Ditch the low flow: Agricultural impacts on flow regimes and consequences for aquatic ecosystem functions
Bibliographic record
Abstract
Abstract Large‐scale, intensive agriculture is a critical activity supporting global food production, yet it has taken a significant toll on the equally critical ecosystem services supplied by global biodiversity. This is particularly true for the planet's most threatened ecosystems: freshwaters. As one of the world's largest agricultural producers, Canada is also home to much of the world's freshwater. As Canada's agricultural capacity expands under climate warming into more northerly latitudes—and in some cases regions with large carbon sinks—it is imperative that this sectoral shift is accompanied by careful management to avoid exacerbating ecosystem service losses. Across Canada, agricultural practices vary in terms of their impact on freshwater ecosystems. Agricultural water extraction, storage behind dams, diversions, dredging and clearing of riparian vegetation can impact more naturalized flow regimes. This review explores the influence of managed low flows on ecosystem functioning in man‐made drainage/irrigation ditch systems. We examine how low flows in these systems can impact ecosystem functions in agricultural watersheds with fragmented natural capital. We provide management options to protect ecosystem functions under a changing climate, recognizing that in agro‐ecosystems, drainage/irrigation ditch systems provide a critical remnant habitat to support biodiversity in otherwise depauperate landscapes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".