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Is the Brood Pattern within a Honey Bee Colony a Reliable Indicator of Queen Quality?

Authors:

Abstract

Failure of the queen is often identified as a leading cause of honey bee colony mortality. However, the factors that can contribute to “queen failure” are poorly defined and often misunderstood. We studied one specific sign attributed to queen failure: poor brood pattern. In 2016 and 2017, we identified pairs of colonies with “good” and “poor” brood patterns in commercial beekeeping operations and used standard metrics to assess queen and colony health. We found no queen quality measures reliably associated with poor-brood colonies. In the second year (2017), we exchanged queens between colony pairs (n = 21): a queen from a poor-brood colony was introduced into a good-brood colony and vice versa. We observed that brood patterns of queens originally from poor-brood colonies significantly improved after placement into a good-brood colony after 21 days, suggesting factors other than the queen contributed to brood pattern. Our study challenges the notion that brood pattern alone is sufficient to judge queen quality. Our results emphasize the challenges in determining the root source for problems related to the queen when assessing honey bee colony health.
insects
Article
Is the Brood Pattern within a Honey Bee Colony
a Reliable Indicator of Queen Quality?
Kathleen V. Lee 1, *, Michael Goblirsch 1, Erin McDermott 2, David R. Tarpy 2and
Marla Spivak 1
1Department of Entomology, University of Minnesota, 1980 Folwell Ave, Suite 219, Saint Paul, MN 55108,
USA; goblirmj@umn.edu (M.G.); spiva001@umn.edu (M.S.)
2Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA;
eemcderm@ncsu.edu (E.M.); drtarpy@ncsu.edu (D.R.T.)
*Correspondence: leex1444@umn.edu; Tel.: +1-651-497-1305
Received: 22 June 2018; Accepted: 29 August 2018; Published: 8 January 2019


Abstract:
Failure of the queen is often identified as a leading cause of honey bee colony
mortality. However, the factors that can contribute to “queen failure” are poorly defined and often
misunderstood. We studied one specific sign attributed to queen failure: poor brood pattern. In 2016
and 2017, we identified pairs of colonies with “good” and “poor” brood patterns in commercial
beekeeping operations and used standard metrics to assess queen and colony health. We found no
queen quality measures reliably associated with poor-brood colonies. In the second year (2017),
we exchanged queens between colony pairs (n= 21): a queen from a poor-brood colony was
introduced into a good-brood colony and vice versa. We observed that brood patterns of queens
originally from poor-brood colonies significantly improved after placement into a good-brood colony
after 21 days, suggesting factors other than the queen contributed to brood pattern. Our study
challenges the notion that brood pattern alone is sufficient to judge queen quality. Our results
emphasize the challenges in determining the root source for problems related to the queen when
assessing honey bee colony health.
Keywords:
Apis mellifera; queen; brood pattern; queen quality; colony health; beekeeping; parasites
and pathogens; pesticides
1. Introduction
The queen is arguably the most important member of a honey bee colony. She is tasked with
the production of daughter workers that forage for resources and care for the brood—eggs, larvae,
and pupae—and sons that support genetic diversity among colonies through mating with virgin queens
from other colonies. The demand by the colony placed on the queen for a sustained, high-reproductive
output underscores the importance of her well-being to a colony’s success. Beekeepers are appreciative
of queen health, as healthy queens ultimately lead to greater revenue generated from the sale of
surplus bees, hive products, and pollination services. Beekeepers rely on various metrics associated
with a queen’s reproductive output when surveying their colonies to establish the health status of their
queens. They then use this information to make management decisions based on whether a queen is
judged to be “good” or “failing”. However, are the signs and symptoms used to discern a good queen
from a failing queen sufficient to inform management decisions? Finding an answer to this question is
needed as queen health is a current issue in the beekeeping industry. Beekeepers repeatedly identify
queen failure as a significant contributor to colony mortality in their responses on annual colony loss
surveys, with commercial beekeepers—beekeepers that manage >500 colonies—ranking it as the first
or second contributing factor [1,2].
Insects 2019,10, 12; doi:10.3390/insects10010012 www.mdpi.com/journal/insects
Insects 2019,10, 12 2 of 17
Queens generally live one to three years. As a queen’s age increases, so does the likelihood of
a supersedure event occurring—when the bees raise a new queen to replace the old queen—or the
queen dying [
3
]. To avoid an interruption in brood production associated with aging queens, it is
common practice for beekeepers to replace older queens annually. However, in recent years, beekeepers
have reported queen failures after introducing young, newly mated queens into colonies. Causes
for why young queens fail are not well understood. One explanation for failure observed in young
queens may be a breakdown in the linkage between sperm stored in the spermatheca and successful
fertilization of worker-destined eggs [
4
]. Approximately 10 days after emergence as an adult, queens
acquire and store all of the sperm they will use throughout their lifetime from a single bout of mating
with 10 to 20 drones that occurs over one or two days [
5
]. Inadequate sperm quantity or quality, either
because of too few matings or inviable sperm in the ejaculate of the drones, could lead to elevated rates
of fertilization failure, to which the colony may respond by supersedure [
4
]. However, sperm viability
appears to be of greater concern than sperm load, as queens are often adequately mated—stored sperm
counts within the queen are high [
6
]—but the viability of stored sperm diminishes over time [
7
,
8
].
Low viability can be an issue as the queen may have smaller brood patches [
9
] or can become a “drone
layer”—laying only unfertilized eggs [
10
]. Queens inbred to related drones produce inviable worker
eggs [5], although this is uncommon in commercial queen production [6,11].
Additional factors that may play a causal role in queen failure are queen infection with pathogens
or exposure to pesticides. There is evidence for negative effects of pathogens on queen physiology
(reviewed previously [
12
]). Infection with deformed wing virus has been associated with ovarian
degeneration [
13
], and an infection of the fungal pathogen Nosema ceranae may lead to relatively higher
vitellogenin levels [
14
] and upregulation of immune genes [
15
]. Physiological changes observed in
queens exposed to different pesticides include lower queen weight [
16
,
17
] and fewer ovarioles [
16
].
Moreover, pesticides have been shown to affect sperm viability in queens, including in-hive chemicals
used by beekeepers to control the parasitic mite Varroa destructor [
18
20
] and agricultural chemicals [
19
].
Pesticide exposure has also been linked to higher rates of queen supersedure [
21
23
] and decreased
survival in combination with a N. ceranae infection [24].
