Introduced honeybees (Apis mellifera) in orchid pollination: surrogate pollinators or pollen wasters?
Bibliographic record
Abstract
Abstract Biological invasion is one of the leading threats to global biodiversity. Invasive species can change the structure and dynamics of landscapes, communities, and ecosystems, and even alter mutualistic relationships across species such as pollination. Orchids are one of the most threatened plant families globally and known to have established specialised pollination mechanism to reproduce, yet the impact of invasive bees on orchid reproduction has not been comprehensively assessed. We conduct a literature survey to document global patterns of the impact of invasive honeybees on orchids’ pollination. We then present a study case from Australian orchids, testing the extent to which introduced honeybees can successfully pollinate orchids across different degrees of habitat alteration, using Diuris brumalis and D. magnifica (Orchidaceae). Globally, Apis mellifera is the principal alien bee potentially involved in orchid pollination. We show that pollinator efficiency and fruit set in D. brumalis is higher in wild habitats in which both native bees and invasive honeybees are present, relative to altered habitat with introduced honeybees only. Pollen removal and fruit set of D. magnifica rise with native bees’ abundance whilst pollinator efficiency decreases with honeybee abundance and increases with habitat size. Complementarily to our findings, our literature survey suggests that the presence of introduced honeybees adversely impacts orchid pollination, likely via inefficient pollen transfer. Given the worldwide occurrence of introduced honeybees, we warn that some orchids may be negatively impacted by these alien pollinators, especially in altered and highly fragmented habitats where natural pollination networks are compromised.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".