Global spatial distribution of Prosopis juliflora - one of the world’s worst 100 invasive alien species under changing climate using multiple machine learning models

• Published in: Environmental monitoring and assessment 2024-02, Vol.196 (2), p.196-196, Article 196
• Main Authors: Pasha, S. Vazeed, Reddy, C. Sudhakar
• Format: Article
• Language: English
• Subjects: Additives; Algorithms; Artificial neural networks; Atmospheric Protection/Air Quality Control/Air Pollution; Bioclimatology; Climate Change; Climate models; Climatic zones; Conservation; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental Monitoring; Geiger counters; Generalized linear models; Global climate; Introduced Species; Invasive species; Learning algorithms; Machine Learning; Mathematical models; Monitoring/Environmental Analysis; Neural networks; Niches; Prosopis; Prosopis juliflora; Spatial distribution; Specificity; Statistical models; Swaths
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-024-12347-1

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 Köppen-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.