Skip to main content

Varroa destructor detection in non-endemic areas

Abstract

Australia is one of the few countries where Apis mellifera are not infested by the parasitic mite Varroa destructor. In Australia a program called the ‘Sugar Shake Team’ has been implemented to detect an incursion of V. destructor, should it occur. The aim of this paper was to provide an estimate of the probability that V. destructor would be detected by the Sugar Shake Team program if an incursion had actually occurred (surveillance system sensitivity) using a scenario tree modelling approach. A secondary aim was to provide commentary on how surveillance for V. destructor incursions into Australia might be made more effective. Our analyses show that if one apiary in the study area was infested with V. destructor, there was only a 0.47% probability that infestation would be detected by the current program. We conclude that in its current form the Sugar Shake Team program does not provide protection to multi-billion-dollar beekeeping, pollination and agricultural industries in Australia. Surveillance for V. destructor can be made more effective by increasing the coverage and frequency of testing, deployment of additional sentinel hives and the use of sticky boards.

Introduction

Varroa destructor is the most lethal parasite of the Western honey bee Apis mellifera (Rosenkranz, Aumeier et al. 2010, Noel et al., 2020), harming bees directly and through their role as a vector of viruses (Francis et al., 2013). Australia is one of the few remaining regions of the world free of V. destructor (Locke 2016, Boncristiani et al., 2020, Fanelli and Tizzani 2020, Roth et al., 2020), and Australian authorities have implemented extensive processes to minimise the risk of an inadvertent incursion of V. destructor.

While the invasion of any species or haplotype of V. destructor is of concern to Australia, the three most important threats are V. destructor Korea and Japan haplotypes and A. mellifera–adapted V. destructor jacobsoni (Roberts et al., 2015). Indeed, if introduced into Australia, all three could immediately spread through the A. mellifera population. There are also strains of V. destructor (from mainland Asia) and V. jacobsoni (from Southeast Asia and the Pacific Islands) that could enter Australia on A. cerana, but, to date, these have not yet demonstrated the ability to reproduce or spread to A. mellifera populations (Anderson and Trueman 2000, Anderson and Fuchs 2015). Over time, both V. destructor and V. destructor on A. cerana have the potential to infest A. mellifera populations similar to the way V. jacobsoni has adapted to A. mellifera in Papua New Guinea (Navajas 2010, Roberts et al., 2015). Identification of an A. cerana incursion is relatively easy due to body colouration and body size that is distinctly different from A. mellifera (Radloff et al., 2010). On the other hand, A. mellifera is prevalent in much of Australia (Cunningham et al., 2002) which means that an A. mellifera colony that had broken quarantine would be difficult to visually identify.

To prevent the establishment of V. destructor in Australia, both state and federal governments have invested resources to minimise the risk of an incursion (Perrone and Malfroy, 2015a, b, PHA, 2016a, Phillips 2020). At international sea and airports, staff from the Australian Quarantine and Inspection Service (AQIS) inspect inbound ships and planes for the presence of honey bees (Phillips 2020). In Victoria, sentinel or bait hives are placed around major international ports to attract colonies that might escape from inbound vessels and these are regularly inspected by quarantine and Agriculture Victoria staff (Boland 2005, Clifford et al., 2011, Heersink et al., 2016, Keeling et al., 2017).

AQIS staff have detected several incursions of V. destructor in Australia in recent years, with all incoming colonies either destroyed or confirmed free of V. destructor promptly after detection (Weatherhead, 2018a, b). Most incursions have been detected by crew on board ships or by port workers who in turn notified AQIS officials. Incursions of A. cerana occurred in Cairns (16.92° S, 145.78° E) in 2007, Townsville (19.26° S, 146.82° E) in 2016 (Ryan 2010) and Melbourne (37.83° S, 144.91° E) in 2018 (Rooth 2018). In all three situations, freedom from V. destructor was declared although the Townsville incursion was infested with V. jacobsoni, identified by molecular methods, that were not considered to be an immediate threat to Australia’s A. mellifera population (Anderson and Fuchs 2015). In spite of intensive eradication efforts, an A. cerana colony infested with V. jacobsoni, believed to be from the 2016 Townsville incursion, was identified in May 2019 (BeeAware 2019). Should an infested invasive A. mellifera colony enter Australia undetected, mites from the colony will spread to other feral and managed colonies in the immediate vicinity. Frey et al. found that about 2.5% of mites in a colony spread to other colonies over a 2-month period, with the most at-risk receiver colonies being up to 1.5 km away (Frey et al., 2015).

In support of quarantine procedures implemented at ports, Australia has developed a compulsory national beekeeping code of practice that formalises the role beekeepers play in minimising the impact of honey bee diseases (PHA, 2016b). To help detect an infested colony that has broken quarantine, the code of practice mandates regular testing for V. destructor using one of several methods, but only for beekeepers who manage more than 50 colonies. Smaller beekeepers, who are much less likely to move their colonies and are less likely to possess the skills necessary to detect V. destructor, are not required, but encouraged, to test for the mite. Several methods are recommended for testing by Australian regulatory authorities, including the sugar shake method (Ellis and Macedo 2001, BeeAware 2014, Vesco and Guido 2014, Macedo et al., 2015), the alcohol wash method (Caron 2015, PHA 2019), drone brood uncapping and the use of sticky boards (Delaplane et al., 2005, Caron 2015).

