Panarchy is a heuristic of complex system change rooted in resilience science. The concept has been rapidly assimilated across scientific disciplines due to its potential to envision and address sustainability challenges, such as climate change and regime shifts, that pose significant challenges for humans in the Anthropocene. However, panarchy has been studied almost exclusively via qualitative research. Quantitative approaches are scarce and preliminary but have revealed novel insights that allow for a more nuanced understanding of the heuristic and resilience science more generally. In this roadmap we discuss the potential for future quantitative approaches to panarchy. Transdisciplinary development of quantitative approaches, combined with advances in data accrual, curation and machine learning, may build on current tools. Combined with qualitative research and traditional approaches used in ecology, quantification of panarchy may allow for broad inference of change in complex systems of people and nature and provide critical information for management of social-ecological systems.

Species abundances and distributions are inherently tied to individuals' decisions about movement within their habitat. Therefore, integrating individual phenotypic variation within a larger ecological framework may provide better insight into how populations structure themselves. Recent evidence for consistent individual differences in behaviour prompts the hypothesis that variation in behavioural types might be related to variation in movement in natural environments. In a multiyear mark-recapture study, we found that individual sticklebacks exhibited consistent individual differences in behaviour both within a standardized testing arena designed to measure exploratory behaviour and within a river. Therefore, we asked whether individual differences in movement in a natural river were related to an individual's exploratory behavioural type. We also considered whether body condition and/or the individual's habitat or social environment use was related to movement. There was no evidence that an individual's exploratory behavioural type was related to movement within the river. Instead, an individual's habitat use and body condition interacted to influence natural movement patterns. Individuals in good condition were more likely to move further in the river, but only if they inhabited a vegetated complex part of the river; body condition had no influence on movement in those individuals inhabiting open areas of the river. Our results suggest that individual traits could help improve predictions about how populations may distribute themselves within patchy and complex environments.

The study of biological invasions of ecological systems has much to offer research on within-host systems, particularly for understanding infections and developing therapies using biological agents. Thanks to the ground-work established in other fields, such as community ecology and evolutionary biology, and to modern methods of measurement and quantification, the study of microbiomes has quickly become a field at the forefront of modern systems biology. Investigations of host-associated microbiomes (e.g., for studying human health) are often centered on measuring and explaining the structure, functions and stability of these communities. This momentum promises to rapidly advance our understanding of ecological networks and their stability, resilience and resistance to invasions. However, intrinsic properties of host-associated microbiomes that differ from those of free-living systems present challenges to the development of a within-host invasion ecology framework. The elucidation of principles underlying the invasibility of within-host networks will ultimately help in the development of medical applications and help shape our understanding of human health and disease.

Understanding the adaptive capacity of ecosystems to cope with change is crucial to management. However, unclear and often confusing definitions of adaptive capacity make application of this concept difficult. In this paper, we revisit definitions of adaptive capacity and operationalize the concept. We define adaptive capacity as the latent potential of an ecosystem to alter resilience in response to change. We present testable hypotheses to evaluate complementary attributes of adaptive capacity that may help further clarify the components and relevance of the concept. Adaptive sampling, inference and modeling can reduce key uncertainties incrementally over time and increase learning about adaptive capacity. Such improvements are needed because uncertainty about global change and its effect on the capacity of ecosystems to adapt to social and ecological change is high.

Concern about the functional consequences of unprecedented loss in biodiversity has prompted biodiversity-ecosystem functioning (BEF) research to become one of the most active fields of ecological research in the past 25 years. Hundreds of experiments have manipulated biodiversity as an independent variable and found compelling support that the functioning of ecosystems increases with the diversity of their ecological communities. This research has also identified some of the mechanisms underlying BEF relationships, some context-dependencies of the strength of relationships, as well as implications for various ecosystem services that mankind depends upon. In this paper, we argue that a multitrophic perspective of biotic interactions in random and non-random biodiversity change scenarios is key to advance future BEF research and to address some of its most important remaining challenges. We discuss that the study and the quantification of multitrophic interactions in space and time facilitates scaling up from small-scale biodiversity manipulations and ecosystem function assessments to management-relevant spatial scales across ecosystem boundaries. We specifically consider multitrophic conceptual frameworks to understand and predict the context-dependency of BEF relationships. Moreover, we highlight the importance of the eco-evolutionary underpinnings of multitrophic BEF relationships. We outline that FAIR data (meeting the standards of findability, accessibility, interoperability, and reusability) and reproducible processing will be key to advance this field of research by making it more integrative. Finally, we show how these BEF insights may be implemented for ecosystem management, society, and policy. Given that human well-being critically depends on the multiple services provided by diverse, multitrophic communities, integrating the approaches of evolutionary ecology, community ecology, and ecosystem ecology in future BEF research will be key to refine conservation targets and develop sustainable management strategies.