Prediction of potential suitable areas of Rhododendron micranthum under different climate scenarios

FENG Xiao, CHEN Feifei, YANG Junli, LIU Hailong, TANG Yuhong, LI Jinfeng, SHI Xiaodeng, JIA Zhongkui, YIN Qun

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2025, Vol. 49 ›› Issue (4) : 161-169.

PDF(4192 KB)
PDF(4192 KB)
JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2025, Vol. 49 ›› Issue (4) : 161-169. DOI: 10.12302/j.issn.1000-2006.202309011

Prediction of potential suitable areas of Rhododendron micranthum under different climate scenarios

Author information +
History +

Abstract

【Objective】This study is dedicated to explore the key environmental variables affecting the distribution of Rhododendron micranthum and predicting its potential geographical distribution under future climate change scenarios in China. The findings aim to support urban greening strategies for winter vegetation-deficient cities and provide scientific guidance for species conservation and management of R. micranthum.【Method】R. micranthum was taken as the research object. Based on 182 distribution points and 34 environmental factors, the MaxEnt model and ArcGIS software, optimized by the R language kuenm software package, were utilized to predict the potential suitable areas. The main environmental variables influencing its geographical distribution were analyzed. Additionally, the spatial distribution patterns and centroid change trends of potential suitable areas in the 2030s (2021-2040), 2050s (2041-2060), and 2070s (2061-2080) under three different greenhouse gas emission scenarios were predicted.【Result】(1) When the optimal model parameters are set as RM = 2.9 and FC = LQPTH, the model achieves the highest prediction accuracy (AUC value is 0.973 2 ± 0.007 1).(2) The primary environmental variables and their corresponding thresholds that impact the distribution of suitable areas for R. micranthum are the lowest temperature of the coldest month, ranging from -13.48 ℃ to 1.02 ℃, and the rainfall during the hottest season, varying between 270.12 mm and 404.76 mm. Within the threshold ranges of these environmental variables, R. micranthum demonstrates a certain level of habitability.(3) Under the current climate conditions, the areas with high, medium, and low suitability for R. micranthum are 43.83×104, 43.79×104, and 62.01×104 km2, respectively. These areas are predominantly located in regions such as Beijing, Gansu, Shannxi, Hebei, Shandong, Henan, Liaoning, and Sichuan. Among them, Beijing has the highest average suitability index, which is 0.801 5.(4) Under future scenarios, higher emission intensities reduced total suitable areas, indicating global warming may hinder R. micranthum growth. Meanwhile, under the SSP126 (low-forcing scenario), the centroid of the suitable areas migrates towards lower latitudes, while under the SSP585 (high-forcing scenario), it migrates towards higher latitudes.【Conclusion】The analysis using the MaxEnt model yields reliable results for predicting the potential distribution of R. micranthum at different stages. In future climate scenarios, the fragmentation degree of suitable areas for R. micranthum in China is expected to increase. Climate conditions associated with low greenhouse gas concentration emissions are favorable for the growth of R. micranthum, whereas those under high greenhouse gas concentration emissions lead to a reduction in its suitable areas.

Key words

Rhododendron micranthum / potential suitable distribution / MaxEnt model / climate change

Cite this article

Download Citations
FENG Xiao , CHEN Feifei , YANG Junli , et al . Prediction of potential suitable areas of Rhododendron micranthum under different climate scenarios[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY. 2025, 49(4): 161-169 https://doi.org/10.12302/j.issn.1000-2006.202309011

