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  • 1
    Language: English
    In: Environmental Modelling and Software, 2004, Vol.19(4), pp.341-343
    Description: In the previous paper, Allessina and Bondavalli (2004) focused their treatment on the history of ascendancy and related measures since that was their primary interest. As reviewer, I wanted to take this opportunity to frame their contribution in the larger field of ecological network...
    Keywords: Engineering ; Environmental Sciences ; Computer Science ; Ecology
    ISSN: 1364-8152
    E-ISSN: 1873-6726
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  • 2
    Language: English
    In: Environmental Modelling and Software, 2007, Vol.22(5), pp.693-700
    Description: Understanding and managing ecosystems as biocomplex wholes is the compelling scientific challenge of our times. Several different system-theoretic approaches have been proposed to study biocomplexity and two in particular, Kauffman's NK networks and Patten's ecological network analysis, have shown promising results. This research investigates the similarities between these two approaches, which to date have developed separately and independently. Kauffman (1993) has demonstrated that networks of non-equilibrium, open thermodynamic systems can exhibit profound order (subcritical complexity) or profound chaos (fundamental complexity). He uses Boolean NK networks to describe system behavior, where is the number of nodes in the network and the number of connections at each node. Ecological network analysis uses a different Boolean network approach in that the pair-wise node interactions in an ecosystem food web are scaled by the throughflow (or storage) to determine the probability of flow along each pathway in the web. These flow probabilities are used to determine system-wide properties of ecosystems such as cycling index, indirect-to-direct effects ratio, and synergism. Here we modify the NK model slightly to develop a fitness landscape of interacting species and calculate how the network analysis properties change as the model's species coevolve. We find that, of the parameters considered, network synergism increases modestly during the simulation whereas the other properties generally decrease. Furthermore, we calculate several ecosystem level goal functions and compare their progression during increasing fitness and determine that at least at this stage there is not a good correspondence between the reductionistic and holistic drivers for the system. This research is largely a proof of concept test and will lay the foundation for future integration and model scenario analysis between two important network techniques.
    Keywords: Boolean Networks ; Coevolution ; Ecological Modeling ; Fitness Landscapes ; Network Analysis ; Engineering ; Environmental Sciences ; Computer Science ; Ecology
    ISSN: 1364-8152
    E-ISSN: 1873-6726
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  • 3
    Language: English
    In: Environmental Modelling and Software, March, 2006, Vol.21(3), p.375(31)
    Description: To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.envsoft.2004.11.007 Byline: Brian D. Fath (a), Stuart R. Borrett (b)(c) Abstract: Network Environ Analysis is a formal, quantitative methodology to describe an object's within system "environ"ment [Patten, B.C., 1978a. Systems approach to the concept of environment. Ohio Journal of Science 78, 206-222]. It provides a perspective of the environment, based on general system theory and input-output analysis. This approach is one type of a more general conceptual approach called ecological network analysis. Application of Network Environ Analysis on ecosystem models has revealed several important and unexpected results [see e.g., Patten, B.C., 1982. Environs: relativistic elementary particles or ecology. American Naturalist 119, 179-219; Patten, B.C., 1985. Energy cycling in the ecosystem. Ecological Modelling 28, 1-71; Fath, B.D., Patten, B.C., 1999a. Review of the foundations of network environ analysis. Ecosystems 2, 167-179], which have been identified and summarized in the literature as network environ properties. To conduct the analysis one needs ecosystem data including the intercompartmental flows, compartmental storages, and boundary input and output flows. The software presented herein uses these data to perform the main network environ analyses and environ properties including unit environs, indirect effects ratio, network homogenization, network synergism, network mutualism, mode partitioning, and environ control. The software is available from The MathWorks MATLAB.sup.[R] Central File Exchange website (http://www.mathworks.com/matlabcentral/fileexchange/loadCategory.do). Author Affiliation: (a) Biology Department, Towson University, Towson, MD 21252, USA (b) Institute of Ecology, University of Georgia, Athens, GA 30602, USA (c) Skiddaway Institute of Oceanography, 10 Ocean Science Circle, Savannah, GA 31411, USA Article History: Received 7 June 2004; Revised 28 October 2004; Accepted 5 November 2004
    Keywords: Oceanography ; Ecology ; Ecosystems
    ISSN: 1364-8152
    Source: Cengage Learning, Inc.
