Skip to main content
SpringerLink
Log in

Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling

  • Published:
Biodiversity & Conservation Aims and scope Submit manuscript

Abstract

Statistical modelling of biological survey data in relation to remotely mapped environmental variables is a powerful technique for making more effective use of sparse data in regional conservation planning. Application of such modelling to planning in the northeast New South Wales (NSW) region of Australia represents one of the most extensive and longest running case studies of this approach anywhere in the world. Since the early 1980s, statistical modelling has been used to extrapolate distributions of over 2300 species of plants and animals, and a wide variety of higher-level communities and assemblages. These modelled distributions have played a pivotal role in a series of major land-use planning processes, culminating in extensive additions to the region's protected area system. This paper provides an overview of the analytical methodology used to model distributions of individual species in northeast NSW, including approaches to: (1) developing a basic integrated statistical and geographical information system (GIS) framework to facilitate automated fitting and extrapolation of species models; (2) extending this basic approach to incorporate consideration of spatial autocorrelation, land-cover mapping and expert knowledge; and (3) evaluating the performance of species modelling, both in terms of predictive accuracy and in terms of the effectiveness with which such models function as general surrogates for biodiversity.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Anderson R.S. and Ashe J.S. 2000. Leaf litter inhabiting beetles as surrogates for establishing priorities for conservation of selected tropical montane cloud forests in Honduras, Central America (Coleoptera; Staphylinidae, Curculionidae). Biodiversity and Conservation 9: 617–653.

    Google Scholar 

  • Anonymous 1992. National Forest Policy Statement: a New Focus for Australia's Forests. Australian Government Publishing Service, Canberra, Australia.

    Google Scholar 

  • Augustin N.H., Mugglestone M.A. and Buckland S.T. 1996. An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology 33: 339–347.

    Google Scholar 

  • Austin M.P. 1998. An ecological perspective on biodiversity investigations: examples from Australian eucalypt forests. Annals of the Missouri Botanical Garden 85: 2–17.

    Google Scholar 

  • Austin M.P. and Meyers J.A. 1996. Current approaches to modeling the environmental niche of eucalypts: implications for management of biodiversity. Forest Ecology and Management 85: 95–106.

    Google Scholar 

  • Austin M.P., Cunningham R.B. and Fleming P.M. 1984. New approaches to direct gradient analysis using environmental scalars and statistical curve-fitting procedures. Vegetatio 55: 11–27.

    Google Scholar 

  • Beard K.H., Hengartner N. and Skelly D.K. 1999. Effectiveness of predicting breeding bird distributions using probabilistic models. Conservation Biology 13: 1108–1116.

    Google Scholar 

  • Belbin L. 1991. Semi-strong hybrid scaling, a new ordination algorithm. Journal of Vegetation Science 2: 491–496.

    Google Scholar 

  • Bennett S., Watson G. and Barratt D. 1997. Species Distribution Modelling Toolkit (SPMODEL). User's Manual. Environment Forest Group, Environment Australia, Canberra, Australia.

    Google Scholar 

  • Bio A.M.F., Alkemade R. and Barendregt A. 1998. Determining alternative models for vegetation response analysis: a non-parametric approach. Journal of Vegetation Science 9: 5–16.

    Google Scholar 

  • Boyce M.S., Vernier P.R., Nielsen S.E. and Schmiegelow F.K.A. 2002. Evaluating resource selection functions. Ecological Modelling 157: 279–298.

    Google Scholar 

  • Brieman L., Friedman J.H., Olshen R.A. and Stone C.J. 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, California.

    Google Scholar 

  • Caro T.M. and O'Doherty G. 1999. On the use of surrogate species in conservation biology. Conservation Biology 13: 805–814.

    Google Scholar 

  • Cleveland W.S., Grosse E. and Shyu W.M. 1992. Local regression models. In: Chambers J.M. and Hastie T.J. (eds) Statistical Models in S. Wadsworth and Brooks, Pacific Grove, California, pp. 309–376.

    Google Scholar 

  • Commonwealth of Australia 1997. Nationally Agreed Criteria for the Establishment of a Comprehensive, Adequate and Representative Reserve System for Forest in Australia. A Report by the Joint ANZECC/MCFFA National Forest Policy Statement Implementation Sub-committee. Commonwealth of Australia, Canberra, Australia.

    Google Scholar 

  • Cowling R.M., Pressey R.L., Lombard A.T., Desmet P.G. and Ellis A.G. 1999. From representation to persistence: requirements for a sustainable system of conservation areas in the species-rich mediterranean-climate desert of southern Africa. Diversity and Distributions 5: 51–71.

