{"data":{"id":"10.5061/dryad.kp854","type":"dois","attributes":{"doi":"10.5061/dryad.kp854","prefix":"10.5061","suffix":"dryad.kp854","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Rabosky, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Rabosky","affiliation":["University of Minnesota"],"nameIdentifiers":[]},{"name":"Goldberg, Emma E.","nameType":"Personal","givenName":"Emma E.","familyName":"Goldberg","affiliation":["University of Michigan"],"nameIdentifiers":[]},{"name":"Rabosky, Daniel L.","nameType":"Personal","givenName":"Daniel L.","familyName":"Rabosky","affiliation":["University of Michigan"],"nameIdentifiers":[]}],"titles":[{"title":"Data from: Model inadequacy and mistaken inferences of trait-dependent speciation"}],"publisher":"Dryad","container":{},"publicationYear":2015,"subjects":[{"subject":"character evolution"},{"subject":"Statistics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Mathematics","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2014-10-20T17:46:10Z","dateType":"Submitted"},{"date":"2015-01-27T20:14:26Z","dateType":"Issued"},{"date":"2015-01-27T20:14:26Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syu131","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["3091484 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Species richness varies widely across the tree of life, and there is great\n interest in identifying ecological, geographic, and other factors that\n affect rates of species proliferation. Recent methods for explicitly\n modeling the relationships among character states, speciation rates, and\n extinction rates on phylogenetic trees- BiSSE, QuaSSE, GeoSSE, and related\n models - have been widely used to test hypotheses about character\n state-dependent diversification rates. Here, we document the disconcerting\n ease with which neutral traits are inferred to have statistically\n significant associations with speciation rate. We first demonstrate this\n unfortunate effect for a known model assumption violation: shifts in\n speciation rate associated with a character not included in the model. We\n further show that for many empirical phylogenies, characters simulated in\n the absence of state-dependent diversification exhibit an even higher Type\n I error rate, indicating that the method is susceptible to additional,\n unknown model inadequacies. For traits that evolve slowly, the root cause\n appears to be a statistical framework that does not require replicated\n shifts in character state and diversification. However, spurious\n associations between character state and speciation rate arise even for\n traits that lack phylogenetic signal, suggesting that phylogenetic\n pseudoreplication alone cannot fully explain the problem. The surprising\n severity of this phenomenon suggests that many trait-diversification\n relationships reported in the literature may not be real. More generally,\n we highlight the need for diagnosing and understanding the consequences of\n model inadequacy in phylogenetic comparative methods.","descriptionType":"Abstract"},{"description":"Analysis scripts and\n simulation results for trait-dependent speciation modelsData file includes\n simulation results, code to repeat simulations, Appendix with\n supplementary methods, and supplementary results.dryad_submit.zip","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"xml":"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