References

[1]
樊星, 秦圆圆, 高翔. IPCC第六次评估报告第一工作组报告主要结论解读及建议[J]. 环境保护, 2021, 49(增刊2):44-48.
FAN X, QIN Y Y, GAO X. Interpretation of the main conclusions and suggestions of IPCC AR6 working group Ⅰ report[J]. Environmental Protection, 2021, 49(S2):44-48.DOI:10.14026/j.cnki.0253-9705.2021.z2.008.
[2]
张华, 赵浩翔, 王浩. 基于MaxEnt模型的未来气候变化情景下胡杨在中国的潜在地理分布[J]. 生态学报, 2020, 40(18):6552-6563.
ZHANG H, ZHAO H X, WANG H. Potential geographical distribution of Populus euphratica in China under future climate change scenarios based on MaxEnt model[J]. Acta Ecologica Sinica, 2020, 40(18):6552-6563.DOI:10.5846/stxb201906111232.
[3]
PUCHALKA R, PAZ-DYDERSKA S, WOZIWODA B, et al. Climate change will cause climatic niche contraction of Vaccinium myrtillus L.and V. vitis-idaea L.in Europe[J]. Science of the Total Environment, 2023,892:164483.DOI:10.1016/j.scitotenv.2023.164483.
[4]
OSTAD-ALI-ASKARI K, GHORBANIZADEH KHARAZI H, SHAYANNEJAD M, et al. Effect of climate change on precipitation patterns in an arid region using GCM models:case study of Isfahan-Borkhar plain[J]. Natural Hazards Review, 2020, 21(2):04020006.DOI:10.1061/(asce)nh.1527-6996.0000367.
[5]
TEWARI V P, VERMA R K, VON GADOW K. Climate change effects in the western Himalayan ecosystems of India:evidence and strategies[J]. Forest Ecosystems, 2017,4:13.DOI:10.1186/s40663-017-0100-4.
[6]
ZHOU H M, MIN X T, CHEN J H, et al. Climate warming interacts with other global change drivers to influence plant phenology:a meta-analysis of experimental studies[J]. Ecology Letters, 2023, 26(8):1370-1381.DOI:10.1111/ele.14259.
[7]
PHILLIPS S J, ANDERSON R P, SCHAPIRE R E. Maximum entropy modeling of species geographic distributions[J]. Ecological Modelling, 2006, 190(3/4):231-259.DOI:10.1016/j.ecolmodel.2005.03.026.
[8]
HAASE C G, YANG A N, MCNYSET K M, et al. GARPTools:R software for data preparation and model evaluation of GARP models[J]. Ecography, 2021, 44(12):1790-1796.DOI:10.1111/ecog.05642.
[9]
BOOTH T H, NIX H A, BUSBY J R, et al. Bioclim:the first species distribution modelling package,its early applications and relevance to most current MaxEnt studies[J]. Diversity and Distributions, 2014, 20(1):1-9.DOI:10.1111/ddi.12144.
[10]
YOON S, LEE W H. Application of true skill statistics as a practical method for quantitatively assessing CLIMEX performance[J]. Ecological Indicators, 2023,146:109830.DOI:10.1016/j.ecolind.2022.109830.
[11]
杨宏, 董京京, 吴桐, 等. 基于MaxEnt模型的迎春樱桃潜在适生区预测[J]. 南京林业大学学报(自然科学版), 2023, 47(4):131-138.
YANG H, DONG J J, WU T, et al. Prediction of potential suitable areas of Cerasus discoidea in China based on the MaxEnt model[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2023, 47(4):131-138.
[12]
SHI X D, YIN Q, SANG Z Y, et al. Prediction of potentially suitable areas for the introduction of Magnolia wufengensis under climate change[J]. Ecological Indicators, 2021,127:107762.DOI:10.1016/j.ecolind.2021.107762.
[13]
YUE Y J, ZHANG P Y, SHANG Y R. The potential global distribution and dynamics of wheat under multiple climate change scenarios[J]. Science of the Total Environment, 2019, 688:1308-1318.DOI:10.1016/j.scitotenv.2019.06.153.
[14]
罗楚滢, 佘济云, 唐子朝. 基于SSPs气候场景的濒危植物银杉潜在分布区预测[J]. 南京林业大学学报(自然科学版), 2024, 48(1):161-168.
LUO C Y, SHE J Y, TANG Z C. Prediction of potential distribution areas of the endangered Cathaya argyrophylla based on shared socio-economic pathways (SSPs) climate scenarios[J]. Journal of Nanjing Forestry University (Natural Sciences Edition), 2024, 48(1):161-168.DOI:10.12302/j.issn.1000-2006.202207027.
