Abstract
In water quality management, delisting decisions for impaired waterbodies bear resemblance to the statistical null hypothesis testing in which the prevailing conditions in the system at hand are compared against a reference (or non-impaired) state. This binary comparison presupposes the existence of a robust delineation of what constitutes a non-impaired state along with the establishment of threshold values for key environmental variables that act as surrogates for the degree of system impairment. Drawing the dichotomy between impaired and non-impaired conditions can be a challenging exercise, as it can be influenced by different trade-offs between environmental priorities and socioeconomic values. In this study, we present an integrative methodological framework that first uses mathematical modelling to reproduce the fundamental relationships between external stressors and ecosystem response and then statistically links the projected patterns with the likelihood to achieve acceptable water quality conditions. Our case study is the Bay of Quinte, Ontario, Canada; an embayment at the northeastern end of Lake Ontario with a long history of eutrophication. Our survey was conducted during the winter and spring of 2014 and included a total of 51 individuals who were actively involved with the Bay of Quinte Remedial Action Plan. Our analysis found that there is a perceptual difference between public and experts in that the latter group tends to more favorably characterize the present conditions, but is also more conservative about the delisting prospects of the system. Statistical analysis showed an average level of confidence lower than 50% about the delisting likelihood when experts were asked to assess the current water quality criteria. We conclude by arguing that the occurrence frequency of extreme conditions (i.e., exceedance of maximum allowable nutrient levels and/or toxic algal blooms) should be an integral component of the delisting process.
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