Speciation and gene flow across an elevational gradient in New Guinea kingfishers

Abstract

Closely related species with parapatric elevational ranges are ubiquitous in tropical mountains worldwide. The gradient speciation hypothesis proposes that these series are the result of in situ ecological speciation driven by divergent selection across elevation. Direct tests of this scenario have been hampered by the difficulty inferring the geographic arrangement of populations at the time of divergence. In cichlids, sticklebacks and Timema stick insects, support for ecological speciation driven by other selective pressures has come from demonstrating parallel speciation, where divergence proceeds independently across replicated environmental gradients. Here, we take advantage of the unique geography of the island of New Guinea to test for parallel gradient speciation in replicated populations of Syma kingfishers that show extremely subtle differentiation across elevation and between historically isolated mountain ranges. We find that currently described high‐elevation and low‐elevation species have reciprocally monophyletic gene trees and form nuclear DNA clusters, rejecting this hypothesis. However, demographic modelling suggests selection has likely maintained species boundaries in the face of gene flow following secondary contact. We compile evidence from the published literature to show that although in situ gradient speciation in labile organisms such as birds appears rare, divergent selection and post‐speciation gene flow may be an underappreciated force in the origin of elevational series and tropical beta diversity along mountain slopes.

Document Details

Document Type
Pub Defense Publication
Publication Date
Oct 01, 2020
Source ID
10.1111/jeb.13698

Entities

People

  • Benjamin G. Freeman
  • Ethan B. Linck
  • John P. Dumbacher

Organizations

  • California Academy of Sciences
  • National Science Foundation
  • United States Department of Defense
  • University of British Columbia
  • University of Washington

Tags

Fields of Study

  • Environmental science

Readers

  • Marine Ecotoxicology
  • Systems Analysis and Design
  • Wetland-Land-Environmental Management.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms