Meta-analyses of the effects of river flow on fish movement and activity
Bibliographic record
Abstract
Estimating the timing and magnitude of fish movements relative to environmental factors can provide insight into why fishes behave as they do. To make broad-level inferences about fish movement in lotic environments, we conducted random-effects meta-analyses on the effects of river flow magnitude on (i) non-migratory movements, (ii) upstream migratory movements, (iii) downstream migratory movements, and (iv) fine-scale activity. We found a significant positive effect of river discharge on non-migratory movements ([Formula: see text] = 0.41 (mean) ± 0.07 (SE), Z = 6.06, p < 0.01, k (sample size) = 27); fishes made larger and (or) more frequent movements during periods of elevated discharge. Furthermore, non-salmonids were more affected by river flow than salmonids. River discharge also had a significant positive effect on the rate, frequency, and probability of upstream migratory movements ([Formula: see text] = 0.23 ± 0.10, Z = 2.24, p < 0.05, k = 8). However, the effect of discharge on the rate of downstream migratory movements ([Formula: see text] = –0.05 ± 0.15, Z = –0.35, p > 0.05, k = 5) and fine-scale activity ([Formula: see text] = 0.01 ± 0.11, Z = 0.08, p > 0.05, k = 5) were not significant. Heterogeneity of effect sizes was evident in all meta-analyses as well as inclusion and (or) publication bias. Collectively, our meta-analyses suggested that changes to a river’s flow regime can modulate non-migratory fish behaviour, the consequences of which need to be explored in terms of habitat use and energy budgets. However, more studies are needed to examine potential effect modifiers and improve sample sizes overall.
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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 | 0.023 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.038 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".