Improving assessment accuracy for lake biological condition by classifying lakes with diatom typology, varying metrics and modeling multimetric indices

Abstract

Site grouping by regions or typologies, site-specific modeling and varying metrics among site groups are four approaches that account for natural variation, which can be a major source of error in ecological assessments. Using a data set from the 2007 National Lakes Assessment project of the USEPA, we compared performances of multimetric indices (MMI) of biological condition that were developed: (1) with different lake grouping methods, ecoregions or diatom typologies; (2) by varying or not varying metrics among site groups; and (3) with different statistical techniques for modeling diatom metric values expected for minimally disturbed condition for each lake. Hierarchical modeling of MMIs, i.e. grouping sites by ecoregions or typologies and then modeling natural variability in metrics among lakes within groups, substantially improved MMI performance compared to using either ecoregions or site-specific modeling alone. Compared with MMIs based on ecoregion site groups, MMI precision and sensitivity to human disturbance were better when sites were grouped by diatom typologies and assessing performance nationwide. However, when MMI performance was evaluated at site group levels, as some government agencies often do, there was little difference in MMI performance between the two site grouping methods. Low numbers of reference and highly impacted sites in some typology groups likely limited MMI performance at the group level of analysis. Varying metrics among site groups did not improve MMI performance. Random forest models for site-specific expected metric values performed better than classification and regression tree and multiple linear regression, except when numbers of reference sites were small in site groups. Then classification and regression tree models were most precise. Based on our results, we recommend hierarchical modeling in future large scale lake assessments where lakes are grouped by ecoregions or diatom typologies and site-specific metric models are used to establish expected metric values.

Keywords: Classification and regression trees; Diatom; Lakes; Multimetric index; Multiple linear regression; Random forest.