Effects of urbanization on small-mammal communities and the population structure of synurbic species: an example of a medium-sized city
Bibliographic record
Abstract
Theories concerning the impact of urbanization on ground-dwelling mammals are mainly based on large-city studies. We investigated whether the negative effects of urbanization are evident in smaller urban areas, where green areas are usually less isolated from their natural surroundings. Livetrapping of small mammals (Rodentia and Soricomorpha) was conducted between 2007 and 2010 in 15 patches within the city of Lublin (Poland) (147.5 km2, population 354 000) and 15 patches in its agricultural surroundings. A decline in species richness and diversity along an urbanization gradient and an increase in the abundance of species best adapted to the city environment (synurbic species) were observed. The main factors influencing ground-dwelling mammals was isolation of green patches, while the management type of green areas had no significant effect. The genus Apodemus Kaup, 1829, particularly the striped field mouse (Apodemus agrarius (Pallas, 1771)), was the most urban biased. Within the city, A. agrarius alters its habitat preferences and life-history parameters (body mass, seasonal fluctuations, winter survival). Understanding the factors influencing ground-dwelling mammals in medium-sized cities will enable measures to be implemented that could reduce the negative effects of urbanization during urban expansion.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".