气候变化对巨柏适宜生境分布的潜在影响和预测

doi:10.17521/cjpe.2023.0218

植物生态学报 ›› 2024, Vol. 48 ›› Issue (4): 445-458.DOI: 10.17521/cjpe.2023.0218 cstr: 32100.14.cjpe.2023.0218

气候变化对巨柏适宜生境分布的潜在影响和预测

吴茹茹¹^,², 刘美珍¹^,³^,^*(), 谷仙⁴, 常馨月⁵, 郭立月¹, 蒋高明¹^,³, 祁如意⁶

¹中国科学院植物研究所植被与环境变化国家重点实验室, 北京 100093
²中国科学院大学研究生院, 北京 100049
³中国科学院大学资源与环境学院, 北京 100049
⁴河北中医药大学药学院, 石家庄 050200
⁵西藏自治区生态环境厅生态环境科学研究院, 拉萨 850000
⁶西藏农牧学院资源与环境学院, 西藏林芝 860000

收稿日期:2023-07-28 接受日期:2023-12-21 出版日期:2024-04-20 发布日期:2024-05-11
通讯作者: * (liumzh@ibcas.ac.cn)
基金资助:
国家自然科学基金(32071604);国家科技基础资源调查专项(2019FY202300)

Prediction of suitable habitat distribution and potential impact of climate change on distribution patterns of Cupressus gigantea

WU Ru-Ru¹^,², LIU Mei-Zhen¹^,³^,^*(), GU Xian⁴, CHANG Xin-Yue⁵, GUO Li-Yue¹, JIANG Gao-Ming¹^,³, QI Ru-Yi⁶

¹State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
²School of Graduate, University of Chinese Academy of Sciences, Beijing 100049, China
³College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
⁴College of China Pharmacy, Hebei University of Chinese Medicine, Shijiazhuang 050200, China
⁵Xizang Research Academy of Eco-environmental Sciences, Xizang Autonomous Region Department of Ecology and Environment, Lhasa 850000, China
⁶Resource and Environment College, Tibet Agricultural & Animal Husbandry University, Nyingchi, Xizang 860000, China

Received:2023-07-28 Accepted:2023-12-21 Online:2024-04-20 Published:2024-05-11
Contact: * (liumzh@ibcas.ac.cn)
Supported by:
National Natural Science Foundation of China(32071604);Special Foundation for National Science and Technology Basic Resources Investigation of China(2019FY202300)

摘要/Abstract

摘要：

随着全球气候变化, 植物的生长和生存模式均受到影响。预测气候变化下物种潜在的地理分布范围变化, 是判断该物种如何响应气候变化的重要手段, 有助于制定科学的物种保护策略。巨柏(Cupressus gigantea)是雅鲁藏布江中游末段及主要支流尼洋河近江河段河岸带的特有物种, 也是国家一级重点保护植物, 其分布范围主要在朗县至米林市丹娘乡的雅鲁藏布江两岸及尼洋河河谷的巨柏公园, 生存受到区域自然资源开发和气候变化的明显影响。该研究基于巨柏当前地理分布数据､地形因子､当前及未来气候条件下的环境因子数据, 利用最大熵(MaxEnt)模型､广义线性模型(GLM)和广义相加模型(GAM)与ArcGIS空间分析分别对西藏当前及未来2081-2100年2种气候变化情景(高排放模式SSP5-8.5和低排放模式SSP1-2.6)下的巨柏生境适宜性进行模拟分析。研究结果表明: (1)巨柏的潜在地理分布范围较狭窄, 适生区集中分布在加查县至米林市的雅鲁藏布江两岸和米林市至工布江达县的尼洋河两岸, 在隆子县､贡嘎县､错那县等地呈零星分布; (2)对巨柏潜在地理分布影响较大的环境因子有最冷季平均气温、温度季节性、昼夜温差月均值和海拔, 适宜范围分别为-1.62-2.14 ℃､565.29-603.78、11.66-12.97 ℃和2 898-3 550 m; (3)三种模型模拟结果均表明与潜在地理分布面积相比, 未来气候变化情景下巨柏的总适生区面积呈现扩大的趋势, 且SSP5-8.5情景下巨柏适生区面积的扩增程度明显高于SSP1-2.6气候情景; (4)与当前适生区质心相比, 未来情景下的巨柏适生区质心整体呈向西南偏移的趋势, 巨柏的分布区也呈向藏西南雅鲁藏布江中游方向较高海拔迁移的趋势。该研究探究了巨柏生长的关键影响因子及巨柏对气候的响应状况, 对巨柏保护具有十分重要的科学意义和实践指导价值。

