Community Perspectives on Engaging Stakeholders for Human-Sumatran Tiger Coexistence in the Bukit Barisan Selatan Landscape
Bibliographic record
Abstract
The Bukit Barisan Selatan (BBS) landscape, a vital habitat for the Sumatran tiger (Panthera tigris sumatrae), is increasingly degraded and fragmented by plantation and agricultural expansion, driving tigers into human-dominated areas and creating conflict zones beyond conservation boundaries. However, despite global frameworks like the Kunming-Montreal 2050 Biodiversity Framework and IUCN advocating coexistence by integrating ecological and socio-economic priorities, the practical implementation at the site level remains uncertain, particularly regarding local perspectives on coexistence and their alignment with competing stakeholder interests. This study analyzes the alignment of parties in the context of the potential coexistence of human-tiger conflicts in the landscape based on local community perspectives. The BBS landscape in this study includes Bukit Barisan Selatan National Park (BBSNP), Pesisir Barat Forest Management Unit (KPH Pesisir Barat), and surrounding villages. We employed literature reviews, interviews, and the MACTOR analysis tool in our analysis. Findings reveal that forest managers (BBSNP, FMU Pesisir Barat) and local communities are key stakeholders, with Forest Rangers as crucial links between conservation policy and communities. Effective communication and coordination by rangers and extension officers foster local compliance, but coexistence remains challenging due to frequent human-tiger conflicts in open areas and mixed-tree plantations, including within conservation zones. Divergence of objectives exists between ecological actors prioritizing tiger population enhancement and socio-economic actors prioritizing human security and land access, necessitating a more balanced and holistic approach to ensure long-term sustainability. The study concludes that integrating ecological and socio-economic objectives through stakeholder collaboration, meeting the needs of local communities, and considering their land rights and access in conservation planning is critical to ensure communities are direct beneficiaries and prevent conflicts of interest in sustainable human-tiger conservation efforts.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".