Long‐term dynamics of coastal dune landscapes and habitat diversity: Insights from a quarter century of resurveys in Castelporziano Presidential Estate
Bibliographic record
Abstract
Abstract Coastal dunes are dynamic ecosystems vulnerable to human impact. Traditional monitoring relies on costly field surveys, but high‐resolution satellite imagery offers an efficient alternative. This study integrates remote sensing (RS) and field data to analyze vegetation and landscape changes over 25 years in the highly protected Castelporziano Presidential Estate. We examined three habitat groups—Herbaceous Dune Vegetation (HDV), Woody Dune Vegetation (WDV), and Broadleaf Mixed Forest (BMF)—using 58 resurveyed plots and land cover maps. Landscape dynamics and vegetation compositional changes were assessed, and temporal patterns were calculated for three buffer sizes (25, 75, and 125 m), using Bray–Curtis dissimilarity and differences in landscape metrics. Random forest models evaluated the relationship between landscape and vegetation compositional changes. The results revealed a reduction in artificial surfaces, greater vegetation encroachment, and clear signs of natural succession. HDV exhibited a shift toward grassland species, reflecting ongoing changes in vegetation composition. WDV experienced the most pronounced compositional change, while BMF showed signs of structural homogenization. Habitat proportion emerged as the strongest predictor of compositional changes, especially at the finest scale. These findings confirm the value of combining RS and field data for long‐term monitoring and provide useful insights for managing coastal dune habitats.
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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 | 0.000 |
| 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".