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SERAPHIM: studying environmental rasters and phylogenetically informed movements.

Simon Dellicour1, Rebecca Rose2, Nuno R Faria3

  • 1Department of Microbiology and Immunology, Rega Institute for Medical Research, Clinical and Epidemiological Virology, KU Leuven-University of Leuven, Minderbroedersstaat 10, Leuven 3000, Belgium.

Bioinformatics (Oxford, England)
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Summary

SERAPHIM is a new computational method that analyzes how environmental factors influence species movement using phylogenetic trees. It helps visualize dispersal history and understand organism spread in different environments.

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Area of Science:

  • Computational biology
  • Phylogenetics
  • Epidemiology

Background:

  • Understanding species movement and dispersal is crucial in ecology and epidemiology.
  • Phylogenetic methods offer insights into evolutionary history and spatial spread.
  • Integrating environmental data with phylogenetic information can reveal movement drivers.

Purpose of the Study:

  • To introduce SERAPHIM (Studying Environmental Rasters and PHylogenetically Informed Movements), a novel computational method.
  • To enable the analysis of environmental factors influencing phylogenetic reconstructions of spatial movement.
  • To provide tools for visualizing dispersal history and assessing the impact of environmental variables on organism spread.

Main Methods:

  • SERAPHIM extracts spatio-temporal data from phylogenetic trees.
  • It calculates environmental "weights" for phylogenetic branches based on raster data.
  • Correlations between movement duration and environmental weights are assessed using randomization procedures.

Main Results:

  • SERAPHIM quantifies the influence of environmental variables on lineage movement.
  • It provides statistical significance for the impact of environment on dispersal.
  • The method visualizes spatial spread and dispersal history.

Conclusions:

  • SERAPHIM offers a robust framework for studying phylogeography in an environmental context.
  • The method is applicable to any phylogeny with spatial and temporal data.
  • It is particularly relevant for emerging infectious disease research and growing population genomic datasets.