Biodiversity & Conservation

, Volume 11, Issue 12, pp 2309–2338 | Cite as

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

  • Simon Ferrier
  • Michael Drielsma
  • Glenn Manion
  • Graham Watson


Regional conservation planning can often make more effective use of sparse biological data by linking these data to remotely mapped environmental variables through statistical modelling. While modelling distributions of individual species is the best known and most widely used approach to such modelling, there are many situations in which more information can be extracted from available data by supplementing, or replacing, species-level modelling with modelling of communities or assemblages. This paper provides an overview of approaches to community-level modelling employed in a series of major land-use planning processes in the northeast New South Wales region of Australia, and evaluates how well communities and assemblages derived using these techniques function as surrogates in regional conservation planning. We also outline three new directions that may enhance the effectiveness of community-level modelling by: (1) more closely integrating modelling with traditional ecological mapping (e.g. vegetation mapping); (2) more tightly linking numerical classification and spatial modelling through application of canonical classification techniques; and (3) enhancing the applicability of modelling to data-poor regions through employment of a new technique for modelling spatial pattern in compositional dissimilarity.

Biodiversity Communities Northeast New South Wales Regional conservation planning Statistical modelling Surrogates 


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  1. Alexander R. and Millington A.C. 2000. Vegetation Mapping: From Patch to Planet. Wiley, New York.Google Scholar
  2. 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
  3. Austin M.P. 1999. The potential contribution of vegetation ecology to biodiversity research. Ecography 22: 465–484.Google Scholar
  4. Austin M.P. and Belbin L. 1982. A new approach to the species classification problem in floristic analysis. Australian Journal of Ecology 7: 75–89.Google Scholar
  5. Belbin L. 1995. PATN Users Guide and Technical Reference. CSIRO Division of Wildlife and Ecology, Canberra, Australia.Google Scholar
  6. Bernert J.A., Eilers J.M., Sullivan T.J., Freemark K.E. and Ribic C. 1997. A quantitative method for delineating regions: an example for the Western Corn Belt Plains Ecoregion of the USA. Environmental Management 21: 405–420.Google Scholar
  7. Brieman L., Friedman J.H., Olshen R.A. and Stone C.J. 1984. Classification and Regression Trees. Wadsworth International Group, Belmont, California.Google Scholar
  8. Burgman M.A. 1987. An analysis of the distribution of plants on granite outcrops in southern Western Australia using Mantel tests. Vegetatio 71: 79–86.Google Scholar
  9. Cawsey E.M., Austin M.P. and Baker B.L. 2002. Regional vegetation mapping in Australia: a case study in the practical use of statistical modelling. Biodiversity and Conservation 11: 2239–2274 (this issue).Google Scholar
  10. Clarke K.R. 1993. Non-parametric multivariate analyses of changes in community structure. Australian Journal of Ecology 18: 117–143.Google Scholar
  11. Clarke K.R. and Warwick R.M. 1998. A taxonomic distinctness index and its statistical properties. Journal of Applied Ecology 35: 523–531.Google Scholar
  12. Cody M.L. 1993. Bird diversity components within and between habitats in Australia. In: Ricklefs R.E. and Schluter D. (eds) Species Diversity in Ecological Communities: Historical and Geographical Perspectives. University of Chicago Press, Chicago, Illinois, pp. 147–158.Google Scholar
  13. De Bruin S. and Gorte B.G.H. 2000. Probabilistic image classification using geological map units applied to land-cover change detection. International Journal of Remote Sensing 21: 2389–2402.Google Scholar
  14. De Sarbo W.S. and Mahajan V. 1984. Constrained classification: the use of a priori information in cluster analysis. Psychometrika 49: 187–215.Google Scholar
  15. Fairbanks D.H.K. and Benn G.A. 2000. Identifying regional landscapes for conservation planning: a case study from KwaZulu-Natal, South Africa. Landscape and Urban Planning 50: 237–257.Google Scholar
  16. Faith D.P. 1996. Conservation priorities and phylogenetic pattern. Conservation Biology 10: 1286–1289.Google Scholar
  17. Faith D.P. and Walker P.A. 1996a. Environmental diversity: on the best-possible use of surrogate data for assessing the relative biodiversity of sets of areas. Biodiversity and Conservation 5: 399–415.Google Scholar
  18. Faith D.P. and Walker P.A. 1996b. How do indicator groups provide information about the relative biodiversity of different sets of areas? On hotspots, complementarity and pattern-based approaches. Biodiversity Letters 3: 18–25.Google Scholar
