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Abstract Freshwater macroinvertebrates are a diverse group and play key ecological roles, including accelerating nutrient cycling, filtering water, controlling primary producers, and providing food for predators. Their differences in tolerances and short generation times manifest in rapid community responses to change. Macroinvertebrate community composition is an indicator of water quality. In Europe, efforts to improve water quality following environmental legislation, primarily starting in the 1980s, may have driven a recovery of macroinvertebrate communities. Towards understanding temporal and spatial variation of these organisms, we compiled the TREAM dataset (Time seRies of European freshwAter Macroinvertebrates), consisting of macroinvertebrate community time series from 1,816 river and stream sites (mean length of 19.2 years and 14.9 sampling years) of 22 European countries sampled between 1968 and 2020. In total, the data include >93 million sampled individuals of 2,648 taxa from 959 genera and 212 families. These data can be used to test questions ranging from identifying drivers of the population dynamics of specific taxa to assessing the success of legislative and management restoration efforts.

Abstract Background Superposition, i.e. the ability of a particle (electron, photon) to occur in different states or positions simultaneously, is a hallmark in the subatomic world of quantum mechanics. Although counterintuitive at first sight, the quantum world has potential to inform macro-systems of people and nature. Using time series and spatial analysis of bird, phytoplankton and benthic invertebrate communities, this paper shows that superposition can occur analogously in redundancy analysis (RDA) frequently used by ecologists. Results We show that within individual ecosystems single species can be associated simultaneously with different orthogonal axes in RDA models, which suggests that they operate in more than one niche spaces. We discuss this counterintuitive result in relation to the statistical and mathematical features of RDA and the recognized limitations with current traditional species concepts based on vegetative morphology. Conclusion We suggest that such “quantum weirdness” in the models is reconcilable with classical ecosystems logic when the focus of research shifts from morphological species to cryptic species that consist of genetically and ecologically differentiated subpopulations. We support our argument with theoretical discussions of eco-evolutionary interpretations that should become testable once suitable data are available.

Abstract Background This paper discusses a paradox in mental health. It manifests as a relationship between adverse “bad” effects (suffering, clinical costs, loss of productivity) in individuals and populations and advantageous “good” aspects of mental disorders. These beneficial aspects (scientific, artistic and political accomplishments) emanate at the societal level through the frequently unprecedented creativity of people suffering from mental disorders and their relatives. Such gains can contribute to societal innovation and problem-solving. Especially in times of accelerated social-ecological change, approaches are needed that facilitate best-possible mental health care but also recognize creative ideas conducive to beneficial clinical and social-ecological innovations as soon as possible. Discussion This paper emphasizes the need to account for creativity as a crucial component in evolving mental health systems and societies. It highlights the need for wide-ranging approaches and discusses how research targeting multiple facets (e.g., brain level, cognitive neuroscience, psychiatry, neurology, socio-cultural, economic and other factors) might further our understanding of the creativity-mental disorder link and its importance for innovating mental health systems and societies. Conclusion Our discussion clarifies that considerable research will be needed to obtain a better understanding of how creativity associated with mental disorders may help to create more sustainable societies on a fast-changing planet through innovative ideas. Given the current-state-of-the-art of research and healthcare management, our discussion is currently speculative. However, it provides a basis for how pros and cons might be studied in the future through transdisciplinary research and collaborations across sectors of society.

Adaptive capacity is a critical component of building resilience in healthcare (RiH). Adaptive capacity comprises the ability of a system to cope with and adapt to disturbances. However, “shocks,” such as the current coronavirus disease 2019 (COVID-19) pandemic, can potentially exceed critical adaptation thresholds and lead to systemic collapse. To effectively manage healthcare systems during periods of crises, both adaptive and transformative changes are necessary. This commentary discusses adaptation and transformation as two complementary, integral components of resilience and applies them to healthcare. We treat resilience as an emergent property of complex systems that accounts for multiple, often disparately distinct regimes in which multiple processes (eg, adaptation, recovery) are subsumed and operate. We argue that Convergence Mental Health and other transdisciplinary paradigms such as Brain Capital and One Health can facilitate resilience planning and management in healthcare systems.

