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http://dx.doi.org/10.13087/kosert.2021.24.5.1

A Comparative Study on HSI and MaxEnt Habitat Prediction Models: About Prionailurus bengalensis  

Yoo, Da-Young (Dept. of Environmental Horticulture and Landscape Architecture, Dankook University)
Lim, Tai-Yang (Dept. of Environmental Horticulture and Landscape Architecture, Dankook University)
Kim, Whee-Moon (Dept. of Environmental Horticulture and Landscape Architecture, Dankook University)
Song, Won-Kyong (School of Environmental Horticulture and Landscape Architecture, Dankook University)
Publication Information
Journal of the Korean Society of Environmental Restoration Technology / v.24, no.5, 2021 , pp. 1-14 More about this Journal
Abstract
Excessive development and urbanization have destroyed animal, plant, habitats and reduced biodiversity. In order to preserve species diversity, habitat prediction studies are have been conducted at home and overseas using various modeling techniques. This study was conducted to suggest optimal habitat modeling research by comparing HSI and MaxEnt, which are widely used among habitat modeling techniques. The study was targeted on the endangered species of Prionailurus bengalensis in nearby areas (5460.35km2) including Cheonan City, and the same data were used for analysis to compare those models. According to the HSI analysis, Prionailurus bengalensis's habitat probability was 74.65% for less than 0.5 and 25.34% for more than 0.5 and the top 30% were forest (99.07%). MaxEnt's analysis showed that 56.22% of those below 0.5 and 43.79% of those above 0.5 were found to have a high explanatory power of 78.3% of AUC. The Paired Wilcoxn test, which evaluated the significance of thoes models, confirmed that the mean difference between the two models was statistically significant (p<0.05). Analysis of the differences in the results of those models using the matrix table shows that score 24.43% HSI and MaxEnt was accordance,12.44% of the 0.0 to 0.2 section, 7.22% of the 0.2 to 0.4 section, 2.73% of the 0.4 to 0.6 section, 1.96% of the 0.6 to 0.8, and 0.08% of the 0.9 to 1.0. To verify where the score difference appears, the result values of those models were reset to values from 1 to 5 and overlaid. Overlapping analysis resulted in 30.26% of the Strongly agree values, 56.77% of the agree values, and 11.92% of the Disagree values. The places where the difference in scores occurs were analyzed in the order of forest (45.23%), agricultural land (34.57%), and urbanization area (7.65%). This confirmed that the analysis of the same target species within the same target site also has differences in forecasts depending on the modelling method. Therefore, a novel analysis method combining the advantages of each modeling in habitat prediction studies should be developed, and future study may be used to select Prionailurus bengalensis and species-protected areas and species protection areas in the future. Further research is judged to require higher accuracy studies through the use of various modeling techniques and on-site verification.
Keywords
Species distribution model; GIS; species Diversity;
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1 Lee SD.Kwon JH.Kim AR and Jung Jh, 2012, A Study on Ecological Evaluation of Habitat Suitability Index using GIS - With a case study of Prionailurus bengalensis in Samjang-Sanchung Road Construction, Korean J EIASS Vol. 21(5): 801-811
2 Lee SG.Jung SG.Park KH.Kim KT and Lee WS, 2010, A Prediction Model and Mapping for Forest-Dwelling Birds Habitat Using GIS, Journal of the Korean Association of Geographic Information Studies 13(1): 62-73   DOI
3 Ahn YJ.Lee DK.Kim HG.Park C.Kim JY and Kim JU, 2015, Estimating Korean Pine(Pinus koraiensis) Habitat Distribution Considering Climate Change Uncertainty - Using Species Distribution Models and RCP Scenarios, Journal of the Korean Society of Environmental Restoration Technology 18(3): 51-64.
4 Baldwin R. A, 2009, Use of Maximum Entropy Modeling in Wildlife Research. Entropy 11: 854-866.   DOI
5 Breiman L, 2001, Random Forests, Machine Learning 45: 5-32.   DOI
6 Cho, N. H., E. S. Kim, B. Lee, J. H. Lim, S. Kan. 2020, Predicting the Potential Distribution of Pinus densiflora and Analyzing the Relationship with Environmental Variable Using MaxEnt Model, Korean Journal of Agricultural and Forest Meteorology. 22(2): 47-56.   DOI
7 Choi TY.Kwon HS.Woo DG and Park CH, 2012, Habitat Selection and Management of the Leopard Cat(Prionailurus bengalensis) in a Rural Area of Korea, Korean Journal of Environment and Ecology 26(3): 322-332.
