Mapping and predicting forest loss in a Sumatran tiger landscape from 2002 to 2050.

Abstract Riau Province in central Sumatra, with its peatland, lowland, and montane forest habitats, was once a stronghold for Sumatran tiger (Panthera tigris sumatrae) populations. Today, Riau may have one of the highest deforestation rates in the world and wildlife populations are dwindling, with n...

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Published in:Journal of Environmental Management Vol. 231; pp. 397 - 405
Main Authors: Poor, Erin E., Shao, Yang, Kelly, Marcella J.
Format: Article
Published: Academic Press Inc. Feb2019
Subjects:
Online Access:View this record in EBSCOhost
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        03014797
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      dt: Feb2019
      vid: 231
      pid: 735
      pub: Academic Press Inc.
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        133750126
        10.1016/j.jenvman.2018.10.065
      ppf: 397
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      tig:
        atl: Mapping and predicting forest loss in a Sumatran tiger landscape from 2002 to 2050.
      aug:
        au:
          Poor, Erin E.
          Shao, Yang
          Kelly, Marcella J.
        affil:
          Virginia Tech, Department of Fish and Wildlife Conservation, 100 Cheatham Hall, 310 W. Campus Drive, Blacksburg, VA 24060, USA
          Virginia Tech, Department of Geography, 115 Major Williams Hall, 220 Stanger Street, Blacksburg, VA 24060, USA
      su:
        Deforestation
        Land cover
        Sumatran tiger
        Forest mapping
        Mountain forests
        Animal populations
      sug:
        subj:
          Deforestation
          Land cover
          Sumatran tiger
          Forest mapping
          Mountain forests
          Animal populations
      keyword:
        Connectivity
        Land cover map
        Land cover prediction
        Palm oil
        Connectivity
        Land cover map
        Land cover prediction
        Palm oil
      ab: Abstract Riau Province in central Sumatra, with its peatland, lowland, and montane forest habitats, was once a stronghold for Sumatran tiger (Panthera tigris sumatrae) populations. Today, Riau may have one of the highest deforestation rates in the world and wildlife populations are dwindling, with natural forest now comprising approximately only 18% of the province, mostly contained within protected areas. Agriculture (acacia, rubber, and oil palm) makes up the majority of Riau's land cover and deforestation for the creation of new plantations is rampant. Natural forest and tigers still remain in Bukit Tigapuluh National Park and Rimbang Baling Wildlife Reserve, which remain connected to tiger populations in montane forest on the western edge of Sumatra. In this study, using freely available Landsat imagery and a maximum likelihood classification algorithm, we create land cover maps for central Sumatra from 2002 to 2016. We then use current land cover, elevation, and slope variables to predict changes from forest to plantation from 2016 to 2050 at five year intervals using a multilayer perceptron neural network. Finally, we compare connectivity based on a 100 km distance threshold (based on potential tiger dispersal) across the landscape and across years. Land cover maps had 80–90% accuracy, and we predict forest in Tesso Nilo and the western edge of the study area to be lost by 2050 given current rates of deforestation. Our connectivity analysis shows that Tesso Nilo and the area between Rimbang Baling and Bukit Tigapuluh are important components for maintaining connectivity throughout the study area. Focusing conservation and rehabilitation efforts on forests close to plantations in flat areas, including Tesso Nilo, is necessary to maintain forests and increase connectivity in Riau to ensure future habitat connectivity for survival of tigers and Sumatra's other diverse endemic species. Highlights • Newly created, accuracy assessed land cover maps are 81.05–92.06% accurate. • From 2002 to 2016, 34.55% of forest in central Riau has been lost. • We predict 58.19% of forest will further be lost from 2016 to 2050. • By 2050 nearly all forest will be lost in Tesso Nilo National Park. • The integral index of connectivity across the landscape decreases by 92%, indicating lack of connected forests for wildlife.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
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