What are the effects of flow-regime changes on fish productivity in temperate regions? A systematic map
Bibliographic record
Abstract
Abstract Background There is growing evidence of the potential negative consequences of altered flow regimes, in terms of magnitude, frequency, timing, duration or season pattern, on fluvial ecosystems and the fisheries they support. The scientific and policy communities have acknowledged the need for a better understanding of the effects of flow alteration on fish productivity. We conducted a systematic map to provide an overview of the existing literature base on the effects of flow-regime changes on direct outcomes of freshwater or estuarine fish productivity in temperate regions to inform stakeholders and policy makers. Methods To identify relevant articles for inclusion in this systematic map, we searched six bibliographic databases, 29 organizational websites, one search engine, and 297 reviews, and solicited grey literature through relevant sources. We screened articles at title and abstract, then by full-text using predefined inclusion criteria. Included studies were coded for key variables of interest, along with a very basic critical appraisal for internal validity (i.e., susceptibility to bias). The quantity and characteristics of the available evidence, knowledge gaps and subtopics with sufficient coverage for full systematic reviewing are reported in a narrative synthesis. The distribution and frequency of examined effects of flow-regime changes on fish productivity outcomes are presented in visual heatmaps. Review findings A total of 1368 studies from 1199 articles were included in the systematic map database and used to identify a number of interesting themes in the evidence base: (1) large evidence bases were found in temperate regions of United States of America (USA), Canada, and Australia; (2) most studies either used a temporal or spatial trend design i.e., lacking a ‘true’ before intervention time period, or no intervention control sites; (3) the most studied causes of altered flow regime were natural (e.g., floods, droughts, climate change), hydroelectric facilities (hydro), and dams with no hydro; and (4) there were clear clusters of studies evaluating effects of changes in magnitude and surrogate measures (e.g., velocity, water depth) on fish productivity outcomes, in particular abundance and diversity metrics. A number of potential knowledge gaps were identified: including geographic (Northern Africa, and possibly parts of Asia), causes of altered flow regime (restoration, land-use change, and water abstraction/extraction/diversion), interventions (flow duration, frequency, rate of change, or timing), outcomes (population viability) and specific intervention/cause/outcome groups (e.g., changes in flow magnitude due to hydro or natural causes and fish survival, performance, and reproduction). A few aspects in methodology were also identified across studies, primarily a lack of true comparators (e.g., temporal or spatial trend designs). Conclusions This map suggests subtopics warranting future evidence synthesis include, examinations into how changes in flow magnitude affects: (1) fish abundance for dams with no hydro causes; (2) fish abundance, diversity/richness, migration, and growth for hydro causes; and (3) fish abundance, diversity/richness, growth, community structure, recruitment, and migrating fish abundance for natural causes. More comprehensive evidence is needed to understand how: (1) fish productivity metrics are affected by changes in flow regime due to restoration, land-use change, and water withdrawal/diversion activities; (2) how fish productivity is affected by changes to components of flow regime other than magnitude (e.g., flow duration, frequency); and (3) changes in flow magnitude due to hydro or natural causes affect fish survival, performance, and reproduction; and (4) changes in flow regime (all causes, all interventions) affect population viability.
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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 | 0.018 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.041 | 0.036 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".