Effective prediction of biodiversity in tidal flat habitats using an artificial neural network |
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| Institution: | 1. Korea Institute of Coastal Ecology, Inc., IT302-802, Ssangyong Technopark III, 36-1 Samjung-Dong, Ojeong-Gu, Bucheon City 421-742, Republic of Korea;2. Department of Life Sciences, College of Science and Engineering, Texas A&M University-Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA;1. Department of Ecology, School of Biology, Aristotle University, 54124 Thessaloniki, Greece;2. Department of Environmental and Natural Resources Management, University of Patras, 30100 Agrinio, Greece;1. University of Melbourne, Department of Mathematics and Statistics, Australia;2. Tel Aviv University, Porter School of Environmental Studies, Israel;3. School of Mathematical and Geospatial Sciences, RMIT University, Australia;4. Tel Aviv University, Zoology Department, Israel;1. Deltares, Delft and CWI, Amsterdam, the Netherlands;2. SURFsara, Amsterdam, the Netherlands;1. Korea Ocean Satellite Center, Korea Institute of Ocean Science & Technology, 787 Haean-ro, Sangrok-gu, Ansan, Gyeonggi-do 426-744, Korea;2. Department of Earth System Sciences, Yonsei University, 134 Shinchon-dong, Seodaemun-gu, Seoul 120-749, Korea |
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| Abstract: | Accurate predictions of benthic macrofaunal biodiversity greatly benefit the efficient planning and management of habitat restoration efforts in tidal flat habitats. Artificial neural network (ANN) prediction models for such biodiversity were developed and tested based on 13 biophysical variables, collected from 50 sites of tidal flats along the coast of Korea during 1991–2006. The developed model showed high predictions during training, cross-validation and testing. Besides the training and testing procedures, an independent dataset from a different time period (2007–2010) was used to test the robustness and practical usage of the model. High prediction on the independent dataset (r = 0.84) validated the networks proper learning of predictive relationship and its generality. Key influential variables identified by follow-up sensitivity analyses were related with topographic dimension, environmental heterogeneity, and water column properties. Study demonstrates the successful application of ANN for the accurate prediction of benthic macrofaunal biodiversity and understanding of dynamics of candidate variables. |
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