Insects 2020, 11, 595; doi:10.3390/insects11090595 www.mdpi.com/journal/insects
Evaluation of Suppressed Mite Reproduction (SMR)
Reveals Potential for Varroa Resistance in European
Honey Bees (Apis mellifera L.)
Fanny Mondet 1,2,*, Melanie Parejo 3,4, Marina D. Meixner 5, Cecilia Costa 6, Per Kryger 7,
Sreten Andonov 8,9, Bertrand Servin 10, Benjamin Basso 2,11, Małgorzata Bieńkowska 12,
Gianluigi Bigio 13, Eliza Căuia 14, Valentina Cebotari 15, Bjorn Dahle 16,17, Marica Maja Dražić 18,
Fani Hatjina 19, Marin Kovačić 20, Justinas Kretavicius 21, Ana S. Lima 22,23, Beata Panasiuk 12,
M. Alice Pinto 22, Aleksandar Uzunov 5,24, Jerzy Wilde 25 and Ralph Büchler 5
1 INRAE, UR 406 Abeilles et Environnement, 84914 Avignon, France
2 UMT PrADE, 84914 Avignon, France; firstname.lastname@example.org
3 Agroscope, Swiss Bee Research Center, 3003 Bern, Switzerland; email@example.com
4 Laboratory of Genetics, University of the Basque Country, 48940 Leioa, Spain
5 LLH Bee Institute, 35274 Kirchhain, Germany; firstname.lastname@example.org (M.D.M.);
email@example.com (A.U.); firstname.lastname@example.org (R.B.)
6 CREA Research Centre for Agriculture and Environment, 40141 Bologna, Italy; email@example.com
7 Department Agroecology, Aarhus University, 4200 Slagelse, Denmark; firstname.lastname@example.org
8 Departement of Animal Biotechnology, FZNH, 1000 Skopje, Macedonia; email@example.com
9 Department of Animal Breeding and Genetics, SLU, 99104 Uppsala, Sweden
10 INRAE, GenPhySE, 31326 Castanet-Tolosan, France; firstname.lastname@example.org
11 ITSAP, 75012 Paris, France
12 Research Institute of Horticulture, 96–100 Skierniewice, Poland; email@example.com (M.B.);
13 Aspromiele, Regional Association of Honey Producers, 15121 Alessandria, Italy;
14 Institute for Beekeeping Research and Development, 013975 Bucharest, Romania;
15 Institute of Zoology, Academy of Sciences of Moldova, 2028 Kishinev, Moldova; firstname.lastname@example.org
16 Norwegian Beekeepers Association, 2040 Kløfta, Norway; email@example.com
17 Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life
Sciences, 1430 Ås, Norway
18 Ministry of Agriculture, 10000 Zagreb, Croatia; firstname.lastname@example.org
19 Department of Apiculture, Institute of Animal Science—Hellenic Agricultural Organization 'DEMETER',
63200 Nea Moudania, Greece; email@example.com
20 Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, 31000 Osijek,
21 National Bee Breeding Association, Virsuliskiu g 33, 05105 Vilnius, Lithuania; firstname.lastname@example.org
22 Centro de Investigação de Montanha, Instituto Politécnico de Bragança, Campus de Santa Apolónia, 5300-
253 Bragança, Portugal; email@example.com (A.S.L.); firstname.lastname@example.org (M.A.P.)
23 CESAM-Ciências, Centro de Estudos do Ambiente e do Mar, Faculdade de Ciências da Universidade de
Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
24 Faculty of Agricultural Sciences and Food, Ss. Cyril and Methodius University, 1000 Skopje, North
25 Apiculture Division, Faculty of Animal Bioengineering, Warmia and Mazury University in Olsztyn,
Sloneczna 48, 10-957 Olsztyn, Poland; email@example.com
* Correspondence: firstname.lastname@example.org; Tel.: +33-43272-2699
Received: 10 July 2020; Accepted: 27 August 2020; Published: 3 September 2020
Insects 2020, 11, 595 2 of 17
Simple Summary: The mite Varroa destructor represents a great threat to honey bees and the
beekeeping industry. The opportunity to select and breed honey bees that are naturally able to fight
the mite stands a sustainable solution. This can be achieved by evaluation of the failure of mite
reproduction (SMR, suppressed mite reproduction). We conducted a large European experiment to
assess the SMR trait in different populations of honey bees spread over 13 different countries, and
representing different honey bee populations. The first goal was to standardize and validate the
SMR evaluation method, and then to compare the SMR trait between the different populations. Our
results indicate that it is necessary to examine at least 35 brood cells infested by a single mite to
reliably estimate the SMR score of any given colony. Several colonies from our dataset display high
SMR scores, indicating that this trait is present within the European honey bee populations. No
major differences could be identified between countries for a given population, or between
populations in different countries. This study shows the potential to increase selection efforts to
breed V. destructor honey bee resistant populations.
