Skip to main content
SpringerLink
Log in
Book cover

Expert Knowledge and Its Application in Landscape Ecology pp 229–251Cite as

Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions

  • Chapter
  • First Online:

Abstract

Conservation planning and management programs typically assume relatively homogeneous ecological landscapes. Such “ecoregions” serve multiple purposes: they support assessments of competing environmental values, reveal priorities for allocating scarce resources, and guide effective on-ground actions such as the acquisition of a protected area and habitat restoration. Ecoregions have evolved from a history of organism–environment interactions, and are delineated at the scale or level of detail required to support planning. Depending on the delineation method, scale, or purpose, they have been described as provinces, zones, systems, land units, classes, facets, domains, subregions, and ecological, biological, biogeographical, or environmental regions. In each case, they are essential to the development of conservation strategies and are embedded in government policies at multiple scales.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Accad A, Low-Choy S, Pullar D, Rochester W (2005) Bioregion classification using model-based clustering: a case study in north eastern Queensland. In: Zerger A, Argent RM (eds), MODSIM 2005 International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, Melbourne, pp 1326–1332

    Google Scholar 

  • Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Modell 157(2–3):101–118

    Article  Google Scholar 

  • Bailey RG (2004) Identifying ecoregion boundaries. Environ Manage 34:S14-S26

    Article  PubMed  Google Scholar 

  • Beier P, Brost B (2010) Use of land facets to plan for climate change: conserving the arenas, not the actors. Conserv Biol 24(3):701–710

    Article  PubMed  Google Scholar 

  • Bensmail H, Celeux G, Raftery AE, Robert CP (1997) Inference in model-based cluster analysis. Statistics Comp 7(1):1–10

    Article  Google Scholar 

  • Cheeseman P, Stutz J (1996) Bayesian classification (AutoClass): theory and results. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds), Advances in knowledge discovery and data mining. American Association for Artificial Intelligence Press/MIT Press, Menlo Park, CA, pp 153–180

    Google Scholar 

  • Christian CS, Stewart GA (1968) Methodology of integrated surveys. In: UNESCO (ed), Aerial survey and integrated studies. Conference Proceedings. UNESCO, Toulouse, pp 233–280

    Google Scholar 

  • Cork S, Sattler P, Alexandra J (2006) Biodiversity theme commentary prepared for the 2006 Australian State of the Environment Committee. Department of the Environment and Heritage, Australian Government, Canberra

    Google Scholar 

  • Cucala L, Marin J-M, Robert CP, Titterington DM (2009) A Bayesian reassessment of nearest-neighbor classification. J Am Stat Assoc 104(485):263–273

    Article  CAS  Google Scholar 

  • Department of the Environment, Water, Heritage and the Arts (2009) Assessment of Australia’s terrestrial biodiversity 2008. Biodiversity Assessment Working Group of the National Land and Water Resources Audit, Australian Government, Canberra

    Google Scholar 

  • Environment Australia (2000) Revision of the interim biogeographic regionalisation for Australia (IBRA) and development of version 5.1 - summary report. Australian Government, Canberra

    Google Scholar 

  • Fraley C, Raftery AE (2006) MCLUST version 3 for R: normal mixture modeling and model-based clustering. Technical Report No. 504. Department of Statistics, University of Washington, Seattle

    Google Scholar 

  • Frühwirth-Schnatter S (2006) Finite mixture and Markov switching models. Springer, New York

    Google Scholar 

  • Gelman A, Carlin JB, Stern HS, Rubin DB (2004) Bayesian data analysis. Chapman and Hall/CRC, New York

    Google Scholar 

  • Georgescu V, Soubeyrand S, Kretzschmar A, Laine AL (2009) Exploring spatial and multitype assemblages of species abundances. Biom J 51(6):979–995

    Article  PubMed  Google Scholar 

  • Gessler PE, Chadwick OA, Chamran F et al (2000) Modeling soil-landscape and ecosystem properties using terrain attributes. Soil Sci Soc Am J 64(6):2046–2056

    Article  CAS  Google Scholar 

  • Goswami M, O’Connor KM, Bhattarai KP (2007) Development of regionalisation procedures using a multi-model approach for flow simulation in an ungauged catchment. J Hydrol 333(2–4):517–531

    Article  Google Scholar 

  • Hardman-Mountford NJ, Hirata T, Richardson KA, Aiken J (2008) An objective methodology for the classification of ecological pattern into biomes and provinces for the pelagic ocean. Remote Sens Environ 112(8):3341–3352

    Article  Google Scholar 

  • Hargrove WW, Hoffman FM (2004) Potential of multivariate quantitative methods for delineation and visualization of ecoregions. Environ Manage 34(S1):39–60

    Google Scholar 

  • Hastie T, Tibshirani R, Friedman JH (2008) The elements of statistical learning (2nd ed). Springer Verlag, New York

    Google Scholar 

  • Ihaka R, Gentleman R (1996) R: a language for data analysis and graphics. J Comput Graph Stat 5(3):299–314

    Article  Google Scholar 

  • Kuhnert PM, Martin TG, Griffiths SP (2010) A guide to eliciting and using expert knowledge in Bayesian ecological models. Ecol Lett 13(7):900–914

    Article  PubMed  Google Scholar 

  • Kynn M (2008) The ‘heuristics and biases’ bias in expert elicitation. J Roy Stat Soc Ser A (Stat Soc) 171:239–264

