气候变化背景下的天山云杉潜在分布区预测

doi:10.13866/j.azr.2024.07.08

Abstract

Abstract:

Picea schrenkiana, one of the most important tree species in the Tianshan Mountains, plays an important role in soil and water conservation in this region. In this study, the potential distribution and dominant climatic factors of current and three climate change scenarios (i.e., low, the medium, and the high greenhouse gas emission scenarios; SSP1-2.6, SSP3-7.0 and SSP5-8.5) in two future time periods (2020-2040 and 2040-2060) were modeled using the maximum entropy model (MaxEnt). The results were: (1) AUC values of the MaxEnt model were all greater than 0.99, indicating that the model had high reliability for predicting the distribution region of P. schrenkiana. The results from the jackknife test and climate factor response curves revealed that isotherm, seasonal temperature variation, annual mean temperature, precipitation in the coldest quarter, precipitation in the wettest month, and precipitation in the driest quarter were the main factors affecting the potential distribution of P. schrenkiana. Overall, temperature is key factor affecting the potential distribution at present, and precipitation, especially precipitation in the coldest quarter, will be the key factor in the future. (2) At present, the potential distribution of P. schrenkiana is mainly in the mountainous regions of the Xinjiang, Qinghai, Inner Mongolia, Xizang, Gansu, Ningxia, Shanxi, and Sichuan provinces. The total potential area of P. schrenkiana is 299.17×10⁴ km² at present, and the area of highest suitability is 49.45×10⁴ km². The potential distribution of P. schrenkiana in future scenarios is still dominated by its currently simulated distribution regions, meaning that the simulated potential area of P. schrenkiana does not significantly change under the different future scenarios. However, the most suitable regions tended to be larger than currently. The potential distribution of P. schrenkiana tended to expand to the west in all scenarios, apart from a migration to the southeast that was predicted under the SSP5-8.5 scenario from 2020 to 2040.

Key words: maximum entropy (MaxEnt) model, climate change, potential distribution area, Picea schrenkiana

ZHOU Jie, WANG Xuhu, DU Weibo, ZHOU Xiaolei, YANG Jie, ZAHNG Xiaowei. Prediction of potential distribution area of Picea schrenkiana under the background of climate change[J].Arid Zone Research, 2024, 41(7): 1167-1176.

