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Projecting suitable habitats considering locational characteristics of major wild vegetables and climate change impacts

  • Choi, Jaeyong (Department of Environment & Forest Resources, Chungnam National University) ;
  • Lee, Sanghyuk (Environment Planning Lab., Korea Environment Institute)
  • Received : 2019.04.08
  • Accepted : 2019.08.13
  • Published : 2019.09.01

Abstract

In this study, we constructed a model of an area where the production and production amount of wild vegetables which are designated as short term income forest products for the whole country are self-sufficient for the representative Eastern Braken fern(Pteridium aquilinum)and Edible aster(Aster scaber). The difference between the existing cultivation site and the model result was examined, and the distribution of the cultivable area was simulated according to the near future climate change by the 2050s. The degree of agreement between the cultivated area and the actual native area was very low at 14.5% for Eastern Braken fern and 12.9% for Edible aster. Using the Maxent model, which has already been proven by many research examples, the cultivation maps through the model can guarantee statistical accuracy by considering many variables. To analyze future location changes, the RCP 4.5 scenario and the RCP 8.5 scenario were applie Edible aster d to predict potential future cultivable areas and compare them to the present. There was no decrease in the cultivable area due to climate change nationwide. However, in the RCP 8.5 scenario for Eastern Braken fern and the RCP 4.5 scenario for Edible aster, declining areas such as Gangwon-do, Jeollabuk-do and Gyeongsangbuk-do showed prominence according to the scenarios. The result of this study suggests that various models can be used for the production of short-term forest productivity maps and it will be used as a climate change impact assessment data for competitive forest products considering the influence of future climate change.

Keywords

References

  1. Barbet-Massin M, Jiguet F, Albert CH, Thuiller W. 2012. Selecting pseudo-absences for species distribution models: How, where and how many? Methods in Ecology and Evolution 3:327-338. https://doi.org/10.1111/j.2041-210X.2011.00172.x
  2. Busby J. 1991. BIOCLIM-a bioclimate analysis and prediction system. Plant Protection Quarterly, Australia.
  3. Choi JY, Lee SH. 2018. Climate change impact assessment of Abies nephrolepis (Trautv.) maxim. in subalpine ecosystem using ensemble habitat suitability modeling. Journal of the Korea Society of Environmental Restoration Technology 21:103-118. [in Korean] https://doi.org/10.13087/kosert.2018.21.1.103
  4. Choi SI. 2018. Income analysis on the cultivation of major wild edible greens. Journal of Korean Forest Society 107:316-323. [in Korean]
  5. Dormann CF, Elith J, Bacher S, Buchmann C, Carl G, Carre G, Lautenbach S. 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
  6. Franklin J, Wejnert KE, Hathaway SA, Rochester CJ, Fisher RN. 2009. Effect of species rarity on the accuracy of species distribution models for reptiles and amphibians in southern California. Diversity and Distributions 15:167-177. https://doi.org/10.1111/j.1472-4642.2008.00536.x
  7. Hijmans RJ, Schreuder M, De La Cruz J, Guarino L. 1999. Using GIS to check co-ordinates of genebank accessions. Genetic Resources and Crop Evolution 46:291-296. https://doi.org/10.1023/A:1008628005016
  8. KFS (Korea Forest Service). 2018. Production of forest products 2017. Korea Forest Service, Daejeon, Korea. [in Korean]
  9. KREI (Korea Rural Economic Institute). 2013. A study on the current status and performance of agroforestry system in Korea. Korea Rural Economic Institute Report, Naju, Korea. [in Korean]
  10. NIMR (National Institute of Meteorological Sciences). 2011. Climate change scenario report for IPCC AR5. NIMR, Jeju, Korea. [in Korean].
  11. Pearson RG, Raxworthy CJ, Nakamura M, Townsend PA. 2007. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. Journal of Biogeography 34:102-117. https://doi.org/10.1111/j.1365-2699.2006.01594.x
  12. Phillips SJ, Dudik M. 2008. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 31:161-175. https://doi.org/10.1111/j.0906-7590.2008.5203.x
  13. Song WK, Kim EY. 2012. A comparison of machine learning species distribution methods for habitat analysis of the Korea water deer (Hydropotes inermis argyropus). Korean Journal of Remote Sensing 28:171-180. [in Korean] https://doi.org/10.7780/kjrs.2012.28.1.171
  14. Syfert MM, Smith MJ, Coomes DA. 2013. The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PloS One 8:e55158. https://doi.org/10.1371/journal.pone.0055158
  15. Thuiller W. 2003. BIOMOD - Optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9:1353-1362. https://doi.org/10.1046/j.1365-2486.2003.00666.x
  16. Warren DL, Glor RE, Turelli M. 2010. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 33:607-611 https://doi.org/10.1111/j.1600-0587.2009.06142.x
  17. Watling JI, Romanach SS, Bucklin DN, Speroterra C, Brandt LA, Pearlstine LG, Mazzotti FJ. 2012. Do bioclimate variables improve performance of climate envelope models? Ecological Modelling 246:79-85 https://doi.org/10.1016/j.ecolmodel.2012.07.018
  18. Woodward FI. 1987. Climate and plant distribution. Cambridge University Press, Cambridge, UK.