Korean Journal of Ecology and Environment (생태와환경)

The Korean Society of Limnology (한국수생태학회)
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Statistical Methods to Evaluate the Occurrence Probability of Exotic Fish in Japan

일본 서식 외래 담수어종의 서식확률 평가를 위한 통계기법 연구

  • Han, Mi-Deok (Department of Environmental Engineering and Biotechnology, Myongji University) ;
  • Chung, Wook-Jin (Department of Environmental Engineering and Biotechnology, Myongji University)
  • 한미덕 (명지대학교 환경생명공학과) ;
  • 정욱진 (명지대학교 환경생명공학과)
  • Received : 2010.09.29
  • Accepted : 2011.05.26
  • Published : 2011.06.30
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Abstract

This study analyzed and modeled the relationships between the probabilities of two exotic species occurrence (i.e. largemouth bass and blue gill) and environmental factors such as climatic and geographical variables using Generalized Additive Models (GAM), Generalized Liner Models and Classification Tree Analysis (CTA). The most moderate occurrence probability of largemouth bass was predicted using GAM with an area under the curve (ADC) of 0.88 and Kappa of 0.42, while those of blue gill was suggested by using CTA with an AUC of 0.92 and Kappa of 0.44. The most significant environmental variable in terms of changes in deviance for both species was the annual air temperature for the occurrence probability. Dams had stronger effect on the occurrence of largemouth bass than blue gill. Model development and prediction for the occurrence probability of fish species and richness are necessary to prevent further spread of exotic fishes such as largemouth bass and blue gill because they can threaten habitats of native river ecosystem through various mechanisms.

본 연구에서는 일본전국을 연구대상지로 선정하고 대상지에 서식하고 있는 대표 외래어종인 배스와 블루길의 공간적 서식분포 특성을 평가하였다. 또한 GAM, GLM, CTA 등의 세가지 통계 기법을 이용하여 일본전국에서의 해당 어종에 대한 공간적 분포패턴을 예측하였다. 그 결과 배스와 블루길 등의 외래어종은 인구 및 댐 풍의 인위적인 환경인자와 유의한 정의 관계를 보임에 따라 외래어종의 확산에 미치는 인간활동의 부정적인 영향이 확인되었다. 또한 회귀모델을 통한 해당어종의 서식확률 예측을 통한 배스와 블루길의 서식 분포는 각각 GAM (AUC: 0.88, Kappa: 0.42)과 CTA (AUC: 0.92, Kappa: 0.44)에 의해서 가장 정확하게 예측되는 것으로 평가되었고, 가장 유의한 환경인자는 연평균기온으로 나타났다. 따라서 각 생물종별로 서식확률을 추정하고 예측하는데 있어서 적합한 통계모델에 대한 검증은 생물종별로 선행되어야 할 필요가 있을 것으로 판단된다. 비록 본 연구의 연구대상지는 일본이지만 국내의 경우도 최근 들어 어류를 포함한 생물조사가 다수의 조사연구에서 광역적으로 시행되고 있기 때문에 본 연구와 같은 다량자료를 이용한 광역 스케일에서의 생물종의 서식확률 및 출현종수에 대한 연구가 충분히 가능할 것으로 판단된다.

