Received: 20 August 2018 | Revised: 4 January 2019 | Accepted: 16 January 2019 DOI: 10.1111/mec.15029 ORIGINAL ARTICLE Integrating life history traits into predictive phylogeography Jack Sullivan1,2* | Megan L. Smith3* | Anahí Espíndola1,4 | Megan Ruffley1,2 | Andrew Rankin1,2 | David Tank1,2 | Bryan Carstens3 1Department of Biological Sciences, University of Idaho, Moscow, Abstract Idaho Predictive phylogeography seeks to aggregate genetic, environmental and taxonomic 2 Institute for Bioinformatics and data from multiple species in order to make predictions about unsampled taxa using Evolutionary Studies, University of Idaho, Moscow, Idaho machine‐learning techniques such as Random Forests. To date, organismal trait data 3Department of Ecology, Evolution and have infrequently been incorporated into predictive frameworks due to difficulties Organismal Biology, The Ohio State University, Columbus, Ohio inherent to the scoring of trait data across a taxonomically broad set of taxa. We re‐ 4Department of Entomology, University of fine predictive frameworks from two North American systems, the inland temperate Maryland, College Park, Maryland rainforests of the Pacific Northwest and the Southwestern Arid Lands (SWAL), by Correspondence incorporating a number of organismal trait variables. Our results indicate that incor‐ Jack Sullivan, Department of Biological porating life history traits as predictor variables improves the performance of the Sciences, University of Idaho, Moscow, ID. Email:
[email protected] supervised machine‐learning approach to predictive phylogeography, especially for Funding information the SWAL system, in which predictions made from only taxonomic and climate vari‐ National Science Foundation, Grant/Award ables meets only moderate success. In particular, traits related to reproduction (e.g., Numbers: DEB 14575199, DEB 1457726, DG‐1343012; NSF GRFP; Ohio State reproductive mode; clutch size) and trophic level appear to be particularly informa‐ University; Institute for Bioinformatics and tive to the predictive framework.