Beyond best management practices: pelagic biogeochemical dynamics in urban stormwater ponds
Bibliographic record
Abstract
Urban stormwater ponds are considered to be a best management practice for flood control and the protection of downstream aquatic ecosystems from excess suspended solids and other contaminants. Following this, urban ponds are assumed to operate as unreactive settling basins, whereby their overall effectiveness in water treatment is strictly controlled by physical processes. However, pelagic microbial biogeochemical dynamics could be significant contributors to nutrient and carbon cycling in these small, constructed aquatic systems. In the present study, we examined pelagic biogeochemical dynamics in 26 stormwater ponds located in southern Ontario, Canada, during late summer. Initially, we tested to see if total suspended solids (TSS) concentration, which provides a measure of catchment disturbance, landscape stability, and pond performance, could be used as an indirect predictor of plankton stocks in stormwater ponds. Structural equation modeling (SEM) using TSS as a surrogate for external loading suggested that TSS was an imperfect predictor. TSS masked plankton-nutrient relationships and appeared to reflect autochthonous production moreso than external forces. When TSS was excluded, the SEM model explained a large amount of the variation in dissolved organic matter (DOM) characteristics (55-75%) but a small amount of the variation in plankton stocks (3-38%). Plankton stocks were correlated positively with particulate nutrients and extracellular enzyme activities, suggesting rapid recycling of the fixed nutrient and carbon pool with consequential effects on DOM. DOM characteristics across the ponds were mainly of autochthonous origin. Humic matter from the watershed formed a larger part of the DOM pool only in ponds with low productivity and low dissolved organic carbon concentrations. Our results suggest that in these small, high nutrient systems internal processes might outweigh the impact of the landscape on carbon cycles. Hence, the overall benefit that constructed ponds serve to protect downstream environments must be weighed with the biogeochemical processes that take place within the water body, which could offset pond water quality gains by supporting intense microbial metabolism. Finally, TSS did not provide a useful indication of stormwater pond biogeochemistry and was biased by autochthonous production, which could lead to erroneous TSS-based management conclusions regarding pond performance.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".