Non-technical summary The United Nations' sustainable development goals (SDGs) articulate societal aspirations for people and our planet. Many scientists have criticised the SDGs and some have suggested that a better understanding of the complex interactions between society and the environment should underpin the next global development agenda. We further this discussion through the theory of social–ecological resilience, which emphasises the ability of systems to absorb, adapt, and transform in the face of change. We determine the strengths of the current SDGs, which should form a basis for the next agenda, and identify key gaps that should be filled. Technical summary The United Nations' sustainable development goals (SDGs) are past their halfway point and the next global development agenda will soon need to be developed. While laudable, the SDGs have received strong criticism from many, and scholars have proposed that adopting complex adaptive or social–ecological system approaches would increase the effectiveness of the agenda. Here we dive deeper into these discussions to explore how the theory of social–ecological resilience could serve as a strong foundation for the next global sustainable development agenda. We identify the strengths and weaknesses of the current SDGs by determining which of the 169 targets address each of 43 factors affecting social–ecological resilience that we have compiled from the literature. The SDGs with the strongest connections to social–ecological resilience are the environment-focus goals (SDGs 2, 6, 13, 14, 15), which are also the goals consistently under-prioritised in the implementation of the current agenda. In terms of the 43 factors affecting social–ecological resilience, the SDG strengths lie in their communication, inclusive decision making, financial support, regulatory incentives, economic diversity, and transparency in governance and law. On the contrary, ecological factors of resilience are seriously lacking in the SDGs, particularly with regards to scale, cross-scale interactions, and non-stationarity. Social media summary The post-2030 agenda should build on strengths of SDGs 2, 6, 13, 14, 15, and fill gaps in scale, variability, and feedbacks.

Human activity causes biome shifts that alter biodiversity and spatial resilience patterns. Rare species, often considered vulnerable to change and endangered, can be a critical element of resilience by providing adaptive capacity in response to disturbances. However, little is known about changes in rarity patterns of communities once a biome transitions into a novel spatial regime. We used time series modeling to identify rare avian species in an expanding terrestrial (southern) spatial regime in the North American Great Plains and another (northern) regime that will become encroached by the southern regime in the near future. In this time-explicit approach, presumably rare species show stochastic dynamics in relative abundance – this is because they occur only rarely throughout the study period, may largely be absent but show occasional abundance peaks or show a combination of these patterns. We specifically assessed how stochastic/rare species of the northern spatial regime influence aspects of ecological resilience once it has been encroached by the southern regime. Using 47 years (1968–2014) of breeding bird survey data and a space-for-time substitution, we found that the overall contribution of stochastic/rare species to the avian community of the southern regime was low. Also, none of these species were of conservation concern, suggesting limited need for revised species conservation action in the novel spatial regime. From a systemic perspective, our results preliminarily suggest that stochastic/rare species only marginally contribute to resilience in a new spatial regime after fundamental ecological changes have occurred.

Spatial regimes (the spatial extents of ecological states) exhibit strong spatiotemporal order as they expand or contract in response to retreating or encroaching adjacent spatial regimes (e.g., woody plant invasion of grasslands) and human management (e.g., fire treatments). New methods enable tracking spatial regime boundaries via vegetation landcover data, and this approach is being used for strategic management across biomes. A clear advancement would be incorporating animal community data to track spatial regime boundaries alongside vegetation data. In a 41,170-hectare grassland experiencing woody plant encroachment, we test the utility of using animal community data to track spatial regimes via two hypotheses. (H1) Spatial regime boundaries identified via independent vegetation and animal datasets will exhibit spatial synchrony; specifically, grassland:woodland bird community boundaries will synchronize with grass:woody vegetation boundaries. (H2) Negative feedbacks will stabilize spatial regimes identified via animal data; specifically, frequent fire treatments will stabilize grassland bird community boundaries. We used 26 years of bird community and vegetation data alongside 32 years of fire history data. We identified spatial regime boundaries with bird community data via a wombling approach. We identified spatial regime boundaries with vegetation data by calculating spatial covariance between remotely-sensed grass and woody plant cover per pixel. For fire history data, we calculated the cumulative number of fires per pixel. Setting bird boundary strength (wombling R2 values) as the response variable, we tested our hypotheses with a hierarchical generalized additive model (HGAM). Both hypotheses were supported: animal boundaries synchronized with vegetation boundaries in space and time, and grassland bird communities stabilized as fire frequency increased (HGAM explained 38% of deviance). We can now track spatial regimes via animal community data pixel-by-pixel and year-by-year. Alongside vegetation boundary tracking, tracking animal community boundaries can inform the scale of management necessary to maintain animal communities endemic to desirable ecological states. Our approach will be especially useful for conserving animal communities requiring large-scale, unfragmented landscapes—like grasslands and steppes.

