Analysis of Potential Habitat Distribution of Invasive Turtles in South Korea

doi:10.16879/jkca.2024.24.2.113

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2024 Vol.24, Issue 2 Preview Page

Research Article

Analysis of Potential Habitat Distribution of Invasive Turtles in South Korea 외래 거북의 잠재서식지 분포 분석: 김소희¹, 조윤주², 박종철³, 백혜준⁴, 석민정⁵, 박영준^6†
Sohee Kim¹, Yunju Cho², Jongchul Park³, Hae-jun Baek⁴, Minjeong Seok⁵, Youngjun Park^6†; ¹국립공주대학교 지리학과 석사과정
²국립공주대학교 지리학과 석사과정
³국립공주대학교 지리학과 부교수
⁴국립생태원 생태조사연구실 연구원
⁵국립생태원 생태조사연구실 연구원
⁶국립생태원 생태조사연구실 선임연구원

¹Master Student, Department of Geography, Kongju National University
²Master Student, Department of Geography, Kongju National University
³Associate Professor, Department of Geography, Kongju National University
⁴Researcher, Conservation Research Division, National Institute of Ecology
⁵Researcher, Conservation Research Division, National Institute of Ecology
⁶Senior Researcher, Conservation Research Division, National Institute of Ecology

31 August 2024. pp. 113~127

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Abstract

This study analyzes the potential habitat distribution of four invasive turtle species (Trachemys spp., Pseudemys concinna, Mauremys sinensis, and Pseudemys nelsoni) designated as invasive species. The potential habitats were predicted using 16 environmental variables, including topography, climate, land cover, and human impact, along with the MaxEnt machine learning model. The results showed that the areas with high occurrence frequencies were similar across all species. The habitats of invasive turtles were closely associated with urbanized areas and regions with high human accessibility. In contrast, the influence of natural factors such as precipitation and temperature was relatively low. The potential habitat analysis demonstrated high simulation efficiency, with an AUC value of 0.913. The findings suggest that monitoring and management efforts should focus on urban and peri-urban areas, including regions near major roads, to effectively control the spread of invasive turtles. This research provides essential foundational data for developing strategies to mitigate the ecological impact of invasive species.

Keywords

Potential Habitat

Invasive Turtle

Invasive Species

MaxEnt

Machine Learning

본 연구는 생태계교란생물로 지정된 네 종의 외래 거북(붉은귀거북 속, 리버쿠터, 중국줄무늬목거북, 플로리다붉은배거북)에 대한 잠재서식지 분포를 분석하였다. 잠재서식지 예측에는 지형, 기후, 토지피복, 인간의 영향 등 16개의 환경변수와 MaxEnt 머신러닝 모델을 이용하였다. 연구 결과는 출현 빈도가 높은 지역이 모든 종에서 유사하다는 것을 보여주었다. 외래 거북의 서식지는 도시화 및 인간의 접근성이 용이한 지역과 연관성이 높았다. 반면, 강수량과 기온과 같은 자연적 요인의 영향은 상대적으로 낮게 나타났다. 잠재서식지 분석 결과는 AUC 값이 0.913으로 높은 모의 효율을 보여주었다. 이는 외래 거북의 확산을 효과적으로 관리하기 위해서 주요 도로 주변을 포함한 도심 및 도시 주변 지역에 대한 모니터링과 관리가 중요함을 시사한다. 이 연구는 외래종으로 인한 생태계 영향을 완화하기 위한 전략 수립에 중요한 기초 자료를 제공한다.

키워드

잠재서식지

외래 거북

생태계교란생물

MaxEnt

머신러닝

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Information

Publisher :The Korean Cartographic Association
Publisher(Ko) :한국지도학회
Journal Title :Journal of the Korean Cartographic Association
Journal Title(Ko) :한국지도학회지
Volume : 24
No :2
Pages :113~127
DOI :https://doi.org/10.16879/jkca.2024.24.2.113

[1] 관계부처 합동, 2019, ｢제2차 외래생물 관리계획(2019~2023)｣.

[2] 구교성･윤가영･김예빈･장이권, 2020, “종분포모델링을 이용한 외래 거북의 서식지 적합성 예측이 실제 분포와 일치할까?,” 한국환경생태학회 학술대회논문집, 30(1), 82-82.

[3] 국립생태원, 2022a, ｢2022년 외래생물 전국 서식실태조사｣.

[4] 국립생태원, 2022b, ｢한국의 생태계교란 외래생물｣.

[5] 김근한･노영희･정휘철･최재용･윤정호, 2017, “국토환경성평가지도 기반 Gap 분석을 이용한 자연환경 우수지역 중 관리취약지역 추출,” 한국지도학회지, 17(2), 111-123. https://doi.org/10.16879/jkca.2017.17.2.111 10.16879/jkca.2017.17.2.111

[6] 김다빈･구경아, 2021, “생태계교란 생물 관리 현황 및 개선방안 연구,” 환경정책, 29(4), 59-81. http://dx.doi.org/10.15301/jepa.2021.29.4.59 10.15301/jepa.2021.29.4.59

[7] 김동언, 2018, “국내 생물다양성 위협 외래생물의 관리제도 및 개선방향,” 환경영향평가, 27(1), 33-55. https://doi.org/10.14249/eia.2018.27.1.33 10.14249/eia.2018.27.1.33

[8] 김세형･김영호, 2017, “한국 말라리아 발생의 공간 분포 변화 및 인문･환경 요인 분석,” 한국지도학회지, 17(1), 71-85. https://doi.org/10.16879/jkca.2017.17.1.071 10.16879/jkca.2017.17.1.071

[9] 김승원･김영호, 2014, “쯔쯔가무시증의 공간 분석 모형 비교,” 한국지도학회지, 14(3), 41-54. http://dx.doi.org/10.16879/jkca.2014.14.3.041 10.16879/jkca.2014.14.3.041

[10] 이도훈･김영채･장민호･김수환･김동언･길지현, 2016, “외래 거북의 국내 현황 및 관리방안,” 환경영향평가, 25(5), 319-332. https://doi.org/10.14249/eia.2016.25.5.319 10.14249/eia.2016.25.5.319

[11] 이동근･김호걸, 2010, “MaxEnt 모형을 이용한 서식지 잠재력 평가 - 하천으로부터의 거리, 하천의 차수, 토지이용을 중심으로-,” 환경복원녹화, 13(6), 161-172.

