Native and alien plant species respond differently to landscape and local factors shaping spontaneous herbaceous vegetation in villages
Bibliographic record
Abstract
• Native species peak in green areas at village edges within semi-natural landscapes. • Archaeophytes thrive in villages in agricultural areas, echoing historic farming. • Neophytes are richer at village edges, regardless of surrounding landscape. • Shading and settlement age promote neophyte and native richness, respectively. • Disturbance reduces natives but increases archaeophyte cover in village green areas. Most urban ecological research focuses on large cities, while smaller settlements remain understudied despite hosting a significant share of both human population and biodiversity. Their unique characteristics, such as lower sealed surface ratio and stronger ties to rural landscapes – highlighting the importance of the wider landscape-scale context – may lead to different ecological dynamics and require tailored planning and management. Here, we investigated how species composition of spontaneous herbaceous vegetation in villages is influenced by landscape-scale and local factors, with particular focus on native, archaeophyte and neophyte species. In 2022, we surveyed vegetation in 64 villages in the Carpathian Basin (Hungary and Romania), sampling public green areas at village centres and edges in contrasting landscape contexts (villages in city agglomeration vs. far from cities, and in semi-natural forested vs. agricultural landscapes). We recorded species richness and relative cover, along with local factors like shading, disturbance, mowing, green area shape, solar radiation, and built-up age. Native species richness was highest at village edges in semi-natural landscapes, whereas archaeophyte cover peaked in villages embedded in agricultural landscapes. Neophyte richness was consistently higher at village edges, regardless of landscape context. Locally, shading increased both neophyte richness and cover. Older settlements promoted higher native richness and cover but resulted in lower archaeophyte cover. Conversely, disturbance reduced native cover while enhancing archaeophyte cover. The divergent responses of the three species groups to landscape and local-scale factors underscore the importance of integrating both landscape-scale planning and local management in managing native and alien species in village green areas.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".