An optical plankton counter (OPC) potentially provides an assessment tool for zooplankton condition in ecosystems that is rapid, economical, and spatially extensive. We collected zooplankton data with an OPC in 20 near-shore regions of 4 of the Laurentian Great Lakes. The zooplankton size information was used to compute mean size, biomass density, and size-spectra parameters for each location. The resulting metrics were analyzed for their ability to discriminate among the Great Lakes. Biomass density provided discrimination among lakes, as did several parameters describing spectra shape and distribution. A proposed zooplankton indicator, mean size (determined with OPC measurements in this study), was found to provide discrimination among lakes. Size-spectra-related parameters added increased ability to discriminate in conjunction with the biomass density (or mean size) metric. A discriminant function analysis of the multiple metrics (mean size, biomass density, and distribution parameters) suggests that a multi metric size-based approach might be used to classify communities among lakes improving a mean-size metric. The feasibility OPCs and size-based metrics for zooplankton assessment was found to have potential for further development as assessment tools for the biological condition of zooplankton communities in the Great Lakes.
Spatial variation is well known to exist in water quality parameters of the Great Lakes nearshore; however, strong patterns for extended reaches also have been observed and found to be robust across seasonal time frames. Less is known about robustness of inter-annual variation within parameters for water quality in the nearshore. We have conducted high-resolution surveys with towed electronic instrumentation in nearshore areas of Lake Superior and have combined several seasons (2001–2005) of measurements from multiple research efforts to investigate how spatial variation compares across years. The combined survey tows ranged across approximately 1200 km of Lake Superior's south shore. In addition to the survey tracks, we also sampled fixed stations to collect calibration data and other parameters not observed by the in situ electronic sensors. The towed sensor data provided information on the spatial and temporal variability of water quality parameters along the nearshore. We found a consistent spatial pattern over time along the south shore of Lake Superior. Nearshore water quality parameters were analyzed with respect to landscape characteristics of the adjacent watersheds (US only) using multivariate stepwise regressions and found to correlate to landscape characterization. The stressor categories of landscape character that best described the nearshore parameters were agriculture-chemical usage and land-cover attributes. Peak nearshore values corresponded with landscape position that had the most altered landuse character (e.g. Duluth/Superior region). The landscape character appears to drive and maintain the spatial pattern in nearshore water quality parameters.
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