The effects of livestock and tourism on vegetation include loss of biodiversity and in some cases species extinction. To evaluate these stressor-effect relationships and provide a tool for managing them in Iran's Lar National Park, we developed a multilayer perceptron (MLP) artificial neural network model to predict vegetation diversity related to human activities. Recreation and restricted zones were selected as sampling areas with maximum and minimum human impacts. Vegetation diversity was measured as the number of species in 210 sample plots. Twelve landform and soil variables were also recorded and used in model development. Sensitivity analyses identified human intensity class and soil moisture as the most significant inputs influencing the MLP. The MLP was strong with R2 values in training (0.91), validation (0.83), and test data sets (0.88). A graphical user interface was designed to make the MLP model accessible within an environmental decision support system tool for national park managers, thus enabling them to predict effects and develop proactive plans for managing human activities that influence vegetation diversity. Integr Environ Assess Manag 2021;17:42-52. © 2020 SETAC.
Keywords: Decision support system; Lar National Park; Multilayer perceptron; Vegetation diversity.
© 2020 SETAC.