Impact of climate change on the potential geographical distribution of Hippophae rhamnoides subsp. sinensis

doi:10.13287/j.1001-9332.202410.025

Abstract

Abstract: Hippophae rhamnoides subsp. sinensis is an important resource plant with considerable medicinal, economic, and ecological value, and an indicator species in the transition zones between forests and grasslands. Predicting the potential geographic distribution of H. rhamnoides subsp. sinensis under climate change can reveal the responses of China’s grassland and forest to global climate change, which is of significance for the conservation and development of its resources. We utilized distribution data of H. rhamnoides subsp. sinensis to predict its suitable habitats under future climate change based on the Biomod2 ensemble model, and analyzed the trend of land use type change in these habitats in conjunction with remote sensing data of land use types in China in 2020. The results showed that the Biomod2 ensemble model significantly improved the accuracy and precision of predicting H. rhamnoides subsp. sinensis compared to single models. The distribution of H. rhamnoides subsp. sinensis was primarily concentrated on both sides of the diagonal from Liaoning to Tibet, situated in forest-grassland ecotone. Under the SSP126 scenario, the suitable habitats for H. rhamnoides subsp. sinensis would initially expand and then contract. Under the SSP585 scenario, they would show a continuous expansion trend. In the context of global warming, the suitable habitats for H. rhamnoides subsp. sinensis would expand. By 2050 and 2070, the area of suitable habitats for H. rhamnoides subsp. sinensis in grasslands would increase, while areas currently occupied by forests, croplands, and developed land would continue to decrease. Under future climate change, the distribution center of H. rhamnoides subsp. sinensis would migrate towards higher-altitude grassland areas. Among the environmental factors affecting the distribution of H. rhamnoides subsp. sinensis, climate variables were predominant, with the highest contribution of rainfall during the warmest season.

Key words: Biomod2 model, Hippophae rhamnoides subsp. sinensis, climate change, potential suitable area, land use type

WANG Wenqiang, YANG Bo, LI Xiaowei, LIANG Yongliang, LI Jingyao. Impact of climate change on the potential geographical distribution of Hippophae rhamnoides subsp. sinensis[J]. Chinese Journal of Applied Ecology, 2024, 35(10): 2813-2821.