8 Fourcade Y.Engler J.O and Secondi J, 2014, Mapping Species Distributions with MAXENT Using a Geographically Biased Sample of Presence Data: A Performance Assessment of Methods for Correcting Sampling Bias, PloS ONE 9(5): 97-122.
9 Choi YH.Hong SJ.Jeon SR and Cho YS, 2019, Site Assessment Using Habitat Suitability Index for Manila Clam Ruditapes philippinarum in Geunso Bay Tidal Flats, Korean J Fish Aquat 52(5): 511-518.
10 Akira D, 2011, Hanwool Academy: Chungnam Development Institute(in Korean), Habitat Ecological Impact Assessment Methodology.
11 Grassman L.I.Tewes M.E.Silvy N.J, 2005, Spatial organization and diet of the leopard cat (Prionailurus bengalensis) in north-central, Thailand Journal of Zoology 266(1): 45-54.   DOI
12 Hanley J. A. and B. J. McNeil, 1983, A method of comparing the areas under receiver operating characteristic curves derived from the same cases, Radiology 148(3): 839-843.   DOI
13 Ho T. K, 1995, Random decision forestsInternational Conference on Document Analysis and Recognition in Sobaeksan National Park, Korean J. Env 17(6): 51-60.
14 Rosner B.Glynn R.J and Lee M.L.T, 2006, Extension of the Rank Sum Test for Clustered Data: Two-Group Comparisons with Group Membership Defined at the Subunit Level, Biometrics 65: 1251-1259.   DOI
15 Raleigh RF, 1984, Habitat suitability information: rainbow trout, Fish and Wildlife Service (in U.S. Department of the Interior)
16 Rho PH and Choung HL, 2006, Alternatives of the Korean Nationwide Survey on Natural Environments to Promote Biodiversity Conservation, Korean J Kei 5(3): 25-56
17 Related Departments of Korea Report, 2020, The 5th National Environmental Comprehensive Plan.
18 Shim YJ.Kim SR.Yoon KB.Jung JW and Park SU and Park YS, 2020a, Evaluation of Alternative Habitats Using Habitat Suitability Index Model of Lutra lutra in Banbyeoncheon Stream, Korean J Env 23(1): 63-76
19 Shim YJ.Kim SR.Yoon KB.Jung JW and Park SU and Park YS, 2020b, A Basic Research for the Development of Habitat Suitability Index Model of Pelophylax chosenicus, Korean J Env 23(1): 49-62
20 Stockwell D. and D. Peters, 1999, The GARP modelling system: problems and solutions to automated spatial prediction, International Journal of Geographical Information Science, 13(2): 143-158.   DOI
21 Song WY and Kim EY, 2012, A Comparison of Machine Learning Species Distribution Methods forHabitat Analysis of the Korea Water Deer (Hydropotes inermis argyropus), Korean J of Remote Sensing 28(1): 171-180   DOI
22 Lim SJ.Kim JY and Park YC, 2015, Analysis of habitat characteristics of leopard cat (Prionailurus bengalensis) in Odaesan National Park, J A&LS 49(3): 99-111
23 Kim WM.Song WK.Kim SY and Hyung EJ, 2017, Habitat Analysis Study of Honeybees (Apis mellifera) in Urban Area Using Species Distribution Modeling -Focused on Cheonan, Journal of the Korea Society of Environmental Restoration Technology 20(3): 55-64.   DOI
24 Chung HI.Choi YY.Ryu JE and Jeon SW, 2020, Accuracy Evaluation of Potential Habitat Distribution in Pinus thunbergii using a Species Distribution Model: Verification of the Ensemble Methodology, Korean J of Climate Change Research 11(1): 37-51.   DOI
25 Wilcoxon F, 1992, Individual Comparisons by Ranking Methods, Breakthroughs in Statistics 196-202.
26 Related Departments of Korea Report, 2020, Fifth National Environmental Report. (in Korean)
27 Kim YS.Yoo MH.Jung BD and Kim JT, 2010, Genetic diversity in Korean Leopard cats (Prionailurus bengalensis euptilura), based on mitochondrial DNA cytochrome b gene sequence analysis, Korean J. Vet Serv 33(4): 353-359.
