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"Everything is not everywhere": Time-calibrated phylogeography of the genus Milnesium (Tardigrada).

Witold MorekBartłomiej SurmaczAlejandro López-LópezŁukasz Michalczyk
Published in: Molecular ecology (2021)
There is ample evidence that macroscopic animals form geographic clusters termed as zoogeographic realms, whereas distributions of species of microscopic animals are still poorly understood. The common view has been that micrometazoans, thanks to their putatively excellent dispersal abilities, are subject to the "Everything is everywhere but environment selects" hypothesis (EiE). One of such groups, <1 mm in length, are limnoterrestrial water bears (Tardigrada), which can additionally enter cryptobiosis that should further enhance their potential for long distance dispersion (e.g., by wind). However, an increasing number of studies, including the most recent phylogeny of the eutardigrade genus Milnesium, seem to question the general applicability of the EiE hypothesis to tardigrade species. Nevertheless, all Milnesium phylogenies published to date were based on a limited number of populations, which are likely to falsely suggest limited geographic ranges. Thus, in order to test the EiE hypothesis more confidently, we considerably enlarged the Milnesium data set both taxonomically and geographically, and analysed it in tandem with climate type and reproductive mode. Additionally, we time-calibrated our phylogeny to align it with major geological events. Our results show that, although cases of long distance dispersal are present, they seem to be rare and mostly ancient. Overall, Milnesium species are restricted to single zoogeographic realms, which suggests that these tardigrades have limited dispersal abilities. Finally, our results also suggest that the breakdown of Gondwana may have influenced the evolutionary history of Milnesium. In conclusion, phylogenetic relationships within the genus seem to be determined mainly by paleogeography.
Keyphrases
  • genetic diversity
  • climate change
  • systematic review
  • machine learning
  • big data
  • risk assessment
  • deep learning
  • finite element