Duration: 5/1-2025  – 2/28-2030

Methodology and theory for the prediction of genetic values and adaptive potential in the wild to generate novel insight into eco-evolutionary processes. 

In order to halt the ongoing biodiversity loss, we need a radically better understanding of key evolutionary and ecological processes of wild populations. With  the rapidly-increasing volumes of high-density genomic data for wild populations, the lack of suitable analytical tools now hinders crucial developments in the field of evolutionary ecology and conservation biology. The methods devloped in this project, funded by an ERC Consolidator Grant, will 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. 

The three major objectives of the GPWILD project are to:

1. Develop a broad set of tools for efficient 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.
3. Quantify key processes affecting eco-evolutionary dynamics in six exceptional wild long-term study systems by applying the proposed methods.

These objectives will be reached in four work packages, developing cutting-edge statistical methodology that builds on the current state-of-the art in animal breeding, human genomics, ecology and evolutionary biology. We are in the lucky situation that we have access to six empirical data sets, namely data from Norwegian house sparrows, Svalbard reindeer, Rum red deer, arctic fox, Dutch great tits and island scrub jays, all provided by collaborators of this project. All methods can be tested and developed around those systems. In addition, extensive simulation studies allow us to experiment with scenarios similar to those expected to be available in the near future. This work also has a wide range of possible applications far beyond the data at hand, which will spark additional research and create valuable new insight into adaptive evolutionary processes and how to best preserve the Earth's biological diversity.

Illustration of project structure: