Using the MaxEnt Algorithm to Predict Habitat Suitability Under Climate Change Scenarios

Forecasting habitat suitability for species under scenarios of climate change is a crucial approach for biodiversity conservation and resource management. This study used the Maximum Entropy (MaxEnt) modelling algorithm to evaluate and predict habitat suitability for [target species] among current and projected climate conditions. Environmental data were extracted from authenticated global databases, and a range of environmental variables, including bioclimatic and topography, were selected to train the MaxEnt model. Future climate data were mapped for the years 2050 and 2070, based on projections from multiple General Circulation Models (GCMs) and Representative Concentration Pathways (RCPs) 4.5 and 8.5. The MaxEnt model's accuracy was estimated using the Area Under the Receiver Operating Characteristics Curve (AUC), and all models demonstrated high predictive performance. The predicted future habitat suitability and estimated percentage changes, distinctly demonstrated significant range shift with contraction of potential suitable habitat or expansion depending on the scenario. [key environmental variables, e.g., temperature seasonality, annual precipitation] were the most important environmental variables to influence distribution in the models. Ultimately, it was clear that the species modeled could be vulnerable to climate change, both in the present and in the future. Considering the potential impacts on biodiversity, it would be prudent to research predictive modeling in conservation planning further. Predictive modeling can yield beneficial outcomes, particularly for considering habitat changes in response to climate impacts, and may aid conservation biologists in developing adaptive responses to reduce the effects of climate change.