气候变化对中国大黄花虾脊兰及其传粉者适生区的影响

doi:10.17520/biods.2019367

生物多样性 ›› 2020, Vol. 28 ›› Issue (7): 769-778. DOI: 10.17520/biods.2019367

所属专题：传粉生物学

• 研究报告: 植物多样性 • 下一篇

气候变化对中国大黄花虾脊兰及其传粉者适生区的影响

余元钧¹^,², 罗火林¹, 刘南南¹, 熊冬金¹, 罗毅波², 杨柏云¹^,^*()

1 南昌大学生命科学学院江西省植物资源重点实验室, 南昌 330031
2 中国科学院植物研究所系统与进化植物学国家重点实验室, 北京 100093

收稿日期:2019-11-20 接受日期:2020-03-23 出版日期:2020-07-20 发布日期:2020-09-29
通讯作者: 杨柏云
作者简介:^* yangboyun@163.com
基金资助:
国家林业局野生动植物保护司“大黄花虾脊兰野生资源系统评估及保护策略”项目;上海市绿化和市容管理局项目(G202401)

Influence of the climate change on suitable areas of Calanthe sieboldii and its pollinators in China

Yuanjun Yu¹^,², Huolin Luo¹, Nannan Liu¹, Dongjin Xiong¹, Yibo Luo², Boyun Yang¹^,^*()

1 Key Laboratory of Plant Resources in Jiangxi Province, School of Life Sciences, Nanchang University, Nanchang 330031
2 State Key Laboratory of Systematic and Evolutionary Botany, Institute of Botany, Chinese Academy of Sciences, Beijing 100093

Received:2019-11-20 Accepted:2020-03-23 Online:2020-07-20 Published:2020-09-29
Contact: Boyun Yang

摘要/Abstract

摘要：

大黄花虾脊兰(Calanthe sieboldii)是典型的大陆与岛屿间间断分布的兰科物种, 适宜分布气候范围狭窄, 同时依赖特殊的传粉者传粉, 包括黄胸木蜂(Xylocopa appendiculata)、赤足木蜂(X. rufipes)和中华绒木蜂(X. chinensis)等3种木蜂属(Xylocopa)昆虫。本文通过R语言Biomod2程序包建立物种分布模型(SDM), 预测了2050年和2070年时大黄花虾脊兰及其传粉者在3种代表浓度路径(RCP2.6、RCP4.5与RCP8.5)下的分布格局, 以期为该濒危植物的保育提供参考。结果表明: 降水相关变量比温度相关变量对大黄花虾脊兰分布的平均解释率更高, 两者分别为25.4%和13.9%。当前大黄花虾脊兰适生区主要集中在华中和华东地区, 未来适生区的增减主要受到气候情景的影响, 其变化范围为-59.0%到34.7%, 并可能向更高海拔的地区移动; 未来木蜂适生区将净收缩16.4%-19.7%, 且主要向西北和东北移动; 因而两者共同分布的面积占大黄花虾脊兰适生区的比例未来相比当前的90.0%可能下降0.5%-11.4%, 表明大黄花虾脊兰分布可能受到未来气候变化和传粉者分布减少的双重影响, 因此对该物种或类似特化传粉的兰科植物进行保育时应当充分考虑传粉者因素。

关键词: 大黄花虾脊兰, 木蜂属, 气候变化, 物种分布模型, 传粉

Abstract

Calanthe sieboldii is an orchid species with a narrow range of suitable habitats that are typically disjunction between continents and islands. Calanthe sieboldii relies on specific pollinators such as Xylocopa carpenter bees, including X. appendiculata, X. rufipes and X. chinensis. This study built a species distribution model (SDM) using the Biomod2 package of R to predict the distribution patterns of C. sieboldii and Xylocopa spp. in China under three different representative concentration pathways in 2050 and 2070. The results show the distribution of C. sieboldii is better explained by precipitation-related variables than temperature-related variables, 25.4% and 13.9%, respectively. The current suitable habitats for C. sieboldii are mainly in Central China and East China. However, future climate change scenarios may shift these habitats, which will shift to higher altitude area, and may change -59.0% to 34.7% dominated by future climate change scenarios. The range of suitable areas for Xylocopa spp. will decrease by 16.4%-19.7%, moving to Northwest and Northeast China. Consequently, as the co-distribution areas between plant and pollinators account for suitable areas C. sieboldii, the orchid’s distribution will decrease by 0.5%-11.4% from 90.0%, indicating that the distribution of C. sieboldii will suffer from future climate change and declining pollinators. Therefore, the pollinator should be considered carefully in the conservation of C. sieboldii or similar specialized-pollination orchid species.

