Brown Carolyn Jm, Arciszewski Tim J, Curry R Allen, Smith D Scott, Munkittrick Kelly R
Department of Biology, Wilfrid Laurier University, Waterloo, ON, Canada.
Canadian Rivers Institute, University of New Brunswick, Fredericton, NB, Canada.
Environ Manage. 2025 Sep;75(9):2183-2197. doi: 10.1007/s00267-025-02260-9. Epub 2025 Aug 16.
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.
大多数环境影响评估（EIA）未能生成有效的监测和预测触发因素，因为缺乏适当的基线数据和预测，尤其是对于生物终点而言。在此，我们提供一个示例，说明如何利用生物数据，特别是鱼类种群数据，来制定监测和预测触发因素，以评估大型水力发电设施——马塔夸克水力发电站计划翻新的影响。我们推荐在收集到足够的生物数据之前制定临时监测触发因素的策略，包括在只有几年数据时使用默认的临界效应大小或数据百分位数。当有足够的数据时，监测触发因素可以基于预测的正常范围，即均值的2倍标准差。我们使用一般线性模型、偏最小二乘回归和弹性网络回归生成了预测触发因素。我们证明，连续4年采样的鱼类种群特征的年际变异性不足以用于有意义的监测和预测触发因素的开发。由于成本以及监管和经济时间框架（当前的做法通常是1年或2年），在未开发地区为新项目收集足够的基线数据将具有挑战性。对于现有项目的变更，如本研究中的项目，或靠近现有开发区的新项目，如果对有效的终点进行了预先考虑，则应该有现有的基线数据。流域内各开发项目之间监测要求的一致性将从长期来看改善监测、建模和预测，并为未来的开发提供参考。
