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Upgrading knowledge on pathogens (particularly viruses) of Australian honey bees

by John Roberts, Denis Anderson and Peter Durr

October 2015

RIRDC Publication No 15/095 RIRDC Project No PRJ-008540

ISBN 978-1-74254-832-6 ISSN 1440-6845

Upgrading knowledge on pathogens (particularly viruses) of Australian honey bees Publication No. 15/095 Project No. PRJ-008540

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Researcher Contact Details

Dr John Roberts CSIRO Clunies Ross Street Canberra ACT 2601

Phone: 02 6246 4019 Email: [email protected]

In submitting this report, the researcher has agreed to RIRDC publishing this material in its edited form.

RIRDC Contact Details

Rural Industries Research and Development Corporation Level 2, 15 National Circuit BARTON ACT 2600

PO Box 4776 KINGSTON ACT 2604

Phone: 02 6271 4100 Fax: 02 6271 4199 Email: [email protected]. Web: http://www.rirdc.gov.au

Electronically published by RIRDC in October 2015 Print-on-demand by Union Offset Printing, Canberra at www.rirdc.gov.au or phone 1300 634 313

Foreword

This research has delivered Australia’s first national survey of honey bee viruses and provides valuable monitoring of endemic pathogens. This knowledge is vital for protecting the biosecurity of Australia’s honey bee industry and addressing concerns from overseas markets that importing Australian honey bees could spread unwanted viruses that threaten their industries. This research is a key step in reestablishing the suspended United States market for Australian honey bees and for maintaining other export markets at risk.

The Australian honey bee and pollination depended industries will benefit from this research through a better understanding of the pathogen landscape affecting honey bee health. There are also potential benefits to beekeepers that wish to export live honey bees through greater market access and security.

This research found five viruses were common in Australian honey bees, but none of these are a concern to overseas export markets. The distribution of endemic pathogens was largely unchanged, although one pathogen had spread to new areas. Several of these established pathogens were highly prevalent throughout the survey and causing management issues for beekeepers.

This research highlights the value of monitoring for biosecurity and pest and disease management. The current honey bee health status reported here should be protected and considered in policy development for importing new genetic stock. Multiple pathogens were also identified in this study that need improved management and should be prioritised as a valuable strategy for increasing industry productivity.

This project was funded from industry revenue that is partially matched by funds provided by the Australian Government.

This report is an addition to RIRDC’s diverse range of over 2000 research publications and it forms part of our Honey Bee and Pollination RD&E program, which aims to support research, development and extension that will secure a productive, sustainable and more profitable Australian beekeeping industry and secure the pollination of Australia’s horticultural and agricultural crops into the future on a sustainable and profitable basis.

Most of RIRDC’s publications are available for viewing, free downloading or purchasing online at www.rirdc.gov.au. Purchases can also be made by phoning 1300 634 313.

Craig Burns Managing Director Rural Industries Research and Development Corporation

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Acknowledgments

We greatly appreciate the assistance and enthusiasm of beekeepers and apiary officers that participated in this study. We especially thank Tiffany Bates, Casey Cooper, Russell Goodman, Daniel Jones, Rob Manning, Trevor Monson, Vicki Simlesa, Doug Somerville and Ian Zadow for their extra help with conducting the survey sampling. Lastly, we thank Saul Cunningham for reviewing this report and Dave Alden of RIRDC for his support as project manager.

Abbreviations

KI Kangaroo Island

KUN Kununurra

NSW New South Wales

NT Northern Territory

QLD Queensland

SA South Australia

TAS Tasmania

VIC Victoria

WA Western Australia

BQCV Black queen cell virus

IAPV Israeli acute paralysis virus

LSV1 Lake Sinai virus 1

LSV2 Lake Sinai virus 2

SBV Sacbrood virus

AFB American foulbrood

EFB European foulbrood

PCR Polymerase chain reaction

FFD Freedom from disease

NPV Negative predictive value

Contents

Foreword ...... iii Acknowledgments ...... iv Abbreviations ...... iv Executive Summary...... vii Introduction ...... 1 Objectives ...... 2 Methodology ...... 2 Survey design ...... 2 Sample collection ...... 2 Analysis of adult bees and Nosema spore estimation ...... 3 Analysis of brood samples ...... 4 Next-generation RNA sequencing for virus detection ...... 4 Disease and management variables influencing the number of viruses ...... 4 Freedom from disease (FFD) analysis ...... 4 Results ...... 6 Sampling level achieved ...... 6 Honey bee viruses detected in Australia ...... 6 Regional differences in virus prevalence ...... 8 Freedom from disease (FFD) analysis for SPV and DWV ...... 9 Next-generation sequencing for virus detection ...... 11 Diversity of virus isolates in Australia ...... 12 Nosema spp. prevalence in Australia ...... 15 Brood diseases detected from hive inspections ...... 16 Pathogen profiles and associations with management ...... 18 Implications ...... 19 Recommendations...... 20 References ...... 21

Tables

Table 1. Sampling periods from five regions across Australia ...... 6

Table 2. Negative predictive values (NPV) for each sampling region giving a probability of freedom from disease. Scenarios with a probability > 0.99 are in bold...... 10

Table 3. Next-generation sequence data from pooled samples collected in VIC, NSW and WA...... 11

Figures

Figure 1. Five sampling regions defined by hive movement restrictions and geographic barriers...... 3

Figure 2. Distribution of sampling locations and apiary origins (if different to sample location). Multiple apiaries were available during almond pollination in northwest VIC and east SA, and during the Rottnest Island breeding program in WA...... 7

Figure 3. Prevalence of five viruses in Australia during winter, spring and autumn (n = sample size). .. 8

Figure 4. Frequency of multiple virus infections in apiaries sampled (n = sample size)...... 9

Figure 5. Maximum likelihood phylogenetic trees of a 585 bp sequence from (A) BQCV isolates and a 474 bp sequence from (B) SBV isolates compared to reference genomes. Two Australian reference strains for BQCV (▲) that are serologically distinct were also included for comparison...... 13

Figure 6. Maximum likelihood phylogenetic trees of a 425 bp sequence from (A) LSV1 isolates and a 559 bp sequence from (B) LSV2 isolates compared to reference genomes...... 14

Figure 7. Maximum likelihood phylogenetic tree of a 418 bp sequence from IAPV isolates compared to a reference genome...... 14

Figure 8. Detection of Nosema apis and Nosema ceranae across Australia (red circles)...... 15

Figure 9. Percentage of samples with average Nosema spore levels estimated as high (>1000 spores), medium (>100 spores), low (<100 spores) or none (n = sample size)...... 16

Figure 10. Brood diseases and hive pests detected during hive inspections. AFB and EFB were confirmed by PCR (n = sample size)...... 17

Figure 11. Signs of brood disease and hive pests detected during inspections. A, American foulbrood; B, European foulbrood; Wax moth larvae faeces on developing brood...... 17

Figure 12. Relationship between the number of viruses detected and Nosema spore counts...... 18

Executive Summary

What the report is about

This report describes the findings of Australia’s first national survey for honey bee pathogens using modern molecular tools. It outlines the current prevalence of honey bee viruses and reports on the spread of endemic pests and diseases to new areas of Australia.