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  • 4
    Language: English
    In: Environmental Modelling and Software, February 2013, Vol.40, pp.1-20
    Description: In order to use environmental models effectively for management and decision-making, it is vital to establish an appropriate level of confidence in their performance. This paper reviews techniques available across various fields for characterising the performance of environmental models with focus on numerical, graphical and qualitative methods. General classes of direct value comparison, coupling real and modelled values, preserving data patterns, indirect metrics based on parameter values, and data transformations are discussed. In practice environmental modelling requires the use and implementation of workflows that combine several methods, tailored to the model purpose and dependent upon the data and information available. A five-step procedure for performance evaluation of models is suggested, with the key elements including: (i) (re)assessment of the model's aim, scale and scope; (ii) characterisation of the data for calibration and testing; (iii) visual and other analysis to detect under- or non-modelled behaviour and to gain an overview of overall performance; (iv) selection of basic performance criteria; and (v) consideration of more advanced methods to handle problems such as systematic divergence between modelled and observed values. ► Numerical, graphical and qualitative methods for characterising performance of environmental models are reviewed. ► A structured, iterative workflow that combines several evaluation methods is suggested. ► Selection of methods must be tailored to the model scope and purpose, and quality of data and information available.
    Keywords: Model Development ; Model Evaluation ; Performance Indicators ; Model Testing ; Sensitivity Analysis ; Engineering ; Environmental Sciences ; Computer Science ; Ecology
    ISSN: 1364-8152
    E-ISSN: 1873-6726
    Source: ScienceDirect Journals (Elsevier)
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  • 5
    Language: English
    In: Environmental Modelling and Software, 2005, Vol.20(4), pp.485-498
    Description: Participation of stakeholders in stewardship of the aquatic environment, including participation from members of the general public, has become much more widespread than was the case a decade or so ago. With this shift, from a former predominantly technocratic stance to something of a democratic stance on the style of management, it becomes important to elucidate public perceptions of environmental behavior. The paper examines this issue: from a rather specific perspective, where the role of time is significant; with a specific purpose in mind; for defining illustrative stakeholder aspirations for the future, whose plausibility is to be assessed against a computational model of lake behavior; and for a specific case study, Lake Lanier in the Chattahoochee watershed of Georgia, USA. Perturbations and variation in the behavior of the aquatic environment span many time frames, from the very short-term response associated with storms, infrastructure failure, transient pollution events, and so on, to the much longer-term, for instance, the biogeochemical 'ageing' of a lake over many decades and more. Our analysis is devoted to data from a survey of stakeholder imagination and perceptions of how the future state of Lake Lanier may evolve in the relatively short term (2-5 years) and in the long term, defined as 25+ years (the span of a generation). Overall, stakeholders are pessimistic and fear that things will be worse in the longer term. Guided largely by thinking on the perspectives of the social solidarities of Cultural Theory, extraction and analysis of sub-samples of the survey responses show that this outlook over the two frames of time is persistent, irrespective of what are, in principle, rather different 'global' attitudes towards the man- environment relationship. Of interest inter alia to the foresight generating procedure, by which the 'reachability' of stakeholder-derived futures for the lake is to be assessed using a computational model of the relevant parts of the science base, is the question of whether the same small number of priorities for further research on lake behavior is robust in the face of the rich variety of aspirations for the future inevitable in a democratic community of stakeholders.
    Keywords: Cultural Theory ; Integrated Environmental Assessment ; Stakeholder Participation ; Engineering ; Environmental Sciences ; Computer Science ; Ecology
    ISSN: 1364-8152
    E-ISSN: 1873-6726
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  • 6
    Language: English
    In: Environmental Modelling and Software, 2006, Vol.21(3), pp.375-405
    Description: Network Environ Analysis is a formal, quantitative methodology to describe an object's within system “environ”ment [Patten, B.C., 1978a. Systems approach to the concept of environment. Ohio Journal of Science 78, 206–222]. It provides a perspective of the environment, based on general system theory and input–output analysis. This approach is one type of a more general conceptual approach called ecological network analysis. Application of Network Environ Analysis on ecosystem models has revealed several important and unexpected results [see e.g., Patten, B.C., 1982. Environs: relativistic elementary particles or ecology. American Naturalist 119, 179–219; Patten, B.C., 1985. Energy cycling in the ecosystem. Ecological Modelling 28, 1–71; Fath, B.D., Patten, B.C., 1999a. Review of the foundations of network environ analysis. Ecosystems 2, 167–179], which have been identified and summarized in the literature as network environ properties. To conduct the analysis one needs ecosystem data including the intercompartmental flows, compartmental storages, and boundary input and output flows. The software presented herein uses these data to perform the main network environ analyses and environ properties including unit environs, indirect effects ratio, network homogenization, network synergism, network mutualism, mode partitioning, and environ control. The software is available from The MathWorks MATLAB Central File Exchange website ( ).
    Keywords: Ecological Modelling ; Ecological Network Analysis ; Network Environ Analysis ; Engineering ; Environmental Sciences ; Computer Science ; Ecology
    ISSN: 1364-8152
    E-ISSN: 1873-6726
    Library Location Call Number Volume/Issue/Year Availability
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