The dual synchronizing influences of precipitation and land use on stream properties in a rapidly urbanizing watershed
Bibliographic record
Abstract
Abstract Conversion of natural ecosystems to urbanized land cover dominated by impervious surfaces can alter hydrologic delivery of terrestrially derived materials to aquatic ecosystems. By changing hydrologic regimes, urbanization has important ramifications for water quality, particularly when considering how land use change might interact with other potential environmental stressors. Here, we analyzed a suite of physical, chemical, and biological time series (2007–2008) in 11 streams in a rapidly urbanizing watershed to determine how spatiotemporal synchrony is structured in urban vs. rural stream ecosystems. We further assessed how a doubling of seasonal precipitation influenced the landscape‐level stream synchrony patterns between years via changes to the local hydrologic regime. Consistent with existing theory and observations, physical variables (i.e., water temperature, flow rate) regulated by external climate features operating at broad spatial scales were more synchronous than the variables associated with fluxes of dissolved solutes (i.e., conductivity, salinity, dissolved nutrients), which are typically controlled by local and internal ecosystem processes. Both urbanization and increased precipitation modified synchrony patterns by increasing the temporal coherence of water flow and the concentration of certain dissolved solutes (i.e., conductivity, salinity). Urban streams exposed to more precipitation exhibited the greatest similarity in physicochemical conditions, suggesting an interaction among the dual stressors of precipitation and urbanization to instill ecosystem synchrony. These results suggest that taking a spatially explicit perspective in understanding urbanization and its interactions with a changing climate is critical for the future management of aquatic resources in highly developed landscapes.
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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 | 0.001 | 0.001 |
| 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".