Paper No. 10
Presentation Time: 10:45 AM


REAVIE, Euan D., Natural Resources Research Institute, University of Minnesota Duluth, 1900 East Camp Street, Ely, MN 55731 and JUGGINS, Steve, School of Geography, Politics and Sociology, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom,

Floral and faunal microfossil indicators have become a mainstay of paleoecological studies in marine and inland aquatic ecosystems. Using uniformitarian principles, modern species-environment relationships are often evaluated to calibrate these microfossils. In freshwater ecosystems, diatoms tend to be the most widely applied subfossil indicator, and modern “training sets” of regional samples are used to calibrate the diatom taxa so that they may be applied to downcore assemblages. Training set development is a major process, but there is poor understanding of the number of samples needed to optimize the power of these microfossil indicators.

Two large, diatom-based training sets from the Great Lakes were investigated to determine optimal sample sizes for inference models: (1) periphyton from coastlines, (2) pelagic phytoplankton. Weighted average models to infer phosphorus concentrations from diatom assemblage data were developed. Training set sample sizes ranging from 10 to the maximum were created through random selection, and performance of each model was evaluated. For each iteration, diatom-inferred nutrient data were related to stressor data (e.g., adjacent agricultural activity) to characterize model ability to track human activities. At least 40-80 samples were needed to capture environmental conditions to such a degree that non-analogue situations should be rare, and so should provide an unambiguous result if the diatom model was applied to any sample assemblage. One should exercise caution when dealing with smaller training sets unless there is certainty that the selected samples reflect the regional variability in species assemblages and environmental conditions. We encourage training set users to employ a similar evaluation to determine whether they have effectively sampled their region of interest. We also encourage the use of a similar optimizing procedure for any microfossil indicator that uses taxonomic information in paleoecological reconstructions.