The concept of tipping points is increasingly being addressed in both fundamental and applied environmental contexts, and is particularly salient in the context of anthropogenic threats, including climate change. Most research on tipping points has been conducted through the lens of a single realm (i.e., freshwater, marine, or terrestrial). Yet, there is both the need and opportunity to learn and share across ecosystems, and to engage in coordinated and comparative research. We aimed to identify priority questions that are germane to freshwater, marine and terrestrial realms, and that, if answered, would improve our ability to understand what tipping points are, why they occur, where they occur, and what to do about them. To help enable such efforts, we assembled a team with diverse expertise to identify key research questions, supplemented by an outreach call distributed via various electronic outlets (e.g., email, websites, social media). The responses were then thematized, evaluated, aggregated or disaggregated, and prioritized. Through workshops, and using a modified Delphi approach, we developed a final list of 18 priority research questions. Key themes that emerged included questions of societal relevance (i.e., why questions), drivers, ecological processes, and sensitivity (i.e., what questions), scale and connectivity (i.e., where questions), and tools, techniques, and resources for implementation (i.e., how questions). These questions frame a research agenda intended to help guide future fundamental and applied research related to tipping points in freshwater, marine, and terrestrial ecosystems.

Global data have served an integral role in characterizing large-scale groundwater systems, identifying their sustainability challenges, and informing on socioeconomic and ecological dimensions of groundwater. These insights have revealed groundwater as a dynamic component of the water cycle and social-ecological systems, leading to an expansion in groundwater science that increasingly focuses on groundwater’s interactions with ecological, socioeconomic, and Earth systems. This shift presents many opportunities that are conditional on broader, more interdisciplinary system conceptualizations, models, and methods that require the integration of a greater diversity of data in contrast to conventional hydrogeological investigations. Here, we catalogue 144 global open access datasets and dataset collections relevant to groundwater science that span elements of the hydrosphere, biosphere, atmosphere, lithosphere, food systems, governance, management, and other socioeconomic system dimensions. The assembled catalogue offers a reference of available data for use in interdisciplinary assessments, and we summarize these data across their primary system, spatial resolution, temporal range, data type, generation method, level of groundwater representation, and institutional location of lead authorship. The catalogue includes 15 groundwater datasets, 23 datasets derived in relation to groundwater, and 106 datasets associated with groundwater. We find the majority of datasets are temporally static and that temporally dynamic data peak in availability during the 2000-2010 decade. Only a small fraction of temporally dynamic data is derived with any direct representation of groundwater, highlighting the need for greater incorporation of groundwater in Earth system models and data collection initiatives across socioeconomic, governance, and environmental science research communities. A small number of countries, led by the USA, Germany, the Netherlands, and Canada, generate most global groundwater data, reflecting a global North bias in the institutional leadership of these data generation activities. We raise three priority themes for future global groundwater data initiatives, which include: data improvements through prioritizing observed and temporally dynamic data; elevating regional and local scale data and perspectives to address challenges relating to equity and bias; and advancing data sharing initiatives founded on reciprocal benefits between global initiatives and data providers.

Over the past decade, corporate investors have increasingly recognized that responsible environmental and social practices are essential to long-term financial success. Despite growing interest, corporate practices remain largely unchanged, and planetary trends are deeply concerning. This review applies a systems analysis lens to understand how financial sector structures and actors influence sustainability outcomes, often in counterproductive ways. Key barriers include the lack of science-based metrics, poor integration of environmental risks, and limited capacity to evaluate complex system dynamics. Current financial practices frequently miss or misinterpret systemic sustainability risks. We identify three critical areas where collaboration between sustainability science and finance is urgently needed: (1) translating scientific insights into financial decision contexts, (2) supporting science-based corporate sustainability reporting, and (3) strengthening environmental impact assessment. Systems thinking helps clarify where financial leverage can drive meaningful, cross-scale change—an essential step toward aligning capital flows with long-term ecological resilience and sustainability goals.

The threat associated with climate change and nature degradation poses complex financial challenges. Our systematic literature review of 88 finance-related publications published between 2015 and early 2022 revealed a gap in research on nature-related financial risks and their connections to climate change, particularly regarding ocean-related risks beyond rising sea levels. Although methods are available to assess these risks, more standardized approaches are needed. Based on this literature review, we developed a typology of climate-nature-finance effects using nine nested causal loop diagrams (CLDs). Our typology illustrates how climate change and environmental degradation can create chain reactions or domino-effects impacting insurance coverage, investors’ confidence, and market stability, leading to broader economic instability. This typology can help practitioners and scholars analyze their exposure to climate change and ecological degradation. Additionally, it can contribute to developing alternative quantitative assessments for studying non-linearities in financial risks. Future research can benefit from addressing the interactions between climate change and nature degradation more effectively and exploring the effects of finance on the environment and society.

Anthropogenic climate change is projected to become a major driver of biodiversity loss, destabilizing the ecosystems on which human society depends. As the planet rapidly warms, the disruption of ecological interactions among populations, species and their environment, will likely drive positive feedback loops, accelerating the pace and magnitude of biodiversity losses. We propose that, even without invoking such amplifying feedback, biodiversity loss should increase nonlinearly with warming because of the non-uniform distribution of biodiversity. Whether these non-uniformities are the uneven distribution of populations across a species’ thermal niche, or the uneven distribution of thermal niche limits among species within an ecological community, we show that in both cases, the resulting clustering in population warming tolerances drives nonlinear increases in the risk to biodiversity. We discuss how fundamental constraints on species’ physiologies and geographical distributions give rise to clustered warming tolerances, and how population responses to changing climates could variously temper, delay or intensify nonlinear dynamics. We argue that nonlinear increases in risks to biodiversity should be the null expectation under warming, and highlight the empirical research needed to understand the causes, commonness and consequences of clustered warming tolerances to better predict where, when and why nonlinear biodiversity losses will occur. This article is part of the discussion meeting issue ‘Bending the curve towards nature recovery: building on Georgina Mace’s legacy for a biodiverse future’.

