Modelling the dynamics of coral reef macroalgae using a Bayesian belief network approach |
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| Authors: | Henk Renken Peter J Mumby |
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| Institution: | 1. Leibniz Center for Tropical Marine Ecology (ZMT), 28359 Bremen, Germany;2. University of Bremen, 28359 Bremen, Germany;1. Laboratory of Environmental Toxicology and Aquatic Ecology, Ghent University, Jozef Plateaustraat 22, B-9000 Ghent, Belgium;2. Unit Environmental Modelling-RMA, Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium;3. Department of Agricultural Economics, Ghent University, Coupure Links 653, B-9000 Ghent, Belgium;1. Australian Rivers Institute, Griffith School of Environment, Griffith University, Nathan, Queensland, Australia;2. Griffith Climate Change Response Program, Australia;3. Griffith School of Engineering, Griffith University, Gold Coast, Queensland, Australia;1. Laboratory of Restoration Ecology, Graduate School of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, 192-8577, Japan;2. Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8564, Japan;3. Center for Regional Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan;4. Central Research Laboratory, Taiheiyo Cement Corporation, 2-4-2 Osaku, Sakura, Chiba, 285-8655, Japan;5. Institute for East China Sea Research, Organization for Marine Science and Technology, Nagasaki University, 1551-7 Taira-machi, Nagasaki, 851-2213, Japan;1. Fisheries Centre, University of British Columbia, AERL, 2202 Main Mall, Vancouver, British Columbia V6T 1Z4, Canada;2. WWF-Malaysia, Suite 1-6-W11, 6th Floor, CPS Tower, Centre Point Complex, 88000 Kota Kinabalu, Sabah, Malaysia |
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| Abstract: | Macroalgae are a major benthic component of coral reefs and their dynamics influence the resilience of coral reefs to disturbance. However, the relative importance of physical and ecological processes in driving macroalgal dynamics is poorly understood. Here we develop a Bayesian belief network (BBN) model to integrate many of these processes and predict the growth of coral reef macroalgae. Bayesian belief networks use probabilistic relationships rather than deterministic rules to quantify the cause and effect assumptions. The model was developed using both new empirical data and quantified relationships elicited from previous studies. We demonstrate the efficacy of the BBN to predict the dynamics of a common Caribbean macroalgal genus Dictyota. Predictions of the model have an average accuracy of 55% (implying that 55% of the predicted categories of Dictyota cover were assigned to the correct class). Sensitivity analysis suggested that macroalgal dynamics were primarily driven by top–down processes of grazing rather than bottom–up nutrification. BBNs provide a useful framework for modelling complex systems, identifying gaps in our scientific understanding and communicating the complexities of the associated uncertainties in an explicit manner to stakeholders. We anticipate that accuracies will improve as new data are added to the model. |
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