DOI QR코드

DOI QR Code

Predicting the suitable habitat distribution of Conyza sumatrensis under RCP scenarios

RCPs 기후변화 시나리오에 따른 큰망초(Conyza sumatrensis)의 적합 서식지 분포 예측

  • Myung-Hyun Kim (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Soon-Kun Choi (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Jaepil Cho (Watershed Management Institute) ;
  • Min-Kyeong Kim (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Jinu Eo (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • So-Jin Yeob (Climate Change Assessment Division, National Institute of Agricultural Sciences) ;
  • Jeong Hwan Bang (Climate Change Assessment Division, National Institute of Agricultural Sciences)
  • 김명현 (국립농업과학원 기후변화평가과) ;
  • 최순군 (국립농업과학원 기후변화평가과) ;
  • 조재필 ((주)유역통합관리연구원) ;
  • 김민경 (국립농업과학원 기후변화평가과) ;
  • 어진우 (국립농업과학원 기후변화평가과) ;
  • 엽소진 (국립농업과학원 기후변화평가과) ;
  • 방정환 (국립농업과학원 기후변화평가과)
  • Received : 2021.09.02
  • Accepted : 2022.02.22
  • Published : 2022.03.31

Abstract

Global warming has a major impact on the Earth's precipitation and temperature fluctuations, and significantly affects the habitats and biodiversity of many species. Although the number of alien plants newly introduced in South Korea has recently increased due to the increasing frequency of international exchanges and climate change, studies on how climate change affects the distribution of these alien plants are lacking. This study predicts changes in the distribution of suitable habitats according to RCPs climate change scenarios using the current distribution of the invasive alien plant Conyza sumatrensis and bioclimatic variables. C. sumatrensis has a limited distribution in the southern part of South Korea. Isothermality (bio03), the max temperature of the warmest month (bio05), and the mean temperature of the driest quarter (bio09) were found to influence the distribution of C. sumatrensis. In the future, the suitable habitat for C. sumatrensis is projected to increase under RCP 4.5 and RCP 8.5 climate change scenarios. Changes in the distribution of alien plants can have a significant impact on the survival of native plants and cause ecosystem disturbance. Therefore, studies on changing distribution of invasive species according to climate change scenarios can provide useful information required to plan conservation strategies and restoration plans for various ecosystems.

기후변화로 인한 지구온난화는 강수량과 기온에 영향을 주며, 다양한 종들의 서식지와 생물다양성에 상당한 영향을 줄 수 있다. 최근 국제 교류의 증가와 기후변화 등의 원인으로 국내로 새롭게 유입되어 정착하는 외래식물이 증가하고 있지만, 기후변화가 이들 외래식물의 국내 분포에 어떤 영향을 주는지에 대한 연구는 부족한 실정이다. 본 연구는 침입외래식물 큰망초(C. sumatrensis)의 현재 분포와 생물기후 변수를 활용하여 RCPs 기후변화 시나리오에 따른 적합 서식지 분포 변화를 예측하였다. 큰망초는 현재 우리나라 남부 지방에서 제한된 분포를 보이고 있으며, 이들의 분포에는 가장 건조한 분기의 평균기온(bio09), 가장 더운 달의 최고기온(bio05), 등온선(bio03)이 영향을 미치는 것으로 나타났다. 기후변화 시나리오에 따라 큰망초의 미래 적합 서식지 면적은 증가할 것으로 전망되었다. 큰망초와 같은 침입외래종의 분포 변화는 자생식물의 생존을 위협할 수 있으며 생태계 교란을 일으킬 수 있다. 따라서 기후변화에 따른 외래종 분포에 대한 연구는 자생식물뿐만 아니라 생물다양성 보전에 중요한 데이터로 활용될 수 있으며, 향후 서식지 복원과 생물자원을 관리하기 위한 정책자료로 활용될 수 있다.

Keywords

Acknowledgement

본 연구는 농촌진흥청 공동연구사업(과제번호: PJ01480801)의 지원에 의해 이루어진 것임.

