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Detecting concerted demographic response across community assemblages using hierarchical approximate Bayesian

Yvonne L Chan1, David Schanzenbach2, Michael J Hickerson3

  • 1Hawai'i Institute of Marine Biology, School of Ocean and Earth Science and Technology, University of Hawai'i at Manoa ylhchan@hawaii.edu.

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This study introduces a new statistical framework to analyze demographic histories across multiple species simultaneously. The method helps understand how communities responded to past climate changes, aiding future conservation efforts.

Keywords:
approximate Bayesian computationcomparative phylogeographyhistorical demographyresponse to climate change

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Area of Science:

  • Ecology
  • Population Genetics
  • Phylogeography

Background:

  • Comparative phylogeography requires methods integrating multi-taxa population data for community-level analysis.
  • Understanding species' demographic tracking is crucial for predicting impacts of climate change, extinctions, and invasions.

Purpose of the Study:

  • To present a statistical framework for detecting concerted demographic histories across ecological assemblages.
  • To estimate the proportion of a community that underwent a synchronized demographic expansion and its timing.

Main Methods:

  • Developed a hierarchical approximate Bayesian computation (hABC) framework.
  • Combined population genetic data from multiple taxa into a single analysis.
  • Validated the approach using simulation experiments and an empirical dataset of Australian avian populations.

Main Results:

  • The hABC framework successfully estimates synchronized demographic pulses within communities.
  • The method accommodates data heterogeneity (e.g., effective population size, mutation rates) across species.
  • Analysis of Australian birds revealed insights into late Pleistocene demographic expansions.

Conclusions:

  • The hABC framework enhances understanding of community responses to historical climate change.
  • It quantifies the proportion of species responding in concert versus independently.
  • This approach is versatile for various phylogeographic datasets, especially with accumulating DNA barcoding data.