Effects of Flow Regimes Altered by Dams on Survival, Population Declines, and Range‐Wide Losses of California River‐Breeding Frogs
Bibliographic record
Abstract
Widespread alteration of natural hydrologic patterns by large dams combined with peak demands for power and water delivery during summer months have resulted in frequent aseasonal flow pulses in rivers of western North America. Native species in these ecosystems have evolved with predictable annual flood-drought cycles; thus, their likelihood of persistence may decrease in response to disruption of the seasonal synchrony between stable low-flow conditions and reproduction. We evaluated whether altered flow regimes affected 2 native frogs in California and Oregon (U.S.A.) at 4 spatial and temporal extents. We examined changes in species distribution over approximately 50 years, current population density in 11 regulated and 16 unregulated rivers, temporal trends in abundance among populations occupying rivers with different hydrologic histories, and within-year patterns of survival relative to seasonal hydrology. The foothill yellow-legged frog (Rana boylii), which breeds only in flowing water, is more likely to be absent downstream of large dams than in free-flowing rivers, and breeding populations are on average 5 times smaller in regulated rivers than in unregulated rivers. Time series data (range = 8 - 19 years) from 5 populations of yellow-legged frogs and 2 populations of California red-legged frogs (R. draytonii) across a gradient of natural to highly artificial timing and magnitude of flooding indicate that variability of flows in spring and summer is strongly correlated with high mortality of early life stages and subsequent decreases in densities of adult females. Flow management that better mimics natural flow timing is likely to promote persistence of these species and others with similar phenology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".