References

  1. Araujo MB and C Rahbek. 2006. How does climate change affect biodiversity? Science 313:1396-1397.  https://doi.org/10.1126/science.1131758
  2. Booth TH. 2018. Species distribution modelling tools and databases to assist managing forests under climate change. For. Ecol. Manag. 430:196-203.  https://doi.org/10.1016/j.foreco.2018.08.019
  3. Bradley BA, DM Blumenthal, DS Wilcove and LH Ziska. 2010a. Predicting plant invasions in an era of global change. Trends Ecol. Evol. 25:310-318.  https://doi.org/10.1016/j.tree.2009.12.003
  4. Bradley BA, DS Wilcove and M Oppenheimer. 2010b. Climate change increases risk of plant invasion in the Eastern United States. Biol. Invasions 12:1855-1872.  https://doi.org/10.1007/s10530-009-9597-y
  5. Bradley BA, M Oppenheimer and DS Wilcove. 2009. Climate change and plant invasions: restoration opportunities ahead? Glob. Change Biol. 15:1511-1521.  https://doi.org/10.1111/j.1365-2486.2008.01824.x
  6. Chappuis E, E Ballesteros and E Gacia. 2012. Distribution and richness of aquatic plants across Europe and Mediterranean countries: patterns, environmental driving factors and comparison with total plant richness. J. Veg. Sci. 23:985-997.  https://doi.org/10.1111/j.1654-1103.2012.01417.x
  7. Chivasa S, EJA Ekpo and RGT Hicks. 2002. New hosts of Turnip mosaic virus in Zimbabwe. Plant Pathol. 51:386. 
  8. Cho JP, JU Kim, SK Choi, SW Hwang and HC Jung. 2020a. Variability analysis of climate extreme index using downscaled multi-models and grid-based CMIP5 climate change scenario data. J. Climate Change Res. 11:123-132.  https://doi.org/10.15531/KSCCR.2020.11.2.123
  9. Cho NH, ES Kim, B Lee, JH Lim and SK Kang. 2020b. Predicting the potential distribution of Pinus densiflora and analyzing the relationship with environmental variable using MaxEnt model. Korean J. Agric. For. Meteorol. 22:47-56. 
  10. Chun YJ, ML Collyer, KA Moloney and JD Nason. 2007. Phenotypic plasticity of native vs. invasive purple loosestrife: a two-state multivariate approach. Ecology 88:1499-1512.  https://doi.org/10.1890/06-0856
  11. Cleland EE, I Chuine, A Menzel, HA Mooney and MD Schwartz. 2007. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22:357-365.  https://doi.org/10.1016/j.tree.2007.04.003
  12. Davidson AM, M Jennions and AB Nicotra. 2011. Do invasive species show higher phenotypic plasticity than native species and, if so, is it adaptive? A meta-analysis. Ecol. Lett. 14:419-431.  https://doi.org/10.1111/j.1461-0248.2011.01596.x
  13. Dormann CF, J Elith, S Bacher, C Buchmann, G Carl, G Carre, JR Marquez, B Gruber, B Lafourcade, PJ Leitao, T Munkemuller, C McClean, PE Osborne, B Reineking, B Schroder, AK Skidmore, D Zurell and S Lautenbach. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. Ecography 36:27-46.  https://doi.org/10.1111/j.1600-0587.2012.07348.x
  14. Du Z, Y He, H Wang, C Wang and Y Duan. 2021. Potential geographical distribution and habitat shift of the genus Ammopiptanthus in China under current and future climate change based on the MaxEnt model. J. Arid Environ. 184:104328. 
  15. Elith J and JR Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Evol. Syst. 40:677-697.  https://doi.org/10.1146/annurev.ecolsys.110308.120159
  16. Fois M, A Cuena-Lombrana, G Fenu and G Bacchetta. 2018. Using species distribution models at local scale to guide the search of poorly known species: Review, methodological issues and future directions. Ecol. Model. 385:124-132.  https://doi.org/10.1016/j.ecolmodel.2018.07.018
  17. Garssen AG, A Baattrup-Pedersen, LA Voesenek, JT Verhoeven and MB Soons. 2015. Riparian plant community responses to increased flooding: A meta-analysis. Glob. Change Biol. 21:2881-2890.  https://doi.org/10.1111/gcb.12921
