Water‐related bird assemblages in an urban pond ‘archipelago’: Winter patterns of bird species occurrence, abundance and richness
Bibliographic record
Abstract
Abstract This study reports on the patterns of species occurrence, abundance and richness of a wintering water‐related bird assemblage in an ‘archipelago’ of 70 small artificial urban ponds (AUPs) embedded in a metropolitan landscape (Rome, central Italy). A total of 20 species in 26 AUPs were sampled. Only the largest AUPs (>0.1 ha) contained all these species, except for Gallinula chloropus. The highest total mean species abundance was observed in the largest ponds, with statistically significant differences evident among size classes. Two significant spatial thresholds in species abundance and richness were observed (between 0.01 and 0.1 ha; between 0.1 and 1 ha in size). The abundance of single species was correlated with their frequency of occurrence. Ponds in urban areas must be larger than 0.1 ha to host a rich winter assemblage of birds, with a further increase in richness noted with a surface area larger than 1 ha. The highest number of species was observed in the larger ponds (>1 ha). The species richness of each AUP is directly correlated to their size (log‐transformed species–area relationship: log S = 3.515 + 0.497 log A; R2 = 0.76). Further research should be conducted to confirm these patterns and to implement information useful for planning and management of artificial ponds in urban areas for this purpose.
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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".