Warm-water coral reefs are facing unprecedented human-driven threats to their continued existence as biodiverse functional ecosystems upon which hundreds of millions of people rely. These impacts may drive coral ecosystems past critical thresholds, beyond which the system reorganises, often abruptly and potentially irreversibly; this is what the Intergovernmental Panel on Climate Change (IPCC, 2022) define as a tipping point. Determining tipping point thresholds for coral reef ecosystems requires a robust assessment of multiple stressors and their interactive effects. In this perspective piece, we draw upon the recent global tipping point revision initiative (Lenton et al., 2023a) and a literature search to identify and summarise the diverse range of interacting stressors that need to be considered for determining tipping point thresholds for warm-water coral reef ecosystems. Considering observed and projected stressor impacts, we endorse the global tipping point revision's conclusion of a global mean surface temperature (relative to pre-industrial) tipping point threshold of 1.2 °C (range 1–1.5 °C) and the long-term impacts of atmospheric CO₂ concentrations above 350 ppm, while acknowledging that comprehensive assessment of stressors, including ocean warming response dynamics, overshoot, and cascading impacts, have yet to be sufficiently realised. These tipping point thresholds have already been exceeded, and therefore these systems are in an overshoot state and are reliant on policy actions to bring stressor levels back within tipping point limits. A fuller assessment of interacting stressors is likely to further lower the tipping point thresholds in most cases. Uncertainties around tipping points for such crucially important ecosystems underline the imperative of robust assessment and, in the case of knowledge gaps, employing a precautionary principle favouring lower-range tipping point values.

Resilience is an increasingly popular concept in research and practice, but quantitative resilience analyses are often disconnected from resilience theory. For example, previous studies argue that diversity, a key attribute for building resilience, and agency, essential for understanding local adaptation and transformation, are critical to understanding resilience. Despite significant progress in integrating them into qualitative frameworks, diversity and agency are rarely incorporated into quantitative social-ecological resilience metrics. This omission is concerning, given the critical role of quantitative resilience metrics in informing resilience-oriented decision-making. This study examines how diversity and agency are represented in quantitative resilience metrics across disciplines, with the goals of (a) assessing how research on social-ecological resilience currently integrates these concepts into quantitative metrics and (b) identifying future opportunities to enhance their inclusion using insights from other fields. Using topic modelling to identify different research fields and facilitate the screening process, we performed a multidisciplinary systematic meta-review of resilience metrics. To understand what types of resilience metrics are used across disciplines and where diversity and agency are more commonly included, we identified six categories of resilience metrics, with “performance under disruption” being the most used category (35%). We found that a limited number of quantitative resilience metrics include diversity and agency, with “system structure” and “compound indicators” being the main sources of diversity and agency, respectively. We further reviewed simulation models applying resilience metrics. The prevalence of performance under disruption metrics is stronger than in reviews (67%) and a similar quantity of metrics including diversity (14%) and agency (5%) was found. Drawing on insights from multiple disciplines, we outline five potential pathways to improve the inclusion of diversity and agency in social-ecological resilience metrics: using network-based metrics, using response and pathway diversity, including diversity and agency in compound indicators, integrating quantitative methodologies outside resilience theory, and improving the application of resilience in simulation models.

The world is currently experiencing a series of dramatic changes, from the consequences of global warming, flooding, forest fires, and drought-induced forest mortality to the COVID-19 pandemic and geopolitical conflicts. These events have elevated the concepts of resilience and tipping points into widespread use across various disciplines. However, each discipline often interprets and defines these concepts differently, leading to inconsistencies and misunderstandings.

David Hilbert once remarked, 'Mathematics is the foundation of all exact knowledge of natural phenomena.' This insight is particularly relevant when discussing resilience and tipping points, as both are deeply rooted in dynamical systems theory. Dynamical systems theory offers a rigorous mathematical framework for understanding complex systems. Thus, a systems perspective is essential for maintaining consistency in how these concepts are defined and applied. Such consistency is crucial for advancing our understanding and improving predictions of real-world systems.

Income inequality is one of the most important measures to indicate socioeconomic welfare and quality of life, and has implications for the environment. Yet, especially at the subnational level, comprehensive global data on income distribution are widely missing. Such data are essential for assessing patterns of inequality within countries and their development over time. Here we created seamless global subnational Gini coefficient and gross national income purchasing power parity per capita datasets for the period 1990–2023 and used these to assess the status and trends of income inequality and income, as well as their interplay. We show that while gross national income has increased for most people globally (94%), inequality has also increased for around 46–59% (depending on the national dataset used) of the global population, while it has decreased for 31–36% and has not shown a significant trend for 10–18%. We illustrate heterogeneities in inequality trends between and within countries, analyse plausible confounding factors related to inequality, and highlight the broad utility of the datasets through a case study that investigates correlations with terrestrial ecological diversity. Our dataset and analyses provide valuable insights for relevant stakeholders to direct future research and make informed decisions at the global, national and subnational levels, addressing societal, economic and environmental challenges caused by inequality.