Exploring Butterfly Diversity Across Three Selected Trails in Endau-Rompin Johor National Park, Johor, Malaysia
Bibliographic record
Abstract
Abstract Endau-Rompin Johor National Park (ERJNP), one of the largest protected areas in southern Peninsular Malaysia, is a critical habitat for diverse flora and fauna, including butterflies (Lepidopteran). However, there is still limited documentation of butterfly diversity and checklist, especially in unexplored habitat patches. This study aimed to update the butterfly checklist in ERJNP, addressing gaps in recent data while investigating butterfly communities along three selected trails to gain a deeper understanding of their diversity and distribution. In line with this, a study on butterfly diversity was conducted for six days from 29th April to 4th May 2024 at three selected trails: Sg. Semawak, Sg. Kawal, and Pamah Meranti. The method employed was fruit-baited traps and aerial netting along three 1 km transects in the selected areas. As for statistical data, Paleontological Statistics (PAST) software was used to analyze the comparison between sampling methods, sampling sites, sampling efforts, and diversity indices. A total of 189 individuals comprising 68 species from six families were recorded. This study also discovered seven (7) new records for ERJNP namely Discophora timora perakensis, Mydosama anapita anapita, Neptis sedata, Arhopala democritus lycaenaria, A. moorei busa, Spindasis lohita senama, and Arnetta verones. Six species recorded in this study are protected under Wildlife Conservation Act [Act 716]. When the site comparison was done, Sg. Kawal recorded the highest diversity value, H’ (3.781), followed by Sg. Semawak (3.158) and Pamah Meranti (2.949). These findings expand the ERJNP butterfly checklist and underscore the importance of targeted conservation efforts to preserve butterfly diversity in this unique ecosystem.
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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".