How do natural changes in flow magnitude affect fish abundance and biomass in temperate regions? A systematic review
Bibliographic record
Abstract
Abstract We conducted a systematic review on the impacts of natural causes of variation or changes in flow magnitude (resulting from climatic variability and broad‐scale drivers such as climate‐induced change) on fish abundance and biomass in temperate regions. Following our systematic review protocol (Birnie‐Gauvin et al., 2021), we examined commercially published and grey literature originally identified during a recent systematic map process (Rytwinski et al., 2020) and a systematic search update. Articles were screened using an a priori eligibility criteria, with consistency checks performed at the title and abstract, and full text screening stages. All eligible articles were assessed for study validity. A narrative synthesis included all available evidence (300 studies from 219 articles), and meta‐analyses (1863 datasets from 193 studies) were conducted where appropriate. Most studies were conducted in the United States (65%) on genera in the Salmonidae and Leuciscidae families (29% and 28%, respectively), and mainly investigated variation in flow magnitude, followed by droughts and floods. We found that fish abundance and biomass responses to changes in natural flow magnitude were mainly negative, but analyses do not support clear generalizable signals across all contexts (e.g. types of changes in flow magnitude, taxa, locations). When exploring reasons for heterogeneity in effect sizes, we found a detectable effect of intervention type on average fish abundance within the first year of the natural change in flow magnitude, with floods and droughts associated with overall negative responses. However, these patterns were more variable when considering between‐year variation or specific taxonomic responses. Our results suggest that while immediate responses were more apparent and relatively consistent within specific types of natural events, fish populations may recover after such events (i.e. ≥2 years post‐natural event); however, most studies were short in duration (<2 years) and longer‐term effects were more variable and may be context dependent. To improve our understanding of species‐specific and population‐level effects, as well as time‐lags in fish responses to natural changes in flow regimes, standardized, long‐term continuous monitoring both before and after a change in flow magnitude are needed to address knowledge gaps. Studies that focus on systems outside North America are recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".