Search

Recently postulated mechanisms and models can help explain the enduring ‘Gaia’ puzzle of environmental regulation mediated by life. Natural selection can produce nutrient recycling at local scales and regulation of heterogeneous environmental variables at ecosystem scales. However, global-scale environmental regulation involves a temporal and spatial decoupling of effects from actors that makes conventional evolutionary explanations problematic. Instead, global regulation can emerge by a process of ‘sequential selection’ in which systems that destabilize their environment are short-lived and result in extinctions and reorganizations until a stable attractor is found. Such persistence-enhancing properties can in turn increase the likelihood of acquiring further persistence-enhancing properties through ‘selection by survival alone’. Thus, Earth system feedbacks provide a filter for persistent combinations of macroevolutionary innovations.

International commitment to the appropriately ambitious Paris climate agreement and the United Nations Sustainable Development Goals in 2015 has pulled into the limelight the urgent need for major scientific progress in understanding and modelling the Anthropocene, the tightly intertwined social-environmental planetary system that humanity now inhabits. The Anthropocene qualitatively differs from previous eras in Earth’s history in three key characteristics: (1) There is planetary-scale human agency. (2) There are social and economic networks of teleconnections spanning the globe. (3) It is dominated by planetary-scale social-ecological feedbacks. Bolting together old concepts and methodologies cannot be an adequate approach to describing this new geological era. Instead, we need a new paradigm in Earth System science that is founded equally on a deep understanding of the physical and biological Earth System – and of the economic, social and cultural forces that are now an intrinsic part of it. It is time...

Some ecosystems undergo abrupt transitions to a new regime after passing a tipping point in an exogenous stressor, for example lakes shifting from a clear to turbid ‘eutrophic’ state in response to nutrient-enrichment. Metrics-based resilience indicators have been developed as early warning signals of these shifts but have not always proved reliable indicators. Alternative approaches focus on changes in the composition and structure of an ecosystem, which can require long-term food-web observations that are typically beyond the scope of monitoring. Here we prototype a network-based algorithm for estimating ecosystem resilience, which reconstructs past ecological networks solely from palaeoecological abundance data. Resilience is estimated using local stability analysis, and eco-net energy: a neural network-based proxy for ‘ecological memory’. We test the algorithm on modelled (PCLake+) and empirical (lake Erhai) data. The metrics identify increasing diatom community instability during eutrophication in both cases, with eco-net energy revealing complex eco-memory dynamics. The concept of ecological memory opens a new dimension for understanding ecosystem resilience and regime shifts, with eco-memory potentially increasing ecosystem resilience by allowing past memorised eco-network states to be recovered after disruptions.

Ecosystems are complex and dynamic making them challenging to understand. We urgently need to assess human impacts on ecosystems which cause changes in structural feedbacks producing large, hard to reverse changes in state and functioning. System dynamics has proven to be a useful and versatile methodology for modelling complex systems given the comparative ease with which feedback loops can be modelled. However, a common issue arises when models become too large and structurally complex to understand the causal drivers of system behaviour. There is a need for an intermediate level of analysis capable of identifying causal driving structures and dynamics, regardless of model complexity. This study investigates Loop Eigenvalue Elasticity Analysis, a structural analysis technique commonly used in business and economic system dynamics models, and evaluates its utility for identifying feedback loop structures responsible for behavioural changes in complex ecological systems. The approach is demonstrated by analysing a simple lake system model that has been extensively studied in the past for its capacity to undertake critical transitions between alternative stable states. We show how the dominance of feedback loops can be tracked through time building influence over the system's behaviour decades prior to the actual collapse in the system. We discuss our findings in the context of studying complex ecosystems and socio-ecological systems.

