Evaluation of conservation interventions using a cellular automata-Markov model

The field of biodiversity conservation increasingly recognizes the need for empirical evaluations of conservation interventions. While the amount of money invested into conservation and the number of protected areas across the globe have been increasing in the past few decades, few well-designed empirical studies try to show what could have happened in the absence of the conservation efforts. In this paper, we propose an empirical method to evaluate such conservation intervention. We integrate a cellular automata-Markov modeling approach and a counterfactual approach showing what may have happened in the absence of a certain conservation intervention. We test this method in a human-dominated tropical landscape in Central India; however, our method is transferable to any other socio-ecological setting. The study area is located in the tropical forests of Central India and has witnessed several management strategies since its declaration as a protected area in the mid-1970s. However, landscape practitioners have identified the revised forest policy of 1988 to play a vital role in this landscape. We chose this particular forest policy as the conservation intervention in our study and tested our method using this landscape as a template. We used remote sensing and GIS techniques to analyze multi-sensor satellite data from the last 30 years to monitor forest transitions and compare them with the counterfactual scenario. Our findings not only shed light on the importance of national-level policies in governing forest dynamics, but also established our proposed method as an effective tool for empirical evaluation of conservation intervention, particularly in developing countries. Published by Elsevier B.V.