References

[1] Araújo MB, Pearson RG. Equilibrium of species’ distributions with climate. Ecography, 2005, 28: 693-695
[2] Spangenberg JH, Neumann W, Klöser H, et al. False hopes, missed opportunities: How economic models affect the IPCC proposals in special report 15 “global warming of 1.5 ℃” (2018). An analysis from the scientific advisory board of BUND. Journal of Applied Business and Economics, 2021, 23: 49-72
[3] Canadell JG, Monteiro PM, Costa MH, et al. Global carbon and other biogeochemical cycles and feedbacks//Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press, 2023: 673-816
[4] 刘国华, 舒洪岚. 未来的希望: 恢复生态学和保护生物学. 江西林业科技, 2003(1): 43-47
[5] Elias MA, Borges FJ, Bergamini LL, et al. Climate change threatens pollination services in tomato crops in Brazil. Agriculture, Ecosystems & Environment, 2017, 239: 257-264
[6] Anderson JT, Song BH. Plant adaptation to climate change: Where are we? Journal of Systematics and Evolution, 2020, 58: 533-545
[7] Zhao GH, Cui XY, Sun JJ, et al. Analysis of the distribution pattern of Chinese Ziziphus jujuba under climate change based on optimized biomod2 and MaxEnt models. Ecological Indicators, 2021, 132: 108256
[8] Zhang KL, Yao LJ, Meng JS, et al. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Science of the Total Environment, 2018, 634: 1326-1334
[9] Breiman L. Random forests. Machine Learning, 2001, 45: 5-32
[10] Elith J, Leathwick JR, Hastie T. A working guide to boosted regression trees. Journal of Animal Ecology, 2008, 77: 802-813
[11] Lek S, Guégan JF. Artificial neural networks as a tool in ecological modelling: An introduction. Ecological Modelling, 1999, 120: 65-73
[12] Guisan A, Edwards TC, Hastie T. Generalized linear and generalized additive models in studies of species distributions: Setting the scene. Ecological Modelling, 2002, 157: 89-100
[13] Xian XQ, Zhao HX, Wang R, et al. Climate change has increased the global threats posed by three ragweeds (Ambrosia L.) in the Anthropocene. Science of the Total Environment, 2023, 859: 160252
[14] 罗玫, 王昊, 吕植. 使用大熊猫数据评估Biomod2和MaxEnt分布预测模型的表现. 应用生态学报, 2017, 28(12): 4001-4006
[15] Fang YQ, Zhang XH, Wei HY, et al. Predicting the invasive trend of exotic plants in China based on the ensemble model under climate change: A case for three invasive plants of Asteraceae. Science of the Total Environment, 2020, 756: 143841
[16] Huang DY, An QJ, Huang SP, et al. Biomod2 modeling for predicting the potential ecological distribution of three Fritillaria species under climate change. Scientific Reports, 2023, 13: 18801
[17] 陈学林, 马瑞君, 孙坤, 等. 中国沙棘属种质资源及其生境类型的研究. 西北植物学报, 2003, 23(3): 451-455
[18] Mei DZG, Ma XJ, Fu FF, et al. Research status and development prospects of sea buckthorn (Hippophae rhamnoides L.) resources in China. Forests, 2023, 14: 2461
[19] 卢顺光, 卢健, 温秀凤. 沙棘植物资源分布与营养学应用综述. 中国水土保持, 2019(7): 45-49
[20] uchowski J. Phytochemistry and pharmacology of sea buckthorn (Elaeagnus rhamnoides; syn. Hippophae rhamnoides): Progress from 2010 to 2021. Phytochemistry Reviews, 2022, 22: 3-33
[21] 马迎梅, 赵子涵, 张娜, 等. 不同种子预处理、培养温度及基质对两种沙棘种子萌发的影响. 西部林业科学, 2023, 52(6): 22-30
[22] 董诗婷, 陈云, 高群玉. 沙棘果生物活性成分及其功能的研究进展. 中国酿造, 2020, 39(2): 26-32
[23] 王璇, 张志伟, 陈志玺, 等. 沙棘果食品开发利用研究进展与发展对策. 保鲜与加工, 2024, 24(1): 75-82
[24] 董敏, 张晶晶, 田丁阳, 等. 中药沙棘的心血管药理作用研究进展. 中医临床研究, 2024, 16(2): 67-71
[25] Ghendov-mosanu A, Cristea E, Patras A, et al. Potential application of Hippophae rhamnoides in wheat bread production. Molecules, 2020, 25: 1272
[26] 张晨凤, 贺丽, 董廷发, 等. 基于Biome-BGC模型的若尔盖不同沙地类型土壤水分植被承载力对气候变化的响应. 生态学杂志, 2024, 43(6): 1833-1840
[27] 范泽孟. 黑河流域植被垂直分布对气候变化的响应. 生态学报, 2021, 41(10): 4066-4076
[28] Fan ZM, Fan B, Yue TX. Terrestrial ecosystem scenarios and their response to climate change in Eurasia. Science China Earth Sciences, 2019, 62: 1607-1618
[29] Smith TB, Wayne RK, Girman DJ, et al. A role for ecotones in generating rainforest biodiversity. Science, 1997, 276: 1855-1857
