Journal of Environmental Impact Assessment (환경영향평가)

Korean Society of Environmental Impact Assessment (한국환경영향평가학회)
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A Management Plan According to the Estimation of Nutria (Myocastorcoypus) Distribution Density and Potential Suitable Habitat

뉴트리아(Myocastor coypus) 분포밀도 및 잠재적 서식가능지역 예측에 따른 관리방향

  • Received : 2018.03.26
  • Accepted : 2018.04.06
  • Published : 2018.04.30
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Abstract

The purpose of this study is to estimate the concentrated distribution area of nutria (Myocastor coypus) and potential suitable habitat and to provide useful data for the effective management direction setting. Based on the nationwide distribution data of nutria, the cross-validation value was applied to analyze the distribution density. As a result, the concentrated distribution areas thatrequired preferential elimination is found in 14 administrative areas including Busan Metropolitan City, Daegu Metropolitan City, 11 cities and counties in Gyeongsangnam-do and 1 county in Gyeongsangbuk-do. In the potential suitable habitat estimation using a MaxEnt (Maximum Entropy) model, the possibility of emergency was found in the Nakdong River middle and lower stream area and the Seomjin riverlower stream area and Gahwacheon River area. As for the contribution by variables of a model, it showed DEM, precipitation of driest month, min temperature of coldest month and distance from river had contribution from the highest order. In terms of the relation with the probability of appearance, the probability of emergence was higher than the threshold value in areas with less than 34m of altitude, with $-5.7^{\circ}C{\sim}-0.6^{\circ}C$ of min temperature of the coldest month, with 15-30mm of precipitation of the driest month and with less than 1,373m away from the river. Variables that Altitude, existence of water and wintertemperature affected settlement and expansion of nutria, considering the research results and the physiological and ecological characteristics of nutria. Therefore, it is necessary to reflect them as important variables in the future habitable area detection and expansion estimation modeling. It must be essential to distinguish the concentrated distribution area and the management area of invasive alien species such as nutria and to establish and apply a suitable management strategy to the management site for the permanent control. The results in this study can be used as useful data for a strategic management such as rapid management on the preferential management area and preemptive and preventive management on the possible spreading area.

본 연구는 국내에 서식하는 뉴트리아의 집중분포지역과 잠재적인 서식가능지역을 예측하여 효과적인 관리방향 설정에 유용한 자료를 제공하고자 하였다. 뉴트리아의 전국 분포 자료를 토대로 CVh(가능도 교차타당성)값을 띠폭(bandwidth)에 적용하여 분포밀도를 분석한 결과, 부산광역시, 대구광역시, 경상남도 소재 11개 시 군, 경상북도 소재 1개 군 등 낙동강수계에 위치한 14개 행정구역 내에서 우선적인 제거가 필요한 집중분포지역이 확인되었다. MaxEnt 모델을 이용한 잠재적인 서식가능지역 예측에서는 낙동강 중 하류 일대와 섬진강 하류, 가화천 일대에서 출현 가능성이 나타났다. 모형의 변수별 기여도는 고도, 건조한 달의 강수량, 가장 추운달의 최저온도, 수계로부터의 거리 순으로 높은 기여도를 보였으며, 출현확률과의 관계를 살펴보면, 고도 34m 이하의 저지대, 가장 추운달의 최저온도가 $-5.7^{\circ}C$이상 $-0.6^{\circ}C$ 이하인 지역, 가장 건조한 달의 강수량이 15-30mm, 수계로부터 1,373m 이하인 지역에서 임계값보다 높은 출현확률을 보였다. 뉴트리아의 생태적 특성과 본 연구결과를 종합하면, 고도, 물과의 접근성 및 이용성, 겨울철 낮은 기온이 뉴트리아의 정착과 확산에 영향을 주는 주요 요인으로 판단되므로 향후 서식가능지역의 검출과 확산 예측 모델링에 있어 중요한 변수로 검토될 수 있다. 뉴트리아와 같은 침입외래생물의 집중분포지역과 관리대상지역을 구분하고 그에 적합한 관리전략을 수립하여 관리현장에 적용하는 것은 영구적인 제어 목적의 관리에 있어 필수적인 사항이다. 본 연구에서 제시된 결과는 우선관리대상지역의 신속한 관리와 확산가능지역에 대한 사전 예방적 관리 등 전략적인 관리의 실행에 있어 유용한 자료로 활용될 수 있다.