Throughout much of Australia, a ‘Sugar Shake Team’ program has, for many years, provided support for a small number of beekeepers to regularly test their colonies for V. destructor using the sugar shake method (Perrone and Malfroy, 2015a, b). At the time of writing, 334 hobby and commercial beekeepers throughout the state of Victoria test at least one of their colonies three times per year using the sugar shake method as part of the Sugar Shake Team program. Another initiative of Agriculture Victoria has been to provide Apistan (the pyrethroid tau-fluvalinate) or Apivar (amitraz) miticide strips and sticky boards to a small number of volunteer beekeepers in order to detect possible infestations. Sticky boards are generally not recommended for use in Australia prior to an incursion since most beekeepers will be unable to identify any fallen mites. The volunteers that use sticky boards send their used mats to a government apiary inspector for assessment and to get a new mat sent to them. It is unlikely that the government will pay for sticky boards to be used by all Sugar Shake Team members, nor will most sugar shake volunteers be prepared to invest the time to perform sticky board testing.

While surveillance programs have been implemented to detect V. destructor presence, to the best of our knowledge, they have not been rigorously assessed in terms of quantifying the probability that V. destructor would be detected if an incursion had actually occurred (surveillance system sensitivity).

The aim of this paper was to provide an estimate of the surveillance system sensitivity of the Sugar Shake Team program. A secondary aim was to provide commentary on how surveillance for V. destructor incursions into Australia might be made more effective. In a more general sense, the approach described in this paper can be applied in other countries to evaluate the effectiveness of programs that have been proposed or implemented to protect animals or plants against other invasive species.

Materials and methods

Three surveillance techniques are used to support Australia’s claim for V. destructor freedom: (1) inspection of colonies for the presence of mites by beekeepers; (2) inspection of colonies by government apiary inspectors while performing routine regulatory tasks; and (3) the Sugar Shake Team program. Of these techniques, only the Sugar Shake Team program was considered suitable for the first objective of this study. We reasoned that passive surveillance for V. destructor by beekeepers or apiary inspectors would only detect V. destructor as an incidental finding and estimation of probabilities associated with this process would likely to be very low and unlikely to make a meaningful contribution to our estimates of the probability that V. destructor would be detected if an incursion of the parasite had actually occurred.

The sugar shake and alcohol wash mite detection methods are similar. Using these tests, the bees are covered in either icing sugar or in alcohol, respectively, before being vigorously shaken (De Jong et al., 1982, Fakhimzadeh 2000, Ellis and Macedo 2001, Macedo et al., 2002, Azizi et al., 2008, Vesco and Guido 2014, Flores et al., 2015, Macedo et al., 2015, Dunaway 2016, de Feraudy et al., 2019, Oliver 2020).

Within-colony V. destructor growth and detection model

A number of within-colony models of V. destructor spread have been developed to estimate the number of V. destructor present in a colony as a function of the number of days since first infestation (Martin 1998, Calis et al., 1999, DeGrandi-Hoffman and Curry 2004, Becher et al., 2013, Becher et al., 2014, Ratti 2015, Ratti et al., 2015, Oliver 2017). We used the model of Oliver (2017), which was based on the model of Martin (1998), to estimate the number of phoretic mites present in an infested colony at the end of every 2 weeks, for a period of 18 months following the date of initial infestation. Oliver’s model was used because it provided estimates of the approximate number of V. destructor present in a colony as a function of the number of days since first infestation, allowing us to determine the likely number of V. destructor that would be present in a sample of around 300 bees taken from a colony at when testing was carried out. The city of Melbourne is comprised of several distinct geographic regions with diverse flora: inner-city suburbs with dense housing and few parks, affluent suburbs with extensive exotic and native flora, industrial areas and outer-suburban areas that are more rural. Each of these areas offers benefits and disadvantages to honey bees. Eucalypts, on which much of Australian commercial beekeeping depends, flower sporadically, with sometimes up to 7 years between flowering events. Melbourne’s climate varies from year to year (Senate Rural and Regional Affairs and Transport References Committee 2014) providing abundant honey 1 year and drought requiring supplemental feeding the following year. Our use of Oliver’s model provided data that the authors believe was sufficiently accurate considering the wide range of geographic regions and colony types that can be found in the Melbourne Metropolitan area.

We assumed that a single mite entered a colony during mid-winter (1 July in the Southern Hemisphere). The Oliver (2017) model was then used to provide estimates of the approximate number of V. destructor present in the colony as a function of the number of days since first infestation. The number of V. destructor mites expected to be present in a random sample of approximately 300 bees taken from a colony at 1-month intervals from the date of first infestation was then calculated assuming there were approximately 40,000 worker bees per colony.

Scenario tree model

The probability that the Sugar Shake Team program would detect V. destructor if the mite was present in the honey bee population at a pre-specified design prevalence (surveillance system sensitivity) was estimated using a scenario tree modelling approach (Martin et al., 2007a, b). The population at risk for our analyses was both managed and feral honeybee colonies in the Melbourne Metropolitan area, Victoria. Our case definition was a honeybee colony infested with V. destructor, and the detection process was the observation of at least one V. destructor mite within an infested colony.

Honey bee populations in the Melbourne Metropolitan area

Implementation of the scenario tree used in this paper required counts of the number of managed colonies in the Melbourne Metropolitan area by post code area. All Australian beekeepers must register with the Department of Primary Industries (or equivalent) in the state in which they live. Beekeepers are asked to provide their home or business address, contact details and details of the number of hives they manage. Beekeepers are not required to report the location of the hives. Under separate legislation, the Australian Honey Bee Industry Biosecurity Code of Practice, each beekeeper is required to keep a record of the location of his/her hives for 3 years (PHA, 2016a, b). Each registered beekeeper is given a unique identifier that must be indelibly marked on each of their hive boxes. A small number of beekeepers do not register with state authorities, and many beekeepers do not provide accurate information on the number of hives they own because if five or more hives are kept a fee is charged. Commercial beekeepers are often reluctant to disclose the location of their hives in case a competitor places hives in the same area which may be a valuable honey-producing location. Commercial beekeepers frequently move their hives inter-state. To minimise the spread of disease, hives moved inter-state are required to have a government-issued health certificate.

