Zooplankton diversity in highly urbanized ponds: The role of road salt is not reflected by watershed impervious cover
Bibliographic record
Abstract
Abstract Urban ecosystems are structured by multiple anthropogenic stressors, yet despite the increasing extent and rate of urbanization worldwide, the ecological consequences of excessive road salt application and other urban phenomena remain poorly resolved. To advance understanding of these drivers and their effects on aquatic ecosystems, we collected zooplankton and associated environmental data from 50 permanent stormwater management ponds in Brampton, Canada. Generalized linear regression analysis revealed that zooplankton richness and diversity were strongly influenced by chloride and nitrate, with chloride having a strong negative effect. Community uniqueness was greatest in ponds with elevated calcium, while the presence of fish and higher pH levels promoted community homogeneity. Redundancy analysis showed that zooplankton beta‐diversity was mainly impacted by water chemistry, which explained the most variation in zooplankton composition (9.2%), whereas watershed impervious cover explained none (0%). Surprisingly, despite strong negative impacts of chloride from road salts on multiple dimensions of zooplankton diversity, structural equation models failed to detect any direct or indirect effects of impervious land cover on zooplankton diversity mediated by its influence of water quality on other biotic factors (e.g., fish presence). These findings highlight the limitations of using impervious surfaces as a proxy for the impacts of urbanization on aquatic ecosystem condition but also suggest that reducing salinization may offer meaningful benefits to biodiversity even in densely populated areas.
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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 | 0.001 |
| 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".