This research is important for ensuring our knowledge of honey bee pathogens in Australia is current and provides valuable monitoring of the impact of endemic pests and diseases. It is also essential for addressing overseas concerns that exotic viruses exist in Australia and could be spread via imports of Australian queens and packaged bees. This research is a key step in reestablishing the trade of Australian bees with the United States and for maintaining other export markets under threat.

Who is the report targeted at?

The report is targeted at the Australian beekeeping industry for the better management of honey bee pathogens and also at decision-makers who develop policy in regards to exports of live bees in the State and Commonwealth Governments and in governments overseas.

Where are the relevant industries located in Australia?

The Australian honey bee industry is represented across all states and territories. Through management of A. mellifera, this industry supplies honey and other bee products, including queens and packaged bees, for domestic users and international export markets. The industry has an estimated gross value of production of $92 million annually with live bee exports estimated to be worth $6 million dollars when the US market was accessible. The honey bee industry also provides paid pollination services to the horticultural and agricultural sectors valued in excess of $4 billion annually.

This research will benefit the honey bee industry as a whole through improved knowledge of the pathogen landscape, but commercial beekeepers that want to export queens and packaged bees may see further benefits from increased market access.

Background

Australia is fortunate to have one of the healthiest honey bee (Apis mellifera) populations in the world, but beekeepers must still manage a range of established pests and diseases. While our long-standing research capability has provided a good understanding of these pathogens, molecular techniques that have become standard practice overseas to diagnose honey bee viruses are yet to be widely adopted in Australia. This has raised concerns with overseas export markets receiving Australian honey bees that viruses may exist here that threaten their industries.

The last published survey for honey bee viruses was conducted in eastern Australia in 1987 using serological methods. Since then there has been little published research on viruses in Australia and several new pests and diseases have arrived, including the Asian honey bee, Apis cerana, in 2007. Examination of this A. cerana population confirmed that no exotic mites or honey bee viruses were introduced, but the United States have also identified the honey bee virus, Slow paralysis virus, as a biosecurity threat that could be present in Australia. There are also several viruses identified from overseas studies that we have very little or no information for in Australia, such as Deformed wing virus, which is a major contributor to global colony losses. Molecular methods are also valuable for monitoring the spread and impact of endemic pathogens, such as Nosema ceranae and European foulbrood, which have not yet spread to all regions of Australia.

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Aims/objectives

The project aimed to upgrade our knowledge of honey bee pathogens in Australia using modern molecular techniques to improve honey bee biosecurity and support establishment of overseas export markets for live honey bees.

The project objectives were to:

1. Determine the presence and distribution of viruses in Australia, in particular Slow paralysis virus and Deformed wing virus

2. Determine the presence of non-viral pathogens that have not yet spread to all areas of Australia, such as European foulbrood and Nosema ceranae

Methods used

Honey bee samples were collected during hive inspections of commercial apiaries across Australia between August 2013 and April 2015. Adult bees collected from multiple hives per apiary were tested for 10 honey bee viruses, N. apis and N. ceranae and spore levels estimated. Diseased brood was collected during hive inspections and tested for either viruses (Slow paralysis virus, Deformed wing virus and Sacbrood virus) or European foulbrood and American foulbrood. The presence of hive pests and chalkbrood was assessed at the hive. Diagnostic PCR tests were used to identify viruses, Nosema species, European foulbrood and American foulbrood. Next-generation sequencing was also used to detect low frequency and novel viruses in adult honey bees from the east coast and WA.

Results/key findings

Five honey bee viruses were detected in Australia, but Slow paralysis virus and Deformed wing virus were not found during the survey. Freedom from disease (FFD) analysis showed good statistical support for this result, with > 0.99 probability FFD for many of the scenarios tested for each sampling region. Black queen cell virus was the most common virus detected followed by Lake Sinai virus 1, Sacbrood virus, Israeli acute paralysis virus and Lake Sinai virus 2. Black queen cell virus and Sacbrood virus are long established honey bee viruses and were common in most regions. Israeli acute paralysis virus and the Lake Sinai viruses are more recently identified from overseas studies and are new records for most regions. Only 17% of samples were virus-free and 56% of samples were infected by more than one virus.

Next-generation sequencing confirmed the PCR virus results with Slow paralysis virus and Deformed wing virus not detected in any sample, although a small number of short virus sequences was found that were 77% – 89% similar to Slow paralysis virus and 77% – 92% similar to Deformed wing virus. This level of identity is generally too small to be considered the same virus, but could represent variant strains. More sequence information and understanding of their pathology in honey bees will be needed to determine their true relationship with these viruses.

Nosema spore levels were generally high across Australia with 92% of samples having medium to high estimates, 6% of samples having low spore levels and only 2% had no detectable Nosema infection. Nosema apis was recorded in all regions except for NT and it was not found north of Bundaberg in QLD. Nosema ceranae was common throughout its known distribution but was more common as a single infection in QLD. Nosema ceranae also does not appear to be displacing N. apis, with 49% of samples having mixed infections where the two species’ distributions overlap.

Importantly, N. ceranae was detected for the first time in one Kununurra (north WA) sample from locally based hives. The confirmed absence of N. ceranae in south WA and its dominance in NT suggests a natural spread from NT to Kununurra via the feral honey bee population. Nosema ceranae was also found in two TAS samples, having previously been detected in only a single beekeeper’s

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hives in 2010. The limited spread of N. ceranae in this region is surprising and may reflect poor climate suitability for this pathogen in TAS.

Lastly, no exotic pathogens or pests such as Varroa and Tropilaelaps mites were detected and no endemic brood diseases or hive pests had increased their distribution. Chalkbrood was the most common brood disease present and 41% of detections were considered to be high infections. European foulbrood was confirmed in only seven samples from NSW, QLD and TAS, although diseased brood showing possible EFB-like symptoms were collected and tested from all regions except NT and Kununurra.

The findings of this research will benefit the Australian honey bee industry by improving current knowledge for honey bee pathogens and assist with accessing export markets for Australian live bees.

Implications for relevant stakeholders

The knowledge developed here on honey bee viruses in Australia will assist industry and government to negotiate the reestablishment of live honey bee trade with the United States and may facilitate the development of new export markets. The absence of both Slow paralysis virus and Deformed wing virus also has implications for the importation of honey bee stock into Australia. The inadvertent introduction of these viruses with imported genetic stock would undermine the current industry health status provided by this study and potentially jeopardise valuable export markets.

This study has also highlighted potential issues with the effectiveness of some pest and disease managements, as reflected by the high prevalence of Nosema infections, multiple viruses and chalkbrood outbreaks. Improving the management of these cryptic pathogens should be given higher priority and seen as a valuable strategy for increasing industry productivity.

Recommendations

The following recommendations are targeted at decision-makers, researchers and beekeepers:

 Future pathogen monitoring and surveillance targeted on fewer pathogens and risk-based sampling adopted.

 Further examination of identified virus sequences showing distant similarity to Slow paralysis virus and Deformed wing virus.

 More research is needed into the pathology of the Lake Sinai viruses and their relationship with other honey bee viruses.

 Greater understanding and demonstration of the effectiveness of different management strategies for reducing Nosema and virus levels.

