Multi‐scaled drivers of rural prairie stream metabolism along human activity gradients
Bibliographic record
Abstract
Summary 1. Stream metabolism is increasingly used for monitoring and assessment of the biological condition of aquatic ecosystems. However, distal environmental drivers, such as land use, are typically not well connected to the proximate controls, such as stream chemistry, that are usually invoked as driving metabolism. This is particularly true for North American prairie streams and for grassland streams worldwide. 2. Stream metabolism was measured at the outflow of 19 subcatchments of the Red River in southern Manitoba, Canada. Subcatchments represented gradients of nutrient‐producing human activities present in the region, that is, wastewater treatment (WWT), livestock production and crop cultivation. Stream metabolism was estimated at all sites using diel changes in dissolved oxygen (DO) concentration over 1 week in the middle of summer. Environmental parameters hypothesised to control stream metabolism were sampled across three spatial scales (stream reach, stream segment and catchment). Model selection using Akaike’s information criterion (AIC) was used to determine linkages between environmental parameters and measures of stream metabolism. 3. Estimated rates of metabolism were within the range of past studies of metabolism in prairie streams, although most streams had negative values of net ecosystem metabolism. However, production‐to‐respiration ratios were >0.5, at all but three sites suggesting that autochthonous production was an important source of organic matter. 4. The a priori model that best predicted gross primary production (GPP) was the intensity of nutrient‐producing human activities (i.e. WWT, livestock and crop cultivation) measured at the catchment scale. Ecosystem respiration (ER) was best predicted by the a priori model comprised of GPP, total nitrogen (TN) and total phosphorus (TP). However, model averaging revealed that prediction of ER could be improved by including riparian cover and removing TP from the model. The positive association between GPP and ER suggested that heterotrophic compartments of the ecosystem were modest contributors to variation in respiration rates. 5. Overall, this study suggests that variation of metabolism in prairie streams of southern Manitoba is controlled by human activities occurring at the catchment scale, a finding consistent with current hierarchically structured riverine paradigms. Moreover, increased understanding of the hierarchical structure of stream metabolism drivers will help to ensure that assessment results can be used more effectively to inform management strategies for prairie ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".