干旱区生态环境知识资源中心(Arid): Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran

Arid

DOI	10.1007/s40333-022-0004-1
	Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran
	Amiri, Mohadeseh; Tarkesh, Mosfata; Shafiezadeh, Mohammad
通讯作者	Amiri, M
来源期刊	JOURNAL OF ARID LAND
ISSN	1674-6767
EISSN	2194-7783
出版年	2022
卷号	14 期号:2 页码:203-224
英文摘要	Invasive species have been the focus of ecologists due to their undesired impacts on the environment. The extent and rapid increase in invasive plant species is recognized as a natural cause of global-biodiversity loss and degrading ecosystem services. Biological invasions can affect ecosystems across a wide spectrum of bioclimatic conditions. Understanding the impact of climate change on species invasion is crucial for sustainable biodiversity conservation. In this study, the possibility of mapping the distribution of invasive Prosopis juliflora (Swartz) DC. was shown using present background data in Khuzestan Province, Iran. After removing the spatial bias of background data by creating weighted sampling bias grids for the occurrence dataset, we applied six modelling algorithms (generalized additive model (GAM), classification tree analysis (CTA), random forest (RF), multivariate adaptive regression splines (MARS), maximum entropy (MaxEnt) and ensemble model) to predict invasion distribution of the species under current and future climate conditions for both optimistic (RCP 2.6) and pessimistic (RCP 8.5) scenarios for the years 2050 and 2070, respectively. Predictor variables including weighted mean of CHELSA (climatologies at high resolution for the Earth's land surface areas)-bioclimatic variables and geostatistical-based bioclimatic variables (1979-2020), physiographic variables extracted from shuttle radar topography mission (SRTM) and some human factors were used in modelling process. To avoid causing a biased selection of predictors or model coefficients, we resolved the spatial autocorrelation of presence points and multi-collinearity of the predictors. As in a conventional receiver operating characteristic (ROC), the area under curve (AUC) is calculated using presence and absence observations to measure the probability and the two error components are weighted equally. All models were evaluated using partial ROC at different thresholds and other statistical indices derived from confusion matrix. Sensitivity analysis showed that mean diurnal range (Bio2) and annual precipitation (Bio12) explained more than 50% of the changes in the invasion distribution and played a pivotal role in mapping habitat suitability of P. juliflora. At all thresholds, the ensemble model showed a significant difference in comparison with single model. However, MaxEnt and RF outperformed the others models. Under climate change scenarios, it is predicted that suitable areas for this invasive species will increase in Khuzestan Province, and increasing climatically suitable areas for the species in future will facilitate its future distribution. These findings can support the conservation planning and management efforts in ecological engineering and be used in formulating preventive measures.
英文关键词	invasive species climate change scenarios partial ROC ensemble forecast Kriging spatial bias
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000765230900001
WOS关键词	SPECIES DISTRIBUTION MODELS ; DISTRIBUTIONS ; PLANTS ; TEMPERATURE ; RISK
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/393311
推荐引用方式 GB/T 7714	Amiri, Mohadeseh,Tarkesh, Mosfata,Shafiezadeh, Mohammad. Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran[J],2022,14(2):203-224.
APA	Amiri, Mohadeseh,Tarkesh, Mosfata,&Shafiezadeh, Mohammad.(2022).Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran.JOURNAL OF ARID LAND,14(2),203-224.
MLA	Amiri, Mohadeseh,et al."Modelling the biological invasion of Prosopis juliflora using geostatistical-based bioclimatic variables under climate change in arid zones of southwestern Iran".JOURNAL OF ARID LAND 14.2(2022):203-224.