Quantile regression analysis as a predictive tool for lake macroinvertebrate biodiversity

Using quantile regression to evaluate limiting responses, only two variables, namely sampling depth and oxygen percent saturation (oxygen content), resulted the ones that best explained all the analyzed metrics of diversity of the macroinvertebrate communities. Depth and oxygen were then used to suggest synthetic models describing the various metrics of potential community diversity. These models can help the environmental agencies responsible for monitoring at the national level in distinguishing entire lakes or part of them with high biodiversity from those in altered conditions and then address remediation efforts toward the water bodies with the most critical conditions. Such approach could also be used to optimize the sampling procedures for the application of the Benthic Quality Index for lakes currently adopted at national level.