mortality within the 4th stage was indicated from the elasticity and matrix model analyses and prior research (Naranjo amp Ellsworth 2005 Asiimwe Ellsworth amp Naranjo 2016) Results

Key factor analysis indicated that mortality during the 4th nymphal stadium was most closely associated with changes in generational mortality both before and after the establishment of exotic aphelinid parasitoids (Table S3) Looking at mortality factors over all stages predation emerged as the key factor and this pattern was again no changed with exotic parasitoid establishment Parasitism was the third to fourth most important factor associated with changes in generational mortality Given the apparent importance of mortality during the 4th nymphal stadium further focus was placed on mortality factors within this stage Again predation was the key factor with parasitism being the least important factor during pre-establishment but the second most important factor after establishment References

Asiimwe P Ellsworth PC amp Naranjo SE (2016) Natural enemy impacts on Bemisia tabaci (MEAM1) dominate plant quality effects in the cotton system Ecological Entomology 41 642-652

Naranjo SE amp Ellsworth PC (2005) Mortality dynamics and population regulation in Bemisia tabaci Entomologia Experimentalis et Applicata 116 93-108

Podoler H amp Rogers D (1975) A new method for the identification of key factors from life-table data Journal of Animal Ecology 44 85-114

Royama T (1996) A fundamental problem in key factor analysis Ecology 77 87-93 Sibly RM amp Smith RH (1998) Identifying key factors using l-contribution analysis Journal

of Animal Ecology 67 17-24

Table S3 Analyses for identifying which life stage mortality and which mortality factors contribute to changes in generational mortality (key factor) of Bemisia tabaci in cotton before and after the establishment of exotic aphelinid parasitoids Maricopa Arizona USA The most influential stages or factors are depicted in bold type FactorStage Key factor analysisdagger

Pre (97-99)

Post (01-10)

All years

Stagepara Egg 0342 0088 0182

N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101

N4 Factor Dislodgement 0149 0043 0088


Predation 0222 0363 0233

Unknown 0163 0065 0123

n = 13 generations for pre-establishment 31 for post-establishment Stage each life stage over all factors (N1 = 1st instar nymph etc) Factors each factor over all life stages N4 factor mortality factors during 4th stadium only and so slopes shown do not sum to unity for key factor analyses daggerValues are the slopes of k-values for stages or factors regressed on total K for the generation

Appendix S3 Details of LTRE analyses Retrospective matrix model analyses A LTRE approach was used to assess the contribution of vital rates to changes in population growth due to parasitoid establishment Following Caswell (1996) the effect onl is given generally by 120582= minus120582 asymp (119886CE=CE minus 119886CE )

FGFHIJ

|(Am+A

r)2 eqn S1

where the superscripts m and r refer to treatment and control (or reference) respectively aij are the elements of the projection matrices andparalparaaijis the sensitivity oflto changes in the matrix elements evaluated on the mean of the treatment and control matrices ([Am+Ar]2) The matrix elements alone are of little interest here because they are composed of vital rates representing both growth (gi) and survival (si) Survival in turn is composed of effects from multiple specific mortality factors (eg predation parasitism dislodgement etc) Caswell (1996) showed that in general the sensitivity oflto changes in any lower-level vital rate x is given by FGFK= FG

FHIJCEFHIJFK

eqn S2

Thus for decomposed lower level vital rates eqn S1 can be expanded to 120582= minus 120582 asymp (120590C=C minus 120590C)

FGFNI

+ (120574C=C minus 120574C)FGFQI

+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI

1 minus 120574C + FGFWI

120574C eqn S4 FGFQI

= FGFUI

minus120590C + FGFWI

120590C eqn S5 Given that survival for any single immature stage is the product of the individual survival values sk from each cause of mortality k 120590C= 119904YY eqn S6 eqn S3 can be further decomposed into specific causes of death by substituting forsi120582= minus 120582 asymp (119904Y=Y minus 119904Y)C

FGFZI[


+ (119865= minus 119865) FGFT+ (120590H= minus 120590H)

FGFN

eqn S7

wheresais adult survival and FGFZI[

= FGFUI

(1 minus 120574C)( 119904CY)119904CYY + FGFWI

120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI

minus 119904CYY + FGFWI

119904CYY eqn S9 generally Recall that there are three sources of mortality for eggs (inviability predation dislodgement) and each of the first three nymphal instars (predation dislodgement unknown) and four sources of mortality for 4th instar nymphs (parasitism predation dislodgement unknown) As an example for the egg stage e the sensitivity ofl with respect to decomposed survival rates for inviability is FG

FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`

120574-119904a-b119904bCZ eqn S10 where the superscripts inv pred and dis denote inviability predation and dislodgement References Caswell H (1996) Analysis of life table response experiments II Alternative parameterizations

for size- and stage-structured models Ecological Modelling 88 73-82

Appendix S4 Factors contributing to program failure

Several elements may have contributed to the lack of efficacy of these newly established parasitoids in the Arizona cotton system First aphelinid parasitoids are not strong dispersers (Hagler et al 2002 Byrne and Bellamy 2003) which does not align well with annual crops such as cotton that must be recolonized each season by both the pest and its natural enemies Low dispersal capacity is supported by studies where parasitism is enhanced when host crops are grown in closer proximity to one another over the seasonal cycle (Naranjo Ellsworth amp Cantildeas 2009) when cotton is embedded in a more diverse cropping system (Karut amp Naranjo 2009) and when host plant diversity in the form of annual and perennial plant borders were deployed to promote parasitoid establishment (Roltsch et al 2008) Ephemeral annual crops pose significant challenges to biological control in general and classical biological control specifically because it selects for certain attributes in introduced natural enemies that are not often the focus of agent selection (Gilstrap 1997 Wiedenmann amp Smith 1997) The relatively poor record of success of classical biological control in annual compared with perennial crops further highlights this challenge (DeBach 1964 Hawkins et al 1999)

Second one of the species established is an autoparasitoid (E sophia) in this system Data and models suggest that the outcomes of biological control that include autoparasitic species can be either positive or negative (Summy et al 1983 Mills amp Gutierrez 1996 Williams 1996 Bogran Heinz amp Ciomperlik 2002 Hunter Collier amp Kelly 2002 Zang Liu amp Wan 2011) By 2006 E sophia had become the dominant aphelinid attacking B tabaci in cotton (Naranjo amp Li 2016) but life tables showed no clear change in parasitism rates or overall host mortality Hence there is no clear evidence that the introduction of this species played a role in the outcome of this program

Third life tables may not have adequately quantified host-feeding and thus additional mortality imposed by both the native or exotic aphelinid parasitoid species This behavior is common in aphelinid parasitoids attacking a wide range of insect species including the parasitoid species examined here (eg Urbaneja et al 2003 Ardeh de Jong amp van Lenteren 2005 Zang amp Liu 2008) Life table observations failed to detect any obvious evidence of host-feeding in B tabaci nymphs although this mortality could have on occasion been incorrectly captured in the unknown category Sucking predators would nearly or most often completely disrupt and evacuate the contents of nymphs and this has a distinctly different appearance than cadavers on which parasitoids had feed (Naranjo amp Ellsworth 2017) It is possible that host-fed nymphs were subsequently attacked by sucking predators or dislodged from the leaf before they could be observed Nonetheless even if exotic parasitoids engaged more readily in host-feeding than their native counterparts this did not translate into higher overall rates of mortality or reduced population growth rates

Fourth the target pest is extremely polyphagous a factor that complicates management by any control tactic One of the initial rationales for implementing a classical biological control program was the fact that parasitoid diversity was low in crops impacted by the pest particularly winter and spring vegetable crops (Hoelmer Schuster amp Ciomperlik 2008) and considerable research examined the fit of exotic parasitoids to specific crops (Gould Hoelmer amp Goolsby 2008) Nonetheless the combination of ephemeral crops with a polyphagous pest well adapted to exploiting these crops may represent an extreme challenge for classical biological control In contrast the fact that native generalist arthropod predators inflict high levels of mortality on B tabaci in cotton as well as a number of other crops (Naranjo Ellsworth amp Cantildeas 2009) suggest that this natural enemy community is well adapted to exploiting ephemeral prey resources

A final consideration is the relatively widespread use of insecticides in the management of B tabaci in all affected crops albeit at lower use rates since the mid-1990s (Naranjo amp Ellsworth 2009) While exotic parasitoid establishment appeared to be unaffected by insecticide use it almost certainly played a role in the dynamics of these host-specific parasitoids and thus the impact they can have on the pest suppression (Gerling amp Naranjo 1998) Although the LTREs were conducted in insecticide free fields they were embedded within a representative cropping system where insecticides were routinely used in adjacent fields for management of B tabaci and other pests of cotton and other crops This is a reality in commercial production systems affected by this pest and likely many other pest species that have been the subject of classical biological control efforts