One sign commonly attributed to failing queens is a poor brood pattern. Brood pattern refers
to wax-capped cells containing pupae, also called sealed brood. A brood pattern is considered to be
poor if
20% of the cells within an area of sealed brood are empty [
25
] and indicates that either the
queen is not laying eggs well or the developing bees are not surviving to eclosion. In addition to queen
quality measures, colony environment may influence brood pattern. Poor brood patterns have been
associated with the fungal pathogen chalkbrood, sacbrood virus, and Nosema spp. infections of >1
million spores per bee [
25
]. Pesticide exposure in the comb [
26
] or lack of adequate nutrition may
impact brood health [
27
], leading to decreased brood viability. In contrast, there also are heritable traits
where worker bees remove diseased or V. destructor infested brood resulting in a worse brood pattern
but a healthier colony [28,29].
The overall objective of this study was to examine if young, failing queens could be a major causal
factor of poor sealed-brood patterns. In the first year of the study, we used standard metrics to assess
queen and colony health in colonies with good-brood and poor-brood patterns. In the second year,
we included additional measures of colony environment to begin to untangle the effects of the queen
and the colony. Our specific objectives were to (1) determine if brood pattern is a reliable indicator of
queen quality, (2) identify colony-level measures associated with poor brood pattern colonies, and (3)
examine the change in brood patterns after queens were exchanged into a colony with the opposite
brood pattern classification.
Insects 2019,10, 12 3 of 17
2. Materials and Methods
2.1. Colony Selection
In 2016 and 2017, we identified colonies with poor sealed brood patterns and good sealed brood
patterns in May and June. For each poor-brood colony, we identified a good-brood colony with the same
management history within a commercial beekeeper operation based in North Dakota, Minnesota,
or Texas. All colonies were headed by queens <6 months old and all queens were produced and mated
in Texas or California. Data were collected from 34 colonies and queens from five operations in 2016,
and 42 different colonies and queens from four operations in 2017 (Figure 1).
Sealed brood patterns were rated using an ordinal scale from 1 (poor) to 5 (excellent) (Figure 2)
by two field technicians with extensive experience in using the rating system (modified from a past
paper [
30
]). A score of <3 was considered to be poor. In 2017, sealed brood pattern was also measured
by quantifying the percent of sealed brood cells by placing a parallelogram large enough to occupy
100 cells over a section of sealed brood, then counting the number of empty cells—cells without
sealed brood—within the parallelogram (described previously [
25
,
31
]). The number of empty cells was
subtracted from 100, and the average was taken from three separate frames containing the fewest empty
cells. A brood pattern with <80% sealed brood was considered to be poor [
25
,
32
]. The parallelogram
method was also used to quantify the queen’s egg-laying pattern by identifying the area of comb
that had the most continuous patch of eggs in each colony, counting the number of empty cells,
and subtracting the number of empty cells from 100.
In 2017, we used a partial reciprocal transplant design to quantify the change in brood patterns
for queens placed into different colony environments (Figure 1b). Pairs of colonies with poor-brood
and good-brood patterns were identified from the same apiary or a nearby apiary with the same
management history. Queens were removed and marked with a paint pen for later identification,
and then placed in queen cages provisioned with food. Queens were then exchanged between
colony pairs, such that a queen previously identified from a poor-brood colony was introduced
into a good-brood colony and a queen from a good-brood colony was introduced into its poor-brood
colony pair. Caged queens were released manually approximately 3 days after introduction into
their new colony. Brood pattern measurements were recorded before the reciprocal exchange and
approximately 21 days after the queen’s release to allow the queens to complete one full worker brood
cycle in their new colony.
2.2. Queen Mating Quality and Morphometric Measurements
In 2016, queens were removed from their colonies and caged individually the same day colony
metrics were recorded (Figure 1a). Cages were provisioned with food and seven worker bees from the
colony where the queen was removed served as attendants. In 2017, only queens still alive after the
exchange were collected. Within two days of being sampled, all queens were shipped live overnight to
the North Carolina State University Queen & Disease Clinic (NCSU-QDC). At the NCSU-QDC, queens
were immobilized by carbon dioxide narcosis and external morphometrics were measured: head width
(mm), thorax width (mm), and wet mass (mg). The spermatheca of each queen was then extracted
and the sperm within was suspended in buffer and differentially (live-dead) dyed in accordance
with the procedure that accompanies the Invitrogen Live-Dead Sperm viability kit (Invitrogen L7011).
Twenty microliters of the sample was then transferred to a cell counting chamber and visualized on
the Nexcelom Vision
®
System [
33
]. The total number of live and dead sperm were counted, and sperm
viability was defined as the percent of live sperm out of the total sperm.
Insects 2019,10, 12 4 of 17
Insects 2018, 9, x FOR PEER REVIEW 4 of 17
Figure 1. Experimental design. (a) In 2016, 17 poor-brood pattern and 17 good-brood pattern colonies
were identified, colony metrics recorded, and queens collected and shipped live for analysis. (b) In
2017, 21 poor-brood pattern and good-brood pattern colony pairs were identified (42 total colonies),
colony metrics recorded, and samples taken. On the same day, queens were exchanged between poor-
brood pattern and good-brood pattern colony pairs. In approximately 24 days, colony metrics were
again recorded, and queens collected and shipped live for analysis.
Figure 2. Sealed brood patterns rated a 1 (left), 3 (middle), and 5 (right). Photo credit: Rob Snyder.
2.3. Colony Measurements
Adult bee populations were estimated by counting the number of frames in the colony that were
fully covered by adult bees (described previously [31,34]). Presence of queen cells—which are
indicators of swarming, supersedure, or queen loss—were noted along with any visual signs of
disease or pests, including American foulbrood (Paenibacillus larvae), European foulbrood
(Melissococcus plutonius), chalkbrood (Ascosphaera apis), sacbrood virus, hive beetles (Aethina tumida),
wax moth (Gallaria melonella), V. destructor mites, deformed wings, and parasitic mite syndrome [35].
Entombed pollen [36] was noted if found.
From a brood frame, approximately 300 worker bees were collected into a 4oz bottle containing
70% ethanol from each colony to quantify the adult bee infestation levels of V. destructor and Nosema
spp. Varroa destructor levels were quantified using an alcohol wash to dislodge the mites from the
adult bees in the sample [37], then the mites and bees were counted and reported as mites per 100
bees. Nosema spp. levels were quantified by counting spores found in a composite sample of 100 bees
[38]. The method of Cantwell [38] does not differentiate between N. apis and N. ceranae, the two
species known to cause infection in US honey bees. If a sample was found to be positive for Nosema
spp. infection, it was assumed to be N. ceranae due to findings from a recent US survey on honey bee
diseases [39].
Figure 1.
