Riparian cottonwood mortality following compound impacts from river water withdrawal and hydroclimatic variation
Bibliographic record
Abstract
Abstract Originating in Glacier Park, Montana, the transboundary St. Mary River has provided a focus for international water sharing for a century. It was dammed and diverted in the USA, and more extensively in Alberta, where it supports Canada's centre for agricultural irrigation. Following water withdrawals, the riparian cottonwoods (Populus angustifolia) collapsed, and we assessed the woodland decline downstream from the St. Mary Dam, through eight sets of aerial photographs over seven decades. These revealed 88% woodland loss from 1951 to 1999 (R2 = 0.984), including a steeper decline with the 1980s drought. Following the implementation of an environmental flow regime that increased minimum flows after 1993, the remnant woodlands were stabilized, with a slight recovery by 2022. Analyses of the historical hydrology revealed compound contributions to the woodland mortality. (1) Annual river flows declined with increasing water diversion, and (2) late summer flows were particularly depleted. (3) Dam operations resulted in abrupt stage recessions and irregular spikes. (4) Accompanying climate warming, the spring snowmelt advanced and late summer flows declined. (5) The greatest climatic influence involved multiple‐year clusters with high versus low flows, corresponding with the Pacific decadal oscillation (PDO) from 1920 to 1990 (R2 = 0.365), but less coupled thereafter. This long‐term study demonstrates the coordination between hydroclimatic variation, water management and the fate of riparian woodlands, which reveal ecosystem health. An environmental flow regime provided benefit, and for other regulated rivers in dry ecoregions, we recommend functional flow regimes that provide sufficient minimum flows and avoid abrupt flow recession or irregular spikes.
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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".