Assessing spatial distribution of Coffea arabica L. in Ethiopia's highlands using species distribution models and geospatial analysis methods

•The extent of Coffea arabica L. were mapped using geospatial data analysis, high resolution satellite and aerial images.•Integrated (Species Distribution Models and geospatial analysis tools) approaches were implemented.•MaxEnt and SVM with pseudo and background absence data showed 12.2% and 23.1% area of indigenous forest, respectively.•GLM and SVM were robust modelling methods using pseudo absence data with TPR = 0.821.•MaxEnt and SVM (TPR = 0.964) were robust modelling methods in case of background absence data.

Though there is an increase in popularity of predictive modelling for assessing the geographical distribution of species, there is still a clear gap on explaining geospatial methods to derive the presence/absence of species in terms of geospatial extent besides the ambiguity of robust models. In this paper, we evaluate four major species distribution modelling methods: Artificial Neural Network (ANN), Support Vector Machines (SVM), Maximum Entropy (MaxEnt) and Generalized Linear Model (GLM) with pseudo absence and background absence data. To investigate the efficacy of these models, we present a case study using Coffea arabica L. species in Ethiopia as there was no species distribution modelling that has been done at a local scale especially in the coffee growing areas. We made predictions on 75% subsets and validation on 25% of the 112 presence of the species records that were collected from field observation and 0.5 m spatial resolution of true colour aerial photographs. Twelve biophysical explanatory variables; climatic, remote sensing based and landscape variables were employed in modelling. The results show that MaxEnt with pseudo absence data and SVM with background absence have highest area of understory coffee presence prediction with 12.2% and 23.1% area coverage of indigenous forest, respectively. The result from the model performance test using True Positive Rate (TPR) shows that GLM and SVM with pseudo absence data performed highest (TPR = 0.821). MaxEnt and SVM were the robust modelling methods (TPR = 0.964) using background absence data.