Experimental design. (
a
) In 2016, 17 poor-brood pattern and 17 good-brood pattern colonies
were identified, colony metrics recorded, and queens collected and shipped live for analysis. (
b
) In 2017,
21 poor-brood pattern and good-brood pattern colony pairs were identified (42 total colonies), colony
metrics recorded, and samples taken. On the same day, queens were exchanged between poor-brood
pattern and good-brood pattern colony pairs. In approximately 24 days, colony metrics were again
recorded, and queens collected and shipped live for analysis.
Insects 2018, 9, x FOR PEER REVIEW 4 of 17
Figure 1. Experimental design. (a) In 2016, 17 poor-brood pattern and 17 good-brood pattern colonies
were identified, colony metrics recorded, and queens collected and shipped live for analysis. (b) In
2017, 21 poor-brood pattern and good-brood pattern colony pairs were identified (42 total colonies),
colony metrics recorded, and samples taken. On the same day, queens were exchanged between poor-
brood pattern and good-brood pattern colony pairs. In approximately 24 days, colony metrics were
again recorded, and queens collected and shipped live for analysis.
Figure 2. Sealed brood patterns rated a 1 (left), 3 (middle), and 5 (right). Photo credit: Rob Snyder.
2.3. Colony Measurements
Adult bee populations were estimated by counting the number of frames in the colony that were
fully covered by adult bees (described previously [31,34]). Presence of queen cells—which are
indicators of swarming, supersedure, or queen loss—were noted along with any visual signs of
disease or pests, including American foulbrood (Paenibacillus larvae), European foulbrood
(Melissococcus plutonius), chalkbrood (Ascosphaera apis), sacbrood virus, hive beetles (Aethina tumida),
wax moth (Gallaria melonella), V. destructor mites, deformed wings, and parasitic mite syndrome [35].
Entombed pollen [36] was noted if found.
From a brood frame, approximately 300 worker bees were collected into a 4oz bottle containing
70% ethanol from each colony to quantify the adult bee infestation levels of V. destructor and Nosema
spp. Varroa destructor levels were quantified using an alcohol wash to dislodge the mites from the
adult bees in the sample [37], then the mites and bees were counted and reported as mites per 100
bees. Nosema spp. levels were quantified by counting spores found in a composite sample of 100 bees
[38]. The method of Cantwell [38] does not differentiate between N. apis and N. ceranae, the two
species known to cause infection in US honey bees. If a sample was found to be positive for Nosema
spp. infection, it was assumed to be N. ceranae due to findings from a recent US survey on honey bee
diseases [39].
Figure 2. Sealed brood patterns rated a 1 (left), 3 (middle), and 5 (right). Photo credit: Rob Snyder.
2.3. Colony Measurements
Adult bee populations were estimated by counting the number of frames in the colony that
were fully covered by adult bees (described previously [
31
,
34
]). Presence of queen cells—which are
indicators of swarming, supersedure, or queen loss—were noted along with any visual signs of disease
or pests, including American foulbrood (Paenibacillus larvae), European foulbrood (Melissococcus
plutonius), chalkbrood (Ascosphaera apis), sacbrood virus, hive beetles (Aethina tumida), wax moth
(Gallaria melonella), V. destructor mites, deformed wings, and parasitic mite syndrome [
35
]. Entombed
pollen [36] was noted if found.
From a brood frame, approximately 300 worker bees were collected into a 4oz bottle containing
70% ethanol from each colony to quantify the adult bee infestation levels of V. destructor and Nosema
spp. Varroa destructor levels were quantified using an alcohol wash to dislodge the mites from the
adult bees in the sample [
37
], then the mites and bees were counted and reported as mites per 100
bees. Nosema spp. levels were quantified by counting spores found in a composite sample of 100
bees [
38
]. The method of Cantwell [
38
] does not differentiate between N. apis and N. ceranae, the two
species known to cause infection in US honey bees. If a sample was found to be positive for Nosema
spp. infection, it was assumed to be N. ceranae due to findings from a recent US survey on honey bee
diseases [39].
Insects 2019,10, 12 5 of 17
A sample of empty wax comb (>3 g) was collected into a 50 mL conical tube and stored at
80
C before shipment on ice to the USDA-AMS lab in Gastonia, North Carolina for pesticide residue
analysis. Wax samples were screened for 175 and 202 pesticides and their metabolites in 2016 and
2017, respectively (analysis methods described previously [
40
]) (see Supplementary Material Dataset
1: S1b. Pesticides 2016 and S1d. Pesticides 2017). Not all pesticide samples were processed due to
cost. Hazard quotients (HQs) and the total number of pesticides detected were used to establish the
pesticide risk in each colony. HQs were calculated by dividing the amount of the pesticide found (ppb)
in the wax sample by the adult bee contact LD
50
reported for adult honey bees (methods described
previously [
23
,
41
]). The LD
50
for each pesticide was obtained primarily by using US EPA Ecotox
Database [
42
]. Additional resources [
23
,
43
,
44
] were used when the adult bee contact LD
50
was not
available through the US EPA Ecotox Database (see Table S1). Wax HQs were considered elevated
if they exceeded a value of 5000 [
23
]. The total number of pesticide residues in the wax sample was
calculated by adding the number of unique pesticides detected for each colony. The HQ and total
number of pesticides detected offer an approximate measure for pesticide exposure in the colony;
however, they do not account for synergistic or sublethal effects, larval toxicity, or adult oral toxicity,
but both have previously been associated with queen failure [23].
2.4. Molecular Analysis
Total RNA was extracted from the remaining queen tissues (after dissection of the spermathecae).
Queens were homogenized in individual microcentrifuge tubes with a plastic pestle in an appropriate
volume of Trizol (Thermo Fisher Scientific, HQ in Waltham, MA, USA) and extraction was performed
by standard phenol-chloroform protocol. Samples were then tested on the NanoDrop for quality and
concentration. RNA concentration was diluted to a normalized 200 ng/uL before cDNA (Biobasic Inc.
in Markham, ON, Canada) was synthesized with the BioBasic Reverse Transcriptase Mix. Reverse
transcription quantitative PCR (rt-qPCR) was performed following a previously described method [
45
]
for detection of the following pathogens: Nosema spp. (universal primer), trypanosome spp. (universal
primer), acute bee paralysis virus (ABPV), black queen cell virus (BQCV), chronic bee paralysis virus
(CBPV), deformed wing virus type A (DWV-A) and type B (DWV-B), Israeli acute paralysis virus
(IAPV), and Lake Sinai virus (LSV). qPCR was performed in triplicate with Power-Up SYBRGreen
Mastermix (Thermo Fisher Scientific, HQ in Waltham, MA, USA) on a 384-well QuantStudio Flex 6
(Thermo Fisher Scientific, HQ in Waltham, MA, USA) and analyzed in the associated software. Cycling
conditions were adapted from the Power-Up SYBR Green protocols. The standard curve for copy
number quantification was determined by running a dilution series of known plasmid standard on
each plate. Results were normalized via GeNorm (reference) to the reference genes Actin, Apo28s,
and GapDH, are reported as presence or absence of the pathogen.
