PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 17, 2025Ecological Processes6 citationsOpen Access

Predicting the invasion potential of two alien plant species (Parthenium hysterophorus L., Asteraceae and Salvia tiliifolia Vahl, Lamiaceae) under the current and future climate change scenarios in Ethiopia

View Full Paper
DMDaniel MeleseMizan Tepi UniversityZWZerihun WolduAddis Ababa UniversityZAZemede AsfawAddis Ababa University

Key Points

  • The study predicts potential distribution of invasive species under climate change, with P. hysterophorus expanding to 76,977 km² by 2050.
  • AUC values were reported as 0.90 for P. hysterophorus and 0.92 for S. tiliifolia, indicating high predictive accuracy of the model.
  • Ensemble modeling combined seven algorithms to assess habitat suitability, showcasing key climatic predictors like BIO9 and BIO18.
  • Findings highlight the need for urgent control measures to manage invasive species threatening agricultural regions in Ethiopia.

Abstract

Abstract Background Invasive alien plants pose serious threats to native ecosystems, agricultural productivity, and biodiversity. Parthenium hysterophorus and Salvia tiliifolia , both originating from the Americas, have become aggressively invasive in Ethiopia since their introduction in the 1970s and 1980s, respectively. This study applies species distribution modeling (SDM) to predict their potential distribution across Ethiopia under current and future climate conditions. An ensemble modeling approach, combining 10 replications of seven algorithms (BRT, RF, GLM, GAM, MaxEnt, MARS, and SVM), was implemented using screened occurrence records and key environmental predictors. Projections were made for the present and for future periods (2050s and 2070s) under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5. Results The ensemble model demonstrated high predictive performance, with AUC values of 0.90 for P. hysterophorus and 0.92 for S. tiliifolia , and corresponding TSS scores of 0.67 and 0.74. Key climatic predictors influencing habitat suitability included BIO9, BIO18, BIO13, BIO4, BIO14, and BIO15. Under current conditions, P. hysterophorus occupies approximately 73,155 km 2 , while S. tiliifolia covers about 47,671 km 2 . P. hysterophorus is projected to expand under a moderate climate scenario (76,977 km 2 in the 2050s) but decline sharply under a high-emissions scenario (26,768 km 2 in the 2070s). In contrast, S. tiliifolia shows continuous expansion under both scenarios, reaching up to 75,843 km 2 by the 2050s. Predicted distributions suggest a high invasion risk across central, northern, southern, eastern, northwestern, southeastern, and southwestern Ethiopia, with substantial overlap with major crop-producing areas, including key teff ( Eragrostis tef , Poaceae) growing regions. Conclusions The findings indicate that both invasive weeds are expected to expand across several regions of Ethiopia, highlighting the urgent need for strengthened control efforts. Continuous monitoring and improved management of forests, agricultural lands, and pastures are essential, as these landscapes remain especially vulnerable to invasion. Proactive measures, including stricter prevention strategies, are critical to limit further spread at both local and national scales. This is particularly important in key agricultural regions such as Amhara, Oromia, Sidama, Southern Nations Nationalities and Peoples Region, Harari, and Dire Dawa, where major crops like teff, sorghum, wheat, and coffee are widely cultivated. Special attention should be given to the major teff-producing regions, which are not only agriculturally important but also culturally significant, and where the impact of these invasive weeds may be especially severe due to the crop’s short stature.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Melese et al. (2025) studied this question.

synapsesocial.com/papers/68d45e6a31b076d99fa5f00ahttps://doi.org/10.1186/s13717-025-00626-9
Ask AI
Helpful
Bookmark
Share
View Full Paper