Assessing the impact of climate change on the emergence of pollinator viruses


Our recent UKRI funded research has highlighted that honey bees are suffering from several emergent diseases caused by RNA viruses, including deformed wing virus (DWV; Martin et al 2012) and chronic bee paralysis virus (CBPV; Budge et al 2020). DWV has been found to spill-over into wild bees, highlighting a further risk to pollinator populations.
We have built some apparatus that simulates extreme weather events by raining on a honey bee colony, preventing it from foraging. We have used this apparatus to highlight that adult honey bees that have been raised in a colony that was unable to forage for just a few days are 4 times more susceptible to oral infection of CBPV. We have also developed reverse engineered DWV with enhanced green fluorescent protein tags (EGFP) to allow the real-time visualisation of tissue infection by the virus infects (Figure 1; Gusachenko et al 2021). Finally we have recently had a paper accepted that used R-INLA models to highlight the climatic drivers of honey bee diseases (Rowland et al accepted; see Figure 1).
Here we propose to combine the use of molecular tools developed at St Andrews with the rain apparatus at Newcastle to determine whether extreme weather events also increase the susceptibility of honey bee to DWV, and observe the routes of infection in compromised honey bees. We will then combine the outputs from the existing R-INLA honey bee disease models with climate predictions to predict the likely future impact climate change will have on the risk of honey bee diseases.


This proposal represents the potential for a student to be exposed to a range of different state-of-the-art techniques.
We will use methods to manipulate honey bee colonies such that eggs from a single queen are laid into two similarly-prepared six frame nucleus colonies. These colonies will either be placed in a rain machine to experience an extreme weather event, or allowed to forage freely for the duration of the larval feeding stage. Once cells are capped, the bees will be removed and exposed to virus as either a pupae or a freshly eclosed bee.
Virus exposure will include wild type DWV and reverse engineered EGFP modified DWV by injection or by feeding orally. Locations of DWV replication will be monitored in EGFP inoculated bees using a Leica TCS SP8 confocal microscope with 10 × HC PL FLUOTAR objective. Mortality and the development of symptoms will be monitored.
Disease and climatic prediction data will be manipulated using the tidyverse package of R. R-INLA models will be implement in the INLA package in R.

Project Timeline

Year 1

Activities have been carefully planned to account for the fact that honey bees are seasonally available pollinators.
In the winter the student will begin by learning to handle data and implement R-INLA disease models in R and how to obtain relevant outputs.
In the summer, the student will become familiar with handling live bees and handling all the different apparatus. We expect some pilot study data in this first year but mainly this will be a period of training.

Year 2

In the winter, the student will obtain climatic predictions for the UK, regionalise these, and assess future regional impact of six different honey bee diseases during the winter using the outputs from the R-INLA disease models.
In the summer the students will run experiments to assess the susceptibility of honey bees given different exposures to extreme weather events using the rain machine and native DWV. Honey bees vary in their physiology depending on the time of year. Experiments will include assessing the pupal and adult stages at three different times of the year Spring (April), summer (June) and winter (September) bees.

Year 3

In the winter the student will use real-time qPCR to test bees exposed to wild-type DWV for the development of virus infection. This will include a full screen of other honey bee pathogens to determine whether increases in susceptibility are limited to viruses or also include pathogens such as microsporidia and bacteria.
In the summer the student will complete their final experiments using EGFP modified DWV to highlight the routes of infection in compromised honey bees.

Year 3.5

This will be a period of writing for the student.

& Skills

This project represent an excellent training opportunity for a student. It is rare to be offered to upskill in computer-based modelling, laboratory skills such as qPCR and handling EGFP labelled viruses, as well as field work manipulating honey bee colonies. David Evans will host the student for a lab rotation to train them in confocal imaging and handling modified honey bee viruses.
The student will be encouraged to join a local beekeeping association to learn apiculture and meet and talk to local beekeepers.
Key skills will include RNA extraction, probe-based qPCR, bee injection, bee feeding, conducting pot studies on adult honey bees, fluorescent confocal microscopy, honey bee colony manipulation, modelling climatic variables, modelling diseases.
The CASE partner also requests annual verbal and written updates from the student for dissemination to beekeepers, which helps the student to upskill in written and verbal communication.

References & further reading

Budge et al 2020
Gusachenko et al 2021 –
Bee Disease Insurance –

Further Information

Giles Budge

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