  19. Faith D.P., Minchin P.R. and Belbin L. 1987. Compositional dissimilarity as a robust measure of ecological distance. Vegetatio 69: 57–68.Google Scholar
  20. Ferrier S. 2002. Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? Systematic Biology 51: 331–363.Google Scholar
  21. 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
  22. Ferrier S., GrayM.R., Cassis G.A. and Wilkie L. 1999a. Spatial turnover in species composition of grounddwelling arthropods, vertebrates and vascular plants in northeastNewSouthWales: implications for selection of forest reserves. In: PonderW. and Lunney D. (eds). The Other 99%. The Conservation and Biodiversity of Invertebrates. Royal Zoological Society of New SouthWales, Sydney, Australia, pp. 68–76.Google Scholar
  23. 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
  24. 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
  25. Ferrier S., Manion G., Drielsma M. and Smith J. 2000b. Ecosystem Classification for the Nandewar Bioregion Derived From a Preliminary Analysis of Abiotic Environmental Variables and Floristic Survey Data. Unpublished report. NSW National Parks and Wildlife Service, Armidale, Australia.Google Scholar
  26. Ferrier S., Watson G., Hines H. and Brown D. 2000c. 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
  27. Ferrier S., Watson G., Pearce J. and Drielsma M. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. I. Species-level modelling. Biodiversity and Conservation 11: 2275–2307 (this issue).Google Scholar
  28. 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
  29. Gauch Jr H.G. 1973. The relationship between sample similarity and ecological distance. Ecology 54: 618–622.Google Scholar
  30. Gordon A.D. 1996. A survey of constrained classification. Computational Statistics and Data Analysis 21: 17–29.Google Scholar
  31. Guisan A. and Zimmermann N.E. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147–186.Google Scholar
  32. Hastie T.J. and Tibshirani R. 1990. Generalised Additive Models. Chapman & Hall, London.Google Scholar
  33. Heikkinen R.K. and Neuvonen S. 1997. Species richness of vascular plants in the subarctic landscape of northern Finland: modelling relationships to the environment. Biodiversity and Conservation 6: 1181–1201.Google Scholar
  34. Hill M.O. 1979. TWINSPAN: A FORTRAN Program for Arranging Multivariate Data in an Ordered Two-Way Table by Classification of the Individuals and Attributes. Department of Ecology and Systematics, Cornell University, Ithaca, NY.Google Scholar
  35. 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
  36. Jongman R.H., ter Braak C.J.F. and van Tongeren O.F.R. 1995. Data Analysis in Community and Landscape Ecology. Cambridge University Press, Cambridge, UK.Google Scholar
  37. Keith D.A. and Bedward M. 1999. Native vegetation of the South East Forests region, Eden, New South Wales. Cunninghamia 6: 1–218.Google Scholar
  38. Lance G.N. and WilliamsW.T. 1975. REMUL: a new divisive polythetic classificatory program. Australian Computer Journal 7: 109–112.Google Scholar
  39. Leathwick J.R., Burns B.R. and Clarkson B.D. 1998. Environmental correlates of tree alpha-diversity in New Zealand primary forests. Ecography 21: 235–246.Google Scholar
  40. Lees B.G. and Ritman K. 1991. Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environmental Management 15: 823–831.Google Scholar
  41. Legendre P. 1993. Spatial autocorrelation: trouble or new paradigm? Ecology 74: 1659–1673.Google Scholar
  42. Legendre P., Lapointe F.-J. and Casgrain P. 1994. Modeling brain evolution from behavior: a permutational regression approach. Evolution 48: 1487–1499.Google Scholar
  43. Lehmann A., Leathwick J.R. and Overton J.McC. 2002. Assessing New Zealand fern diversity from spatial predictions of species assemblages. Biodiversity and Conservation 11: 2217–2238 (this issue).Google Scholar
  44. Lenihan J.M. 1993. Ecological response surfaces for North American boreal tree species and their use in forest classification. Journal of Vegetation Science 4: 667–680.Google Scholar
  45. Lwanga J.S., Balmford A. and Badaza R. 1998. Assessing fern diversity: relative species richness and its environmental correlates in Uganda. Biodiversity and Conservation 7: 1387–1398.Google Scholar
  46. Mackey B.G., Nix H.A., Stein J.A. and Cork S.E. 1989. Assessing the representativeness of theWet Tropics of Queensland World Heritage Property. Biological Conservation 50: 279–303.Google Scholar
  47. 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
  48. Manly B.F.J. 1986. Randomization and regression methods for testing for associations with geographical, environmental and biological distances between populations. Researches on Population Ecology 28: 201–218.Google Scholar
  49. Manly B.F.J. 1991. Randomization and Monte Carlo Methods in Biology. Chapman & Hall, London.Google Scholar
