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PhD Position Subproject 11: Economic implications of species mixture and structural diversity (RTG 2300) ‘Enrichment of European beech forests with conifers: impacts of functional traits on ecosystem functioning’The interdisciplinary Research Training Group 2300 was established in 2017. The PhD students enrolled in this program investigate ecological challenges and societal demands on forests in a changing climate. The program focuses on European temperate forests. A central goal is to uncover the impact of mixed compared to monospecific forests on ecosystem functions and processes with a special focus on the importance of functional traits. Our research approaches involve field studies in European beech, Norway spruce and Douglas fir forests and their mixtures, experimental studies to uncover cause-effect relationships, and statistical as well as economic analyses. We hypothesize that mixtures of European beech with conifers show higher functional diversity than pure European beech stands, resulting in higher resilience and providing multiple ecosystem services.
Subproject 11 entitled ‘Economic implications of species mixture and structural diversity’ offers one position in bio-economic modelling (65 % TV-L E13). The main objective of this subproject is to clarify the level and stability of economic returns and ecosystem services in admixed European beech stands and their functional relationship with species identity, mixture, and stand structural diversity. This improved understanding shall support the identification of potential management pathways, in terms of species composition and silvicultural interventions, which balance production and conservation-oriented goals. The subproject will involve risk-sensitive bio-economic modelling coupled with simulation of growth and tree structures in close cooperation with RTG Partners (particularly SP 8 and 9). For this position candidates should have completed a very good M.Sc. in forest sciences, ecological sciences, environmental economics or a related field with advanced knowledge of forest economics and skills in bio-economic and/or statistical programming. Find more information here.