An integrating approach of cellular automata and ecological network to predict the impact of land use change on connectivity

Abstract

As a primary concern in biodiversity conservation, understanding the impact of land use/cover change (LUCC) on functional connectivity has the overarching significance. Yet, LUCC simulation models used in previous studies are insufficient to tackle the complex and non-linear interactions of land-use components, and thus may not be able to provide the accurate impact assessments. This paper aimed to fill this gap and to find a solution that avoids significant connectivity loss in the future LUCC. The artificial neural network (ANN) based cellular automata (CA) and graph theory were combined, to explore potential changes in functional connectivity under alternative urban expansion scenarios. Results show that, both urban land and forested land will increase in all of future scenarios; however, functional connectivity of each species will still decrease except under a population change based scenario. The increase in forested land and the contrary connectivity loss proved the strong barrier effect caused by urban expansion. The impacts vary by species: small forest mammals might benefit more from the gain in the stepping-stone-like patches, while the intra-patch connectivity loss is the primary threat for large terrestrial mammals. Finally, we identified the key connecting patches (i.e., stepping-stones) and suggested that those patches should be prioritized for protection.