Agricultural intensification has significantly increased yields and fed growing populations across the planet, but has also led to considerable environmental degradation. In response an alternative process of ‘Sustainable Intensification’ (SI), whereby food production increases while environmental impacts are reduced, has been advocated as necessary, if not sufficient, for delivering food and environmental security. However, the extent to which SI has begun, the main drivers of SI, and the degree to which degradation is simply ‘offshored’ are uncertain. In this study we assess agroecosystem services in England and two contrasting sub-regions, majority-arable Eastern England and majority-pastoral South-Western England, since 1950 by analysing ecosystem service metrics and developing a simple system dynamics model. We find that rapid agricultural intensification drove significant environmental degradation in England in the early 1980s, but that most ecosystem services except farmland biodiversity began to recover after 2000, primarily due to reduced livestock and fertiliser usage decoupling from high yields. This partially follows the trajectory of an Environmental Kuznets Curve, with yields and GDP growth decoupling from environmental degradation above ~£17,000 per capita per annum. Together, these trends suggest that SI has begun in England. However, the lack of recovery in farmland biodiversity, and the reduction in UK food self-sufficiency resulting in some agricultural impacts being ‘offshored’, represent major negative trade-offs. Maintaining yields and restoring biodiversity while also addressing climate change, offshored degradation, and post-Brexit subsidy changes will require significant further SI in the future.

We present a methodological approach for constructing an agent-based model (ABM) to assess community food security and variation among livelihood trajectories, using rural Malawi as a case study. The approach integrates both quantitative and qualitative data to explore how interactions between households and the environment lead to the emergence of community food availability, access, utilisation and stability over time. Results suggest that livelihoods based upon either non-agricultural work or farming are most stable over time, but agricultural labourers, dependent upon the availability of casual work, demonstrate limited capacity to step-up livelihood activities. The scenario results suggest that population growth and increased rainfall variability are linked to significant declines in food utilisation and stability by 2050. Taking a systems approach may help to enhance the sustainability of livelihoods, target efforts and promote community food security. We discuss transferability of the methodological approach to other case studies and scenarios. © 2018, University of Surrey. All rights reserved. SD was supported by an EPSRC Doctoral Training Centre grant (EP/G03690X/1). MS provided the village-level data collected under an ESPA Early Career Fellowship Grant (FELL-2014-104) with support from the Ecosystem Services for Poverty Alleviation (ESPA) programme. The expert workshop was organized with the support of the ‘Attaining Sustainable Services from Ecosystems using Trade-off Scenarios’ project (ASSETS; http: //espa-assets.org/; NE-J002267-1), which is also funded with support from ESPA. The ESPA programme is funded by the Department for International Development (DFID), the Economic and Social Research Council (ESRC) and the Natural Environment Research Council (NERC). SB was supported by two grants: BizkaiaTalent, Marie Curie Actions (People, Grant agreement n. 267230) and Juan de la Cierva incorporación (Grant IJCI-2014-22590).

Effective responses to climate change may demand a radical shift in human lifestyles away from self-interest for material gain, towards self-restraint for the public good. The challenge then lies in sustaining cooperative mitigation against the temptation to free-ride on others’ contributions, which can undermine public endeavours. When all possible future scenarios entail costs, however, the rationale for contributing to a public good changes from altruistic sacrifice of personal profit to necessary investment in minimizing personal debt. Here we demonstrate analytically how an economic framework of costly adaptation to climate change can sustain cooperative mitigation to reduce greenhouse gas emissions. We develop game-theoretic scenarios from existing examples of insurance for adaptation to natural hazards exacerbated by climate-change that bring the debt burden from future climate events into the present. We model the as-yet untried potential for leveraging public contributions to mitigation from personal costs of adaptation insurance, by discounting the insurance premium in proportion to progress towards a mitigation target. We show that collective mitigation targets are feasible for individuals as well as nations, provided that the premium for adaptation insurance in the event of no mitigation is at least four times larger than the mitigation target per player. This prediction is robust to players having unequal vulnerabilities, wealth, and abilities to pay. We enumerate the effects of these inequalities on payoffs to players under various sub-optimal conditions. We conclude that progress in mitigation is hindered by its current association with a social dilemma, which disappears upon confronting the bleak consequences of inaction.

Migration is often mentioned as a major potential impact of climate change for small island states, especially low-lying atolls. Understanding future migration flows, including the potential role of environmental change, requires an interdisciplinary approach, focusing both on environmental and socio-economic factors. This paper presents a detailed analysis of contemporary migration decision-making processes in a small island nation—the Maldives—based on a survey conducted in 2015. The results challenge the view that climate change is influencing contemporary migration behaviour in the Maldives. The survey shows how attitudes influence intention to migrate both internally and internationally. Existing analysis of the national census shows a strong urbanisation trend, with significant net migration to the capital island Male and its environs, dominating national migration flows. People consider perceived employment and educational opportunities, quality of health services, and expectations about general quality of life, happiness, and social environment. In addition, many Maldivians have a high intention to migrate internationally. Hence, the reduction of barriers to international migration by, for example, establishment of international migrant networks, or policies enabling migration from the Maldives, is likely to increase international migration. Maldivians widely express knowledge and concern about climate change and sea-level rise, recognising the high vulnerability of the island nation. However, such considerations are not presently important in their decisions about migration.