Key words: Calanthe sieboldii, Xylocopa, climate change, species distribution model, pollination

余元钧, 罗火林, 刘南南, 熊冬金, 罗毅波, 杨柏云 (2020) 气候变化对中国大黄花虾脊兰及其传粉者适生区的影响. 生物多样性, 28, 769-778. DOI: 10.17520/biods.2019367.

Yuanjun Yu, Huolin Luo, Nannan Liu, Dongjin Xiong, Yibo Luo, Boyun Yang (2020) Influence of the climate change on suitable areas of Calanthe sieboldii and its pollinators in China. Biodiversity Science, 28, 769-778. DOI: 10.17520/biods.2019367.

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链接本文: https://www.biodiversity-science.net/CN/10.17520/biods.2019367

https://www.biodiversity-science.net/CN/Y2020/V28/I7/769

图/表 6

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编号 Number	变量描述 Variable description	方差膨胀因子 Variance inflation factor	校正判定系数 Adjusted R² (%)
Bio1	年平均气温 Annual mean temperature	2,162.4	13.2
Bio2	昼夜温差月均值 Mean diurnal range	968.6	22.1
Bio3	等温性 Isothermality	231.1	9.6
Bio4	温度季节性 Temperature seasonality	3,230.3	19.3
Bio5	最暖月最高温 Max temperature of warmest month	27,969.6	8.1
Bio6	最冷月最低温 Min temperature of coldest month	127,767.6	12.9
Bio7	年均温差 Temperature annual range	146,914.7	21.1
Bio8	最湿季均温 Mean temperature of wettest quarter	11.5	21.5
Bio9	最干季均温 Mean temperature of driest quarter	42.8	11.3
Bio10	最暖季均温 Mean temperature of warmest quarter	2,009.7	6.3
Bio11	最冷季均温 Mean temperature of coldest quarter	11,340.5	12.5
Bio12	年均降水量 Annual precipitation	105.3	29.9
Bio13	最湿月降水量 Precipitation of wettest month	207.9	27.8
Bio14	最干月降水量 Precipitation of driest month	332.1	26.6
Bio15	降水季节性 Precipitation seasonality	20.2	17.7
Bio16	最湿季降水量 Precipitation of wettest quarter	228.8	29.9
Bio17	最干季降水量 Precipitation of driest quarter	382.3	27.0
Bio18	最暖季降水量 Precipitation of warmest quarter	54.5	15.4
Bio19	最冷季降水量 Precipitation of coldest quarter	40.4	28.6

编号 Number	变量描述 Variable description	方差膨胀因子 Variance inflation factor	校正判定系数 Adjusted R² (%)
Bio1	年平均气温 Annual mean temperature	2,162.4	13.2
Bio2	昼夜温差月均值 Mean diurnal range	968.6	22.1
Bio3	等温性 Isothermality	231.1	9.6
Bio4	温度季节性 Temperature seasonality	3,230.3	19.3
Bio5	最暖月最高温 Max temperature of warmest month	27,969.6	8.1
Bio6	最冷月最低温 Min temperature of coldest month	127,767.6	12.9
Bio7	年均温差 Temperature annual range	146,914.7	21.1
Bio8	最湿季均温 Mean temperature of wettest quarter	11.5	21.5
Bio9	最干季均温 Mean temperature of driest quarter	42.8	11.3
Bio10	最暖季均温 Mean temperature of warmest quarter	2,009.7	6.3
Bio11	最冷季均温 Mean temperature of coldest quarter	11,340.5	12.5
Bio12	年均降水量 Annual precipitation	105.3	29.9
Bio13	最湿月降水量 Precipitation of wettest month	207.9	27.8
Bio14	最干月降水量 Precipitation of driest month	332.1	26.6
Bio15	降水季节性 Precipitation seasonality	20.2	17.7
Bio16	最湿季降水量 Precipitation of wettest quarter	228.8	29.9
Bio17	最干季降水量 Precipitation of driest quarter	382.3	27.0
Bio18	最暖季降水量 Precipitation of warmest quarter	54.5	15.4
Bio19	最冷季降水量 Precipitation of coldest quarter	40.4	28.6