Introduction

Australia is fortunate to have one of the healthiest honey bee (Apis mellifera) populations in the world. Our relative isolation and stringent biosecurity practices have kept several significant pests and pathogens from our shores, in particular parasitic mites like Varroa and Tropilaelaps. Australian beekeepers must still manage a range of established pests and diseases but our long-standing diagnostic capability has provided a good understanding of these pathogens affecting our honey bees. However while molecular techniques have become standard practice to diagnose honey bee viruses overseas, they are yet to be widely adopted in Australia. Virus research has been limited in Australia and has typically relied on less sensitive serological methods (Anderson 1984). This has raised concerns with overseas export markets receiving Australian honey bees that there are gaps in our knowledge on viruses.

The last published survey for honey bee viruses was conducted across the eastern states of Australia in 1987 and used serological methods available at the time (Hornitzky 1987). Since then honey bee virology has continued sporadically, mostly at CSIRO (Anderson 1991; Anderson & Giacon 1992; Anderson & Gibbs 1988; 1989; Roberts & Anderson 2014), and much of this work is unpublished (D. Anderson, pers. comm.). Then in 2007, an incursion of the Asian honey bee, Apis cerana, was detected in Cairns in far north QLD. This invasive honey bee had potentially introduced exotic mites and pathogens and was a major concern for overseas trading partners. This threat and the lack of published virus surveys resulted in the United States, one of our largest export markets, prohibiting Australian honey bees in 2010. To address this and our own biosecurity concerns, multiple examinations of this A. cerana population have confirmed that no exotic mites were introduced and a recent study conducted by the authors have found no evidence for exotic honey bee viruses being introduced or spread to local A. mellifera colonies (Roberts & Anderson 2013). However the United States have also identified the honey bee virus, Slow paralysis virus (SPV), as a biosecurity threat that could be present in Australia. This virus is rare, having only been detected in Britain, Fiji and Western Samoa, but can cause colony losses particularly in association with Varroa mites (de Miranda et al. 2010b). Although not detected by serological methods in the previous 1987 survey, an analysis with more sensitive molecular techniques is needed to determine the current presence of SPV in Australia.

An analysis of Australian honey bees with molecular methods is vital for upgrading our knowledge of the current pathogen landscape. In addition to SPV, several other honey bee viruses have been identified from overseas studies that we have very little or no information for in Australia (Lanzi et al. 2006; Runckel et al. 2011). Deformed wing virus (DWV) is arguably the most significant of these viruses, as it is a major contributor to global colony losses in association with Varroa destructor (Martin et al. 2012). Molecular methods are also valuable for monitoring the spread and impact of endemic pathogens. For instance, Nosema ceranae is an emerging pathogen that has spread across Australia’s eastern states, but is yet to be found in WA (Hornitzky 2011). Similarly, European foulbrood (EFB, Melissococcus pluton) is common across the east coast, but is not recorded in WA or NT (McKee et al. 2003).

Here we have used molecular diagnostic tools to conduct a national pathogen survey of Australian honey bees. This survey aimed to determine the presence of honey bee viruses, in particular SPV and DWV for their impact on biosecurity, and update the current distribution of endemic pathogens, such as N. ceranae and EFB, which have not yet spread to all regions of Australia. The outcomes of this study will improve the biosecurity knowledge of the honey bee industry to assist overseas trade of live honey bees and management of endemic pathogens.

Objectives

1. Determine the presence and distribution of viruses (and strains of viruses) in Australia using molecular techniques, with particular emphasis on Slow paralysis virus and Deformed wing virus

2. Determine the presence of non-viral pathogens (particularly European foulbrood and Nosema ceranae) considered not present in certain Australian states using molecular techniques

Methodology

Survey design

The survey was conducted over 2 years between August 2013 and April 2015. The survey was designed to cover five distinct beekeeping regions covering all states and territories of Australia (Figure 1). These regions were defined based on current hive movement restrictions and previous analysis showing the connectedness of the east coast (Gordon et al. 2014). The east coast region included QLD, NSW, VIC and SA, as there are no geographic barriers restricting honey bee movement between these states. South WA was a distinct region because there is no movement of honey bees or untreated equipment into the state and there are substantial desert barriers with neighbouring states. There is also no return movement of honey bees or untreated equipment from Kununurra (KUN) in north WA. Beekeepers from south WA can currently take hives to KUN to provide pollination, but not return them to south WA because of the recent introduction of small hive beetle to this area. NT and KUN were considered one region despite movement restrictions between states because there are no geographic barriers. Lastly, TAS and Kangaroo Island (KI) were distinct regions because they are islands and have movement restrictions in place.

Within these regions, apiaries of commercial beekeepers were randomly selected for sampling and opportunities around pollination were used to achieve a maximum number of samples. To ensure consistency in sampling, the principal researcher collected all samples and conducted all hive inspections except for five samples that were provided by beekeepers. To determine the sampling level required, we used information on the number of registered beekeepers with more than 250 hives in each state and the likely degree of pathogen spread between hives to estimate the number of apiaries to sample in each region. Using the R package “FFD” (Kopacka et al. 2013), we estimated a sampling level to detect pathogens present at more than 5% prevalence needed 65 apiaries across the east coast, 29 apiaries from south WA and 22 apiaries from TAS. The small number of commercial beekeepers in NT, KUN and KI were all sampled. Sample collection

For each sample, eight hives were randomly chosen as a representation of the apiary visited. From each hive, two brood frames were inspected to record and collect suspected diseased brood (i.e. Chalkbrood, European foulbrood, American foulbrood or virus infection) and hive pests (i.e. Small hive beetle, Wax moth and exotic mites). Approximately 25 adult bees were collected from the brood comb of each inspected hive and pooled into a screw-top container to create a combined apiary sample of approximately 200 adult bees that would be tested for viruses and Nosema species. Brood samples suspected of disease were collected into separate samples tubes to be tested in the laboratory. Hive pests and Chalkbrood ‘mummies’ were identified and recorded at the hive. All samples were collected on ice, transported frozen to the laboratory and then stored at -20°C until needed.

Figure 1. Five sampling regions defined by hive movement restrictions and geographic barriers.

Analysis of adult bees and Nosema spore estimation

Each adult bee sample was split into four sub-samples of 50 bees and placed into separate sterile zip- lock bags. Bees were then crushed in 5 mL of 0.05 M potassium phosphate buffer using a rolling cylinder. Once crushed, two 500 μl aliquots were collected in 1.7 ml centrifuge tubes for downstream RNA and DNA extraction and 5 μl examined for Nosema spores on a microscope slide at 400x magnification under a light microscope (Leica Microsystems). The level of Nosema spores was coarsely estimated as low (<100 spores), medium (<1000 spores), high (>1000 spores) or no spores being present and averaged across the four sub-samples.

For each sub-sample, DNA was extracted from one 500 μl aliquot using the High Pure PCR template preparation kit (Roche Diagnostic) and PCR tested for N. apis and N. ceranae. RNA was extracted from the other 500 μl aliquot by firstly clearing the sample by adding 50 μl of diethyl ether and 100 μl of chloroform, vigorously shaking for 30 seconds and then centrifugation for 2 minutes at 7,000 g. The supernatant, which contains possible virus particles, was collected and RNA extracted using the Purelink viral RNA extraction kit (Invitrogen) following the manufacturer’s instructions. Extracted RNA was converted to cDNA using the Tetro cDNA synthesis kit (Bioline) and PCR tested for 10 honey bee viruses; Slow paralysis virus (SPV), Deformed wing virus (DWV), Sacbrood virus (SBV), Black queen cell virus (BQCV), Kashmir bee virus (KBV), Israeli acute paralysis virus (IAPV), Acute bee paralysis virus (ABPV), Chronic bee paralysis virus (CBPV), Lake Sinai virus 1 (LSV1) and Lake Sinai virus 2 (LSV2).

