Urbanization and agriculture influence stream dissolved organic matter quality variability more than decomposition rates and macroinvertebrate diversity across seasonal time scales
Bibliographic record
Abstract
In the era of the Anthropocene, humans have impacted over half of the Earth’s surface. Urbanized and agricultural land use pressures have easily become some of the dominating forces shaping ecosystems today, revealing similar impacts on freshwater ecosystems. Streams and rivers are among the most heavily impacted due to the influence of catchment land use on stream water quality and ecological condition. Structural and functional indicators collected by biomonitoring programs are underused as tools for targeting stream restoration efforts. In the present study we applied a novel combination of indicators—dissolved organic matter (DOM) composition, cotton strip decomposition, and benthic invertebrate sampling—to determine if streams highly impacted by urbanized and agricultural land use across Windsor-Essex (southwestern Ontario, Canada) were consistent by season, anthropogenic land use or some combination of both. Overall, our results suggest that agricultural and urban streams are indeed degraded at a similar level, with high decomposition rates and low levels of macroinvertebrate diversity. Moreover, DOM quality proved to be the most effective indicator, integrating insights from both decomposition and macroinvertebrate indices while remaining stable seasonally. Microbial humic-like DOM correlated positively with decomposition rates, and negatively with invertebrate species richness. Our findings show that function changes in stream ecological condition can be effectively tracked by structural indicators like DOM composition. We suggest that these measures should be incoporated into monitoring programs to develop functional indicators for targeting stream restoration.
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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 | 0.000 | 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) | 0.001 | 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".