Abstract: In the fight against the Varroa destructor mite, selective breeding of honey bee (Apis mellifera
L.) populations that are resistant to the parasitic mite stands as a sustainable solution. Selection
initiatives indicate that using the suppressed mite reproduction (SMR) trait as a selection criterion
is a suitable tool to breed such resistant bee populations. We conducted a large European
experiment to evaluate the SMR trait in different populations of honey bees spread over 13 different
countries, and representing different honey bee genotypes with their local mite parasites. The first
goal was to standardize and validate the SMR evaluation method, and then to compare the SMR
trait between the different populations. Simulation results indicate that it is necessary to examine at
least 35 single-infested cells to reliably estimate the SMR score of any given colony. Several colonies
from our dataset display high SMR scores indicating that this trait is present within the European
honey bee populations. The trait is highly variable between colonies and some countries, but no
major differences could be identified between countries for a given genotype, or between genotypes
in different countries. This study shows the potential to increase selective breeding efforts of V.
destructor resistant populations.
Keywords: Varroa; honey bee; SMR (suppressed mite reproduction); breeding; selection; resistance
The invasive parasitic mite Varroa destructor is one of the main drivers of honey bee (Apis mellifera
L.) colony losses [1–5]. In Europe, where the mite was first introduced in the 1970s, varroosis is a
major challenge for beekeeping [6–8]. In many countries, beekeepers frequently employ organic or
synthetic acaricides to avoid losing their colonies. However, resistance to chemical treatments can
evolve, rendering their application useless [9–12]. In addition, mite treatments with
chemotherapeutics may cause adverse effects on the honey bees [13,14] and can leave residues in hive
To overcome these issues, a sustainable approach with a long-term perspective needs to be
developed. Selecting and breeding honey bee stock able to counteract the varroa mite would
contribute to such a strategy. Populations of honey bees capable of surviving varroa infestations
without treatment are well-described, and detailed investigations have provided insights regarding
the underlying mechanisms [17–21]. Investigations of relevant traits to be utilized for selection
towards increased varroa resistance already started in the 1990s, and since then, several breeding
programs have yielded promising results [22–25]. In this paper, we use the term resistance according
to the definition of , since the fitness of the mite is compromised.
Among the numerous mechanisms known to limit varroa mite population growth, suppression
of mite reproduction (SMR) seems to play an important role and has been observed in the naturally
resistant populations from Gotland and Avignon [27,28]. This trait first described by  refers to
Insects 2020, 11, 595 3 of 17
mites that enter a brood cell to complete their reproductive cycle, but eventually do not produce any
mature and mated female progeny. Suppression of mite reproduction is considered a colony-level
trait and defined by the proportion of worker brood cells containing non-reproducing mother mites.
The trait was found to be heritable  and was utilized in U.S. breeding programs since the late
1990s [31–33]. Such lines are used by several commercial beekeepers, but no large-scale beekeeping
practices that abstain from regular varroa treatments have been reported so far. In European selection
programs, however, the trait has not yet received much attention, and data about the variability of
mite reproductive success and the distribution of the trait in nonresistant, managed honey bee
populations across Europe are missing. Moreover, when initiating any breeding attempts on the SMR
trait in a given environment, it is important to screen the local population for the presence and
variability of SMR and thus evaluate its potential for selection.
Several different mechanisms may trigger the SMR phenotype and may originate from host
and/or parasite features. SMR can indirectly result from adult bee behaviors such as varroa-sensitive
hygiene (VSH) or recapping behaviors [34,35]. Mechanisms of physiology or behavior of the brood
may also influence the ability of varroa to reproduce [36–38], but remain unknown. Parasite features
may also influence varroa reproduction, such as variation in mite genotypes [39,40], or the
physiological status of mites invading cells.
To provide baseline data for regional breeding programs, we initiated a common study to
evaluate the present of SMR in local European honey bee populations according to geographical
locations and genotypes. We developed a common protocol to accurately identify the proportion of
non-normally reproducing mites in a given colony, and to ensure data compatibility among the
participants. We conducted simulations to estimate the accuracy of the SMR estimates and optimize
future research. We also discuss potential mechanisms that may be present in different breeding
stocks, such as behavior of the bees, physiological features of the brood, or parasite features.
