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As the tropical deforestation crisis continues, innovative schemes are being developed to reduce this loss, such as the sale of forest carbon credit. Nevertheless, to address this ongoing and pervasive loss, governments, protected area managers and donors need to know where to invest their limited conservation resources for greatest success. At the moment this prioritisation is rarely done objectively, so there is a need for new methods that predict the efficacy of different approaches. In this study, we focus on forest loss in and around one of Indonesia’s largest protected areas, Kerinci Seblat National Park (KSNP), and evaluate the effectiveness of several forest protection scenarios. First, forest loss patterns from 1985 to 2002 were mapped for the southern end of the KS region and the correlates of deforestation were determined using a logistic regression analysis. This highlighted the critical threat posed to the forest by its proximity to the forest edge and to settlements, as well as its elevation and slope. This regression model was then used to map the predicted risk of remaining forest being cleared and was combined with field data to model the results of three law enforcement scenarios up to the year 2020. This found that a strategy that concentrated patrol effort at the four main access points was found to avoid the most deforestation. These results show that modelling the impact of different protection strategies can provide important insights and could be used more widely in deforestation mitigation and designing conservation landscapes.

Modelling the effectiveness of enforcement strategies for avoiding tropical deforestation in Kerinci Seblat National Park, Sumatra