Modelling habitat conversion in miombo woodlands: insights from Tanzania

Alex L. Lobora*, Cuthbert L. Nahonyo, Linus K. Munishi, Tim Caro, Charles Foley, Colin M. Beale

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)391-403
Number of pages13
JournalJournal of Land Use Science
Issue number5
Early online date29 May 2017
Publication statusPublished - 3 Sep 2017


  • Bayesian
  • INLA
  • land use
  • miombo
  • REDD
  • Spatio-temporal

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