Heritability and microevolution of hormonal traits
APPLICATION DEADLINE CLOSED JAN 15!
Abstract Our goal is to unravel evolutionary patterns in flexible physiological traits such as circulating hormone concentrations. Unlike stable traits like morphology, the evolution of highly flexible traits like hormones is still hardly understood. Since hormones regulate a wide range of fitness-relevant traits, it is important to understand their scope for plastic or micro-evolutionary changes to understand the potential of populations to adapt to the rapid ongoing changes in environmental conditions world-wide. We are currently focusing on the ‘stress’ hormone corticosterone, assessing circulating concentrations as well as responses to environmental and social conditions in wild populations of great tits (Parus major). We are assembling a growing data base of hormone concentrations of individuals taken at multiple times of year, together with measures of fitness and genetic pedigree. We routinely collect correlative data on hormone variation relative to environmental and social variation and have embarked on experimental approaches to establish hormonal reaction norms.
We are looking for an enthusiastic PhD student to continue and expand the field and data base work to quantify the plasticity and heritability of corticosterone traits. We welcome the use of experimental approaches such as reaction norms to quantify hormonal responses of individuals. We are also able to conduct experiments in captivity. Experience in work with birds, hormones or data bases are advantageous but not required. A driver’s licence is required for field work.
Interested candidates are asked to submit a research statement stating what excites them about the heritability and microevolution of hormonal traits and what they may want to work on. In case of interest in further discussing research ideas, students are encouraged to contact Prof. Michaela Hau email@example.com.
Keywords hormone, corticosterone, microevolution, heritability, great tit, field work, data base
Main advisor Michaela Hau, MPIO Seewiesen