The ‘Anthropocene’ concept provides a conceptual framework that encapsulates the current global situation in which society has an ever-greater dominating influence on Earth System functioning. Simulation models used to understand earth system dynamics provide early warning, scenario analysis and evaluation of environmental management and policies. This paper aims to assess the extent to which current models represent the Anthropocene and suggest ways forward. Current models do not fully reflect the typical characteristics of the Anthropocene, such as societal influences and interactions with natural processes, feedbacks and system dynamics, tele-connections, tipping points, thresholds and regime shifts. Based on an analysis of current model representations of Anthropocene dynamics, we identify ways to enhance the role of modeling tools to better help us understand Anthropocene dynamics and address sustainability issues arising from them. To explore sustainable futures (‘safe and operating spaces’), social processes and anthropogenic drivers of biophysical processes must be incorporated, to allow for a spectrum of potential impacts and responses at different societal levels. In this context, model development can play a major role in reconciling the different epistemologies of the disciplines that need to collaborate to capture changes in the functioning of socio-ecological systems. Feedbacks between system functioning and underlying endogenous drivers should be represented, rather than assuming the drivers to be exogenous to the modelled system or stationary in time and space. While global scale assessments are important, the global scale dynamics need to be connected to local realities and vice versa. The diversity of stakeholders and potential questions requires a diversification of models, avoiding the convergence towards single models that are able to answer a wide range of questions, but without sufficient specificity. The novel concept of the Anthropocene can help to develop innovative model representations and model architectures that are better suited to assist in designing sustainable solutions targeted at the users of the models and model results.

Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.

The ability of species to modulate environmental conditions and resources has long been of interest. In the past three decades the impacts of these biotic modifiers have been investigated as ‘ecosystem engineers’, ‘niche constructors’, ‘facilitators’ and ‘keystone species’. This environmental modulation can vary spatially from extremely local to global, temporally from days to geological time, and taxonomically from a few to a very large number of species. Modulation impacts are pervasive and affect, inter alia, the climate, structural environments, disturbance rates, soils and the atmospheric chemical composition. Biotic modifiers may profoundly transform the projected environmental conditions, and consequently have a significant impact on the predicted occurrence of the focal species in species distribution models (SDMs). This applies especially when these models are projected into different geographical regions or into the future or the past, where these biotic modifiers may be absent, or other biotic modifiers may be present. We show that environmental modulation can be represented in SDMs as additional variables. In some instances it is possible to use the species (e.g. biotic modifiers) in order to reflect the modulation. This would apply particularly to cases where the effect is the result of a single or a small number of species (e.g. elephants transforming woodland to grassland). Where numerous species generate an effect (such as tree species making a forest, or grasses facilitating fire) that modulates the abiotic environment, the effect itself might be a better descriptor for the aggregated action of the numerous species. We refer to this ‘effect’ as the modulator. Much of the information required to incorporate environmental modulation effects in SDMs is already available from remote-sensing data and vegetation models.

Life has significantly altered the Earth's atmosphere, oceans and crust. To what extent has it also affected interior geological processes? To address this question, three models of geological processes are formulated: mantle convection, continental crust uplift and erosion and oceanic crust recycling. These processes are characterised as non-equilibrium thermodynamic systems. Their states of disequilibrium are maintained by the power generated from the dissipation of energy from the interior of the Earth. Altering the thickness of continental crust via weathering and erosion affects the upper mantle temperature which leads to changes in rates of oceanic crust recycling and consequently rates of outgassing of carbon dioxide into the atmosphere. Estimates for the power generated by various elements in the Earth system are shown. This includes, inter alia, surface life generation of 264 TW of power, much greater than those of geological processes such as mantle convection at 12 TW. This high power results from life's ability to harvest energy directly from the sun. Life need only utilise a small fraction of the generated free chemical energy for geochemical transformations at the surface, such as affecting rates of weathering and erosion of continental rocks, in order to affect interior, geological processes. Consequently when assessing the effects of life on Earth, and potentially any planet with a significant biosphere, dynamical models may be required that better capture the coupled nature of biologically-mediated surface and interior processes.

