A comparative analysis of protected area expansion strategies for biodiversity and ecosystem services: a case study of Hainan Island
Bibliographic record
Abstract
Introduction The allocation of limited resources to simultaneously protect biodiversity and provide ecosystem services (ESs) is a critical global challenge in achieving the Kunming–Montreal Global Biodiversity Framework. One common strategy for this challenge is to expand existing protected areas (PAs), but the efficiency of different expansion methods for biodiversity and ESs remains unknown. Methods This study investigated two strategies, preserving existing PAs (“locking”) versus reassessing their boundaries (“unlocking”), to evaluate their effectiveness in achieving biodiversity and ES targets. The study used Marxan, a spatial modeling tool, to assess the effectiveness of PAs expansion strategies on Hainan Island in China. Results and discussion The current PAs system, which encompasses 8.82% of the island, is inadequate for protecting the target levels of biodiversity and ES. We experimented with expanding the PAs to 15% using both strategies. The results revealed that, compared with the “unlocking” strategy, the “locking” strategy favored ES protection (66.49% vs 86.84%), but did so at the expense of biodiversity conservation. In contrast, the “unlocking” strategy required a larger area for expansion and led to increased habitat fragmentation compared wtih the “locking” approach. These findings underscore the need for a strategic approach to expanding PAs and balancing between biodiversity conservation and ES provision. This study offers valuable insights that could be used for broader applications in PAs management and biodiversity conservation planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".