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

doi:10.17521/cjpe.2023.0218

Abstract

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

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[J]. Chin J Plant Ecol, 2024, 48(4): 445-458.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2023.0218

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

Figures/Tables 11

References 58

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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	°

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	°

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

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

	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