References

Ardeh MJ de Jong PW amp van Lenteren JC (2005) Selection of Bemisia nymphal stages for oviposition or feeding and host-handling times of arrhenotokous and thelytokous Eretmocerus mundus and arrhenotokous E eremicus Biocontrol 50 449-463

Bogran CE Heinz KM amp Ciomperlik MA (2002) Interspecific competition among insect parasitoids Field experiments with whiteflies as hosts in cotton Ecology 83 653-668

Byrne DN amp Bellamy DE (2003) Migration by the sweet potato whitefly Bemisia tabaci and its aphelinid parasitoid Eretmocerus eremicus Journal of Insect Science 3 33 httpsdoiorg101093jis3133

DeBach P ed (1964) Biological Control of Insect Pests and Weeds Reinhold Publishing New York USA

Gerling D Alomar O amp Arnoacute J (2001) Biological control of Bemisia tabaci using predators and parasitoids Crop Protection 20 779-799

Gilstrap FE (1997) Importation biological control in ephemeral crop habitats Biological Control 10 23-29

Gould J Hoelmer K amp Goolsby J (eds) (2008) Classical Biological Control of Bemisia tabaci in the United States A Review of Interagency Research and Implementation Springer Dordrecht-Heidelberg-London-New York

Hagler JR Jackson CG Henneberry TJ amp Gould JR (2002) Parasitoid mark-release-recapture techniques - II Development and application of a protein marking technique for Eretmocerus spp parasitoids of Bemisia argentifolii Biocontrol Science and Technology 12 661-675

Hawkins BA Mills NJ Jervis MA amp Price PW (1999) Is the biological control of insects a natural phenomenon Oikos 86 493-506

Hoelmer K Schuster D amp Ciomperlik M (2008) Indigenous parasitoids of Bemisia in the USA and potential for non-target impacts of exotic parasitoid introductions Classical Biological Control of Bemisia tabaci in the United States A Review of Interagency Research and Implementation (eds J Gould K Hoelmer amp J Goolsby) pp 307-327 Springer Dordrecht-Heidelberg-London-New York

Hunter MS Collier TR amp Kelly SE (2002) Does an autoparasitoid disrupt host suppression provided by a primary parasitoid Ecology 83 1459-1469

Karut K amp Naranjo SE (2009) Mortality factors affecting Bemisia tabaci populations on cotton in Turkey Journal of Applied Entomology 133 367-374

Mills NJ amp Gutierrez AP (1996) Prospective modelling in biological control An analysis of

the dynamics of heteronomous hyperparasitism in a cotton-whitefly-parasitoid system Journal of Applied Ecology 33 1379-1394

Naranjo S amp Li S (2016) Long term dynamics of aphelinid parasitoids attacking Bemisia tabaci Biological Control 93 56-64


Naranjo SE amp Ellsworth PC (2017) Methodology for developing life tables for sessile insects in the field using the whitefly Bemisia tabaci in cotton as a model system Journal of Observed Experiments ndash Environment (in press)

Naranjo SE Ellsworth PC amp Cańas L (2009) Mortality and populations dynamics of Bemisia tabaci within a multi-crop system Proceedings of the Third International Symposium on Biological Control of Arthropods Christchurch New Zealand (eds PG Mason DR Gillespie amp CD Vincent) pp 202-207 USDA Forest Service Publ FHTET-2008-06

Roltsch WJ Pickett CH Simmons GS amp Hoelmer KA (2008b) Habitat management for the establishment of Bemisia natural enemies Classical Biological Control of Bemisia tabaci in the United States A Review of Interagency Research and Implementation (eds J Gould K Hoelmer amp J Goolsby) pp 243-257 Springer Dordrecht-Heidelberg-London-New York

Summy KR Gilstrap FE Hart WG Caballero JM amp Saenz I (1983) Biological control of the citrus blackfly (Homoptera Aleyrodidae) in Texas Environmental Entomology 12 782-786

Urbaneja A Stansly PA Beltran D Sanchez E amp Gallego A (2003) Life history of Eretmocerus mundus Mercet (Hymenoptera Aphelinidae) on Bemisia tabaci biotype Q (Homoptera Aleyrodidae) using sweet pepper and tomato Bulletim of OILBSROP 26 67

