Landscape indicators as a tool for explaining heavy metal concentrations in urban streams
Bibliographic record
Abstract
In urban watersheds, the toxicity and persistence of metals found in urban runoff has serious consequences for aquatic organisms. Landscape indicators - measures of the amount and arrangement of land cover - have been widely used as proxies for monitoring stream water quality. Yet, landscape indicators of heavy metal concentrations remain largely unexamined. Here, we investigated the utility of landscape indicators for explaining spatio-temporal trends of heavy metal concentrations in 58 streams throughout the Greater Vancouver Region in British Columbia, Canada. We asked: 1) How effective are landscape indicators in explaining instream heavy metal concentrations over different spatio-temporal scales? 2) Does explanatory power differ for landscape composition versus configuration indicators? We developed landscape indicators using a high resolution (5-m) land cover map and then used these landscape indicators to develop statistical models explaining Copper (Cu), Lead (Pb), Zinc (Zn), Cadmium (Cd) and Iron (Fe) concentrations in streams. Overall, riparian indicators explained 5–11% more variation of wet season metals than dry season, whereas watershed indicators only explained more variability in Pb during the dry season. Combining indicators mapped across multiple scales improved explanatory power of models, explaining >60% of the variability in heavy metal concentrations, regardless of season. When considering spatial arrangement of land cover, edge contrast between deciduous patches and impervious surroundings explained as much as 39% of the variability in Cu concentrations. Our approach is cost-effective, transportable, and especially useful in cities where high resolution land cover is readily available yet resources for in-stream monitoring are scarce.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".