Journal of the Korean Society of Environmental Restoration Technology (한국환경복원기술학회지)

The Korea Society of Environmental Restoration Technology (한국환경복원기술학회)
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A Comparative Study on HSI and MaxEnt Habitat Prediction Models: About Prionailurus bengalensis

HSI와 MaxEnt를 통한 삵의 서식지 예측 모델 비교 연구

  • 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)
  • 유다영 (단국대학교 환경원예.조경학과) ;
  • 임태양 (단국대학교 환경원예.조경학과) ;
  • 김휘문 (단국대학교 환경원예.조경학과) ;
  • 송원경 (단국대학교 환경원예.조경학부)
  • Received : 2021.01.20
  • Accepted : 2021.10.17
  • Published : 2021.10.31
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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

Acknowledgement

이 논문은 환경부의 재원으로 한국환경산업기술원의 "도시생태 건강성 증진 기술개발사업의 지원을 받아 연구되었습니다. (2019002770001)"

References

  1. 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.
  2. Akira D, 2011, Hanwool Academy: Chungnam Development Institute(in Korean), Habitat Ecological Impact Assessment Methodology.
  3. Baldwin R. A, 2009, Use of Maximum Entropy Modeling in Wildlife Research. Entropy 11: 854-866. https://doi.org/10.3390/e11040854
  4. Breiman L, 2001, Random Forests, Machine Learning 45: 5-32. https://doi.org/10.1023/A:1010933404324
  5. 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. https://doi.org/10.5532/KJAFM.2020.22.2.47
  6. 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.
  7. 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.
  8. 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. https://doi.org/10.15531/KSCCR.2020.11.1.37
  9. 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.
  10. 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. https://doi.org/10.1017/S095283690500659X
  11. 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. https://doi.org/10.1148/radiology.148.3.6878708
  12. Ho T. K, 1995, Random decision forestsInternational Conference on Document Analysis and Recognition in Sobaeksan National Park, Korean J. Env 17(6): 51-60.
  13. 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. https://doi.org/10.1111/j.1755-263X.2010.00097.x
  14. 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
  15. 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
  16. 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. https://doi.org/10.13087/KOSERT.2017.20.3.55
  17. 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.
  18. 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
  19. 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. https://doi.org/10.1371/journal.pone.0233043
  20. 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. https://doi.org/10.1002/jwmg.842
  21. 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. https://doi.org/10.17663/JWR.2017.19.4.433
  22. 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.
  23. 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
  24. 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
  25. 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 https://doi.org/10.11108/KAGIS.2010.13.1.062
  26. 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
  27. 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. https://doi.org/10.1016/j.gecco.2014.11.009
  28. 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 https://doi.org/10.1644/11-MAMM-A-394.1
  29. 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
  30. 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
  31. Phillips S.J..Anderson R.P..Schapire R.E, 2006, Maximum entropy modeling of species geographic distributions, Ecological Modelling. 190: 2261-259
  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. https://doi.org/10.1111/j.0906-7590.2006.04700.x
  33. Related Departments of Korea Report, 2020, Fifth National Environmental Report. (in Korean)
  34. Raleigh RF, 1984, Habitat suitability information: rainbow trout, Fish and Wildlife Service (in U.S. Department of the Interior)
  35. 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
  36. Related Departments of Korea Report, 2020, The 5th National Environmental Comprehensive Plan.
  37. 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. https://doi.org/10.1111/j.1541-0420.2006.00582.x
  38. 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
  39. 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
  40. 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 https://doi.org/10.7780/KJRS.2012.28.1.171
  41. 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. https://doi.org/10.1080/136588199241391
  42. Wilcoxon F, 1992, Individual Comparisons by Ranking Methods, Breakthroughs in Statistics 196-202.
  43. 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. https://doi.org/10.1093/icesjms/fsv223
  44. Chungnam Development Research Institute, 2015, Academy Hanul 1765, Tanaka Akira, Habitat Ecological Impact Assessment Methodology.
  45. World Economic Forum, 2020, The Global Risks Report.