Edge-driven land degradation in protected tropical forests: Spatial insights from Bangladesh for targeted restoration
Bibliographic record
Abstract
Tropical forest degradation, particularly within protected areas (PAs), represents an insidious form of land degradation that undermines ecosystem resilience, biodiversity, and carbon storage. Yet, degradation dynamics along PA boundaries remain poorly quantified at fine spatial scales. This study provides a spatially explicit assessment of edge-driven forest degradation in three ecologically significant PAs in Bangladesh—Bhawal National Park (BNP), Teknaf Wildlife Sanctuary (TWS), and Rema-Kalenga Wildlife Sanctuary (RKWS)—over a 23-year period (2001–2023). Leveraging the Hansen Global Forest Change dataset at 30 m resolution, we quantified tree cover loss across concentric buffer zones (0–500 m, 500–1000 m, and 1000–1500 m) and stratified results by canopy density to identify structurally vulnerable forest patches. Results reveal that degradation is spatially concentrated along PA edges: RKWS and TWS experienced the highest losses in the 0–500 m zone (12.4 ha/ha and 11.7 ha/ha, respectively). High-canopy forests (>75% cover) were disproportionately affected, contributing to 85% of total loss in RKWS. BNP exhibited a more diffuse degradation pattern, while TWS and RKWS showed intensified edge fragmentation. These findings expose a critical governance blind spot: legal protection does not ensure ecological integrity without spatially informed management. By integrating fine-scale remote sensing with landscape metrics, this study introduces a transferable methodology for identifying degradation-prone zones and prioritizing spatially targeted restoration. The approach offers broader utility for sustainable land management, particularly in tropical nations balancing conservation mandates with socio-demographic pressures. Insights are directly relevant to achieving ecosystem restoration targets under the Bonn Challenge and the UN Decade on Ecosystem Restoration.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".