In 2017 before queens were exchanged between pairs of colonies, >50 adult bees were collected
from a brood frame into a 50 mL conical tube from each colony. Samples were frozen immediately using
dry ice or liquid nitrogen, stored at
80
C, and then shipped on dry ice to the NCSU-QDC. Samples
were analyzed by rt-qPCR for pathogens (see above), the storage protein vitellogenin (Vg), heat shock
protein HSP70 ab-like, and the immune peptides defensin and hymenoptacin. For each colony-level
sample, 5 g (approximately 50 bees) were extracted. The entire 5 g sample was homogenized in an
appropriate volume of Trizol and extracted by standard phenol-chloroform extraction. The rest of the
extraction was performed as above. Expression levels of the immune genes, HSP70 ab-like and Vg
were determined via
∆∆
Ct analysis as compared to the reference gene Actin, not by standard curve
quantitation. These genes were tested as they can indicate the health of the bees: Vg can influence
the lifespan and decrease the oxidative stress of worker bees [
46
49
], relatively higher values for the
immune genes suggest an upregulated immune system [
50
,
51
], and the upregulation of heat shock
proteins suggests a response to stressors resulting in denatured proteins [
52
]. Upregulation indicates
that the immune system is more active—potentially in response to a pathogen or other stressor—and
is costly to the individual bee.
Insects 2019,10, 12 6 of 17
2.5. Statistical Analysis
We used the statistical program R for all analyses [
53
]. All statistical assumptions were visually
checked, and if violated an appropriate test was used—the nonparametric Kruskal–Wallis test or the
Welch’s t-test for unequal variances—or the data were transformed. Summary data are reported as
means
±
SD unless otherwise noted. Statistical comparisons were considered significant if
α
< 0.05.
All raw data can be found in Supplementary Material Dataset 1.
To ensure brood patterns were different between poor-brood and good-brood colonies,
we compared the brood pattern scores—rating in 2016 and percent sealed in 2017—between the
two groups using a Kruskal–Wallis test for the 2016 data and a Welch’s t-test for the 2017 data. For the
2017 data, a simple linear regression was used to examine the relationship between the two methods
of measuring sealed brood patterns.
For objectives 1 and 2, we used odds ratios (
±
95% confidence intervals) to compare the odds
of a pathogen occurring in a poor-brood queen or colony compared to a good-brood queen or
colony [
54
]. An odds ratio value significantly >1 indicates a positive association, and an odds ratio
value significantly <1 indicates a negative association. To calculate the odds ratio in cases where no
pathogen was detected, the Haldane–Anscombe correction was used [
55
,
56
]. We used lme4 in R [
53
,
57
]
to perform analyses using linear mixed effects models to compare the relationships between queen or
colony measures and the binary brood pattern classification of good-brood or poor-brood. The brood
pattern classification was used as a fixed effect, and beekeeper as a random factor with random slopes
for the effect of the brood pattern classification. p-values were obtained by using likelihood ratio tests
comparing the full model with the brood classification as a factor to the model without the brood
pattern classification. The effect levels are reported as the estimate ±standard errors.
For objective 3, we compared the sealed brood pattern of each queen in her original colony to her
sealed brood pattern approximately 21 days after being released into her new colony. We predicted that
if colony environment had an effect on brood pattern, then the pattern should either improve when
a queen from a poor-brood colony was placed into a good-brood colony or worsen when a queen from
a good-brood colony was placed into a poor-brood colony: the change in brood pattern (after minus
before the exchange) would be significantly different than zero using a t-test. In addition, we examined
the relationship between the brood pattern before the exchange, and the change in brood pattern (after
minus before the exchange) using a simple linear regression. We predicted that the queens with best or
worst brood patterns before the exchange would have the largest change in brood pattern after they
the queens were transferred to their reciprocal colonies. We also compared queen egg patterns before
and after the exchange using a t-test and a simple linear regression. Data were excluded from these
analyses if the queen was not found after the exchange.
3. Results
3.1. Brood Pattern Classifications
Brood patterns were significantly different between good-brood and poor-brood pattern colonies
in 2016 based on the brood rating scale (H= 25.6, df = 1, p< 0.01) and 2017 based on the percent of cells
sealed (t
23.93
= 10.01, p< 0.01), confirming that the poor-brood and good-brood classifications were
different. In 2016, the mean brood rating was for 4.0
±
0.4 good-brood colonies (n= 17) and
1.9 ±0.5
for poor-brood pattern colonies (n= 17). In 2017, the mean percent sealed brood was
93.0 ±2.9%
for good-brood colonies (n= 21) and 72.1
±
9.1% for poor-brood colonies (n= 21). The brood rating
scale was highly correlated to the percent brood measure in 2017 (R
2
= 0.90, F
1,79
= 731.2, p< 0.01),
suggesting that the rating method sufficiently and accurately categorized brood patterns.
Insects 2019,10, 12 7 of 17
3.2. Measures Associated with Queens
Sperm number and sperm viability assessed from the queen spermathecae and queen
morphometrics are summarized in Table 1. Data obtained from queens judged to be on average
“high quality” from US commercial queen producers [
6
,
11
] are included for comparison. In general,
the queen morphometrics, and number and viability of sperm in the spermathecae of the queens from
our study were similar or higher than the previous studies. In 2017, three queens did not survive until
the second sampling: one queen from a good-brood colony and two queens from poor-brood colonies.
One queen from a poor-brood colony in Operation 1 in 2017 had a sperm viability of 1.0%, which was
examined as a possible error as it was more than 2 standard deviations from the mean. This queen
continued to lay fertilized worker bee eggs, which is contrary to what would be expected from queens
with similar levels of sperm viability [
9
]. Due to the biological improbability of the results, the data for
this queen was removed from sperm viability analyses. The percent sperm viability was not different
between the two brood pattern classification groups in either 2016 (
χ2
= 2.5, df = 1, p= 0.11) or 2017
(
χ2= 0.02
, df = 1, p= 0.90). In 2016, queens from poor-brood colonies tended to have fewer sperm than
good-brood colonies, but the difference was not significant (
χ2
= 3.3, df = 1, p= 0.07). There was no
difference in sperm count between brood pattern groups in 2017 (
χ2
= 0.27, df = 1, p= 0.61). For both
years, the average sperm count for both queen groups was over the 3 million sperm count threshold to
be considered adequately mated [
58
] (Table 1). None of the queen mating or morphometric measures
could be reliably associated with queens from poor-brood colonies.
