A novel approach for assessing factors affecting biodiversity based on networks analysis |
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Affiliation: | 1. Department of Civil Engineering, Faculty of Engineering, University of Maragheh, P.O. Box 55136-553, Maragheh, East Azerbaijan, Iran;2. Faculty of Civil Engineering, University of Tabriz, Tabriz, East Azerbaijan, Iran;3. GTEV-ReX Limited, Swindon, Wilts. United Kingdom;4. Department of Earth Sciences, Faculty of Natural Sciences, Tabriz, East Azerbaijan, Iran;5. Institute of Environment, University of Tabriz, Iran;1. National CAD Support Software Engineering Research Center, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China;2. Department of Mechanical Engineering, McGill University, Montreal, Quebec, Canada;3. School of Power and Mechanical Engineering, Wuhan University, Wuhan, Hubei 430072, China |
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Abstract: | We propose in this work a novel approach aiming at assessing cause and effect relationships between variables that can affect target biodiversity issues. These cause–effect relations are used to build a network whose nodes represent variables linked by directed arcs. The arcs have associated a value that represents trends of cause–effect relations. An important novelty of this approach is the use of product and addition operations between trends of cause–effect relations for assessing factors that can affect target variables. For the analysis of the network we use the concept of paths. Paths are defined as sequences of cause–effect relations from source variables to target variables. For example, the path from population increment that causes effects on the increment of transport routes, which in turn causes effects on the loss of vegetation cover. This approach was applied to the assessment of vegetation cover in the Morelos State, México during the period 2000–2010. The results show a promising practical alternative to assess the potential effects on biodiversity issues based on the analysis of the paths represented in the network. |
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Keywords: | Networks Path analysis Trends Least square method |
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