Evolutionary Ecology

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Behavioural Ecology

Prof. Dr. Niels Dingemanse

sp_Photo1We conduct evolutionary behavioural ecology research, asking questions about the adaptive evolution of behavioural strategies, and their genetic architectures, within an ecological context. Our research is motivated by behavioural ecology, life-history, and quantitative genetics theory, and seeks to test predictions and assumptions of adaptive theory using observational and experimental approaches in the laboratory and the wild.

We monitor 12 nest box populations of a passerine bird model (the great tit) south of Munich since 2010, providing unique longitudinal data of breeders and their offspring with respect to various key life-history and behavioural traits. We can thus uniquely study the action of selection and why it may favour a modular trait structure, such as represented by the integration between life-history and behavioural strategies (‘pace-of-life syndromes’) in the wild.

We primarily focus on behaviours mediating the trade-off between current and future reproduction, such as aggressiveness and exploratory tendency, and use large-scale population-level environmental manipulations (predation risk, resource availability) to study how the interaction between ecological and population-level processes shape the adaptive evolution of trait integration in the wild. We use genetic pedigrees to estimate parameters key in testing evolutionary theory in the wild, while laboratory studies of pedigreed insect models (field crickets) further allow us to study how the interaction between genes and environment (e.g. diet, competitive regimes) shaped the genetic architecture of life-history behaviour.

Our group is further at the forefront of the development of statistical tools to quantify multi-level and multi-variate variation. For example, we have developed with a team of international collaborators an educational software package entitled SQUID (Statistical Quantification of Individual Differences) that enables self-teaching and research into mixed-effects modelling analyses and optimal sampling designs.


For more details on our current research at the LMU see the webpage “Research”.