Aim: Two of the oldest observations in plant geography are the increase in plant diversity from the poles towards the tropics and the global geographic distribution of vegetation physiognomy (biomes). The objective of this paper is to use a process-based vegetation model to evaluate the relationship between modelled and observed global patterns of plant diversity and the geographic distribution of biomes. Location: The global terrestrial biosphere. Methods: We implemented and tested a novel vegetation model aimed at identifying strategies that enable plants to grow and reproduce within particular climatic conditions across the globe. Our model simulates plant survival according to the fundamental ecophysiological processes of water uptake, photosynthesis, reproduction and phenology. We evaluated the survival of an ensemble of 10,000 plant growth strategies across the range of global climatic conditions. For the simulated regional plant assemblages we quantified functional richness, functional diversity and functional identity. Results: A strong relationship was found (correlation coefficient of 0.75) between the modelled and the observed plant diversity. Our approach demonstrates that plant functional dissimilarity increases and then saturates with increasing plant diversity. Six of the major Earth biomes were reproduced by clustering grid cells according to their functional identity (mean functional traits of a regional plant assemblage). These biome clusters were in fair agreement with two other global vegetation schemes: a satellite image classification and a biogeography model (kappa statistics around 0.4). Main conclusions: Our model reproduces the observed global patterns of plant diversity and vegetation physiognomy from the number and identity of simulated plant growth strategies. These plant growth strategies emerge from the first principles of climatic constraints and plant functional trade-offs. Our study makes important contributions to furthering the understanding of how climate affects patterns of plant diversity and vegetation physiognomy from a process-based rather than a phenomenological perspective.

Models that demonstrate environmental regulation as a consequence of organism and environment coupling all require a number of core assumptions. Many previous models, such as Daisyworld, require that certain environment-altering traits have a selective advantage when those traits also contribute towards global regulation. We present a model that results in the regulation of a global environmental resource through niche construction without employing this and other common assumptions. There is no predetermined environmental optimum towards which regulation should proceed assumed or coded into the model. Nevertheless, polymorphic stable states that resist perturbation emerge from the simulated co-evolution of organisms and environment. In any single simulation a series of different stable states are realised, punctuated by rapid transitions. Regulation is achieved through two main subpopulations that are adapted to slightly different resource values, which force the environmental resource in opposing directions. This maintains the resource within a comparatively narrow band over a wide range of external perturbations. Population driven oscillations in the resource appear to be instrumental in protecting the regulation against mutations that would otherwise destroy it. Sensitivity analysis shows that the regulation is robust to mutation and to a wide range of parameter settings. Given the minimal assumptions employed, the results could reveal a mechanism capable of environmental regulation through the by-products of organisms.

The principle of Maximum Entropy Production (MaxEP) has been successfully used to reproduce the steady states of a range of non-equilibrium systems. Here we investigate MaxEP and maximum heat flux extremum principles directly via the simulation of a Rayleigh-Bernard convective system implemented as a lattice gas model. Heat flux and entropy production emerges in this system via the resolution of particle interactions. In the spirit of other related works, we use a reductionist approach, creating a lattice-Boltzmann model to produce steady-convective states between reservoirs of different temperatures. Convection cells emerge that show meta-stability where a given lattice size is able to support a range of convective states. Slow expansion and contraction of the model lattice, implemented by addition and subtraction of vertices, shows hysteresis loops where stable convection cells are expanded to regions wherein they become meta-stable, and eventually transition into more stable configurations. The maximally stable state is found to be that which maximises the rate of heat transfer, which is only equivalent to maximum internal entropy production in a strong forcing regime, while it is consistent with minimising entropy production in a weak forcing case. These results demonstrate the utility of lattice-Boltzmann models for future studies of non-equilibrium systems, and highlight the importance of dissipation and forcing rates in disambiguating proposed extremum principles.

