Comparing two approaches to land use/cover change modeling and their implications for the assessment of biodiversity loss in a deciduous tropical forest

Land use/cover change (LUCC) modeling is an important approach to evaluating global biodiversity loss and is the topic of a wide range of research in ecology, geography and environmental social science. This paper reports on development and assessment of maps of change potential produced by two spatially explicit models and applied to a Tropical Deciduous Forest in western Mexico. The first model, DINAMICA EGO, uses the weights of evidence method which generates a map of change potential based on a set of explanatory variables and past trends involving some degree of expert knowledge. The second model, Land Change Modeler (LCM), is based upon neural networks. Both models were assessed through Relative Operating Characteristic and Difference in Potential. At the per transition level, we obtained better results using DINAMICA. However, when the per transition susceptibilities are combined to compose an overall change potential map, the map generated using LCM is more accurate because neural networks outputs are able to express the simultaneous change potential to various land cover types more adequately than individual probabilities obtained through the weights of evidence method. An analysis of the change potential obtained from both models, compared with observed deforestation and selected biodiversity indices (species richness, rarity, and biological value) showed that the prospective LUCC maps tended to identify locations with higher biodiversity levels as the most threatened areas as opposed to areas that had actually undergone deforestation. Overall however, the approximate assessment of biodiversity given by both models was more accurate than a random model.