Investigating the impacts of field‐realistic exposure to a neonicotinoid pesticide on bumblebee foraging, homing ability and colony growth
Bibliographic record
Abstract
Summary The ability to forage and return home is essential to the success of bees as both foragers and pollinators. Pesticide exposure may cause behavioural changes that interfere with these processes, with consequences for colony persistence and delivery of pollination services. We investigated the impact of chronic exposure (5–43 days) to field‐realistic levels of a neonicotinoid insecticide (2·4 ppb thiamethoxam) on foraging ability, homing success and colony size using radio frequency identification (RFID) technology in free‐flying bumblebee colonies. Individual foragers from pesticide‐exposed colonies carried out longer foraging bouts than untreated controls (68 vs. 55 min). Pesticide‐exposed bees also brought back pollen less frequently than controls indicating reduced foraging performance. A higher proportion of bees from pesticide‐exposed colonies returned when released 1 km from their nests; this is potentially related to increased orientation experience during longer foraging bouts. We measured no impact of pesticide exposure on homing ability for bees released from 2 km, or when data were analysed overall. Despite a trend for control colonies to produce more new workers earlier, we found no overall impacts of pesticide exposure on whole colony size. Synthesis and applications. This study shows that field‐realistic neonicotinoid exposure can have impacts on both foraging ability and homing success of bumblebees, with implications for the success of bumblebee colonies in agricultural landscapes and their ability to deliver crucial pollination services. Pesticide risk assessments should include bee species other than honeybees and assess a range of behaviours to elucidate the impact of sublethal effects. This has relevance for reviews of neonicotinoid risk assessment and usage policy world‐wide.
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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".