2. Materials and Methods
2.1. Honey Bee Colonies and Sampling Strategy
This study was conducted by 17 laboratories in 13 European countries (Table 1) during the
summer and fall of 2015 and 2016. A total of 414 colonies, distributed in 68 apiaries and managed by
the participating institutes or by partner beekeepers, were evaluated.
The experimental colonies originated from stock maintained at the participating institutes and,
according to the expert opinion of the respective experimenters, belonged to European subspecies,
local hybrids, or local populations of Apis mellifera (A. m. carnica, A. m. caucasica, A. m. cecropia, A. m.
iberiensis, A. m. ligustica, A. m. macedonica, A. m. mellifera, A. m. carpatica, Buckfast, and hybrids of
Carnica, Ligustica, and Mellifera), referred hereafter as “genotypes”. While no genetic screening was
employed to confirm subspecies origin, the populations represent distinct local populations and can
be considered as different genotypes. Sampled colonies were randomly chosen from each local
population. We also included 23 colonies from two populations that were preselected for varroa
resistance: A. m. mellifera hybrids from a French varroa-surviving population  and colonies
containing A. m. mellifera hybrid queens artificially inseminated with semen collected from colonies
of a VSH (Varroa sensitive hygiene) breeding program (Danka et al., USDA Baton Rouge, USA) .
A summary of participating institutes, laboratories, respective genotypes, and the number of
investigated samples is presented in Table 1.
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Table 1. Sampling strategy throughout Europe and corresponding genotypes.
Country Laboratory Genotypes
Buckfast, mellifera mix, VSH
Buckfast, caucasica, carnica mix,
carnica 105 35
R. of Moldova
IZASM carpatica 23 13
carnica, caucasica, mellifera
SIC: single-infested brood cells.
2.2. Evaluation of Mite Non-Reproduction
A reliable assessment of mite offspring stages requires a considerable level of knowledge, skill,
and experience of the evaluator. In particular, female protonymphs and male offspring are difficult
to differentiate [6,41], and their correct identification can be challenging. To improve and promote
the reliability of measurements in the present study, a standardized protocol was developed,
including detailed photographs of the respective developmental stages of bee pupae and mite
offspring, which was shared among all participants of the experiment . In addition, all
participants of the study had the opportunity to gain experience by attending a training workshop
where the scoring method was demonstrated and practiced.
To determine the proportion of mites that had infested brood cells but failed to reproduce, a
frame containing capped, worker brood at late developing stages (pupae with purple eyes and white
body or older, i.e., at least 7 days postcapping) was sampled from each colony and assessed in the
laboratory. Frames were dissected fresh when possible, or after storage (for 1–6 months) at −20 °C.
On each frame containing combs, brood cells were randomly selected and carefully opened
under a stereo-microscope. If a cell was infested, the developmental stage of the pupa was scored.
Three stages were distinguished, according to morphological characteristics: <7 days postcapping
(pupae with eyes lighter than dark purple), 7–9 days postcapping (pupae with dark eyes and light
body coloration), and 10–12 days postcapping (pupae with dark body coloration). The main criterium
to differentiate between postcapping day 9 and 10 was the presence of grey wing pads at day 10. In
addition, the composition of the mite family was carefully assessed by recording the number of
foundress mites, the stage of the eldest female offspring, and, optionally, the presence and stage of
male offspring. A table describing the normal development of mite offspring with regard to the
development of bee pupae was used to determine whether the foundress mite would have produced
at least one mated daughter by the time the bee emerged from the cell (Figure 1).
Only single-infested brood cells (one foundress mite only) containing pupae older than 7 days
postcapping were scored, as it is impossible to determine the success of mite reproduction in earlier
stages and in case of multiple infestations. A foundress mite was considered reproductive if it was
Insects 2020, 11, 595 5 of 17
accompanied by offspring at least as old as described in the chart (Figure 1). In the 7–9 days
postcapping stage, normally reproducing mites have at least one deutonymph or adult son and one
deutonymph daughter. In the 10–12 days postcapping stage, normally reproducing mites have at
least one adult son and one adult daughter. The foundress was considered non-reproductive if the
offspring was younger (delayed reproduction), if the male was missing (no male), or if no offspring
was present (infertile mite). Further details on the protocol and more illustrations can be found at
On each comb, cells were dissected until at least 10, or if possible 35, single-infested cells were
identified. Brood infestation rate was estimated as the number of cells containing mites over the total
number of screened cells, and the SMR score was calculated as the proportion of infested cells
containing non-reproducing mites.
