Modeling Invasive Plant Species in Kenya’s Northern Rangelands

Abstract

Kenya is composed of diverse geographic regions and is heavily impacted by climatic variability. Habitat heterogeneity has led to a diverse number of plants and animals. Invasive species, however, threaten this biodiversity. This study mapped the current distribution of Acacia reficiens and Opuntia spp. using occurrence data, then applied a species distribution model to identify where suitable habitats occur under current and projected climatic scenarios under Representative Climate Pathways (R) 2.6 and 8.5. Occurrences of the two invasive plant species were sampled using an android-based application and a GPS (Global Positioning System) device. Predictor variables included: elevation, distance to streams and rivers, human population density, and vegetation indices (monthly Normalized Difference Vegetation Indices (NDVI) and Enhanced Vegetation Indices (EVI) derived from MODIS products 1-km spatial resolution). The mean of 25 replicates was used in identifying suitable habitats. We evaluated model performance using the average test AUC, mean testing omission rate metrics, and mean regularized training gain. The predictive models for both species performed better than random chance (p < 0.05). Mean test AUC values of 0.96 and 0.97 for A. reficiens and Opuntia spp. respectively, were achieved and their associated 95% confidence intervals showed the fitted models realized the high discriminative ability to differentiate optimal conditions for invasive plant species from random pseudo-absence points. The mean test AUC results for A. reficiens (0.97 ± 0.02) and Opuntia spp. (0.985 ± 0.01) were regarded as high. The models yielded moderate test gain values of 2.4 and 2.7, respectively. The model predictions show the distributions of A. reficiens and Opuntia spp. may increase under future climatic scenarios; with current extents estimated at 339,000 and 183,000 ha, respectively, with projected future spread reaching 732,800 and 206,900 ha, respectively, by 2070. Data on mapping, monitoring, and assessment of the invasive species can provide governments with insight into how the poor and vulnerable people are affected by the loss and degradation of biodiversity and ecosystems due to the spread of such species. This information is key in achieving the Sustainable Development Goals 15 (SDG) of the UN, aimed at the protection, restoration, and promotion of sustainable use of terrestrial ecosystems.