Korean journal of applied entomology (한국응용곤충학회지)

Korean Society of Applied Entomology (한국응용곤충학회)
권호 표지
DOI QR코드

DOI QR Code

Prediction of Changes in Habitat Distribution of the Alfalfa Weevil (Hypera postica) Using RCP Climate Change Scenarios

RCP 기후변화 시나리오 따른 알팔파바구미(Hypera postica)의 서식지 분포 변화 예측

  • Kim, Mi-Jeong (Division of Ecological Conservation, Bureau of Ecological Research, National Institute of Ecology) ;
  • Lee, Heejo (Division of Ecological Conservation, Bureau of Ecological Research, National Institute of Ecology) ;
  • Ban, Yeong-Gyu (Division of Ecological Conservation, Bureau of Ecological Research, National Institute of Ecology) ;
  • Lee, Soo-Dong (Department of Landscape Architecture, Gyeongnam National University of Science and Technology) ;
  • Kim, Dong Eon (Division of Ecological Conservation, Bureau of Ecological Research, National Institute of Ecology)
  • 김미정 (국립생태원 생태보전연구실) ;
  • 이희조 (국립생태원 생태보전연구실) ;
  • 반영규 (국립생태원 생태보전연구실) ;
  • 이수동 (경남과학기술대학교 조경학과) ;
  • 김동언 (국립생태원 생태보전연구실)
  • Received : 2017.12.07
  • Accepted : 2018.07.03
  • Published : 2018.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

Climate change can affect variables related to the life cycle of insects, including growth, development, survival, reproduction and distribution. As it encourages alien insects to rapidly spread and settle, climate change is regarded as one of the direct causes of decreased biodiversity because it disturbed ecosystems and reduces the population of native species. Hypera postica caused a great deal of damage in the southern provinces of Korea after it was first identified on Jeju lsland in the 1990s. In recent years, the number of individuals moving to estivation sites has concerned scientists due to the crop damage and national proliferation. In this study, we examine how climate change could affect inhabitation of H. postica. The MaxEnt model was applied to estimate potential distributions of H. postica using future climate change scenarios, namely, representative concentration pathway (RCP) 4.5 and RCP 8.5. As variables of the model, this study used six bio-climates (bio3, bio6, bio10, bio12, bio14, and bio16) in consideration of the ecological characteristics of 66 areas where inhabitation of H. postica was confirmed from 2015 to 2017, and in consideration of the interrelation between prediction variables. The fitness of the model was measured at a considered potentially useful level of 0.765 on average, and the warmest quarter has a high contribution rate of 60-70%. Prediction models (RCP 4.5 and RCP 8.5) results for the year 2050 and 2070 indicated that H. postica habitats are projected to expand across the Korean peninsula due to increasing temperatures.

기후변화는 곤충의 성장, 발육, 생존, 생식력, 분포범위 등 생활사의 변수들에 영향을 준다. 특히 외래곤충의 경우 생태계 정착 및 확산이 빨라지고 있으며, 생태계 교란, 토착종 감소 등 생물다양성을 감소시키는 직접적인 원인 중 하나이다. 알팔파바구미는 1990년대 제주도에서 처음 발견 후 남부지방에 대량 발생하여 농업해충으로 인식되었다. 최근 하면처로 이동하는 개체에 의한 밭작물의 피해와 여러 시군에서 서식이 확인되며 확산의 우려되고 있다. 본 연구에서는 기후변화가 알팔파바구미에 미치는 영향에 대해 파악하였다. 미래의 기후 시나리오 RCP 4.5와 RCP 8.5에서 알팔파바구미의 잠재적 분포를 추정하기 위해 MaxEnt 모델을 적용하였다. 모형의 변수는 2015~2017년까지 알팔파바구미의 서식이 확인된 66개 지점과 종의 생태특성 및 예측변수간 상관성을 고려한 6개(bio3, bio6, bio10, bio12, bio14, bio16)의 생물기후를 사용하였다. 예측된 모형의 적합도는 평균 0.765로 잠재력이 의미 있는 값이며, 최고 따뜻한 분기의 평균기온(bio10)이 60~70%로 높은 기여도를 나타냈다. 2050년과 2070년의 시나리오(RCP 4.5, RCP 8.5)에 대한 모형의 결과는 한반도 전역에서 알팔파바구미의 분포 변화를 보여 주었으며, 기온상승에 따른 전국적 확산이 예측되었다.