    Google Scholar 

  • Cox D.R. 1958. Two further applications of a model for binary regression. Biometrika 45: 562–565.

    Google Scholar 

  • Dinerstein E., Powell G., Olson D., Wikramanayake E., Abell R., Loucks C., Underwood E., Allnutt T., Wettengel W., Ricketts T., Strand H., O'Connor S. and Burgess N. 2000. A Workbook for Conducting Biological Assessments and Developing Biodiversity Visions for Ecoregion-Based Conservation. Part 1: Terrestrial Ecoregions. Conservation Science Program, World Wildlife Fund, Washington, DC.

    Google Scholar 

  • Edwards Jr, T.C., Deshler E.T., Foster D. and Moisen G.G. 1996. Adequacy of wildlife habitat relation models for estimating spatial distributions of terrestrial vertebrates. Conservation Biology 10: 263–270.

    Google Scholar 

  • Elith J. 2000. Quantitative methods for modeling species habitat: comparative performance and an application to Australian plants. In: Ferson S. and Burgman M. (eds) Quantitative Methods for Conservation Biology. Springer-Verlag, New York, pp. 39–58.

    Google Scholar 

  • Environment Australia 1999. Response to Disturbance of Forest Species, Upper North East and Lower North East Regions. A Project Undertaken As Part of the NSW Comprehensive Regional Assessments. Resource and Conservation Division, Department of Urban Affairs and Planning, Sydney, Australia.

    Google Scholar 

  • Ferrier S. 1984. The Status of the Rufous Scrub-Bird Atrichornis Rufescens: Habitat, Geographical Variation and Abundance. Ph.D. Thesis. University of New England, Armidale, Australia.

    Google Scholar 

  • Ferrier S. 1991. Computer-based spatial extension of forest fauna survey data: current issues, problems and directions. In: Lunney D. (ed) Conservation of Australia's Forest Fauna. Royal Zoological Society of NSW, Sydney, Australia, pp. 221–227.

    Google Scholar 

  • Ferrier S. 1992a. Development of a Predictive Modelling Module for E-RMS. Unpublished consultancy report prepared by NSW National Parks and Wildlife Service. Australian National Parks and Wildlife Service, Canberra, Australia.

    Google Scholar 

  • Ferrier S. 1992b. Environmental Resource Mapping System. User's Manual. New South Wales National Parks and Wildlife Service, Sydney, Australia.

    Google Scholar 

  • Ferrier S. 1997. Biodiversity data for reserve selection: making best use of incomplete information. In: Pigram J.J. and Sundell R.C. (eds) National Parks and Protected Areas: Selection, Delimitation and Management. Centre for Water Policy Research, University of New England, Armidale, Australia, pp. 315–329.

    Google Scholar 

  • Ferrier S. 2000. Applications and directions post-1995. In: Brown D., Hines H., Ferrier S. and McKay K. (eds) Establishment of a Biological Information Base for Regional Conservation Planning in Northeast New SouthWales, Phase 1 (1991–1995). Occasional Paper no. 26, New SouthWales National Parks and Wildlife Service, Sydney, Australia, pp. 151–154.

    Google Scholar 

  • Ferrier S. 2002. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Systematic Biology 51: 331–363.

    Google Scholar 

  • Ferrier S. and Smith A.P. 1990. Using geographical information systems for biological survey design, analysis and extrapolation. Australian Biologist 3: 105–116.

    Google Scholar 

  • Ferrier S. and Watson G. 1994. Modelling the spatial distribution of forest fauna and flora: approaches to reducing and evaluating prediction error. In: Anonymous (ed) Proceedings of International Biodiversity Conference: Conserving Biological Diversity in Temperate Forest Ecosystems – Towards Sustainable Management. Centre for Resource and Environmental Studies, Australian National University, Canberra, Australia, pp. 81–82.

    Google Scholar 

  • Ferrier S. and Watson G. 1997. An Evaluation of the Effectiveness of Environmental Surrogates and Modelling Techniques in Predicting the Distribution of Biological Diversity. Environment Australia, Canberra, Australia.

    Google Scholar 

  • Ferrier S., Gray M.R., Cassis G.A. and Wilkie L. 1999a. Spatial turnover in species composition of grounddwelling arthropods, vertebrates and vascular plants in northeast New South Wales: implications for selection of forest reserves. In: Ponder W. and Lunney D. (eds) The Other 99%. The Conservation and Biodiversity of Invertebrates. Royal Zoological Society of New South Wales, Sydney, Australia, pp. 68–76.