None of the pathogens tested had significantly higher odds of being associated with queens
from poor-brood pattern colonies (Table 2). The 2016 data for Operation 1 were not included in the
PCR results because those samples were lost. Twenty-three percent of queens from 2016 and 78% of
queens from 2017 had no pathogens detected from the panel of common honey bee pathogens used
for screening. Moreover, ABPV, CBPV, trypanosomes spp., and Nosema spp. were not detected in any
queens from 2016 or 2017. In both years, DWV-B was the most prevalent virus found in queen bees,
followed by DWV-A. BQCV, LSV, and IAPV had low prevalence as they were found in only one or two
queens in either 2016 or 2017.
Insects 2019,10, 12 8 of 17
Table 1.
The current study’s summary of queen quality results compared to the results from previous studies [
6
,
11
], including the number of queens tested (n) and the
mean (
±
SD) values of morphometric and mating quality measures. Queens from this study are compared between brood pattern groups, with the queens from 2017
classified by their source colony status (before the exchange) of good-brood or poor-brood.
Paper (Year) Brood Pattern nSperm Count, Millions (Range) Poorly Mated 1Sperm Viability (%) Weight (mg) Thorax Width (mm) Head Width (mm)
This study (2016) Good-brood 17 6.74 ±1.95
(2.55–9.37) 6% 83.7 ±6.3 223.9 ±17.1 4.89 ±0.15 3.79 ±0.14
Poor-brood 17 5.07 ±2.51
(0.52–8.09) 24% 78.3 ±11.2 216.5 ±23.0 4.85 ±0.18 3.8 ±0.12
This study (2017) Good-brood 19 5.69 ±1.82
(1.39–8.4) 11% 78.0 ±6.4 223.6 ±27.9 4.93 ±0.19 3.83 ±0.10
Poor-brood 18 5.88 ±1.57
(2.8–8.1) 6% 78.3 ±10.9 2231.8 ±23.0 4.94 ±0.19 3.83 ±0.09
Delaney et al. (2011)
NA 114 3.99 ±1.50
(0.2–9.0) 18.6% NA 184.8 ±21.7 4.35 ±0.19 3.62 ±0.12
Tarpy et al. (2012) NA 61 4.37 ±1.45 13.6%, & 1
virgin queen 83.7 ±3.3 218.7 ±20.7 4.34 ±0.23 3.45 ±0.23
1Percent of queens tested that had <3 million sperm in their spermatheca [58]. 2Queen with 1% sperm viability removed from summary.
Insects 2019,10, 12 9 of 17
3.3. Measures Associated with Colony Environment
3.3.1. Adult Bee Pathogens
None of the pathogens tested had significantly higher odds of being associated with a poor-brood
pattern colony (Table 2). Varroa destructor levels were not different between good-brood and poor-brood
colonies for either year, and overall levels were low with few colonies having a mite load higher than
a treatment threshold of 3 mites per 100 bees [
34
,
59
]. Worker bees from poor-brood colonies were
not more likely to be over the threshold of >1 Nosema spp. million spores per bee as quantified by
microscopy [
39
], nor be more likely to test positive for Nosema spp. as determined by PCR. In 2017,
all worker bee samples tested positive for LSV and 35 samples also tested positive for Nosema spp.
However, no 2017 queen tested positive for LSV or Nosema spp., suggesting that the queen was not
vertically transmitting these pathogens and the workers did not transmit them to her.
Table 2.
Summary of the odds ratios (95% CI range) and the percent of positive pathogen detections
using PCR for worker bee samples (5 g composite sample) in 2017 and queen samples in 2016 and 2017
from colonies with good-brood or poor-brood patterns. Only pathogens with positive detections are
included; chronic bee paralysis virus was not found in any samples. Also included are the comparisons
between poor-brood and good-brood colonies with symptoms of the brood disease chalkbrood,
and worker bee samples with Varroa destructor mite levels >3 mites per 100 bees, and Nosema spp. levels
>1 million spores per bee as determined by microscopy. No pathogen had significantly higher odds of
being in a poor-brood pattern colony or queen.
% of Samples with Positive
Detections
Sample Type (Year) Factor Good-Brood Poor-Brood Odds Ratio (95% CI)
Queens (2016)
No pathogens detected 33 13 0.31 (0.05–1.93)
Black Queen Cell Virus 7 0 0.31 (0.01–8.29)
Deformed Wing Virus type A 40 60 2.25 (0.52–9.70)
Deformed Wing Virus type B 53 73 2.41 (0.52–11.1)
Lake Sinai Virus 13 0 0.17 (0.01–3.96)
Queens (2017) 1
No pathogens detected 79 78 0.93 (0.2–4.47)
Deformed Wing Virus type A 5 11 2.25 (0.19–27.22)
Deformed Wing Virus type B 16 17 1.07 (0.19–6.13)
Israeli Acute Paralysis Virus 5 0 0.33 (0.01–8.73)
Worker bees (2016) >3 Varroa mites per bee 6 6 1.00 (0.06–17.41)
>1 million Nosema spores per bee, by microscopy
12 18 1.61 (0.23–11.09)
Worker bees (2017) 2
>3 Varroa mites per bee 0 5 3.15 (0.12–81.74)
>1 million Nosema spores per bee, by microscopy
33 48 1.82 (0.52–6.33)
Acute Bee Paralysis Virus 10 5 0.48 (0.04–5.68)
Black Queen Cell Virus 38 19 0.38 (0.09–1.55)
Deformed Wing Virus type A 5 14 3.33 (0.32–34.99)
Deformed Wing Virus type B 24 43 2.40 (0.64–9.03)
Israeli Acute Paralysis Virus 19 5 0.21 (0.02–2.09)
Lake Sinai Virus 100 100 1.00 (0.02–52.74)
Trypanosomes 10 19 2.24 (0.36–13.78)
Nosema spp., by PCR 90 86 0.63 (0.09–4.23)
Brood disease (2016) Chalkbrood 6 0 0.31 (0.01–8.27)
Brood disease (2017) Chalkbrood 352 76 2.91 (0.78–10.89)
1
Queens in 2017 were sampled after the queen exchange but classified by their source colony status (before the
exchange) of good-brood or poor-brood.
2
Sampled before the queen exchange.
3
Accounts for chalkbrood found
before and/or after queen exchange.
3.3.2. Brood Pathogens
It was not always possible to choose poor-brood colonies with no clinical signs of disease. Due to
the near ubiquity of chalkbrood in 2017, we chose five good-brood and six poor-brood colonies with
chalkbrood before the exchange that had
5 cells presenting symptoms of infection. After the exchange,
52% of good-brood and 76% of poor-brood colonies had chalkbrood symptoms. However, chalkbrood
was not more likely to be found in poor-brood pattern colonies (Table 2). For comparison, only one
good-brood colony had chalkbrood in 2016. No other brood diseases were found in either year.
