Lichen elemental content bioindicators for plot environment including pollution load are a popular and cost-effective tool to help assess environmental health. Our objective was development of lichen elemental bioindicators for air pollution in the USA upper Midwest as a model for the USA Forest Service Forest Inventory and Analysis Program (FIA) in eastern USA (E USA) and other large-scale programs. Trained non-specialists collected single-species composite samples using rigorous protocols. Elemental data from combustion and ICP-OES were validated based on values and replicate variability. Data were converted between species with GLM or regression, for equivalence in analysis. Environmental variables for each site represented climate, modeled pollution load, and land cover. Patterns were analyzed with scatterplots, Principal Components Analysis, regression, and simple plus partial correlations.
Validated data were compiled for 20 elements in 203 samples from five lichen species and 83 sites; data conversion between species was successful. Considering accepted ecological theories and many environmental variables generated some unexpected conclusions. “Multiple causation” — Flavoparmelia caperata (often used for lichen elemental analysis) and Physcia aipolia plus stellaris (not used before but successful) between them covered the study area; site partitioning was linked more to local forest cover than local air pollution. Pb levels were more strongly linked to developed land cover than other factors including local pollution. “Scale- and context-dependency” — One species’ bioindicator usefulness was limited by its pollution-sensitivity, an expected outcome. Two species had limited usefulness because of anomalous elemental data linked with non-specialist identification issues and lichen community context, an unexpected outcome. Species conversion models were as expected both scale- and context-dependent. Conflicting interpretations of Al and Fe as air pollution bioindicators in this study, but often as signals of soil contamination in Europe, might be resolved by invoking both multiple causation and scale-dependency.