Wiedenmann RN amp Smith JW (1997) Attributes of natural enemies in ephemeral crop habitats Biological Control 10 16-22

Williams T (1996) Invasion and displacement of experimental populations of a conventional parasitoid by a heteronomous hyperparasitoid Biocontrol Science and Technology 6 603-618

Zang LS amp Liu TX (2008) Host-feeding of three parasitoid species on Bemisia tabaci biotype B and implications for whitefly biological control Entomologia Experimentalis et Applicata 127 55-63

Zang LS Liu TX amp Wan FH (2011) Reevaluation of the value of autoparasitoids in biological control PLoS ONE 6 e20324 doi101371 journalpone0020324


1emsp |emspINTRODUC TION

Classicalbiologicalcontrolhasbeenakeytechnologyintheman-agementof invasive arthropodpests globally forover120yearsThe goal is to provide long-lasting pest control by reestablishinguppertrophic level linksthroughthe introductionofnaturalene-miesfromthepestrsquosnativeregion (DeBach1964)Recentexam-inationofclassicalbiologicalcontrolprogrammes indicatesabouta33rateofsuccessinestablishingexoticagentsanda10rateofachievingsome levelof satisfactorycontrolof targeted insectpests (Cock etal 2016) Although a risky endeavour given thelow chances of success the few economic analyses conductedforinsecttargetsindicateamedianbenefittocostratioofaround801withmanyprogrammesprovidinglong-termpestsuppressionwithcontinuingeconomicbenefits(NaranjoEllsworthampFrisvold2015)Classicalbiological controlprogrammesare complexwithlongtimehorizonsandwithmanycriticalstepsrequiredtoachievesuccess(DeBach1964Hokkanen1985vanDriescheampHoddle2000) The final phase should involve evaluation of overall out-comesoftheprogrammeinecologicalsociologicalandeconomictermsastepthatisfrequentlyoverlookedduelargelytothelackofpersonnelandfinancialresourcesduringthelaterstagesoftheprogramme (McEvoy amp Coombs 1999 van Driesche amp Hoddle2000)

Lifetablesandassociatedmatrixmodelshavenotbeenwidelyappliedinclassicalbiologicalcontrolbuttheycanprovideimport-ant insight on the impact of introduced species on targeted pestpopulationsProspectiveapproacheshavebeenusefulforidentify-ing lifestagevulnerabilities suggestingpotentialagents toexploitthese vulnerabilities and predicting the impact on pest popula-tionsbyplannedorongoingbiologicalcontrolprogrammes(BarkerampAddison2006Davisetal2006MainesKnochelampSeastedt2013Mills2005SheaampKelly1998)Retrospectiveanalysescanidentify the contribution of specific vital rates and enhance ourunderstanding of factors contributing to the success or failure ofaprogramme (CattonLalondeBuckleyampdeClerck-Floate2016DauerMcEvoyampvanSickle2012McEvoyampCoombs1999)Lifetable response experiments (LTRE) are a retrospective approachwhereby changes in population growth due to an environmentalvariablecanbedecomposedintotheindividualcontributionsoftheunderlyingvitalrates(Caswell1996)LTREshavehadbroadappli-cation in investigating such areas as species conservation and ec-otoxicology (Caswell 2001) and are ideally suited to investigatingpestpopulationresponsestoclassicalbiologicalcontrolwherethe

environmentalchangeisrepresentedbytheadditionofnewnaturalenemyspeciesintoanexistingcommunity

Several interrelated hypotheses should be supported if in-troduced species have provided successful biological controlIntroductionsshouldsupplynewandhigherlevelsofmortalityandthismortalityshouldbeassociatedwithdeclinesinpestpopulationgrowthInadditionthenewsourceofmortalitymightdisplaysomelevelofregulatorydensitydependenceProspectiveandretrospec-tiveapproachesbasedonlifetablematrixmodelandLTREanalyseswereusedtoecologicallyevaluateandinformaclassicalbiologicalcontrolprogrammetargetingBemisia tabaciGennadius(MEAM1)aninsectpestofglobalsignificance

