Evolutionary biology considers how organisms and populations change over multiple generations, and so is naturally focused on issues of sustainability through time. Yet, sustainability science rarely incorporates evolutionary thinking and most scientists and policy makers do not account for how evolutionary processes contribute to sustainability. Understanding the interplay between evolutionary processes and nature's contribution to people is key to sustaining life on Earth.

Evolution, the change in gene frequencies within populations, is a process of genetically based modification by descent, providing the raw material essential for adaptation to environmental change. Therefore, it is crucial that we understand evolutionary processes if we aim for a sustainable planet. We here contribute to this development by describing examples of contemporary, rapid evolutionary changes of concern for sustainability, specifically highlighting the global spread of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and how the evolutionary toolbox allowed tracking the origins and evolution of SARS-CoV-2 in real time and predicting potential future outbreaks. We also consider how urban development accelerates evolutionary processes such as altered phenotypic and physiological changes and the spread of infectious and zoonotic diseases. We show the importance of evolutionary concepts and techniques for public-health decision making. Many examples of the potential of evolutionary insights contributing to crucial sustainability challenges exist, including infectious and zoonotic diseases, ecosystem and human health, and conservation of natural resources. We thus join recent calls advocating for a stronger collaboration between evolutionary biologists and the sustainability community, increasing interdisciplinarity and the awareness about the knowledge of evolutionary processes for decision making and policies.

Evolution is fundamental to sustaining life on Earth and should be incorporated in sustainability measures and policies.

Information on social aspects of climate change intervention, such as behavioral choices and public acceptance, are often not included in global climate models. As a result, they have been critiqued for not adequately reflecting ‘real world’ conditions. At the same time, these models are important and influential policy tools. To improve these models, calls are being made for more interaction – or integration – between the social science and modelling research communities. Yet, it remains unclear how to achieve this. Responding to this gap, we explore what kind of integration is currently taking place, how, and opportunities for further development.

The importance of social drivers of climate change interventions, or social aspects, is currently underrepresented in computational modelling projections. These parameters are largely excluded from estimates of technical mitigation potential, feasibility, and tools like integrated assessment models (IAMs) and other large-scale models that influence the development of climate policies and notable bodies like the Intergovernmental Panel on Climate Change. This paper contributes to calls being made within the research community to address this gap and strengthen linkages between modelling practices and social science insights. Using nature-based solutions (NbS) as a framing, we present the results of a critical literature review and interviews with multidisciplinary experts reflecting on the current state of integration around IAMs and opportunities to better capture social aspects within large-scale modelling processes. Our findings confirm the need to incorporate social aspects in IAMs, but highlight that how this happens in practice may depend on context, project objectives, or pragmatic choices rather than conceptual notions about what ‘good’ integration is. Nevertheless, some integration strategies are better than others, and concerns about data limitations and low capacity of the IAM community for engaging in integration can be overcome with sufficient support and complementary efforts from the broader research community.

Integrating social aspects in large-scale models requires complementary efforts from the broader research community.

Modern agriculture is associated with numerous environmental predicaments, such as land degradation, water pollution, and greenhouse gas emission. Socio-economically, it is characterized by a treadmill of technological change, increased mechanization, and economic consolidation, while depressing economic returns to farmers. A root cause is the dominance of annual plants cultivated in monocultures. Annual crops require the yearly clearing of vegetation resulting in soil erosion and other forms of ecosystem degradation. Monocultures are susceptible to agricultural pests and weeds. By contrast, perennial polycultures informed by natural ecosystems, promise more sustainable agroecosystems with the potential to also revitalize the economic foundation of farming and hence rural societies.