Assessing chronic effects of chemical pollution on biodiversity using mean species abundance relationships.

Abstract

Since chemical pollution poses a persistent threat to freshwater ecosystems and biodiversity, innovative methodologies are required to address the ecological risks associated with such pollutants. This study predicts the long-term impacts of chemicals based on an equation that describes the time-dependency of the median lethal and effect concentration (L(E)C50) with the Critical Body Residue concept. This way, the methodology can predict Species Sensitivity Distributions (SSDs) for any given time point. The methodology was extended to predict the Mean Species Abundance Relationships (MSAR) as an indicator of biodiversity. To test and validate the methodology, data from a case study with six freshwater arthropods exposed short- and long-term to imidacloprid was used. The potentially affected fraction of species (PAF) and its opposite (1-PAF) was used to validate the MSAR framework itself. The accuracy of the predicted chronic median lethal concentration (LC50) values was species-dependent. However, except for one species, all predicted chronic LC50 values were within the 95% Confidence Intervals (CIs) of the fits based on only acute data. The mean differences between the predicted and calculated MSARs were between 2% and 6%. The predicted MSARs generally underestimated the impact of imidacloprid. However, all predicted MSARs were either similar or lower than the calculated 1-PAF, and their CIs covered the calculated MSARs. Thus, the study found that the presented methodology is useful for predicting the long-term effects of chemical pollutants.