简介：Background:Inthispaper,aregressionmodelforpredictingthespatialdistributionofforestcockchaferlarvaeintheHessianRiedregion(Germany)ispresented.Theforestcockchafer,anativebioticpest,isamajorcauseofdamageinforestsinthisregionparticularlyduringtheregenerationphase.ThemodeldevelopedinthisstudyisbasedonasystematicsampleinventoryofforestcockchaferlarvaebyexcavationacrosstheHessianRied.Theseforestcockchaferlarvaedatawerecharacterizedbyexcesszerosandoverdispersion.Methods:Usingspecificgeneralizedadditiveregressionmodels,differentdiscretedistributions,includingthePoisson,negativebinomialandzero-inflatedPoissondistributions,werecompared.Themethodologyemployedallowedthesimultaneousestimationofnon-linearmodeleffectsofcausalcovariatesand,toaccountforspatialautocorrelation,ofa2-dimensionalspatialtrendfunction.Inthevalidationofthemodels,boththeAkaikeinformationcriterion(AIC)andmoredetailedgraphicalproceduresbasedonrandomizedquantileresidualswereused.Results:ThenegativebinomialdistributionwassuperiortothePoissonandthezero-inflatedPoissondistributions,providinganearperfectfittothedata,whichwasproveninanextensivevalidationprocess.Thecausalpredictorsfoundtoaffectthedensityoflarvaesignificantlyweredistancetowatertableandpercentageofpureclaylayerinthesoiltoadepthof1m.Modelpredictionsshowedthatlarvadensityincreasedwithanincreaseindistancetothewatertableuptoalmost4m,afterwhichitremainedconstant,andwithareductioninthepercentageofpureclaylayer.Howeverthislattercorrelationwasweakandrequiresfurtherinvestigation.The2-dimensionaltrendfunctionindicatedastrongspatialeffect,andthusexplainedbyfarthehighestproportionofvariationinlarvadensity.Conclusions:Assuchthemodelcanbeusedtosupportforestpractitionersintheirdecisionmakingforregenerationandforestprotecti