PCR reactions for each pathogen were carried out in a total volume of 10 μl containing 1 x PCR buffer, 1.5 mM MgCl2, 0.2 mM dNTPs, 0.4 μM forward primer, 0.4 μM reverse primer, 1 U Taq DNA polymerase (New England Biolabs) and 2 μl DNA template or 1 μl cDNA template. PCR cycling conditions and primers sequences for each pathogen are shown in Table 1. PCR reactions were analysed on 1.5% agarose gels stained with GelRed (Jomar Biosciences) and viewed under a UV illuminator. Positive PCR products were sequenced then analysed with MEGA6 (Tamura et al. 2011). Analysis of brood samples

For brood samples suspected of EFB and AFB infection, DNA was extracted using the High Pure PCR template preparation kit (Roche Diagnostic). Brood suspected of virus infection was prepared as above with a clearing diethyl ether/chloroform step, following by RNA extraction with the Purelink viral RNA extraction kit (Invitrogen) and conversion to cDNA with the Tetro cDNA synthesis kit (Bioline). Brood samples were only tested for SPV, DWV and SBV, and all PCR reactions carried out as described above. Next-generation RNA sequencing for virus detection

Pooled adult bee samples from five sampling periods (Western Australia – October 2013, Western Australia – October 2014, Victoria/New South Wales – August 2013, Victoria/New South Wales – April 2014, Victoria/New South Wales – August 2014) were chosen for next-generation RNA sequencing on the Illumina HiSeq 2500. Each pooled sample was first processed to optimise recovery of RNA viruses and reduce host (honey bee) RNA. This involved combining 1 ml of homogenised bee extract per sample per period (ranged from 14 to 18 samples per period) into a screw-cap centrifuge tube and adjusting with 0.05 M potassium phosphate buffer for a total volume of 20 ml. Then 3 ml diethyl ether and 3 ml chloroform were added, shaken vigorously then centrifuged at 6,000 rpm for 30 minutes (J-E Avanti centrifuge). Supernatants were transferred to Ultraclear SW28 tubes (Beckman Coultier) and centrifuged at 21 500 rpm for 3.5 hours at 4 °C (Beckman L-80 ultracentrifuge). Pelleted samples were dissolved in 1 ml of 0.05 M potassium phosphate buffer before being passed through a 0.22 μm bacterial filter. We then mixed 340 μl of each filtered sample with 10 μl of RNase, 10 μl DNase and 40 μl of DNase I buffer and incubated at 37°C for 2 hours. RNA was extracted from the treated samples using the Purelink viral RNA extraction kit. Illumina libraries were prepared from the extracted RNA and 100 bp paired-end sequences were generated on an Illumina HiSeq 2500 at the Biomolecular Research Facility (Australian National University, Canberra). Raw data were quality trimmed and mapped to reference virus genomes retrieved from the NCBI GenBank database. Raw data were also assembled de novo to identify novel virus genomes.

Disease and management variables influencing the number of viruses

To explore factors influencing the number of viruses in each apiary, we used a Generalised Linear Modelling (GLM) approach to model the number (proportion) of viruses in each apiary as a function of management, biosecurity and disease variables collected or measured during the survey (e.g. Nosema spores levels, total hives operating, use of a barrier system etc.). Model building was done using the “glm” function of the “stats” module within R version 3.2.0. Initial model building showed “overdispersion”, and to account for this we used a quasi-binomial error term. Alternative models were compared using a likelihood ratio test (LRT) based on an analysis of deviance and the F test, with a 0.05 significance threshold. Freedom from disease (FFD) analysis

For each of the five sampling regions surveyed we calculated the Negative Predictive Value (“NPV”). For this we assumed that each could be considered a two-stage random sampling design (Cameron & Baldock 1998), with the first stage being the apiary, and the second stage the region. For each stage the

sensitivity of the sampling and testing to correctly classify the unit (apiary or region) was determined assuming it was present at different scenarios of prevalence within the apiary and within the region. As both viral diseases are highly infectious, we assumed that if they were present, then the intra-apiary prevalence might vary from 10 to 30%. In contrast, due to the relatively large distances between apiaries and biosecurity measures used by beekeepers, the regional prevalence would be much lower (1 to 10%). The test sensitivity to detect the viruses at a hive level was assumed to be 95%, and the test specificity 100%, as any possible positives would be further tested.

The calculated NPV can be interpreted as the probability of the regions being free from disease, given that all the sampled apiaries were free (Martin et al. 2007). As calculated this makes no assumptions about the prior probability of freedom, assuming that the disease is as likely to be present as to be absent. However, more recent approaches recognise the benefit of incorporating previous knowledge about the disease status, using Bayes’ Rule (Andersson et al. 2014). In the specific case of SPV and DWV there is considerable evidence of the absence of the viruses within Australia based on the lack of the distinctive clinical signs and the absence of a “pathway” for introduction due to Australia’s strict quarantine requirements. Therefore, as well as calculating the conventional NPV with a uniformed prior belief in freedom (i.e. 0.50), we also calculated it for each prevalence scenario for each region with prior beliefs of FFD set at 0.95 and 0.99.

All FFD calculation were done using the “Analysis of simple 2-stage freedom survey” web-site provided as part of the “EpiTools” suite of epidemiological calculators (Sergeant 2015) .

Results

Sampling level achieved

Samples from 1,240 hives representing 155 apiaries were collected over 11 sampling periods to achieve good coverage of the five regions (Table 1, Figure 2). The majority of VIC, NSW and all SA samples were collected during winter to take advantage of large-scale almond pollination in northeast VIC and east SA. Additional VIC and NSW apiaries and the KI apiaries were sampled in autumn. TAS and QLD apiaries were sampled in spring, except for five QLD apiaries sampled during almond pollination. Sampling in south WA was conducted in both spring and autumn, and apiaries were sampled from NT and KUN in winter.

Table 1. Sampling periods from five regions across Australia Region Location of sampling Apiaries sampled Season Date East coast Northwest VIC1 9 VIC, 6 NSW, 5 QLD and 2 SA Winter Aug-13 East coast VIC 9 VIC Autumn Apr-14 East coast North NSW 8 NSW Autumn May-14 East coast Northeast SA1 21 SA Winter Aug-14 East coast Northwest VIC1 10 VIC and 7 NSW Winter Aug-14 East coast Bundaberg to Bowen, QLD 19 QLD Spring Oct-14 South WA South WA 14 South WA Spring Oct-13 South WA South WA 15 South WA Autumn Mar-14 NT/KUN NT and Kununurra, WA 7 NT and 3 Kununurra, WA Winter Jun-14 TAS TAS 15 TAS Spring Nov-14 KI Kangaroo Island, SA 4 Kangaroo Island, SA Autumn Apr-15

1 samples collected during almond pollination

Honey bee viruses detected in Australia

Diagnostic PCR markers were used to determine the presence of 10 common honey bee viruses in Australian honey bees. Of these, five honey bee viruses were consistently detected across 155 apiaries sampled across Australia. BQCV was the most common virus detected being found in 65% of adult bee samples following by LSV1 (37%), SBV (35%), IAPV (21%) and LSV2 (21%) (Figure 3). An additional 124 brood samples suspected of virus infection were also analysed for SBV, SPV and DWV. No additional viruses were detected, but 27 brood samples were positive for SBV. These results support the absence of SPV and DWV in Australia.