    Google Scholar 

  • Ferrier S., Pearce J., Drielsma M., Watson G., Manion G., Whish G. and Raaphorst S. 1999b. Evaluation and Refinement of Techniques for Modelling Distributions of Species, Communities and Assemblages. Unpublished report prepared for Environment Australia. New South Wales National Parks and Wildlife Service, Sydney, Australia.

    Google Scholar 

  • Ferrier S., Brown D. and Hines H. 2000a. Environmental GIS database. In: Brown D., Hines H., Ferrier S. and McKay K. (eds) Establishment of a Biological Information Base for Regional Conservation Planning in Northeast New South Wales, Phase 1 (1991–1995). Occasional Paper no. 26, New South Wales National Parks and Wildlife Service, Sydney, Australia, pp. 33–76.

    Google Scholar 

  • Ferrier S., Brown D., Hines H., Scotts D., Griffiths S. and Hunter J. 2000b. Introduction. In: Brown D., Hines H., Ferrier S. and McKay K. (eds) Establishment of a Biological Information Base for Regional Conservation Planning in Northeast New South Wales, Phase 1 (1991–1995). Occasional Paper no. 26, New South Wales National Parks and Wildlife Service, Sydney, Australia, pp. 15–28.

    Google Scholar 

  • Ferrier S., Pressey R.L. and Barrett T.W. 2000c. A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement. Biological Conservation 93: 303–325.

    Google Scholar 

  • Ferrier S., Watson G., Hines H. and Brown D. 2000d. Predictive modelling of biological data. In: Brown D., Hines H., Ferrier S. and McKay K. (eds) Establishment of a Biological Information Base for Regional Conservation Planning in Northeast New South Wales, Phase 1 (1991–1995). Occasional Paper no. 26, New South Wales National Parks and Wildlife Service, Sydney, Australia, pp. 97–130.

    Google Scholar 

  • Ferrier S., DrielsmaM., Manion G. and Watson G. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodiversity and Conservation 11: 2309–2338 (this issue).

    Google Scholar 

  • Fielding A.H. and Bell J.F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24: 38–49.

    Google Scholar 

  • Finkel E. 1998. Ecology: software helps Australia manage forest debate. Science 281: 1789–1791.

    Google Scholar 

  • Flather C.H. and King R.M. 1992. Evaluating performance of regional wildlife habitat models: implications to resource planning. Journal of Environmental Management 34: 31–46.

    Google Scholar 

  • Franklin J. 1995. Predictive vegetation mapping: geographic modelling of biospatial patterns in relation to environmental gradients. Progress in Physical Geography 19: 474–499.

    Google Scholar 

  • Franklin J. 1998. Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. Journal of Vegetation Science 9: 733–748.

    Google Scholar 

  • Gioia P. and Pigott J.P. 2000. Biodiversity assessment: a case study in predicting richness from the potential distributions of plant species in the forests of southwestern Australia. Journal of Biogeography 27: 1065–1078.

    Google Scholar 

  • Groves C., Valutis L., Vosick D., Neely B., Wheaton K., Touval J. and Runnels B. 2000. Designing a Geography of Hope: a Practitioner's Handbook for Ecoregional Conservation Planning, 2nd ed. The Nature Conservancy, Washington, DC.

    Google Scholar 

  • Guisan A. and Zimmermann N.E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147–186.

    Google Scholar 

  • Hanski I. 1999a. Habitat connectivity, habitat continuity, and metapopulations in dynamic landscapes. Oikos 87: 209–219.

    Google Scholar 

  • Hanski I. 1999b. Metapopulation Ecology. Oxford University Press, New York.

    Google Scholar 

  • Hastie T.J. and Tibshirani R. 1990. Generalised Additive Models. Chapman & Hall, London.

    Google Scholar 

  • Hines H., Brown D. and Scotts D. 2000. Biological surveys. In: Brown D., Hines H., Ferrier S. and McKay K. (eds) Establishment of a Biological Information Base for Regional Conservation Planning in Northeast New SouthWales, Phase 1 (1991–1995). Occasional Paper no. 26, New SouthWales National Parks and Wildlife Service, Sydney, Australia, pp. 77–96.

    Google Scholar 

  • Humphries C.J., Williams P.H. and Vane-Wright R.I. 1995. Measuring biodiversity value for conservation. Annual Review of Ecology and Systematics 26: 93–111.

    Google Scholar 

  • Hutchinson M.F., Belbin L., Nicholls A.O., Nix H.A., McMahon J.P. and Ord K.D. 1997. BioRap Rapid Assessment of Biodiversity, Vol 2. Spatial Modelling Tools. Australian BioRap Consortium, CSIRO, Canberra, Australia.

