Bee diversity in apple orchards of the Lower Himalaya: research synthesis, a new field study, and future needs
Bibliographic record
Abstract
In northern India and surrounding countries of the Lower Himalaya, apple is an important cash crop that contributes significantly to state economies and farmer livelihoods. Apple cultivation is shifting to higher elevations to counter declining fruit yields associated with climate change. Pollinator scarcity is another factor linked to declines in fruit yield and quality. To advance understanding of bee diversity and pollination ecology in apples for this region, we compiled a taxonomically updated list of bee taxa associated with apple orchards using records from existing literature and a new field study. Our list includes 25 bee genera, 75 named species, and numerous morphospecies. Common genera also feature prominently in apple studies elsewhere in the world. Apis cerana and A. mellifera were the most frequently reported visitors to apple flowers; Bombus, Ceratina, Lasioglossum, and Syrphidae flies were the most common non-Apis floral visitors. Bee species richness was inversely correlated with elevation and pollination deficit whereas bee abundance was not. Therefore, apples grown at higher elevations may experience more favourable growing conditions but also incur greater pollination deficits that are linked to reduced bee richness. This underscores the importance of conserving bee diversity to safeguard pollination services and farmer livelihoods in the region. Our literature review further highlights the need for more tools to identify the regional bee fauna, more thoroughly documented and standardised study methods to build capacity within the research community and aid comparative studies, and more expansive cataloguing and monitoring of pollinator communities to better understand the diversity, roles, and status of bees throughout this under-studied region.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".