The Utility of Fish Population Monitoring and Forecast Trigger Development for Designing Adaptive Aquatic Monitoring...

Area of Science:

• Environmental Science
• Ecology
• Hydroelectric Engineering

Background:

• Environmental Impact Assessments (EIAs) frequently lack robust monitoring and forecasting capabilities.
• This deficiency stems from inadequate baseline data, particularly for biotic endpoints.
• Effective environmental management necessitates improved data collection and predictive modeling.

Purpose of the Study:

• To demonstrate the development of monitoring and forecast triggers using biotic data (fish populations).
• To assess the impacts of refurbishing the Mactaquac Hydroelectric Generating Station.
• To recommend strategies for establishing effective environmental monitoring and forecasting.

Main Methods:

• Utilized fish population data to develop monitoring and forecast triggers.
• Employed interim strategies like default critical effect sizes and data percentiles for limited datasets.
• Applied statistical models including general linear models, partial least squares regression, and elastic net regression for forecasting.
• Analyzed 4 years of consecutive fish population data.

Main Results:

• Demonstrated that 4 years of interannual fish population variability was insufficient for developing meaningful monitoring and forecast triggers.
• Highlighted challenges in collecting sufficient baseline data for new projects due to time and cost constraints.
• Showcased methods for developing triggers based on predicted normal ranges (e.g., 2x standard deviation) with sufficient data.

Conclusions:

• Effective environmental monitoring and forecasting require substantial baseline biological data.
• Strategies for interim trigger development are necessary when data is limited.
• Coordinated monitoring requirements across watershed developments can enhance long-term prediction and management.