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dc.contributor.authorFitzpatrick, Matthew C.
dc.contributor.authorGotelli, Nicholas J.
dc.contributor.authorEllison, Aaron M.
dc.date.accessioned2013-11-20T19:43:51Z
dc.date.issued2013
dc.identifierQuick submit: 2013-10-22T10:27:33-04:00
dc.identifier.citationFitzpatrick, Matthew C., Nicholas J. Gotelli, and Aaron M. Ellison. 2013. MaxEnt versus MaxLike: empirical comparisons with ant species distributions. Ecosphere 4 (5): art55.en_US
dc.identifier.issn2150-8925en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:11327042
dc.description.abstractMaxEnt is one of the most widely used tools in ecology, biogeography, and evolution for modeling and mapping species distributions using presence-only occurrence records and associated environmental covariates. Despite its popularity, the exponential model implemented by MaxEnt does not directly estimate occurrence probability, the natural quantity of interest when modeling species distributions. Instead, MaxEnt generates an index of relative habitat suitability. MaxLike, a newly introduced maximum-likelihood technique, has been shown to overcome the problem of directly estimating the probability of occurrence using presence-only data. However, the performance and relative merits of MaxEnt and MaxLike remain largely untested, especially when modeling species with relatively few occurrence data that encompass only a portion of the geographic range of the species. Using geo-referenced occurrence records for six species of ants in New England, we provide comparisons of MaxEnt and MaxLike. We show that by most quantitative metrics, the performance of MaxLike exceeds that of MaxEnt, regardless of whether MaxEnt models account for sampling bias and include greater model complexity than implemented in MaxLike. More importantly, for most species, the relative suitability index estimated by MaxEnt often was poorly correlated with the probability of occurrence estimated by MaxLike, suggesting that the two methods are estimating different quantities. For species distribution modeling, MaxLike, and similar models that are based on an explicit sampling process and that directly estimate probability of occurrence, should be considered as important alternatives to the widely-used MaxEnt framework.en_US
dc.description.sponsorshipOrganismic and Evolutionary Biologyen_US
dc.language.isoen_USen_US
dc.publisherEcological Society of Americaen_US
dc.relation.isversionofdoi:10.1890/ES13-00066.1en_US
dash.licenseLAA
dc.subjectecological niche modelingen_US
dc.subjectmyrmecologyen_US
dc.subjectNew Englanden_US
dc.subjectoccurrence probabilityen_US
dc.subjectpresence-only dataen_US
dc.subjectspecies distribution modelingen_US
dc.titleMaxEnt versus MaxLike: empirical comparisons with ant species distributionsen_US
dc.typeJournal Articleen_US
dc.date.updated2013-10-22T14:28:31Z
dc.description.versionAccepted Manuscripten_US
dc.rights.holderMC Fitzpatrick, NJ Gotelli, and AM Ellison
dc.relation.journalEcosphereen_US
dash.depositing.authorEllison, Aaron M.
dc.date.available2013-11-20T19:43:51Z
dc.identifier.doi10.1890/ES13-00066.1*
workflow.legacycommentsCan post manuscript per http://www.sherpa.ac.uk/romeo/issn/2150-8925/en_US
dash.contributor.affiliatedEllison, Aaron
dc.identifier.orcid0000-0003-4151-6081


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