Environmental Monitoring and Assessment

, Volume 115, Issue 1–3, pp 69–85 | Cite as

Spatial Scale of Autocorrelation of Assemblages of Benthic Invertebrates in Two Upland Rivers in South-Eastern Australia and Its Implications for Biomonitoring and Impact Assessment in Streams



Spatial autocorrelation in ecological systems is a critical issue for monitoring (and a general understanding of ecological dynamics) yet there are very few data available, especially for riverine systems. Here, we report here on assemblage-level autocorrelation in the benthic-invertebrate assemblages of riffles in two adjacent, relatively pristine rivers in south-eastern Victoria, Australia (40-km reaches of the Wellington [surveys in summers of 1996 and 1997] and Wonnangatta Rivers [survey in summer of 1996 only], with 16 sites in each river). We found that analyses were similar if the data were resolved to family or to species level. Spatial autocorrelation was assessed by using Mantel-tests for the data partitioned into different sets of spatial separations of survey sites (e.g. 0–6 km, 6–12 km, etc.). We found strong small-scale (≤6 km) autocorrelation in the Wellington River, which is consistent with known dispersal abilities of many aquatic invertebrates. Surprisingly, there were strong negative correlations at longer distance classes for the Wellington River in one of the two summers (20–40 km) and the Wonnangatta River (12–20 km). That two largely unimpacted, adjacent rivers should have such different autocorrelation patterns suggests that impact assessment cannot assume dependence or independence of sites a priori. We discuss the implications of these results for use of “reference” sites to assess impacts at nominally affected sites.


ausrivas baci designs dispersal drift mantel tests oviposition riffles rivpacs 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. ANZECC: 1992, Australian Water Quality Guidelines for Fresh and Marine Waters. Canberra.Google Scholar
  2. Bagge, P.: 1995, ‘Emergence and upstream flight of lotic mayflies and caddisflies (Ephemeroptera and Trichoptera) in a lake outlet, central Finland’, Entomologica Fennica 6, 91–97.Google Scholar
  3. Barrett, G., Silcocks, A., Barry, S., Cunningham, R. and Poulter, R.: 2003, The New Atlas of Australian Birds. Birds Australia. Birds Australia Royal Australasian Ornithologists Union. Melbourne.Google Scholar
  4. Brittain, J. E. and Eikeland, T. J.: 1988, ‘Invertebrate drift. A review’, Hydrobiologia 166,77–93.CrossRefGoogle Scholar
  5. Casgrain, P. and Legendre, P.: 2001, The R package for multivariate and spatial analysis. Départemente de sciences biologiques, Université de Montréal, Montréal, Canada.Google Scholar
  6. Clarke, K. R.: 1993, ‘Non-parametric multivariate analyses of changes in community structure’, Austr. J. Ecol. 18, 117–143.Google Scholar
  7. Clarke, K. R. and Gorley, R. N.: 2001, PRIMER v5. – PRIMER-E, Plymouth, UK.Google Scholar
  8. Commonwealth of Australia: 2001, Australian Water Resources Assessment 2000. National Land and Water Resources Audit. Canberra, Australia.Google Scholar
  9. Cooper, S. D., Diehl, S., Kratz, K. and Sarnelle, O.: 1998, ‘Implications of scale for patterns and processes in stream ecology’, Austr. J. Ecol. 23, 27–40.Google Scholar
  10. Coysh, J., Nichols, S., Simpson, J., Norris, R., Barmuta, L., Chessman, B. and Blackman, P.: 2000, AUStralian RIVer Assessment System AUSRIVAS. National River Health Program Predictive Model Manual. Cooperative Research Centre for Freshwater Ecology, University of Canberra, Canberra.Google Scholar
