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Swedish Museum of Natural History

Swedish Museum of Natural History

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23 Projects, page 1 of 5
  • Funder: EC Project Code: 301572
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  • Funder: EC Project Code: 885088
    Overall Budget: 203,852 EURFunder Contribution: 203,852 EUR

    Previous studies on extinct Pleistocene megafauna attempted to decipher species’ responses to environmental change through genetic studies and palaeodietary reconstruction. However, none of these studies addressed the issue whether changes in palaeoecology represent evolutionary processes or are instead a result of environmentally induced plasticity. The present research project proposes novel ways of addressing the aforementioned question through an interdisciplinary approach including palaeogenomics, ancient epigenomics and palaeoecology. The project will focus on a case study of cave bear populations in the Romanian Carpathians, a key region of their distribution prior extinction showing the most dramatic dietary differentiation among cave bears across their entire geographical range. We will explore the response of Late Pleistocene cave bear populations to environmental heterogeneity, and determine the genetic and epigenetic processes that led to these differences in Romanian cave bears. To achieve this, we will carry out next-generation sequencing of cave bears from different time-related populations with various dietary patterns. This dataset will constitute the most complete cave bear genome dataset ever analysed, allowing us to gain an unprecedented understanding of cave bear evolutionary history and moving forward the field of evolutionary epigenomics. This project will provide new research avenues that the experienced researcher will be able to exploit in order to reach and reinforce a position of professional maturity and independence. The experienced researcher will also be trained in cutting edge scientific techniques and analyses, as well as a number of skills that are transferable between countries and sectors (including management, grant writing, communication and teaching), and that will facilitate her development into a research leader.

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  • Funder: EC Project Code: 898120
    Overall Budget: 203,852 EURFunder Contribution: 203,852 EUR

    Statistical analysis of phylogenetic models is one of the most active areas of research in computational biology today with wide applications in the Theory of Evolution, epidemiology, forensics, etc. Current phylogenetic software packages limit the user to the set of phylogenetic models and inference strategies that have been pre-programmed in the tool. Inference under certain important phylogenetic models is very difficult with the Markov chain Monte-Carlo strategy implemented in current packages for phylogenetic analysis. The new paradigm of probabilistic programming, coming from computational statistics and theoretical computer science, solves the model expression problem and enables the user to implement novel inference methods. We utilize probabilistic programming to automatically generate Sequential Monte Carlo (SMC) inference machinery for MCMC-hard problems in phylogentics. SMC algorithms may be more efficient, provide unbiased solutions, and provide likelihoods estimates for model comparison. The goal of the proposed research is to carry out some of the first applications of probabilistic programming to real-world problems of empirical interest in evolutionary biology. The objectives are (1) to design and implement statistical inference machinery for complex diversification models with variable tree topology and a trait-dependent branching process under probabilistic programming, (2) to do a pilot study on the applicability of this inference machinery by studying the effect of the orogeny of the Andes on Neotropical biodiversity, and (3) contribute to the design and implementation of a novel probabilistic programming language for phylogenetics, TreePPL, by utilizing the insights gained from (1) and (2). We also propose dissemination and communication measures that target scientists and the general public throughout Europe and in particular new and aspiring EU member states.

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  • Funder: EC Project Code: 796877
    Overall Budget: 173,857 EURFunder Contribution: 173,857 EUR

    This proposed project investigates novel ways of predicting the effects of future climate change on the survival of animal species. We will focus on a case study of two genetically and morphologically distinct wolf populations living in Eurasia. One of these survived the climate changes that have occurred over the past 40,000 years, whereas the other did not. We will describe the details of this extinction and replacement event, and determine the genetic processes that led to this difference between the two populations. To achieve this, we will carry out next-generation sequencing of wolf ancient DNA samples, and take advantage of ancient samples already collected and sequenced from across Eurasia. Together, this dataset will constitute the largest ancient wolf genome dataset ever collected. The project will end with a workshop that includes experts in climate and population viability modeling, and conservation practicioners, to explore the ways that the findings can be incorporated into future climate change mitigation planning. This workshop will provide new research avenues that the experienced researcher will be able to exploit in order to reach and reinforce a position of professional maturity and independence. The experienced researcher will also be trained in cutting edge scientific techniques and analyses, as well as a number of skills that are transferable between countries and sectors (including management, grant writing, communication and teaching), and that will facilitate his development into a research leader.

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  • Funder: EC Project Code: 273412
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