Green landscape and macrophyte cover influence macroinvertebrate taxonomic and functional composition in urban waterbodies at multiple spatial scales
Bibliographic record
Abstract
Abstract Urban waterbodies provide important services to humans and play a considerable role in biodiversity conservation. Yet, we still know very little about how urban pond ecosystems may respond to ongoing and future stresses operating at multiple spatial scales. Here we examined the littoral macroinvertebrates in 20 urban waterbodies as an indicator community to assess how local waterbody condition and urban land use affected their taxonomic and functional composition. Although macroinvertebrates were diverse (total richness of 60 taxa ranging from 10 to 41), they were dominated by two major taxonomic groups, the Diptera Chironomidae (36%) and the Annelida Oligochaeta (22%), which largely represented the dominant functional group of the Collectors-Gatherers (63%). Fuzzy clustering identified four different types of communities based on taxonomic and functional groups. These reflected inversed gradients in the dominance of Collectors-Gatherers versus ponds with higher abundances of Herbivores (Gastropoda Pulmonata, Hemiptera, Trichoptera), Collectors-Filterers (Gastropoda Prosobranchia, Crustacea Ostracoda), Predators (Odonata), and Parasites (Nematoda, Hydracarina). Distance-based redundancy analysis identified macrophyte cover and green landscape (parks and buildings with yards) within a 100 m radius as the best drivers of macroinvertebrate taxonomic and functional composition. We also noted a comparable variance explained by models that included parks within a 500 m radius or buildings with yards within a 2000 m radius. Our results have implications for urban landscape management as it suggests that human alteration in the urban landscape can be transmitted at least up to 2000 m from ponds.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".