Browse > Article
http://dx.doi.org/10.5532/KJAFM.2020.22.2.47

Predicting the Potential Distribution of Pinus densiflora and Analyzing the Relationship with Environmental Variable Using MaxEnt Model  

Cho, NangHyun (Department of Environmetal Science, Kangwon National University)
Kim, Eun-Sook (Forest Ecology and Climate Change Division, National Institute of Forest Science)
Lee, Bora (Forest Ecology and Climate Change Division, National Institute of Forest Science)
Lim, Jong-Hwan (Forest Ecology and Climate Change Division, National Institute of Forest Science)
Kang, Sinkyu (Department of Environmetal Science, Kangwon National University)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.22, no.2, 2020 , pp. 47-56 More about this Journal
Abstract
Decline of pine forests happens in Korea due to various disturbances such as insect pests, forest fires and extreme climate, which may further continue with ongoing climate change. For conserving and reestablishing pine forests, understanding climate-induced future shifts of pine tree distribution is a critical concern. This study predicts future geographical distribution of Pinus densiflora, using Maximum Entropy Model (MaxEnt). Input data of the model are locations of pine tree stands and their environmental variables such as climate were prepared for the model inputs. Alternative future projections for P. densiflora distribution were conducted with RCP 4.5 and RCP 8.5 climate change scenarios. As results, the future distribution of P. densiflora steadily decreased under both scenarios. In the case of RCP 8.5, the areal reductions amounted to 11.1% and 18.7% in 2050s and 2070s, respectively. In 2070s, P. densiflora mainly remained in Kangwon and Gyeongsang Provinces. Changes in temperature seasonality and warming winter temperature contributed primarily for the decline of P. densiflora., in which altitude also exerted a critical role in determining its future distribution geographic vulnerability. The results of this study highlighted the temporal and spatial contexts of P. densiflora decline in Korea that provides useful ecological information for developing sound management practices of pine forests.
Keywords
Pinus densiflora; Climate change; Maximum entropy model; Geographic distribution; RCP scenarios;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Allen, C. D., A. K. Macalady, H. Chenchouni, D. Bachelet, N. McDowell, M. Vennetier, T. Kitzberger, A. Rigling, D. D. Breshears, and E. T. Hogg, 2010: A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecology and Management 259(4), 660-684.   DOI
2 Berger, A. L., S. A. D. Pietra, and V. J. D. Pietra, 1996: A maximum entropy approach to natural language processing. Computational Linguistics 22(1), 39-71.
3 Bertrand, R., J. Lenoir, C. Piedallu, G. Riofrio-Dillon, P. Ruffray, C. Vidal, J.-C. Pierrat, and J.-C. Gegout, 2011: Change in plant community composition lag behind climate warming in lowland forests. Nature 479, 517-520.   DOI
4 Choi, J., P. S. Lee, and S. H. Lee, 2015: Anticipation of the future suitable cultivation areas for Korean pines in Korean peninsula with climate change. Journal of Korean Society of Environmental Restoration Technology 18(1), 103-113. (in Korea with English abstract)   DOI
5 Chun, J. H., and C.-B. Lee, 2013: Assessing the effects of climate change on the geographic distribution of Pinus densiflora in Korea using ecological niche model. Agricultural and Forest Meteorology 15(4), 219-233. (in Korea with English abstract)   DOI
6 Franklin, J., 2009: Mapping species distributions: spatial inference and prediction. Cambridge University Press.
7 IPCC, 2014: Synthesis Report. Conrtibution of Working Group I, II and III to the Firth Assessment Report of the Intergovernmental Panel on Climate Change (Core Writign Team, R. K. Rachauri, and L. A. Meyer, eds). IPCC, Geneva, Switzerland, 151pp.
8 Kang, S. K., J.-H. Lim., E. S. Kim, and N. H. Cho, 2016: Modelling analysis of climate and soil depth effects on pine tree dieback in Korea using BIOME-BGC. Korean Journal of Agricultural and Forest Meteorology 18(4), 242-252. (in Korea with English abstract)   DOI
9 Kim, H. G., D.-K. Lee, Y. W. Mo, S. H. Kil, P. Chan, and S. J. Lee, 2013: Prediction of landslides occurrence probability under climate change using MaxEnt model. Journal of Environmental Impact Assessment 22(10), 30-50. (in Korea with English abstract)
10 Kim, D. W., J. C. Park, and D.-H. Jang, 2017a: Analysis of the possibility for drought detection of spring season using SPI and NDVI. Journal of the association of Korean geographers 6(2), 165-174. (in Korea with English abstract)   DOI
11 Kim, J. B., E. S. Kim, and J.-H. Lim, 2017b: Topographic and meteorological characteristics of pinus densiflora dieback areas in Sogwang-Ri, Uljin. Korean Journal of Agricultural and Forest Meteorology 19(1), 10-18. (in Korea with English abstract)   DOI
12 Kim, K. T., and J. S. Park, 2006: Correlation analysis of vegetation index and drought index. Wetlands research 8(1), 49-58. (in Korea with English abstract)
13 Kim, T.-G., Y. G. Cho, and J.-G. Oh, 2015: Prediction model of pine forests' distribution change according to climate change. Korean Society of Limnology 48(4), 229-237. (in Korea with English abstract)
14 KEI(Korea Environment Institute), 2001: Climate change impacts assessment and adaptation measures on ecosystem. II - Forest eco-climate model development. 107pp.