These results are quite different from the last virus survey, which reported SBV in 14% of samples and BQCV, CBPV, KBV and Cloudy wing virus (CWV) all in less than 5% of samples (Hornitzky 1987). Comparison of the two surveys is difficult because of the different sampling strategy (dead/expelled adult bees versus live adult bees) and detection method (serology versus molecular). However the difference in prevalence for BQCV is remarkable and may suggest this virus has become more common. BQCV is typically associated with queen rearing causing occasional death of developing queen larvae and pupae (Bailey & Woods 1977), but overseas studies are also now finding this virus is common in adult honey bees (Ravoet et al. 2013; Singh et al. 2010). The sensitivity of current molecular methods has likely contributed to the increased detection, but it is possible BQCV has become more prevalent due to unknown biotic and abiotic factors.

apiary sampled apiary origin multiple apiaries (e.g. almonds)

Interestingly, CBPV and KBV were not detected here (CWV was not tested for as there are no molecular markers developed). CBPV was uncommon in the earlier survey, so its absence here could reflect the different sampling strategies or it could be below the detection sensitivity of this survey. KBV is genetically and serologically very similar to IAPV and so the earlier survey may have detected virus strains that are now labelled IAPV (de Miranda et al. 2010a). IAPV was first characterised from Israeli honey bees in 2007 and was soon implicated in colony collapse disorder in the US after it was detected in Australian packaged bees (Cox-Foster et al. 2007). Since then the concerns around IAPV have reduced as it became clear that US colony losses are multifactorial (Cornman et al. 2012). The detection of IAPV in this survey is not surprising and may not be new, as KBV has long been studied in Australia by serology without molecular confirmation (Anderson 1991; Anderson & Gibbs 1982). However, the low prevalence of IAPV in WA and its absence from NT, KUN and KI suggest that this virus could have more recent origins in Australia.

LSV1 and LSV2 are new records for most regions of Australia, having been first identified by the authors in Cairns, QLD, in both A. mellifera and A. cerana (Roberts & Anderson 2013). Here, both viruses were common, except LSV1 was not found in KUN and LSV2 was not found in QLD, TAS or KUN (Figure 3). These viruses were first characterised in US surveys using next-generation sequencing approaches, but apart from knowing they infect honey bees, we have no knowledge of their impact on colonies. The prevalence of these viruses in Australia suggests they are important in the pathogen landscape and warrant further investigation for how they affect honey bee health.

Regional differences in virus prevalence

Virus prevalence patterns were generally consistent across regions and sampling periods with several exceptions (Figure 3). Winter samples collected during almond pollination had high frequency of BQCV (>75%) with the four other viruses common at lower levels (15-51%), except LSV2 was not found in QLD samples. In contrast, no viruses were detected in KUN and only LSV1 was detected in NT. This is despite all hives in KUN originating at some point from south WA, and NT also receives queens and packaged bees from south WA. The lower sample sizes of this region could limit virus detection, but the absence of viruses may be more likely due to seasonal and climatic differences with southern states. Further sampling at different times of year may find additional viruses in these areas.

WINTER 100% BQCV 80% LSV1 60% SBV 40% IAPV 20% LSV2 0% VIC (n=20) NSW (n=13) QLD (n=5) SA (n=23) KUN (n=3) NT (n=7)

SPRING AUTUMN 100% 100% 80% 80% 60% 60% 40% 40% 20% 20% 0% 0% QLD (n=18) WA (n=14) TAS (n=15) VIC (n=9) NSW (n=8)WA (n=15) KI (n=4)

Figure 3. Prevalence of five viruses in Australia during winter, spring and autumn (n = sample size).

LSV1 and LSV2 were not found in QLD spring samples and LSV2 was not detected in TAS or the VIC and NSW autumn samples. While this result suggests LSV2 may only appear in winter, this virus was common in WA during spring and autumn and was also the most common virus detected in KI during autumn. Therefore other factors such as interactions with other pathogens may shape the prevalence of this virus.

Virus prevalence in the WA autumn samples was also notably different to the east coast. Both SBV and LSV1 were at high frequency (80%) compared to VIC and NSW (22-44%). LSV2 was common in WA (53%), but not detected at all in VIC and NSW. Meanwhile IAPV was common in VIC (22%) and NSW (38%), but not detected in WA autumn samples. Virus prevalence in the WA spring samples was more similar with the east coast, but with higher frequency of BQCV (71%) and only a single detection of IAPV. This result suggests different factors may influence the viral landscape in WA such as climate and the absence/presence of certain pathogens.

Lastly, for the closed bee population on KI only BQCV, LSV1 and LSV2 were detected. Similarly to NT and KUN, sampling in other seasons may reveal more viruses in this population. However, the presence of LSV1 and LSV2 is interesting because it suggests these viruses are long established in Australia.

Overall, virus infections were very common in Australian honey bees with only 17% samples being free of these viruses. A high proportion of samples were infected by multiple viruses (56%), particularly in WA and the east coast where between 11% and 21% of samples were infected by four viruses and one SA sample was infected by all five viruses (Figure 4). The impact of these infections on hives is unclear as viruses do affect honey bee health and contribute to overall pathogen loads, but they are often present as asymptomatic infections (Anderson & Gibbs 1988). The high occurrence of multiple viruses may also be a reflection of hive stress from other pathogens or poor nutrition.

WINTER SPRING AUTUMN

100%

80% 5 viruses 60% 4 viruses 3 viruses 40% 2 viruses 20% 1 virus 0 viruses 0% All VIC NSW QLD SA NT/KUN QLD WA TAS VIC NSW WA KI (n=155) (n=20) (n=13) (n=5) (n=23) (n=10) (n=18) (n=14) (n=15) (n=9) (n=8) (n=15) (n=4) Figure 4. Frequency of multiple virus infections in apiaries sampled (n = sample size).

Freedom from disease (FFD) analysis for SPV and DWV

As was largely expected on the basis of previous work, our survey did not detect the presence of the SPV and DWV. To provide statistical support in terms of probability that Australia is truly free of these viruses, we applied a “Freedom From Disease” analysis, broadly following the guidelines provided by the OIE in the Terrestrial Animal Health Code (Article 1.4.6) and recently used to analyse a survey to determine that New Zealand was free of Israeli acute paralysis virus (McFadden et al. 2014).

Based on the NPV calculated for each sampling region, there was good statistical support for “freedom from disease” in each region for a range of scenarios (Table 2). The two largest sampling regions, east coast and south WA, had > 0.99 probability of freedom from disease under all scenarios assuming > 0.2 within apiary prevalence and > 0.1 apiary level prevalence. The three smaller regions (NT & KUN, TAS and KI) also had > 0.99 probability of freedom from disease for these scenarios but only when assuming a prior FFD belief of 0.95 or 0.99. Overall, this analysis supports with high confidence that SPV and DWV are not present in Australia.

Table 2. Negative predictive values (NPV) for each sampling region giving a probability of freedom from disease. Scenarios with a probability > 0.99 are in bold.

