The postdoctoral researcher will leverage the data time series of species and genetic diversity to model past ecosystem dynamics in order to forecast possible future responses to climate changes. They will first use species distribution models together with data on past and future climatic conditions to predict species and genetic accumulation over time as a function of distance to refugia and biological traits. Second, the candidate will use a recently developed machine learning approach to calibrate a process-based model of arctic ecosystem dynamic. Processmodels are required to describe ecosystem dynamics and anticipate their response to climate change, but previous efforts lacked both ecosystem time series and a numerical parameterisation approach. By combining sedimentary ancient DNA time series with a neural network approach, the postdoctoral research will calibrate the parameters of a model of ecosystem dynamics including processes of abiotic niche filtering, colonisation, competition and herbivory. The model will be used to explain species and genetic diversity build-up during the Holocene and predict future trajectories under climate change.
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[Website The Arctic University of Norway]