This project is funded by the Academy of Finland, PI: I. Žliobaitė, duration: 2018 -- 2022.

Summary: Biospheric data record the history of life and its environmental context over time and space. Analysis of such continuously evolving data requires advanced machine learning methods, with a particular focus on tracking changing data distribution along with evolving communities of organisms, and tailoring learned models to work across extremely long time scales. This project will develop such computational techniques and a methodological platform for tracking and analyzing environmental change from global biospheric data including the fossil record, and make the results available to the research community and policy makers via a well established fossil database infrastructure in Helsinki. In addition to broad applicability for comparative paleobiology studies, the proposed methodology will help to more accurately reconstruct the evolutionary context of early hominins, and better understand the ongoing anthropogenic global change.

The project is funded by The Research Council of Norway, duration: 2018 -- 2022.

PIs: N. Chr. Stenseth, J. M. Nordboten, M. Fortelius and I. Žliobaitė.

Summary: The project will investigate whether – and how – macroevolution can be fully understood as a result of microevolutionary processes. This will be achieved by integrating microevolutionary theory with theories on macroevolution, hence determining whether we can explain macroevolution within the framework of the Modern Synthesis or need to go beyond it.

The project is funded by Fundacio CAPES, Brazil. University of Helsinki (I. Žliobaitė's group) is a project partner.

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