Multivariate statistics as a tool for model-based prediction of floodplain vegetation and fauna |
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| Institution: | 1. UFPB – Federal University of Paraíba, Center for Technology, Graduate Program in Civil and Environmental Engineering, Brazil;2. UFPB – Federal University of Paraíba, Center for Exact and Natural Sciences, Department of Geosciences, Brazil;3. UFPB – Federal University of Paraíba, Center for Technology, Department of Civil and Environmental Engineering, Brazil;1. Department of Disaster Management, Begum Bekeya University, Rangpur, 5400, Bangladesh;2. Department of Agro-food Safety and Crop Protection, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju, Republic of Korea;3. Trakya University, Laboratory Technology Program, Ipsala, Edirne, Turkiye;4. Institute of National Analytical Research and Service (INARS), Bangladesh Council of Scientific and Industrial Research (BCSIR), Dhanmondi, Dhaka, 1205, Bangladesh;5. Department of Biochemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia;6. Department of Disaster Management, Alagappa University, Karaikudi, Tamilnadu, India;7. Department of Development Studies, Daffodil International University, Dhaka 1216, Bangladesh;1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore;1. Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, Kerala, 690 525, India;2. Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, Kerala, 690 525, India;3. Department of Electronics and Communication Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, Kerala, 690 525, India;4. Center for Cybersecurity Systems and Networks, Amrita Vishwa Vidyapeetham, Amritapuri Campus, Kollam, Kerala, 690 525, India;1. School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, PR China;2. Environmental Change Institute, University of Oxford, Oxford, UK |
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| Abstract: | Robust prediction models for the spatial distribution of grassland vegetation, molluscs and carabids in a study area at the Middle Elbe (Germany) had to be generated by means of multivariate statistical methods. An appropriate study design has been developed. Data of all three taxons as well as numerous parameters of the abiotic environment were ascertained. Canonical correspondence analysis (CCA) detected clear dependencies between the occurrence of biotic objects and mainly hydrological parameters. By use of Arc/Info applications CANOGEN and CANORES, it was possible to extrapolate the models from the sample plots to the whole study area. The predicted spatial distribution of nearly all in the field studies examined species could be depicted with these instruments. Comparison between investigated and predicted distribution of species showed high correspondence.Robustness of the models was proved by interchanging model parameters for different study years and also in applying models at a second study area located 40 km upstream of the original study area.As a second method for further investigation, logistic regression was used to build generalised linear models (GLM) for potential indicator species in the study area. |
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