Chapter 9 . Macroinvertebrate and Stream Habitat Relationships
189
Another example for the fact that ecological patterns underlie multivariate
factors is indicated by the relationship between mean species richness and pR
(Rildrew and Townsend 1987). Streams with a pR as high as 6.5 but low
alkalinity (low Ca2+) often show similar features as acidic waters with pR< 5.5
(Willoughby and Mappin 1988). Effects of pR on aquatic fauna are different at
different water temperatures (Rynes 1970). Food supply also depends on current
speed. either to convey particles to filter feeding organisms or to deposit detritus
(RellaweIl 1986). Toxicity of ammonia and hydrogen sulfide to aquatic organism
is dependent on both temperature and pR conditions.
These and other examples illustrate the multivariate effects of different habitat
conditions on distributions of macroinvertebrates. To study the effect of individual
variable while keeping all other variables at their respective means ignores this
fact. Further research needs to consider techniques for multivariate sensitivity
analysis in order to elucidate aquatic habitat conditions.
9.5
Conclusions
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 too1.
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.
189
Another example for the fact that ecological patterns underlie multivariate
factors is indicated by the relationship between mean species richness and pR
(Rildrew and Townsend 1987). Streams with a pR as high as 6.5 but low
alkalinity (low Ca2+) often show similar features as acidic waters with pR< 5.5
(Willoughby and Mappin 1988). Effects of pR on aquatic fauna are different at
different water temperatures (Rynes 1970). Food supply also depends on current
speed. either to convey particles to filter feeding organisms or to deposit detritus
(RellaweIl 1986). Toxicity of ammonia and hydrogen sulfide to aquatic organism
is dependent on both temperature and pR conditions.
These and other examples illustrate the multivariate effects of different habitat
conditions on distributions of macroinvertebrates. To study the effect of individual
variable while keeping all other variables at their respective means ignores this
fact. Further research needs to consider techniques for multivariate sensitivity
analysis in order to elucidate aquatic habitat conditions.
9.5
Conclusions
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 too1.
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.