[15]
谢登峰, 童芬, 杨丽娟, 等. MaxEnt模型下的外来入侵种香丝草在中国的潜在分布区预测[J]. 四川大学学报(自然科学版), 2017, 54(2):423-428.
XIE D F, TONG F, YANG L J, et al. Potential distributions of an invasive species Conyza bonariensis(Compositae) in China as predicted by MaxEnt[J]. Journal of Sichuan University (Natural Science Edition), 2017, 54(2):423-428.
[16]
RAMOS R S, KUMAR L, SHABANI F, et al. Risk of spread of tomato yellow leaf curl virus (TYLCV) in tomato crops under various climate change scenarios[J]. Agricultural Systems, 2019, 173:524-535.DOI:10.1016/j.agsy.2019.03.020.
[17]
MACKAY M, GARDINER S E. A model for determining ex situ conservation priorities in big Genera is provided by analysis of the subgenera of Rhododendron (Ericaceae)[J]. Biodiversity and Conservation, 2017, 26(1):189-208.DOI:10.1007/s10531-016-1237-0.
[18]
孙娜. 照山白和耳叶杜鹃叶中的化学成分及其生物活性研究[D]. 武汉: 华中科技大学, 2019.
SUN N. Study on chemical constituents and biological activities of leaves of Dioscorea nipponica and Rhododendron auriculatum[D]. Wuhan: Huazhong University of Science and Technology, 2019.
[19]
张艳红, 赵凤军, 王文平, 等. 辽宁耐寒杜鹃花资源调查[J]. 辽东学院学报(自然科学版), 2010, 17(2):98-102,132.
ZHANG Y H, ZHAO F J, WANG W P, et al. Resource investigation into hardy Rhododendron in Liaoning[J]. Journal of Eastern Liaoning University (Natural Science), 2010, 17(2):98-102,132.DOI:10.14168/j.issn.1673-4939.2010.02.012.
[20]
LU Y P, LIU H C, CHEN W, et al. Conservation planning of the genus Rhododendron in northeast China based on current and future suitable habitat distributions[J]. Biodiversity and Conservation, 2021, 30(3):673-697.DOI:10.1007/s10531-020-02110-6.
[21]
黄跃新, 王利宏. 照山白在园林绿化中应用的可行性研究[J]. 河北林果研究, 2012, 27(1):83-85.
HUANG Y X, WANG L H. The feasibility study of application of Rhododendron micranthum in landscape[J]. Hebei Journal of Forestry and Orchard Research, 2012, 27(1):83-85. DOI:10.3969/j.issn.1007-4961.2012.01.024.
[22]
柴冰. 照山白根化学成分及活性研究[D]. 北京: 北京协和医学院, 2020.
CHAI B. Studies on chemical constituents and activities of roots of Dioscorea zingiberensis[D]. Beijing: Peking Union Medical College, 2020.DOI: 10.27648/d.cnki.gzxhu.2020.000845.
[23]
蒋淑磊, 白霄霞, 李国松, 等. 照山白杜鹃愈伤组织诱导及植株再生技术[J]. 分子植物育种, 2022, 20(5):1629-1634.
JIANG S L, BAI X X, LI G S, et al. Callus induction and plant regeneration technology of Rhododendron micranthum[J]. Molecular Plant Breeding, 2022, 20(5):1629-1634.DOI:10.13271/j.mpb.020.001629
[24]
YI S J, QIAN Y Q, HAN L, et al. Selection of reliable reference genes for gene expression studies in Rhododendron micranthum Turcz[J]. Scientia Horticulturae, 2012, 138:128-133.DOI:10.1016/j.scienta.2012.02.013.
[25]
LI D X, LI Z X, LIU Z W, et al. Climate change simulations revealed potentially drastic shifts in insect community structure and crop yields in China’s farmland[J]. Journal of Pest Science, 2023, 96(1):55-69.DOI:10.1007/s10340-022-01479-3.
[26]
GUISAN A, TINGLEY R, BAUMGARTNER J B, et al. Predicting species distributions for conservation decisions[J]. Ecology Letters, 2013, 16(12):1424-1435.DOI:10.1111/ele.12189.
[27]
SU B D, HUANG J L, MONDAL S K, et al. Insight from CMIP6 SSP-RCP scenarios for future drought characteristics in China[J]. Atmospheric Research, 2021,250:105375.DOI:10.1016/j.atmosres.2020.105375.
[28]