Figure 1. Staging chart used to determine the reproductive success of female varroa mites.
Photographs show the average appearance of the development of mite progeny (first two eggs—
upper part) in relation to the bee pupal stage (lower part). For bees, the main characteristics used to
determine each stage are indicated below the photographs. For mites, the normally expected stage of
the eldest female and male offspring are indicated above the photographs. If the eldest progeny was
at a younger stage than the one corresponding to the illustration of a given bee stage, then the
foundress mite was classified as non-reproducing. The solid line placed between day 9 (bees with
colored thorax, and white wing pads) and 10 (bees with grey wing pads) days postcapping separates
the period before and after which we should expect adult female varroa offspring. Source:
Insects 2020, 11, 595 6 of 17
2.3. Simulation Analyses
Theoretical calculations were performed to determine the influence of the number of infested
cells opened on the precision of SMR estimation. Given a number of single-infested cells (SIC) opened
(i) and the true SMR value (s), the observed number of non-reproducing cells (r) can be considered
as random. To account for the sampling variability in r, the sampling process was averaged over all
possible values of r:
where (̃|,) follows a beta distribution with parameters (1+r, 1+i-r) and (|,) a binomial
distribution of parameters i and s. Hence, the distribution of SMR estimates (̃) is a mixture of beta
distributions. These distributions were derived for varying values of SIC and SMR.
In a second step, to improve the estimation of SMR, all colonies were modelled jointly to gather
strength across colonies and get more robust estimates of SMR for colonies that have been evaluated
with few SIC. This hierarchical approach assumes that true SMR values of colonies arise from a
common beta-distribution Beta (a, b). In a Bayesian setting, the beta distribution is the prior
distribution on SMR. Because many colonies were evaluated on SMR, the parameters of the prior
distribution can actually be learned from the data using an empirical Bayes strategy: first, raw values
of the SMR estimates are obtained (r/SIC), then, based on the global distributions of these estimates,
prior distribution parameters are estimated via maximum likelihood. Finally, posterior means are
calculated for all colonies and used as robust estimates of SMR.
2.4. Statistical Analyses
All statistical analyses and figures were generated in the R environment (Version 3.3.1). Colonies
were considered as individuals. The relationship between the brood infestation rate and the SMR
score was tested using Pearson correlation tests. Due to the nature of the experimental design and
the data (proportion data), analyses were performed using generalized linear models (GLM—
package lme4), and as a quasi-distribution was fitted, Fisher tests were subsequently used. A
generalized linear mixed-effects model (GLMM) was used in the case of the comparison within the
carnica group. To account for the fact that colonies could belong to the same apiary in this latter case,
the identity of apiaries was included as a random factor, along with treatment effect as a fixed
explanatory variable (genotype, country, or SMR level). Pairwise comparisons of factor levels were
performed using post-hoc tests (fdr or Tukey).
3.1. Variability of SMR in Different European Countries
Descriptive analysis based on the complete dataset (≥10 SIC per colony, n = 414 colonies) resulted
in an overall average SMR score of 32.8% ± 16.8 and a median of 31.4%. The SMR score of colonies
that had not been preselected for varroa resistance varied between 0 and 100%. Overall, most colonies
displayed a score between 0 and 50%, with 15.9% of the colonies showing an SMR score equal to or
greater than 50% (Figure 2A).
However, the median and range of SMR scores varied substantially between the different
countries where the study was performed, ranging between 4.0% ± 10.6 (Denmark) and 24.5% ± 16.9
(Italy) (Figure 2B).
Insects 2020, 11, 595 7 of 17
Figure 2. Distribution of the suppression of mite reproduction (SMR) score within Europe. (A)
Histogram of distribution of the total data and (B) SMR scores by country for each of the 13 sampled
countries. Grey histogram bars indicate colonies with a varroa-resistance potential (SMR score ≥ 50%).
Boxplot widths are proportional to the sample size which is indicated below each bar or plot. The
dashed line represents the average SMR score among the sampled European countries. ° Indicate data
points distributed outside 1.5 interquartile space.