气候模式 Climate models	年代 Time	气候情景 Scenarios	当前分布区 Current range (km²)	丢失 Loss (km²)	增加 Gain (km²)	未来分布区 Future range (km²)	适生区净变化率 Net changes in suitable area (%)
CCSM4	2050	RCP2.6	424,281	64,782	138,733	498,232	17.4
		RCP4.5	424,281	148,569	121,545	397,257	-6.4
		RCP8.5	424,281	242,163	100,146	282,264	-33.5
	2070	RCP2.6	424,281	40,928	188,254	571,607	34.7
		RCP4.5	424,281	176,090	92,036	340,227	-19.8
		RCP8.5	424,281	303,405	79,212	200,088	-52.8
HadGEM2-AO	2050	RCP2.6	424,281	120,838	185,188	488,631	15.2
		RCP4.5	424,281	188,459	149,785	385,607	-9.1
		RCP8.5	424,281	223,285	138,763	339,759	-19.9
	2070	RCP2.6	424,281	135,160	196,285	485,406	14.4
		RCP4.5	424,281	267,545	120,198	276,934	-34.7
		RCP8.5	424,281	330,576	80,286	173,991	-59.0
FGOALS-g2	2050	RCP2.6	424,281	94,930	153,479	482,830	13.8
		RCP4.5	424,281	148,569	121,545	397,257	-6.4
		RCP8.5	424,281	242,163	100,146	282,264	-33.5
	2070	RCP2.6	424,281	40,928	188,254	571,607	34.7
		RCP4.5	424,281	176,090	92,036	340,227	-19.8
		RCP8.5	424,281	303,405	79,212	200,088	-52.8

气候模式 Climate models	年代 Time	气候情景 Scenarios	当前分布区 Current range (km²)	丢失 Loss (km²)	增加 Gain (km²)	未来分布区 Future range (km²)	适生区净变化率 Net changes in suitable area (%)
CCSM4	2050	RCP2.6	424,281	64,782	138,733	498,232	17.4
		RCP4.5	424,281	148,569	121,545	397,257	-6.4
		RCP8.5	424,281	242,163	100,146	282,264	-33.5
	2070	RCP2.6	424,281	40,928	188,254	571,607	34.7
		RCP4.5	424,281	176,090	92,036	340,227	-19.8
		RCP8.5	424,281	303,405	79,212	200,088	-52.8
HadGEM2-AO	2050	RCP2.6	424,281	120,838	185,188	488,631	15.2
		RCP4.5	424,281	188,459	149,785	385,607	-9.1
		RCP8.5	424,281	223,285	138,763	339,759	-19.9
	2070	RCP2.6	424,281	135,160	196,285	485,406	14.4
		RCP4.5	424,281	267,545	120,198	276,934	-34.7
		RCP8.5	424,281	330,576	80,286	173,991	-59.0
FGOALS-g2	2050	RCP2.6	424,281	94,930	153,479	482,830	13.8
		RCP4.5	424,281	148,569	121,545	397,257	-6.4
		RCP8.5	424,281	242,163	100,146	282,264	-33.5
	2070	RCP2.6	424,281	40,928	188,254	571,607	34.7
		RCP4.5	424,281	176,090	92,036	340,227	-19.8
		RCP8.5	424,281	303,405	79,212	200,088	-52.8

年代 Time	气候情景 Scenarios	大黄花虾脊兰适生区 Suitable areas of Calanthe sieboldii (km²)	木蜂适生区 Suitable areas of Xylocopa spp. (km²)	共同分布区 Co-distribution areas (km²)	共同分布区占大黄花虾脊兰适生区比例 Proportion of co-distribution areas among suitable areas of Calanthe sieboldii (%)
当前 Current	-	424,281	3,326,189	382,189	90.0
2050	RCP2.6	498,232	2,725,353	460,145	92.4
	RCP4.5	397,257	2,672,072	347,495	87.5
	RCP8.5	282,264	2,780,090	255,319	90.4
2070	RCP2.6	571,607	2,895,034	511,387	89.5
	RCP4.5	340,227	2,747,877	294,294	86.5
	RCP8.5	200,088	2,561,470	157,259	78.6

气候变化对中国大黄花虾脊兰及其传粉者适生区的影响

Influence of the climate change on suitable areas of Calanthe sieboldii and its pollinators in China

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