Global spatial distribution of Chromolaena odorata habitat under climate change: random forest modeling of one of the 100 worst invasive alien species

Abstract

Anthropogenic activities and global climate change increase the risk of Chromolaena odorata invasion and habitat expansion. To predict its global distribution and habitat suitability under climate change, a random forest (RF) model was employed. The RF model, utilizing default parameters, analyzed species presence data and background information. The model revealed that the current spatial distribution of C. odorata covers 7,892,447 km2. Predictions for 2061– 2080 indicate expansion of suitable habitat (42.59 and 46.30%), reduction of suitable habit (12.92 and 12.20%), and preservation of suitable habitat (87.08 and 87.80%) under the SSP (Shared Socio-economic Pathway) 2-4.5 and SSP5-8.5 scenarios, respectively, in comparison to the present distribution. Currently, C. odorata is predominantly found in South America, with limited presence in other continents. However, the data suggest that climate change will elevate the global invasion risk of C. odorata worldwide, particularly in Oceania, Africa, and Australia. Countries such as Gambia, Guinea-Bissau, and Lesotho, which currently have unsuitable habitats, are predicted to have highly suitable habitats with climate change, supporting the idea that global habitat expansion for C. odorata will occur due to climate change. This study indicates that proper management of C. odorata is crucial during the early invasion phase.

Assessing the Potential Distribution of Oxalis latifolia, a Rapidly Spreading Weed, in East Asia under Global Climate Change

“…Several factors contribute to the predictive performance of SDM, including the size and resolution of the study area and the threshold used for modeling [ 34 ]. Other important factors include the type of species, number of species occurrence records, and environmental variables used in the models [ 35 ] as well as the modeling algorithms and global circulation models utilized [ 36 ] and the methods for model evaluation and validation [ 36 , 37 ].…”

Ecological insights and environmental threats of invasive alien plantChromolaena odorata: Prospects for sustainable management

“…The global diversity and distribution of C. odorata remarkably increased during the past decades (Adhikari et al, 2023;Kishore et al, 2024;Ni et al, 2023). Several models such as Random forest (RF) model assisted in assessing the spatial distribution of C. odorata (Adhikari et al, 2023). In this respect, RF model predicted that C. odorata covers 7,892,447 km 2 of the global landscape.…”

“…Among global continents, Adhikari et al (2023) predicted the highest spatial distribution of C. odorata in South America (76.23%), followed by Africa (30.47%), Australia (21.79%), Oceania (19.95%), Asia (13.15%), North America (6.38%), and Europe (0.43%). Therefore, the global spread of C. odorata is observed in countries belonging to all continents, except the pristine landscapes of Antarctica.…”

“…Studies on invasion ecology therefore need to be prioritized and revitalized for maintaining the environmental sustainability (Gioria & Pyšek, 2016;Zavaleta-Cortijo et al, 2020). The environmental and health risks of invasive alien plants are further exacerbated under the event of climate change (Adhikari et al, 2023;Blumenthal et al, 2016;Hulme, 2012Hulme, , 2017Pérez et al, 2022). In this vein, the distribution pattern of Chromolaena odorata (L.) R.M.…”

Projected Impacts of Climate Change on the Range Expansion of the Invasive Straggler Daisy (Calyptocarpus vialis) in the Northwestern Indian Himalayan Region

“…Comprehending the distribution range and patterns of invasive plant species is difficult, and predicting their potential spread is even more challenging. Of late, studies have focused on computational models for estimating the likelihood of invasive species’ spatio-temporal spread using current knowledge of their distribution and different environmental variables [ 16 , 17 ]. Ecological niche modeling (ENM) predicts a species’ potential spread based on species presence and absence data, which helps in determining the strength of the relationship between a species and its environment [ 18 , 19 ].…”