  18. Gill NS and F Sangermano. 2016. Africanized honeybee habitat suitability: a comparison between models southern Utah and southern California. Appl. Geogr. 76:14-21.  https://doi.org/10.1016/j.apgeog.2016.09.002
  19. Guan BC, HJ Guo, SS Chen, DM Li, X Liu, X Gong and G Ge. 2020. Shifting ranges of eleven invasive alien plants in China in the face of climate change. Ecol. Inf. 55:101024. 
  20. Halvorsen R, S Mazzoni, JW Dirksen, E Naesset, T Gobakken and M Ohlson. 2016. How important are choice of model selection method and spatial autocorrelation of presence data for distribution modelling by MaxEnt? Ecol. Model. 328:108-118.  https://doi.org/10.1016/j.ecolmodel.2016.02.021
  21. Hao JH, S Qiang, QQ Liu and F Cao. 2009. Reproductive traits associated with invasiveness in Conyza sumatrensis. J. Syst. Evol. 47:245-254.  https://doi.org/10.1111/j.1759-6831.2009.00019.x
  22. Hegland SJ, A Nielsen, A Lazaro, AL Bjerknes and O Totland. 2009. How does climate warming affect plant-pollinator interaction? Ecol. Lett. 12:184-195.  https://doi.org/10.1111/j.1461-0248.2008.01269.x
  23. Hernandez PA, CH Graham, LL Master and DL Albert. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 5:773-785. 
  24. Hijiman RJ, J van Etten, J Cheng, M Mattiuzzi, M Summer, JA Greenberg and A Ghosh. 2017. Raster: Geographic Data Analysis and Modeling, version 2.6-7. http://cran.r-project.org/web/packages/raster/ 
  25. Hirabayashi Y, R Mahendran, S Koirala, L Konoshima, D Yamazaki, S Watanabe, H Kim and S Kanae. 2013. Global flood risk under climate change. Nat. Clim. Change 3:816-821.  https://doi.org/10.1038/nclimate1911
  26. IPCC. 2018. Summary for policymakers. p. 32. In: Global Warming of 1.5℃. An IPCC Special Report on the Impacts of Global Warming of 1.5℃ above Pre-Industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty. World Meteorological Organization. Geneva, Switzerland. 
  27. Ji W, G Gao and J Wei. 2021. Potential global distribution of Daktulosphaira vitifoliae under climate change based on MaxEnt. Insects 12:347. 
  28. Jorda C, I Font, P Martinez, M Juarez, A Ortega and A Lacasa. 2001. Current status and new natural hosts of Tomato yellow leaf curl virus (TYLCV) in Spain. Plant Dis. 85:445-445. 
  29. Kim MH, SW Choi, C Jung, KJ Ahn, YJ Oh, YJ Song, SI Kwon, J Eo and HK Nam. 2018. Indicator Species of Climate Change in Agroecosystem of Korea. National Institute of Agricultural Sciences. Wanju, Korea. 
  30. Koch R, JS Almeida-Cortez and B Kleinschmit. 2017. Revealing areas of high nature conservation importance in a seasonally dry tropical forest in Brazil: Combination of modelled plant diversity hot spots and threat patterns. J. Nat. Conserv. 35:24-39.  https://doi.org/10.1016/j.jnc.2016.11.004
  31. Kramer-Schadt S, J Niedballa, JD Pilgrim, B Schroder, J Lindenborn, V Reinfelder, M Stillfried, I Heckmann, AK Scharf, DM Augeri, SM Cheyne, AJ Hearn, J Ross, DW Macdonald, J Mathai, J Eaton, AJ Marshall, G Semiadi, R Rustam, H Bernard, R Alfred, H Samejima, JW Duckworth, C Breitenmoser-Wuersten, JL Belant, H Hofer and A Wilting. 2013. The importance of correcting for sampling bias in MaxEnt species distribution models. Divers. Distrib. 19:1366-1379.  https://doi.org/10.1111/ddi.12096
  32. Kriticos DJ, RW Sutherst, JR Brown, SW Adkins and GF Maywald. 2003. Climate change and the potential distribution of an invasive alien plant: Acacia nilotica ssp. indica in Australia. J. Appl. Ecol. 40:111-124.  https://doi.org/10.1046/j.1365-2664.2003.00777.x
  33. Lankau R, V Nuzzo, G Spyreas and AS Davis. 2009. Evolutionary limits ameliorate the negative impact of an invasive plant. Proc. Natl. Acad. Sci. U.S.A. 106:15362-15367.  https://doi.org/10.1073/pnas.0905446106
  34. Lee YH, SH Hong, CS Na, SI Sohn, MH Kim, CS Kim and YJ Oh. 2016. Predicting the suitable habitat of Amaranthus viridis based on climate change scenarios by MaxEnt. Korean J. Environ. Biol. 34:240-245.  https://doi.org/10.11626/KJEB.2016.34.4.240
