Analyzing the landscape characteristics promoting the establishment and spread of gorse (<i>Ulex europaeus</i>) along roadsides
Bibliographic record
Abstract
Abstract The International Union for Conservation of Nature listed gorse (Ulex europaeus, Fabaceae), a heliophilous evergreen shrub, as one of the world's 100 worst invasive species. Over the years, multiple attempts have been made for controlling gorse, including biological methods, but they have not been fully successful. This study aims to investigate some aspects that still remain unexplored such as the relationship between anthropogenic disturbances with the spatial mechanisms of species spread. We aimed to fill this gap by analyzing the role of transportation network configurations and landscape context on gorse propagation. We surveyed the presence of gorse in southern Brazilian forest‐grassland mosaics by completing landscape‐level road transects coupled with remote sensing, to evaluate land use and landscape structure. A binary logistic regression model was performed to test the influence of independent variables (e.g., road orientation, type and category in addition to distance to the nearest habitat patch of forest, grassland and anthropogenic area) on gorse occurrence. Our results showed that the structure of road networks can facilitate the spread of heliophilous taxa like gorse. Specifically, local, paved, NWSE and NS‐oriented (which exhibited high light exposure) roads had the highest probability of finding gorse. This suggests that human transportation activity (traffic, road construction, and maintenance) constitutes a significant dispersal agent for the species. In addition, the landscape matrix context also played a significant role; gorse was most prevalent along roadsides close to urban or agricultural areas than to forests and grasslands. No area was completely free from disturbances, such as fire, livestock grazing, and silviculture, in these seminatural landscapes. We concluded that, disturbances affecting small‐scale processes, at roadside and adjacent habitat patches, were probably as important factors explaining gorse occurrence as the urban impact. Furthermore, the current work also emphasizes the need of understanding the complexity of interacting factors. Our work has important implications for ecosystem conservation and habitat management, and consequently should be considered a prior step in establishing new approaches for gorse control.
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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 | 0.000 |
| 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".