关键词: 气候变化, 巨柏, 潜在分布区模拟, 最大熵模型, 广义线性模型, 广义相加模型, 适生分析

Abstract:

Aims The growth and survival patterns of species might be affected by global climate change. Predicting changes of the potential range of geographic distribution for a certain species under climate change is important to understand the response of the species to climate change, and helps to formulate scientific conservation strategies for the species. Cupressus gigantea is an endemic species of both the Yarlung Zangbo River and one of the first class National Protection Wild Plants. However, this species faces dual pressures from resource development and climate change. We here research the distribution pattern on the both side of the Yarlung Zangbo River from Nang County to Danniang Town of Nyingchi.
Methods In this investigation, we simulated the habitat suitability of C. gigantea under current and two climate change scenarios from 2081 to 2100 (SSP5-8.5: high emission scenario and SSP1-2.6: low emission scenario) in Xizang by using maximum entropy models (MaxEnt), generalized liner models (GLM) and generalized additive models (GAM) with ArcGIS spatial analysis based on the current actual geographical distribution information. All the data were obtained by field investigation, with topographic factor variables and environmental data being collected or predicted under current and future climatic conditions, respectively.
Important findings The results of the study show that: (1) The potential geographical distribution of C. gigantea was narrow, with the suitable distribution areas beings concentrated in Gyaca County to Gongbo’gyamda County, through which the Yarlung Zangbo River in Xizang. And others were scatterly distributed in Lhünzê County, Gonggar County, and Cona County in eastern Xizang. (2) Environmental factors which had significant impacts on the potential geographic distribution of C. gigantea were the average air temperature of the coldest quarter, temperature seasonality, mean of monthly (maximum temperature-minimum temperature), and altitude with suitable ranges of -1.62-2.14 °C, 565.29-603.78, 11.66-12.97 °C and 2 898-3 550 m, respectively. (3) The general suitable area of C. gigantea under future climate change scenarios tended to expand compared with the potential geographic distribution area, and its expansion of the suitable area range of C. gigantea under the SSP5-8.5 climate scenario was much higher than the SSP1-2.6 climate scenario. (4) The center of mass in the future scenario displayed an overall trend to the southwest, and the distribution area of C. gigantea also showed a trend to the southwest of the middle reaches of the Southwest Yarlung Zangbo River. Our findings provided the scientific significance and practical guidance for investigating key environmental factors for the growth, restoration and conservation of C. gigantea.

Key words: climate change, Cupressus gigantea, potential distribution area modeling, maximum entropy model (MaxEnt), generalized liner model (GLM), generalized additive model (GAM), suitable analysis

吴茹茹, 刘美珍, 谷仙, 常馨月, 郭立月, 蒋高明, 祁如意. 气候变化对巨柏适宜生境分布的潜在影响和预测. 植物生态学报, 2024, 48(4): 445-458. DOI: 10.17521/cjpe.2023.0218

WU Ru-Ru, LIU Mei-Zhen, GU Xian, CHANG Xin-Yue, GUO Li-Yue, JIANG Gao-Ming, QI Ru-Yi. Prediction of suitable habitat distribution and potential impact of climate change on distribution patterns of Cupressus gigantea. Chinese Journal of Plant Ecology, 2024, 48(4): 445-458. DOI: 10.17521/cjpe.2023.0218

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链接本文: https://www.plant-ecology.com/CN/10.17521/cjpe.2023.0218

https://www.plant-ecology.com/CN/Y2024/V48/I4/445

图/表 11

图1 用于模型模拟的巨柏数据分布图。

Fig. 1 Distribution of Cupressus gigantea data for model simulation.

表1 用于预测巨柏在西藏地区潜在地理分布的环境变量

Table 1 Environmental variables used to predict the potential geographic distribution of Cupressus gigantea in Xizang

变量类型 Variable type	变量代码 Variable code	环境变量描述 Environment variable description	单位 Unit
生物气候变量 Bioclimatic variables	Bio1	年平均气温 Annual mean temperature	℃
	Bio2^*	昼夜温差月均值 Mean of monthly (maximum temperature‒minimum temperature)	℃
	Bio3	等温性 Isothermality	/(×100)
	Bio4^*	温度季节性(标准差×100) Temperature seasonality (standard deviation × 100)
	Bio5	最暖月最高气温 Max temperature of the warmest month	℃
	Bio6	最冷月最低气温 Min temperature of the coldest month	℃
	Bio7	年气温变化范围 Temperature annual range (Bio5 - Bio6)	℃
	Bio8	最湿季平均气温 Mean temperature of the wettest quarter	℃
	Bio9	最干季平均气温 Mean temperature of the driest quarter	℃
	Bio10	最热季平均气温 Mean temperature of the warmest quarter	℃
	Bio11^*	最冷季平均气温 Mean temperature of the coldest quarter	℃
	Bio12	年降水量 Annual precipitation	mm
	Bio13	最湿月降水量 Precipitation of the wettest month	mm
	Bio14	最干月降水量 Precipitation of the driest month	mm
	Bio15	降水量季节性变异系数 Variation of the precipitation seasonality	%
	Bio16	最湿季度降水量 Precipitation of the wettest quarter	mm
	Bio17	最干季度降水量 Precipitation of the driest quarter	mm
	Bio18	最暖季度降水量 Precipitation of the warmest quarter	mm
	Bio19	最冷季度降水量 Precipitation of the coldest quarte	mm
地形因子变量 Terrain factor variables	Al^*	海拔 Altitude	m
	Sl^*	坡度 Slope	°
	As^*	坡向 Aspect	°