Figures/Tables 9

Fig. 1

Tab. 1

Tab. 2

Tab. 3

Fig. 2

Fig. 3

Fig. 4

Tab. 4

Fig. 5

References 33

[1]	张爱平, 王毅, 熊勤犁, 等. 末次间冰期以来3种云杉属植物的历史分布变迁及避难所[J]. 应用生态学报, 2018, 29(7): 2411-2421.
	[Zhang Aiping, Wang Yi, Xiong Qinli, et al. Distribution changes and refugia of three spruce taxa since the last interglacial[J]. Chinese Journal of Applied Ecology, 2018, 29(7): 2411-2421.]
[2]	方精云. 我国森林植被带的生态气候学分析[J]. 生态学报, 1991, 11(4): 377-387.
	[Fang Jingyun. Ecolomatological analysis of the forest zones in China[J]. Acta Ecologica Sinica, 1991, 11(4): 377-387.]
[3]	曹雪萍, 王婧如, 鲁松松, 等. 气候变化情景下基于最大熵模型的青海云杉潜在分布格局模拟[J]. 生态学报, 2019, 39(14): 5232-5240.
	[Cao Xueping, Wang Jingru, Lu Songsong, et al. Simulation of the potential distribution patterns of Picea crassifolia in climate change scenarios based on the maximum entropy (Maxent) model[J]. Acta Ecologica Sinica, 2019, 39(14): 5232-5240.]
[4]	钱灵颖, 黄智洵, 杨盛昌, 等. 厦门市重点保护植物空间优先保护格局研究[J]. 生态学报, 2021, 41(11): 4367-4378.
	[Qian Lingying, Huang Zhixun, Yang Shengchang, et al. Study on spatial conservation priorty pattern of key protected plants in Xiamen[J]. Acta Ecologica Sinica, 2021, 41(11): 4367-4378.]
[5]	管凤荣. 结合物种分布模型的蜀柏毒蛾在四川的发生分布及影响因子分析[D]. 成都: 四川农业大学, 2018.
	[Guan Fengrong. The Distribution and Analysis of Influence Factors of Parocneria orienta in Sichuan with Species Ditribution Model[D]. Chengdu: Sichuan Agricultural University, 2018.]
[6]	徐文力, 李庆康, 杨潇, 等. 气候变化情景下西藏入侵植物印加孔雀草的潜在分布预测[J]. 生态学报, 2022, 42(17): 7266-7277.
	[Xu Wenli, Li Qingkang, Yang Xiao, et al. Prediction of potential distribution of the invasive plant Tagetes minuta L. (Wild Marigold) in Tibet under climate change[J]. Acta Ecologica Sinica, 2022, 42(17): 7266-7277.]
[7]	Zhang K, Yao L, Meng J, 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.
[8]	邢丁亮, 郝占庆. 最大熵原理及其在生态学研究中的应用[J]. 生物多样性, 2011, 19(3): 295-302. doi: 10.3724/SP.J.1003.2011.08318
	[Xing Dingliang, Hao Zhanqing. The principle of maximum entropy and its applications in ecology[J]. Biodiversity Science, 2011, 19(3): 295-302.] doi: 10.3724/SP.J.1003.2011.08318
[9]	吴艳, 王洪峰, 穆立蔷. 物种分布模型的研究进展与展望[J]. 高师理科学刊, 2022, 42(5): 66-70.
	[Wu Yan, Wang Hongfeng, Mu Liqiang. Research progress and prospect of species distribution models[J]. Journal of Science of Teachers’ College and University, 2022, 42(5): 66-70.]
[10]	刘婷, 曹家豪, 齐瑞, 等. 基于GIS和MaxEnt模型分析气候变化背景下紫果云杉的潜在分布区[J]. 西北植物学报, 2022, 42(3): 481-491.
	[Liu Ting, Cao Jiahao, Qi Rui, et al. Research of potential geographical distribution of Picea purpurea based on GIS and MaxEnt under different climate conditions[J]. Acta Botanica Boreali-Occidentalia Sinica, 2022, 42(3): 481-491.]
[11]	张世林, 高润红, 高明龙, 等. 气候变化背景下中国樟子松潜在分布预测[J]. 浙江农林大学学报, 2023, 40(3): 560-568.
	[Zhang Shilin, Gao Runhong, Gao Minglong, et al. Prediction of the potential distribution pattern of Pinus sylvestris var. mongolica in China under climate change[J]. Journal of Zhejiang Agricultural and Forestry University, 2023, 40(3): 560-568.]
[12]	张艳芳. 基于MaxEnt模型预测花烟草全球潜在适生区的研究[D]. 泰安: 山东农业大学, 2023.
	[Zhang Yanfang. Prediction of Global Potential Suitable Habitats of Nicotiana alata Link et Otto Based on MaxEnt Model[D]. Tai’an: Shandong Agricultural University, 2023.]
[13]	李晓辰, 贡璐, 魏博, 等. 气候变化对新疆雪岭云杉潜在适宜分布及生态位分化的影响[J]. 生态学报, 2022, 42(10): 4091-4100.
	[Li Xiaochen, Gong Lu, Wei Bo, et al. Effects of climate change on potential distribution and niche differentiation of Picea schrenkiana in Xinjiang[J]. Acta Ecologica Sinica, 2022, 42(10): 4091-4100.]
[14]	刘梦婷, 王振锡, 王雅佩, 等. 新疆天山云杉林群落分布格局及环境解释[J]. 林业科学研究, 2019, 32(6): 90-98.
	[Liu Mengting, Wang Zhenxi, Wang Yapei, et al. Plant communities pattern of Picea tianschanica forest and their interrelations with invironmental factors in Tianshan area[J]. Forestry Research, 2019, 32(6): 90-98.]
[15]	范晓聪. 天山云杉ABI5的克隆和原核表达[D]. 杭州: 浙江农林大学, 2021.
	[Fan Xiaocong. Cloning and Prokaryotic Expression of ABI5 From Picea schrenkiana[D]. Hangzhou: Zhejiang Agricultural and Forestry University, 2021.]
[16]	周小东, 常顺利, 王冠正, 等. 天山北坡中段雪岭云杉径向生长对气候变化的响应[J]. 干旱区研究, 2023, 40(8): 1215-1228.
	[Zhou Xiaodong, Chang Shunli, Wang Guanzheng, et al. Radial growth response of Picea schrenkiana to climate change in the middlesection of the northern slope of the Tianshan Mountains[J]. Arid Zone Research, 2023, 40(8): 1215-1228.]
[17]	李宗英, 罗庆辉, 许仲林. 西天山雪岭云杉林分密度对森林生物量分配格局和异速生长的影响[J]. 干旱区研究, 2021, 38(2): 545-552.