Insects 2019,10, 12 10 of 17
3.3.3. Worker Bee Vitellogenin, Immune Genes, and Heat Shock Protein
Vg levels in worker bees from poor-brood colonies were 0.90
±
0.36 (standard error) higher than
Vg levels in workers bees from good-brood colonies. This difference was significant (
χ2
= 13.1, df = 1,
p< 0.01), but may not be biologically relevant as it was under one ct cycle. We found no differences
between the worker bees from good-brood and poor-brood colonies for defensin (
χ2
= 1.3, df = 1,
p= 0.26
), hymenoptacin (
χ2
= 0.7, df = 1, p= 0.39), or Hsp70ab-like (
χ2
= 1.9, df = 1, p= 0.16). However,
the levels of these genes were all significantly higher in Operation 1’s worker bees from poor-brood
colonies (n= 6) compared to the worker bees from good-brood colonies (n= 6): defensin (H= 7.4,
p< 0.01
), hymenoptacin (H= 8.3, p< 0.01), and Hsp70ab-like (H= 5.0, p< 0.05) (Figure 3). Vg was not
different between good-brood and poor-brood colonies for Operation 1 (H= 0.8, p= 0.38). No other
significant differences were found for the immune genes or heat shock protein genes. These results
suggest that the worker bee immune systems in Operation 1’s poor-brood colonies were upregulated.
Figure 3.
The transcription levels (means
±
95% CI)) relative to the reference gene actin for the two
immune gene peptides defensin (
a
) and hymenoptacin (
b
), and the heat shock protein HSP70ab-like (
c
).
The significance asterisks indicate that the only significant comparisons were between the worker bees
from good-brood colonies (light grey) compared poor-brood colonies (dark grey) within Operation 1.
3.3.4. Colony Pesticide Levels
Twenty-eight beeswax samples were processed for pesticides in 2016 and 24 samples in 2017
(results summarized in Table S1). The pesticide data are not directly comparable between years as
there were different chemicals tested each year. In 2016, there was a range of 5–16 pesticides detected
per sample, and a range of 9–31 pesticides detected per sample in 2017. In 2016, the most common
pesticide class found was varroacides—pesticides used to control V. destructor—with 44% of pesticides
found belonging to this class (Figure S1). Fungicides were most common in 2017 with 45% of pesticides
found belonging to that class, followed by varroacides at 24%.
Overall HQ levels were low. Excluding Operation 5, the mean HQ in 2016 was 38
±
71 (
n= 22
).
For Operation 5, there was a high incidence of cyfluthrin (pyrethroid insecticide) in both the good-brood
and poor-brood colonies resulting in higher HQs: 2093
±
1940 (n= 6). One good-brood colony had
an HQ >5000. In 2017, the mean HQ for good-brood colonies was 677
±
801 (n= 12) and
1160 ±894
(n= 12) for poor-brood colonies. All HQs in 2017 were <5000. The log transformed HQs were not
significantly different between good-brood and poor-brood colonies in 2016 (
χ2
= 0.03, df = 1,
n= 28
,
p= 0.86
) nor in 2017 (
χ2
= 1.97, df = 1, n= 24, p= 0.16). However, the total number of pesticide residues
Insects 2019,10, 12 11 of 17
in 2016 was significantly higher in poor-brood compared to good-brood colonies (
χ2
= 5.00, df = 1,
n= 24
,p< 0.05), with poor-brood colonies having 1.9
±
0.7 (standard errors) more pesticides detected.
In 2017, there was a trend toward more pesticides in poor-brood colonies, but this result was not
significant (χ2= 3.8, df = 1, n= 28, p= 0.051).
3.4. Brood Pattern Change
The change in sealed brood patterns for queens from poor-brood colonies exchanged into
good-brood colonies was significantly different than zero with a mean increase of 11.6
±
9.9 more
sealed cells (t
17
= 5.0, p< 0.01) (Figure 4a), indicating better patterns after the exchange. The brood
patterns for queens from good-brood colonies were also significantly different after the exchange
into poor-brood colonies with a mean of 8.0
±
10.9 fewer sealed cells (t
18
= 3.2, p< 0.01), indicating
worse patterns after the exchange. The linear regression of the starting brood pattern against the
change in brood pattern was significant (R
2
= 0.50, F
1,35
= 36.38, p< 0.01), suggesting that queens
with initially poor patterns tended to have improved patterns after the exchange and queens with
initially better brood patterns tended to have worse patterns after the exchange (Figure 4b). This result
implies that colony environment impacted the sealed brood pattern. To account for the potential effect
of chalkbrood on sealed brood patterns, we removed the colonies with signs of chalkbrood after the
exchange from the dataset and re-examined the relationship between the starting sealed brood pattern
and the change in brood pattern. The relationship was still significant (R
2
= 0.48, F
1,14
= 14.99, p< 0.01),
suggesting that the change in brood patterns was not only due to chalkbrood.
Queens from poor-brood colonies had significantly worse egg patterns compared to queens from
good-brood colonies before the exchange, with an average of 84.7
±
16.0% sealed for poor-brood
colonies compared to an average of 94.9
±
4.7% sealed for good brood colonies (t
19.7
= 2.6, p< 0.05).
When the same <80% cut-off for a poor sealed brood pattern was used for the egg patterns, one queen
from a good-brood colony and four queens from poor-brood colonies had “poor” egg patterns before
the queen exchange. Queens from good-brood colonies transferred into poor-brood colonies had
a mean egg pattern of 95.8
±
3.4% after the exchange, and queens from poor-brood colonies transferred
to good-brood colonies had a mean egg pattern of 90.8
±
6.1%. While the difference in egg pattern
was still significantly different between groups (t
26.41
= 3.1, p< 0.01), only one queen, originally from
a poor-brood colony, had a “poor” egg pattern of <80% after the exchange.
The change in egg pattern after queens were reciprocally transferred was not different than zero
for queens from either good-brood (t
18
= 0.6, p= 0.58) or poor-brood colonies (t
17
= 1.5, p= 0.16)
(Figure 5a), suggesting that egg patterns did not change after the queen exchange based on the binary
sealed brood classification. Queens from good-brood colonies had good patterns before and after
they were exchanged into a potentially worse colony environment. Egg patterns for queens from
poor-brood colonies did not improve on average after the exchange and the variability in egg pattern
change was higher for these queens. While there was no difference in the egg pattern change when
classified by the binary good or poor sealed brood classification, the queens that initially had the worst
egg patterns had better patterns after being exchanged, and the queens with good egg patterns had
similar or worse patterns after the exchange (R
2
= 0.87, F
1,16
= 115.5, p< 0.01) (Figure 5b). This result
suggests that colony environment may have influenced the egg patterns for queens with initially the
worst egg patterns as those patterns improved after the exchange. However, it is unclear why some of
the good egg patterns for queens from poor-brood colonies were worse after the exchange as their egg
laying potential was high.
