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DOMINATING PARAMETERS OF IMPAIRMENT AND RECOVERY
Streams draining agricultural basins are impaired by many stressors among which
channel alteration, loss of riparian buffers, siltation, reduction of watershed buffering
capacity, and nutrient loads are most important. Identifying the dominant stress may
not be simple and the abatement activities may be directed to a stress that may not
bring about the desired improvement of integrity to the best ecological status. New
models quantitatively identifying the impact of multiple stressors and threshold of
impairment and recovery are needed.
The current STAR (Science To Achieve Results) project conducted at Northeastern
University (Boston, Massachusetts) is developing non-linear models of watershed
biotic integrity and loading by extracting knowledge from large data containing
indices of biotic integrity and their metrics and stresses from several states. The
basic model concept is a hierarchical, four-layer progression of risks from landscape
and hydrologic/hydraulic stresses and diffuse and point source pollutant inputs to
instream impacts causing risks to aquatic biota. Four risks can be considered: habitat
degradation, water pollution by pollutants, sediment contamination and fragmentation
(Novotny et al., 2005). Artificial Neural Net (ANN) non-linear layered models are highly
compatible with the hierarchical risk propagation modelling concept.
The ANN models identified clusters (states) of the fish Index of Biotic Integrity (IBI)
(Virani et al. 2005) and its metrics. Data sets containing more than 50 parameters
measured several times at about 2000 sites in Maryland and Ohio were analysed.
The Self Organizing Maps (SOM) of the fish IBI and its metrics were developed
by unsupervised ANN learning. SOM (Kohonen, 2001) is a data clustering and
visualization technique which converts complex, non-linear relationships between
high-dimensional data vectors into simple geometric relationships on an easy to
visualize low-dimensional display (usually a 2-dimensional space). In SOM analysis,
each neuron unit has a different weighted connection to each and every one of
the SOM output layer. These weights model the influence of an input variable (fish
IBI metrics) to the sites patterned in an SOM neuron. SOM is an effective data
clustering tool with its output emphasizing the salient features of the data and
allowing the subsequent automatic formation of clusters of similar data items. SOMs
of environmental variables (habitat, chemistry, and macro-invertebrates) were then
overlaid over the SOMs of the metrics and overall fish IBI to identify the parameters
that showed a similar SOM pattern (Virani et al., 2005).
The three clusters of fish IBI metrics recognized in Ohio reflect the quality of the
fish community. The overall fish IBIs in the clusters indicated that sites in Cluster
I had ‘superior’ fish composition, sites in Cluster II were intermediate, and sites in
Cluster III were inferior. However, overall IBIs varied within each cluster and there
was a minor overlap because the overall IBI is a summation of scoring of metrics.
Hence the same IBI can be achieved by many variants of metric scores. Because
each neuron of SOM contains several physical monitoring sites, it was possible to
locate the clusters regionally and put them on a map (Figure 2). It can be seen that
most Cluster III sites were located in the highly agricultural northwest corner of the
state (dominated by monocultural corn growing) and around the Cleveland – Akron
industrial area. The best Cluster I sites were in the hilly more pristine eastern and
southern parts of the state.
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