Keywords

References

  1. Abbas A. 1988. Impact du Ragondin (Myocastor coypus Molina) sur une Culture de Mais (Zea mays L.) dans le Marais Poitevin. Acta Oecol-Oec Appl. 9(2): 173-189.
  2. Aliev FF. 1966. Numerical Changes and the Population Structure of the Coypu (Myocastor coypus) in Different Countries. Saugetierkd Mitt. 15: 238-242.
  3. Aliev FF. 1968. Contribution to the Study of Nutria- Migrations (Myocastor coypus). Saugetierkd Mitt. 16: 301-303.
  4. ArcGIS Pro. Tool Reference; [Cited 2017 February 8]. Available from: http://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/kernel-density.htm
  5. Baker SJ. 2010. Control and Eradication of Invasive Mammals in Great Britain. Rev Sci Tech. 29: 311-327. https://doi.org/10.20506/rst.29.2.1981
  6. Bar-Ilan A, Marder J. 1983. Adaptations to Hypercapnic Conditions in the Nutria (Myocastor coypus)-in vivo and in vitro $CO_2$ Titration Curves. Comp. Biochem. Physiol. 75(4): 603-608. https://doi.org/10.1016/0300-9629(83)90427-9
  7. Borgnia M, Galante ML, Cassini MH. 2000. Diet of the Coypu (Nutria, Myocastor coypus) in Agro- Systems of Argentinean Pampas. J Wildl Manage. 64(2): 354-361. https://doi.org/10.2307/3803233
  8. Bounds DL. 2000. Nutria: An Invasive Species of National Concern. Wetland Journal. 12(3): 9-16.
  9. Cronk QCB, Fuller JL. 1995. Plant Invaders: The Threat to Natural Ecosystems. Biodivers Conserv. London. 241p.
  10. D’adamo P, Guichon ML, Bo RF, Cassini MH. 2000. Habitat Use by Myocastor coypus in Agro-Systems of the Argentinean Pampas. Acta Theriol. 45: 25-33. https://doi.org/10.4098/AT.arch.00-3
  11. Doncaster CP, Micol T. 1989. Annual Cycle of a Coypu (Myocastor coypus) Population: Male and Female Strategies. J. Zool. (London). 217: 227-240. https://doi.org/10.1111/j.1469-7998.1989.tb02484.x
  12. Franklin J. 2009. Mapping Species Distributions Spatial Inference and Prediction. Cambridge University Press.
  13. Gosling LM. 1974. The Coypus in East Anglia. Transactions of the Norfolk and Norwich Naturalists’ Society. 23: 49-59.
  14. Gosling LM, Hudson LW, Addison GC. 1980. Age Estimation of Coypus (Myocastor coypus) from Eye lens Weight. J Appl Ecol. 17: 641-648. https://doi.org/10.2307/2402642
  15. Gosling LM, Baker SJ. 1981. Coypu (Myocastor coypus) Potential Longevity. J. Zool. (London). 197: 285-312.
  16. Gosling LM, Baker SJ, Skinner JR. 1983. A Simulation Approach to Investigating the Response of a Coypu Population to Climatic Variation. Bull. OEPP. 13(2): 183-192. https://doi.org/10.1111/j.1365-2338.1983.tb01597.x
  17. Guichón ML, Doncaster CP, Cassini MH. 2003. Population Structure of Coypus (Myocastor coypus) in Their Region of Origin and Comparison with Introduced Populations. J. Zool. 261(3): 265-272. https://doi.org/10.1017/S0952836903004187
  18. Hall ER. 1981. The Mammals of North America. Second Edition. John Wiley and Sons, New York, 2: 601-1181.
  19. Heibl C, Renner SS. 2012. Distribution Models and a Dated Phylogeny for Chilean Oxalis Species Reveal Occupation of New Habitats by Different Lineages, not Rapid Adaptive Radiation. Syst Biol. 61(5): 823-834. https://doi.org/10.1093/sysbio/sys034
