Unrestricted ramping rates and long‐term trends in the food web metrics of a boreal river
Bibliographic record
Abstract
Abstract Long‐term monitoring of the food web of a regulated hydropeaking river was conducted to assess if previously documented effects of changing ramping rates (RRs) were maintained with the addition of 6 years of data. Using carbon and nitrogen stable isotope analyses, we hypothesized that: (1) macroinvertebrates and fish inhabiting areas below peaking hydrodams would be higher in δ15N and lower in δ13C due to increased flow velocity and the influence of light respired dissolved inorganic carbon, relative to those sampled from areas with a natural flow regime; (2) the increase in δ15N of macroinvertebrates would lead a shorter food web length in the regulated river, but δ13C and niche width would be similar between the restricted and unrestricted RR periods (i.e., the BACI analysis); and (3) isotopic metrics (e.g., δ13C, δ15N, niche width [SEAB], and food chain length [Δ15N]) would correlate with variations in flow characteristics through time. Consistent with previous analysis conducted over a shorter time period, a shift toward higher δ15N values was observed for both fish and invertebrates, but, contrarily, only invertebrates (not fish) had a lower δ13C value downstream of the dam. Over the long term, the before‐after‐control‐impact analysis found no effect of RRs on any of the food web metrics, implying that the change in operation did not affect the river food web. However, analysis of the time series data indicated that flow metrics and trophic metrics were often correlated, including a negative effect of RR (invertebrates) and discharge (fish) on food chain length. This study illustrates the difficulty in detecting changes in food web structure and function under changing flow regime influenced by natural and anthropogenic effects. As such, this study highlights the need for considering large spatial and temporal scales to differentiate between confounding effects of climate, natural variability, and altered flow regimes on food webs in regulated rivers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".