a b s t r a c t
A recentseriesofpapersbyCharlesT.Perrettiandcollaboratorshaveshownthatnonparametric
forecasting methodscanoutperformparametricmethodsinnoisynonlinearsystems.Suchasituation
can arisebecauseoftwomainreasons:theinstabilityofparametricinferenceproceduresinchaotic
systemswhichcanleadtobiasedparameterestimates,andthediscrepancybetweentherealsystem
dynamics andthemodeledone,aproblemthatPerrettiandcollaboratorscall “the truemodelmyth”.
Should ecologistsgoonusingthedemandingparametricmachinerywhentryingtoforecastthe
dynamics ofcomplexecosystems?Orshouldtheyrelyontheelegantnonparametricapproachthat
appears sopromising?Itwillbeherearguedthatecologicalforecastingbasedonparametricmodels
presents twokeycomparativeadvantagesovernonparametricapproaches.First,thelikelihoodof
parametric forecastingfailurecanbediagnosedthankstosimpleBayesianmodelcheckingprocedures.
Second, whenparametricforecastingisdiagnosedtobereliable,forecastinguncertaintycanbe
estimated onvirtualdatageneratedwiththe fitted todataparametricmodel.Incontrast,nonparametric
techniquesprovideforecastswithunknownreliability.Thisargumentationisillustratedwiththesimple
theta-logistic modelthatwaspreviouslyusedbyPerrettiandcollaboratorstomaketheirpoint.Itshould
convince ecologiststosticktostandardparametricapproaches,untilmethodshavebeendevelopedto
assess thereliabilityofnonparametricforecasting.
a b s t r a c tA recentseriesofpapersbyCharlesT.Perrettiandcollaboratorshaveshownthatnonparametricforecasting methodscanoutperformparametricmethodsinnoisynonlinearsystems.Suchasituationcan arisebecauseoftwomainreasons:theinstabilityofparametricinferenceproceduresinchaoticsystemswhichcanleadtobiasedparameterestimates,andthediscrepancybetweentherealsystemdynamics andthemodeledone,aproblemthatPerrettiandcollaboratorscall “the truemodelmyth”.Should ecologistsgoonusingthedemandingparametricmachinerywhentryingtoforecastthedynamics ofcomplexecosystems?Orshouldtheyrelyontheelegantnonparametricapproachthatappears sopromising?Itwillbeherearguedthatecologicalforecastingbasedonparametricmodelspresents twokeycomparativeadvantagesovernonparametricapproaches.First,thelikelihoodofparametric forecastingfailurecanbediagnosedthankstosimpleBayesianmodelcheckingprocedures.Second, whenparametricforecastingisdiagnosedtobereliable,forecastinguncertaintycanbeestimated onvirtualdatageneratedwiththe fitted todataparametricmodel.Incontrast,nonparametrictechniquesprovideforecastswithunknownreliability.Thisargumentationisillustratedwiththesimpletheta-logistic modelthatwaspreviouslyusedbyPerrettiandcollaboratorstomaketheirpoint.Itshouldconvince ecologiststosticktostandardparametricapproaches,untilmethodshavebeendevelopedtoassess thereliabilityofnonparametricforecasting.
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