Suchergebnisse

Shifts in the ecosystems distribution as the result of climate change are of interest for decision-makers in biodiversity conservation at local and European level. This paper presents the use of modeling technique, Maxent (Maximum entropy modeling) and BIOCLIM (environmental envelope model), to estimate the impact of climate change on the Alpine bioregion of Continental Europe for improving the management policy in support of stopping biodiversity loss. The European Union priority habitat 6230 occurring in mountain areas and sub-mountain areas of the Carpathians was selected for modeling being of high priority conservation status in the Natura 2000 network of protected area. Maxent and BIOCLIM were used to create spatial distribution models for Mesophilous oligotrophic mountain pasture and Subalpine oligotrophic pastures. Models were run with 1950–2000 averaged bioclimatic data and double atmospheric CO2 concentration scenario in perspective of the year 2050. In our analyses we have included once all 6320 mapped habitat with Nardus grasslands. Under 1950–2000 climate scenario, both models exhibited high AUC values (> 0.9). The predicted geographical distribution of Mesophilous oligotrophic mountain pasture and Subalpine oligotrophic pastures coded as VNG and PON habitat modeled by Maxent and BIOCLIM shows differences between the modeling approaches, with Maxent predicting smaller areas (12% less) of suitable habitat than BIOCLIM. For the future climate scenario (double CO2) the surface with PON+VNG decreases by 31% for Maxent and 26% for BIOCLIM. However both models show significant shifts of the Nardus habitat due to climate change. The distribution maps obtained indicate vulnerability areas to biodiversity loss and of interest to be monitored. The output of models will contribute to the Black Sea Catchment Observation Systems to be further accessible to scientists, decision-makers and the general public.

The Sustainable Development Goals (SDGs) established to be achieved by 2030 are an ensemble of 17 goals to address global environmental and social economic concerns [1]. SDG 15 concerns the protection of terrestrial ecosystems to halt biodiversity loss. Target 15.9 states that by 2020, ecosystem and biodiversity values should be integrated into national and local planning, and is related to Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020, which also involves integrating biodiversity values into national accounting and reporting systems [2]. The importance of maintaining ecosystem integrity is becoming widely recognized, not only to halt biodiversity loss, but also to preserve Nature's benefits to human well-being, and has been included in many other targets such as the EU 2020 Biodiversity Strategy's target 2, which requires the restoration of at least 15% of degraded ecosystems as well as the establishment of green infrastructures to enhance ecosystem services (ES) [3]. The Green Infrastructures (GI) framework is used as a policy tool and promotes the multi-functional use of landscapes to improve biodiversity conservation and benefits to society. It is formulated as a "strategically planned network of natural and semi-natural areas" [4] and is based on three main pillars: key habitats for target species, connectivity and ES [5]. As part of ERA-PLANET's GEOEssential project (Essential Variables workflows for resource efficiency and environmental management), our study aims at demonstrating how the GI framework can be implemented at any geographical area or time-period through reproducible modeling workflows from field data to Essential Variables (EV) data products and policy relevant indicators to monitor and inform advances towards environmental targets. A proof of concept workflow was already set in place for computing the indicator 15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem, while other workflows will follow. The execution platform is the GEOEssential Virtual Laboratory, a cloud-based virtual platform which enables access to, and execution of workflows for the ecosystem science community of practice and even more. REFERENCES: 1. UNSD, 2016. Sustainable Development Goals Report. https://unstats.un.org/sdgs/report/2016/ (accessed 18 May 2018). 2. CBD Secretariat, 2010. The Strategic Plan for Biodiversity 2011-2020, and the Aichi Biodiversity Targets. Secretariat of the Convention on Biological Diversity, Nagoya. 3. European Commission, 2011. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions - Our life insurance, our natural capital: an EU biodiversity strategy to 2020, Brussels. 4. European Commission, 2013. Green infrastructure (GI) - Enhancing Europe's Natural Capital, Brussels. 5. Liquete, C., Kleeschulte, S., Dige, G., Maes, J., Grizzetti, B., Olah, B., & Zulian, G., 2015. Mapping green infrastructure based on ecosystem services and ecological networks: A Pan-European case study. Environmental Science & Policy, 54, 268–280.

Nature forms interdependent networks in a landscape, which is key to the survival of species and the maintenance of genetic diversity. Nature provides crucial socio-economic benefits to people, but they are typically undervalued in politi- cal decisions. This has led to the concept of Green Infrastructure (GI), which defines an interlinked network of (semi-) natural areas with high ecological values for wildlife and people, to be conserved and managed in priority to preserve biodiversity and ecosystem services. This relatively new concept has been used in different contexts, but with widely diverging interpretations. There is no apparent consensus in the scientific literature on the methodology to map and implement GI. This paper serves as an informed primer for researchers that are new to GI mapping understand the key principles and terminology for the needs of their own case-study, and as a framework for more advance researchers will- ing to contribute to the formalization of the concept. Through a literature review of articles on creating GI networks, we summarized and evaluated commonly used methods to identify and map GI. We provided key insights for the assessment of diversity, ecosystem services and landscape connectivity, the three 'pillars' on which GI identification is based accord- ing to its definition. Based on this literature review, we propose 5 theoretical levels toward a more complex, reliable and integrative approach to identify GI networks. We then discuss the applications and limits of such method and point out future challenges for GI identification and implementation.

