New Zealand Journal of Marine and Freshwater Research abstracts

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Biological assessment of rivers in the Manawatu-Wanganui region of New Zealand using a predictive macroinvertebrate model

Michael K. Joy
Russell G. Death

Institute of Natural Resources-Ecology
Massey University
Private Bag 11 222
Palmerston North, New Zealand
email: mikejoy@clear.net.nz

Abstract   This study presents a river invertebrate and classification system (RIVPACS) type bioassessment methodology for the Manawatu-Wanganui region of New Zealand. Aquatic macroinvertebrates and related physico-chemical data were collected at 127 sites, with minimal human impacts (reference sites) in 2000. The reference sites were classified into five groups based on their macroinvertebrate data using TWINSPAN. These biotic groupings were then applied to their corresponding physico-chemical data and discriminant functions were obtained to assign sites into the biotic groups using the physico-chemical data. The discriminant functions correctly allocated 72% of the sites to the correct classification group using a jack-knife validation. The probabilities from the discriminant functions were used to predict macroinvertebrate assemblages and these were compared with observed macroinvertebrate assemblages. The model was then used to assess the health of 29 test sites with known impacts. All test sites were assessed as impacted based on the 10th percentile of the reference data. To evaluate the temporal reliability of the model, data available for 11 sites sampled in 1997 and 2000 were run through the model. The results of this comparison showed little variation in O/E ratios over time and the two sites classed as impacted in 1997 were also classed as impacted in 2000.

Keywords   biomonitoring; macroinvertebrates; predictive models; RIVPACS

M02081 Received 9 October 2002; accepted 11 February 2003; Published 20 June 2003
New Zealand Journal of Marine and Freshwater Research, 2003, Vol. 37: 367-379
0028-8330/03/3702-0367 $7.00 © The Royal Society of New Zealand 2003

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