Influences of urban stormwater management ponds on wetlandscape connectivity
Bibliographic record
Abstract
Wetlands have been degraded and destroyed in many human-dominated landscapes worldwide, threatening vital ecosystem services. Landscape connectivity is critical for wetland biota, but is often reduced among remaining wetlands. With urban development, stormwater management (SWM) ponds are installed to hold runoff and associated contaminants. Unfortunately, SWM ponds do not adequately replace the ecosystem services and functions provided by a connected wetlandscape. We use a graph theory approach to analyze landscape connectivity of wetlands and stormwater management ponds in seven municipalities in southern Ontario, Canada. We analyze changes in connectivity through time, alongside potential effects of SWM pond creation on connectivity. We calculate the number of links (NL) and components (NC) at the landscape-level (i.e. connectivity within a municipality), and the probability of connection and two of its components (i.e. dPCflux and dPCconnector) at the habitat-level (i.e. contributions of individual wetlands and SWM ponds to landscape connectivity). Our results suggest that landscape connectivity has decreased with wetland loss, while SWM pond construction has improved connectivity. Wetlands appear to be more connected over the landscape, while SWM ponds might act as stepping-stones for species moving between wetlands. Our results point towards the need to protect and restore wetlands to safeguard critical ecosystem services. Stormwater ponds may not provide habitat of equivalent quality to wetlands; however, their strategic placement within urban areas could improve wetlandscape connectivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".