Holocene vegetation, fire, climate dynamics and human impact - comparing multi-site evidences from the northern forest-steppes and boreal forests (taiga) transition in Mongolia

• Applicant: Professor Dr. Hermann Behling
• Subject Area: Ecology and Biodiversity of Plants and Ecosystems
• Term: since 2025
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 562445286

Understanding the interactions between the different factors that govern the long-term dynamics of the Mongolian boreal forest (taiga and subtaiga) represents a major challenge since this ecosystem is one of the most sensitive to ongoing global change. New sedimentary archives from northern Mongolia (Lake Khovsgol and Khangai Jargalant region) will allow to understand the functioning of these ecosystems during the Holocene, and will enhance the research already carried out on forest-steppes ecosystems, which make the transition between these forests and the Mongolian steppes, along a North-South gradient. Still unclear is since when and how intensive the human impact was in forest-steppe to the boreal forest (taiga) transition zone in northern Mongolia and whether the steppe areas are natural or due to human impacts. These new archives will thus make it possible to better understand and characterize, on a regional scale, how the vegetation of these forests has varied over time and how it has been shaped by environmental factors (climate, fires), but also humans, notably since the Bronze Age (Afanasievo, Andronovo, Karassouk cultures, Mongol empires, Chinese Liao, Jin and Yuan dynasties). These sedimentary archives will be analysed to highlight the past vegetation (pollen), climate (pollen, brGDGTs, XRF, transfer functions, non-pollen palynomorphs), fires (macro-coal) and human impact dynamics (pollen, non-pollen palynomorphs, macro-charcoals) during the Holocene. Two innovative methods will be used for climate reconstruction with the analysis of molecular biomarkers brGDGTs, independent of human activities and the automatic recognition of pollen grains using convolutional neural networks machine-learning models. This project will be part of an international collaboration (Germany, Mongolia, France, Switzerland) in several disciplines (palaeoecology, geography, geology, archaeology, botany, ecology, nature conservation, etc.) and will contribute to the protection and management of the boreal biome.

• DFG Programme: Research Grants