基于SSPs气候场景的濒危植物银杉潜在分布区预测

doi:10.12302/j.issn.1000-2006.202207027

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (1): 161-168.doi: 10.12302/j.issn.1000-2006.202207027

基于SSPs气候场景的濒危植物银杉潜在分布区预测

罗楚滢¹(), 佘济云¹^,^*(), 唐子朝²

1.中南林业科技大学林学院,湖南长沙 410004
2.湖南省农林工业勘察设计研究总院,湖南长沙 410007

收稿日期:2022-07-10 修回日期:2023-03-09 出版日期:2024-01-30 发布日期:2024-01-24
通讯作者: 佘济云
基金资助:
长沙市科技局基础研究项目(kq1801082)

Prediction of potential distribution areas of the endangered Cathaya argyrophylla based on shared socio-economic pathways (SSPs) climate scenarios

LUO Chuying¹(), SHE Jiyun¹^,^*(), TANG Zichao²

1. College of Forestry, Central South University of Forestry and Technology, Changsha 410004, China
2. Hu’nan Prospecting Designing and Research General Institute for Agriculture and Forestry Industry, Changsha 410007, China

Received:2022-07-10 Revised:2023-03-09 Online:2024-01-30 Published:2024-01-24
Contact: SHE Jiyun

摘要/Abstract

摘要：

【目的】探讨影响濒危植物银杉(Cathaya argyrophylla)种群发生的关键因子及预测其在当前与未来的潜在分布区,为银杉天然种质资源的保护和引种栽培提供理论依据。【方法】基于银杉的41个样本点,结合气候、地形、土壤、辐射因子数据,采用MaxEnt模型预测当前情景下银杉的潜在分布区,评估不同环境因子对银杉地理分布的影响,同时以未来共享社会经济数据(SSPs)作为气候数据,预测银杉未来的潜在分布区。【结果】当前气候条件下,银杉核心适生区面积约为1.325×10⁵ km²,约占我国国土面积的1.38%;最干月份降水量、下行紫外线辐射、海拔、最冷月份最低温度对银杉分布影响的累计贡献率为91.9%,是影响银杉地理分布的主导因子。未来3种气候情景下,银杉核心适生区面积均有增加,但仍以黔、湘、桂、渝为中心。【结果】当前气候条件下,银杉核心适生区面积约为1.325×10⁵ km²,约占我国国土面积的1.38%,最干月份降水量、下行紫外线辐射、海拔、最冷月份最低温度对银杉分布影响的累计贡献率为91.9%,是影响银杉地理分布的主导因子。未来3种气候情景下,银杉核心适生区面积均有增加,但仍以黔、湘、桂、渝为中心。以生存概率大于0.5作为适应范围,则适宜银杉生存的主要环境条件为:最干月份降水量18.03~215.63 mm,下行紫外线辐射1 070 728 ~1 437 806 W/m²,海拔493.68 ~ 1 731.10 m,最冷月最低温-1.01~4.05 ℃。【结论】最干月份降水量、下行紫外线辐射可以被认定是影响银杉潜在分布的主要因子。在3种未来气候情景下,银杉的核心适生区均呈扩张趋势,扩张区主要分布于云南、浙江、福建、贵州4省。此外,中间发展路径情景(SSP2-4.5)更适合银杉的生长与繁殖。

关键词: 银杉, 共享社会经济路径(SSPs), 潜在分布区, 气候因子, 濒危植物

Abstract:

【Objective】This study explored key factors affecting the occurrence of endangered Cathaya argyrophylla populations and predicted their potential distribution areas in the present and future, providing a theoretical basis for the conservation and introduction of C. argyrophylla natural resources. 【Method】Based on 41 sample sites of C. argyrophylla, the MaxEnt model was used to predict the potential distribution areas of C. argyrophylla under the current scenario by combining climate, topography, soil and radiation factor data, and to assess the effects of different environmental factors on the geographical distribution of C. argyrophylla. Also, future shared socio-economic pathways (SSPs) were used as climate data to predict future changes in the growth distribution areas of C. argyrophylla. 【Result】Under the current climate conditions, the core suitable area for C. argyrophylla is approximately 1.325 × 10⁵ km², accounting for approximately 1.38% of China’s land area. It has a cumulative contribution rate of 91.9% to the distribution of C. argyrophylla due to precipitation in the driest month, downward ultraviolet radiation, altitude, and the lowest temperature in the coldest month. They are the dominant factor affecting the geographical distribution of C. argyrophylla. If the survival probability is greater than 0.5 as the adaptation range, the main environmental conditions suitable for the survival of C. argyrophylla are: precipitation in the driest month is 18.03~215.63 mm, downward ultraviolet radiation is 1 070 728~1 437 806 W/m², altitude is 493.68~1 731.10 m, and the lowest temperature in the coldest month is -1.01~4.05 ℃. In the future, under SSP1-2.6, SSP2-4.5, and SSP5-8.5, the core suitable areas for C. argyrophylla will increase. However, they will still be centered in Guizhou, Hunan, Guangxi, and Chongqing.【Conclusion】Precipitation during the driest month and downward ultraviolet radiation were the main factors influencing the potential distribution of C. argyrophylla. Under the three future climate scenarios, the core area suitable for C. argyrophylla showed an expanding trend. The expansion areas were mainly distributed in Yunnan, Zhejiang, Fujian and Guizhou. The intermediate development scenario (SSP2-4.5) was more suitable for the growth and reproduction of C. argyrophylla. This study provides an important theoretical basis for the conservation of C. argyrophylla planting resources.

