Protected areas shape the distribution of tourism across rural Europe
Bibliographic record
Abstract
Abstract Protected areas (PAs) have expanded rapidly in recent decades to help mitigate the ongoing biodiversity crisis but are under increasing human pressures that jeopardize their conservation effectiveness. Tourism in PAs may galvanize efforts towards biodiversity conservation, but it can also be a major source of threats, leading to multiple adverse social and ecological impacts. Considering the recent Kunming‐Montreal Global Biodiversity Framework, which aims to protect at least 30% of terrestrial ecosystems by 2030, understanding and assessing how and which PAs shape the spatial distribution of tourism is of great importance to biodiversity management and policy. In this study, we used a geospatial data set that describes the location and capacity of tourism accommodation in 81,185 local administrative units (LAUs) covering over 28 countries across rural Europe. After estimating the number of nights spent per LAU, we modelled the influence of PAs on the distribution of tourism throughout Europe while controlling for other social, economic and environmental covariates, but also for spatial autocorrelation. We reveal a positive link between highly protected PAs and the number of nights spent by tourists in LAUs. The attractiveness of these PAs for tourists may pose a conservation paradox, that is, highly protected PAs, which aim to safeguard nature tend to attract a disproportionate number of tourists, which may lead to nature degradation. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.001 | 0.003 |
| 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) | 0.003 | 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".