The Population Ecology Group covers four main research areas: Species networks and dynamics are the focus of the Community Population Ecology section. Local adaptation, temporal population dynamics and behavioural ecology are the focus of the Evolutionary Population Ecology section. And the genomic architecture of phenotypic polymorphisms and reproductive barriers involved in speciation are the focus of the Molecular Population Ecology section. Furthermore, we are active in the development of biostatistical and survey methods as part of the Population Ecology Methods section. For specific ongoing projects see our projects site.
Evolutionary population ecology
Molecular population ecology
Community population ecology
A central aspect for understanding population dynamics of individual species is an understanding of their environment, including the interactions among different species and trophic levels. Grassland habitats in particular offer a perfect model system for studying the interactions between plant- and consumer communities of different diversities or land-use regimes. The Jena ExperimentExternal link with its long-term manipulation of plant species richness in replicated plots offers a unique opportunity for uncovering species interactions. We aim to evaluate how changes in plant diversity can influence the ability of ecosystems to support complex consumer communities, by analyzing trait-based interaction networks, time series and recovery after disturbance events. Another large, collaborative, long-term project that we are involved in is the Nutrient NetworkExternal link that evaluates the global impacts of altered nutrient budgets on grassland ecosystems. The network provides insights into how nutrient-mediated changes in plant productivity or stoichiometry affect the consumer communities and ecosystem functions as herbivory or predation.
Population ecology methods
Methods development is rapid in ecological and evolutionary research. This includes statistical methods that become more and more sophisticated. We contribute to methods development and application with a particular focus on mixed effects models. Mixed effects models represent a particularly flexible class of statistical models that can be used to model complex datasets. We apply mixed effects models for estimating behavioral variability, for estimating genetic variation in complex pedigrees and for estimating spatial structure. An added complexity to mixed effects models is the generalization to non-normal trait distributions. Some traits and some biological phenomena are inherently non-normal and this represents particular challenges to statistical modelling and interpretation of the results. We contribute to the dissemination of new statistical developments by theoretical contributions, simulations and software development.