A comparison between Ensemble and MaxEnt species distribution modelling approaches for conservation: A case study with Egyptian medicinal plants

Understanding the relationship between the geographical distribution of taxa and their environmental conditions is a key concept in ecology and conservation. The use of ensemble modelling methods for species distribution modelling (SDM) have been promoted over single algorithms such as Maximum Entropy (MaxEnt). Nevertheless, we suggest that in cases where data, technical support or computational power are limited, for example in developing countries, single algorithm methods produce robust and accurate distribution maps. We fit SDMs for 114 Egyptian medicinal plant species (nearly all native) with a total of 14,396 occurrence points. The predictive performances of eight single-algorithm methods (maxent, random forest (rf), support-vector machine (svm), maxlike, boosted regression trees (brt), classification and regression trees (cart), flexible discriminant analysis (fda) and generalised linear models (glm)) were compared to an ensemble modelling approach combining all eight algorithms. Predictions were based originally on the current climate, and then projected into the future time slice of 2050 based on four alternate climate change scenarios (A2a and B2a for CMIP3 and RCP 2.6 and RCP 8.5 for CMIP5). Ensemble modelling, MaxEnt and rf achieved the highest predictive performances based on AUC and TSS, while svm and cart had the poorest performance. There is high similarity in habitat suitability between MaxEnt and ensemble predictive maps for both current and future emission scenarios, but lower similarity between rf and ensemble, or rf and MaxEnt. We conclude that single-algorithm modelling methods, particularly MaxEnt, are capable of producing distribution maps of comparable accuracy to ensemble methods. Furthermore, the ease of use, reduced computational time and simplicity of methods like MaxEnt provides support for their use in scenarios when the choice of modelling methods, knowledge or computational power is limited but the need for robust and accurate conservation predictions is urgent.