Modeling Invasive Plant Species in Kenya’s Northern Rangelands - Ouko, Edward; Omondi, Steve; Mugo, Robinson; Wahome, Anastasia; Kasera, Kenneth; Nkurunziza, Emmanuel; Kiema, John; Flores, Africa; Adams, Emily C.; Kuraru, Samson; Wambua, Margaret (Frontiers in Environmental Science, 2020)
Kenya is composed of diverse geographic regions and is impacted by climatic variability. This diversity in conditions has led to a diverse number of plants and animals. Invasive species however, threatens this biodiversity. This study mapped the current distribution of A. reficiens and Opuntia species using occurrence data, then applied the model to identify where suitable environments the species are likely to occur under current and future climatic conditions under Representative Climate Pathways (RCPs) 2.6 and 8.5. The current distribution of the two invasive plant species was sampled in the field using an android based application and a GPS (Global Positioning System) device. Distal variables including: Elevation, human settlements, distance to rivers and vegetation indices (Monthly Normalized Difference Vegetation Indices (NDVI) and, Enhanced Vegetation Indices (EVI) derived from MODIS products 1km spatial resolution) were used as predictors. A mean of 25 replicates was used in identifying suitable niches. The model performance was evaluated using the average test AUC, average testing omission rate metrics and average regularized training gain. The predictive models for both species performed better than random prediction (P < 0.05). Average test AUC values (0.96 and 0.97 A.reficiens and Opuntia species respectively) and their associated 95% confidence intervals showed the fitted models had high discriminative ability to differentiate suitable environments for invasive plant species from random background points. The average test AUC values for A. reficiens (0.97 ± 0.01) and Opuntia species (0.96 ± 0.02) were high. Both models yielded moderate model gain values of 2.4 and 2.7 respectively. The model predictions show distribution of both A.reficiens and Opuntia species are likely to extend under future climatic scenarios; with current extents estimated at 291,900 ha and 28,700 ha respectively. Data on mapping, monitoring and assessment of the invasive species can provide governments with insight into how the poor and vulnerable are affected by the loss and degradation of biodiversity and ecosystems due to the spread of such species. This will directly or indirectly help in a achieving the Sustainable Development Goals (SDG) of the UN’s Agenda for SDG 15: Protect, restore and promote sustainable use of terrestrial ecosystems.