Insects 2019,10, 12 12 of 17
Insects 2018, 9, x FOR PEER REVIEW 12 of 17
of the good egg patterns for queens from poor-brood colonies were worse after the exchange as their
egg laying potential was high.
Figure 4. Changes in sealed brood pattern for the partial reciprocal transplant experiment in 2017. (a)
Comparison of the change in the sealed brood pattern—a queen’s percent of sealed brood cells after
the exchange minus her percent of sealed brood cells before the exchange—to the initial brood pattern
classification of good (light grey) or poor (dark grey). Positive values indicate an improved brood
pattern after the exchange and negative values indicate the pattern was worse after the exchange. (b)
The potential for change in brood pattern based on the variability in the starting brood patterns.
Figure 5. Changes in egg pattern for the partial reciprocal transplant experiment in 2017. (a)
Comparison of the change in the egg pattern—a queen’s egg pattern after the exchange minus her egg
pattern before the exchange—to the initial sealed brood pattern classification of good (light grey) or
poor (dark grey). Positive values for the change in egg pattern indicate that the brood pattern
improved after the exchange and negative values indicate the pattern was worse after the exchange.
(b) The potential for change in egg pattern based on variability in the starting egg patterns.
Figure 4.
Changes in sealed brood pattern for the partial reciprocal transplant experiment in 2017.
(
a
) Comparison of the change in the sealed brood pattern—a queen’s percent of sealed brood cells
after the exchange minus her percent of sealed brood cells before the exchange—to the initial brood
pattern classification of good (light grey) or poor (dark grey). Positive values indicate an improved
brood pattern after the exchange and negative values indicate the pattern was worse after the exchange.
(b) The potential for change in brood pattern based on the variability in the starting brood patterns.
Insects 2018, 9, x FOR PEER REVIEW 12 of 17
of the good egg patterns for queens from poor-brood colonies were worse after the exchange as their
egg laying potential was high.
Figure 4. Changes in sealed brood pattern for the partial reciprocal transplant experiment in 2017. (a)
Comparison of the change in the sealed brood pattern—a queen’s percent of sealed brood cells after
the exchange minus her percent of sealed brood cells before the exchange—to the initial brood pattern
classification of good (light grey) or poor (dark grey). Positive values indicate an improved brood
pattern after the exchange and negative values indicate the pattern was worse after the exchange. (b)
The potential for change in brood pattern based on the variability in the starting brood patterns.
Figure 5. Changes in egg pattern for the partial reciprocal transplant experiment in 2017. (a)
Comparison of the change in the egg pattern—a queen’s egg pattern after the exchange minus her egg
pattern before the exchange—to the initial sealed brood pattern classification of good (light grey) or
poor (dark grey). Positive values for the change in egg pattern indicate that the brood pattern
improved after the exchange and negative values indicate the pattern was worse after the exchange.
(b) The potential for change in egg pattern based on variability in the starting egg patterns.
Figure 5.
Changes in egg pattern for the partial reciprocal transplant experiment in 2017. (
a
) Comparison
of the change in the egg pattern—a queen’s egg pattern after the exchange minus her egg pattern
before the exchange—to the initial sealed brood pattern classification of good (light grey) or poor (dark
grey). Positive values for the change in egg pattern indicate that the brood pattern improved after the
exchange and negative values indicate the pattern was worse after the exchange. (
b
) The potential for
change in egg pattern based on variability in the starting egg patterns.
4. Discussion
The results of this study suggest that a poor sealed brood pattern is not a reliable indicator
of queen quality and is not necessarily a sign of queen failure. Queens from both good-brood and
Insects 2019,10, 12 13 of 17
poor-brood colonies had sperm counts, sperm viability, body sizes, and weights that were comparable
to queens considered to be of high quality in other studies [
6
,
11
]. Queens from poor-brood colonies
were not more likely to have <3 million sperm in their spermathecae, which has been considered the
threshold for being poorly mated [
58
]. There were no differences in pathogen detections between the
sets of queens, including viruses, Nosema spp., and trypanosomes.
The partial reciprocal transplant of queens in 2017 revealed that the sealed brood patterns of
queens from poor-brood colonies improved significantly after they were placed into colonies with
good patterns, suggesting an influence of colony environment on the sealed brood pattern rather than
solely the queens’ egg-laying capacity. None of the worker bee pathogen or immune gene measures
were reliably associated with poor patterns. Levels of HQs in wax combs did not differ between
brood pattern classifications. More specifically, Operation 5 reported issues with queens not being
accepted by colonies in the spring of 2016; we found the highest HQs in those colonies. However,
queen acceptance problems and high HQs were not found in other operations in this study. The total
number of pesticides detected in wax combs was significantly higher in colonies with poorer patterns
in 2016 and trended that way in 2017. Pesticide exposure may have influenced brood survivorship and
thus brood pattern, but this warrants further investigation.
In this study, we differentiated between queen and colony measures as possible causes of
poor sealed brood patterns, but the queen and her colony are not mutually exclusive. Every colony
phenotype is a result of both environment and genetics: how a queen’s offspring interacts with the
environment, which includes nutrition, pesticides, pathogens, and beekeeper management practices.
After the queen exchange in 2017, we allowed queens to lay for 21 days before removing her from
the colony for sampling. It is possible that if we had left the queen in the colony and sampled after
6 weeks—when the worker bees would have been progeny of the transferred queen—that we would
have been able to see if the designation of poor or good brood patterns held with the new work force.
Replacing the queen could result in a better brood pattern if the colony environment remained the
same and the new workers were better able to thrive in that environment.
For practical purposes, the questions important to beekeepers are action-based: under what
conditions will the colony improve if the queen is replaced? Further studies on brood pattern could help
elucidate the cause(s) and indicate management steps to take. A full reciprocal transplant—exchanging
queens between two good-brood colonies, between two poor-brood colonies, and the same queen
exchanges performed in this study—could help tease out colony vs. queen effects on brood pattern
by controlling for the influence of transferring queens and the changes in environmental conditions
that occur as the season progresses. Further studies could investigate colony effects on egg laying
patterns by caging the queen on a frame, noting the egg pattern, then following the brood viability over
time. Collecting longitudinal data on pathogens and immune genes could help determine if the brood
pattern changes as these factors change. Additional measures could be included to more thoroughly
judge queen quality, including the number of patrilines [
60
] and queen pheromone profile [
61
,
62
].