Scenario assumptions Scenario results Within Probability of freedom (NPV) assuming Region Apiary level Apiary level Region level apiary different prior FFD “beliefs” prevalence sensitivity sensitivity prevalence 0.50 0.95 0.99 0.01 0.485 0.660 0.974 0.995 0.1 0.05 0.55 0.944 0.947 0.997 0.999 0.1 0.996 0.996 1.000 1.000 East coast 0.01 0.636 0.733 0.981 0.996 (97/510 0.2 0.05 0.8147 0.987 0.988 0.999 1.000 apiaries 0.1 1.000 1.000 1.000 1.000 sampled) 0.01 0.690 0.763 0.984 0.997 0.3 0.05 0.9317 0.994 0.994 1.000 1.000 0.1 1.000 1.000 1.000 1.000 0.01 0.499 0.666 0.974 0.995 0.1 0.05 0.55 0.749 0.799 0.987 0.998 0.1 0.937 0.941 0.993 0.999 South WA 0.01 0.738 0.793 0.986 0.997 (29/32 0.2 0.05 0.8147 0.932 0.936 0.996 0.999 apiaries 0.1 0.995 0.995 1.000 1.000 sampled) 0.01 0.844 0.865 0.992 0.998 0.3 0.05 0.9317 0.976 0.976 0.999 1.000 0.1 0.999 0.999 1.000 1.000 0.01 0.550 0.690 0.977 0.996 0.1 0.05 0.55 0.550 0.690 0.977 0.996 0.1 0.550 0.690 0.977 0.996 NT/KUN 0.01 0.815 0.844 0.990 0.998 (10/10 0.2 0.05 0.8147 0.815 0.844 0.990 0.998 apiaries 0.1 0.815 0.844 0.990 0.998 sampled) 0.01 0.932 0.936 0.996 0.999 0.3 0.05 0.9317 0.932 0.936 0.996 0.999 0.1 0.932 0.936 0.996 0.999 0.01 0.375 0.615 0.968 0.994 0.1 0.05 0.55 0.609 0.719 0.980 0.996 0.1 0.756 0.804 0.987 0.998 TAS 0.01 0.556 0.692 0.977 0.996 (15/22 0.2 0.05 0.8147 0.802 0.835 0.990 0.998 apiaries 0.1 0.912 0.919 0.995 0.999 sampled) 0.01 0.635 0.733 0.981 0.996 0.3 0.05 0.9317 0.867 0.883 0.993 0.999 0.1 0.952 0.954 0.998 1.000 0.01 0.550 0.690 0.977 0.996 0.1 0.05 0.55 0.550 0.690 0.977 0.996 0.1 0.550 0.690 0.977 0.996 KI 0.01 0.815 0.844 0.990 0.998 (4/4 0.2 0.05 0.8147 0.815 0.844 0.990 0.998 apiaries 0.1 0.815 0.844 0.990 0.998 sampled) 0.01 0.932 0.936 0.996 0.999 0.3 0.05 0.9317 0.932 0.936 0.996 0.999 0.1 0.932 0.936 0.996 0.999

Next-generation sequencing for virus detection

To increase the sensitivity of detecting SPV and DWV we used next-generation sequencing to identify virus genomes in five pooled adult bee samples from VIC, NSW and WA. Short sequence reads are randomly generated from detected virus genomes, with more reads created for viruses that are in higher abundance in the sample. For each sample, we used analysis software to map these short sequence reads to reference virus genomes available from the NCBI database. This analysis identified all five viruses detected by PCR in each sample, with a high number of reads covering the full genome in most cases (Table 3). SPV or DWV were not clearly detected in any sample, although a small number of short virus sequences was found that were 77% – 89% similar to SPV and 77% – 92% similar to DWV. This level of identity is generally too small to be considered the same virus, but could represent variant strains of these viruses as both SPV and DWV are part of virus complexes with 83% – 84% identity between variant strains (de Miranda et al. 2010b; Moore et al. 2011). More sequence information and understanding of their pathology in honey bees will be needed to determine the true relationship of these virus sequences with SPV and DWV.

Further insight was gained by examining the read counts as a representation of virus abundance in the samples. Read counts within a sample often varied from the virus frequency detected by PCR. For example, LSV1 was the second most common virus detected across all samples but consistently had the lowest number of reads. In contrast, IAPV was often less common than LSV1 but had significantly higher reads in the VIC/NSW samples. These differences likely reflect the infection levels of these viruses and may be a useful for understanding the impact of these viruses on hive health.

A number of other insect viruses and plant viruses were also detected by next-generation sequencing, although read counts for these “non-honey bee” viruses were generally low. Strains related to eight recognised insect viruses were detected, including Aphid lethal paralysis virus and Rhopalosiphum padi virus that have also been found in several recent honey bee surveys around the world. Strains of 24 different plant viruses were also detected, including viruses of vegetable crops, pastures and fruit trees. Of interest was Prunus necrotic ringspot virus in samples collected during almond pollination and Tobacco ringspot virus which was recently reported to infect US honey bees (Li et al. 2014). Finally, several novel insect and plant virus genomes were found through this sequencing approach, some with near complete genomes. These results highlight the diversity and complexity of the viral landscape in honey bees and further investigation will be needed to determine the importance of these viruses for honey bee health and also the role of honey bees as vectors of plant viruses.

Table 3. Next-generation sequence data from pooled samples collected in VIC, NSW and WA.

WA Spring WA Autumn VIC/NSW Autumn VIC/NSW winter 2013 VIC/NSW winter 2014 Viruses Reads Length Identity Reads Length Identity Reads Length Identity Reads Length Identity Reads Length Identity BQCV 15,783,935 100% 93% 7,307,478 100% 93% 17,058,783 100% 93% 61,987,720 100% 93% 46,024,806 100% 93% LSV1 43,437 100% 84% 138,707 100% 84% 246,402 100% 93% 484,077 100% 93% 618,229 100% 93% SBV 1,771,600 100% 97% 412,927 100% 97% 18,123,069 100% 99% 13,746,880 100% 99% 308,194 100% 99% IAPV 6683 100% 95% 6693 100% 95% 44,231,274 100% 95% 1,164,649 100% 95% 18,399,024 100% 95% LSV2 80,079 100% 92% 529,391 100% 92% 144,837 100% 94% 453,276 100% 94% 2,954,340 100% 94% SPV 3 1% 80% 15 7% 89% 24 8% 80% 0 15 4% 77% DWV 0 32 7% 83% 6 1% 77% 313 1% 92% 1 1% 82% VDV1 0 243 13% 78% 0 0 0 1 Varroa destructor virus (VDV) is closely related to DWV

Diversity of virus isolates in Australia

A phylogenetic sequence analysis revealed considerable diversity among isolates of the five viruses present in Australia. In some cases there seemed to be geographic groupings for this diversity, but often isolates from different regions were more similar and grouped together on the phylogenetic trees. For instance BQCV isolates were quite variable, but had little statistical support for any clear groupings (Figure 5). Comparison with two serologically distinct strains (identified as B1 and B4) previously isolated from eastern Australia (Roberts & Anderson 2013) showed that most isolates were more similar to the B4 strain, but a small number of distinct isolates (which included all TAS isolates) grouped with the B1 strain.