Keywords

References

  1. Akcakaya, H.R., McCarthy, M.A., Pearce, J.L., 1995. Linking landscape data with population viability analysis: management options for the helmeted honeyeater Lichenostomus melanops cassidix. Biol. Conserv. 73(2), 169-176. https://doi.org/10.1016/0006-3207(95)90045-4
  2. Baba, K., 1983. The discovery of the alfalfa weevil, Hypera postica G. in Japan. Plant Prot. Kyushu (in Japanese) 12(2), 151-160.
  3. Bae, S., Mainali, B.P., Choi, B., Yoon, Y., Kim, H., 2014. Control efficacy of environment-friendly agricultural materials against alfalfa weevil, Hypera postica Gyllenhal at Chinese milk vetch field. Korean J. Pestic. Sci. 18(1), 26-32. https://doi.org/10.7585/kjps.2014.18.1.26
  4. Bale, J.S., 2002. Insects and low temperatures: from molecular biology to distributions and abundance. Philos. Trans. R. Soc. London B: Biol. Sci. 357(1423), 849-862. https://doi.org/10.1098/rstb.2002.1074
  5. Bang, S.W., Kim, M.H., Noh, T.H., 2004. Development of integrated management plan for abating the threats from invasive alien species in Korea. Korea Environment Institute RE-02, 325 pp.
  6. Carey, J.R., 1993. Applied demography for biologists: with special emphasis on insects Oxford University Press, New York.
  7. Cothran, W.R., Summers, C.G., 1972. Sampling for the Egyptian alfalfa weevil: A comment on the sweep-net method 1 2. J. Econ. Entomol. 65(3), 689-691. https://doi.org/10.1093/jee/65.3.689
  8. Crossman, N.D., Bryan, B.A., Cooke, D.A., 2011. An invasive plant and climate change threat index for weed risk management: Integrating habitat distribution pattern and dispersal process. Ecol. Indic. 11(1), 183-198. https://doi.org/10.1016/j.ecolind.2008.10.011
  9. Dowling, C.R., 2015. Using MaxEnt modeling to predict habitat of mountain pine beetle in response to climate change. Ph.D. Thesis, University of Southern California.
  10. Elith, J., Kearney, M., Phillips, S., 2010. The art of modelling range-shifting species. Methods Ecol. Evol. 1(4), 330-342. https://doi.org/10.1111/j.2041-210X.2010.00036.x
  11. Enkegaard, A., 1993. The poinsettia strain of the cotton whitefly, Bemisia tabaci (Homoptera; Aleyrodidae), biological and demographic parameters on poinsettia (Euphorbia pulcherrima) in relation to temperature. Bull. Entomol. Res. 83, 535-546. https://doi.org/10.1017/S0007485300039961
  12. Ferrier, S., Drielsma, M., Manion, G., Watson, G., 2002a. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodiversity Conserv. 11(12), 2309-2338. https://doi.org/10.1023/A:1021374009951
  13. Ferrier, S., Watson, G., Pearce, J., Drielsma, M., 2002b. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling. Biodiversity Conserv. 11(12), 2275-2307. https://doi.org/10.1023/A:1021302930424
  14. Fleming, R.A., Candau, J.N., 1998. Influences of climatic change on some ecological processes of an insect outbreak system in Canada's boreal forests and the implications for biodiversity. Environ. Monit. Assess. 49(2), 235-249. https://doi.org/10.1023/A:1005818108382
  15. Franklin, J., 2009. Mapping species distributions: spatial inference and prediction. Cambridge University Press.
  16. Gallien, L., Douzet, R., Pratte, S., Zimmermann, N.E., Thuiller, W., 2012. Invasive species distribution models-how violating the equilibrium assumption can create new insights. Glob. Ecol. Biogeogr. 21(11), 1126-1136. https://doi.org/10.1111/j.1466-8238.2012.00768.x
  17. Guisan, A., Graham, C.H., Elith, J., Huettmann, F., 2007a. Sensitivity of predictive species distribution models to change in grain size. Divers. Distrib. 13(3), 332-340. https://doi.org/10.1111/j.1472-4642.2007.00342.x
  18. Guisan, A., Zimmermann, N.E., Elith, J., Graham, C.H., Phillips, S., Peterson, A.T., 2007b. What matters for predicting the occurrences of trees: techniques, data or species' characteristics Ecol. Monogr. 77(4), 615-630. https://doi.org/10.1890/06-1060.1
  19. Hanley, J.A., McNeil, B.J., 1983. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiol. 148(3), 839-843. https://doi.org/10.1148/radiology.148.3.6878708
  20. Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G., Jarvis, A., 2005. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25(15), 1965-1978. http://www.worldclim.org https://doi.org/10.1002/joc.1276
