Implementing Honey Bee Surveillance for the Detection of Avocado Sunblotch Disease in California.

Abstract

Avocado sunblotch viroid (ASBVd) represents a complex and significant hurdle for the avocado industry and growers. The viroid's uneven distribution within host tissues makes traditional leaf-based indexing logistically intensive and prone to sampling errors. This study evaluates a honey bee-mediated surveillance framework as a scalable alternative for orchard monitoring. Beehives were strategically positioned across four commercial orchards in Southern California, located directly under the canopy and at distances of 100 and 200 meters from confirmed infected trees. A total of 83 samples, comprising foragers, hive-bound workers, and stored pollen, were processed for RNA extraction and reverse transcription to cDNA and then screened using an integrated platform of Recombinase Polymerase Amplification (RPA), CRISPR-Cas12a, and solid-state nanopore-array sensing. Results were further validated and quantified via droplet digital PCR (ddPCR). ASBVd was successfully detected in both honey bee and pollen samples collected near and 100 m away from infected trees, with stored pollen exhibiting a significantly higher prevalence and viral titer. The RPA-CRISPR-nanopore platform identified nine pollen and one bee-positive samples with viroid loads down to 1.57 copies/µL, all of which were confirmed by ddPCR. Additionally, we used ddPCR to identify three more low-titer bee and pollen samples with viroid loads down to 0.52 copies/µL. This dual-detection framework combines RPA-CRISPR-nanopore for rapid field screening with ddPCR for sensitive confirmation of low-titer samples. By using honey bees as efficient biological sensors, this system offers a scalable proof-of-concept approach for monitoring ASBVd in avocado orchards.