28 Kearney M.R.Wintle B.A and Porter W.P, 2010, Correlative and mechanistic models of species distribution provide congruent forecasts under climate change, Conservation Letters 3(3): 203-213.   DOI
29 Kim CY.Kim LG.Srel JU.Son SH.Kim SO., 2012, Analyse the Home Range of Leopard Cat(Prionailurus bengalensis) near forest living in Gayasan National Park of Korea, Korean Soc Env 22(2): 207-211
30 Kim TG.Yang DH.Cho YH and Song KH and Oh JG, 2016, Habitat Distribution Change Prediction of Asiatic Black Bears (Ursus thibetanus) Using MAXENT Modeling Approach, Korean J Ksl 49(3): 197-207
31 McCarthy J.L.Wibisono H.T and McCarthy K.P, 2015, Assessing the distribution and habitat use of four felid species in Bukit Barisan Selatan National Park, Sumatra, Indonesia Global Ecology and Conservation 3: 210-221.   DOI
32 Pilar A.H.Graham C.H.Master L.L and Albert D.L, 2006, The effect of sample size and species characteristics on performance of different species distribution modeling methods, Ecography 29: 773-785.   DOI
33 Yu W.Yi Q and Chen Y, 2015, Modelling the effects of climate variability on habitat suitability of jumbo flying squid, Dosidicus gigas in the Southeast Pacific Ocean off Peru, ICES Journal of Marine Science 73(2): 239-249.   DOI
34 Chungnam Development Research Institute, 2015, Academy Hanul 1765, Tanaka Akira, Habitat Ecological Impact Assessment Methodology.
35 World Economic Forum, 2020, The Global Risks Report.
36 Phillips S.J.Dudik M.E and Schapire R, 2004, Proceedings of the Twenty-First International Conference on Machine Learning (A Maximum Entropy Approach to Species Distribution Modeling) 655-662
37 Phillips S.J..Anderson R.P..Schapire R.E, 2006, Maximum entropy modeling of species geographic distributions, Ecological Modelling. 190: 2261-259
38 Kwon HS.Seo CW and Park CH, 2012, Development of Species Distribution Models and Evaluation of Species Richness in Jirisan region, Korean J KSIS, 20(3): 11-18
39 Mohamed A..Sollmann R..Bernard H.. Ambu L. N..Lagan P..Mannan S.. Hoffer H. and Wilting A, 2013, Density and habitat use of the leopard cat (Prionailurus bengalensis) in three commercial forest reserves in Sabah, Malaysian Borneo, Journal of Mammalogy 94(1): 82-89   DOI
40 Park YS.Chang MH.Cha JY.Cho DG and Kim SH and Lee SW, 2015, A Study on Site Selection for Reeve's turtle(Maunemys reevesii) Habitats Using Habitat Suitability Index, Korean J Env 18(3): 109-118
41 Lee DK.Baek GH.Park C and Kim HG, 2011, Spatial Planning of Climate Adaptation Zone to Promote Climate Change Adaptation for Endangered Species, Korean J Env 14(6): 111-117.
42 Latif Q.S.Saab V.A and Markus A, 2020, Development and evaluation of habitat suitability models for nesting white-headed woodpecker (Dryobates albolarvatus) in burned forest, PloS ONE 15(5): e0233043.   DOI
43 Latif Q.S.Saab V.A and Mellen K, 2015, Evaluating Habitat Suitability Models for Nesting White-Headed Woodpeckers in Unburned Forest, Journal of Wildlife Management 79(2): 263-273.   DOI
44 Lee BE.Kim J.Kim NI and Kim JG, 2017, Evaluation on Replacement Habitat of Two Endangered Species, Aster altaicus var. uchiyamae and Polygonatum stenophyllum Using Habitat Suitability Index, Journal of Wetlands Research 19(4): 433-442.   DOI
45 Lee HJ.Cha JY and Kim YC, 2014, Home Range Analysis of Three Midium-Sized Mammals, Journal of the Korean Society of Environmental Restoration Techology 17(6): 51-60