Riparian Seedling Mortality from Simulated Water Table Recession, and the Design of Sustainable Flow Regimes on Regulated Rivers
Bibliographic record
Abstract
Seasonal water limitation exerts a strong ecological filter on stream communities in semiarid regions. For first‐year riparian willow and poplar tree seedlings, desiccation from rapidly declining river flows can limit reproduction, especially on rivers in which flow regulation and land conversion have limited the amount of area available for recruitment. We investigated survivorship of first‐year riparian seedlings to simulated river stage declines, focusing on the three dominant species in California's heavily regulated San Joaquin Basin: Fremont cottonwood, Goodding's black willow, and sandbar willow. Seedlings grown in mesocosms were subjected to water table decline rates typical in spring on unregulated and regulated rivers. We compared species' differences in survival time and fit the empirical data to accelerated failure time models that predicted time until death as a function of drawdown rate, initial seedling size, and maternal line. We used Akaike information criteria to select the best model for each species. Water table decline rates ≥ 6 cm/day were lethal to all species. At an intermediate rate (3 cm/day) survival varied most among species (12–38%) and was highest for Goodding's black willow. Failure time models indicated no maternal effects on survival but that initial seedling size was important for cottonwood. Using these models, we simulated survivable flow scenarios on the Tuolumne River (CA) and assessed the survivability of actual flow releases in two representative years. This modeling approach shows promise for optimizing flow releases to restore pioneer riparian habitat on regulated rivers in some of the world's most water‐limited regions.
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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) | 0.001 | 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".