Trondheim. GPWILD, an ERC grant that helps address the biodiversity crisis

In December 2024, we at the Department of Mathematical Sciences at NTNU received the great news that my project “Prediction of genetic values and adaptive potential in the wild (GPWILD)” received funding through a Consolidator Grant by the European Research Council (ERC).

A major goal of the project is to develop novel methodology to analyze large-scale genomic data, which are currently generated at more and more affordable costs for many wild animal populations. These types of data are now opening up new ground-breaking opportunities to answer central questions in conservation biology and evolutionary ecology, for example to predict and understand whether or when populations are able to escape extinction. However, the lack of suitable methodology to exploit genomic data from wild study systems is now holding back major and crucial developments.
To address these limitations, the GPWILD project combines cutting-edge statistical and machine learning methodology and theory from ecology and evolutionary biology, and builds on the current state-of-the art in animal breeding and human genomics, where the analysis of genomic data has led to massive progress. The team, which currently is being built up, will be composed of a cross-disciplinary group of researchers. Together we will contribute towards the development of methods that will lead to better understanding and prediction of evolution and the robustness of wild animal populations, with the ultimate goal to help preserving biodiversity.

There is in fact wide recognition of the importance to understand how human-driven environmental change is impacting biodiversity. Despite this being one of the most pressing questions of our time, we currently lack a comprehensive understanding of the capacity for wild populations to adapt and survive [1].
The urgent need for a better understanding and management of wild populations is reflected by the fact that the Convention on Biological Diversity, which has near universal participation among countries, recently agreed to have restoration completed or underway on at least 30\% of degraded ecosystems on Earth by 2030 [1].
However, the failure to meet previously set biodiversity goals [2] and the action plans of international consortia, such as the EU Biodiversity strategy for 2030 [3], confirm that we have not yet sufficiently well understood how wild-living plant and animal populations evolve and adapt to modified environmental conditions.

The two main strategies for wild animal or plant populations to avoid extinction are to either adapt in a plastic way, meaning that individuals adjust their phenotypes on a short time scale, or by adaptive evolution, where selection pressure ultimately alters the genetic material of individuals in a population. Only in the second case, that is, in the presence of adaptive evolution of the genome, can the changes be passed on to the next generations, which thus has the potential to rescue species on a permanent time scale. In either case it is extremely hard to predict and understand whether or when species can keep up with the current pace of environmental change. Deeper insight into evolutionary processes in wild populations are therefore urgently needed, and the methodology developed in the ERC project will deliver exactly this. All novel methods will be developed around six study systems where the data are owned by national and international collaborators, including data from arctic fox, reindeer, red deer and three different bird species.

The advancements from GPWILD will also open up many new research opportunities by laying
the foundation for the wide use of genomic data that are now generated at higher and higher pace for more and more wild populations. Powerful predictions across populations and time scales will help answer many open questions in biodiversity conservation and evolutionary ecology,
and will improve our understanding of causes and consequences of populations’ abilities to adapt and survive in changing environments. The insight into adaptive evolutionary processes generated by my project is overdue and will help address the dramatic loss of biodiversity and the restoration of natural ecosystems. Ultimately, this is in line with several UN sustainability goals.