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The Biodiversity Forecasting Toolkit: Answering the ‘how much’, ‘what’, and ‘where’ of planning for biodiversity persistence

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  • Drielsma, Michael
  • Ferrier, Simon
  • Howling, Gary
  • Manion, Glenn
  • Taylor, Subhashni
  • Love, Jamie

Abstract

This research reports on a new approach to conservation assessment that seeks to extend the target-based model traditionally underpinning systematic conservation planning. The Biodiversity Forecasting Tool (BFT) helps answer three important questions relating to regional biodiversity persistence: ‘how much’ biodiversity can persist for a given land-management scenario; ‘what’ habitats to focus conservation effort on; and ‘where’ in the landscape to undertake conservation action. The tool integrates fine-scaled variability in vegetation composition and structure with spatial context, which is critical for ensuring the viability of populations. Thus, a raster data framework is employed which deems each location or gridcell in a landscape as contributing to biodiversity benefits to various degrees. At its simplest, just two spatial inputs, vegetation community types and vegetation condition, are needed. Drawing on, as a case-study, a broad-scale biodiversity assessment for NSW, Australia, this paper reports on the successful application of the BFT tool for a variety of functions ranging from interactive scenario evaluation through to conservation benefits mapping.

Suggested Citation

  • Drielsma, Michael & Ferrier, Simon & Howling, Gary & Manion, Glenn & Taylor, Subhashni & Love, Jamie, 2014. "The Biodiversity Forecasting Toolkit: Answering the ‘how much’, ‘what’, and ‘where’ of planning for biodiversity persistence," Ecological Modelling, Elsevier, vol. 274(C), pages 80-91.
    • Handle: RePEc:eee:ecomod:v:274:y:2014:i:c:p:80-91
    DOI: 10.1016/j.ecolmodel.2013.11.028
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    References listed on IDEAS

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    1. Ana S. L. Rodrigues & Sandy J. Andelman & Mohamed I. Bakarr & Luigi Boitani & Thomas M. Brooks & Richard M. Cowling & Lincoln D. C. Fishpool & Gustavo A. B. da Fonseca & Kevin J. Gaston & Michael Hoff, 2004. "Effectiveness of the global protected area network in representing species diversity," Nature, Nature, vol. 428(6983), pages 640-643, April.
    2. Drielsma, Michael & Ferrier, Simon & Manion, Glenn, 2007. "A raster-based technique for analysing habitat configuration: The cost–benefit approach," Ecological Modelling, Elsevier, vol. 202(3), pages 324-332.
    3. C. R. Margules & R. L. Pressey, 2000. "Systematic conservation planning," Nature, Nature, vol. 405(6783), pages 243-253, May.
    4. Meppem, Tony & Gill, Roderic, 1998. "Planning for sustainability as a learning concept," Ecological Economics, Elsevier, vol. 26(2), pages 121-137, August.
    5. Williams, Kristen J. & Reeson, Andrew F. & Drielsma, Michael J. & Love, Jamie, 2012. "Optimised whole-landscape ecological metrics for effective delivery of connectivity-focused conservation incentive payments," Ecological Economics, Elsevier, vol. 81(C), pages 48-59.
    6. Sterman, John., 1994. "Learning in and about complex systems," Working papers 3660-94., Massachusetts Institute of Technology (MIT), Sloan School of Management.
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    Cited by:

    1. Drielsma, Michael J. & Love, Jamie & Taylor, Subhashni & Thapa, Rajesh & Williams, Kristen J., 2022. "General Landscape Connectivity Model (GLCM): a new way to map whole of landscape biodiversity functional connectivity for operational planning and reporting," Ecological Modelling, Elsevier, vol. 465(C).

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