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dc.contributor.authorMa, Qianqian
dc.date.accessioned2014-10-03T04:30:25Z
dc.date.available2014-10-03T04:30:25Z
dc.date.issued2014-05
dc.identifier.otherma_qianqian_201405_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/ma_qianqian_201405_phd
dc.identifier.urihttp://hdl.handle.net/10724/30531
dc.description.abstractVarious ecological network measures (e.g. cycling index, indirect effects index, and ascendency) have been defined to capture holistic or system-wide properties of ecosystems. These system-wide measures are defined based on ecological network models, and often have complex computations. According to Jorgensen et al. (2005), these indicators usually require a more profound understanding of how they can be used in environmental management and which health aspects they are able to cover. All projects in this dissertation are aimed to advance our understanding of system-wide ecological measures. This dissertation consists of pathway-based analysis of individual system-wide measures, and a comprehensive comparison of all measures using statistical analysis. (I) Pathway-based computation of ecological network measures System-wide measures are defined based on algebraic computations, which have two major disadvantages: (1) these algebraic formulations are often too complex to be comprehended, therefore it is hard to verify how well these formulas represent their intended meanings; and (2) these algebraic formulations are mostly applicable to steady-state models only, which greatly limits their applications. In this dissertation, I utilize a stochastic individual-based algorithm called Network Particle Tracking (NPT) to simulate the ecosystem models, and investigate pathway-based computation of these measures. Through this work, we aim (1) to develop simpler and more intuitive formulations that quantify and help interpret existing indicators as an alternative to the conventional algebraic formulations; (2) to search for novel measures that inform us about ecosystem structure and function; and (3) to extend the applicability of these useful but limited indicators to dynamic ecosystem models. (II) Statistical analysis of ecological network measures Several earlier works have studied the relationships among ecological network measures, focusing on a few widely used measures such as cycling index, indirect effect, and amplification. There are forty, or perhaps even more system-wide measures proposed to capture holistic properties of ecosystems. Through a comprehensive comparison of all measures simultaneously, this work investigates and uncovers some interesting relationships among measures. For example, we found out that ascendency, a widely used indicator, is highly correlated with total system throughput. This work will be potentially helpful for selecting measures in ecological network analysis.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectSystems ecology
dc.subjectEcological network analysis
dc.subjectSystem-wide measures
dc.subjectIndirect effects
dc.subjectCycling index
dc.subjectCluster analysis
dc.subjectNetwork Particle Tracking
dc.subjectPathway-based
dc.subjectCompartmental systems
dc.titlePathway-based and statistical analysis of ecological network measures
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentBiological and Agricultural Engineering
dc.description.majorBiological and Agricultural Engineering
dc.description.advisorCaner Kazanci
dc.description.committeeCaner Kazanci
dc.description.committeeWilliam Tollner
dc.description.committeeJohn Schramski
dc.description.committeeBernard Patten


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