[30] Zhan C, Liang C, Zhao L, et al. Vegetation dynamics and its response to climate change in the Yellow River Basin, China. Frontiers in Environmental Science, 2022, 10: 892747
[31] Loehle C. Disequilibrium and relaxation times for species responses to climate change. Ecological Modelling, 2018, 384: 23-29
[32] Singh CP, Mohapatra J, Mathew JR, et al. Long-term observation and modelling on the distribution and patterns of alpine treeline ecotone in Indian Himalaya. Journal of Geomatics, 2021, 15: 68-84
[33] 张晓玮, 蒋玉梅, 毕阳, 等. 基于MaxEnt模型的中国沙棘潜在适宜分布区分析. 生态学报, 2022, 42(4): 1420-1428
[34] 谢婧妍, 贺晓慧, 朱丽, 等. 气候变化背景下云南沙棘在中国的潜在地理分布. 防护林科技, 2023(1): 24-29
[35] Su BD, Huang JL, Mondal SK, et al. Insight from CMIP6 SSP-RCP scenarios for future drought characte-ristics in China. Atmospheric Research, 2020, 250: 105375
[36] Sun JJ, Jiao W, Wang Q, et al. Potential habitat and productivity loss of Populus deltoides industrial forest plantations due to global warming. Forest Ecology and Management, 2021, 496: 119474
[37] Allouche O, Tsoar A, Kadmon R. Assessing the accuracy of species distribution models: Prevalence, Kappa and the true skill statistic (TSS). Journal of Applied Ecology, 2006, 43: 1223-1232
[38] Bebber DP, Field E, Gui H, et al. Many unreported crop pests and pathogens are probably already present. Global Change Biology, 2019, 25: 2703-2713
[39] Gao ML, Zhao GH, Zhang SN, et al. Priority conservation area of Larix gmelinii under climate change: Application of an ensemble modeling. Frontiers in Plant Science, 2023, 14: 1177307
[40] 徐新良, 刘纪远, 张树文, 等. 中国多时期土地利用土地覆被遥感监测数据集(CNLUCC). 北京: 中国科学院地理科学与资源研究所数据注册与出版系统, 2018
[41] Hao TX, Elith J, Lahoz-monfort JJ, et al. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution mo-dels. Ecography, 2020, 43: 549-558
[42] Wang YL, Liu HC, Xu JK, et al. Prediction of suitable planting areas of Rubia cordifolia in China based on a species distribution model and analysis of specific secondary metabolites. Industrial Crops and Products, 2023, 206: 117651
[43] Hebbar KB, Ramesh SV, Bhat R. Predicting current and future climate suitability for arecanut (Areca catechu L.) in India using ensemble model. Heliyon, 2024, 10: e26382
[44] 荣文文, 黄祥, 牛攀新, 等. 基于最大熵模型的中药材木贼麻黄潜在适生区. 生态学报, 2023, 43(20): 8631-8646
[45] 刘桂芳, 卢鹤立. 全球变暖背景下的中国西部地区气候变化研究进展. 气象与环境科学, 2009, 32(4): 69-73
[46] 刘瑞香. 不同土壤水分条件对中国沙棘和俄罗斯沙棘的光合和蒸腾作用的影响. 内蒙古大学学报: 自然科学版, 2006, 26(2): 163-169
[47] Yan YJ, Zhao CJ, Xie YP, et al. Nature reserves and reforestation expend the potential habitats for endangered plants: A model study in Cangshan, China. Journal for Nature Conservation, 2024, 77: 126533
[48] 杜社妮, 白岗栓, 李代琼. 中国沙棘、俄罗斯沙棘和俄罗斯沙棘×中国沙棘光合特性及影响因子. 水土保持通报, 2008, 28(4): 26-32
[49] 靳甜甜, 傅伯杰, 刘国华, 等. 不同坡位沙棘光合日变化及其主要环境因子. 生态学报, 2011, 31(7): 1783-1793
[50] Chen IC, Hill JK, Ohlemüller R, et al. Rapid range shifts of species associated with high levels of climate warming. Science, 2011, 333: 1024-1026
[51] 吴绍洪, 尹云鹤, 郑度, 等. 青藏高原近30年气候变化趋势. 地理学报, 2005, 60(1): 3-11
[52] Brandt JS, Haynes MA, Kuemmerle T, et al. Regime shift on the roof of the world: Alpine meadows converting to shrublands in the southern Himalayas. Biological Conservation, 2013, 158: 116-127
[53] Bertrand R, Lenoir J, Piedallu C, et al. Changes in plant community composition lag behind climate warming in lowland forests. Nature, 2011, 479: 517-520
[54] 牟艳玲. 中国北方森林潜在分布及未来变化趋势. 硕士论文. 兰州: 兰州大学, 2010
[55] Sankey TT, Germino MJ. Assessment of juniper encroachment with the use of satellite imagery and geospatial data. Rangeland Ecology & Management, 2008, 61: 412-418
[56] Coop JD, GivnisHTJ. Spatial and temporal patterns of recent forest encroachment in montane grasslands of the Valles Caldera, NM, USA. Journal of Biogeography, 2007, 34: 914-927
[57] Briggs JM, Knapp AK, Blair JM, et al. An ecosystem in transition: Causes and consequences of the conversion of mesic grassland to shrubland. BioScience, 2005, 55: 243-254