Application of the Australian river bioassessment system (AUSRIVAS) in the Brantas River, East Java, Indonesia

Hart, Barry T.; Davies, P. A.; Humphrey, C. L.; Norris, RH; Sudaryanti, Sri; Trihadiningrum, Yulinah

doi:10.1006/jema.2001.0424

Cited by 20 publications

(7 citation statements)

References 5 publications

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“…impoundment, pollution or introduction of exotic species). The selection of reference sites was based on findings of Sudaryanti et al (2001), Hart et al (2001) and Tejerina-Garro et al (2005). Sudaryanti et al (2001) assessed the impact of different human activities on riverine ecology of river Brantas, Indonesia, and indicated that it is difficult to find any un-disturbed reference site in developing countries due to anthropogenic activities in catchment area of aquatic resources.…”

Section: Validation Of Reference Sites and Variations In Overall Ibi mentioning

confidence: 99%

Assessment of an index of biological integrity (IBI) to quantify the quality of two tributaries of river Chenab, Sialkot, Pakistan

Qadir

Malik

2008

Hydrobiologia

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Section: Validation Of Reference Sites and Variations In Overall Ibi mentioning

confidence: 99%

Assessment of an index of biological integrity (IBI) to quantify the quality of two tributaries of river Chenab, Sialkot, Pakistan

Qadir

Malik

2008

Hydrobiologia

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“…Plafkin et al 1989;Wright et al 1993;Simpson and Norris 2000;Hart et al 2001;Reynoldson et al 2001). It is a fundamental tenet of RBA protocols that the samples collected are representative of the community at a site (Nichols and Norris 2006).…”

Section: Introductionmentioning

confidence: 97%

What do qualitative rapid assessment collections of macroinvertebrates represent? A comparison with extensive quantitative sampling

Gillies

Hose

Turak³

2008

Environ Monit Assess

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It is a fundamental tenet of Rapid Biological Assessments (RBA) that the samples collected reflect the community from which they are drawn. As with any biological sampling, RBA collections are subject to sampling error resulting in the omission of some taxa. The aim of this study is to compare the composition of RBA samples with an estimate of community structure based on extensive quantitative sampling. We used logistic regression to explore the relationships between the frequency of a taxon being collected in an RBA sample and its biological and ecological traits, namely its abundance, distribution, body size and habit. RBA samples and quantitative estimates of community structure were made in riffles in the Kangaroo and Nepean Rivers, New South Wales, Australia. Single RBA samples may collect up to 63% of the taxa that are collected by extensive quantitative sampling at a site. The frequency of a taxon being recorded in an RBA sample was significantly and positively related to all traits tested indicating a bias in the collection methods towards large, abundant and widely distributed taxa. Accordingly, taxa missed by RBA sampling were generally small, narrowly distributed or rare. These findings enhance our understanding of what RBA samples represent, and the bias and source of errors associated with RBA sampling. This study also quantifies the utility of RBA methods for biodiversity assessment.

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“…In the end, and for all configurations, the OE ratios were calculated in a similar way as in RIVPACS/AUSRIVAS models (Wright et al ., ; Hart et al ., ). The expected value for a site, E( x ), is given by the sum of probabilities of taxa predicted with a probability higher than x % for a given site.…”

Section: Methodsmentioning

confidence: 99%

Combining Multiple Machine Learning Algorithms to Predict Taxa Under Reference Conditions for Streams Bioassessment

Feio

Viana-Ferreira

Costa

2013

River Research & Apps

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In the present study, we tested the potential of combining three machine learning techniques in a bioassessment tool to more accurately predict the pool of expected taxa at a site. This tool, the Hydra, uses the best performing technique from Support Vector Machines (SVM), Multi‐layer Perceptron and K‐Nearest Neighbour (KNN), to predict the taxa expected at a stream site, and further evaluates the quality of a site, though a classification system based on observed/expected values, similar to that used in River Invertebrate Prediction and Classification System (RIVPACS) models. To test the procedure, we used a dataset composed of 137 training sites, 15 validation sites and 174 test sites (potentially disturbed) from Portuguese streams. The combined use of three machine learning techniques was more effective in the prediction of invertebrate taxa at a site than their individual use. The three methods were always tested for all invertebrate taxa, but from the three techniques tested, SVM and KNN were most often the best performing techniques (the most accurate among the three for a higher number of taxa) in the prediction of invertebrate taxa with the present dataset. The combination of all algorithms implemented in Hydra resulted in good models for stream bioassessment (e.g. SD OE50 < 0.2, regression of O vs E: R2 > 0.6, Spearman correlations with global degradation >0.7). We also found no advantage in removing rare taxa from the training dataset, and 50% accuracy is the most adequate accuracy level for calculation of OE ratios through Hydra. Future work should consist of comparing the performance of this technique with others, such as the RIVPACS models, using the same datasets. Considering the flexibility of this technique, self‐adjustment and easy implementation through a website (aquaweb.uc.pt), we expect it to be also useful in the prediction of other aquatic elements such as fishes and algae. Copyright © 2013 John Wiley & Sons, Ltd.

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Application of the Australian river bioassessment system (AUSRIVAS) in the Brantas River, East Java, Indonesia

Cited by 20 publications

References 5 publications

Assessment of an index of biological integrity (IBI) to quantify the quality of two tributaries of river Chenab, Sialkot, Pakistan

Assessment of an index of biological integrity (IBI) to quantify the quality of two tributaries of river Chenab, Sialkot, Pakistan

What do qualitative rapid assessment collections of macroinvertebrates represent? A comparison with extensive quantitative sampling

Combining Multiple Machine Learning Algorithms to Predict Taxa Under Reference Conditions for Streams Bioassessment

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