Simple models of complex phenomena provide powerful insights and suggest low-level mechanistic descriptions. The Earth system arises from the interaction of subsystems with multi-scale temporal and spatial variability; from the microbial to continental scales, operating over the course of days to geological time. System-level homeostasis has been demonstrated in a number of conceptual, artificial life, models which share the advantage of a thorough and transparent analysis. We reintroduce a general model for a coupled life-environment model, concentrating on a minimal set of assumptions, and explore the consequences of interaction between simple life elements and their shared, multidimensional environment. In particular stability, criticality and transitions are of great relevance to understanding the history, and future of the Earth system. The model is shown to share salient features with other abstract systems such as Ashby's Homeostat and Watson and Lovelock's Daisyworld. Our generic description is free to explore high-dimensional, complex environments, and in doing so we show that even a small increase in the environmental complexity gives rise to very complex attractor landscapes which require a much richer conception of critical transitions and hysteresis

Complex systems are frequently characterised as systems of many components whose interactions drive a plethora of emergent phenomena. Understanding the history and future behaviour of planet Earth, arguably the most complex known system in the universe, is an ambitious goal and remains at the core of complexity science. From the establishment of the planet’s magnetic dipole, to the interplay between life and it’s environment. The dynamics across all scales are characterised by their numerous interacting components.

Models which explore the possibilities of emergent self-regulation in the Earth system often assume the timescales associated with changes in various sub-systems to be predetermined. Given their importance in guiding the fixed point dynamics of such models, relatively little formalism has been established. We analyse a classic model of environmental self-regulation, Daisyworld, and interpret the original equations for model temperature, changes in insolation, and self-organisation of the biota as an important separation of timescales. This allows a simple analytical solution where the model is reduced to two states while retaining important characteristics of the original model. We explore the consequences of relaxing some key assumptions. We show that increasing the rate of change of insolation relative to adaptation of the biota shows a sharp transition between regulating, and lifeless states. Additionally, in slowing the rate of model temperature change relative to the adapting biota we derive expressions for the damping rate of fluctuations, along with a threshold beyond which damped oscillations occur. We relax the assumption that seeding occurs globally by extending this analysis to solve a two-dimensional cellular automata Daisyworld. We conclude by reviewing a number of previous Daisyworld models and make explicit their respective timescales, and how their behaviour can be understood in light of our analysis.

The Earth, with its core-driven magnetic field, convective mantle, mobile lid tectonics, oceans of liquid water, dynamic climate and abundant life is arguably the most complex system in the known universe. This system has exhibited stability in the sense of, bar a number of notable exceptions, surface temperature remaining within the bounds required for liquid water and so a significant biosphere. Explanations for this range from anthropic principles in which the Earth was essentially lucky, to homeostatic Gaia in which the abiotic and biotic components of the Earth system self-organise into homeostatic states that are robust to a wide range of external perturbations. Here we present results from a conceptual model that demonstrates the emergence of homeostasis as a consequence of the feedback loop operating between life and its environment. Formulating the model in terms of Gaussian processes allows the development of novel computational methods in order to provide solutions. We find that the stability of this system will typically increase then remain constant with an increase in biological diversity and that the number of attractors within the phase space exponentially increases with the number of environmental variables while the probability of the system being in an attractor that lies within prescribed boundaries decreases approximately linearly. We argue that the cybernetic concept of rein control provides insights into how this model system, and potentially any system that is comprised of biological to environmental feedback loops, self-organises into homeostatic states., Author Summary Life on Earth is perhaps greater than three and a half billion years old and it would appear that once it started it never stopped. During this period a number of dramatic shocks and drivers have affected the Earth. These include the impacts of massive asteroids, runaway climate change and increases in brightness of the Sun. Has life on Earth simply been lucky in withstanding such perturbations? Are there any self-regulating or homeostatic processes operating in the Earth system that would reduce the severity of such perturbations? If such planetary processes exist, to what extent are they the result of the actions of life? In this study, we show how the regulation of environmental conditions can emerge as a consequence of life's effects. If life is both affected by and affects it environment, then this coupled system can self-organise into a robust control system that was first described during the early cybernetics movement around the middle of the twentieth century. Our findings are in principle applicable to a wide range of real world systems - from microbial mats to aquatic ecosystems up to and including the entire biosphere.

