Classification of waterbodies is a necessary step for aquatic biological as
sessment. Geographic classes, such as physiographic provinces, marine bioge
ographic provinces, and ecoregions are frequently used as a classification
framework for conducting bioassessments. An alternative to such a priori cl
assification of waterbodies is to let the biological data tell us, using va
rious statistical exploratory techniques to identify classes that do not ne
cessarily conform to geographic stratification. In assessment of new sites,
it is often necessary to identify or predict which of several classes the
new site may belong to. We classified benthic macroinvertebrate assemblages
from streams of Wyoming, using 3 alternative methods: a priori establishme
nt of ecoregions, adaptive modification of the ecoregions, and a posteriori
clustering. Classification strengths were compared using similarity dendro
grams. Clustering separated the sites better than ecoregions, but the clust
ers showed strong ecoregional affinities. The modified ecoregions were equa
l to the clusters in classification strength. We conclude that an iterative
process that includes generation of hypotheses, exploratory data analysis,
and evaluation and modification of hypotheses is most likely to produce ro
bust classifications, regardless of specific analytical approaches used.