表2 巨柏适宜生境预测中GLM和GAM模型的AUC值与TSS值

Table 2 AUC and TSS values for GLM and GAM model in predicting the potential geographic distribution of Cupressus gigantea

指标 Indicator	算法 Algorithm
指标 Indicator	GLM	GAM
AUC	0.985	0.979
TSS	0.976	0.952

图2 巨柏适宜生境预测中不同模型的变量重要性。A, MaxEnt模型。B, GAM和GLM模型。GAM, 广义相加模型; GLM, 广义线性模型; MaxEnt, 最大熵模型。Al, 海拔; As, 坡向; Bio2, 昼夜温差月均值; Bio4, 温度季节性; Bio11, 最冷季平均气温; Sl, 坡度。

Fig. 2 Variable importance of different models in predicting the potential geographic distribution of Cupressus gigantea. A, MaxEnt model. B, GAM and GLM modes. GAM, generalized additive models; GLM, generalized liner models; MaxEnt, maximum entropy models. Al, altitude; As, aspect; Bio2, mean of monthly (maximum temperature‒minimum temperature); Bio4, temperature seasonality; Bio11, mean temperature of the coldest quarter; Sl, slope.

图3 巨柏存在概率对气候因子的响应曲线。

Fig. 3 Response curves of existence probability of Cupressus gigantea to climate factors.

图4 巨柏在西藏的潜在地理分布。A-C分别为MaxEnt、GAM、GLM模型当前气候情境下的模拟结果。D-F分别为MaxEnt、GAM、GLM模型SSP1-2.6情境下的模拟结果。G-I分别为MaxEnt、GAM、GLM模型SSP5-8.5情境下的模拟结果。GAM, 广义相加模型; GLM, 广义线性模型; MaxEnt, 最大熵模型; SSP1-2.6, 低排放情景; SSP5-8.5, 高排放情景。

Fig. 4 Potential geographic distribution of Cupressus gigantea in Xizang. A-C are the simulation results under current scenario of MaxEnt, GAM, and GLM models, respectively. D-F are the simulation results under SSP1-2.6 scenario of MaxEnt, GAM, and GLM models, respectively. G-I are the simulation results under SSP5-8.5 scenario of MaxEnt, GAM, and GLM models, respectively. GAM, generalized additive models; GLM, generalized liner models; MaxEnt, maximum entropy models; SSP1-2.6, low emission scenario; SSP5-8.5, high emission scenario.

表3 不同时期不同气候情景下巨柏适生区的变化

Table 3 Changes of distribution area of Cupressus gigantea in different periods and different scenarios

	模型 Model	时期/气候情景 Period/climate scenarios	适生区面积 Suitable area (× 10³ km²)	扩增面积 Gain area (× 10³ km²)	保留面积 Unchanged area (× 10³ km²)	收缩面积 Loss area (× 10³ km²)	范围变化 Range change (%)	损失率 Percentage loss (%)	增益率 Percentage gain (%)
总适生区 General suitable area	MaxEnt	当前 Current	8.95
		2100s/SSP1-2.6	10.47	3.98	6.49	2.46	16.98	27.49	44.47
		2100s/SSP5-8.5	11.70	5.32	6.38	2.57	30.73	28.72	59.44
	GLM	当前 Current	11.12
		2100s/SSP1-2.6	11.88	3.57	8.30	2.82	6.77	25.35	32.12
		2100s/SSP5-8.5	16.74	5.98	10.76	5.61	50.47	50.44	53.78
	GAM	当前 Current	14.47
		2100s/SSP1-2.6	14.48	4.34	10.14	4.33	0.05	29.92	29.96
		2100s/SSP5-8.5	17.92	5.93	10.18	4.29	23.82	29.67	40.96
中高适生区 Medium and highly suitable area	MaxEnt	当前 Current	3.10
		2100s/SSP1-2.6	3.71	0.98	2.74	0.37	19.68	11.94	31.61
		2100s/SSP5-8.5	4.39	1.89	2.50	0.60	41.61	19.35	60.97
	GLM	当前 Current	4.67
		2100s/SSP1-2.6	6.56	2.49	4.07	0.59	40.62	12.74	53.36
		2100s/SSP5-8.5	7.87	3.80	4.07	0.59	68.72	12.74	81.46
	GAM	当前 Current	4.93
		2100s/SSP1-2.6	6.79	2.47	4.33	0.61	37.65	12.32	49.96
		2100s/SSP5-8.5	8.16	3.91	4.25	0.69	65.36	13.95	79.31

