Predictionofspecies’distributionsiscentraltodiverseapplicationsinecology,evolutionandconservationscience.Thereisincreasingelectronicaccesstovastsetsofoccurrencerecordsinmuseumsandherbaria,yetlittleeffectiveguidanceonhowbesttousethisinformationinthecontextofnumerousapproachesformodellingdistributions.Tomeetthisneed,wecompared16modellingmethodsover226speciesfrom6regionsoftheworld,creatingthemostcomprehensivesetofmodelcomparisonstodate.Weusedpresence-onlydatatofitmodels,andindependentpresence-absencedatatoevaluatethepredictions.Alongwithwell-establishedmodellingmethodssuchasgeneralisedadditivemodelsandGARPandBIOCLIM,weexploredmethodsthateitherhavebeendevelopedrecentlyorhaverarelybeenappliedtomodellingspecies’distributions.Theseincludemachine-learningmethodsandcommunitymodels,bothofwhichhavefeaturesthatmaymakethemparticularlywellsuitedtonoisyorsparseinformation,asistypicalofspecies’occurrencedata.Presence-onlydatawereeffectiveformodellingspecies’distributionsformanyspeciesandregions.Thenovelmethodsconsistentlyoutperformedmoreestablishedmethods.Theresultsofouranalysisarepromisingfortheuseofdatafrommuseumsandherbaria,especiallyasmethodssuitedtothenoiseinherentinsuchdataimprove.
NOVEL METHODS IMPROVE PREDICTION OF SPECIES’ DISTRIBUTIONS FROM OCCURRENCE DATA
Document Number:
WS-VME-09/P01
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Abstract