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Detalle de: Congruent evolutionary responses of European steppe biota to late Quaternary climate change: insights from convolutional neural network based demographic modeling

Title Congruent evolutionary responses of European steppe biota to late Quaternary climate change: insights from convolutional neural network based demographic modeling
Abstract

Quaternary climatic oscillations had a large impact on European biogeography. Alternation of cold and warm stages caused recurrent glaciations, massive vegetation shifts and large-scale range alterations in many species. The Eurasian steppe biome and its grasslands are a noteworthy example; they underwent climate-driven, large-scale contractions during warm stages and expansions during cold stages. Here, we evaluate the impact of these range alterations on the late Quaternary demography of phylogenetically unrelated plant and insect species, typical of the Eurasian steppes. We contrast three explicit demographic hypotheses by applying a novel approach combining Convolutional Neural Networks with Approximate Bayesian Computation. We identified congruent demographic responses of cold stage expansions and warm stage contractions across all species, but also species-specific effects. The demographic history of Eurasian steppe biota reflects major paleoecological turning points of the late Quaternary, and emphasizes the role of the climate as a driving force behind patterns of genetic variance.

Apa Reference Kirschner, P., Perez, M., Záveská, E., Sanmartin, I., Marquer, L., Schlick-Steiner, B., Alvarez, N., Steinere, F. & Schönswetter, P. (2021). Congruent evolutionary responses of European steppe biota to late Quaternary climate change: insights from convolutional neural network based demographic modeling. 10.21203/rs.3.rs-534143/v1
Doi 10.21203/rs.3.rs-534143/v1
Publication Date 2 Jun 2021
Publication Type Preprint
Researcher Isabel Sanmartín Bastida
Ontology Artificial Intelligence, Population genomics, Phylogenetic Analysis, Bayesian statistics, Population dynamics, Deep Learning in Biology, Invertebrates, Climate change adaptation
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