Data on beekeepers, hive numbers and beekeeper home address, summarised by post code area, were provided by Agriculture Victoria in May 2019. Using a Geographic Information System, a circular area of 10-km radius was drawn around the point location of the Port of Melbourne (37.8332° S, 144.9125° E) and all post code areas falling within or overlapping this circular area designated as a high V. destructor incursion risk area (Figure 1). Swarms of bees seldom fly further than 5 km from their home colony (Winston 1991), so we reasoned that approximately 10 km from the most likely site of entry (the Port of Melbourne) would be the outer limits of a high-risk area. The remainder of the Melbourne Metropolitan area was designated as a low V. destructor incursion–risk area. We assumed the probability of V. destructor being present in the high-risk area was twice that of the low-risk area.

Figure 1.
figure 1

Map showing the boundaries of post code areas in the Melbourne Metropolitan area showing the location of the Port of Melbourne (●). The shaded areas show those post code areas that fall within a 10-km radius around the Port of Melbourne.

We further assumed that the number of feral honeybee colonies in the Melbourne Metropolitan area was twice that of managed honey bee colonies (Utaipanon et al., 2020) and that a feral colony could be thought of as an apiary comprised of a single colony. This gives rise to a total of approximately 30,300 colonies (10,272 managed colonies and approximately 20,000 feral colonies) and 23,300 apiaries (3322 managed apiaries and approximately 20,000 feral ‘apiaries’) in the Melbourne Metropolitan area, as of May 2019. In the Agriculture Victoria database, nucleus colonies were not included in the managed colony group.

Table I provides details of the managed and feral honeybee population at risk in the Melbourne Metropolitan area, as of May 2019. Data is obtained from Agriculture Victoria (private email). Table II provides details of the input parameters for the scenario tree model used to estimate the probability of detecting a V. destructor–infested colony.

Table I Counts of managed apiaries and colonies and approximate counts of feral colonies in the greater Melbourne Metropolitan area. Data obtained from Agriculture Victoria (private email, Agriculture Victoria, May 2019).
Table II Input parameters for a scenario tree model to estimate the probability of detecting a V. destructor–infested colony

Design prevalence

To estimate the sensitivity of the V. destructor surveillance system (and the probability that the population was free of V. destructor if no infested colonies were detected), it was necessary to specify a design prevalence defined as the prevalence of apiary-level infestation below which the population may be considered ‘free’ of disease (Martin et al., 2007a, b). Specification of a design prevalence was necessary because absolute proof of disease freedom requires testing of the entire population simultaneously using a perfect test.

Data provided by Agriculture Victoria (private email) showed that a total of 213 managed apiaries (comprised of 561 colonies) were in the defined high-risk area (the area covered by any post code within 10 km of the Port of Melbourne). These counts are likely to be an underestimate since an unknown proportion of beekeepers do not register with the state government for the reasons outlined above. Also, many colonies may not be kept at the registered address of the beekeeper, but at other locations. Most hives in the Melbourne Metropolitan area are kept by hobbyists, and it is likely that the hives owned by hobbyists remain at a single location throughout the year. Commercial beekeepers, whose business is registered in the Melbourne Metropolitan area, are likely to move their colonies from one location to another on a regular basis.

The aim of a surveillance program is to detect the presence of V. destructor quickly, increasing the likelihood of an effective control and eradication response. For this reason, the apiary-level design prevalence (PA*) was set to 1 in 23,300 (equivalent to 4.3 per 100,000 apiaries). The within-apiary design prevalence (PC*) for apiaries that were V. destructor positive was set to 0.8. This means that in an apiary that was V. destructor positive, eight out of 10 colonies were expected to be V. destructor positive. This relatively high within-apiary prevalence was used because mites that leave an infested colony are likely to populate nearby colonies (DeGrandi-Hoffman et al., 2016, Messan et al., 2017, Noel et al., 2020, Messan et al., 2021).

Apiary- and colony-level surveillance system sensitivity calculations

We assumed that the sugar shake method was the only method used for V. destructor detection and the diagnostic sensitivity of the sugar shake method was 0.65.

Figure 2 is a diagram showing the scenario tree for V. destructor detection. The probability of a colony being detected V. destructor positive was reasoned to be dependent on: (1) its geographic location, in either the high- or low-risk area; (2) the probability that the apiary was infested, dependent on the apiary-level design prevalence; (3) the colony type (managed or feral) which determined whether or not testing for V. destructor would take place; (4) the probability that a colony within an apiary selected for testing was infested, dependent on the colony-level design prevalence (the proportion of V. destructor–infested colonies within V. destructor–positive apiaries); and (5) the diagnostic sensitivity of the sugar shake method.

Figure 2.
figure 2

Scenario tree model of apiary and colony selections and testing for V. destructor. Key: SS surveillance system.

We calculated the adjusted relative risk, 퐴푅, for apiaries in the high- and low-risk areas as a function of the area-level relative risk estimates and the proportion of apiaries in each area, using the approach described by Martin et al., 2007a, b Briefly, the adjusted relative risk allowed us to estimate the probability that an apiary selected at random from a given area (high risk, low risk) was V. destructor positive. Acknowledging lack of independence in the data arising from the possibility of testing two or more colonies from the same apiary, the apiary-level sensitivity of detection, 푆푒, was given by:

$$ {\mathrm{S}}_{\mathrm{eA}}=1-{\left[1-\mathrm{P}\left(\mathrm{pos}\right)\mathrm{j}\right]}^{\mathrm{n}} $$
(1)

In Eq. 1, 푃푟 (푝표푠) equals the product of the sum of the positive limb probabilities for the 푗th risk area and 푛 equals the number of colonies tested in each apiary. The probability that an apiary was V. destructor negative given that all colonies tested returned a negative result, the apiary-level negative predictive value (푁푃푉) , was:

$$ \mathrm{NP}{\mathrm{V}}_A=1-\left(\ {\mathrm{S}}_{\mathrm{eA}}\times {\mathrm{AR}}_{\mathrm{j}}\times {P}_{A^{\ast }}\right) $$
(2)