3.2. Protocol Improvement to Estimate a Reliable SMR Score
Estimation of the true SMR score varies depending on the true SMR score itself and on the
number of cells opened (Figure 3A). The variation is nonsymmetrical for small and large values of
SMR when SIC is small (<10). For instance, a high SMR score (0.7) is more likely under- than
overestimated, while a low SMR score (0.2) is more likely over- than underestimated. Irrespective of
the SMR score there is high variability in its estimate in particular for small SIC. For instance, with 10
SIC and a true SMR value of 0.35, the measured SMR score can be overestimated to be higher than
0.5 with a probability of 24%, and it can even be overestimated to be higher than 0.7 with a probability
of 4%. The variability was further quantified by looking at the cumulative density function of the
SMR estimate (Figure 3B). It is clear that 10 SIC is not sufficient to obtain an SMR score with a
satisfying variability (max. ± 30% of raw variability, Table S1). Thirty-five SIC stands as a minimum
requirement, with more acceptable variability (max. ± 20% of raw variability). This result is confirmed
by the Empirical Bayes distribution, where the distance to the identical distribution is acceptable
when the number of SIC is greater than 30 (Figure 3C). In this shrinkage approach, it appears clearly
that colonies assessed with only a few SIC (<15) have estimated values highly shrunk compared to
their raw value, which highlights once again their low reliability. SMR analyses using 100 SIC would
provide ideal reduced variability (ca. ± 12%), but practical aspects in the field and lab must be
considered to evaluate the feasibility of such a standard.
Insects 2020, 11, 595 8 of 17
Figure 3. SMR estimates reliability. (A) Density of distribution of the SMR estimate, depending on the
SMR value (s = 0.2, 0.35, 0.5 or 0.7) and the number of opened single-infested cells (SIC, no. of SIC =
10, 35, or 100). (B) Precision of the SMR estimate (95% quantiles), depending on the SMR estimate.
Three examples of SIC numbers are represented (10, 35, and 100) to illustrate the fact that using 10
SIC does not give a reliable SMR estimate. (C) “Empirical Bayes” (EB) distribution with the initial
distribution of raw SMR values (top) and comparison to the EB estimates. The distance to the identical
distribution (black line) is acceptable when the number of SIC is greater than 30 (pale brown).
3.3. Variability of SMR in Different Honey Bee Genotypes
Based on the data of colonies with at least 35 SIC, significant differences of SMR were observed
between the different genotypes (Figure 4A—GLM: F = 6.758, p < 1.49 × 10−7): A. m. caucasica, A. m.
ligustica, A. m. mellifera, together with Carnica and Ligustica hybrids had higher SMR scores than A.
m. carnica, Buckfast, A. m. carpatica, and Mellifera hybrids.
A. m. carnica genotypes were sampled in three different countries (Germany, Croatia, and
Poland). Colonies from Germany tended to exhibit higher SMR scores than those from Croatia and
Poland, but the differences were not significant (Figure 4B—GLMM: χ2 = 4.85, p = 0.088). It is
important to note that within a given genotype and within a given country, the variability of the SMR
score can be particularly high. For instance, in Germany, A. m. carnica bees displayed SMR scores
between 14.2% and 65.7%.
Colonies from preselected populations with increased varroa resistance displayed a significantly
higher SMR score than unselected ones (Figure 5—GLM: F = 86.32, p = 6.77 × 10−7).
Insects 2020, 11, 595 9 of 17
Figure 4. Variation of the SMR score according to the honey bee genotype. (A) Scores in the nine
different genotypes. (B) Scores in the A. m. carnica genotype, sampled in three different countries. n-
values are indicated below each bar. Different letters indicate significant differences between groups.
Buck, Buckfast; Carn, Carnica; Carp, Carpatica; Cauc, Caucasica; Ligu, Ligustica; Melli, Mellifera; mix,
hybrid; De, Germany; Hr, Croatia; Pol, Poland.
Figure 5. Effect of preselection for varroa resistance on the SMR score. Adjusted mean (± standard
error) SMR scores between an unselected population (Uns, unselected) and two populations selected
using varroa-resistance-related criteria (survival: Surv, VSH: VSH). Sample sizes are indicated below
each bar and different letters indicate significant differences between groups.
3.4. Putative Mechanisms for SMR
Based on the data of colonies with at least 35 SIC (n = 159 colonies, from 10 different countries),
no significant correlation could be identified between the rate of brood infestation and the SMR score
(Figure 6A—t = −1.48, p = 0.14).
In addition, the reason for classifying an infested brood cell as non-reproductive was
investigated based on the following mite physiological criteria: delayed reproduction, missing male,
or infertile (no offspring). These three criteria were analyzed in six countries (Germany, France,
Poland, Croatia, Moldova, and Romania) where colonies containing at least 10 non-reproducing
mites could be identified (Figure 6B). The proportion of cells being classified as non-reproductive due
to the absence of a male was significantly different between countries (GLM: F = 6.68, p = 3.33 × 10−8).
