Landscape fragmentation of the Natura 2000 network and its surrounding areas
Bibliographic record
Abstract
Habitat loss from anthropogenic development has led to an unprecedented decline in global biodiversity. Protected areas (PAs) exist to counteract this degradation of ecosystems. In the European Union, the Natura 2000 (N2k) network is the basis for continent-wide conservation efforts. N2k is the world's largest coordinated network of protected areas. However, threats to ecosystems do not stop at the borders of PAs. As measured by a landscape fragmentation metric, anthropogenic development can affect the interiors of PAs. To ensure the long-term viability of the N2k network of PAs, this paper attempts to quantify the degree to which N2k sites are insulated from development pressures. We use a comprehensive dataset of effective mesh density (seff) to measure aggregate fragmentation inside and within a 5 km buffer surrounding N2k sites. Our results show a strong correlation (R² = 0.78) between fragmentation (seff) within and around N2k sites. This result applies to all biogeographical regions in Europe. Only a narrow majority (58.5%) of N2k sites are less fragmented than their surroundings. Remote and mountainous regions in northern Europe, the Alps, parts of Spain, and parts of eastern Europe show the lowest levels of fragmentation. These regions tend to hold the largest N2k sites as measured by area. In contrast, central and western Europe show the highest fragmentation levels within and around N2k sites. 24.5% of all N2k sites are classified as highly to very-highly fragmented. N2k PA age since initial protection does not correlate with the difference in exterior and interior fragmentation of N2k PAs. These results indicate that PAs in Europe are not sheltered from anthropogenic pressures leading to fragmentation. Hence, we argue that there is a high potential for improving PA efficacy by taking pre-emptive action against encroaching anthropogenic fragmentation and by targeting scarce financial resources where fragmentation pressures can be mitigated through enforced construction bans inside PAs.
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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 | 0.004 | 0.013 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".