	[Li Zongying, Luo Qinghui, Xu Zhonglin. Effects of stand density on the biomass allocation and tree height-diameter allometric growth of Picea schrenkiana forest on the northern slope of the western Tianshan Mountains[J]. Arid Zone Research, 2021, 38(2): 545-552.]
[18]	王燕, 赵士洞. 天山云杉林生物生产力的地理分布[J]. 植物生态学报, 2000(2): 186-190.
	[Wang Yan, Zhao Shidong. Productivity pattern of Picea schrenkiana var. Tianschanica forest[J]. Chinese Journal of Plant Ecology, 2000(2): 186-190.]
[19]	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[J]. 生态学报, 2022, 42(4): 1420-1428.
	[Zhang Xiaowei, Jiang Yumei, Bi Yang, et al. Identification of potential distribution area for Hippophae rhamnoides subsp. sinensis by the MaxEnt model[J]. Acta Ecologica Sinica, 2022, 42(4): 1420-1428.]
[20]	胡永云. 复杂气候系统和全球变暖[J]. 物理, 2022, 51(1): 10-15.
	[Hu Yongyun. The complex climate system and global warming[J]. Physics, 2022, 51(1): 10-15.]
[21]	李建宇, 陈燕婷, 郭燕青, 等. 基于MaxEnt预测未来气候条件下钻叶紫菀在中国的潜在适生区[J]. 植物保护, 2023, 49(2): 92-102.
	[Li Jianyu, Chen Yanting, Guo Yanqing, et al. Potential suitabie areas of Symphyotrichum subulatum based on MaxEnt under future climate scenarios[J]. Plant Protection, 2023, 49(2): 92-102.]
[22]	陈新美, 雷渊才, 张雄清, 等. 样本量对MaxEnt模型预测物种分布精度和稳定性的影响[J]. 林业科学, 2012, 48(1): 53-59.
	[Chen Xinmei, Lei Yuancai, Zhang Xiongqing, et al. Impact of sample size and spatial distribution on species distribution model[J]. Scientia Silvae Sinicae, 2012, 48(1): 53-59.]
[23]	柳晓燕, 李俊生, 赵彩云, 等. 基于MAXENT模型和ArcGIS预测豚草在中国的潜在适生区[J]. 植物保护学报, 2016, 43(6): 1041-1048.
	[Liu Xiaoyan, Li Junsheng, Zhao Caiyun, et al. Prediction of potential suitable area of Ambrosia artemisiifolia L. in China based on MAXENT and ArcGIS[J]. Journal of Plant Protection, 2016, 43(6): 1041-1048.]
[24]	王露露, 伊力哈木·亚尔买买提. 未来气候情景下2种新疆特有树种潜在适生区预测[J]. 北京林业大学学报, 2022, 44(6): 10-22.
	[Wang Lulu, Yilihamu Yaermaimaiti. Prediction of the potential distribution of two endemic tree species in Xinjiang of western China under future climate scenarios[J]. Journal of Beijing Forestry University, 2022, 44(6): 10-22.]
[25]	王晓帆, 段雨萱, 金露露, 等. 基于优化的最大熵模型预测中国高山栎组植物的历史、现状与未来分布变化[J]. 生态学报, 2023, 43(16): 6590-6604.
	[Wang Xiaofan, Duan Yuxuan, Jin Lulu, et al. Prediction of historical, present and future distribution of Quercus sect. Heterobalanus based on the optimized MaxEnt model in China[J]. Acta Ecologica Sinica, 2023, 43(16): 6590-6604.]
[26]	赵晓冏, 巩娟霄, 赵莎莎, 等. 样本量及其空间分布对物种分布模型的影响[J]. 兰州大学学报(自然科学版), 2018, 54(2): 208-215.
	[Zhao Xiaojiong, Gong Juanxiao, Zhao Shasha, et al. Impact of sample size and spatial distribution on species distribution model[J]. Journal of Lanzhou University (Natural Sciences), 2018, 54(2): 208-215.]
[27]	李岳峰. 新疆天山雪岭云杉物候变化及其气候影响因子分析[D]. 乌鲁木齐: 新疆大学, 2021.
	[Li Yuefeng. Phenology Changes of Schrenk Spruce Forest and Their Climatic Impact Factor in Tianshan[D]. Urumqi: Xinjiang University, 2021.]
[28]	郯俊岭, 江志红, 马婷婷. 基于贝叶斯模型的中国未来气温变化预估及不确定性分析[J]. 气象学报, 2016, 74(4): 583-597.
	[Tan Junling, Jiang Zhihong, Ma Tingting. Projections of future climate change and uncertainty over China based on bayesian model averaging[J]. Acta Meteorologica Sinica, 2016, 74(4): 583-597.]
[29]	李宁宁, 张爱平, 张林, 等. 气候变化下青藏高原两种云杉植物的潜在适生区预测[J]. 植物研究, 2019, 39(3): 395-406. doi: 10.7525/j.issn.1673-5102.2019.03.010
	[Li Ningning, Zhang Aiping, Zhang Lin, et al. Predicting potential distribution of two species of spruce in Qinghai-Tibet Plateau under climate change[J]. Bulletin of Botanical Research, 2019, 39(3): 395-406.] doi: 10.7525/j.issn.1673-5102.2019.03.010
[30]	尹锴. 天山云杉林土壤种子库研究[D]. 乌鲁木齐: 新疆农业大学, 2006.
	[Yin Kai. Study on Soil Seed Bank Picea schrenkiana Forest[D]. Urumqi: Xinjiang Agricultural University, 2006.]
[31]	Wu G, Liu X, Chen T, et al. The positive contribution of WUE to the resilience of Schrenk spruce (Picea schrenkiana) to extreme drought in the western Tianshan Mountains, China[J]. Acta Physiologiae Plantarum, 2020, 42: 1-16.
[32]	罗庆辉, 徐泽源, 许仲林. 天山雪岭云杉林生物量估测及空间格局分析[J]. 生态学报, 2020, 40(15): 5288-5297.
	[Luo Qinghui, Xu Zeyuan, Xu Zhonglin. Estimation and spatial pattern analysis of biomass of Picea schrenkiana forests[J]. Acta Ecologica Sinica, 2020, 40(15): 5288-5297.]
[33]	万辛如, 程超源, 白德凤, 等. 气候变化的生态影响及适应对策[J]. 中国科学院院刊, 2023, 38(3): 518-527.
	[Wan Xinru, Cheng Chaoyuan, Bai Defeng, et al. Ecological impacts of climate change and adaption strategies[J]. Bulletin of the Chinese Academy of Sciences, 2023, 38(3): 518-527.]

