Identification of wild bees and honey bees using non-lethal molecular methods

This great diversity of species, all differing in terms of morphology, mobility, phenology, nesting sites and more importantly floral preference, is essential for the pollination of a wide variety of wild and cultivated plants. The health of bee populations is threatened by many factors such as intensive agricultural practices and land management.

Background and challenges

Negative impacts on the health of honey bees is documented but impacts on the abundance and species richness of wild bees needs a better assessment. The ability to easily, quickly and unambiguously identify an individual to species level is a methodological impediment in many research fields, taxonomy, population and conservation biology and for the study of mutualistic interaction networks such as plants-pollinators. So far, the identification of insects has been carried out using identification keys based on morphological features of individuals, which often requires the skills of experts with limited availabilities.

Objectives

In BEEDET, we proposed to develop a non-lethal protocol to identify wild bees from the traces they may have left  when foraging on flowers (hair, saliva, faeces, etc.). The experimental design is based on strawberry plants, commonly used as a pollinometers to assess pollination service in different agroforestry landscapes. Three conditions were defined: a negative control consisting of enclosed strawberry plants in the absence of bee, a positive control consisting of enclosed strawberry plants in the presence of three wild bee species, and a test condition consisting of strawberry plants exposed to open air and local pollinator species. For each experimental condition, four strawberry plants were used and approximately ten flowers per plant were collected and stored at -20°C. DNA extraction was performed using the DNeasy Blood & Tissue Kits and PCR amplification was obtained by targeting a 220 bps fragment of the insect 16S minibarcode (Clarke et al., 2014). Illumina sequencing (Miseq) and bioinformatic analyses are underway.

INRAE units involved

• Département BAP
  • DYNAFOR Joint Research Unit - Agriforestry Landscape Dynamics and Ecology
• Département GA
  • GENPHYSE Joint Research Unit - Genetics, Physiology and Livestock Systems