Background Elevational treelines in the Urals respond positively to industrial warming. If ongoing forest expansion keeps pace with future global change it will squeeze vast areas of pristine alpine tundra habitats, possibly to extinction. However, the rate of climate change may outrun a treeline advance. Furthermore, maladaptation and poor genetic connectivity in the landscape may constrain the adaptation of local tree populations. Hence, to unravel the important factors for treeline migration and provide realistic predictions of the future treeline position we need to combine a genetic survey with dendroecological data to fed into a spatially explicit model that can simulate trait adaptations.
We are looking for 1 PhD student (m/f/d) within the Russian-German DFG funded project бз.limate-induced treeline dynamics in the Ural Mountains: drivers, constraints, and the role of genetic adaptationби (UralTreelines, https://gepris.dfg.de/gepris/projekt/448651799 ) who is enthusiastic to address timely scientific questions about land surface processes in the Arctic and consequences on ecosystems, especially on vegetation dynamics, starting as soon as possible. The main tasks lay in the interface of model development, molecular data generation and data analyses. This involves development of the individual-based forest model LAVESI by including dynamic adaptation of traits relevant for simulated tree species in the Ural Mountains. Further, effective seed dispersal rates need to be adjusted based on distances inferred from microsatellite-based genotyping. Following a validation of the model, the importance of the newly implemented processes for historical treeline dynamics shall be evaluated. With the enhanced model, we aim to improve our understanding about responses of boreal forests in the Urals under climate warming in the next decades and to estimate if the potential migration would ultimately cause a tundra loss.
Tasks You will
- Improve the microsatellite assay, genotype the samples and run the parentage analyses.
- Interpret literature-based results and own parentage data to implement dynamically adapting traits of tree individuals into the individual-based forest model LAVESI (in programming language C++).
- Parameterize the model for elevational treelines and variables adapted for the larch species present in the Urals and analyze the impact of potential processes (seed dispersal, adaptation) by running sensitivity studies.
- Force simulations with the newly parameterised and enhanced model and evaluate future population dynamics and treeline advances under potential genetic constraints using climate scenarios for the 21st century.
- A suitable publication activity as well as the presentation of the achieved results at international conferences are expected.