  11. Cressie, N. A. C.: 1993, Statistics for Spatial Data. Wiley, New York.Google Scholar
  12. Crosskey, R. W.: 1990, The Natural History of Black Flies. Wiley, Chichester, UK.Google Scholar
  13. Delettre, Yannick, R. and Morvan, N.: 2000, ‘Dispersal of adult aquatic Chironomidae (Diptera) in agricultural landscapes’, Freshw. Biology 44, 399–411.CrossRefGoogle Scholar
  14. Diniz-Filho, J. A. F., Bini, L. M. and Hawkins, B. A.: 2003, ‘Spatial autocorrelation and red herrings in geographical ecology’, Global Ecol. Biogeography 12, 53–64.CrossRefGoogle Scholar
  15. Doisy, K. E. and Rabeni, C. F.: 2001, ‘Flow conditions, benthic food resources, and invertebrate community composition in a low-gradient stream in Missouri J. N. Am. Benthol. Soc. 20, 17–32.CrossRefGoogle Scholar
  16. Erman, N. A.: 1986, ‘Movements of self-marked caddisfly larvae Chryandra centralis (Trichoptera: Limnephilidiae.) in a Sierran spring stream, California, U.S.A.’, Freshw. Biology 16, 455–464.Google Scholar
  17. Faith, D. P., Humphrey, C. L. and Dostine, P. L.: 1991, ‘Statistical power and BACI designs in biological monitoring: Comparative evaluation of measures of community dissimilarity based on benthic macroinvertebrate communities in Rockhole Mine Creek, Northern Territory, Australia’, Austr. J. Marine Freshw. Res. 42, 589–602.CrossRefGoogle Scholar
  18. Franklin, R. B. and Mills, A. L.: 2003, ‘Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field’, Microbiol. Ecol. 44, 335–346.CrossRefGoogle Scholar
  19. Glaister, A.: 1999. Guide to the Identification of Australian Elmidae Larvae (Insecta: Coleoptera). Volume 21 of CRCFE/MDFRC Identification Guide Series. CRCFE/MDFRC, Albury.Google Scholar
  20. Goedmakers, A. and Pinkster, S.: 1981, ‘Population dynamics three gammarid species (Crustacea: Amphipoda) in a French chalk stream 3. Migration’, Bijdragen tot de Dierkunde 51, 145–180.Google Scholar
  21. Gornall, R., Hollingsworth, P. and Preston, C.: 1998, ‘Evidence for spatial structure and directional gene flow in a population of an aquatic plant,’ Potamogeton coloratus. Heredity 80, 414–421.Google Scholar
  22. Haines, A. T., Finlayson, B. L. and McMahon, T. A.: 1988, ‘A global classification of river regimes’, Appl. Geography 8, 255–272.CrossRefGoogle Scholar
  23. Hemsworth, R. J. and Brooker, M. P.: 1979, ‘The rate of downstream displacement of macroinvertebrates in the upper Wye, Wales’, Holarctic Ecol. 2, 130–136.Google Scholar
  24. Hershey, A. E., Pastor, J., Peterson, B. J. and Kling, G. W.: 1993, ‘Stable isotopes resolve the drift paradox for baetis mayflies in an arctic river’, Ecology 74, 2315–2325.CrossRefGoogle Scholar
  25. Holme, S.: 1979, ‘A simple sequentially rejective multiple test procedure’, Scand. J. Statistics 6, 65–70.Google Scholar
  26. Hruby, T.: 1987, ‘Using similarity measures in benthic impact assessments’, Environ. Monit. Assess. 8, 163–180.CrossRefGoogle Scholar
  27. Hughes, J. M. R. and James, B.: 1989, ‘A hydrological regionalisation of streams in Victoria, Australia and implications for steam ecology’, Austr. J. Marine Freshw. Res. 40, 303–326.CrossRefGoogle Scholar
  28. Hynes, H. B. N.: 1970, The Ecology of Running Waters. Liverpool University Press, Liverpool.Google Scholar
