Skip to main content

Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks

  • Chapter
Ecological Informatics

11.5 Conclusions

The sensitivity analyses by means of validated ANN models can contribute to improved understanding of the ecology of streams and rivers. The interpretation of resulting sensitivity curves may reveal impacts of environmental conditions on the occurrence of macroinvertebrate taxa. Such additional knowledge can be useful for the bioindication of stream habitats by means of macroinvertebrate assemblages, and enhance our capacity to monitor and mitigate stream ecosystems. The shape of the sensitivity curves of taxa would indicate how important it is to manage disturbances within certain bounds in order to maintain healthy aquatic ecosystems. Taxa with a threshold response to a disturbance appear to be eliminated at a stream site that proves to be beyond a certain disturbance level. Taxa with ramp responses would gradually become rarer as disturbance intensified. The identification of such threshold conditions would provide catchment and water resource managers with a powerful tool.

Overall it can be concluded that ANN provide a powerful tool for stream modelling allowing the user not only to achieve highly accurate predictions but discover information on general trends in the data. Therefore, this methodology can efficiently be applied to determine ecological requirements of stream organisms that are not fully understood.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 289.00
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Barmuta LA (1990) Interaction between the effects of substratum, velocity and location on stream benthos: an experiment. Australian Journal of Marine and Freshwater Research, 41, 557–573

    Article  Google Scholar 

  • Bunn SE, Edward DH, NR Loneragan (1986) Spatial and temporal variation in the macroinvertebrate fauna of streams of the northern jarrah forest, Western Australia. Freshwater Biology, 16, 67–91

    Google Scholar 

  • Chon TS, Park YS, Moon KH, Cha EY (1996) Patternizing communities by using an artificial neural network. Ecol. Modelling 90, 69–78

    Article  Google Scholar 

  • Conrick DL, Cockayne BJ (Eds) (2000) Biological Monitoring and Assessment of Freshwaters using Macroinvertebrates. Background Information, Sampling and Analytical Procedures. Queensland Department of Natural Resources, Brisbane

    Google Scholar 

  • Downes BJ, Lake PS, Glaister A, Webb JA (1998) Scales and frequencies of disturbance: rock size, bed packing and variation among upland streams. Freshwater Biology 40, 625–639

    Article  Google Scholar 

  • Giller PS, Malmqvist B (1998) The Biology of Streams and Rivers. Oxford, NewYork, Oxford University Press

    Google Scholar 

  • Hawking JH, Smith FJ (1997) Colour Guide to Invertevrates of Australian Inland Water. Albury, NSW. CRC for Freshwater Ecology Identification Guide No.8

    Google Scholar 

  • Hellawell JM (1986) Biological indicators of freshwater pollution and environmental management. London & New York. Elsevier

    Google Scholar 

  • Hildrew AG, Townsend CR (1987) Organization in freshwater benthic communities. In: Organization of communities: past and present. Eds J.H.R. Gee and P.S. Giller. Oxford, Blackwell

    Google Scholar 

  • Hoang H, Recknagel F, Marshall J, Choy S (2001) Predictive Modelling of Macroinvertebrate Assemblages for Stream Habitat Assessments in Queensland (Australia). Ecological Modelling 146,1–3, 195–206

    Article  Google Scholar 

  • Hoang H (2001) Predicting Freshwater Habitat Conditions by the Distribution of Maroinvertebrates Using Artificial Neural Network. Thesis submitted for Master of Applied Science. Adelaide University, Adelaide

    Google Scholar 

  • Hynes HBN (1970) The Ecology of Running Waters. Liverpool University Press, Liverpool, p 555

    Google Scholar 

  • Lampert W, Sommer U (1997) Limnoecology: The Ecology of Lakes and Streams. New York, Oxford University Press

    Google Scholar 

  • Lawrence JF, Britton EB (1991) Coleoptera. Insects of Australia. CSIRO. Carlton, Victoria. Melbourne University press. 2: 543–683

