New Insight into the Identification and Activity Pattern of Asiatic Black Bear in a Protected Area of Northeastern Bangladesh
Bibliographic record
Abstract
The monitoring of wildlife habitats is crucial for effective conservation efforts, particularly where biodiversity faces significant threats. This study aimed to monitor the biodiversity of wild mammals in a protected area (PA) of Northeastern Bangladesh, with a particular focus on detecting previously unrecorded species using camera traps. We deployed nine camera traps across 19 locations inside the PAs of Satchari National Park (SNP) from May 2024 to April 2025. Further, the camera-trap data were analyzed to evaluate the existing wild mammals, along with their activity patterns and seasonal variations, in SNP. Our study identified the Asiatic black bear in SNP for the first time, representing a significant contribution to biodiversity records of Bangladesh. Among the other frequently documented wild mammals were the wild boar, northern pig-tailed macaque, and barking deer, whereas less commonly detected species included the crab-eating mongoose and jungle cat. Activity pattern analysis of Asiatic black bear revealed a predominantly nocturnal-to-crepuscular behavior, with distinct bimodal peaks during early morning and evening. The present study showed that the Asiatic black bear was active in pre-monsoon and winter; however, it was absent during the rainy season, suggesting seasonal habitat use or detectability challenges. This is the first study to confirm the presence of Asiatic black bears in PAs of SNP using camera traps. These findings also highlight the importance of long-term biodiversity monitoring for continued conservation efforts to protect the diverse wildlife of SNP. The detection of previously undocumented wild mammals highlights the ecological importance of SNP, urging authorities to tighten the ongoing conservation initiatives. Understanding the diel and seasonal activity patterns would instruct the timing of conservation and habitat management strategies. This study also makes the integration of camera-trap monitoring into long-term biodiversity management to guide evidence-based conservation policies in Bangladesh’s 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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".