  29. Jackson, J. K. and Resh, V. H.: 1992, ‘Variation in genetic structure among populations of the caddisfly Helicopsyche borealis from three steams in northern California,’ U.S.A. Freshw. Biology 27, 29–42.CrossRefGoogle Scholar
  30. Jackson, J. K., McElravy, P. and Resh, V. H.: 1999, ‘Long-term movements of self-marked caddisfly larvae Trichoptera: Sericostomatidae. in a California coastal mountain stream,’ Freshw. Biology 42, 525–536.CrossRefGoogle Scholar
  31. Johnson, D.: 1996, The Vision Splendid: The Fight for the Alpine National Park. Volume 1 of The Environmental Papers. Victorian National Parks Association, Melbourne.Google Scholar
  32. Kienel, U. and Kumke, T.: 2002, ‘Combining ordination techniques and geostatistics to determine the patterns of diatom distributions at Lake Lama, Central Siberia,’ J. Paleolimnol. 28, 181–194.CrossRefGoogle Scholar
  33. Koenig, W. D.: 1999, ‘Oaks, acorns, and the geographical ecology of acorn woodpeckers,’ J. Biogeography 26, 159–165.CrossRefGoogle Scholar
  34. Koenig, W. D. and Knops, J. M. H.: 1998, ‘Testing for spatial autocorrelation in ecological studies,’ Ecography 21, 423–429.CrossRefGoogle Scholar
  35. Land Conservation Council: 1982, Report on the Alpine Study Area. Land Conservation Council, Melbourne.Google Scholar
  36. Legendre, P.: 1993, ‘Spatial autocorrelation: trouble or new paradigm?,’ Ecology 74, 1659–1673.CrossRefGoogle Scholar
  37. Legendre, P. and Fortin, M. J.: 1989, ‘Spatial pattern and ecological analysis,’ Vegetatio 80, 107–138.CrossRefGoogle Scholar
  38. Legendre, P., Dale, M. R. T., Fortin, M. J., Gurevitch, J., Hohn, M. and Myers, D.: 2002, ‘The consequences of spatial structure for the design and analysis of ecological field surveys,’ Ecography 25, 601–615.CrossRefGoogle Scholar
  39. Legendre, P., Dale, M. R. T., Fortin, M. J., Casgrain, P. and Gurevitch, J.: 2004, ‘The consequences of spatial structures on the results of field experiments,’ Ecology 85, 3202–3214.Google Scholar
  40. Legendre, P. and Legendre, L.: 1998, Numerical Ecology, Second English edition. Elsevier, Amsterdam.Google Scholar
  41. Lekve, K., Boulinier, T., Stenseth, N. C., Gjøsæ ter, J., Fromentin, J. -M., Hines, J. E. and Nichols, J. D.: 2002, ‘Spatio-Temporal Dynamics of Species Richness in Coastal Fish Communities,’ Proceedings of the Royal Society of London Series B: Biological Sciences 269, 1781–1789.Google Scholar
  42. Lichstein, J. W., Simons, T. R., Shriner, S. A. and Franzreb, K. E.: 2002, ‘Spatial autocorrelation and autoregressive models in ecology,’ Ecol. Monographs 72, 445–463.Google Scholar
  43. Lloyd, N. J.: 2001, Spatial Autocorrelation of Benthic Invertebrate Assemblages in Two Upland Victorian Streams. PhD thesis. Monash University, Melbourne, Australia.Google Scholar
  44. Lloyd, N. J., Mac Nally, R. and Lake, P. S.: In press, Spatial Autocorrelation of Assemblages of Benthic Invertebrates and its Relationship to Environmental Factors in two Upland Rivers in Southeastern Australia. Diversity and Distributions.Google Scholar
  45. Lloyd, N. J., Mac Nally, R. and Lake, P. S.: in Review, Spatial Autocorrelation of Assemblages of Benthic Invertebrates in two Upland Rivers in South-Eastern Australia.Google Scholar
  46. Logan, M.: 2000, ‘Measuring fine particle size distributions,’ Austr. Wildlife Res. 27, 191–194.CrossRefGoogle Scholar
  47. Mac Nally, R. C.: 1995, ‘On large-scale dynamics and community structure in forest birds: Lessons from some eucalypt forests of south-eastern Australia,’ Philos. Transact. Royal Soc. London B 350, 369–379.Google Scholar
