Multivariate techniques relating macroinvertebrate and environmental data from a river catchment

Ruse, L. P.

doi:10.1016/s0043-1354(96)00217-5

Cited by 29 publications

(25 citation statements)

References 8 publications

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“…Dissolved oxygen is also known as an important factor regulating benthic macroinvertebrate community composition (e.g. Ruse 1996;Weigel et al 2003;Chaves et al 2005) and low values may indicate organic pollution. Conductivity integrates several variables like natural mineral content of the water due to geology, but also minerals from pollutant degradation (effluents of wastewater treatment plants) and inorganic pollutants.…”

Section: Performance Of the Initial Ann Modelmentioning

confidence: 99%

Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks

et al. 2007

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The effect of environmental conditions on river macrobenthic communities was studied using a dataset consisting of 343 sediment samples from unnavigable watercourses in Flanders, Belgium. Artificial neural network models were used to analyse the relation among river characteristics and macrobenthic communities. The dataset included presence or absence of macroinvertebrate taxa and 12 physicochemical and hydromorphological variables for each sampling site. The abiotic variables served as input for the artificial neural networks to predict the macrobenthic community. The effects of the input variables on model performance were assessed in order to identify the most diagnostic river characteristics for macrobenthic community composition. This was done by consecutively eliminating the least important variables and, when beneficial for model performance, adding previously removed ones again. This stepwise input variable selection procedure was tested not only on a model predicting the entire macrobenthic community, but also on three models, each predicting an individual taxon. Additionally, during each step of the stepwise leave-one-out procedure, a sensitivity analysis was performed to determine the response of the predicted macroinvertebrate taxa to the input variables applied. This research illustrated that a combination of input variable selection with sensitivity analyses can contribute to the development of reliable and ecologically relevant ANN models. The river characteristics predicting presence or absence of the benthic macroinvertebrates best were the Julian day, conductivity, and dissolved oxygen content. These conditions reflect the importance of discharges of untreated wastewater that occurred during the period of investigation in nearly all Flemish rivers.

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Section: Performance Of the Initial Ann Modelmentioning

confidence: 99%

Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks

et al. 2007

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“…First, seasonal effects have not been considered although a dataset containing both spring and autumn samples is seen as optimal (Furse et al 1984;Ruse 1996;Verdonschot 2000). The taxonomic resolution at a species-level classification is desired for an in-depth analysis of macroinvertebrate responses to environmental variables (Furse et al 1984;Pardo and Armitage 1997;Verdonschot 2000;Adriaenssens et al 2004).…”

Section: Discussionmentioning

confidence: 99%

Application of clustering techniques for the characterization of macroinvertebrate communities to support river restoration management

et al. 2006

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The European Water Framework Directive prescribes that the development of a river assessment system should be based on an ecological typology taking the biological reference conditions of each river type as a starting point. Aside from this assessment, water managers responsible for river restoration actions also need to know the steering environmental factors to meet these reference conditions for biological communities in each ecological river type. As such, an ecological typology based on biological communities is a necessity for efficient river management. In this study, different clustering techniques including the Sørensen similarity ratio, ordination analysis and self-organizing maps were applied to come to an ecological classification of a river. For this purpose, a series of sites within the Zwalm river basin (Flanders, Belgium) were monitored. These river sites were then characterized in terms of biotic (macroinvertebrates), physical-chemical and habitat variables. The cluster analysis resulted in a series of characteristic biotic communities that are found under certain environmental conditions, natural as well as human-influenced. The use of multiple clustering techniques can be of advantage to draw more straightforward and robust conclusions with regard to the ecological classification of river sites. The application of the clustering techniques on the Zwalm river basin, allowed for distinguishing five mutually isolated clusters, characterized by their natural typology and their pollution status. On the basis of this study, one may conclude that river management could benefit from the use of clustering methods for the interpretation of large quantities of data. Furthermore, the clustering results might enable the development of a cenotypology useful for efficiently steering river restoration and enabling river managers to meet a good ecological status in most of the rivers as set by the European Water Framework Directive.

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“…The concentration of suspended solids seemed to be an important factor in explaining the macroinvertebrate community structure and is indeed an extremely important problem in the monitored region because of the important height differences, the removal of natural banks, and intensive agriculture activities along the watercourses. Temporal dependency was included into the multivariate analysis, but was not observed to be as important as was reported by Ruse [4].…”

Section: Analysis Of Land-use Impacts On Aquatic Macroinvertebrate Comentioning

confidence: 95%

“…To reduce the dominating influence of abundant taxa in the multivariate analysis, abundance data of macroinvertebrates were log (n + 1) transformed [4,22,23]. Because of the skewed distribution of the physical-chemical variables, log transformation was applied to become a log normal distribution [5,23,24] and land-use variables were log (n + 1) transformed to be appropriate for use in multivariate analysis.…”

Section: Study Areamentioning

confidence: 99%

“…Biological indices can be used to assess the effect of land use, although it is not possible to detect specific cause-effect relationships without controlled experimental research. However, by means of multivariate techniques, contemporary environmental data can be used to explain biological variation [4,5] and can reveal likely relationships between land-use changes and macroinvertebrate taxa. Then potential biological indicators can be assigned.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Land-Use Impact on Macroinvertebrate Communities in the Zwalm River Basin (Flanders, Belgium) Using Multivariate Analysis and Geographic Information Systems

Adriaenssens

Goethals

Pauw

2002

The Scientific World JOURNAL

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Relationships between land-use and river water quality assessed by means of biological and physical-chemical variables and habitat characteristics were analysed for the Zwalm River basin in Flanders (Belgium). The research focussed on three zones within this river basin, each characterized by different land uses, and consequently, different types of pollution, mainly of diffuse origin. Environmental data have been integrated within a Geographic Information System. Possible relationships between aquatic ecosystem and land-use variables were searched for by means of multivariate analysis.

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Multivariate techniques relating macroinvertebrate and environmental data from a river catchment

Cited by 29 publications

References 8 publications

Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks

Analysis of macrobenthic communities in Flanders, Belgium, using a stepwise input variable selection procedure with artificial neural networks

Application of clustering techniques for the characterization of macroinvertebrate communities to support river restoration management

Assessment of Land-Use Impact on Macroinvertebrate Communities in the Zwalm River Basin (Flanders, Belgium) Using Multivariate Analysis and Geographic Information Systems

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