In Eq. 2, 퐴푅 equals the adjusted relative risk for the 푗th area and the expression 퐴R × 푃 can be thought of as the probability that apiaries in the 푗푡ℎ risk area were infested (the effective probability of infestation for the 푗푡ℎ risk area). The surveillance system sensitivity 푆푆푒 was then equal to one minus the product of each of the 퐿 apiary-level negative predictive values for those apiaries that were tested:

$$ SSe=1-\prod \limits_{l=1}^L{NPV}_A $$
(3)

Probability of freedom

The posterior probability that honey bee colonies in the Melbourne Metropolitan area were free of V. destructor (푃퐹푟푒푒) at the stated design prevalence was estimated using a prior estimate that V. destructor was actually present in the population (nominally set to 0.50) and the surveillance system sensitivity, calculated using Eq. 3 (Martin et al., 2007a, b):

$$ Pfree=\frac{1- prior}{1- prior\times SSe} $$
(4)

If prior = 0.5, Eq. 4, after rearranging, simplifies to:

$$ Pfree=\frac{1}{\left(2- SSe\right)} $$
(5)

Results

Estimated probability of detecting V. destructor in an infested colony

We assumed V. destructor first infested a managed colony in the high-risk area on 1 July in a given year. Using data from the V. destructor growth model (Oliver 2017), we calculated the number of V. destructor mites in an infested colony and the number of V. destructor mites likely to be in a sample of 300 bees taken from an infested colony at different points in time following the date of first infestation (300 is the approximate number of bees sampled from a colony for either the alcohol wash or sugar shake method). If a colony was first infested on 1 July in year 푌, it was not until April in the following year (year 푌 + 1) that around 50 mites would be estimated to be present in the colony and that at least one mite would probably be present in a sample of 300 bees selected for testing (Figure 3). If sampling was carried out monthly between 1 June and 1 August of year 푌 + 1 (winter in the Southern Hemisphere), we estimate that at least one V. destructor mite would be present in two of the three samples. If sampling were carried out monthly between 1 September and 1 November in year 푌 + 1 (spring in the Southern Hemisphere), 15 to 17 months after the date of first infestation, we estimate that at least one V. destructor mite would be present in all three samples (Figure 3).

Figure 3.
figure 3

Stacked bar charts showing the expected number of V. destructor mites present and detected and the number of V. destructor mites present and not detected in a random sample of 300 bees taken from an infested colony as a function of the number of days since first infestation (assumed to be 1 July, mid-winter in the Southern Hemisphere) for a the alcohol wash method (diagnostic sensitivity 0.95) and b using the sugar shake method (diagnostic sensitivity 0.65). The horizontal dashed line identifies those months where one or more V. destructor mites were likely to be in a sample of 300 bees.

On 1 July of year 1 of the model, 푌, colony strength was set at 16,000 adult bees (Seeley 1978, Winston 1991, Seeley 2010, Winston 2014, Tautz and Steen 2018). The number of infested colonies was set at 1, while the daily reproduction rate for V. destructor 푟 was set to 0.021. The daily reproduction rate is the number of eggs a female V. destructor will lay each day (Medina et al., 2002, Corrêa-Marques et al., 2003, Oliver 2017). Using these input parameters, Oliver’s model predicted a maximum colony strength (number of bees) at 48,000 in January, 푌 + 1. By early winter (May 푌 + 1), the number of bees in the colony had fallen to 18,000, before increasing again during the Spring.

Estimated surveillance system sensitivity

Given an overall apiary-level design prevalence (푃) of 4.3 per 100,000, the effective probability of infestation was 8.2 per 100,000 for apiaries in the high-risk area and 4.1 per 100,000 for apiaries in the low-risk area. A total of 213 of the 3322 registered apiaries (equivalent to 561 of the 10,272 managed colonies) in the Melbourne Metropolitan area were estimated to be in the high-risk area.

All beekeepers were eligible to take part in the Sugar Shake Team program with 200 apiaries (25 in the high-risk area and 175 in low-risk area) selected at random for testing. Within each selected apiary, a single colony was selected at random for testing. Testing was not carried out in feral colonies. Our scenario tree model shows that if one of the 23,300 feral and managed apiaries in the Melbourne Metropolitan area was infested, the probability of detecting V. destructor using the current Sugar Shake Team program, testing 200 colonies out of 30,000 managed and feral colonies, was 0.47%. Assuming a prior probability of V. destructor freedom of 0.50, sugar shake testing was conducted monthly and the probability of V. destructor incursion per month was 0.001 after 10 years of testing and returning negative tests on each occasion, the probability that the population was actually free of V. destructor would only be 0.57.

Figure 4 shows the probability of detecting an infested colony as a function of the number of apiaries sampled (100 to 500 apiaries) assuming 1, 10 or 20 infested apiaries present in the Melbourne Metropolitan area. Even with 500 apiaries tested and a relatively high design prevalence of 67 V. destructor–positive apiaries per 100,000 (equivalent to 20 infested colonies in the Melbourne Metropolitan area), the probability of selecting an infested colony after a single round of Sugar Shake Team testing was only 20%. The size of the feral population (which are not sampled and tested) and the relatively low number of managed colonies sampled hinder V. destructor detection.

Figure 4.
figure 4

Line plots showing the probability that a V. destructor–infested apiary will be detected as V. destructor positive as a function of the number of apiaries tested using the sugar shake method, assuming there are 1, 10 or 20 V. destructor–infested apiaries in the greater Melbourne Metropolitan area, assuming a sufficient number of bees were sampled from a colony to ensure at least one mite was present.

