Testing for the presence of cryptic diversity in tail-dropper slugs (Prophysaon) using molecular data. (7th June 2018)

• Record Type: Journal Article
• Authors: Smith, Megan L; Ruffley, Megan; Rankin, Andrew M; Espíndola, Anahí; Tank, David C; Sullivan, Jack; Carstens, Bryan C
• Is Part Of: Biological journal of the Linnean Society. Volume 124:Number 3(2018)
• Page Start: 518
• Page End: 532
• Publication Date: 2018-06-07
• Subjects: approximate Bayesian computation -- cryptic diversity -- phylogeography -- posterior predictive simulations; Biology -- Periodicals; Evolution (Biology) -- Periodicals; 570
• DOI: 10.1093/biolinnean/bly067
• Languages: English
• ISSNs: 0024-4066

Abstract

Abstract: The Pacific Northwest of North America contains two disjunct temperate rainforests, one in the Coastal and Cascades Ranges and another in the Northern Rocky Mountains. These rainforests harbour > 200 disjunct and endemic taxa, with coastal and inland populations separated by the Columbia Basin. For several taxa, molecular data have revealed cryptic diversity structured across the Columbia Basin. Here, we use information from previously studied taxa and a machine-learning framework to predict that tail-dropper slugs in the genus Prophysaon ( Prophysaon andersoni, Prophysaon coeruleum, Prophysaon dubium and the Prophysaon vanattae/Prophysaon humile complex) should lack cryptic diversity. This prediction is supported by results from species distribution models (SDMs), which suggest that all taxa lacked suitable habitat in the inland rainforests during the Last Glacial Maximum. We collected COI data and tested these predictions using approximate Bayesian computation and found that models of recent dispersal between inland and coastal populations received strong support. Finally, we used posterior predictive simulations to show that the best model was a reasonable fit to the data for all taxa. Our study highlights the utility of predictive modelling in a comparative phylogeographical framework and illustrates how posterior assessments of model fit can improve confidence in model-based phylogeographical analysis.