气候变化领域集成服务门户(CCPortal): Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models

CCPortal

DOI	10.1007/s10661-024-12347-1
	Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models
	Pasha, S. Vazeed; Reddy, C. Sudhakar
发表日期	2024
ISSN	0167-6369
EISSN	1573-2959
起始页码	196
结束页码	2
卷号	196 期号:2
英文摘要	Climate change is one of the factors contributing to the spread of invasive alien species. As a result, it is critical to investigate potential invasion dynamics on a global scale in the face of climate change. We used updated occurrence data, bioclimatic variables, and Koppen-Geiger climatic zones to better understand the climatic niche dynamics of Prosopis juliflora L. (Fabaceae). In this study, we first compared several algorithms-MaxEnt, generalized linear model (GLM), artificial neural network (ANN), generalized boosted model (GBM), generalized additive model (GAM), and random forest (RF)-to investigate the relationships between species-environment and climate for mesquite. We identified the global climate niche similarity sites (NSSs) using the coalesce approach. This study focused on the current and future climatic suitability of P. juliflora under two global circulation models (GCMs) and two climatic scenarios, i.e., Representative Concentration Pathways (RCPs), 4.5 and 8.5, for 2050 and 2070, respectively. Sensitivity, specificity, true skill statistic (TSS), kappa coefficient, and correlation were used to evaluate model performance. Among the tested models, the machine learning algorithm random forest (RF) demonstrated the highest accuracy. The vast swaths of currently uninvaded land on multiple continents are ideal habitats for invasion. Approximately 9.65% of the area is highly suitable for the establishment of P. juliflora. Consequently, certain regions in the Americas, Africa, Asia, Europe, and Oceania have become particularly vulnerable to invasion. In relation to RCPs, we identified suitable area changes (expansion, loss, and stability). The findings of this study show that NSSs and RCPs increase the risk of invasion in specific parts of the world. Our findings contribute to a cross-border continental conservation effort to combat P. juliflora expansion into new potential invasion areas.
英文关键词	Machine learning; SDM; Niche similarity sites (NSSs)
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Sciences
WOS记录号	WOS:001149304800002
来源期刊	ENVIRONMENTAL MONITORING AND ASSESSMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/290136
作者单位	Department of Space (DoS), Government of India; Indian Space Research Organisation (ISRO); National Remote Sensing Centre (NRSC)
推荐引用方式 GB/T 7714	Pasha, S. Vazeed,Reddy, C. Sudhakar. Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models[J],2024,196(2).
APA	Pasha, S. Vazeed,&Reddy, C. Sudhakar.(2024).Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models.ENVIRONMENTAL MONITORING AND ASSESSMENT,196(2).
MLA	Pasha, S. Vazeed,et al."Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models".ENVIRONMENTAL MONITORING AND ASSESSMENT 196.2(2024).