  50. Margules C.R. and Pressey R.L. 2000. Systematic conservation planning. Nature 405: 243–253.Google Scholar
  51. McCullagh P. and Nelder J.A. 1989. Generalized Linear Models, 2nd Edition. Chapman & Hall, London.Google Scholar
  52. McKenzie N.L., Belbin L., Margules C.R. and Keighery G.J. 1989. Selecting representative reserve systems in remote areas. Biological Conservation 50: 239–261.Google Scholar
  53. McNaughton S.J. 1994. Conservation goals and the configuration of biodiversity. In: Forey P.L., Humphries C.J. and Vane-Wright R.I. (eds). Systematics and Conservation Evaluation. Clarendon Press, Oxford, UK, pp. 41–62.Google Scholar
  54. Michaelsen J., Davis F.W. and Borchert M. 1987. A non-parametric method for analyzing hierarchical relationships in ecological data. Coenoses 2: 39–48.Google Scholar
  55. Mielke P.W., Berry K.J. and Johnson E.S. 1976. Multi-response permutation procedures for a priori classifications. Communications in Statistics part A-Theory and Methods 5: 1409–1424.Google Scholar
  56. 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
  57. 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
  58. Nix H.A. 1991. Biogeography: pattern and process. In: Nix H.A. and Switzer M.A. (eds). Rainforest Animals: Atlas of Vertebrates Endemic to Australia's Wet Tropics. Australian National Parks and Wildlife Service, Canberra, Australia, pp. 11–39.Google Scholar
  59. Nix H.A., Faith D.P., Hutchinson M.F., Margules C.R., West J., Allison A., Kesteven J.L., Natera G., Slater W., Stein J.L. and Walker P. 2000. The BioRap Toolbox: a National Study of Biodiversity Assessment and Planning for Papua New Guinea. Consultancy Report to the World Bank. Centre for Resource and Environmental Studies, Australian National University, Canberra, Australia.Google Scholar
  60. 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
  61. Noss R.F. 1990. Indicators for monitoring biodiversity: a hierarchical approach. Conservation Biology 4: 355–364.Google Scholar
  62. NSW NPWS 1999a. Derived Forest Ecosystems: an Evaluation of Surrogacy Value and Internal Biological Variation. 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
  63. NSW NPWS 1999b. Forest Ecosystem Classification and Mapping for Upper and Lower North East CRA 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
  64. Oksanen J. and Tonteri T. 1995. Rate of compositional turnover along gradients and total gradient length. Journal of Vegetation Science 6: 815–824.Google Scholar
  65. 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
  66. Poulin R. and Morand S. 1999. Geographical distances and the similarity among parasite communities of conspecific host populations. Parasitology 119: 369–374.Google Scholar
  67. 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
  68. Simmons M.T. and Cowling R.M. 1996. Why is the Cape Peninsula so rich in plant species? An analysis of the independent diversity components. Biodiversity and Conservation 5: 551–573.Google Scholar
  69. Smouse P.E., Long J.C. and Sokal R.R. 1986. Multiple regression and correlation extensions of the Mantel test of matrix correspondence. Systematic Zoology 35: 627–632.Google Scholar
  70. Strahler A.H. 1980. The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote Sensing of Environment 10: 1135–1163.Google Scholar
  71. ter Braak C.J.F. 1986. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67: 1167–1179.Google Scholar
  72. Whittaker R.H. 1972. Evolution and measurement of species diversity. Taxon 21: 213–251.Google Scholar
  73. Whittaker R.H. 1977. Evolution of species diversity in land communities. Evolutionary Biology 10: 1–67.Google Scholar
  74. Wilson M.V. and Mohler C.L. 1983. Measuring compositional change along gradients. Vegetatio 54: 129–141.Google Scholar
  75. Winsberg S. and De Soete G. 1997. Multidimensional scaling with constrained dimensions: CONSCAL. British Journal of Mathematical and Statistical Psychology 50: 55–72.Google Scholar
  76. Wohlgemuth T. 1998. Modelling floristic species richness on a regional scale: a case study in Switzerland. Biodiversity and Conservation 7: 159–177.Google Scholar
  77. Woinarski J.C.Z., Price O. and Faith D.P. 1996. Application of a taxon priority system for conservation planning by selecting areas which are most distinct from environments already reserved. Biological Conservation 76: 147–159.Google Scholar
  78. Yee T.W. and Mitchell N.D. 1991. Generalized additive models in plant ecology. Journal of Vegetation Science 2: 587–602.Google Scholar

Copyright information

© Kluwer Academic Publishers 2002

Authors and Affiliations

  • Simon Ferrier
    • 1
  • Michael Drielsma
    • 1
  • Glenn Manion
    • 1
  • Graham Watson
    • 1
  1. 1.New South Wales National Parks and Wildlife ServiceArmidaleAustralia

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