Modelling habitat conversion in miombo woodlands: insights from Tanzania
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Taylor & Francis Group
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Research Article published by Taylor & Francis Group
Understanding the drivers of natural habitat conversion is a major challenge, yet predicting where future losses may occur is crucial to preventing them. Here, we used Bayesian analysis to model spatio-temporal patterns of land-use/cover change in two protected areas designations and unclassified land in Tanzania using time-series satellite images. We further investigated the costs and benefits of preserving fragmenting habitat joining the two ecosystems over the next two decades. We reveal that habitat conversion is driven by human population, existing land-use systems and the road network. We also reveal the probability of habitat conversion to be higher in the least protected area category. Preservation of habitat linking the two ecosystems saving 1640 ha of land from conversion could store between 21,320 and 49,200 t of carbon in the next 20 years, with the potential for generating between US$ 85,280 and 131,200 assuming a REDD+ project is implemented.
Understanding the drivers of natural habitat conversion is a major challenge, yet predicting where future losses may occur is crucial to preventing them. Here, we used Bayesian analysis to model spatio-temporal patterns of land-use/cover change in two protected areas designations and unclassified land in Tanzania using time-series satellite images. We further investigated the costs and benefits of preserving fragmenting habitat joining the two ecosystems over the next two decades. We reveal that habitat conversion is driven by human population, existing land-use systems and the road network. We also reveal the probability of habitat conversion to be higher in the least protected area category. Preservation of habitat linking the two ecosystems saving 1640 ha of land from conversion could store between 21,320 and 49,200 t of carbon in the next 20 years, with the potential for generating between US$ 85,280 and 131,200 assuming a REDD+ project is implemented.
Keywords
Spatio-temporal, land use