    Google Scholar 

  • Kremen C. 1994. Biological inventory using target taxa: a case study of the butterflies of Madagascar. Ecological Applications 4: 407–422.

    Google Scholar 

  • Lambeck R.J. 1997. Focal species: a multi-species umbrella for nature conservation. Conservation Biology 11: 849–856.

    Google Scholar 

  • Lawes M.J. and Piper S.E. 1998. There is less to binary maps than meets the eye: the use of species distribution data in the Southern African Sub-region. South African Journal of Science 94: 207–210.

    Google Scholar 

  • Leathwick J.R. 1995. Climatic relationships of some New Zealand forest tree species. Journal of Vegetation Science 6: 237–248.

    Google Scholar 

  • Leathwick J.R. 1998. Are New Zealand's Nothofagus species in equilibrium with their environment? Journal of Vegetation Science 9: 719–732.

    Google Scholar 

  • Legendre P. 1993. Spatial autocorrelation: trouble or new paradigm? Ecology 74: 1659–1673.

    Google Scholar 

  • Lehmann A., Overton J.McC. and Leathwick J.R. 2002. GRASP: generalized regression analysis and spatial predictions. Ecological Modelling 157: 187–205.

    Google Scholar 

  • Maddock A. and du Plessis M.A. 1999. Can species data only be appropriately used to conserve biodiversity? Biodiversity and Conservation 8: 603–615.

    Google Scholar 

  • Manly B.J.F., McDonald L.L. and Thomas D.L. 1993. Resource Selection by Animals: Statistical Design and Analysis for Field Studies. Chapman & Hall, London.

    Google Scholar 

  • Margules C.R. and Austin M.P. 1994. Biological models for monitoring species decline: the construction and use of data bases. Proceedings of the Royal Society, London, B 344: 69–75.

    Google Scholar 

  • Margules C.R. and Pressey R.L. 2000. Systematic conservation planning. Nature 405: 243–253.

    Google Scholar 

  • Margules C.R. and Redhead T.D. 1995. Guidelines for Using the BioRap Methodology and Tools. CSIRO, Canberra, Australia.

    Google Scholar 

  • McCullagh P. and Nelder J.A. 1989. Generalized Linear Models, 2nd ed. Chapman & Hall, London.

    Google Scholar 

  • Miller J. and Franklin J. 2002. Predictive vegetation modelling with spatial dependence: vegetation alliances in the Mojave Desert. Ecological Modelling 157: 225–245.

    Google Scholar 

  • Miller M.E., Hui S.L. and Tierney W.M. 1991. Validation techniques for logistic regression models. Statistics in Medicine 10: 1213–1226.

    Google Scholar 

  • Moore D.M., Lees B.G. and Davey S.M. 1991. A new method for predicting vegetation distributions using decision tree analysis in a geographic information system. Environmental Management 15: 59–71.

    Google Scholar 

  • Murphy A.H. and Winkler R.L. 1992. Diagnostic verification of probability forecasts. International Journal of Forecasting 7: 435–455.

    Google Scholar 

  • Myers N., Mittermeier R.A., Mittermeier C.G., da Fonseca G.A.B. and Kent J. 2000. Biodiversity hotspots for conservation priorities. Nature 403: 853–858.

    Google Scholar 

  • Noss R.F. 1987. From plant communities to landscapes in conservation inventories: a look at the Nature Conservancy (USA). Biological Conservation 41: 11–37.

    Google Scholar 

  • Noss R.F. 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4: 355–364.

    Google Scholar 

  • NSW NPWS 1999. Modelling Areas of Habitat Significance for Vertebrate Fauna and Vascular Flora in North East NSW. A Project Undertaken As Part of the NSW Comprehensive Regional Assessments. Resource and Conservation Division, Department of Urban Affairs and Planning, Sydney, Australia.

    Google Scholar 

  • Olson D.M. and Dinerstein E. 1998. The Global 200: a representation approach to conserving the Earth's most biologically valuable ecoregions. Conservation Biology 12: 502–515.

    Google Scholar 

  • Pearce J. and Ferrier S. 2000a. Evaluating the predictive performance of habitat models developed using logistic regression. Ecological Modelling 133: 225–245.

    Google Scholar 

  • Pearce J. and Ferrier S. 2000b. An evaluation of alternative algorithms for fitting species distribution models using logistic regression. Ecological Modelling 128: 127–147.

    Google Scholar 

  • Pearce J. and Ferrier S. 2001. The practical value of modelling relative abundance of species for regional conservation planning. Biological Conservation 98: 33–43.