Key words: Cathaya argyrophylla, shared socio-economic pathways(SSPs), potential distribution area, climatic factor, endangered plant

中图分类号:

Q948.2

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 Chuying, SHE Jiyun, TANG Zichao. 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 Science Edition), 2024, 48(1): 161-168.DOI: 10.12302/j.issn.1000-2006.202207027.

图/表 5

表1

环境变量及基于最大熵模型的各环境变量贡献率"

变量类型 type of factors	环境变量 environment variables	贡献率/% contribution rate
土壤因子 soil factor	土壤参考深度soil reference depth	0.7
	顶层土壤质地topsoil texture	0.3
	淤泥含量silt content	0.6
	沙含量sediment concentration	0.1
	黏土含量clay content	0.1
	酸碱度potential of hydrogen	0.1
	土壤有效含水量available soil water amount	—
	有机碳含量organic carbon content	0.5
气候因子 climatic factor	年平均气温(bio1)annual mean temperature	—
	平均气温日较差(bio2)mean diurnal range	—
	等温性(bio3)isothermality	1.9
	气温季节性变动系数(bio4) temperature seasonality	—
	最热月最高温(bio5) max temperature of the warmest month	—
	最冷月最低温(bio6) min temperature of the coldest month	2.1
	气温年较差(bio7)temperature annual range	—
	最湿季度平均温度(bio8) mean temperature of the wettest quarter	—
	最干季度平均温度(bio9) mean temperature of the driest quarter	—
	最暖季度平均温度(bio10) mean temperature of the warmest quarter	—
	最冷季度平均温度(bio11) mean temperature of the coldest quarter	—
	年降水量(bio12)annual precipitation	—
	最湿月份降水量(bio13) precipitation of the wettest month	—
	最干月份降水量(bio14) precipitation of the driest month	66.1
	降水量季节性变化(bio15) precipitation seasonality	0.8
	最干季度降水量(bio16) precipitation of the wettest quarter	—
	最湿季度降水量(bio17) precipitation of the driest quarter	0.6
	最暖季度降水量(bio18) precipitation of the warmest quarter	0.4
	最冷季度降水量(bio19) precipitation of the coldest quarter	—
地形因子 topographic factor	海拔(高程) elevation	9.1
	坡度slope	1.5
	坡向aspect	0.5
辐射因子 radiation factor	下行紫外线辐射 surface downward UV radiation flux	14.6

表1

图1

图2

表2

图3

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气候变化情景 climate change scenario	核心适生区面积/×10⁴km² core suitable area				变化率/% change rate
气候变化情景 climate change scenario	扩张 expand	保留 hold	缩减 reduce	整体变化量 change amount	扩张 expand	保留 hold	缩减 reduce
2050s(SSP1-2.6)	5.59	7.67	5.47	+0.12	0.58	0.80	0.57
2050s(SSP2-4.5)	19.60	9.49	3.65	+15.95	2.03	0.98	0.38
2050s(SSP5-8.5)	10.26	8.14	4.99	+5.27	1.06	0.85	0.52
2070s(SSP1-2.6)	7.06	8.00	5.13	+1.93	0.73	0.83	0.53
2070s(SSP2-4.5)	16.35	9.61	3.52	+12.83	1.70	1.00	0.37
2070s(SSP5-8.5)	9.65	8.52	4.62	+5.03	1.00	0.88	0.48
2090s(SSP1-2.6)	12.18	8.44	4.69	+7.49	1.26	0.88	0.49
2090s(SSP2-4.5)	14.44	8.41	4.72	+9.72	1.50	0.87	0.49
2090s(SSP5-8.5)	11.24	8.40	4.74	+6.50	1.17	0.87	0.49

基于SSPs气候场景的濒危植物银杉潜在分布区预测

Prediction of potential distribution areas of the endangered Cathaya argyrophylla based on shared socio-economic pathways (SSPs) climate scenarios

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