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An ensemble of spatially explicit land-cover model projections: prospects and challenges to retrospectively evaluate deforestation policy

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Abstract

Ensemble techniques, common in many disciplines, have yet to be fully exploited with spatially explicit projections from land-change models. We trial a land-change model ensemble to assess the impact of policies designed to conserve tropical rainforest at the municipality scale in Brazil, noting the achievements made and challenges ahead. Four spatial model frameworks that were calibrated with the same predictor variables produced 21 counterfactual simulations of the actual landscape. Individual projections with a uniform calibration period gave estimates that between 29 and 68% of the simulated deforestation was saved, but lacked an uncertainty estimate, whilst batch projections from two different model frameworks provided a more dependable mean estimate that 38 and 49% deforestation was prevented with an uncertainty range of 1900 and 1000 km2. The consensus ensembles used agreement between the projections and found that the seven examples with a uniform calibration period produced an error margin of ±435.94 km2 and a prevented forest loss estimate of 50%. Using all 21 projections with diverse calibration periods improved these errors to ±179.26 km2 with a 53% estimate of prevented forest loss. Whilst we achieved a method of combining projections of different frameworks to reduce uncertainty of individual modelling frameworks, demonstrating a control model and accounting for non-linear conditions are challenges that will provide better confidence in this method as an operational tool. Such retrospective evidence could be used to make timely rewards for efforts of governments and municipalities to support tropical forest conservation and help mitigate deforestation.

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Acknowledgements

Spatial data sets were supplied by Imazon, Brazil, the socioeconomic data from Instituto Nacional de Pesquisas Espaciais, Brazil, and prepared at Imperial College by Igor Lysenco. Funding for the land-cover modelling Tansley working group was from Natural Environment Research Council Biodiversity & Ecosystem Service Sustainability grant, NERC/PR100027. AVB, IMDR and RME were supported by European Research Council grant 281986. This paper represents a contribution to Imperial College’s initiative in Grand Challenges in Ecosystems and the Environment. We also thank the feedback of the anonymous referees on earlier drafts of this paper.

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Authors and Affiliations

  1. Department of Life Sciences, Imperial College of London, Silwood Park, Ascot, SL5 7PY, UK

    Andrew V. Bradley, Isabel M. D. Rosa & Robert M. Ewers

  2. Biodiversity Conservation, German Centre for Integrative Biodiversity Research (iDiv), Deutscher Pl. 5E, 04103, Leipzig, Germany

    Isabel M. D. Rosa

  3. Martin Luther University Halle-Wittenberg, Halle, Germany

    Isabel M. D. Rosa

  4. Imazon, Travessa Dom Romualdo de Seixas, 1698, Ed Zion Business, Umarizal, Belém, Brazil

    Amintas Brandão Jr.

  5. Center for Sustainability and the Global Environment (SAGE), Nelson Institute for Environmental Studies, University of Wisconsin, 1710 University Avenue, Madison, WI, 53726, USA

    Amintas Brandão Jr.

  6. Clark Labs, Clark University, 950 Main Street, Worcester, 01610, MA, USA

    Stefano Crema

  7. Graduate School of Geography, Clark University, 950 Main Street, Worcester, 01610, MA, USA

    Carlos Dobler

  8. Centre for Water Systems, University of Exeter, North Park Road, Exeter, EX4 4QF, UK

    Simon Moulds

  9. Department of Civil and Environmental Engineering, Imperial College of London, South Kensington Campus, London, SW7 2AZ, UK

    Simon Moulds

  10. Computational Science Laboratory, Microsoft Research, 21 Station Road, Cambridge, CB1 2FB, UK

    Sadia E. Ahmed

  11. AB Sustain, 64 Innovation Way, Peterborough, PE2 6FL, UK

    Sadia E. Ahmed

  12. Environmental Change Institute, Oxford University Centre for the Environment, South Parks Road, Oxford, OX1 3QY, UK

    Tiago Carneiro

  13. TerraLAB, Computer Science Department, Federal University of Ouro Preto, Ouro Preto, Brazil

    Tiago Carneiro

  14. Microsoft Digital, Microsoft, 2 Kingdom Street, London, W2 6BD, UK

    Matthew J. Smith

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  1. Andrew V. Bradley
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  2. Isabel M. D. Rosa
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  3. Amintas Brandão Jr.
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  5. Carlos Dobler
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  6. Simon Moulds
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  7. Sadia E. Ahmed
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  10. Robert M. Ewers
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Correspondence to Andrew V. Bradley.

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Bradley, A.V., Rosa, I.M.D., Brandão, A. et al. An ensemble of spatially explicit land-cover model projections: prospects and challenges to retrospectively evaluate deforestation policy. Model. Earth Syst. Environ. 3, 1215–1228 (2017). https://doi.org/10.1007/s40808-017-0376-y

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