Integrating Different Data Sources Using a Bayesian Hierarchical Model to Unveil Glacial Refugia

Mauricio Campos, Bo Li, Guillaume de Lafontaine, Joseph Napier, Feng Sheng Hu

Research output: Contribution to journalArticlepeer-review

Abstract

Rapid anthropogenic climate change has elevated the interest in studying the biotic responses of species during the Last Glacial Maximum. During this period, species retreated to highly spatially restricted geographic regions where survival was possible, known as glacial micro-refugia, from which they migrated and expanded when conditions became more suitable. Several distinct sources of evidence have contributed to developing a new understanding of how these regions might have impacted the sustainability of the natural populations of many species. Pollen records in Eastern Beringia have been used to explore the possibility that the region harbored glacial refugia for several plants from the arctic tundra and/or the boreal forest biomes common to the region. Our study focuses on Alnus viridis and Picea glauca, two predominant species of arcto-boreal vegetation. We propose to integrate genomic, SDM, and existing fossil data in a hierarchical Bayesian modeling (HBM) framework to determine whether multiple refugia existed in isolated geographic areas. This study demonstrates how the flexibility of HBMs makes the formal synthesis of such disparate data sources feasible. Our results highlight the regions of plausible refugia that can guide future investigations into studying the role of glacial refugia during climate change. Supplementary materials accompanying this paper appear online.

Keywords

  • Data integration
  • Genetic
  • INLA
  • Pollen fossil
  • Spatial process
  • Species distribution models

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