The Utility of Fish Population Monitoring and Forecast Trigger Development for Designing Adaptive Aquatic Monitoring Plans for Large Industrial Developments
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".