Modeling Aquatic Macroinvertebrate Richness Using Landscape Attributes

We used a rapid, repeatable, and inexpensive geographic information system (GIS) approach to predict aquatic macroinvertebrate family richness using the landscape attributes stream gradient, riparian forest cover, and water quality. Stream segments in the Allegheny River basin were classified into eight habitat classes using these three landscape attributes. Biological databases linking macroinvertebrate families with habitat classes were developed using life habits, feeding guilds, and water quality preferences and tolerances for each family. The biological databases provided a link between fauna and habitat enabling estimation of family composition in each habitat class and hence richness predictions for each stream segment. No difference was detected between field collected and modeled predictions of macroinvertebrate families in a paired t-test. Further, predicted stream gradient, riparian forest cover, and total phosphorus, total nitrogen, and suspended sediment classifications matched observed classifications much more often than by chance alone. High gradient streams with forested riparian zones and good water quality were predicted to have the greatest macroinvertebrate family richness and changes in water quality were predicted to have the greatest impact on richness. Our findings indicate that our model can provide meaningful landscape scale macroinvertebrate family richness predictions from widely available data for use in focusing conservation planning efforts.Peer reviewe