Biodiversity trends in urban storm water ponds
Bibliographic record
Abstract
City environments are eliminating habitat for many species thereby reducing biodiversity ; wetlands in particular are being threatened. Storm water ponds built in urban areas to collect runoff and prevent flooding can undergo succession over time to develop into quasi-wetlands. This study examined 38 storm water ponds (SWP) relative to five natural (NAT) wetlands in Canada's National Capital Region to place the SWPs in context as a biodiversity resource in the area. Odonates (dragonflies and damselflies) were used as biological indicators. Odonates are closely associated with vegetation throughout their life cycles, so both odonates and vegetation were surveyed twice during summer 2015. Odonates were sampled in one or more circuits around each pond for a total effort of one hour during peak flight conditions. Vegetation sampling was closely timed with sampling of the odonates, using the interrupted belt transect method. Sampled quadrats were selected based on preliminarily observed maximum diversity. Only circuit 1 odonates were used for analyses, and they were standardized by distance travelled (pond size). There was no statistical difference in the odonate richness between pond types. There was greater vegetation richness at the SWPs, and a higher density of non-native plants in comparison to the NAT wetlands. Odonate richness was unaffected by pond size and age. However, vegetation richness decreased with both pond size and age. There was no significant correlation between standardized odonate richness and vegetation richness. However, the percentage of female odonates ovipositing increased with vegetation richness. SWPs are potentially good sites for management or enhancement to better support the presence of wildlife, helping to replace disappearing urban wetland habitat, while still performing their primary task of holding storm water runoff.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".