On Developing Sustainable Digital Ecosystems and their Spatial-temporal Knowledge Management

The research aims to assess the sustainment of multiple ecosystems with viable and adaptable models. We propose an Information System (IS) modelling approach and examine the sustainment between ecosystems through connectable multidimensional IS artefacts. For example, humans survive in healthy and hassle-free environments for long-term economic benefits. We conceptualize human, healthcare, and environmental ecosystems are connectable, and the interconnectivity depends on how the ecologies are supportive together and with each other. The ecosystems emerge and grow with data heterogeneity challenges, which can disorganize ecological connectivity, impeding the implementation of resilient digital ecosystems. The development of multidimensional repositories is added motivation to explore connectivity, for which Attribute Journey Mapping and Modelling (AJMM) method is sought. Map views are computed to successfully interpret and establish connectivity, including coherency between attributes of multiple digital ecosystems. Besides, Big Data has changed the ecological research direction with which the coexistence between human-healthcare-environment ecosystems is assessed

Modeling and control of complex dynamic systems: Applied mathematical aspects

The concept of complex dynamic systems arises in many varieties, including the areas of energy generation, storage and distribution, ecosystems, gene regulation and health delivery, safety and security systems, telecommunications, transportation networks, and the rapidly emerging research topics seeking to understand and analyse. Such systems are often concurrent and distributed, because they have to react to various kinds of events, signals, and conditions. They may be characterized by a system with uncertainties, time delays, stochastic perturbations, hybrid dynamics, distributed dynamics, chaotic dynamics, and a large number of algebraic loops. This special issue provides a platform for researchers to report their recent results on various mathematical methods and techniques for modelling and control of complex dynamic systems and identifying critical issues and challenges for future investigation in this field. This special issue amazingly attracted one-hundred-and eighteen submissions, and twenty-eight of them are selected through a rigorous review procedure

Arctic air pollution: Challenges and opportunities for the next decade

The Arctic is a sentinel of global change. This region is influenced by multiple physical and socio-economic drivers and feedbacks, impacting both the natural and human environment. Air pollution is one such driver that impacts Arctic climate change, ecosystems and health but significant uncertainties still surround quantification of these effects. Arctic air pollution includes harmful trace gases (e.g. tropospheric ozone) and particles (e.g. black carbon, sulphate) and toxic substances (e.g. polycyclic aromatic hydrocarbons) that can be transported to the Arctic from emission sources located far outside the region, or emitted within the Arctic from activities including shipping, power production, and other industrial activities. This paper qualitatively summarizes the complex science issues motivating the creation of a new international initiative, PACES (air Pollution in the Arctic: Climate, Environment and Societies). Approaches for coordinated, international and interdisciplinary research on this topic are described with the goal to improve predictive capability via new understanding about sources, processes, feedbacks and impacts of Arctic air pollution. Overarching research actions are outlined, in which we describe our recommendations for 1) the development of trans-disciplinary approaches combining social and economic research with investigation of the chemical and physical aspects of Arctic air pollution; 2) increasing the quality and quantity of observations in the Arctic using long-term monitoring and intensive field studies, both at the surface and throughout the troposphere; and 3) developing improved predictive capability across a range of spatial and temporal scales

'Raising the bar' : improving the standard and utility of weed and invasive plant research

Fil: Murray, Justine V.. Water for Healthy Country Flagship; AustraliaFil: Lehnhoff, Erik A.. Montana State University; Estados UnidosFil: Neve, Paul. University of Warwick; Reino UnidoFil: Poggio, Santiago Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Webber, Bruce L.. CSIRO Ecosystems Sciences; Australia. The University of Western Australia; Australi

Shocks in coupled socio-ecological systems: what are they and how can we model them?

Coupled socio-ecological systems (SES) are complex systems characterized by self-organization, non-linearities, interactions among heterogeneous elements within each subsystem, and feedbacks across scales and among subsystems. When such a system experiences a shock or a crisis, the consequences are difficult to predict. In this paper we first define what a shock or a crisis means for SES. Depending on where the system boundary is drawn, shocks can be seen as exogenous or endogenous. For example, human intervention in environmental systems could be seen as exogenous, but endogenous in a socio-environmental system. This difference in the origin and nature of shocks has certain consequences for coupled SES and for policies to ameliorate negative consequences of shocks. Having defined shocks, the paper then focuses on modelling challenges when studying shocks in coupled SES. If we are to explore, study and predict the responses of coupled SES to shocks, the models used need to be able to accommodate (exogenous) or produce (endogenous) a shock event. Various modelling choices need to be made. Specifically, the ‘sudden’ aspect of a shock suggests the time period over which an event claimed to be a shock occurred might be ‘quick’. What does that mean for a discrete event model? Turning to magnitude, what degree of change (in a variable or set of variables) is required for the event to be considered a shock? The ‘surprising’ nature of a shock means that none of the agents in the model should expect the shock to happen, but may need rules enabling them to generate behaviour in exceptional circumstances. This requires a certain design of the agents’ decision-making algorithms, their perception of a shock, memory of past events and formation of expectations, and the information available to them during the time the shock occurred

Freshwater ecosystem services in mining regions : modelling options for policy development support

The ecosystem services (ES) approach offers an integrated perspective of social-ecological systems, suitable for holistic assessments of mining impacts. Yet for ES models to be policy-relevant, methodological consensus in mining contexts is needed. We review articles assessing ES in mining areas focusing on freshwater components and policy support potential. Twenty-six articles were analysed concerning (i) methodological complexity (data types, number of parameters, processes and ecosystem-human integration level) and (ii) potential applicability for policy development (communication of uncertainties, scenario simulation, stakeholder participation and management recommendations). Articles illustrate mining impacts on ES through valuation exercises mostly. However, the lack of ground-and surface-water measurements, as well as insufficient representation of the connectivity among soil, water and humans, leave room for improvements. Inclusion of mining-specific environmental stressors models, increasing resolution of topographies, determination of baseline ES patterns and inclusion of multi-stakeholder perspectives are advantageous for policy support. We argue that achieving more holistic assessments exhorts practitioners to aim for high social-ecological connectivity using mechanistic models where possible and using inductive methods only where necessary. Due to data constraints, cause-effect networks might be the most feasible and best solution. Thus, a policy-oriented framework is proposed, in which data science is directed to environmental modelling for analysis of mining impacts on water ES