Geographic clustering was more evident with the other four viruses, with isolates often clustering by state. An exception to this for SBV was a distinct group of isolates (Group 2) from across the east coast region (Figure 5). There was also often clear separation between isolates from WA and other states, particularly for LSV1 and LSV2 (Figure 6). Interestingly for IAPV, the single WA isolate clustered with a distinct group consisting mostly of QLD isolates (Figure 7.)

These results highlight clear genetic differences between virus isolates that could relate to important biological differences, such as virulence (Domingo & Holland 1997). This diversity needs to be considered when managing viruses, particularly in developing honey bee host tolerance, as its effectiveness may vary between regions.

VIC 106 WA 15 NSW 4 WA 6 NSW 119 WA 2 WA 28 VIC 9 A WA 11 B WA 4 NSW 110 WA 8 VIC 105 WA 20 WA 22 VIC 140 WA 10 VIC 116 QLD 117 Group 1 WA 12 QLD 117 WA 7 NSW 109 VIC 133 QLD 8 VIC 7 NSW 127 VIC 106 NSW 120 SA 102 SA 9 SA 19 AUST B4 strain SA 11 SA 7 SA 104 SA 9 VIC 107 SA 15 VIC 4 SA 18 NSW 124 NSW 126 SA 14 GroupGroup 1 1 NSW 129 SA 11 TAS 3 SA 2 TAS 5 SA 20 Group 1 SA 16 TAS 2 SA 14 TAS 33 NSW 110 NSW 141 TAS 6 QLD 113 SA R7 WA 20 VIC 131 WA 16 VIC 105 SA 17 WA 7 VIC 108 WA 6 QLD 7 SA 8 WA 119 VIC 3 WA 17 VIC 112 NSW 120 WA 19 QLD 1 QLD 115 WA26 NSW 8 SA 102 QLD 123 SA 6 WA 30 VIC 132 WA 18 NSW 119 NSW 125 WA 27 QLD 118 NSW 5 SA 17 VIC 130 QLD 11 QLD 3 BQCV genome (NC_003784) NSW 1 WA 5 VIC 107 Group 2 WA 17 Group 2 AUST B1 strain QLD 17 NSW 2 Group 2 SA 16 TAS 11 Group 2 TAS 8 SA 13 TAS 13 TAS 6 SBV genome (NC_002066)

0.005 0.005 Figure 5. Maximum likelihood phylogenetic trees of a 585 bp sequence from (A) BQCV isolates and a 474 bp sequence from (B) SBV isolates compared to reference genomes. Two Australian reference strains for BQCV (▲) that are serologically distinct were also included for comparison.

VIC 132 VIC 8 VIC 105 A VIC 134 B VIC 3 NSW 110 WA 21 SA 6 81 NT 6 WA 27 TAS 2 WA19 SA 7 Group 1 Group 1 SA 9 WA 30 SA 4 91 WA 25 SA 10 NT 8 65 WA 28 NT 3 TAS 3 LSV2 genome (HQ888865) TAS 8 KI 5 Group 2 KI 5 67 SA 102 SA 7 SA 11 SA 102 WA 18 WA 2 KI 4 WA 22 Group 3 WA 28 NSW 126

WA 3 85 VIC 132 WA 16 Group 2 WA 27

WA 25 0.005 WA 12 WA 4 WA 8 WA 17 WA 24 LSV1 genome (HQ871931)

0.1 Figure 6. Maximum likelihood phylogenetic trees of a 425 bp sequence from (A) LSV1 isolates and a 559 bp sequence from (B) LSV2 isolates compared to reference genomes.

SA 4 SA 21 85 SA 104 SA 6 77 Group 1 SA 8

89 NSW 110 90 ACT 64 NSW 124 VIC 116 TAS 22 99 Group 2 77 TAS 2 62 TAS 13 TAS 1 Group 3 IAPV genome (NC_009025) QLD 17 83 Group 4 QLD 19 QLD 15

100 QLD 3 VIC 131 QLD 117 QLD 118 Group 5 89 NSW 2 NSW 6 WA 7

0.005 Figure 7. Maximum likelihood phylogenetic tree of a 418 bp sequence from IAPV isolates compared to a reference genome.

Nosema spp. prevalence in Australia

Nosema apis was recorded in all regions except for NT and it was not found north of Bundaberg in QLD (Figure 8). Nosema ceranae was common throughout its distribution but was more common as a single infection in QLD. Consistent with previous studies, N. ceranae does not appear to be displacing N. apis, with 49% of samples having mixed infections where the two species’ distributions overlap. However, the absence of N. apis in northern regions of Australia suggests a climatic barrier for this species. The exception to this was the presence of N. apis in KUN, although this sample had recently originated from south WA.

Importantly, N. ceranae was found to have increased its range in Australia. It was detected for the first time in one KUN (northern WA) sample from locally based hives that had been in KUN for at least 12 months (having originally come from south WA). Based on the confirmed absence of N. ceranae in south WA and the dominance of N. ceranae in NT, we suspect that N. ceranae has spread to KUN naturally via the feral bee population.

Nosema ceranae was also found in two TAS samples, having previously been detected in only a single beekeeper’s hives in 2012. One of these positive samples was from the same beekeeper and now a second beekeeper’s hives have become infected with N. ceranae. This result is interesting as there is no direct link between these two beekeeping operations to facilitate the spread of N. ceranae, but also because this pathogen has not spread further throughout TAS over this time. Commercial hives are moved across the state for leatherwood flowering and pollination contracts, but perhaps the cooler climate of TAS has inhibited the further spread of N. ceranae throughout the state.

Figure 8. Detection of Nosema apis and Nosema ceranae across Australia (red circles).

Nosema spore levels were generally high across Australia, regardless of sampling period, with 92% of samples having medium to high estimates (Figure 9). Only 6% of samples had low spore levels and only 2% had no detectable Nosema infection. The highest levels of Nosema were found in SA and KI, where all samples had high spore levels. Interestingly, these samples had very different Nosema species profiles. For instance, 83% of SA samples were mixed infections whereas KI samples were single N. apis infections. Similarly, high spore levels in TAS and QLD spring samples can be mostly attributed to single N. apis and single N. ceranae infections, respectively. Some of the lowest spore levels were found in KUN and NT in winter, which is likely due to the dry tropical climate and the generally low manipulation and movement of hives in this region. While winter and spring are typically peak periods for Nosema (particularly N. apis), the generally high spore levels in all sampling periods and the few samples with low infections suggest there may be other contributing factors. Ensuring good nutrition and minimal hive manipulations are well known management strategies to

reduce Nosema levels. However, these results indicate that many beekeepers have struggled to implement these strategies effectively and further investigation of this issue is warranted.

WINTER SPRING AUTUMN

100%

80%

60% High Medium 40% Low 20% None

0% All VIC NSW QLD SA NT/KUN QLD WA TAS VIC NSW WA KI (n=155) (n=20) (n=13) (n=5) (n=23) (n=10) (n=18) (n=14) (n=15) (n=9) (n=8) (n=15) (n=4)

Figure 9. Percentage of samples with average Nosema spore levels estimated as high (>1000 spores), medium (>100 spores), low (<100 spores) or none (n = sample size).

Brood diseases detected from hive inspections

Three brood diseases, AFB, EFB and Chalkbrood, and two hive pests, Small hive beetle and Wax moth were identified during the 1,240 hive inspections (Figure 10). No exotic pathogens or pests, such as Varroa and Tropilaelaps mites were detected and no endemic brood diseases or hive pests had increased their distribution.