[12] 이성우･정경우･박영준･박종철, 2021, “최대 엔트로피 모델을 활용한 경주시의 소나무재선충병 위험지역 분포 예측,” 한국지도학회지, 21(3), 43-57. https://doi.org/10.16879/jkca.2021.21.3.043 10.16879/jkca.2021.21.3.043

[13] 정지화･이우신, 2019, “안산갈대습지에 서식하는 붉은귀거북(Trachemys scripta elegans)의 활동 요인과 시기에 따른 활동 차이,” 한국환경생태학회지, 33(6), 645-653.

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[15] Baldwin, R.A., 2009, Use of Maximum Entropy Modeling in Wildlife Research, Entropy 2009, 11, 854-866. https://doi.org/10.3390/e11040854 10.3390/e11040854

[16] Dunn, O.J., 1964, Multiple Comparisons Using Rank Sums, Technometrics, 6(3), 241-252. 10.1080/00401706.1964.10490181

[17] Feldman, M.L., 2005, The Red eared Slieder Turtle(Trachemys scripta elegans) in New Zealand, Proceedings of the 13th Australian Vertebrate Pest Conference, Manaaki Whenua-Landcare Research, 96-101.

[18] Fick, S.E. and Hijmans, R.J., 2017, WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas, International Journal of Climatology, 37(2), 4302–4317. 10.1002/joc.5086 10.1002/joc.5086

[19] Hijmans, R.J., Phillips, S., Leathwick, J., and Elith, J., 2023, Package ‘dismo’, https://cran.r-project.org/web/packages/dismo/dismo.pdf

[20] IPBES, 2019, The Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, Brondizio, E.S., Settele, J., Diaz, S., and Ngo, H.T. eds., IPBES secretariat, Bonn, Germany. https://doi.org/10.5281/zenodo.3831673 10.5281/zenodo.3831673

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[23] Oregon Sea Grant, 2016, Species Guides (Red-Eared Slieder), https://seagrant.oregonstate.edu

[24] Paini, D.R., Sheppard, A.W., Cook, D.C., De Barro, P.J., Worner, S.P., and Thomas, M.B., 2016, Global threat to agriculture from invasive species, PNAS, 113(27), 7575-7579. https://doi.org/10.1073/pnas.1602205113 10.1073/pnas.160220511327325781PMC4941431

[25] Park, J., Kim, J., Song, K., Jeong, T., and Park. S., 2018, Mapping potential habitats for the management of exportable insects in South Korea, Journal of Asia-Pacific Biodiversity, 11(1), 11-20. https://doi.org/10.1016/j.japb.2017.12.002 10.1016/j.japb.2017.12.002

[26] Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., and Müller, M., 2011, pROC: an open-source package for R and S+ to analyze and compare ROC curves., BMC Bioinformatics, 12(77). https://doi.org/10.1186/1471-2105-12-77 10.1186/1471-2105-12-7721414208PMC3068975

[27] Robin, X., Turck, N., Hainard, A., Tiberti, N., Lisacek, F., Sanchez, J.C., Müller, M., Siegert, S., Doering, M., and Billings Z., 2023, Package ‘pROC’, https://cran.r-project.org/web/packages/pROC/pROC.pdf

[28] Swets, J.A., 1988, Measuring the Accuracy of Diagnostic Systems, Science, 240(4857) 1285-1293. 10.1126/science.3287615 10.1126/science.32876153287615

[29] van Dijk, P.P., 2011, Mauremys reevesii (errata version published in 2016), The IUCN Red List of Threatened Species 2011: e.T170502A97431862. http://dx.doi.org/10.2305/IUCN.UK.2011-1.RLTS.T170502A6783291.en 10.2305/IUCN.UK.2011-1.RLTS.T170502A6783291.en

[30] van Dijk, P.P., Harding, J., and Hammerson, G.A., 2011, Trachemys scripta (errata version published in 2016), The IUCN Red List of Threatened Species 2011: e.T22028A97429935. https://dx.doi.org/10.2305/IUCN.UK.2011-1.RLTS.T22028A9347395.en 10.2305/IUCN.UK.2011-1.RLTS.T22028A9347395.en

[31] Wyneken, J., Godfrey, M.H., and Bels, V., 2007, Biology of Turtles: From Structures to Strategies of Life, 1st ed., CRC Press. https://doi.org/10.1201/9781420004977 10.1201/9781420004977

[32] 국가교통DB, https://www.ktdb.go.kr

[33] 국토정보플랫폼, https://map.ngii.go.kr

[34] 디지털트윈국토, https://www.vworld.kr

[35] 코리아크레딧뷰로, https://www.koreacb.com

[36] 환경공간정보서비스, https://egis.me.go.kr

Journal of the Korean Cartographic Association ISSN:1598-6160(Print) 한국지도학회지

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