Discussion

Our analyses show that the probability of detecting a V. destructor infested colony in the first year to 18 months after an incursion, if 200 colonies out of 30,000 managed and feral colonies were tested, was only 0.47%. During this time, the mite is likely to have migrated to other colonies and become widely distributed. As a consequence, the Sugar Shake Team is not fit for purpose and needs restructuring if it is to be a fit-for-purpose surveillance tool. The low sugar shake test sensitivity, 0.65, in itself is not a concern. More important is that currently only about 300 out of approximately 30,000 colonies are tested each year and feral colonies are not tested. Also, during the first 9 months immediately following an incursion, it is unlikely that V. destructor will be detected in a colony if it is present. This means that by the time V. destructor becomes detectable, the opportunity to achieve control and eradication may be missed. The importance of this is that government and industry need to then consider putting minimal effort into eradication and move directly to industry-wide management of a widely prevalent pathogen.

In addition to the inadvertent importation of V. destructor into Australia, the importation of other mellifera pathogens is a concern for any country with agriculture dependant on intensively managed honeybees. Examples include Tropilaelaps clareae (de Guzman et al., 2017) and V. jacobsoni (Mattu and Sharma 2018) which infest A. mellifera in Asia while the small hive beetle, Aethina tumida (endemic in sub-Saharan Africa), has been introduced in North America, Australia, Europe (where it gained entry via Italy) and a small number of other countries (Giangaspero and Turno 2015). Other examples include the global spread of Nosema ceranae (Goblirsch 2017), an obligate microsporidium of the honey bee.

The possible introduction of tracheal mites, Acarapis woodi (Pettis and Wilson 1996), is also a concern in many countries, including Australia. Africanised ‘killer’ bees in the Americas, resulting from the importation of A. mellifera scutellata into Brazil in 1957 (Whitfield et al., 2006) has caused severe safety issues for both beekeepers and the general public due to their aggressiveness. While the subject matter of the analyses conducted in this paper is unlikely to be of direct interest to beekeepers in other countries, given V. destructor is already present in most areas of the world (Traynor et al., 2020), we propose that our methodological approach could be used to assist in the design of surveillance programs for other honeybee pathogens of importance such as those listed above.

Study limitations

There are two main limitations to the analyses described in this paper. First is the quality of government data on registered beekeepers, which may lead to inaccurate estimates of the density and distribution of managed colonies. Second is the use of Oliver’s model to describe both feral and managed colonies. Although Oliver’s model is based on empirical data, his model estimates the growth of V. destructor, numbers, and honeybees in a managed colony. There are differences between feral and managed colonies which the Oliver model does not take into account. Other models may lead to different results, although the general conclusions are likely to be similar (Corrêa-Marques et al., 2003, Odemer 2020). The authors believe that Oliver’s model which is based on the peer-reviewed model of Martin (1998) is sufficiently accurate to draw general conclusions about the likely increase in V. destructor numbers within a colony over time since first infestation.

Recommendations

A secondary aim of the study was to provide commentary on how surveillance for V. destructor incursions into Australia might be made more effective since we conclude that the Sugar Shake Team program does not provide protection to the multi-billion-dollar beekeeping, pollination and agricultural industries in Australia. In this regard, we make the following recommendations regarding V. destructor surveillance in Australia:

  1. 1.

    The surveillance system sensitivity of the sugar shake program can be improved if testing is carried out more frequently throughout the year (e.g. every month). Concentrating test effort in high-risk areas and switching from monthly testing to weekly testing during the winter, using sticky boards and modified bases, will enhance surveillance system sensitivity.

  2. 2.

    An alternative approach, to saturate managed colonies in greater Melbourne with Varroa-resistant queens, particularly within 10 km of possible ports of entry, may delay the spread of the mite. A disadvantage is that the mite will live only in feral colonies where they will remain undetected until they move into colonies outside Melbourne.

  3. 3.

    Increase manual monitoring for invasive species around ports of entry. In Australia, all detected incursions of V. destructor at ports were first reported either by port workers or by crew of ships. V. destructor awareness campaigns targeting port workers and the crew of ships should be enhanced.

  4. 4.

    Increase the number of sentinel hives around ports, with and without bees, with frequent monitoring.

  5. 5.

    If a colony is found to contain invasive mites, bait traps containing sugar and fipronil should be deployed to kill all colonies within a 5-km radius of the site of detection. This may be unacceptable to some urban hobby beekeepers so engagement with this sector is required well in advance to explain the rationale for this strategy and to solicit endorsement.

  6. 6.

    Investigate the feasibility of placing bait traps containing fipronil on all incoming ships so that swarms may be killed while at sea.

  7. 7.

    Better pre-incursion preparedness, e.g. develop a Varroa-resistant queen breeding facility, using VSH genetics from queens or sperm sourced overseas, and encourage their widespread use by both commercial and hobby beekeepers.

  8. 8.

    Model the ways in which an incursion can spread from Port of Melbourne, and the optimum ways the incursion can be stopped or delayed.

We conclude that the approach described in this study could form the basis of a more generalised, fit-for-purpose honeybee surveillance system for use in both Australia and other countries of the world.

References

  • Anderson, D. and S. Fuchs (2015). “Two genetically distinct populations of Varroa jacobsoni with contrasting reproductive abilities on Apis mellifera.” Journal of Apicultural Research 37(2): 69-78.

    Article  Google Scholar 

  • Anderson, D. and J. Trueman (2000). “Varroa jacobsoni (Acari: Varroidae) is more than one species.” Experimental and Applied Acarology 24(3): 165-189

    CAS  PubMed  Article  Google Scholar 

  • Azizi, H., E. Sadeghi, M. Taghdiri and A. Vardanjani (2008). “The comparative evaluation of the laboratory methods of separation mite varroa from the mature honeybee.” Research Journal of Parasitology 3(4): 123 - 129.

