Detecting unwanted changes associated with localized human activities in aquatic ecosystems requires defining the value of an indicator expected at a site in the absence of development. Ideally, adequate and comparable baseline data will be collected at an exposure location before that development, but this is rarely done. Instead, comparisons are made using various designs to overcome the inadequate or missing baseline data. Commonly these comparisons are done over short periods, using information from local reference sites to estimate variability expected at the exposed site. Results of these truncated designs are often evaluated using p values that may have little bearing on ecologically relevant changes. To remedy the reliance of studies on small datasets collected at reference sites, other designs emphasize regional analyses, but these may be insensitive to site-specific changes. Some designs also may forego discussing the consequences of detecting any differences. A new monitoring framework has been proposed to use existing solutions, simplify analysis, and focus on the detection of meaningful changes. It is illustrated here by using data on fish health from a large-scale, long-term program in the Moose River basin in northern Ontario. This framework advocates interpretation of data at multiple scales: within-site, locally, and regionally. The primary focus is on estimating a range from a probability distribution of historical data collected at a specific location where 95% of future observations are predicted to occur. Changes at the exposed site are also compared with historical and contemporary expectations from proximate and regional reference sites. Critical effect sizes also can be derived from regional reference data to evaluate the magnitude of differences observed between any 2 sites. Any unexpected changes inform future monitoring decisions provided by a priori guidance. Adoption of this framework extends the utility of monitoring programs in which commitments to long-term collections have been made, advocates harmonization of studies over time and space, and focuses attention on unusual observations.