Bibliographic record
Abstract
Abstract The human population is rapidly urbanizing, and the negative impacts of urban cover on biodiversity and ecosystem function are expected to increase. Trophic dynamics have been hypothesized to change with urbanization, with consequences for biodiversity and function. Here, I review recent progress in this area by focusing on how urbanization affects dietary sources, trophic interactions and the functional ecology of synanthropic species. Urbanization affects primary autochthonous production in terrestrial and aquatic ecosystems by replacing and fragmenting natural areas with impervious cover, increasing nutrient supply, changing hydrological regimes, and altering the composition and seasonality of primary producers. The responses of primary production differ between climatic regions or across hydrological regimes. Urbanization can also change the availability of subsidies (autochthonous vs. allochthonous resources) because many urban species feed on human food (anthropogenic subsidies) and because of changes in the plant composition and physical characteristics of riparian zones. Urbanization can change the composition of consumers by decreasing the abundance of apex predators, releasing mesopredators, as well as the introduction of non‐native omnivores. Few direct experiments have been conducted on trophic interactions in urban ecosystems. They broadly suggest that urbanization weakens herbivory and predation, but that it might increase competition between synanthropic and urbanophobic species. However, the outcomes of these interactions are highly context‐specific. The reliance of synanthropic species on anthropogenic subsidies appears to be an important aspect of urban trophic ecology. However, more research is needed to understand how dietary flexibility, especially in relation to anthropogenic subsidies, contributes to the physiology and population dynamics of synanthropes. Urbanization can dramatically change trophic dynamics in the urban ecosystem with implications for biodiversity patterns, management and conservation. However, it is clear that a broader and more mechanistic understanding of the urban food webs is needed. This can be accomplished through inclusion of functional trophic metrics in monitoring efforts, the use of stable isotope food web metrics, the use of multi‐trophic‐level experiments and a more detailed study of the functional ecology of synanthropes. A plain language summary is available for this article.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".