Diversity, Patterns of Distribution, and Bayesian Projected Trends of the Ecological Impact of Invasive Alien Plant Species in the Mount Cameroon Region

Invasive alien plant species (IAS) are a major threat to biodiversity, ecosystem function, and sustainable land use, especially in tropical hotspots like the Mount Cameroon region. Despite its ecological significance, comprehensive data on the diversity, distribution, and projected impacts of IAS in this region have been lacking. This study systematically inventoried IAS, analysed their spatial and habitat distributions, assessed their effects on native plant communities, and modelled future expansion trends. Between September 2019 and November 2021, a field survey was conducted across 120 permanent plots (1,000 m² each) in four representative sites—Buea, Bakingili, Limbe, and Idenau-covering roadsides, farmlands, and forests. Species identification, cover estimation, and habitat characterisation followed standardised protocols. Diversity indices (Shannon-Wiener, evenness, richness, Sørensen similarity) were calculated for invaded and uninvaded plots. GIS mapping and Bayesian hierarchical modeling were employed to assess spatial patterns, environmental drivers, and project IAS expansion from 2020 to 2030. A total of 25 IAS from 16 families and 24 genera were identified, with Asteraceae and Poaceae being most salient. Annual herbs constituted 70% of the IAS flora. Species richness and evenness were highest in Buea (H’=2.57, S=17, E=0.91) and lowest in Bakingili. Roadsides and farmlands exhibited significantly higher IAS abundance than forests (p=0.036). Bayesian projections indicated a progressive expansion of IAS, with Chromolaena odorata, Tithonia diversifolia, and Eleusine indica expected to be the most aggressive invaders, and the cumulative IAS-occupied area projected to exceed 75,000 m² by 2030. Invaded sites showed marked declines in native species diversity and evenness. These findings highlight the urgent need for targeted, site-specific management interventions, particularly along roadsides and for rapidly expanding species. Integrating field inventories, GIS, and Bayesian modeling provides robust insights for prioritising eradication and control strategies to mitigate the ecological and economic impacts of IAS in the Mount Cameroon region.