图5 巨柏当前与其他时期潜在适宜分布区的空间格局变化。A-C, 分别为SSP1-2.6情景下MaxEnt、GAM、GLM模型结果较当前情景下总适生区的空间格局变化。D-F, 分别为SSP5-8.5情境下MaxEnt、GAM、GLM模型结果较当前情景下总适生区的空间格局变化。G-I, 分别为SSP1-2.6情境下MaxEnt、GAM、GLM模型结果较当前情景下中高适生区的空间格局变化。J-L, 分别为SSP5-8.5情境下MaxEnt、GAM、GLM模型结果较当前情景下中高适生区的空间格局变化。GAM, 广义相加模型; GLM, 广义线性模型; MaxEnt, 最大熵模型; SSP1-2.6, 低排放情景; SSP5-8.5, 高排放情景。

Fig. 5 Changes in spatial patterns of potentially suitable distribution areas for Cupressus gigantea. A-C are the changes in spatial patterns of total fitness zones in MaxEnt, GAM, and GLM model results of SSP1-2.6 over the current scenario, respectively. D-F are the changes in spatial patterns of total fitness zones in MaxEnt, GAM, and GLM model results of SSP5-8.5 over the current scenario, respectively. G-I are the changes in spatial patterns of medium and high fitness zones in MaxEnt, GAM, and GLM model results of SSP1-2.6 changes in spatial pattern of medium and high fitness zones over the current scenario. J-L are the changes in spatial patterns of medium and high fitness zones in MaxEnt, GAM, and GLM model results of SSP5-8.5 changes in spatial pattern of medium and high fitness zones over the current scenario. GAM, generalized additive models; GLM, generalized liner models; MaxEnt, maximum entropy models; SSP1-2.6, low emission scenario; SSP5-8.5, high emission scenario.

图6 未来气候条件下巨柏的总适生区质心迁移路线。GAM, 广义相加模型; GLM, 广义线性模型; MaxEnt, 最大熵模型; SSP1-2.6, 低排放情景; SSP5-8.5, 高排放情景。

Fig. 6 The center of gravity of Cupressus gigantea general suitable area under future climate conditions. GAM, generalized additive models; GLM, generalized liner models; MaxEnt, maximum entropy models; SSP1-2.6, low emission scenario; SSP5-8.5, high emission scenario.

表4 不同时期不同气候情景下巨柏质心的变化

Table 4 Changes in the center of mass of Cupressus gigantea in different periods and different scenarios

模型 Model	时期/气候情景 Periods/climate scenarios	经度 Longitude (° E)	纬度 Latitude (° N)	迁移距离 Migration distance (km)
MaxEnt	当前 Current	94.88	29.62
	2100s/SSP1-2.6	94.24	29.39	48.41
	2100s/SSP5-8.5	94.10	29.05	82.37
GLM	当前 Current	94.25	29.74
	2100s/SSP1-2.6	94.48	29.56	30.22
	2100s/SSP5-8.5	92.61	29.36	164.08
GAM	当前 Current	94.05	29.34
	2100s/SSP1-2.6	93.26	28.70	104.35
	2100s/SSP5-8.5	92.60	28.90	149.06

图7 实际调查所得巨柏分布区与当前气候条件下模拟所得高适生区分布。A, MaxEnt模型所得高适生区。B, GAM模型所得高适生区。C, GLM模型所得高适生区。GAM, 广义相加模型; GLM, 广义线性模型; MaxEnt, 最大熵模型。

Fig. 7 Distribution of Cupressus gigantea from actual surveys and simulation of highly suitable area under current climate conditions. A, Highly suitable area from the MaxEnt model. B, Highly suitable area from the GAM model. C, Highly suitable area from the GLM model. GAM, generalized additive models; GLM, generalized liner models; MaxEnt, maximum entropy models.

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气候变化对巨柏适宜生境分布的潜在影响和预测

Prediction of suitable habitat distribution and potential impact of climate change on distribution patterns of Cupressus gigantea

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