The origin of urban communities: From the regional species pool to community assemblages in city
Bibliographic record
Abstract
Abstract Aim Cities worldwide are characterized by unique human stressors that filter species based on their traits, potentially leading to biodiversity loss. The knowledge of which species are filtered and at which scale is important to gain a more mechanistic understanding of urban community assembly and to develop strategies to manage human impact on urban ecosystems. We investigate the ecological mechanisms shaping urban community assembly, taking into account changes across scales, taxa and urban green space types. Location City of Zurich, Switzerland. Taxon Carabid beetles and wild bees. Methods We use a large species occurrence and trait dataset with a high spatial resolution to assess the filtering effect of a medium‐sized city on a regional pool of potential colonists. We then assess the filtering from the urban pool to five widely distributed types of urban green spaces. Results We found that our model city selects for functionally similar but taxonomically diverse bee and carabid beetle species from the regional species pool. Within the city, community assembly processes vary among green space types and taxa resulting in important changes in community taxonomic and functional composition. Main conclusions Our findings suggest that urban community assembly is a multi‐scale process dominated by the strong environmental filtering from a regional to an urban species pool. This leads to the selection of species pre‐adapted to urban conditions. Spatial habitat heterogeneity within and among UGS types can maintain an important taxonomic diversity within cities. However, increasing urban functional diversity would require stronger management efforts that consider regional ecological processes.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".