Impact of stresses imposed on macroinvertebrate communities in two suburban streams

The aim of the project was to determine factors which explain the distribution of macroinvertebrates in two Melbourne streams both drastically affected by urbanisation. A detailed description is given of Dandenong Creek, flowing through the south-eastern suburbs, and Darebin Creek, in the northern suburbs, emphasising stream features likely, or known, to influence the drift and benthic fauna. Faunal sampling was carried out in Dandenong Creek from June 1992 until July 1993, and in Darebin Creek from February 1995 until March 1998. Physicochemical parameters were also recorded. The collected data, together with previously existing data, were analysed using multivariate analyses: non-metric multi-dimensional scaling (NMDS); analysis of similarities (ANOSIM); matching biotic and abiotic variables using BIOENV, and principal component analysis (PCA). Various biotic and diversity indices were calculated in an attempt to identify the major factors responsible for the failure of the fauna to recover from previously more seriously degraded water quality. The contribution of drift to the colonisation potential in Dandenong Creek appeared to be impacted by retarding basins, underground barrel-draining and channelization. Results also indicated that increased conductivity adversely affected the fauna in the lower reaches of Dandenong Creek. It was concluded that in Darebin Creek, high nutrient levels, as well as other pollutants, had resulted in low macroinvertebrate diversity in both the drift and benthos. If, as this study suggests, faunal diversity is a valid measure of stream health, the following factors need to be addressed for catchment-wide, stream management: lack of riparian zone vegetation (increasing bank erosion and making the benthic habitat unstable, with greater temperature variability); control of stormwater runoff (flow variability, increased conductivity, nutrient levels, sediment loads, sewage effluent, industrial discharges and heavy metals), and to modify retarding basins to increase stream continuity.

High performance liquid chromatography with two simultaneous on-line antioxidant assays : evaluation and comparison of espresso coffees

The antioxidant profiles of various espresso coffees were established using HPLC with UV-absorbance detection and two rapid, simultaneous, on-line chemical assays that enabled the relative reactivity of sample components to be screened. The assays were based on (i) the colour change associated with reduction of the 2,2´-diphenyl-1-picrylhydrazyl radical (DPPH•); and (ii) the emission of light (chemiluminescence) upon reaction with acidic potassium permanganate. Results from the two approaches were similar and reflected the complex array of antioxidant species present in the samples. However, some differences in selectivity were observed. Chromatograms generated with the chemiluminescence assay contained more peaks, which was ascribed to the greater sensitivity of the reagent towards minor, readily oxidisable sample components. The three coffee samples produced closely related profiles, signifying their fundamentally similar chemical compositions and origin. Nevertheless, the overall intensity and complexity of the samples in both UV absorption and antioxidant assay chromatograms were aligned with the manufacturers description of flavour intensity and character.

Large versus small wood in streams : the effect of wood dimension on macroinvertebrate communities

Conventionally, most research and restoration involving in-stream wood focuses on large wood (>0.1 m diameter), excluding any smaller pieces. However, this may neglect a major component of in-stream habitat, as small wood can constitute the majority of pieces, particularly in small streams. The ecological benefit of large wood is well established, but corresponding benefits associated with small wood (0.05-0.1 m diameter) have not been demonstrated. To test the effect of wood dimension on macroinvertebrate community composition, we compared the fauna occupying large wood habitats with that occupying small wood at eight streams in south-eastern Australia. The relationships between wood dimensions and its macroinvertebrate fauna were complex. Community composition did not vary with wood dimension, and no significant correlations were found between other macroinvertebrate attributes (including family richness and evenness) and wood dimension, including diameter. However, analysis of covariance suggested that large wood supported a greater diversity and abundance of macroinvertebrates, indicating that the method of analysis could influence the result. Adjustment for differences in sample dimension using rarefaction determined that these findings were likely to be a result of the surface area and volumes sampled varying with the dimension of the wood. Per unit surface area, and per unit volume, small wood supported a similar number of families to large wood. Thus we conclude that, relative to the available surface area, small and large wood can be equivalent in their contribution to the available habitat in a stream. Therefore, the potential value of small wood as a habitat resource warrants its explicit consideration for inclusion in ecological and rehabilitation studies.

Evaluation of the fuzzy ARTMAP neural network using off-line and on-line learning strategies

This paper describes an experimental study of the Fuzzy ARTMAP (FAM) neural network as an autonomous learning system for pattern classification tasks. A benchmark database of radar signals from ionosphere has been employed for the system to classify arbitrary sequences of pattern into distinct categories. A number of simulations have been conducted systematically to evaluate the applicability and usefulness of FAM in this context. First, we identify the 'optimum' parameter settings of FAM for the problem at hand, and investigate the effects of different training schemes and learning rules on classification results, using an off-line learning methodology. We then examine a voting strategy to improve classification accuracy by combining results from multiple FAM classifiers. In addition to off-line learning, we evaluate the prospect of using FAM as an autonomously learning pattern classification system for on-line, non-stationary environments. The performance of FAM is comparable with other reported results, but with the added advantage of on-line and incremental learning.