数据图层编号	数据说明	数据图层编号	数据说明	数据图层编号	数据说明
bio1	年平均温度/℃	bio8	最湿季度的平均温度/℃	bio14	最干月的降水量/mm
bio2	每月温差的平均值/℃	bio9	最干季度的平均温度/℃	bio15	季节性降水量变异系数/%
bio3	等温性/%	bio10	最热季度的平均温度/℃	bio16	最湿季度的降水量/mm
bio4	季节性温度变异	bio11	最冷季度的平均温度/℃	bio17	最干季度的降水量/mm
bio5	最热月的最高温度/℃	bio12	年降水量/mm	bio18	最热季度的降水量/mm
bio6	最冷月的最低温度/℃	bio13	最湿月的降水量/mm	Bio19	最冷季度的降水量/mm
bio7	温度年较差/℃

时期	情景	训练集AUC
当前（2000—2020年）	-	0.991
2020—2040年	SSP1-2.6	0.990
	SSP3-7.0	0.992
	SSP5-8.5	0.992
2040—2060年	SSP1-2.6	0.992
	SSP3-7.0	0.991
	SSP5-8.5	0.991

变量	描述	贡献率/％	变量	描述	贡献率/％
bio19	最冷季度的降水量	17.3	bio11	最冷季度的平均温度	2.8
bio3	等温性	15.8	bio2	每月温差的平均值	1.4
bio4	季节性温度变异	12.6	bio12	年降水量	0.4
bio1	年平均温度	12.0	bio7	温度年较差	0.3
bio13	最湿月的降水量	10.6	bio6	最冷月的最低温度	0.2
bio17	最干季度的降水量	7.6	bio5	最热月的最高温度	0.1
bio18	最热季度的降水量	7.1	bio16	最湿季度的降水量	0
bio9	最干季度的平均温度	4.6	bio8	最湿季度的平均温度	0
bio14	最干月的降水量	3.7	bio10	最热季度的平均温度	0
bio15	季节性降水量变异系数	3.3

情景	时期	高适生区	面积变化比例%	中适生区	面积变化比例%	低适生区	面积变化比例%	总适生区	面积变化比例%
当前	-	49.45	-	85.48	-	164.24	-	299.17	-
SSP1-2.6	2020—2040年	63.43	28.27	78.78	-7.84	165.29	0.64	307.50	2.78
SSP1-2.6	2040—2060年	62.61	26.61	74.30	-13.08	145.70	-11.29	282.61	-5.54
SSP3-7.0	2020—2040年	60.73	22.81	75.90	-11.21	152.62	-7.08	289.25	-3.32
SSP3-7.0	2040—2060年	65.86	33.19	86.09	0.71	153.92	-6.28	305.87	2.24
SSP5-8.5	2020—2040年	64.06	29.54	82.63	-3.33	141.94	-13.58	288.63	-3.52
SSP5-8.5	2040—2060年	64.56	30.56	87.24	2.06	154.53	-5.91	306.33	2.39