    Article  Google Scholar 

  • Becher, M., J. Osborne, P. Thorbek, P. Kennedy and V. Grimm (2013). “Towards a systems approach for understanding honeybee decline: a stocktaking and synthesis of existing models.” Journal of Applied Ecology 50(4): 868-880.

    Article  PubMed  Google Scholar 

  • Becher, M., V. Grimm, P. Thorbek, J. Horn, P. Kennedy and J. Osborne (2014). “BEEHAVE: a systems model of honeybee colony dynamics and foraging to explore multifactorial causes of colony failure.” Journal of Applied Ecology 51(2): 470 - 482.

    Article  PubMed  Google Scholar 

  • BeeAware. (2014). “Sugar shaking.” Retrieved 24 July 2019, 2019, from https://www.beeaware.org.au/wp-content/uploads/2014/03/Sugar-shaking.pdf.

  • BeeAware. (2019). “Varroa mites detected again at townsville port.” Retrieved 24 July 2019, 2019, from https://beeaware.org.au/archive-news/varroa-mites-detected-again-at-townsville-port/.

  • Boland, P. (2005). A Review of the National Sentinel Hive Program. B. Australia. Australia, Biosecurity Australia.

  • Boncristiani, H., J. Ellis, T. Bustamante, J. Graham, C. Jack, B. Kimmel, A. Mortensen and D. Schmehl (2020). “World honey bee health: the global distribution of western honey bee (Apis mellifera L.) pests and pathogens.” Bee World 98(1): 2 - 6.

    Article  Google Scholar 

  • Calis, J., I. Fries and S. Ryries (1999). “Population modelling of Varroa jacobsoni Oud.” Apidologie 30: 111 - 124.

    Article  Google Scholar 

  • Caron, D. (2015). Tools for varroa management: a guide to effective varroa sampling & control. H. H. Coalition. US, Honeybee Health Coalition.

  • Clifford, D., S. Barry, D. Cook, R. Duthie and D. Anderson (2011). “Using simulation to evaluate time to detect incursions in honeybee biosecurity in Australia.” Risk Analysis 31(12):1961-1968.

    PubMed  Article  Google Scholar 

  • Corrêa-Marques, M., L. Medina, S. Martin and D. De Jong (2003). “Comparing data on the reproduction of Varroa destructor.” Genetic Molecular Research 2(1): 1-6

    Google Scholar 

  • Cunningham, S., F. FitzGibbon and T. Heard (2002). “The future of pollinators for Australian agriculture.” Australian Journal of Agricultural Research 53(8): 893.

    Article  Google Scholar 

  • de Feraudy, L., U. Marsky and J. Danihlik (2019). Efficiency of Varroa monitoring methods: The benefits of standardized monitoring devices. Apimondia. Montreal

    Google Scholar 

  • de Guzman, L., G. Williams, K. Khongphinitbunjong and P. Chantawannakul (2017). “Ecology, Life History, and Management of Tropilaelaps Mites.” Journal of Economic Entomology 110(2): 319 - 332

    PubMed  Article  Google Scholar 

  • De Jong, D., D. De Andrea Roma and L. Goncalves (1982). “A comparative analysis of shaking solutions for the detection of varroa jacobsoni on adult honey bees.” Apidologie 13(3): 297 - 306.

    Article  Google Scholar 

  • DeGrandi-Hoffman, G. and R. Curry (2004). “A mathematical model of Varroa mite (Varroa destructor Anderson and Trueman) and honeybee (Apis mellifera L.) population dynamics.” International Journal of Acarology 30(3): 259 - 274.

    Article  Google Scholar 

  • DeGrandi-Hoffman, G., F. Ahumada, V. Zazueta, M. Chambers, G. Hidalgo and E. W. deJong (2016). “Population growth of Varroa destructor (Acari: Varroidae) in honey bee colonies is affected by the number of foragers with mites.” Exp Appl Acarol 69(1): 21-34.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Delaplane, K., J. Berry, J. Skinner, J. Parkman and W. Hood (2005). “Integrated pest management against Varroa destructor reduces colony mite levels and delays treatment threshold.” Journal of Apicultural Research 44(4): 157 - 162.

    Article  Google Scholar 

  • Dunaway, D. (2016). “Meaningful Mite Monitoring.” Bee sCene 32(2): 3.

    Google Scholar 

  • Ellis, M. and P. Macedo. (2001). “Using the sugar roll technique to detect varroa mites in honey bee colonies.” NebGuide Retrieved 24 July 2019, 2019, from https://www.researchgate.net/profile/Marion_Ellis/publication/266211649_G01-1430_Using_the_Sugar_Roll_Technique_to_Detect_Varroa_Mites_in_Honey_Bee_Colonies/links/54 b804450cf2c27adc4870fd/G01-1430-Using-the-Sugar-Roll-Technique-to-Detect-Varroa-Mites-inHoney-Bee-Colonies.pdf?origin=publication_detail.

  • Fakhimzadeh, K. (2000). Potential of super-fine ground, plain white sugar dusting as an ecological tool for the control of Varroasis in the honey bee (Apis mellifera). American Bee Journal. US, Dadant. 140: 487–491.

    Google Scholar 

  • Fanelli, A. and P. Tizzani (2020). “Spatial and temporal analysis of varroosis from 2005 to 2018.” Research in Veterinary Science 131: 215 - 221.

    PubMed  Article  Google Scholar 

  • Flores, J., S. Gil and F. Padilla (2015). “Fiabilidad de los principales métodos de diagnóstico de Varroa destructor en colonias de abejas.” Archivos de Zootecnia 64(246): 161 - 166.