During the past two centuries, the world has undergone deep societal, political, and economical changes that heavily affected human life. The above changes contributed to an increased awareness about the deep impact that policy decisions have at the local and the global level. Therefore, there is a strong need that policy-making and decision-making processes for a sustainable development be based on the best available knowledge about Earth system and environment. The recent advance of information technologies enables running complex models that use the large amount of Earth Observation datasets available. However, data and model interoperability are still limited to the syntactic level allowing to access and process datasets independently of their structural characteristics (data format, co- ordinate reference systems, service interface, .) but with no clear reference to their content (the semantic level) and context of use (the pragmatic level). This poses heavy limitations to the reusability of scientific processes and related workflows. The paper presents a general framework to address this issue through the design of a Knowledge Base sup- porting data and model semantic (and pragmatic) interoperability. In this framework, a general ontology rep- resents the knowledge generation process for policy relevant decision-making, while multiple vocabularies formalize the semantics of data and models, identifying different types of observables, process variables, and indicators/indices. To evaluate the proposed approach to semantic interoperability of data and models, the Knowledge Base has been integrated with an advanced model-sharing framework, and a proof-of-concept has been developed for the assessment of one of the indicators of the Sustainable Development Goals defined by the United Nations.

Intro -- Preface -- About the 2nd Springer Conference of the Euro-Mediterranean Journal for Environmental Integration (EMCEI-2), Tunisia 2019 -- Section 1: Engineering Applications for Environmental Management -- Section 2: Process Control, Simulations and Intensification for Environmental Management -- Section 3: Ecotoxicology, Environmental Safety and Bioremediation -- Section 4: Biotechnology for Environmental Management -- Section 5: Climate-Change-Related Effects on the Environment and Ecological Systems -- Section 6: Natural Resources, Agriculture and the Environment -- Section 7: Smart Technologies for Environmentally Friendly Energy Production -- Section 8: Remote Sensing and GIS for Environmental Monitoring and Management -- Section 9: Environmental Impacts of Natural Hazards and Environmental Risk Assessment -- Section 10: Sustainable Management of Marine and Coastal Environments -- Section 11: Sustainable Management of the Urban Environment -- Section 12: Sustainable Management of the Indoor and Built Environment -- Section 13: Environmental-Change-Related Impacts on Human Health -- About the Conference Steering Committee -- Contents -- About the Editors -- Engineering Applications for Environmental Management: Adsorption-Oriented Processes Using Conventional and Non-conventional Adsorbents -- Efficiency of Hybrid Process of Coagulation/Flocculation Followed by Membrane Filtration for the Treatment of Synthetic Vegetable Oil Refinery Wastewater -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 3.1 Coagulation/Flocculation Treatment -- 3.2 Dead-End Filtration -- 3.3 Cross-Flow Filtration -- 3.4 Combination of CF and CFF Processes -- 4 Discussion -- 5 Conclusion -- References -- Fe/Clay Composite as Catalysts for Textile Wastewater Treatment -- 1 Introduction -- 2 Materials and Methods -- 3 Results -- 3.1 Catalysts.

Several holistic approaches are based on the description of socio-ecological systems to address the sustainability challenge. Essential Variables (EVs) have the potential to support these approaches by describing the status of the Earth system through monitoring and modeling. The different classes of EVs can be organized along the envi- ronmental policy framework of Drivers, Pressures, States, Impacts and Responses. The EV concept represents an opportunity to strengthen monitoring systems by providing observations to seize the fundamental dimensions of the Earth system The Group on Earth Observation (GEO) is a partnership of 113 nations and 134 participating organizations in 2021 that are dedicated to making Earth Observation (EO) data available globally to inform about the state of the environment and enable data-driven decision processes. GEO is building the Global Earth Observation System of Systems, a set of coordinated and independent EO, information and processing systems that interoperate to provide access to EO for users in the public and private sectors. The progresses made in the development of various classes of EVs are described with their main policy targets, Internet links and key references The paper reviews the literature on EVs and describes the main contributions of the EU GEOEssential project to integrate EVs within the work plan of GEO in order to better address selected environmental policies and the SDGs. A new GEO-EVs community has been set to discuss about the current status of the EVs, exchange knowledge, experiences and assess the gaps to be solved in their communities of providers and users. A set of four traits characterizing an EV was put forward to describe the entire socio-ecological system of planet Earth: Es- sentiality, Evolvability, Unambiguity, and Feasibility. A workflow from the identification of EO data sources to the final visualization of SDG 15.3.1 indicators on land degradation is demonstrated, spanning through the use of different EVs, the definition of the knowledge base on this indicator, the implementation of the workflow in the VLab (a cloud-based processing infrastructure), the presentation of the outputs on a dedicated dashboard and the corresponding narrative through a story map.