Ecosystem functioning and community structure are recognized as key components of ecosystem integrity, but comprehensive, standardized studies of the responses of both structural and functional indicators to different types of anthropogenic pressures remain rare. Consequently, we lack an empirical basis for (i) identifying when monitoring ecosystem structure alone misses important changes in ecosystem functioning, (ii) recommending sets of structural and functional metrics best suited for detecting ecological change driven by different anthropogenic pressures, and (iii) understanding the cumulative effects of multiple, co-occurring stressors on structure and function. We investigated variation in community structure and ecosystem functioning of stream ecosystems along three gradients (10–16 independent stream sites each) of increasing impact arising from agriculture, forestry and river regulation for hydropower, respectively. For each stream, we quantified variation in (i) the abiotic environment, (ii) community composition of four organism groups and (iii) three basal ecosystem processes underpinning carbon and nutrient cycling in streams. We assessed the responsiveness of multiple biodiversity, community structure and ecosystem functioning indicators based on variance explained and effect size metrics. Along a gradient of increasing agricultural impact, diatoms and fish were the most responsive groups overall, but significant variation was detected in at least one aspect of community composition, abundance and/or biodiversity of every organism group . In contrast, most of our functional metrics did not vary significantly along the agricultural gradient, possibly due to contrasting, antagonistic effects of increasing nutrient concentrations and turbidity on ecosystem process rates. The exception was detritivore-mediated litter decomposition which increased up to moderate levels of nutrient. Impacts of river regulation were most marked for diatoms, which were responsive to both increasingly frequent hydropeaking and to increasing seasonal river regulation. Among functional indicators, both litter decomposition and algal biomass accrual declined significantly with increasing hydropeaking. Few structural or functional metrics varied with forest management, with macroinvertebrate diversity increasing along the forestry gradient, as did algal and fungal biomass accrual. Together, these findings highlight the challenges of making inferences about the impacts of anthropogenic disturbances at the ecosystem level based on community data alone, and pinpoint the need to identify optimal sets of functional and structural indicators best suited for detecting ecological changes associated with different human activities.

Managing social-ecological systems toward desirable regimes requires learning about the system being managed while preparing for many possible futures. Adaptive management (AM) and scenario planning (SP) are two systems management approaches that separately use learning to reduce uncertainties and employ planning to manage irreducible uncertainties, respectively. However, each of these approaches have limitations that confound management of social-ecological systems. Here, we introduce iterative scenarios (IS), a systems management approach that is a hybrid of the scopes and relationships to uncertainty and controllability of AM and SP that combines the "iterativeness" of AM and futures planning of SP. Iterative scenarios is appropriate for situations with high uncertainty about whether a management action will lead to intended outcomes, the desired benefits are numerous and cross-scale, and it is difficult to account for the social implications around the natural resource management options. The value of iterative scenarios is demonstrated by applying the approach to green infrastructure futures for a neighborhood in the city of Cleveland, Ohio, U.S., that had experienced long-term, systemic disinvestment. The Cleveland green infrastructure project was particularly well suited to the IS approach given that learning about environmental factors was necessary and achievable, but what would be socially desirable and possible was unknown. However, iterative scenarios is appropriate for many social-ecological systems where uncertainty is high as IS accommodates real-world complexity faced by management.

A key challenge of the Anthropocene is to confront the dynamic complexity of systems of people and nature to guide robust interventions and adaptations across spatiotemporal scales. Panarchy, a concept rooted in resilience theory, accounts for this complexity, having at its core multiscale organization, interconnectedness of scales, and dynamic system structure at each scale. Despite the increasing use of panarchy in sustainability research, quantitative tests of its premises are scarce, particularly as they pertain to management consequences in ecosystems. In this study we compared the physicochemical environment of managed (limed) and minimally disturbed reference lakes and used time series modeling and correlation analyses to test the premises of panarchy theory: (1) that both lake types show dynamic structure at multiple temporal scales, (2) that this structure differs between lake types due to liming interacting with the natural disturbance regime of lakes, and (3) that liming manifests across temporal scales due to cross-scale connectivity. Hypotheses 1 and 3 were verified whereas support for hypothesis 2 was ambiguous. The literature suggests that liming is a "command-and-control" management form that fails to foster self-organization manifested in lakes returning to pre-liming conditions once management is ceased. In this context, our results suggest that redundance of liming footprints across scales, a feature contributing to resilience, in the physicochemical environment alone may not be enough to create a self-organizing limed lake regime. Further research studying the broader biophysical lake environment, including ecological communities of pelagic and benthic habitats, will contribute to a better understanding of managed lake panarchies. Such insight may further our knowledge of ecosystem management in general and of limed lakes in particular.

Studying ecosystem dynamics is critical to monitoring and managing linked systems of humans and nature. Due to the growth of tools and techniques for collecting data, information on the condition of these systems is more widely available. While there are a variety of approaches for mining and assessing data, there is a need for methods to detect latent characteristics in ecosystems linked to temporal and spatial patterns of change. Resilience-based approaches have been effective at not only identifying environmental change but also providing warning in advance of critical transitions in social-ecological systems (SES). In this study, we examine the usefulness of one such method, Fisher Information (FI) for spatiotemporal analysis. FI is used to assess patterns in data and has been established as an effective tool for capturing complex system dynamics to include regimes and regime shifts. We employed FI to assess the biophysical condition of eighty-five Swedish lakes from 1996-2018. Results showed that FI captured spatiotemporal changes in the Swedish lakes and identified distinct spatial patterns above and below the Limes Norrlandicus, a hard ecotone boundary which separates northern and southern ecoregions in Sweden. Further, it revealed that spatial variance changed approaching this boundary. Our results demonstrate the utility of this resilience-based approach for spatiotemporal and spatial regimes analyses linked to monitoring and managing critical watersheds and waterbodies impacted by accelerating environmental change.