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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.