In comparison to Germany, a higher proportion of cells with no male varroa was identified in France
(Fisher post-hoc: t = −7.38, p = 5.2 × 10−11). Similarly, the proportion of cells being classified as non-
reproductive due to infertility was significantly different (GLM: F = 6.56, p = 2.59 × 10−5). In
comparison to Germany, Moldova (Fisher post-hoc: t = −3.25, p = 0.0016) and Poland (Fisher post-hoc:
t = −3.98, p = 0.00013) had a lower proportion of infertile varroa infested cells.
The cause for reproduction failure of the foundress varroa females was further investigated on
the same dataset in relation to the degree of SMR of the colony, which was categorized as low (<34%,
less than the average SMR in the study), medium (35–49%), or high (>50%, corresponding to
Insects 2020, 11, 595 10 of 17
potentially resistant colonies) (Figure 6C). Similar proportions of non-reproducing cells due to the
absence of a male were detected in all three SMR categories (GLM: F = 1.11, p = 0.33). A similar result
was found for the proportion of infertile cells (GLM: F = 0.75, p = 0.48) and for the proportion of
delayed cells (GLM: F = 2.25, p = 0.11). Overall, no correlation was found between the SMR score and
the proportion of absent males (Pearson: t = 1.52, p = 0.13), infertile foundresses (Pearson: t = 0.62, p =
0.54), or delayed reproduction (Pearson: t = −1.65, p = 0.10).
Figure 6. Putative mechanisms for SMR. (A) Relationship between brood infestation rates and SMR
scores (n = 159). (B) Analyses of causes for non-reproduction (n = 105) according to the country, and
(C) according to the level of the SMR score. N-values are indicated below each bar.
4.1. SMR Trait in European Colonies
Suppression of mite reproduction (SMR) has been recognized as an important trait for survival
in naturally resistant honey bee populations [24,28] and has been successfully implemented in
breeding programs in the U.S. [22,31,43,44]. In contrast, beyond investigations in naturally resistant
populations [21,27,28], the distribution of the SMR trait in European honey bee populations has not
yet received major scientific attention. Most breeding efforts for varroa resistance in Europe have,
until recently, relied on the introduction of nonlocal resistant stock, however, these attempts have
not been successful [28,45]. A contributing factor to the failure of such attempts could result from
genotype-environment interactions  favoring colonies’ adaptation to the prevailing
environmental conditions . When initiating any breeding attempts on the SMR trait in a given
environment, it is important to screen the local population for the presence and variability of SMR
and thereby evaluate its potential for selection.
In the present study, we screened 414 colonies across the entire European continent to provide
a comprehensive dataset that describes the underlying variation of mite non-reproduction in Europe,
which may serve as baseline data for selection decisions in prospective breeding programs. To obtain
a first general overview on the distribution and variability of SMR in Europe with manageable input
of labor, each participating laboratory investigated its colonies based on at least 10 single-infested
cells. Based on these data, we observed a great variability of SMR across the different honey bee
populations, with a mean proportion of non-reproducing mites reaching an overall score of 32.8%,
and close to 16% of colonies exceeding a score of 50% (from observations based on minimum 10
In a next step, the variation of mite reproduction success in different honey bee genotypes was
explored further by examining at least 35 single-infested cells. The results showed that some
genotypes performed significantly better than others with three of them (one each of caucasica,
ligustica, and mellifera origin) exhibiting comparatively high scores. However, the number of
colonies investigated was quite small, and additional experiments are needed to confirm this
observation. Considering that on average between 5% and 20% of mite foundresses remain infertile
in European honey bees , and mite reproduction rates ranging from 0.78 to 0.9 have been reported
from mite-susceptible control colonies in previous studies [27,48], the present results indicate that a
Insects 2020, 11, 595 11 of 17
considerable proportion of the honey bee populations in Europe may hold the potential to select for
increased resistance (sensu ) to V. destructor. This is also supported by the fact that the SMR scores
of colonies originating from preselected stock were consistently and significantly higher than the
scores of unselected genotypes. The present scores observed in both the French surviving population
(0.47 ± 0.12) and the VSH hybrid genotype (0.57 ± 0.11) were in the range of previous results from the
French population (0.59 ± 0.02) and that reported from the mite-surviving population from Gotland
(0.48 ± 0.02) [27,28].
