Blue-green algae enhanced performance of diatom-based multimetric index on defining lake condition under high level of human disturbance

Degradation of lake conditions could result from many stressors generated by human disturbance. Accurately defining lake ecological condition by multimetric index (MMI) method is of great importance for tracking source of stressors and lake management. For algal assessment, seldom have structural and functional attributes of soft-bodied planktonic algae metrics, one important dimension of biological condition, been used to develop MMI in conjunction with diatom metrics. Another thing is that some researchers found MMI method does not perform well in mid- and high-disturbed lakes. To test the aforementioned questions, we used data sets of the 2007 National Lake Assessment project of the USEPA to develop MMIs with and without using soft-bodied planktonic algae metrics for plains and lowlands area (PLNLOW, high disturbed region of the US) and across the conterminous US. Compared to site groups modeled by single diatom assemblages, we found integrating soft-bodied planktonic algae metric (especially blue-green algae metric) into developing MMIs can significantly improve performance of MMI in PLNLOW region. The separation powers of MMIs of five level III ecoregions, developed by incorporating blue-green algae metric, are consistently higher than those developed by single diatom assemblages (p-value = 0.029). However, when blue-green algae metric was applied to develop MMI along with diatom metrics in the national scale assessment, performances of MMIs are similar to that developed by diatom metrics (0.14