Mapping the distribution of invasive Prosopis juliflora in Ethiopia using MODIS and climate predictors

Background/Question/Methods

The distribution of invasive Prosopis juliflora in invaded ranges has not been properly mapped. We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitats of P. juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250-m spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran MaxEnt models using non-correlated variables and the 143 species-occurrence points. MaxEnt generated probability surfaces were converted into binary maps using the 10- percentile logistic threshold values. Performances of models were evaluated using Area Under the Receiver Operating Characteristic Curve (AUC).

Results/Conclusions

The results indicate that the extent of P. juliflora invasion is approximately 3,605 Km² in the Afar region (AUC = 0.94), while the potential habitat for future infestations is 5,024 Km² (AUC = 0.95). Our analyses demonstrates that time-series of MODIS vegetation indices and species occurrence points can be used with MaxEnt modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and guide land managers and policy makers in containing P. juliflora. Our methods can be replicated for managing invasive species in other East African countries.