    Google Scholar 

  • Leathwick JR, Snelder T, Chadderton WL et al (2011) Use of generalised dissimilarity modelling to improve the biological discrimination of river and stream classifications. Freshw Biol 56(1):21–38

    Article  Google Scholar 

  • Loveland TR, Merchant JM (2004) Ecoregions and ecoregionalization: geographical and ecological perspectives. Environ Manage 34(S1):1–13

    Google Scholar 

  • Low-Choy S, O’Leary R, Mengersen K (2009) Elicitation by design in ecology: using expert opinion to inform priors for Bayesian statistical models. Ecology 90(1):265–277

    Article  Google Scholar 

  • Mackey BG, Berry SL, Brown T (2008) Reconciling approaches to biogeographical regionalization: a systematic and generic framework examined with a case study of the Australian continent. J Biogeogr 35(2):213–229

    Article  Google Scholar 

  • McMahon G, Gregonis SM, Waltman SW et al (2001) Developing a spatial framework of common ecological regions for the conterminous United States. Environ Manage 28(3):293–316

    Article  PubMed  CAS  Google Scholar 

  • McMahon G, Wiken EB, Gauthier DA (2004) Toward a scientifically rigorous basis for developing mapped ecological regions. Environ Manage 34:S111-S124

    Article  PubMed  Google Scholar 

  • Morgan MG, Terrey J (1990) Natural regions of western New South Wales and their use for environmental management. Proc Ecol Soc Australia 16:67–73

    Google Scholar 

  • Morgan G (2000) Landscape health in Australia: a rapid assessment of the relative condition of Australia’s bioregions and subregions. Environment Australia, Australian Government, Canberra

    Google Scholar 

  • Natural Resource Management Ministerial Council (2004) Directions for the national reserve ­system – a partnership approach. Department of the Environment and Heritage, Australian Government, Canberra

    Google Scholar 

  • Neldner VJ, Wilson BA, Thompson EJ, Dillewaard HA (2005) Methodology for survey and ­mapping of regional ecosystems and vegetation communities in Queensland, version 3.0. Environmental Protection Agency, Queensland Government, Brisbane

    Google Scholar 

  • Rochester W, Accad A, Low-Choy SJ et al (2004) Final report UQ-EPA subregion classification project. The University of Queensland, Brisbane

    Google Scholar 

  • Sattler P, Creighton C (2002) Australian terrestrial biodiversity assessment 2002. National Land and Water Resources Audit, Australian Government, Canberra

    Google Scholar 

  • Sattler P, Williams R (eds) (1999) The conservation status of Queensland’s bioregional ecosystems. Environmental Protection Agency, Queensland Government, Brisbane

    Google Scholar 

  • Snelder T, Lehmann A, Lamouroux N et al (2010) Effect of classification procedure on the performance of numerically defined ecological regions. Environ Manage 45(5):939–952

    Article  PubMed  Google Scholar 

  • Stanton JP, Morgan G (1977) The rapid selection and appraisal of key endangered sites: the Queensland case study. School of Natural Resources, University of New England, Armidale

    Google Scholar 

  • Thackway R, Cresswell ID (1995) An interim biogeographic regionalisation for Australia: a framework for setting priorities in the national reserves system cooperative program, version 4.0. Australian Nature Conservation Agency, Canberra

    Google Scholar 

  • Young PAR, Cotterell MA (1993) A conservation assessment of the South-eastern Queensland 2001 region (draft report). Department of Environment, Queensland Government, Brisbane

    Google Scholar 

  • Young PAR, Dillewaard HA (1999) Chapter 12: Southeast Queensland. In: Sattler PS, Williams RD (eds), The conservation status of Queensland’s bioregional ecosystems. Environmental Protection Agency, Queensland Government, Brisbane, pp 12.1-12.75

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CSIRO Ecosystem Sciences, 1700, Canberra, ACT, 2601, Australia

    Kristen J. Williams

  2. Cooperative Research Centre for National Plant Biosecurity and Faculty of Science and Technology, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4001, Australia

    Samantha Low-Choy

  3. CSIRO Marine and Atmospheric Research, 2583, Brisbane, QLD, 001, Australia

    Wayne Rochester

  4. Faculty of Science and Technology, Queensland University of Technology, 2 George Street, Brisbane, QLD, 4001, Australia

    Clair Alston

Authors
  1. Kristen J. Williams
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samantha Low-Choy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wayne Rochester
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Clair Alston
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kristen J. Williams .

Editor information

Editors and Affiliations

  1. Ontario Forest Research Institute, Ontario Ministry of Natural Resources, Queen St. East 1235, Sault Ste. Marie, P6A 2E5, Ontario, Canada

    Ajith H. Perera

  2. Dept. Zoology, Biodiversity & Spatial Information Ctr., North Carolina State University, Raleigh, 27695, North Carolina, USA

    C. Ashton Drew

  3. Fac. Natural Resources &, Environmental Studies, University of Northern British Columbia, University Way 3333, Prince George, V2N 4Z9, British Columbia, Canada

    Chris J. Johnson

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Williams, K.J., Low-Choy, S., Rochester, W., Alston, C. (2012). Using Bayesian Mixture Models That Combine Expert Knowledge and GIS Data to Define Ecoregions. In: Perera, A., Drew, C., Johnson, C. (eds) Expert Knowledge and Its Application in Landscape Ecology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1034-8_12

Download citation

Publish with us

Policies and ethics

Search

Navigation