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Identification of Crucial Stepping Stone Habitats for Biodiversity Conservation in Northeastern Madagascar Using Remote Sensing and Comparative Predictive Modeling

Biodiversity and conservation(2020)

引用 30|浏览11
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摘要
Madagascar is a global biodiversity hotspot of conservation concern. The decline of natural forest habitats due to shifting cultivation has been one of the major land use changes during the last decades. We analyzed satellite images between 1990 and 2018 from northeastern Madagascar to evaluate the contribution of nine variables (e.g., topographic, demographic, forest protection) to explain past forest loss, predict future deforestation probabilities to define important areas that require further conservation attention. Forest cover declined by 21% since 1990 and the once continuous rain forest belt of the region is disrupted twice, in the center and at the southern limit of the study region. Status of forest protection and proximity to the forest edge were identified as most important predictors, but all variables contributed to explaining the observed pattern of deforestation. At least 20% of the 3136 villages in the area were established since 1990 at the expense of previously forested areas. This housing sprawl was mainly driven by accessibility, decreasing landscape connectivity. To conserve the unique biodiversity of the region, the expansion of protected forests and active reforestation measures are urgently needed. Sustainable land use planning and forest management integrating the needs of local land users and conservation priorities should be promoted. We see the highest potential for external stakeholders (e.g., national NGOs) to implement targeted interventions embedded in community-based approaches. Our land cover maps and predictive modeling highlight crucial areas that could act as stepping stone habitats for dispersing or retreating species and therefore important locations to intensify conservation measures.
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关键词
Land use change,Remote sensing,Landscape connectivity,Agroforestry,Protected areas,Artificial neural networks
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