    CAS  Article  Google Scholar 

  • Francis, R., S. Nielsen and P. Kryger (2013). “Varroa-virus interaction in collapsing honey bee colonies.” PLoS One 8(3): e57540.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  • Frey, E., H. Schnell and P. Rosenkranz (2015). “Invasion of Varroa destructor mites into mite-free honey bee colonies under the controlled conditions of a military training area.” Journal of Apicultural Research 50(2): 138 - 144.

    Article  Google Scholar 

  • Giangaspero, M. and P. Turno (2015). “Aethina tumida, an exotic parasite of bees.” Clinical Microbiology: Open Access 04(05).

  • Goblirsch, M. (2017). “Nosema ceranae disease of the honey bee (Apis mellifera).” Apidologie 49(1): 131 - 150.

    Article  Google Scholar 

  • Heersink, D., P. Caley, D. Paini and S. Barry (2016). “Quantifying the Establishment Likelihood of Invasive Alien Species Introductions Through Ports with Application to Honeybees in Australia.” Risk Analysis 36(5): 892 - 903.

    PubMed  Article  Google Scholar 

  • Keeling, M., S. Datta, D. Franklin, I. Flatman, A. Wattam, M. Brown and G. Budge (2017). “Efficient use of sentinel sites: detection of invasive honeybee pests and diseases in the UK.” Journal of the Royal Society Interface 14(129):20160908.

    PubMed Central  Article  PubMed  Google Scholar 

  • Locke, B. (2016). “Natural Varroa mite-surviving Apis mellifera honeybee populations.” Apidologie 47, 467–482

    Article  Google Scholar 

  • Macedo, P., J. Wu and M. Ellis (2002). “Using inert dusts to detect and assess varroa infestations in honey bee colonies.” Journal of Apicultural Research 41: 3-7

    Article  Google Scholar 

  • Macedo, P., J. Wu and M. Ellis (2015). “Using inert dusts to detect and assess varroa infestations in honey bee colonies.” Journal of Apicultural Research 41(1-2): 3 - 7

    Article  Google Scholar 

  • Martin, S. (1998). “A population model for the ectoparasitic mite Varroa jacobsoni in honey bee (Apis mellifera) colonies.” Ecological Modelling 109(3): 267 - 281.

    Article  Google Scholar 

  • Martin, P., A. Cameron, K. Barfod, E. Sergeant and M. Greiner (2007a). “Demonstrating freedom from disease using multiple complex data sources 2: case study--classical swine fever in Denmark.” Preventative Veterinary Medicine 79(2-4): 98 - 115.

    CAS  Article  Google Scholar 

  • Martin, P., A. Cameron and M. Greiner (2007b). “Demonstrating freedom from disease using multiple complex data sources 1: a new methodology based on scenario trees.” Preventive Veterinary Medicine 79(2-4): 71-97.

    CAS  PubMed  Article  Google Scholar 

  • Mattu, V. and I. Sharma (2018). “Mite Varroa jacobsoni and its impact on Apis mellifera L.” Indian Journal of Entomology 80(4).

  • Medina, L., S. Martin, L. Espinosa-Montano and F. Ratnieks (2002). “Reproduction of Varroa destructor in worker brood of Africanized honey bees (Apis mellifera).” Exp Appl Acarol 27(1-2): 79 - 88.

    PubMed  Article  Google Scholar 

  • Messan, K., G. DeGrandi-Hoffman, C. Castillo-Chavez and Y. Kang (2017 ). “Migration effects on population dynamics of the honeybee-mite interactions.” Mathematical Modeling of Natural. Phenomena 12(2): 84 - 115.

    Article  Google Scholar 

  • Messan, K., M. Rodriguez Messan, J. Chen, G. DeGrandi-Hoffman and Y. Kang (2021). “Population dynamics of varroa mite and honeybee: effects of parasitism with age structure and seasonality.” Ecological Modelling 440.

  • Navajas, M. (2010). Tracking the colonisation history of the invasive species varroa destructor. Trends in Acarology. S. M and B. J. Dordrecht, Netherlands, Springer,: 375.

  • Noel, A., Y. Le Conte and F. Mondet (2020). “Varroa destructor: how does it harm Apis mellifera honey bees and what can be done about it?” Emerging Topics in Life Sciences 4(1): 45 – 57

  • Odemer, R. (2020). “Reproductive capacity of Varroa destructor in four different honey bee subspecies.” Saudi Journal of Biological Sciences 27(1): 247 - 250.

    PubMed  Article  Google Scholar 

  • Oliver, R. (2017). “Building a Varroa Model.” Retrieved 15 April 2019, 2019, from http://scientificbeekeeping.com/the-varroa-problem-part-12/.

  • Oliver, R. (2020). “Mite Monitoring Methods.” Sick Bees Retrieved 15 April 2020, 2020, from http://scientificbeekeeping.com/varroa-management/mite-monitoring-methods/.

  • Perrone, S. and S. Malfroy (2015a). “BeeForce Australia Part II.” Bee World 91(3): 70 - 74.

    Article  Google Scholar 

  • Perrone, S. T. and S. Malfroy (2015b). “BeeForce Australia Part I.” Bee World 91(2): 36 - 37.

    Article  Google Scholar 

  • Pettis, J. and W. Wilson (1996). “Life history of the honey bee tracheal mite (Acari: Tarsonemidae).” Annals of the Entomological Society of America 89(3): 368 - 374.

    Article  Google Scholar 

  • PHA. (2016a). “Australian honey bee industry biosecurity code of practice.” 24 July 2019, 2019, from http://beeaware.org.au/wp-content/uploads/2017/09/Australian-Honey-Bee-IndustryBiosecurity-Code-of-Practice.pdf.

  • PHA. (2016b). “National honey bee pest surveillance program.” Retrieved 24 July 2019, 2019, from https://nbpsp.planthealthaustralia.com.au/public.php?page=pub_home&program=5.