  48. Mantel, N.: 1967, ‘The detection of disease clustering and a generalised regression approach,’ Cancer Res. 27, 209–220.Google Scholar
  49. Marchant, R., Metzeling, L., Graesser, A. and Suter, P.: 1989, ‘A subsampler for samples of benthic invertebrates,’ Bull. Austr. Soc. Limnol. 12, 49–52.Google Scholar
  50. Metzeling, L., Graesser, P., Suter, P. and Marchant, R.: 1984, ‘The distribution of aquatic macroinvertebrates in the upper catchment of the Latrobe River, Victoria,’ Occas. Papers Museum of Victoria 1, 1–62.Google Scholar
  51. Neves, R. J.: 1979, ‘Movements of larval and adult Pycnopsyche guttifer Walker (Trichoptera: Limnephilidae) along Factory Brook, Massachusetts,’ Am. Midland Naturalist 102, 51–58.CrossRefGoogle Scholar
  52. Otto, C. and Sjöström, P.: 1986, ‘Drifting behaviour of insect larvae,’ Hydrobiologia 131, 77–86.CrossRefGoogle Scholar
  53. Parkes, D., Newell, G. and Cheal, D.: 2003, ‘Assessing the quality if native vegetation: The ‘habitat hectares’ approach,’ Ecol. Manage. Restoration 4, S29–S38.CrossRefGoogle Scholar
  54. Perry, J. N., Liebhold, A. M., Rosenberg, M. S., Dungan, J., Miriti, M., Jakomulska, A. and Citron-Pousty, S.: 2002, ‘Illustrations and guidelines for selecting statistical methods for quantifying spatial pattern in ecological data,’ Ecography 25, 578–600.CrossRefGoogle Scholar
  55. Pinckney, J. and Sandulli, R.: 1990, ‘Spatial autocorrelation analysis of meiofaunal and microalgal populations on an intertidal sandflat: Scale linkage between consumers and resources,’ Estuarine Coastal Shelf Sci. 30, 341–354.Google Scholar
  56. Pontasch, K. W. and Brusven, M. A.: 1988, ‘Diversity and community comparison indices: Assessing macroinvertebrate recovery following a gasoline spill,’ Water Res. 22, 619–626.CrossRefGoogle Scholar
  57. Poole G. C.: 2002, ‘Fluvial landscape ecology: Addressing uniqueness within the river discontinuum,’ Freshw. Biology 47, 641–660.CrossRefGoogle Scholar
  58. Rice, S. P., Greenwood, M. T. and Joyce, C. B.: 2001, ‘Tributaries, sediment sources, and longitudinal organization of macroinvertebrate fauna along river systems,’ Can. J. Fish. Aquatic Sci. 58, 824–840.CrossRefGoogle Scholar
  59. Robinson, C. T., Reed, L. M. and Minshall, G. W.: 1992, ‘Influence of flow regime on life history, production, and genetic structure of Baetis tricaudatus Ephemeroptera and Hesperoperla pacifica Plecoptera,’ J. N. Am. Benthol. Soc. 11, 278–289.CrossRefGoogle Scholar
  60. Salas, M. R.: 1981, A Reconnaissance Survey of the Vegetation and Sedimentary Record of Lake Tali Karng. BSc (Hons) Thesis, Monash University, Melbourne.Google Scholar
  61. Schreiber, E. S. G.: 1988, Seasonal and Diel Variations in Macroinvertebrate Drift in a Temperate Australian Upland Stream. MSc thesis, Monash University.Google Scholar
  62. Soininen, J., Paavola, R. and Muotka, T.: 2004, ‘Benthic diatom communities in boreal streams: Community structure in relation to environmental and spatial gradients,’ Ecography 27, 330–342.CrossRefGoogle Scholar
  63. Sokal, R. R.: 1986, Spatial Data Analysis and Historical Processes. In: Diday, E., Lebart, L., Pagès, J. P., and Thomassone, R. (eds.), Data Analysis and Informatics IV, pp. 29–43. Amsterdam: Elsevier Science.Google Scholar