    Google Scholar 

  • Lindegaard C, Brodersen KP (1995) Distribution of Chironomidae in the river continuum. In: Chironomids: from Genes to Ecosystems. Ed. P.S. Cranston. Melbourne, CSIRO

    Google Scholar 

  • Norton RA, Williams DD, Hogg ID, Palmen SC (1988) Biology of the orbatid mite: Mucronorthrus nasalis (Acari: oribatida: trhypochthoniidae) from small cold water springbok in eastern Canada. Canadian Journal of Zoology, 66, 622–629

    Article  Google Scholar 

  • Orth DJ, Maughan OE (1983) Microhabitat preferences of benthic fauna in a woodland stream. Hydrobiologia, 106, 157–168

    Article  Google Scholar 

  • Pudmenzky A, Marshall JC, Choy SC (1998) Preliminary application of artificial neural network model for predicting macroinvertebrates in rivers. Freshwater Biological Monitoring Report No. 9, The State of Queensland, Department of Natural Resources, Rocklea

    Google Scholar 

  • Quinn JM, Hickey CW (1994) Hydraulic parameters and benthic invertebrate distributions in two gravel bed New Zealand rivers. Freshwater Biology 18, 521–528

    Google Scholar 

  • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by backpropagation errors. Nature 323, 533–536

    Article  Google Scholar 

  • Reynoldson TB, Norris RH, Resh VH, Day KE, Rosenberg DM (1997) The reference condition: a comparison of multimetric and multivariate approaches to assess water-quality impairment using benthic macroinvertebrates. Journal of the North American Benthological Society 16(4), 833–852

    Article  Google Scholar 

  • Schleiter IM, Borchardt D, Wagner R, Dapper T, Schmidt KD, Schmidt HH, Werner H (1999) Modelling water quality, bioindication and population dynamics in lotic ecosystems using neural networks. Ecological Modelling 120, 271–286

    Article  Google Scholar 

  • Simpson S, Norris R, Barmuta L, Blackman P (1997) Australian River Assessment System — National River Health Program Predictive Model Manual (first draft). CRC Freshwater Ecology, University of Canberra, Canberra, ACT

    Google Scholar 

  • Smith BJ (1996) Identification keys to Families and Genera of Bivalve and Gastropod Moluscs foun in Australian Inland waters. Albury, NSW. CRC for Freshwater Ecology Identification Guide No.6

    Google Scholar 

  • Statzner B, Gore JA, Resh VH (1988) Hydraulic stream ecology: observed patterns and potential applications. Journal of North American Benthological Society, 7, 307–360

    Article  Google Scholar 

  • Strahler AN (1957) Quantitative analysis of watershed geomorphology. Transactions of the American Geophysical Union, 38, 913–920

    Google Scholar 

  • Suter PJ (1996) Baetidae. In: Mayfly Nymphs of Australia. A guide to Genera. Eds. J.C. Dean and P.J. Suter. Albury, NSW. CRC for Freshwater Ecology Identification Guide No.7

    Google Scholar 

  • Walley WJ, Fontama VN (1998) Neural network predictors of average score per taxon and number of families at unpolluted river sites in Great Britain. Water Research 32(2), 613–622

    Article  CAS  Google Scholar 

  • Willoughby LG, Mappin RG (1988) Distribution of Ephemerella ignita (Ephemeroptera) in streams: The role of pH and food resources. Freshwater Biology, 19, 145–155

    Google Scholar 

  • Wright JF (1995) Development and use of a system for predicting the macroinvertebrate fauna in flowing waters. Australian Journal of Ecology, 20, 181–197

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Hoang, H., Recknagel, F., Marshall, J., Choy, S. (2006). Elucidation of Hypothetical Relationships between Habitat Conditions and Macroinvertebrate Assemblages in Freshwater Streams by Artificial Neural Networks. In: Recknagel, F. (eds) Ecological Informatics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-28426-5_11

Download citation

Publish with us

Policies and ethics