Chalkbrood was the most common brood disease present and 26% of detections were considered high infections by the author. The majority of these high infections were in the east coast, with only one high infection identified in a WA autumn sample. Incidentally, this sample also had the only AFB infection detected in WA. Chalkbrood was not identified in KUN or NT, although additional NT brood samples sent to the authors had chalkbrood infections.

AFB was detected at low levels in all regions except SA, NT and KI (Figure 11). This pathogen is yet to be reported from NT and KI, but is known to occur in SA. AFB was not detected in any samples collected in winter during almond pollination, which is when all SA samples were collected. The absence of AFB at this time likely reflects the efforts of beekeepers to provide healthy hives.

EFB was confirmed in only seven samples from NSW, QLD and TAS, although diseased brood showing possible EFB-like symptoms were collected and tested from all regions except KUN and NT (Figure 11). EFB infections can be difficult to diagnose purely from symptoms as other stressors such as poor nutrition or chilling can have a similar result (Forsgren 2010). This result confirms the current distribution is unchanged with WA, NT and KI still free of EFB.

Wax moth was common at low levels in all regions, being identified either directly as larvae or indirectly from larval faeces on disturbed brood (Figure 11). The frequency of Wax moth disturbed brood was surprisingly common, even in populous hives, and possibly has an underappreciated impact on brood production in some circumstances.

Lastly, Small hive beetle was found only within its currently defined distribution and showed an expected trend of higher frequency in QLD. Small hive beetle was also found at low levels in KUN, where it has occurred since its accidental introduction in 2007. However anecdotally from local beekeepers and from the author’s observations, Small hive beetle numbers may be reducing and may not be well suited for this environment.

100%

80%

60%

40%

20%

0% VIC (n=29) NSW (n=21) QLD (n=24) SA (n=23) WA (n=29) KUN (n=3) NT (n=7) TAS (n=14) KI (n=4)

American foulbrood European foulbrood Chalkbrood Small hive beetle Wax moth

Figure 10. Brood diseases and hive pests detected during hive inspections. AFB and EFB were confirmed by PCR (n = sample size).

A B C

Figure 11. Signs of brood disease and hive pests detected during inspections. A, American foulbrood; B, European foulbrood; Wax moth larvae faeces on developing brood.

Pathogen profiles and associations with management

Viruses are often considered secondary pathogens compared to brood diseases and Nosema, but it is likely they provide an indication of hive stress. We used a generalised linear modelling approach to explore the effect of non-viral pathogens (Nosema spore counts, high chalkbrood infection and clinical SBV infection), environmental factors (sampling season, region and episystem) and several management factors (apiary size, total hives registered, queen breeder, pollination service provider, barrier system and hive movement frequency) on the number of viruses present in a sample. This analysis revealed that the combination of Nosema spore counts and episystem had a significant effect on virus number (P = 0.0146, spore counts; P = 0.0004, episystem 1 and 2, P = 0.004, episystem 1 and 3). The episystem is an extension of sampling region by encompasses all the biological and environmental factors of an epidemiological system in that region (Tabachnick 2010). Because this analysis found no significant difference between region 4 or 5 (TAS and KI) with region 1 (East coast), they were combined into one episystem. The strong regional/episystem effect on viruses and Nosema spore counts is likely explained by environmental differences between these regions and also the absence of several pathogens and pests in WA and NT/KUN. The number of viruses detected appeared to increase with higher Nosema spore levels (Figure 12). This supports the theory that increased hive stress from high Nosema infections may result in more infections from secondary pathogens such as viruses.

Figure 12. Relationship between the number of viruses detected and Nosema spore counts.

Implications

The knowledge developed here on honey bee viruses in Australia has important benefits for industry by gaining access to international export markets for live honey bees. In particular, these results address the concerns of the United States by showing with a good degree of confidence that SPV is not present in Australian honey bees. This information will assist industry and government to negotiate the reestablishment of live honey bee trade with the United States. This may also facilitate the development of trade opportunities for new markets by supporting the health status of Australian honey bees.

The absence of DWV is also significant for the honey bee industry, as this virus has significant impact on colonies overseas and would be an unwelcome addition to Australia’s pathogen landscape. Based on overseas studies, it was believed DWV likely existed at low prevalence in Australia and the arrival of Varroa would spread and increase infection levels of this virus (Martin et al. 2012). Our results do not support this, but it is highly likely DWV would be introduced with Varroa and add to the impact on honey bee colonies.

The absence of both SPV and DWV has implications for the importation of honey bee stock into Australia. Any imports carry the risk of introducing unwanted pests and diseases, however access to genetic stock with valuable characteristics can be beneficial for industry development and productivity. Importation protocols for honey bee queens and semen are currently being reviewed and a recent RIRDC-supported project has developed a molecular test for detecting Africanised honey bee genetics in imported semen, which may give access to genetic stock from new areas (Chapman et al. 2015). The inadvertent introduction of SPV or DWV with imported genetic stock needs to be considered and managed to avoid undermining the current industry health status provided by this study and potentially jeopardising valuable export markets.

New detections of N. ceranae in KUN and TAS have important implications for local biosecurity and demonstrate the value of regular monitoring. Nosema ceranae was not found in south WA and seems to have spread naturally to KUN from NT. Current protocols for the movement of used equipment between KUN and south WA will need to be reviewed to ensure they are adequate for preventing both Small hive beetle and N. ceranae from spreading further in WA. The two detections of N. ceranae in TAS suggest this pathogen has spread little in several years. This may be influenced by climate or local management practices, but could present an opportunity for further reducing the spread and impact of this pathogen in TAS.

This study has also highlighted potential issues with the effectiveness of some pest and disease managements, as reflected by the high prevalence of Nosema infections, multiple viruses and chalkbrood outbreaks. Environmental conditions will have important influence on pathogen levels and poor conditions at times during this survey have likely added to increased pathogen prevalence. However, Nosema levels shown in this study have been similarly high in earlier studies and taken together suggest Nosema species are not being effectively managed. A potential reason for this is the cryptic nature of Nosema infections and a lack of quantified impacts on hive health and productivity. However, it is clear that Nosema and viruses contribute to overall pathogen loads and will add to hive stress. Therefore, improving the management of these cryptic pathogens should be given higher priority and seen as a valuable strategy for increasing industry productivity.

Recommendations

 The scale of the survey (Australia wide, 17 different pests and diseases) imposed limitations on sampling level. Future pathogen monitoring and surveillance activities would likely benefit from being targeted on fewer pathogens and adopting risk-based sampling to focus on key areas and time periods. For example, further surveillance for SPV could be targeted to packaged bee exporters and queen breeders and limited to autumn sampling when SPV is typically at highest prevalence.

 Next-generation sequencing identified multiple short virus sequences showing closest similarity to SPV and DWV. Further clarifying the relationship of these potential viruses with SPV and DWV may be beneficial for maintaining export markets and understanding any effect on honey bee health.

 Lake Sinai viruses are common in Australian honey bees yet we have no understanding of their impact on colony health. More research is needed into the pathology of these viruses and their relationship with other honey bee pathogens.

 More effort is needed to reduce the prevalence of Nosema spp. and viruses in Australia. Greater detailed understanding and demonstration of the effectiveness of different management strategies for reducing pathogen levels would assist beekeepers to make more informed decisions and encourage industry adoption.

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