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Bayesian Skyline Plots disagree with range size changes based on Species Distribution Models for Holarctic birds.

Eleanor F MillerRhys E GreenAndrew BalmfordPierpaolo Maisano DelserRobert BeyerMarius SomveilleMichela LeonardiWilliam AmosAndrea Manica
Published in: Molecular ecology (2021)
During the Quaternary, large climate oscillations impacted the distribution and demography of species globally. Two approaches have played a major role in reconstructing changes through time: Bayesian Skyline Plots (BSPs), which reconstruct population fluctuations based on genetic data, and Species Distribution Models (SDMs), which allow us to back-cast the range occupied by a species based on its climatic preferences. In this paper, we contrast these two approaches by applying them to a large data set of 102 Holarctic bird species, for which both mitochondrial DNA sequences and distribution maps are available, to reconstruct their dynamics since the Last Glacial Maximum (LGM). Most species experienced an increase in effective population size (Ne , as estimated by BSPs) as well as an increase in geographical range (as reconstructed by SDMs) since the LGM; however, we found no correlation between the magnitude of changes in Ne and range size. The only clear signal we could detect was a later and greater increase in Ne for wetland birds compared to species that live in other habitats, a probable consequence of a delayed and more extensive increase in the extent of this habitat type after the LGM. The lack of correlation between SDM and BSP reconstructions could not be reconciled even when range shifts were considered. We suggest that this pattern might be linked to changes in population densities, which can be independent of range changes, and caution that interpreting either SDMs or BSPs independently is problematic and potentially misleading.
Keyphrases
  • mitochondrial dna
  • genetic diversity
  • copy number
  • computed tomography
  • electronic health record
  • machine learning
  • gene expression
  • working memory
  • artificial intelligence
  • deep learning
  • decision making