How do changes in flow magnitude due to hydroelectric power production affect fish abundance and diversity in temperate regions? A systematic review protocol
Bibliographic record
Abstract
Abstract Background Natural flow regimes are an essential component of healthy fluvial systems, but hydropower production alters flow components, disrupting natural processes and impacting species reliant on them. Establishing improved management of flow regimes requires a better understanding of how fish respond to altered flow components, such as flow magnitude. We recently conducted a systematic map to provide a summary of the existing literature base on the impacts of flow regime changes on direct outcomes of freshwater or estuarine fish productivity. Of those studies considering the impacts of flow magnitude changes due to hydropower, studies on fish abundance, biomass and diversity responses were identified as subtopics (i.e., knowledge clusters) that had sufficient coverage for further review. This systematic review proposes to estimate how fish abundance, biomass and diversity are affected by alterations in flow magnitude due to hydropower production. Methods This systematic review will use evidence identified during a systematic map process. An updated English language search will be performed using six bibliographic databases, Google Scholar, and networking tools to include academic and grey literature published after 2016. Eligibility screening will be conducted at two stages: (1) title and abstract, and (2) full-text. We will include all studies that evaluate the impact of changes to (or manipulations of) flow magnitude due to hydropower on fish abundance, density, biomass, yield, species richness, composition or diversity indices. The focus of this review will be on the downstream fluvial effects of flow magnitude changes and include hydroelectric facilities where water moves via gravity or by active pumping. Any freshwater or estuarine fish species or species group in temperate regions will be considered. Included eligible studies will undergo a critical appraisal that will assess the internal study validity. We will extract information on study characteristics, intervention/exposure and comparator details, measured outcomes, and effect modifiers. A narrative synthesis will describe the quantity and characteristics of available evidence, and where sufficient numbers of similar studies are available, meta-analysis will be conducted to estimate an overall mean and variance of effect.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".