  21. Hill, M.P., Bertelsmeier, C., Clusella-Trullas, S., Garnas, J., Robertson, M.P., Terblanche, J.S., 2016. Predicted decrease in global climate suitability masks regional complexity of invasive fruit fly species response to climate change. Biol. Invasions 18(4), 1105-1119. https://doi.org/10.1007/s10530-016-1078-5
  22. Hong, K.J., Kim, J.Y., 2002. Detected pest of curculionoidea from plant quarantiner ('00-'01). Proc. Kor. Soc. Appl. Entomol. Conf., 58 pp.
  23. Keith, D.A., Elith, J., Simpson, C.C., 2014. Predicting distribution changes of a mire ecosystem under future climates. Divers. Distrib. 20(4), 440-454. https://doi.org/10.1111/ddi.12173
  24. Kim, D.H., Kim, K.S., Lee, S.C., Hyun, J.U., Lim, H.C., 2003. Survey of introduced exotic pest in Jeju island. Rep. Res. Exp. RDA, pp. 411-417.
  25. Kumar, S., Stohlgren, T.J., 2009. MaxEnt modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J. Ecol. Nat. Environ. 1(4), 94-98.
  26. Kwon, T.S., Lee, C.M., Kim, S.S., 2014. Northward range shifts in Korean butterflies. Clim. Chang. 126(1-2), 163-174. https://doi.org/10.1007/s10584-014-1212-2
  27. Lee, H.S., Kwon, J.H., Chung, B.K., Kim, T.S., 2012. Scouting methods for larva and adult alfalfa weevil, Hypera postica (Coleoptera: Curculionidae) on Chinese milkvetch, Astragalus sinicus L. Korean J. Appl. Entomol. 51(1), 67-72. https://doi.org/10.5656/KSAE..2012.01.1.071
  28. Merow, C., Smith, M.J., Silander, J.A., 2013. A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 36(10), 1058-1069. https://doi.org/10.1111/j.1600-0587.2013.07872.x
  29. Metcalf, R.L., Luckman, W.H., 1994. Introduction to insect pest management. A Wiley-Interscience Publication, Wiley, New York.
  30. Miller, C.D.F., Mukerji, M.K., Guppy, J.C., 1972. Notes on the spatial pattern of Hypera postica (Coleoptera: Curculionidae) on alfalfa. Can. Entomol. 104(12), 1995-1999. https://doi.org/10.4039/Ent1041995-12
  31. Morimoto, K., 1987. Establishment of the alfalfa weevil in Japan. Honeybee Sci. 5, 257-259.
  32. Nix, H.A., 1986. A biogeographic analysis of Australian elapid snakes, in: Longmore, R. (Ed.), Atlas of elapid snakes of Australia. Australian Flora and Fauna Series No. 7, Australian Government Publishing Service, Canberra, pp. 4-15.
  33. Park, S.U., Koo, K.A., Kong, W.S., 2016. Potential impact of climate change on distribution of warm temperate evergreen broad-leaved trees in the Korean peninsula. J. Korean Geogr. Soc. 51(2), 201-217.
  34. Pearce, J., Lindenmayer, D., 1998. Bioclimatic analysis to enhance reintroduction biology of the endangered helmeted honeyeater (Lichenostomus melanops cassidix) in southeastern Australia. Restor. Ecol. 6(3), 238-243. https://doi.org/10.1046/j.1526-100X.1998.00636.x
  35. Peterson, A.T., Soberon, J., Pearson, R.G., Anderson, R.P., Martinez-Meyer, E., Nakamura, M., Araujo, M.B., 2011. Ecological niches and geographic distributions. Princeton University Press, Princeton and Oxford.
  36. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3), 231-259. https://doi.org/10.1016/j.ecolmodel.2005.03.026
  37. Rushton, S.P., Ormerod, S.J., Kerby, G., 2004. New paradigms for modelling species distributions?. J. Appl. Ecol. 41(2), 193-200. https://doi.org/10.1111/j.0021-8901.2004.00903.x
  38. Secretariat of the Convention on Biological Diversity, 2010. Global biodiversity outlook 3. Montreal, Canada, pp. 12-48.
  39. Seo, C.W., Park, Y.R., Choi, Y.S., 2008. Comparison of species distribution models according to location data. J. Korean Soc. Geospatial Inf. Syst. 16(4), 59-64.
  40. Sutherst, R.W., 2000. Climate change and invasive species: a conceptual framework. Invasive Species in a Changing World, pp. 211-240.
  41. Thuiller, W., 2003. BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change. Glob. Chang. Biol. 9(10), 1353-1362. https://doi.org/10.1046/j.1365-2486.2003.00666.x
  42. Zahiri, B., Fathipour, Y., Khanjani, M., Moharramipour, S., Zalucki, M.P., 2010. Preimaginal development response to constant temperatures in Hypera postica (Coleoptera: Curculionidae) picking the best model. Environ. Entomol. 39, 177-178. https://doi.org/10.1603/EN08239