  35. Levine JM, M Vila, CM D'Antonio, JS Dukes, K Grigulis and S Lavorel. 2003. Mechanisms underlying the impacts of exotic plant invasions. Proc. R. Soc. Lond. B 270:775-781.  https://doi.org/10.1098/rspb.2003.2327
  36. Li J, G Fan and Y He. 2020a. Predicting the current and future distribution of three Coptis herbs in China under climate change conditions, using the MaxEnt model and chemical analysis. Sci. Total Environ. 698:134141. 
  37. Li Y, M Li, C Li and Z Liu. 2020b. Optimized maxent model predictions of climate change impacts on the suitable distribution of Cunninghamia lanceolata in China. Forests 11:302. 
  38. Liu J, HD Luo, WZ Tan and L Hu. 2012. First report of a leaf spot on Conyza sumatrensis caused by Phoma macrostoma in China. Plant Dis. 96:148. 
  39. Miller-Rushing AJ, DW Inouye and RB Primack. 2008. How well do first flowering dates measure plant responses to climate change? The effects of population size and sampling frequency. J. Ecol. 96:1289-1296.  https://doi.org/10.1111/j.1365-2745.2008.01436.x
  40. Muscarella R, PJ Galante, M Soley-Guardia, RA Boria, JM Kass, M Uriarte and RP Anderson. 2014. ENMeval: an R package for conducting spatially independent evaluations and estimating optimal model complexity for maxent ecological niche models. Methods Ecol. Evol. 5:1198-1205.  https://doi.org/10.1111/2041-210X.12261
  41. O'Donnell MS and DA Ignizio. 2012. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geological Survey Data Series 691(10):4-9. 
  42. Ortega-Huerta MA and AT Peterson. 2008. Modeling ecological niches and predicting geographic distributions: a test of six presence-only methods. Rev. Mex. Biodivers. 79:205-2016. 
  43. Park JU, T Lee, DG Kim and S Shin. 2020. Prediction of potential habitats and distribution of the marine invasive sea squirt, Herdmania momus. Korean J. Environ. Biol. 38:179-188.  https://doi.org/10.11626/KJEB.2020.38.1.179
  44. Person RG, CJ Raxworthy, M Nakamura and AT Peterson. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. J. Biogeogr. 34:102-117. 
  45. Phillips SJ, RP Anderson and RE Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3-4):231-259.  https://doi.org/10.1016/j.ecolmodel.2005.03.026
  46. Root TL, JT Price, KR Hall, SH Schneider, C Rosenzweig and JA Pounds. 2003. Fingerprints of global warming on wild animals and plants. Nature 421:57-60.  https://doi.org/10.1038/nature01333
  47. Steinkamp J and T Hickler. 2015. Is drought-induced forest dieback globally increasing? J. Ecol. 103:31-43.  https://doi.org/10.1111/1365-2745.12335
  48. Swets J. 1988. Measuring the accuracy of diagnostic systems. Science 240:1285-1293.  https://doi.org/10.1126/science.3287615
  49. Taylor S, L Kumar, N Reid and DJ Kriticos. 2012. Climate change and the potential distribution of an invasive shrub, Lantana camara L. PLoS One 7:e35565. 
  50. Vila M and J Weiner. 2004. Are invasive plant species better competitors than native plant species? Evidence from pairwise experiments. Oikos 105:229-238.  https://doi.org/10.1111/j.0030-1299.2004.12682.x
  51. Vila M, JL Espinar, M Hejda, PE Hulme, V Jarosik, JL Maron, J Pergl, U Schaffner, Y Sun and P Pysek. 2011. Ecological impacts of invasive alien plants: a meta-analysis of their effects on species, communities and ecosystems. Ecol. Lett. 14:702-708.  https://doi.org/10.1111/j.1461-0248.2011.01628.x
  52. Wilting A, A Cord, AJ Hearn, D Hesse, A Mohamed, C Traeholdt, SM Cheyne, S Sunarto, MA Jayasilan, J Ross, AC Shapiro, A Sebastian, S Dech, C Breitenmoser, J Sanderson, JW Duckworth and H Hofer. 2010. Modelling the species distribution of flat-headed cats (Prionailurus planiceps), an endangered South-East Asian small felid. PLoS One 5:e9612. 
  53. Woodward FI and FK Woodward. 1987. Climate and Plant Distribution. Cambridge University Press. Cambridge, UK.