Keywords

References

  1. Akaike, H. 1974. A new look at the statistical model identification. IEEE Transactions on Automatic Control 19: 716-723. https://doi.org/10.1109/TAC.1974.1100705
  2. Amarasinghe, U.S. and R.L. Welcomme. 2002. An analysis of fish species richness in natural lakes. Environmental Biology of Fishes 65: 327-339. https://doi.org/10.1023/A:1020558820327
  3. Azuma, M. 1992. Ecological release in feeding behavior: the case of bluegills in Japan. Hydrobiologia 244: 269-276. https://doi.org/10.1007/BF00007042
  4. Chambers, J.M. and T.J. Hastie. 1992. Statistical Models in S. AT&T Bell Laboratories.
  5. Fielding, A.H. and J.F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/ absence models. Environmental Conservation 24: 38-49. https://doi.org/10.1017/S0376892997000088
  6. Fukushima, M., S. Kameyama, M. Kaneko, K. Nakeko and E.A. Steel. 2007. Modelling the effects of dams on freshwater fish distributions in Hokkaido, Japan. Freshwater Biology 52: 1511-1524. https://doi.org/10.1111/j.1365-2427.2007.01783.x
  7. Gonzalez-Taboada, F.C. Nores and M.A. Alvarez. 2007. Breeding bird species richness in Spain: assessing diversity hypothesis at various scales. Ecography 30: 241-250. https://doi.org/10.1111/j.0906-7590.2007.04824.x
  8. Guisan, A. and N.E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147-186. https://doi.org/10.1016/S0304-3800(00)00354-9
  9. Han, M. and G. Lee. 2008. Modelling of spatial distribution of the species richness of fishes, plants, and birds using environmental factors on a wide-ranging scale. Korean Journal of Environment and Ecology 22: 347-355.
  10. Han, M., M. Fukushima and T. Fukushima. 2008a. Species richness of exotic and endangered fishes in Japan's reservoirs. Environmental Biology of Fishes 83: 409-416. https://doi.org/10.1007/s10641-008-9362-6
  11. Han, M., M. Fukushima, S. Kameyama, T. Fukushima and B. Matsushita. 2008b, How do dams affect freshwater fish distributions in Japan? Statistical analysis of native and nonnative species with various life histories. Ecological Research 23: 735-743. https://doi.org/10.1007/s11284-007-0432-6
  12. Holmquist, J.G., J.M. Schmidt-Gengenbach and B.B. Yoshioka. 1998. High dams and marine-freshwater linkages: Effects on native and introduced fauna in the Caribbean. Conservation and Biology 12: 621-630. https://doi.org/10.1046/j.1523-1739.1998.96427.x
  13. Landis, J.R. and G.G. Koch. 1977. A one-way components of variance model for categorical data. Biometrics 33: 671-679. https://doi.org/10.2307/2529465
  14. Leathwick, J.R. 1998. Are New Zealand's Nothofagus species in equilibrium with their environment?. Journal of Vegetation Science 9: 719-732. https://doi.org/10.2307/3237290
  15. Leathwick, J.R., J. Elith and T. Hastie. 2006. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modeling of species distributions. Ecological Modelling 199: 188-196. https://doi.org/10.1016/j.ecolmodel.2006.05.022
  16. Luoto, M., R. Virkkala, R.K. Heikkinen and K. Rainio. 2004. Predicting bird species richness using remote sensing in boreal agricultural-forest mosaics. Ecological Modelling 14: 1946-1962.
  17. Marchetti, M.P., J.L. Lockwood and T. Light. 2006. Effects of urbanization on California's fish diversity: differentiation, homogenization and the influence of spatial scale. Biological Conservation 127: 310-318. https://doi.org/10.1016/j.biocon.2005.04.025
  18. Ministry of Land, Infrastructure, Transport and Tourism, Japan. 2005. The report of the National Census on River Environments (http://www.mlit.go.jp/river/IDC/database/ databasetop.html).
  19. Neter, J., M.H. Kutner, C.J. Nachtsheim and W. Wasserman. 1996. Applied linear regression models, 3rd edn. Times Mirror Higher Education Group, Chicago.
  20. Quist, M.C., W.A. Hubert and F.J. Rahel. 2005. Fish assemblage structure following impoundment of a Great Plains River. Western North American Naturalist 65: 53-63.
  21. Scheffer, M., G.J. van Geest, K. Zimmer, E. Jeppesen, M. Sondergaard, M.G. Butler, M.A. Hanson, S. Declerck and L. De Meester. 2006. Small habitat size and isolation can promote species richness: second-order effects on biodiversity in shallow lakes and ponds. Oikos 112: 227- 231. https://doi.org/10.1111/j.0030-1299.2006.14145.x
  22. Swets, J.A. 1988. Measuring the accuracy of diagnostic systems. Science 240: 1285-1293. https://doi.org/10.1126/science.3287615
  23. Word, D.F. 2007. Modelling the potential geographic distribution of invasive ant species in New Zealand. Biological Invasions 9: 723-735. https://doi.org/10.1007/s10530-006-9072-y
  24. Yodo, T. and K. Iguchi. 2004. A review on the black bass problem referring to the historical background in Japan. Bulletin of Fisheries Research Agency 12: 10-24.
  25. Yuma, M., K. Hosoya and Y. Nagata. 1998. Distribution of the freshwater fishes of Japan: an historical overview. Environmental Biology of Fishes 52: 97-124. https://doi.org/10.1023/A:1007451417556
  26. Zhao, S., J. Fang, C. Peng, Z. Tang and S. Piao. 2006. Patterns of fish species richness in China's lakes. Global Ecology and Biogeography 15: 386-394. https://doi.org/10.1111/j.1466-822X.2006.00236.x