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Vegetation,herbivores and fires in savanna ecosystems: A network perspective
Institution:1. Department of Life Sciences, University of Parma, Viale Usberti 11/A, Parma, Italy;2. Biology Program, Faculty of Natural Sciences and Mathematics, Universidad del Rosario, Kr 26 No 63B-48, Bogotá, Colombia;1. Department of Forestry and Wildlife Management, Faculty of Applied Ecology and Agricultural Sciences, Inland Norway University of Applied Sciences, NO-2480, Koppang, Norway;2. Department of Conservation Biology, The University of Dodoma, P.O. Box 338, Dodoma, Tanzania;1. Institute for Complex Analysis of Regional Problems of Far-Eastern Branch of Russian Academy of Sciences, 679016, 4, Sholom-Aleihem St., Birobidjan, Russian Federation;2. Institute of Automation and Control Processes of the Russian Academy of Sciences, Far Eastern Branch, 690041, 5, Radio St., Vladivostok, Russian Federation;1. Posgrado en Fitosanidad, Entomología y Acarología, Colegio de Postgraduados, Km 36.5, Carretera México-Texcoco, Montecillo, 56230 Texcoco, Estado de México, Mexico;2. Laboratorio de Análisis y Referencia en Sanidad Forestal, Dirección General de Gestión Forestal y de Suelos, Secretaría del Medio Ambiente y Recursos Naturales, Av. Progreso Núm. 3, 04100 Coyoacán, México, D.F., Mexico;3. Departamento de Biología Animal, Facultat de Biología, Universitat de Barcelona, Av. Diagonal 645, 08028 Barcelona, Spain;1. Institute of Optimization and Operations Research, Ulm University, Ulm, Germany;2. Instituto de Computação, Universidade Federal Fluminense, Niterói, RJ, Brazil;3. Petrobras, Brazil
Abstract:The dynamics of savanna ecosystems depends on the interplay between multiple factors such as grazing, browsing, fires, rainfall regime and interactions between grass and woody vegetation. In most modelling applications this interplay may not be fully understood because some of these drivers enter the models as dynamically independent factors. In this paper we consider such factors as dynamic variables. To analyze their interplay we focus on the structure of the interactive network of variables and exploit the properties of signed digraphs using the algorithm of Loop Analysis. Qualitative signed digraphs for the savanna ecosystem are developed and their predictions used to interpret patterns of abundance observed in case studies selected from the literature. The outcomes of this exercise unveil that: 1) the structure of the interactions is appropriate locus for the explanation of patterns observed in savannas; 2) signed digraph can help disentangling causative mechanisms by linking correlation patterns, source of change and network structure. This study highlights that central to the understanding of savanna dynamics is our ability to diagram the important relationships and understand how they interrelate with sources of variations to cause ecosystem change.
Keywords:Savanna ecosystems  Cause and effect mechanisms  Complex systems  Fires  Loop analysis  Positive feedback
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