    Google Scholar 

  • Pearce J., Ferrier S. and Scotts D. 2001a. An evaluation of the predictive performance of distributional models for flora and fauna in northeast NSW. Journal of Environmental Management 62: 171–184.

    Google Scholar 

  • Pearce J., Cherry K., Drielsma M., Ferrier S. and Whish G. 2001b. Incorporating expert opinion and finescale vegetation mapping into statistical models of faunal distribution. Journal of Applied Ecology 38: 412–424.

    Google Scholar 

  • Polasky S., Camm J.D., Solow A.R., Csuti B., White D. and Ding R. 2000. Choosing reserve networks with incomplete species information. Biological Conservation 94: 1–10.

    Google Scholar 

  • Pressey R.L. 1998. Algorithms, politics and timber: an example of the role of science in a public, political negotiation process over new conservation areas in production forests. In: Wills R. and Hobbs R. (eds) Ecology for Everyone: Communicating Ecology to Scientists. Surrey Beatty and Sons, Sydney, Australia, pp. 73–87.

    Google Scholar 

  • Pressey R.L. 1999. Applications of irreplaceability analysis to planning and management problems. Parks 9(1): 42–51.

    Google Scholar 

  • Pressey R.L., Humphries C.J., Margules C.R., Vane-Wright R.I. and Williams P.H. 1993. Beyond opportunism: key principles for systematic reserve selection. Trends in Ecology and Evolution 8: 124–128.

    Google Scholar 

  • Simberloff D. 1998. Flagships, umbrellas, and keystones: is single-species management passé in the landscape era? Biological Conservation 83: 247–257.

    Google Scholar 

  • Smith T.B., Bruford M.W. and Wayne R.K. 1993. The preservation of process: the missing element of conservation programs. Biodiversity Letters 1: 164–167.

    Google Scholar 

  • Soberón J.M., Llorente J.B. and Oñate L. 2000. The use of specimen-label databases for conservation purposes: an example using Mexican Papilionid and Pierid butterflies. Biodiversity and Conservation 9: 1441–1466.

    Google Scholar 

  • Stockwell D.R.B., Davey S.M., Davis J.R. and Noble I.R. 1990. Using induction of decision trees to predict Greater Glider density. AI Applications 4(4): 33–43.

    Google Scholar 

  • ter Braak C.J.F. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67: 1167–1179.

    Google Scholar 

  • Thackway R. and Creswell I.D. 1997. A bioregional framework for planning the national system of protected areas in Australia. Natural Areas Journal 17: 241–247.

    Google Scholar 

  • Vane-Wright R.I. 1996. Identifying priorities for the conservation of biodiversity: systematic biological criteria within a socio-political framework. In: Gaston K.J. (ed) Biodiversity: a Biology of Numbers and Difference. Blackwell, Oxford, UK, pp. 309–341.

    Google Scholar 

  • Watson G. 1996. Predictive Species Modelling: User Manual. Unpublished report. Environment Australia, Canberra, Australia.

    Google Scholar 

  • Williams P.H. and AraÚjo M.B. 2000. Using probability of persistence to identify important areas for biodiversity conservation. Proceedings of the Royal Society, London, B 267: 1959–1966.

    Google Scholar 

  • Yee T.W. and Mitchell N.D. 1991. Generalized additive models in plant ecology. Journal of Vegetation Science 2: 587–602.

    Google Scholar 

  • Zaniewski A.E., Lehmann A. and Overton J.McC. 2002. Predicting species distribution using presenceonly data: a case study of native New Zealand ferns. Ecological Modelling 157: 259–278.

    Google Scholar 

Download references

Author information

Author notes

  1. Graham Watson

    Present address: Department of Land and Water Conservation, University of New England, P.O. Box 245, Armidale, NSW, 2351, Australia

  2. Jennie Pearce

    Present address: Canadian Forest Service, 1219 Queens St East, Sault Ste. Marie, Ontario, Canada, P6A 2E5

Authors and Affiliations

  1. Wildlife Service, New South Wales National Parks, P.O. Box 402, Armidale, NSW, 2350, Australia

    Simon Ferrier, Graham Watson, Jennie Pearce & Michael Drielsma

Authors
  1. Simon Ferrier
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Graham Watson
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jennie Pearce
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Michael Drielsma
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ferrier, S., Watson, G., Pearce, J. et al. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling. Biodiversity and Conservation 11, 2275–2307 (2002). https://doi.org/10.1023/A:1021302930424

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1021302930424

Search

Navigation