2emsp |emspMATERIAL S AND METHODS

21emsp|emspStudy system

The invasive B tabaci(sweetpotatowhitefly)isakeyeconomicpestof multiple annual crops in agricultural production regions in thesouthern tier of theUSA including cottonGossypium hirsutum L(OliveiraHenneberryampAnderson2001)Classicalbiologicalcon-trol was immediately considered a viable approach for pestman-agementduetopastsuccesswithotherwhiteflyspecies (BellowsBezark Paine Ball amp Gould 1992 Onillon 1990) and limitedparasitoiddiversity in affectedcrops (Hoelmer1996) Inpart theprogramme introduced about 30 populations of Eretmocerus spp and Encarsia spp nymphal parasitoids into the lowdesert agricul-tural production areas of California andArizona (GouldHoelmeramp Goolsby 2008) Eretmocerus sp from Ethiopia and the heter-onomoushyper-parasitoidEncarsia sophia (GiraultandDodd) fromPakistanbecamepermanentlyestablishedinArizonaaround2001largelydisplacingseveralnativeaphelinidspeciespreviouslyattack-ingthepest(NaranjoampLi2016)

22emsp|emspStudy sites

Study sites were located on the asymp900ha University of ArizonaMaricopaAgriculturalCenterfarminMaricopaArizonaUSAThefarmgrowsamixofcropsrepresentativeofagriculturalproductionin central Arizona including agronomic (cotton wheat sorghum alfalfa)andvegetablecropsField-basedlifetabledataonimmatureB tabaciwere generated on insecticide-free cotton from1997 to2010Cottonvarietiesrepresentativeofcommercialproductionintheareawereplantedmid-Apriltoearly-Mayeachyearandgrown

Analysesdeployedhereshouldbemorewidelyappliedtoassessingandimprovingbiologicalcontrolgenerally

K E Y W O R D S

Bemisia tabaciclassicalbiologicalcontrolelasticitylifetableresponseexperimentmatrixmodelparasitismpopulationgrowthpredation














γiwhere






(1)MB=dB∕(

1minusdA

)

(2)(1minus

prodj


prodjminus1

1[1minusMk])














(3)mminus


k)

aij

aij

xk|(Am+Ar

)∕2


3emsp |emspRESULTS














StageFactor



Egg



Smallnymph

Predation 0002(091) 0003(080) 0003(071) minus0015(047) 0012(048) 00159(020)


Largenymph

Parasitism 0009(078) 0082 (0002) 0048 (001) 0034(044) 0106(0016) 0078 (001)

Predation 0016(068) 0008(084) 0039(018) 0090(009) minus0021(073) 0061(021)

Dislodgement 0015(060) 0012(060) 0006(071) 0028(049) 0029(034) 0021(036)

Allnymph

Parasitism 0012(069) 0065 (001) 0041(003) 0028(053) 0102(002) 0075(002)

n 14 31 45 11 23 34




















Factorstage



Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567




Predation 3394 4076 3874

Unknown 1812 0883 1157



Predation 0955 1720 1494

Unknown 0739 0576 0624















ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References







































γiwhere






(1)MB=dB∕(

1minusdA

)

(2)(1minus

prodj


prodjminus1

1[1minusMk])














(3)mminus


k)

aij

aij

xk|(Am+Ar

)∕2


3emsp |emspRESULTS














StageFactor



Egg



Smallnymph

Predation 0002(091) 0003(080) 0003(071) minus0015(047) 0012(048) 00159(020)


Largenymph

Parasitism 0009(078) 0082 (0002) 0048 (001) 0034(044) 0106(0016) 0078 (001)

Predation 0016(068) 0008(084) 0039(018) 0090(009) minus0021(073) 0061(021)

Dislodgement 0015(060) 0012(060) 0006(071) 0028(049) 0029(034) 0021(036)

Allnymph

Parasitism 0012(069) 0065 (001) 0041(003) 0028(053) 0102(002) 0075(002)

n 14 31 45 11 23 34




















Factorstage



Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567




Predation 3394 4076 3874

Unknown 1812 0883 1157



Predation 0955 1720 1494

Unknown 0739 0576 0624















ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References







































(3)mminus


k)

aij

aij

xk|(Am+Ar

)∕2


3emsp |emspRESULTS














StageFactor



Egg



Smallnymph

Predation 0002(091) 0003(080) 0003(071) minus0015(047) 0012(048) 00159(020)


Largenymph

Parasitism 0009(078) 0082 (0002) 0048 (001) 0034(044) 0106(0016) 0078 (001)