  64. Statzner, B. and Borchardt, D.: 1994, Longitudinal Patterns and Processes along Streams: Modelling Ecological Responses to Physical Gradients. In: Giller, P., Hildrew, A. G., and D.G., R. (eds.), Aquatic Ecology: Scale, Patterm and Process, pp. 113–140. Oxford: Blackwell Scientific Publications.Google Scholar
  65. Statzner, B. and Higler, B.: 1986, ‘Stream hydraulics as a major determinant of benthic invertebrate zonation patterns,’ Freshw. Biology 16, 127–139.CrossRefGoogle Scholar
  66. Storch, D., Gaston, K. J. and Cepak, J.: 2002, ‘Pink landscapes: 1/f spectra of spatial environmental variability and bird community composition,’ Proc. Royal Soc. London Series B: Biol. Sci. 269, 1791–1796.CrossRefGoogle Scholar
  67. Surber, E. W.: 1937, ‘Rainbow trout and bottom fauna production in one mile of stream,’ Transact. Am. Fish. Soc. 66, 193–202.CrossRefGoogle Scholar
  68. Tobin, P. C.: 2004, ‘Estimation of the spatial autocorrelation function: consequences of sampling dynamic populations in space and time,’ Ecography 27, 767–775.CrossRefGoogle Scholar
  69. Townsend, C. R. and Hildrew, A. G.: 1976, ‘Field experiments on the drifting, colonization and continuous redistribution of stream benthos, J. Animal Ecol. 45, 759–772.CrossRefGoogle Scholar
  70. Townsend, C. R., Doledec, S., Norris, R., Peacock, K. and Arbuckle, C.: 2003, ‘The influence of scale and geography on relationships between stream community composition and landscape variables: Description and prediction,’ Freshw. Biology 48, 768–785.CrossRefGoogle Scholar
  71. Underwood, A. J.: 1992, ‘Beyond BACI: The detection of environmental impacts on populations in the real, but variable, world,’ Exp. Marine Ecol. 161, 145–178.CrossRefGoogle Scholar
  72. Walsh, C. J.: 1997, ‘A multivariate method for determining optimal sample size in the analysis of macroinvertebrate samples,’ Marine Freshw. Res. 48, 241–248.CrossRefGoogle Scholar
  73. Waters, T. F.: 1965, ‘Interpretation of invertebrate drift in streams,’ Ecology 46, 327–234.CrossRefGoogle Scholar
  74. Watkins, A. J. and Wilson, J. B.: 1992, ‘Fine-scale community structure of lawns,’ J. Ecol. 80, 15–24.CrossRefGoogle Scholar
  75. Wiens, J. A.: 1984, Resource systems, populations and communities. In: P. W. Price, C. N. Slobodchikoff, and W. S. Gaud (eds.), A New Ecology: Novel Approaches to Interactive Systems, pp. 397–436. Wiley, New York, NY, USA.Google Scholar
  76. Wiens, J. A.: 2002, ‘Riverine landscapes: taking landscape ecology into the water,’ Freshw. Biology 47, 501–515.CrossRefGoogle Scholar
  77. Wildi, O.: 1990, ‘Sampling with multiple objectives and the role of spatial autocorrelation,’ Coenoses 5, 51–60.Google Scholar
  78. Wright, J. F., Moss, D., Armitage, P. D. and Furse, M. T.: 1984, ‘A preliminary classification of running water sites in Great Britain based on macroinvertebrate species and the prediction of community type using environmental data,’ Freshw. Biology 14, 221–256.CrossRefGoogle Scholar
  79. Wrona, F. J., Culp, J. M. and Davies, R. W.: 1982, ‘Macroinvertebrate sampling: A simplified apparatus and approach,’ Can. J. Fish. Aquatic Sci. 39, 1051–1054.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  • Natalie J Lloyd
    • 2
  • Ralph Mac Nally
    • 1
    • 2
  • P. S. Lake
    • 1
    • 2
  1. 1.Australian Centre for Biodiversity: Analysis, Policy and Management, School of Biological SciencesMonash UniversityAustralia
  2. 2.Cooperative Research Centre for Freshwater Ecology, School of Biological SciencesMonash UniversityAustralia

Personalised recommendations