  20. Huh J. 2012. Bandwidth Selections based on Cross-Validation for Estimation of a Discontinuity Point in Density. Journal of the Korean Data & Information Science Society. 23(4): 765-775. [Korean Literature] https://doi.org/10.7465/jkdi.2012.23.4.765
  21. Kil JH, Lee DH, Kim YC. 2015. Effective Management of Invasive Nutria (Myocastor coypus) in the UK and the USA. Ecol. Resil. Infrastruct. 2(4): 265-273. [Korean Literature] https://doi.org/10.17820/eri.2015.2.4.265
  22. Kumar S, Stohlgren TJ. 2009. Maxent Modeling for Predicting Suitable Habitat for Threatened and Endangered Tree Canacomyrica monticola in New Caledonia. J. Ecol. Nat. Environ. 1(4): 094-098.
  23. LeBlanc DJ. 1994. Nutria. Prevention and Control of Wildlife damage. B71-B80.
  24. Lee DH, Kil JH, Yang BG. 2012. Ecological Characteristics for the Sustainable Management of Nutria (Myocastor coypus) in Korea. National Institute of Environmental Research. [Korean Literature]
  25. Lee DH, Kil JH, Kim DE. 2013. The Study on the Distribution and Inhabiting Status of Nutria (Myocastor coypus) in Korea. Korean J. Environ. Ecol. 27: 316-326. [Korean Literature]
  26. Lee DH, Kil JH. 2015. Analysis of the best practices for nutria management in Europe and North America. National Institute of Ecology. 144p. [Korean Literature]
  27. Lee DH, Lee MS, Kim YC, Kim IR, Kim HK, Jeong DG, Lee JR, Kim JH. 2017. Complete Mitochondrial Genome of the Invasive Semi-Aquatic Mammal, Nutria Myocastor coypus (Rodentia; Myocastoridae). Conserv Genet Resour. 1-4.
  28. Lee SH, Cho KH, Lee WJ. 2016. Prediction of Potential Distributions of Two Invasive Alien Plants, Paspalum distichum and Ambrosia artemisiifolia, Using Species Distribution Model in Korean Peninsula. Ecol. Resil. Infrastruct. 3(3): 189-200. [Korean Literature] https://doi.org/10.17820/eri.2016.3.3.189
  29. Leuven RSEW, Velde GVD, BaijensI, Snijders J, Zwart CVD, Lenders HJR, Vaate ABD. 2009. The River Rhine: a Global Highway for Dispersal of Aquatic Invasive Species. Biol Invasions. 11: 1989-2008. https://doi.org/10.1007/s10530-009-9491-7
  30. Lockwood JL, Hoopes MF, Marchetti MP. 2007. Invasion Ecology. Blackwell Publishing, Oxford, UK.
  31. Lowe S, Browne M, Boudjelas S, Poorter MD. 2000. 100 of the World's Worst Invasive Alien Species: a Selection from the Global Invasive Species Database. Invasive Species Specialist Group. Auckland. New Zealand.
  32. Minor ES, Tessel SM, Engelhardt KAM, Lookingbill TR. 2009. The Role of Landscape Connectivity in Assembling Exotic Plant Communities: a Network Analysis. Ecology, 90: 1802-1809. https://doi.org/10.1890/08-1015.1
  33. Miura S. 1976. Disposal of Nutria in Okayama Prefecture. J. Mammal. Soc. Jpn. 6: 231-237.
  34. National Institute of Ecology. 2015. The Study on the Inhabitation Status of Nutria (Myocastor coypus). National Institute of Ecology Press. Seocheon. 186p. [Korean Literature]
  35. National Institute of Ecology. 2017. The Study on the Inhabitation Status of Nutria (Myocastor coypus). National Institute of Ecology Press. Seocheon. 197p. [Korean Literature]