  • PHA. (2019). “Alcohol washing.” Retrieved 15 April 2020, 2020, from https://beeaware.org.au/wpcontent/uploads/2014/03/Alcohol-washing.pdf.

  • Phillips, C. (2020). “The force of varroa: anticipatory experiences in beekeeping biosecurity.” Journal of Rural Studies 76: 58 - 66.

    Article  Google Scholar 

  • Radloff, S., C. Hepburn, H. Randall Hepburn, S. Fuchs, S. Hadisoesilo, K. Tan, M. Engel and V. Kuznetsov (2010). “Population structure and classification of Apis cerana.” Apidologie 41(6): 589 - 601.

    Article  Google Scholar 

  • Ratti, V. (2015). Predictive Modeling of the Disease Dynamics of the Honeybee-Varroa destructor Virus Systems. PhD, University of Guelph.

    Google Scholar 

  • Ratti, V., P. Kevan and H. Eberl (2015). “A Mathematical Model of the Honeybee-Varroa destructor Acute Bee Paralysis Virus System with Seasonal Effects.” Bulletin of Mathematical Biology 77(8):1493 - 1520.

    PubMed  Article  Google Scholar 

  • Roberts, J., D. Anderson and W. Tay (2015). “Multiple host shifts by the emerging honeybee parasite, Varroa jacobsoni.” Molecular Ecology 24(10): 2379 - 2391.

    CAS  PubMed  Article  Google Scholar 

  • Rooth, M. (2018). “Varroa mite detected at Port of Melbourne on a ship from United States.” 2019(31 May 2019).

  • Rosenkranz, P., P. Aumeier and B. Ziegelmann (2010). “Biology and control of Varroa destructor.” Journal of Invertebrate Pathology 103 Suppl 1: S96 - S119.

    PubMed  Article  Google Scholar 

  • Roth, M., J. Wilson, K. Tignor, A. Gross and M. Messenger (2020). “Biology and Management of Varroa destructor (Mesostigmata: Varroidae) in Apis mellifera (Hymenoptera: Apidae) Colonies.” Journal of Integrated Pest Management 11(1).

  • Rural and Regional Affairs and Transport References Committee (2014). Future of the Beekeeping and Pollination Service Industries in Australia. Commonwealth Government, Canberra.

    Google Scholar 

  • Ryan, T. (2010). Estimating the Potential Public Costs of the Asian Honey Bee Incursion, RIRDC. 10/026.

  • Seeley, T. (1978). “Life History Strategy of the Honey Bee, Apis mellifera.” Oecologia 32(1): 109 - 118.

    PubMed  Article  Google Scholar 

  • Seeley, T. (2010). Honeybee Democracy. US, Princeton University Press.

  • Tautz, J. and D. Steen (2018). The honey factory. Australia, Black Inc.

    Google Scholar 

  • Traynor, K., F. Mondet, J. de Miranda, M. Techer, V. Kowallik, M. Oddie, P. Chantawannakul and A. McAfee (2020). “Varroa destructor: a complex parasite, crippling honey bees worldwide.” Trends in Parasitology 36(7): 592-606.

    CAS  PubMed  Article  Google Scholar 

  • Utaipanon, P., T. Schaerf, N. Chapman, M. Holmes and B. Oldroyd (2020). “Using trapped drones to assess the density of honey bee colonies: a simulation and empirical study to evaluate the accuracy of the method.” Ecological Entomology.

  • Vesco, U. and G. Guido (2014). Sugar shaking for varroa monitoring: verifying repeatability, mite recovery rate and bee sample precision. ApiBio / ApiOrganica: 3rd World Symposium of Organic Beekeeping. Castel S. Pietro Terme (Italy)

  • Weatherhead, T. (2018a). Incursions into Australia to Date, Sentinel Hive Protections and Gaps. Third Australian Bee Congress. Gold Coast, Queensland, Australia.

  • Weatherhead, T. (2018b). Incursions into Australia to Date, Sentinel Hive Protections and Gaps. Third Australian Bee Congress. Gold Coast.

    Google Scholar 

  • Whitfield, C., S. Behura, S. Berlocher, A. Clark, J. Johnston, W. Sheppard, D. Smith, A. Suarez, D. Weaver and N. Tsutsui (2006). “Thrice out of Africa: ancient and recent expansions of the honey bee, Apis mellifera.” Science 314(5799): 642-645.

    CAS  PubMed  Article  Google Scholar 

  • Winston, M. (1991). The Biology of the Honey Bee. US, Harvard University Press.

  • Winston, M. (2014). Bee Time: Lessons from the Hive. US, Harvard University Press.

Download references

Availability of data and materials

Data and materials will be made available on any reasonable request.

Funding

Funding for the paper was provided by the University of Melbourne, Australia

Author information

Authors and Affiliations

Authors

Contributions

RO, 70%; MS, 20%; JPS, 10%. All authors have read the manuscript and approved the content.

Corresponding author

Correspondence to Robert Owen.

Ethics declarations

Ethical approval

The paper was based on modelling and ethical approval was not necessary.

Competing interests

The authors declare that they have no competing interests

Additional information

Handling Editor: Cedric Alaux

Détection de Varroa destructor dans les zones non endémiques.

surveillance / arbres de scenario / Australie / biosécurité / acarien.

Nachweis von Varroa destructor in nicht-endemischen Gebieten.

Überwachung / Szenariobaum / Australien / Biosicherheit / Milbe.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Owen, R., Stevenson, M. & Scheerlinck, JP. Varroa destructor detection in non-endemic areas. Apidologie 52, 900–914 (2021). https://doi.org/10.1007/s13592-021-00873-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13592-021-00873-7

Keywords

  • surveillance
  • scenario tree
  • Australia
  • biosecurity
  • mites