The Utility of Fish Population Monitoring and Forecast Trigger Development for Designing Adaptive Aquatic Monitoring Plans for Large Industrial Developments

Most Environmental Impact Assessments (EIAs) fail to generate effective monitoring and forecast triggers because there is a lack of appropriate baseline data and forecasting, especially for biotic endpoints. Herein, we provide an example of how to develop monitoring and forecast triggers with biotic data, specifically fish populations, to assess impacts of a planned refurbishment of the Mactaquac Hydroelectric Generating Station, a large hydroelectric facility. We recommend strategies for developing interim monitoring triggers until sufficient biological data is collected, including default critical effect sizes or data percentiles when there are only a few years of data. When there is sufficient data the monitoring trigger can be based on the predicted normal range, i.e., 2x standard deviation of the means. We generated forecast triggers with the general linear model, partial least squares regression, and elastic net regression. We demonstrate that interannual variability of fish population characteristics sampled consecutively for 4 years was insufficient for meaningful monitoring and forecast trigger development. Collecting sufficient baseline data for new projects in an undeveloped area will be challenging due to costs and regulatory and economic time frames as current practice is generally 1 or 2 years. Changes to existing projects, such as in this study, or new projects near existing development should have existing baseline data - if forethought is given as to effective endpoints. The alignment of monitoring requirements between developments within a watershed will improve monitoring, modelling, and prediction over the long term and for consideration of future developments.