Predation 0016(068) 0008(084) 0039(018) 0090(009) minus0021(073) 0061(021)

Dislodgement 0015(060) 0012(060) 0006(071) 0028(049) 0029(034) 0021(036)

Allnymph

Parasitism 0012(069) 0065 (001) 0041(003) 0028(053) 0102(002) 0075(002)

n 14 31 45 11 23 34




















Factorstage



Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567




Predation 3394 4076 3874

Unknown 1812 0883 1157



Predation 0955 1720 1494

Unknown 0739 0576 0624















ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References



























3emsp |emspRESULTS














StageFactor



Egg



Smallnymph

Predation 0002(091) 0003(080) 0003(071) minus0015(047) 0012(048) 00159(020)


Largenymph

Parasitism 0009(078) 0082 (0002) 0048 (001) 0034(044) 0106(0016) 0078 (001)

Predation 0016(068) 0008(084) 0039(018) 0090(009) minus0021(073) 0061(021)

Dislodgement 0015(060) 0012(060) 0006(071) 0028(049) 0029(034) 0021(036)

Allnymph

Parasitism 0012(069) 0065 (001) 0041(003) 0028(053) 0102(002) 0075(002)

n 14 31 45 11 23 34




















Factorstage



Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567




Predation 3394 4076 3874

Unknown 1812 0883 1157



Predation 0955 1720 1494

Unknown 0739 0576 0624















ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References

































StageFactor



Egg



Smallnymph

Predation 0002(091) 0003(080) 0003(071) minus0015(047) 0012(048) 00159(020)


Largenymph

Parasitism 0009(078) 0082 (0002) 0048 (001) 0034(044) 0106(0016) 0078 (001)

Predation 0016(068) 0008(084) 0039(018) 0090(009) minus0021(073) 0061(021)

Dislodgement 0015(060) 0012(060) 0006(071) 0028(049) 0029(034) 0021(036)

Allnymph

Parasitism 0012(069) 0065 (001) 0041(003) 0028(053) 0102(002) 0075(002)

n 14 31 45 11 23 34




















Factorstage



Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567




Predation 3394 4076 3874

Unknown 1812 0883 1157



Predation 0955 1720 1494

Unknown 0739 0576 0624















ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References












































Factorstage



Stage Egg 3291 2319 2643

N1 1276 1121 1167

N2 1507 0992 1144

N3 1354 1167 1223

N4 3415 3683 3567




Predation 3394 4076 3874

Unknown 1812 0883 1157



Predation 0955 1720 1494

Unknown 0739 0576 0624















ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References






































ACKNOWLEDG EMENTS




ORCID


R E FE R E N C E S



































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References































































$ amp()+-

[0 1(333456+ amp)]


()+-[0 1(333456+ amp)]















(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References







































(ha)

Total

exp area

(ha)

No

generations

Associated

study











2008 DP 164B2RF 072 38 3 para

2009 DP 164B2RF 072 26 3 para

2010 DP 1044B2RF 060 22 3 para







Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References




























Egg se(1-ge) 0 0 0 0 F

N1 se(ge) sN1(1-g1) 0 0 0 0

N2 0 sN1(g1) sN2(1-g2) 0 0 0

N3 0 0 sN2(g2) sN3(1-g3) 0 0

N4 0 0 0 sN3(g3) sN4(1-g4) 0









se(ge)

se(1-ge)

F




Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References



























Introduction










Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References



























Pre (97-99)

Post (01-10)

All years


N1 -0008 0113 0076

N2 0069 0016 0038

N3 0001 0058 0041

N4 0597 0725 0661




Predation 0333 0519 0378

Unknown 0009 0037 0101



Predation 0222 0363 0233

Unknown 0163 0065 0123



FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References



























FGFHIJ

|(Am+A

r)2 eqn S1


FHIJCEFHIJFK

eqn S2


FGFNI


+ (119865= minus 119865) FSFT

eqn S3 where FGFNI

= FGFUI



= FGFUI



FGFZI[



FGFN

eqn S7


= FGFUI


120574C( 119904CY)119904CYY eqn S8

FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References


























FGFQI

= FGFUI



FZI^_= FG

FU(1 minus 120574-)119904a-b119904bCZ +

FGFW`









References
































References


























retrospective analysis of a classical biological control ... 2019/naranj… · 2.3.2 | estimation...

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