The local climate represents the primary selection pressure acting on vegetation, but competitive interactions between plant strategies determine their composition. We link growth and reproduction characteristics from different plant strategies, that emerge from climatic constraints, to their competitive abilities and calculate explicitly their spatial dynamics. DIVE (Dynamics and Interactions of VEgetation), a simple generic model is built, that calculates population dynamics in the presence of perturbations, seed and resource competition. To understand the impacts of competition and perturbations on the population dynamics, a range of sensitivity experiments are conducted. DIVE simulations feature successional dynamics from fast-growing towards slow-growing plant strategies and as such corresponds to widely observed characteristics of terrestrial vegetation. Perturbations, seed and resource competition were found to affect succession and diversity, with the community composition at steady state ranging from competitive exclusion to coexistence and total extinction. We conclude that linking ecophysiological characteristics of vegetation to competition is a valid approach to determine population dynamics. Furthermore, incorporating mechanisms of perturbations and competition may be essential in order to effectively predict the response of community dynamics to changing environmental conditions.

The Maximum Entropy Production (MEP) principle has been remarkably successful in producing accurate predictions for non-equilibrium states. We argue that this is because the MEP principle is an effective inference procedure that produces the best predictions from the available information. Since all Earth system processes are subject to the conservation of energy, mass and momentum, we argue that in practical terms the MEP principle should be applied to Earth system processes in terms of the already established framework of non-equilibrium thermodynamics, with the assumption of local thermodynamic equilibrium at the appropriate scales.

Daisyworld is a simple planetary model designed to show the long-term effects of coupling between life and its environment. Its original form was introduced by James Lovelock as a defense against criticism that his Gaia theory of the Earth as a self-regulating homeostatic system requires teleological control rather than being an emergent property. The central premise, that living organisms can have major effects on the climate system, is no longer controversial. The Daisyworld model has attracted considerable interest from the scientific community and has now established itself as a model independent of, but still related to, the Gaia theory. Used widely as both a teaching tool and as a basis for more complex studies of feedback systems, it has also become an important paradigm for the understanding of the role of biotic components when modeling the Earth system. This paper collects the accumulated knowledge from the study of Daisyworld and provides the reader with a concise account of its important properties. We emphasize the increasing amount of exact analytic work on Daisyworld and are able to bring together and summarize these results from different systems for the first time. We conclude by suggesting what a more general model of life-environment interaction should be based on.

A long standing debate within ecology is to what extent ecosystem complexity and stability are related. Landmark theoretical studies claimed that the more complex an ecosystem, the more unstable it is likely to be. Stability in an ecosystems context can be assessed in different ways. In this paper we measure stability in terms of a model ecosystem's ability to regulate environmental conditions. We show how increasing biodiversity in this model can result in the regulation of the environment over a wider range of external perturbations. This is achieved via changes to the ecosystem's resistance and resilience. This result crucially depends on the feedback that the organisms have on their environment.

This article is a contribution to the PAGES Focus 4 ‘Regional Integration’ theme. ABSTRACT: Humanity faces a major global challenge in achieving wellbeing for all while simultaneously ensuring that the biophysical processes and ecosystem services that underpin wellbeing are exploited within scientifically informed boundaries of sustainability. We propose a framework for defining the safe and just operating space for humanity that integrates social wellbeing into the original planetary boundaries concept (Rockström et al. 2009ab) for application at regional scales. We argue that such a framework can: (1) increase the policy impact of the boundaries concept as most governance takes place at the regional rather than planetary scale; (2) contribute to the understanding and dissemination of complexity thinking throughout governance and policy making; (3) act as a powerful metaphor and communication tool for regional equity and sustainability. We demonstrate the approach in two rural Chinese localities where we define the safe and just operating space that lies between an environmental ceiling and a social foundation from analysis of time series drawn from monitored and palaeoecological data and from social survey statistics respectively. Agricultural intensification has led to poverty reduction though not eradicated it but at the expense of environmental degradation. Currently the environmental ceiling is exceeded for degraded water quality at both localities even though the least well met social standards are for available piped water and sanitation. The conjunction of these social needs and environmental constraints around the issue of water access and quality illustrates the broader value of the safe and just operating space approach for sustainable development.