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dc.contributor.authorFroman Walmsley, Sam
dc.contributor.authorMorrissey, Michael
dc.date.accessioned2021-11-10T13:30:05Z
dc.date.available2021-11-10T13:30:05Z
dc.date.issued2021-11-09
dc.identifier.citationFroman Walmsley , S & Morrissey , M 2021 , ' Causation, not collinearity : identifying sources of bias when modelling the evolution of brain size and other allometric traits ' , Evolution Letters , vol. Early View . https://doi.org/10.1002/evl3.258en
dc.identifier.issn2056-3744
dc.identifier.otherPURE: 275886833
dc.identifier.otherPURE UUID: bf88fa43-70f3-48a8-a2aa-3e96dc4e3d2b
dc.identifier.urihttp://hdl.handle.net/10023/24304
dc.descriptionSFW is supported by a Fellowship from Fulbright Canada. MBM is supported by a University Research Fellowship from the Royal Society (London, UF130398).en
dc.description.abstractMany biological traits covary with body size, resulting in an allometric relationship. Identifying the evolutionary drivers of these traits is complicated by possible relationships between a candidate selective agent and body size itself, motivating the widespread use of multiple regression analysis. However, the possibility that multiple regression may generate misleading estimates when predictor variables are correlated has recently received much attention. Here, we argue that a primary source of such bias is the failure to account for the complex causal structures underlying brains, bodies, and agents. When brains and bodies are expected to evolve in a correlated manner over and above the effects of specific agents of selection, neither simple nor multiple regression will identify the true causal effect of an agent on brain size. This problem results from the inclusion of a predictor variable in a regression analysis that is (in part) a consequence of the response variable. We demonstrate these biases with examples and derive estimators to identify causal relationships when traits evolve as a function of an existing allometry. Model mis-specification relative to plausible causal structures, not collinearity, requires further consideration as an important source of bias in comparative analyses.
dc.format.extent11
dc.language.isoeng
dc.relation.ispartofEvolution Lettersen
dc.rightsCopyright © 2021 The Authors. Evolution Letters published by Wiley Periodicals LLC on behalf of Society for the Study of Evolution (SSE) and European Society for Evolutionary Biology (ESEB). This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.en
dc.subjectAllometryen
dc.subjectBrain sizeen
dc.subjectCausal inferenceen
dc.subjectCoevolutionen
dc.subjectComparative methodsen
dc.subjectCorrelated response to selectionen
dc.subjectReceiprocal evolutionen
dc.subjectHA Statisticsen
dc.subjectQH301 Biologyen
dc.subjectNDASen
dc.subject.lccHAen
dc.subject.lccQH301en
dc.titleCausation, not collinearity : identifying sources of bias when modelling the evolution of brain size and other allometric traitsen
dc.typeJournal articleen
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews.Centre for Biological Diversityen
dc.contributor.institutionUniversity of St Andrews.School of Biologyen
dc.contributor.institutionUniversity of St Andrews.St Andrews Bioinformatics Uniten
dc.identifier.doihttps://doi.org/10.1002/evl3.258
dc.description.statusPeer revieweden


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