Prediction of potential distribution area of Picea schrenkiana under the background of climate change

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 33

Related Articles 15

Metrics

Comments

Recommended 0

[1]	LYU Zhuangzhuang, QIAO Qingqing, DONG Sunyi, WANG Dong. Paleoclimatic evolution and driving mechanisms in arid areas of inland Asia during the Middle Miocene Climatic Optimum in the context of global climate warming [J]. Arid Zone Research, 2024, 41(8): 1309-1322.
[2]	LIANG Shuanghe, NIU Zuirong, JIA Ling. Analysis of runoff changes and attribution in the main stream of Zuli River in the past 65 years [J]. Arid Zone Research, 2024, 41(6): 928-939.
[3]	TANG Kexin, GUO Jianbin, HE Liang, CHEN Lin, WAN Long. Characteristics of the spatial and temporal evolution of Gross Primary Productivity and its influencing factors in China’s drylands [J]. Arid Zone Research, 2024, 41(6): 964-973.
[4]	YANG Fei, ZHANG Wentao, ZHANG Feimin, WANG Chenghai. Climate characteristics and variation in the Qilian Mountains from 1961 to 2022 [J]. Arid Zone Research, 2024, 41(10): 1627-1638.
[5]	ZHANG Yin, SUN Congjian, LIU Geng, CHAO Jinlong, GENG Tianwei. Response of NDSI in the Tarim River Basin mountainous areas to climate change over the past 20 years [J]. Arid Zone Research, 2024, 41(10): 1639-1648.
[6]	CHENG Qian, QI Yue, LIU Mingchun, ZHANG Peng, DING Wenkui, LI Xingyu, REN Liwen, YANG Hua. Characteristics of ecology and water resource changes in the Shiyang River Basin under the background of climate change and human activities [J]. Arid Zone Research, 2024, 41(10): 1672-1684.
[7]	FAN Yuke, REN Ju, WANG Runlong, ZHOU Dongdong, PAN Zikai, ZHANG Xiaowei, ZHOU Xiaolei. Prediction of potential suitable distribution area of Pinus bungeana in China under the background of climate change [J]. Arid Zone Research, 2024, 41(10): 1719-1730.
[8]	ZHAO Yuqi, WEI Tianxing. Changes in vegetation cover and influencing factors in typical counties of the Loess Plateau from 1990 to 2020 [J]. Arid Zone Research, 2024, 41(1): 147-156.
[9]	HU Guanglu,TAO Hu,JIAO Jiao,BAI Yuanru,CHEN Haizhi,MA Jin. Runoff trend and attribution analysis of the Zhengyi Gorge in the middle reaches of the Heihe River [J]. Arid Zone Research, 2023, 40(9): 1414-1424.
[10]	ZHOU Xiaodong, CHANG Shunli, WANG Guanzheng, ZHANG Yutao, YU Shulong, ZHANG Tongwen. Radial growth response of Picea schrenkiana to climate change in the middle section of the northern slope of the Tianshan Mountains [J]. Arid Zone Research, 2023, 40(8): 1215-1228.
[11]	MENG Chengfeng, ZHONG Tao, ZHENG Jianghua, WANG Nan, LIU Zexuan, REN Xiangyuan. Analysis of temporal and spatial characteristics and driving forces of Kunlun glacial lakes [J]. Arid Zone Research, 2023, 40(7): 1094-1106.
[12]	ZHAO Yanfen, PAN Borong. Potential geographical distributions of Tugarinovia in China under climate change scenarios [J]. Arid Zone Research, 2023, 40(6): 949-957.
[13]	YAO Chunyan, LIU Honghu, LIU Jing. Variation of runoff and sediment in the headwaters of the Yangtze River from 1980 to 2020 [J]. Arid Zone Research, 2023, 40(5): 726-736.
[14]	TIAN Shengchuan, ZHAO Shanchao, ZHENG Xinjun, WANG Yugang, LI Yan. Water source of spruce (Picea schrenkiana) at different altitudes in the Tianshan Mountains during the growing season [J]. Arid Zone Research, 2023, 40(3): 436-444.
[15]	DAI Jun, HU Haizhu, MAO Xiaomin, ZHANG Ji. Future climate change trends in the Shiyang River Basin based on the CMIP6 multi-model estimation data [J]. Arid Zone Research, 2023, 40(10): 1547-1562.