Potential geographical distribution of Ambrosia trifida in Xinjiang under climate change

doi:10.11686/cyxb2020040

Abstract

Abstract:

Determining the potential distribution of invasive species and their responses to climate change on a regional scale is of great significance for the early warning， specific prevention， and control of invasive species. Ambrosia trifida （giant ragweed） is a globally recognized invasive weed. This plant is now widely distributed in the Yili Valley in Xinjiang which locaded in Central Asian hub of the“Belt and Road”. To predict the potential spread of A. trifida in Xinjiang， a Maxent niche model was used to project its potential distribution under the current climate and the climate in the 2050s and 2070s under two climate change scenarios （RCP4.5 and RCP8.5）. The results indicated that the suitable habitat area of giant ragweed in Xinjiang could extend to 24.01×10⁴ km²， accounting for 14% of the total area of Xinjiang under the current climate conditions. Under the RCP4.5 scenario， its suitable habitat area was predicted to increase to 37.36×10⁴ km² and 39.23×10⁴ km² by the 2050s and 2070s， respectively. Under the RCP8.5 scenario， its suitable habitat area was predicted to increase to 39.45×10⁴ km² and 42.94×10⁴ km² by the 2050s and 2070s， respectively. Under the two future climate scenarios， the potential suitable habitat area will tend to increase and shift northwards， and the decreasing areas will be mainly concentrated in the Junggar Basin. Among all the environmental factors， precipitation and temperature made the strongest contributions to distribution， with total contribution rates of 40.1% and 56.0%， respectively. The individual factors making the strongest contributions to giant ragweed distribution were precipitation in the driest month （36.2%） and temperature seasonality （29.1%）. Farmland and construction land were identified as the areas with the highest risk of giant ragweed invasion. On the basis of these results， we suggest that management strategies should focus not only on predicted habitats， but also on farmland， grassland， and roadsides where there is sufficient water and strong disturbance by humans or animals.

Key words: biological invasion, climate change, Maxent niche model, giant ragweed, suitable threshold

Qian-qian MA, Tong LIU, He-gan DONG, Han-yue WANG, Wen-xuan ZHAO, Rui-li WANG, Yan LIU, Le CHEN. Potential geographical distribution of Ambrosia trifida in Xinjiang under climate change[J]. Acta Prataculturae Sinica, 2020, 29(12): 73-85.

Figures/Tables 13

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模式名称 Model name	单位名称及所属国家 Company name and country
ACCESS1-0	CSIRO-BOM，澳大利亚 Australia
BCC-CSM1-1	BCC，中国 China
CCSM4	NCAR，美国 America
CNRM-CM5	CNRM，意大利 Italy
GFDL-CM3	NOAA GEDL，美国 America
GISS-E2-R	NASA GISS，美国 America
HadGEM2-AO	NIMA/KMA，韩国/英国 Korea/ United Kingdom
HadGEM2-CC	MOHC，英国 United Kingdom
HadGEM2-ES	MOHC，英国 United Kingdom
INMCM4	UNM，俄罗斯 Russia
IPSL-CM5A-LR	IPSL，法国 France
MIROC-ESM-CHEM	MIROC，日本 Japan
MIROC-ESM	MIROC，日本 Japan
MIROC5	MIROC，日本 Japan
MPI-ESM-LR	MPI-M，德国 Germany
MRI-CGCM3	MPI-M，德国 Germany
NorESM1-M	NCC，挪威 Norway

模式名称 Model name	单位名称及所属国家 Company name and country
ACCESS1-0	CSIRO-BOM，澳大利亚 Australia
BCC-CSM1-1	BCC，中国 China
CCSM4	NCAR，美国 America
CNRM-CM5	CNRM，意大利 Italy
GFDL-CM3	NOAA GEDL，美国 America
GISS-E2-R	NASA GISS，美国 America
HadGEM2-AO	NIMA/KMA，韩国/英国 Korea/ United Kingdom
HadGEM2-CC	MOHC，英国 United Kingdom
HadGEM2-ES	MOHC，英国 United Kingdom
INMCM4	UNM，俄罗斯 Russia
IPSL-CM5A-LR	IPSL，法国 France
MIROC-ESM-CHEM	MIROC，日本 Japan
MIROC-ESM	MIROC，日本 Japan
MIROC5	MIROC，日本 Japan
MPI-ESM-LR	MPI-M，德国 Germany
MRI-CGCM3	MPI-M，德国 Germany
NorESM1-M	NCC，挪威 Norway

代号Code	变量Variables	贡献率Percentage contribution (%)
Bio14 (mm)	最干月降水Precipitation of driest month	36.2
Bio4 (×100)	温度季节性变化标准差Temperature seasonality	29.1
Bio1 (℃×10)	年均温Annual mean temperature	18.3
Bio8 (℃×10)	最湿季均温Mean temperature of wettest quarter	4.1
Bio12 (mm)	年降水Annual precipitation	3.5
Bio7 (℃×10)	气温年范围Temperature annual range	3.1
Bio3	等温性Isothermality	1.4
Elevation (m)	海拔Elevation	1.1
Slope	坡度Slope	1.1
Soil	土壤类型Soil	0.9
Land	土地利用类型Land	0.8
Bio15	降水季节性变异系数Precipitation seasonality (coefficient of variation)	0.4

代号Code	变量Variables	贡献率Percentage contribution (%)
Bio14 (mm)	最干月降水Precipitation of driest month	36.2
Bio4 (×100)	温度季节性变化标准差Temperature seasonality	29.1
Bio1 (℃×10)	年均温Annual mean temperature	18.3
Bio8 (℃×10)	最湿季均温Mean temperature of wettest quarter	4.1
Bio12 (mm)	年降水Annual precipitation	3.5
Bio7 (℃×10)	气温年范围Temperature annual range	3.1
Bio3	等温性Isothermality	1.4
Elevation (m)	海拔Elevation	1.1
Slope	坡度Slope	1.1
Soil	土壤类型Soil	0.9
Land	土地利用类型Land	0.8
Bio15	降水季节性变异系数Precipitation seasonality (coefficient of variation)	0.4

相对适应性等级 Suitability classes	划分标准 Classification criteria
非适生区Unsuitable category	<0.1000
最不适区Most unsuitable	<0.0006
重度不适区Severe unsuitable	0.0006~0.0046
中度不适区Moderate unsuitable	0.0046~0.0131
轻度不适区Slightly unsuitable	0.0131~0.1000
适生区Suitable category	>0.1000
轻度适生区Slightly suitable	0.1000~0.1356
中度适生区Moderate suitable	0.1356~0.1896
显著适生区Noteworthy suitable	0.1896~0.2560
最适区域Most suitable	>0.2560