Seasonal Variation of Forest Butterfly Diversity in Tropical Lowland Nepal
Bibliographic record
Abstract
ABSTRACT Butterfly populations and diversity vary with the seasons due to bioclimatic factors, particularly precipitation and temperature. Their seasonality in the tropical region of Nepal has not been well studied, and climatic factors have yet to be incorporated into research. Hence, this study examined the seasonal variation of forest butterfly diversity, community composition, and the effect of precipitation and temperature on diversity in the tropical lowland of Nepal. Butterflies were sampled for a year using Pollard Walk and checklist methods. Different forms of diversity indices, similarity/dissimilarity, and indicator species analysis were performed using data from the Pollard Walk. Generalized Linear Mixed Models were employed to assess the effect of precipitation and temperature on species richness and abundance. Data from the checklist method was utilized to account for the overall species richness. A total of 115 butterfly species from six families were documented. The diversity and community composition varied significantly between the seasons, with two seasonal peaks of richness: pre‐monsoon and post‐monsoon. Species richness and abundance also varied significantly among the families. Species such as Euthalia aconthea , Hypolimnas misippus , Jamides celeno, and Vanessa indica were found to be strong indicators for particular seasons. Nymphalidae was the richest, most abundant, and most diverse family. Different families exhibited noticeable variations in diversity throughout the seasons. Species richness and abundance were positively affected by increased temperature but negatively affected by increased precipitation. The present study highlights the significance of seasonal shifts for butterfly diversity in a tropical region. The seasonality of butterflies in the study area may have also been influenced by anthropogenic activities and human‐created habitat heterogeneity, resulting in the dominance of generalist species during specific seasons.
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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 | 0.000 |
| 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".