Addressing among-group variation in covariate effects using multilevel models

Multilevel models are used to model processes associated with hierarchical data structures. Despite infrequent use in the biological and environmental sciences, the use of these models with hierarchically-structured data conveys multiple advantages. These include the assessment of whether covariate effects differ among groups or clusters, and separate estimation of covariate effects by hierarchical level (thereby addressing atomistic and aggregation fallacy concerns). We illustrate these advantages using larval mayfly count data derived from annual surveys on the Mississippi River and a continuous covariate (water depth).