Classifying habitat characteristics of wetlands using a self-organizing map |
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| Affiliation: | 1. Department of Life Sciences, Yeungnam University, Gyeongsan 38541, Republic of Korea;2. National Institute of Ecology (NIE), Seocheon 33657, Republic of Korea;1. Ecology and Environmental Modelling Laboratory, Department of Environmental Science, The University of Burdwan, Purba Bardhaman, 713104, India;2. Department of Geography, The University of Burdwan, Purba Bardhaman, 713104, West Bengal, India;3. Department of Basic Sciences and Humanities, Institute of Engineering & Management, Sector -V, Salt Lake City, Kolkata 700091, West Bengal, India;1. Shijiazhuang Institute of Railway Technology, Shijiazhuang 050018, China;2. School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD 4072, Australia;3. College of Forestry, Beijing Forestry University, Beijing 100083, China;4. Pingwu Panda Valley Family Farm, Pingwu 622550, China;5. The Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China;6. UQ Spatial Epidemiology Laboratory, School of Veterinary Science, University of Queensland, Gatton 4343, Australia;1. Environmental Systems Engineering, Faculty of Engineering and Applied Science, University of Regina, Regina, Saskatchewan S4S 0A2, Canada;2. Johnson Shoyama Graduate School of Public Policy, University of Regina, Saskatchewan S4S 0A2, Canada;1. Achutha Menon Centre for Health Science Studies, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, Kerala, India;2. Department of Health and Family Welfare, Government of Punjab, India;1. Warnell School of Forestry and Natural Resources, University of Georgia, 180 E. Green Street, Athens, GA 30602, USA;2. Tall Timbers, 13093 Henry Beadel Drive, Tallahassee, FL 32312, USA;1. Department of Zoology, Entomology & Fisheries Sciences, Makerere University, P.O. Box 7062, Kampala, Uganda;2. Department of Biology, Coe College, 1220 1st Avenue NE, Cedar Rapids, IA 52402, USA |
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| Abstract: | Wetlands are nutrient-rich and biodiverse ecosystems that provide habitats for various animals and plants and protect against flooding. Classification of wetlands provides information to conservation planners and resource managers for ecosystem service determination. Many ecological case studies illuminate the self-organizing map (SOM) as a robust and powerful data classification and visualization tool. In this study, we use the SOM to analyze the habitat characteristics of inland wetlands in South Korea. We surveyed the plants, benthic macroinvertebrates, and bird species inhabiting 530 nationwide wetlands for four years from 2016 to 2019. Nine environmental features, including the proportion of urban area, farmland, grassland, a forest within a 1 km buffer zone, distance from the river and nearest wetland, area, perimeter, and average slope of wetland polygons, were used to train the SOM and examine the habitat characteristics of the surveyed living components. A map size of 10 × 11 pixels was considered for SOM training, and the output data were classified into eight clusters. Based on the occurrence frequency of the surveyed species group, most species were distributed in all clusters, whereas some dominated in specific clusters. We believe that our study contributes significantly to the literature because it highlights the significance of the SOM approach to cluster wetlands with dependent habitats and provides ecological information to build sustainable wetland conservation policies. |
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