Prevalence and abundance of bees visiting major conventionally-managed agricultural crops in Brazil

Abstract

This study brings together data from a series of local pollinator surveys undertaken in major conventionally-managed agricultural crops in Brazil to determine the presence and abundance of bees visiting flowers within crops compared with adjacent off-crop habitats. Surveys were undertaken within crops and in adjacent off-crop areas using broadly the same methodology in flowering soybean, drybean, maize, citrus, coffee, rice, and cotton and in sugarcane immediately after harvest. The bee species present were assessed twice per day at three times during crop flowering (post-harvest for sugar-cane). Pan traps were used to collect species present within the crop and off-crop twice per day. Drybeans, citrus, and coffee flowers displayed a consistent medium to high abundance of honey bees based on effort-adjusted transect and spot counts. In contrast, fields with flowering soybeans and cotton showed in-crop lower abundance and rice, corn, and harvested sugar cane showed consistently low numbers or no honey bees. In almost all cases honey bees were the most prevalent and abundant flower visitors within the studied crops. Where non-Apis bees were observed they often belong to the Meliponini (primarily Trigona) or the genera Bombus or Xylocopa. The off-crop assessments of species presence using pan traps showed a far wider diversity of species with greater proportional representation of solitary bee species. Overall, whilst honey bees accounted for the majority of observed bees visiting flowers in the agricultural fields, the off-crop habitat contained a greater abundance and diversity of non-Apis bee populations.

Keywords:

• pollinators
• Brazil
• agriculture
• survey

Acknowledgments

The authors would like to thank Sigrun Bocksch from Eurofins Agroscience Services Ecotox GmbH and local staff from Eurofins do Brasil for the field surveys which formed the basis of this work, Johannes Lückmann from Rifcon for assistance in data mining and Emily Scorgie, Syngenta for statistical advice.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

This work was funded by BASF SE, Bayer CropScience AG and Syngenta.

Supplementary material

Supplementary material is available for this article at: https://doi.org/10.1080/00218839.2019.1655132.