Introduced honey bees (Apis mellifera) potentially reduce fitness of cavity-nesting native bees through a male-bias sex ratio, brood mortality and reduced reproduction
Bibliographic record
Abstract
Introduction In Australia, as well as many other regions of the world, European honey bees Apis mellifera are an introduced species and may harm native bee fauna by competing with them for food resources. Field studies have revealed negative associations between honey bee and native bee abundance, but whether this translates to fitness costs for native bees is unclear. Methods Using drilled wooden-block trap nests, we evaluated whether honey bee abundance is associated with fitness parameters (number of nests, provisioned cells per nest, offspring number, mortality rate, sex ratio, and body size) of cavity-nesting native bees over 2 years. We also conducted palynological analyses to measure pollen resource overlap and evaluate whether this impacts native bee fitness. Results and discussion Greater honey bee abundance was associated with a male-biased sex ratio in the native bee progeny across years and an increased mortality rate of native bee progeny in the first year. Most non-significant associations were also in the directions predicted from honey bees adversely impacting native bee fitness. In the first year, greater pollen morphospecies overlap was associated with fewer provisioned cells. In conclusion, we demonstrated that honey bees have the potential to have harmful consequences for native bee fitness.
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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 | 0.000 | 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) | 0.002 | 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".