Prediction of genetic values and adaptive potential in the wild

In order to halt the ongoing biodiversity loss, we need a radically better understanding of key evolutionary and ecological processes of wild populations.

The revolutionary methods of my project will enable us to utilize the rapidly-increasing volumes of high-density genomic data for wild populations, and open up unseen opportunities for genomic modeling of eco-evolutionary processes in wild systems.

As a result, we will gain an improved understanding of why some species can cope better with the expected global changes of the environment than others.

My three major objectives are to: (1) develop a broad set of tools for effcient and accurate prediction of evolutionary change and adaptive evolutionary potential in spatially and temporally structured populations, (2) provide a general theoretical foundation for the developments of new methods to estimate accuracy in important parameters of eco-evolutionary dynamics, and (3) quantify key processes affecting eco-evolutionary dynamics in six exceptional wild long-term study systems by applying the proposed methods.

I will do so by developing cutting-edge statistical methodology that builds on the current state-of-the art in animal breeding, human genomics, ecology and evolutionary biology.

As an innovative statistician with cross-disciplinary expertise and long collaborations with biologists, I am uniquely positioned for this task.

All methods will be developed and tested around the six empirical data sets, as well as via extensive simulation studies that allow us to experiment with scenarios similar to those expected to be available in the near future.

My work will expand current research boundaries, both in terms of methods and theory, and has a wide range of possible applications far beyond the data at hand, which will spark additional research.

Most importantly, the project will create valuable new insight into adaptive evolutionary processes and how to best preserve the Earth's biological diversity.