Water availability and proximity to natural areas influence terrestrial plant and macroinvertebrate communities in urban stormwater infrastructures
Bibliographic record
Abstract
Abstract Stormwater infrastructures are primarily used for managing water runoff, but these environments can also foster biodiversity. Despite extensive literature about certain taxa found in these human-made environments, the terrestrial plants and macroinvertebrates present there remain understudied. Here, we compared alpha and beta diversity of plant and macroinvertebrate communities and assessed the influence of landscape characteristics on their composition in different types of urban stormwater infrastructures. Plants and macroinvertebrates were identified at the bottom and on the banks of 54 infrastructures (dry basins, wet basins with and without a water channel and retention ponds) in Quebec City and Trois-Rivieres, in Eastern Canada. Results showed poor and homogenous plant and macroinvertebrate communities in dry basins. Wet basins had the highest plant diversity, with more facultative wetland species. Wet basins with and without water channel had similar plant and macroinvertebrate composition, with the most heterogeneous communities. Retention ponds (with permanent water) had distinct communities with fewer plant species than wet basins. Macroinvertebrate and plant diversity decreased when excluding data from the banks of retention ponds from the analyses. The presence of natural areas around the infrastructures significantly influenced communities within a 2000 m and 500 m radius for plant and macroinvertebrate communities, respectively. Wetland plant species were generally found in infrastructures close to natural areas, whereas generalist species were associated with disturbed environments. Our results suggest that enhancing diversity of the stormwater infrastructure types at the regional and local (microhabitat) scales will maximize diversity of plants and macroinvertebrates.
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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 | 0.001 | 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) | 0.001 | 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".