4.2. Factors Affecting Measurement of SMR
While the present results indicate that honey bee colony resistance to V. destructor in many
European bee populations could indeed be improved by selection for increased SMR, several factors
may present a challenge towards the creation and implementation of such a selection approach.
Obtaining a reliable estimate of the ability of a given colony to suppress mite reproduction is difficult
and labor intensive. In addition, the score can be influenced by a number of different factors, such as
the amount of worker and drone brood available  or the mite load of the colonies. The number of
offspring per mite tends to decrease with high infestation levels , which may bias observed SMR
scores towards increased values. Despite high infection rates found in some colonies (up to 80% in
the brood), no correlation was observed between mite loads and SMR scores in the present study.
However, such effects are complex and need to be further explored as they can possibly cancel each
other out. For instance, colonies with high SMR expression may regulate the total amount of V.
destructor in the colony, while colonies with low mite infestations may not trigger behaviors resulting
in high SMR values. Further studies are necessary to confirm if SMR can also be influenced by other
environmental factors, similarly to what has been shown for hygienic behavior and food availability,
or virus infections [51–53]. In addition, although SMR has been described as a heritable trait [30,54],
heritability estimates are currently unavailable for any population.
The reliability of the SMR score is highly dependent on the number of single-infested cells that
are opened and assessed. While the best estimate for the score of a given colony requires the
assessment of a high number of single-infested brood cells, as close as possible to the total number of
such cells in the colony, this is obviously not a practicable approach. In previous studies, the number
of observations per colony was not exactly specified but typically varied between 10 and 35 [27,49,55].
To evaluate the reliability of scoring we performed simulation analyses based on different numbers
of observations and different levels of SMR. The results showed that estimation of the SMR score
based on small numbers of single-infested cells varied widely, and scoring based on ten observations
may lead to under- or overestimation of the true SMR score in the range of 30%. Even when the
scoring was based on 35 single-infested cells, there remained considerable variation around the true
Repeated measurements as is done for VSH  could increase reliability, however, the time
window for SMR scoring is very narrow, which adds another level of complexity to applying this
trait for selection. In most colonies, mite infestation levels in the brood can be very low early in the
season , and a level that enables assessment of SMR with manageable input of time and labor is
only possible after the peak of development, in the short period between late summer and early fall.
To complicate matters further, as a high expression of the SMR trait results in a decrease of brood
infestation, in such colonies it may become increasingly difficult to find enough single-infested cells
for a reliable scoring even late in the season. One possible alternative could be to measure SMR in
brood frames that were exposed to high infestation levels by placing them into mite-donor colonies
during the open stage. However, this alternative procedure does not really reflect the natural
situation, and the introduction of mites from a foreign origin may result in a biased score .
Together, these challenges may lay behind the reluctance of breeders to integrate and use the SMR
trait in selection programs .
Insects 2020, 11, 595 12 of 17
4.3. Triggers for SMR
The development of a simple bioassay to score the SMR potential of a colony is a challenging
task, and the factors responsible for SMR require further investigation. Several different mechanisms
may trigger the SMR phenotype and may originate from host and/or parasite features. Host factors
seem to be central in some populations, as a change of queen can lead to a change in SMR phenotype
: (i) SMR can indirectly result from varroa-sensitive hygiene (VSH) behavior, when adult bees
preferentially target brood infested by reproducing mites , but leave brood cells containing non-
reproducing mites untouched. (ii) Female mites escaping from VSH-targeted cells may survive and
enter a new cell for reproduction, but due to their previous aborted reproduction cycle, could face an
increased risk of reproduction failure. This mechanism remains to be confirmed, even if a high
correlation between the level of VSH and SMR was found in some populations [34,60,61]. (iii)
Recapping, which consists of the opening and subsequent recapping of the targeted cells by the bees,
may also influence the reproductive capacity of mites within the targeted brood. In an artificial
uncapping/recapping experiment, it was shown that targeted cells have a lower varroa reproduction
rate , and naturally surviving populations displaying increased SMR scores also display high
recapping rates [21,35]. (iv) Physiological or behavioral features of the brood itself may also influence
the ability of varroa to reproduce [36–38], even though the exact mechanisms through which the
brood may impair varroa reproduction remain unknown.
Nonetheless, parasite features may also influence varroa reproduction. Recent research has
shown that genetic variation in mites is higher than previously assumed [39,40], and such results may
contribute to an improved understanding of the interactions between the genotypes of host colonies
and their parasites. The physiological status of mites invading cells could also play a significant role.