15 KFS(Korea Forest Service), 2016: Survey Report of National pine forest Resources. 9pp.
16 KFS(Korea Forest Service), 2017: National pine forest monitoring. 1pp.
17 Kumar, S., J. Graham, A. M. West, and P. H. Evangelista, 2014: Using district-level occurrences in maxent for predicting the invasion potential of an exotic insect pest in India. Computers and Electronics in Agriculture 103, 55-62.   DOI
18 Lee, H. W., 2012: Prediction of Spatial Distribution and Forest Carbon Storage on Pinus densiflora and Quercus spp. Stands in Korea using 4th Forest Cover Map and HyTAG Model (Master Dissertation, Korea University, South Korea) (in Korea with English abstract)
19 Lim, J.-H., 2016: Climate change-induced dieback of evergreen conifers in Korea and options for adaptation. Proceedings of 2016 International Climate Change Adaptation Symposium on Forest Management for Enhancing Resilience to Climate Change, Seoul, Korea. 53-76.
20 Lee, S.-H., P. S.-H. Lee, S. A. Lee, S.-Y. Ji, and J. Choi., 2015: Predicting the changes in cultivation areas of walnut trees (Juglans sinensis) in Korea due to climate change impacts. Korean Journal of Agricultural and Forest Meteorology 17(4), 399-410. (in Korea with English abstract)   DOI
21 Lee, Y.-H., Y.-J. Oh, S.-H. Hong, C.-S. Na, Y.-E. Na, C.-S. Kim, and S.-I. Sohn, 2015: Predicting the suitable habitat of invasive alien plant Conyza bonariensis based on climate change scenarios. Climate Change Research 6(3), 243-248. (in Korea with English abstract)   DOI
22 Mather, J. R., and G. A. Yoshioka, 1968: The role of climate in the distribution of vegetation. Journal of the Association of American Geographers 58, 29-41.   DOI
23 McDowell, N., W. T. Pockman, C. D. Allen, D. D. Breshears, N. Cobb, T. Kolb, J. Plaut, J. Sperry, A. West, D. G. Williams, and E. A. Yepez, 2008: Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? New Phytologist 178, 719-739.   DOI
24 Phillips, S. J., and M. Dudik, 2008: Modeling of species distributions with MaxEnt: new extensions and a comprehensive evaluation. Ecography 1, 161-175.   DOI
25 NIFOS(National Institute of Forest Science), 2012: Economic Species (1) Pine tree. 250.
26 NIFOS(National Institute of Forest Science), 2014: Predicting Changes of Productive Areas for Major Species under Climate Change in Korea.
27 Phillips, S. J., R. P. Anderson, and R. E. Schapire, 2006: Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231-259.   DOI
28 Rosas, T., L. Galiano, R. Ogaya, J. Peñuelas, and J. M. Vilalta, 2013: Dynamics of non-structural carbohydrates in three Mediterranean woody species following long-term experimental drought. Frontiers in Plant Science 4, 400pp.   DOI
29 Walther, G. R., E. P. Convery, A. Menzel, C. Parmesan, R. J. C. Beebee, J. M. Fromentin, O. Hoegh-Guldberg, and F. Bairlein, 2002: Ecological responses to recent climate change. Nature 416, 389-395.   DOI
30 Zhang, X., M. A. Friedl, C. B. Schaaf, and A. H. Strahler, 2004: Climate controls on vegetation phonological patterns in northern mid- and high latitudes inferred from MODIS data. Journal of Global change biology 10, 1133-1145.   DOI
31 Seo, D. J., C.Y. Oh, K. S. Woo, and J. C. Lee., 2013: A study on ecological niche of Pinus densiflora forests according to the environment factors. Korean Journal of Agricultural and Forest Meteorology 15(3), 153-160. (in Korea with English abstract)   DOI
32 Song, W. K., 2015: Habitat analysis of Hyla suweonensis in the breeding season using species distribution modeling. Journal of Korean Society of Environmental Restoration Technology 18(1), 71-82. (in Korea with English abstract)   DOI
33 Stephenson, N., 1990: Climatic control of vegetation distribution: The role of the water balance. The American Naturalist 135(5), 649-670.   DOI
34 Thuiller, W., 2003: BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9(10), 1353-1362.   DOI