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Ecosystem Health: Biocomplexity, Modeling, and Solutions

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 4089

Special Issue Editor


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Guest Editor
Future Ecosystems Lab, Institute of Environment and Ecology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: biocomplexity; collective dynamics; ecology and evolution of biological and socio-technological systems; systemic risk analysis; decision science; complex networks; network science; information theory; stochastic processes; fractals; uncertainty; ecohydrology; hydrodynamics; sustainability; ecosystem health; ecodesign; ecosystem modeling; data science; biomimicry; bio-inspired design; macroecology; physiophysics; ecosystem pathology; forecasting; interdisciplinary applications of statistical physics; design by analogy; food systems; physio-linguistics; microbiome; epigenetics; environment; aquatic and marine ecosystems
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Special Issue Information

Dear Colleagues,

Ecosystem health is a primary grand challenge for the sustainability of our planet. Degradation of the environment is alarming in many areas around the world considering both terrestrial and aquatic ecosystems experiencing extreme phenomena. In this Special Issue, we welcome papers that highlight evidence and solutions of ecosystem health broadly defined (for instance, considering both ecological indicators of biodiversity and species population status, human indicators of effects and hazards, or their nexus with relevance to environmental sciences, biology, public health, and medicine) based on modeling the biocomplexity of ecosystems at any spatiotemporal and biological scales—from the gene to the population/continental scale—or across scales. This view of ecosystem health is bringing population health into sustainability quantitatively. Papers focused on (1) collective “omics” phenomena such as the microbiome, epigenetics, and the envirome linking humans and the environment, (2) pattern/process trajectories and solutions considering climate change and ecosystem management/design, and (3) technology or other sectors to address ecosystem sustainability issues are welcome. Papers concentrated on novel multiscale methods for predicting ecosystem biocomplexity, such as complex networks and information theory, are also extremely encouraged.

Prof. Dr. Matteo Convertino
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • biocomplexity
  • ecosystems
  • modeling
  • multiscale
  • population health
  • systemic risk
  • networks
  • decisions
  • technology
  • sustainability

Published Papers (1 paper)

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Research

24 pages, 8988 KiB  
Article
Is the World Becoming a Better or a Worse Place? A Data-Driven Analysis
by Salvatore F. Pileggi
Sustainability 2020, 12(1), 88; https://doi.org/10.3390/su12010088 - 20 Dec 2019
Cited by 7 | Viewed by 3261
Abstract
Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about [...] Read more.
Is the World becoming a better or a worse place to live? In this paper, we propose a tool that can help to answer the question by combining a number of global indicators belonging to multiple categories. The proliferation of statistical data about various aspects of the World performance may suggest that it should be “easy” to evaluate the overall success of human enterprise on this planet. Moreover, it also points out the intrinsic importance in the selection of indicators. However, people have different values, biases, and preferences about the importance of various indicators, making it almost impossible to find an objective answer to this question. To address the variety and the heterogeneity of available indicators and world views, we present the analysis of global World performance as a multi-criteria decision problem, making sure that the assessment method remains as transparent as possible. By dynamically selecting a set of indicators of interest, defining the weights that we attach to various indicators and specifying the desired trends associated with each indicator, we make the assessment adaptive to individual values. We also try to deal with the inherent bias that may exist in the set of indicators that are chosen. As a study case, from various data sets that are openly available online, we have selected several that are most relevant and easy to interpret in the context of the question in the title of the paper. We demonstrate how the choice of personal preferences, or weights, can strongly change the result. Our method also provides analysis of the weights space, showing how results for particular value sets compare to the average and extreme (optimistic and pessimistic) combinations of weights that may be chosen by users. Full article
(This article belongs to the Special Issue Ecosystem Health: Biocomplexity, Modeling, and Solutions)
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