Planting of neonicotinoid-coated corn raises honey bee mortality and sets back colony development
Bibliographic record
Abstract
Worldwide occurrences of honey bee colony losses have raised concerns about bee health and the sustainability of pollination-dependent crops. While multiple causal factors have been identified, seed coating with insecticides of the neonicotinoid family has been the focus of much discussion and research. Nonetheless, few studies have investigated the impacts of these insecticides under field conditions or in commercial beekeeping operations. Given that corn-seed coating constitutes the largest single use of neonicotinoid, our study compared honey bee mortality from commercial apiaries located in two different agricultural settings, i.e. corn-dominated areas and corn-free environments, during the corn planting season. Data was collected in 2012 and 2013 from 26 bee yards. Dead honey bees from five hives in each apiary were counted and collected, and samples were analyzed using a multi-residue LC-MS/MS method. Long-term effects on colony development were simulated based on a honey bee population dynamic model. Mortality survey showed that colonies located in a corn-dominated area had daily mortality counts 3.51 times those of colonies from corn crop-free sites. Chemical analyses revealed that honey bees were exposed to various agricultural pesticides during the corn planting season, but were primarily subjected to neonicotinoid compounds (54% of analysed samples contained clothianidin, and 31% contained both clothianidin and thiamethoxam). Performance development simulations performed on hive populations' show that increased mortality during the corn planting season sets back colony development and bears contributions to collapse risk but, most of all, reduces the effectiveness and value of colonies for pollination services. Our results also have implications for the numerous large-scale and worldwide-cultivated crops that currently rely on pre-emptive use of neonicotinoid seed treatments.
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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) | 0.001 | 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".