  36. National Institute of Environmental Research. 2006. A Study of Detailed Survey on Invasive Alien Species in Korea and Designation of Invasive Alien Species in Foreign Countries. National Institute of Environmental Research Press. Incheon. 408p.
  37. Phillips SJ, Anderson RP, Schapire RE. 2006. Maximum Entropy Modeling of Species Geographic Distributions. Ecol Modell. 190: 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  38. Phillips SJ, Dudik M. 2008. Modeling of Species Distributions with Maxent: New Extension and a Comprehensive Evaluation. Ecography. 31: 161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  39. SCBD. Secretariat of the Convention on Biological Diversity. 2014. Global Biodiversity Outlook 4. Secretariat of the Convention on Biological Diversity, Montreal, Canada.
  40. Sheffels TR. 2013. Status of Nutria (Myocastor coypus) Populations in the Pacific Northwest and Development of Associated Control and Management Strategies, with and Emphasis on Metropolitan Habitats. Thesis for degree of Doctor of Philosophy. Portland State University, Portland, USA.
  41. Silverman BW. 1986. Density Estimation for Statistics and Data Analysis. New York: Chapman and Hall. 76p.
  42. Skyriené G, Paulauskas A. 2012. Distribution of Invasive Muskrats (Ondatra zibethicus) and Impact on Ecosystem. Ekologija. 58: 357-367.
  43. Song WK. 2015. Habitat Analysis of Hyla suweonensis in the Breeding Season Using Species Distribution Modeling. J. Korean Env. Res. & Reveg. Tech. 18(1): 71-82. [Korean Literature]
  44. Wilcove DS, Rothstein D, Dubow J, Phillips A, Losos E. 1998. Quantifying Threats to Imperiled Species in the United States. Bioscience. 48: 607-615. https://doi.org/10.2307/1313420
  45. With KA. 2002. The Landscape Ecology of Invasive Spread. Conservation Biology. 16: 1192-1203. https://doi.org/10.1046/j.1523-1739.2002.01064.x
  46. Woods CA, Howland EB. 1979. Adaptive Radiation of Capromyid Rodents: Anatomy of the Masticatory Apparatus. J. Mammal. 60: 95-116. https://doi.org/10.2307/1379762
  47. Woods CA, Contreras L, Willner-Chapman G, Whidden HP. 1992. Myocastor coypus. Mammalian Species. 398: 1.
  48. World Clim - Global Climate Data. WorldClim Version2. bio 30s; [Cited 2017 July 13]. Available from: http://worldclim.org/version2
  49. Worton BJ. 1989. Kernel Methods for Estimating the Utilization Distribution in Home-Range Studies. Ecology. 70: 164-168. https://doi.org/10.2307/1938423
  50. Worton BJ. 1995. Using Monte Carlo Simulation to Evaluate Kernel Based Home-Rang Estimators. J. Wildl. Manage. 59: 794-800. https://doi.org/10.2307/3801959
  51. Yoo SH, Lee KS, Park CH. 2013. MCP, Kernel Density Estimation and LoCoH Analysis for the Core Area Zoning of the Red-crowned Crane's Feeding Habitat in Cheorwon, Korea. Korean J. Environ. Ecol. 27(1): 11-21. [Korean Literature]
  52. Zietsman L. 2011.Observations on Environmental Change in South Africas. SUN Press. 177p.

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