For instance, there are strong indications that mite reproductive success may be reduced after
prolonged periods on adult bees without access to brood. Otten  describes significant seasonal
differences in mite reproductive success, with lowest levels (70%) in late winter and high values (up
to 90%) in July. Recent research also indicates that mite reproduction success decreases after
broodless periods, for instance caused by prolonged caging of the queen or application of trapping
combs as integrated varroa control measures , or in the context of swarming .
The failure of mites to reproduce, regardless if depending on host and/or parasite mechanisms
or environmental factors, can be characterized by three different features of mite reproduction:
infertility of the mite, i.e., total absence of offspring, absence of the male, which will prevent the
mating of the female offspring, or a delay in egg laying and/or offspring development which will
prevent mites from reaching the adult stage before the developing bee emerges. Each of these features
may potentially be linked more specifically to one or more host/parasite mechanisms regulating
varroa reproduction. In this study, the three possible features were identified in all investigated
populations, with the delay being the most frequent reason for mite reproduction failure. In France,
the proportion of male absence was higher than in all other populations studied, while infertility was
particularly low in Moldova and Poland (Figure 6B). The proportions of the three features that form
SMR do not vary according to the SMR level of the colony, suggesting that all three features may be
important to support SMR. The history of host-parasite interactions may have shaped the different
mechanisms observed in this study. Further studies would be necessary to understand the link
between the mite reproduction features, and the host-parasite mechanism that regulate mite
4.4. Comparing SMR to Other Means of Selection for Varroa Resistance
The considerable amount of work involved in scoring SMR, and as we have shown, a
considerable level of variability in measurement, can question the advantages of this trait compared
to other traits known to be related to resistance. The assays currently available to assess the detection
and uncapping of varroa parasitized brood through VSH behavior equally are time-consuming and
more restrictive than the SMR assay in terms of brood and mite requirements, and also display high
levels of variability in the outcome . The development of a bioassay allowing to study the bees’
behavior without the need to have mites to infest the cells would facilitate the study of both SMR and
Insects 2020, 11, 595 13 of 17
VSH traits. The development of molecular markers are a great hope in this direction, but despite
several recent findings  no commercial service is currently available.
The advantage of the SMR phenotype is that it encompasses several possible mechanisms
leading to resistance, such as action from the adult bees through VSH or impairment of varroa
reproduction by the brood. An alternative and even more straightforward method of resistance
evaluation would be to follow the growth of the varroa population in colonies over time. It can be
done with far less work than VSH or SMR scoring, and colonies with a lower growth during the
season are expected to survive better than those with more mites. The value of this approach is
however hampered, since many environmental factors can influence the varroa load in a colony, such
as the influx of mites from neighboring colonies , which probably explains the low heritability of
varroa population growth [30,66].
Letting nature do the selection in order to breed from the surviving colonies, sometimes referred
to as the Bond method  or Darwinian beekeeping , has gained attention and some
attractiveness due to the repeated finding of populations able to survive without treatments
[17,19,68]. As discussed above, however, so far attempts to bring such honey bees bred from “natural
selection” into beekeeping on a wider scale have failed.
SMR stands as a complex trait, as it can be triggered by several host and/or parasite mechanisms,
influenced by a wide variety of environmental factors, and remains challenging to phenotype
accurately in the field. In the present study, it was not possible to identify the specific mechanism
underlying the SMR trait, and we found large uncertainty in its estimation when few cells are
investigated. Nevertheless, SMR implies lower mite population growth and, thus, remains a trait of
great importance for the development of selection strategies to improve the ability of honey bee
colonies to fight infestation by one of the most important honey bee enemies, the mite V. destructor.
Supplementary Materials: The following are available online at www.mdpi.com/2075-4450/11/9/595/s1, Table
S1: Simulation results for the precision of the SMR estimates.
Author Contributions: Conceptualization and methodology, F.M., C.C., M.D.M., S.A., A.U., R.B.; experiments
and data acquisition, F.M., C.C., M.P., B.B., M.B., G.B., E.C., V.C., B.D., M.M.D., F.H., M.K., J.K., P.K., A.S.L., B.P.,
M.A.P., J.W., R.B.; data analysis: S.A., F.M., B.S.; writing, F.M., M.D.M., M.P., P.K., R.B. All authors have read
and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Acknowledgments: we wish to thank all technicians involved for their tedious efforts, our colleagues from the
RNSBB (research network for sustainable bee breeding) for valuable discussions and the COLOSS research
association for providing a networking platform.
Conflicts of Interest: The authors declare no conflict of interest.
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