辛晓歌, 吴统文, 张洁, 等. BCC模式及其开展的CMIP6试验介绍[J]. 气候变化研究进展, 2019, 15(5):533-539.
XIN X G, WU T W, ZHANG J, et al. Introduction of BCC models and its participation in CMIP6[J]. Climate Change Research, 2019, 15(5):533-539.DOI:10.12006/j.issn.1673-1719.2019.039.
[29]
WU T W, LU Y X, FANG Y J, et al. The Beijing climate center climate system model (BCC-CSM):the main progress from CMIP5 to CMIP6[J]. Geoscientific Model Development, 2019, 12(4):1573-1600.DOI:10.5194/gmd-12-1573-2019.
[30]
LI S Y, MIAO L J, JIANG Z H, et al. Projected drought conditions in northwest China with CMIP6 models under combined SSPs and RCPs for 2015-2099[J]. Advances in Climate Change Research, 2020, 11(3):210-217.DOI:10.1016/j.accre.2020.09.003.
[31]
施晓灯. 红花玉兰适生区域研究[D]. 北京: 北京林业大学, 2021.DOI: 10.26949/d.cnki.gblyu.2021.000344.
ZHOU X D. Study on the suitable region of Magnolia grandiflora[D]. Beijing: Beijing Forestry University, 2021.
[32]
COBOS M E, TOWNSEND PETERSON A, BARVE N, et al. Kuenm:an R package for detailed development of ecological niche models using Maxent[J]. PeerJ, 2019,:7e6281.DOI:10.7717/peerj.6281.
[33]
ZHUO Z H, XU D P, PU B, et al. Predicting distribution of Zanthoxylum bungeanum Maxim.in China[J]. BMC Ecology, 2020, 20(1):46.DOI:10.1186/s12898-020-00314-6.
[34]
OUYANG X H, PAN J L, WU Z T, et al. Predicting the potential distribution of Campsis grandiflora in China under climate change[J]. Environmental Science and Pollution Research International, 2022, 29(42):63629-63639.DOI:10.1007/s11356-022-20256-4.
[35]
ZHANG K L, YAO L J, MENG J S, et al. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change[J]. Science of the Total Environment, 2018, 634:1326-1334.DOI:10.1016/j.scitotenv.2018.04.112.
[36]
YU F Y, WANG T J, GROEN T A, et al. Climate and land use changes will degrade the distribution of rhododendrons in China[J]. Science of the Total Environment, 2019, 659:515-528.DOI:10.1016/j.scitotenv.2018.12.223.
[37]
颜佳滢, 吴志峰, 申健, 等. 未来气候变化对粤港澳地区杜鹃花适生区的影响[J]. 生态学报, 2022, 42(13):5481-5492.
YAN J Y, WU Z F, SHEN J, et al. Effect of future climate change on suitable areas of Rhododendrons in Guangdong-Hong Kong-Macao region[J]. Acta Ecologica Sinica, 2022, 42(13):5481-5492.DOI:10.5846/stxb202011062842.
[38]
纪忠萍, 温晶, 方一川, 等. 近50年广东冬半年降水的变化及连旱成因[J]. 热带气象学报, 2009, 25(1):29-36.
JI Z P, WEN J, FANG Y C, et al. Variation of precipitation during winter half-year and cause of continuous drought in Guangdong during the past 50 years[J]. Journal of Tropical Meteorology, 2009, 25(1):29-36.DOI:10.3969/j.issn.1004-4965.2009.01.004.
[39]
云英英, 范秋云, 史佑海. 基于最大熵模型的海南杜鹃在中国的潜在地理分布[J]. 热带作物学报, 2024, 45(5):1031-1039.
YUN Y Y, FAN Q Y, SHI Y H. Potential geographical distribution of Rhododendron hainanense based on MaxEnt model[J]. Chinese Journal of Tropical Crops, 2024, 45(5):1031-1039.DOI:10.3969/j.issn.1000-2561.2024.05.018.
[40]
高晓宁, 赵冰, 刘旭梅, 等. 4个杜鹃花品种对干旱胁迫的生理响应及抗旱性评价[J]. 浙江农林大学学报, 2017, 34(4):597-607.
GAO X N, ZHAO B, LIU X M, et al. Physiological response to drought stress and drought resistance evaluation of four Rhododendron cultivars[J]. Journal of Zhejiang A & F University, 2017, 34(4):597-607.DOI:10.11833/j.issn.2095-0756.2017.04.005.
PDF(4192 KB)

Accesses

Citation

Detail

Sections
Recommended
The full text is translated into English by AI, aiming to facilitate reading and comprehension. The core content is subject to the explanation in Chinese.

/