To make the study more robust, it could be done at different times of year and with different ages
of queens.
An important lesson from this study was that it was difficult to find queens with poor brood
patterns without signs of brood disease. If queen failure is a leading cause of colony loss, then other
symptoms besides poor brood patterns are likely to be more relevant. Beekeepers report multiple
symptoms associated with younger queens failing, including stunted colony growth, relatively low
brood production, irregular egg laying pattern, supersedure of apparently healthy queens, or queen
death without replacement. These different symptoms may be attributed to different causes, so defining
the specific symptoms and measures used to identify “failing” queens is critical to make progress in
mitigating queen failures. Specifying details like queen age can make a difference in interpretations
of measures like sperm viability that can decrease as queens age [
7
,
8
]. Quantifying the prevalence of
different definitions of queen failure could help research target issues, and a specific definition would
allow for the work to be repeatable.
Insects 2019,10, 12 14 of 17
Operation 1 serves as an example of why a specific definition of queen failure matters. Operation
1 selected colonies for us to sample that matched a different definition of “queen failure”: colonies were
selected based on relatively small amounts of brood—19 of approximately 800 inspected colonies—and
we sampled those colonies with the worst brood patterns. In these preselected poor-brood colonies
the immune systems of the worker bees were upregulated, making it appear that colony environment
influenced sealed brood pattern. Because sealed brood pattern was not the primary symptom used to
identify the colonies, in effect we were examining a different type of failure. The definition of “failing”
used by Operation 1 may be more relevant to beekeepers, although it again may not reliably be tied to
queen quality.
5. Conclusions
Brood pattern alone was an insufficient proxy of queen quality. In future studies, it is important to
define the specific symptoms of queen failure being studied in order to address issues in queen health.
Supplementary Materials:
The following are available online at http://www.mdpi.com/2075-4450/10/1/12/s1,
Table S1: Pesticide summary for good-brood and poor-brood colonies in 2016 and 2017, Figure S1: The relative
percent of pesticide classes found in beeswax samples from 2016 and 2017, and Supplementary Materials Dataset
1: S1a. Data 2016, S1b. Pesticides 2016, S1c. Data 2017, S1d. Pesticides 2017.
Author Contributions:
Conceptualization, D.R.T., M.S., and K.L.; Methodology, D.R.T., K.L., M.S., M.G., and E.M.;
Validation, K.L., M.G., E.M., D.R.T., and M.S.; Formal Analysis, K.L.; Investigation, K.L. and M.G.; Resources,
D.R.T. and M.S.; Data Curation, K.L.; Writing-Original Draft Preparation, K.L., M.G., E.M., D.R.T., and M.S.;
Writing-Review & Editing, K.L., M.G., E.M., D.M., and M.S.; Visualization, K.L.; Supervision, D.R.T. and M.S.;
Project Administration, D.R.T. and M.S.; Funding Acquisition, D.R.T. and M.S.
Funding:
This research was funded by USDA National Institute of Food and Agriculture, grant number
2016-07962; and a North Central SARE Partnership Grant, grant number ONC16-019.
Acknowledgments:
We would like to thank Dennis vanEngelsdorp for his suggestions to improve the manuscript,
Megan Mahoney for help in collecting queens, Deniz Chen for processing queen samples, the anonymous
reviewers for their suggestions that strengthened the manuscript, and the beekeepers for their participation,
support, and ideas.
Conflicts of Interest:
The authors declare no conflict of interest. The founding sponsors had no role in the design
of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the
decision to publish the results.
References
1.
Lee, K.V.; Steinhauer, N.; Rennich, K.; Wilson, M.E.; Tarpy, D.R.; Caron, D.M.; Rose, R.; Delaplane, K.S.;
Baylis, K.; Lengerich, E.J.; et al. A national survey of managed honey bee 2013–2014 annual colony losses in
the USA. Apidologie 2015,46, 292–305. [CrossRef]
2.
Kulhanek, K.; Steinhauer, N.; Rennich, K.; Caron, D.M.; Sagili, R.R.; Pettis, J.S.; Ellis, J.D.; Wilson, M.E.;
Wilkes, J.T.; Tarpy, D.R.; et al. A national survey of managed honey bee 2015–2016 annual colony losses in
the USA. J. Apic. Res. 2017,56, 328–340. [CrossRef]
3. Szabo, T.I. Length of Life of Queens in Honey Bee Colonies. Am. Bee J. 1993,133, 723–724.
4.
Baer, B.; Collins, J.; Maalaps, K.; den Boer, S.P.A. Sperm use economy of honeybee (Apis mellifera) queens.
Ecol. Evol. 2016,6, 2877–2885. [CrossRef] [PubMed]
5.
Koeniger, G.; Koeniger, N.; Ellis, J.D.; Connor, L. Mating Biology of Honey Bees (Apis mellifera); Wicwas Press
LLC: Kalamazoo, MI, USA, 2014; ISBN 978-1-878075-38-3.
6.
Tarpy, D.R.; Keller, J.J.; Caren, J.R.; Delaney, D.A. Assessing the Mating ‘Health’ of Commercial Honey Bee
Queens. J. Econ. Entomol. 2012,105, 20–25. [CrossRef] [PubMed]
7.
Lodesani, M.; Balduzzi, D.; Galli, A. A study on spermatozoa viability over time in honey bee (Apis mellifera
ligustica) queen spermathecae. J. Apic. Res. 2004,43, 27–28. [CrossRef]
8.
Tarpy, D.R.; Olivarez, R. Measuring sperm viability over time in honey bee queens to determine patterns in
stored-sperm and queen longevity. J. Apic. Res. 2014,53, 493–495. [CrossRef]
9.
Collins, A.M. Relationship between semen quality and performance of instrumentally inseminated honey
bee queens. Apidologie 2000,31, 421–429. [CrossRef]
Insects 2019,10, 12 15 of 17
10.
Collins, A.M.; Williams, V.; Evans, J.D. Sperm storage and antioxidative enzyme expression in the honey bee,
Apis mellifera.Insect Mol. Biol. 2004,13, 141–146. [CrossRef] [PubMed]
11.
Delaney, D.A.; Keller, J.J.; Caren, J.R.; Tarpy, D.R. The physical, insemination, and reproductive quality of
honey bee queens (Apis mellifera L.). Apidologie 2011,42, 1–13. [CrossRef]
12.
Amiri, E.; Strand, M.; Rueppell, O.; Tarpy, D. Queen Quality and the Impact of Honey Bee Diseases on
Queen Health: Potential for Interactions between Two Major Threats to Colony Health. Insects
2017
,8, 48.
[CrossRef] [PubMed]
13.
Gauthier, L.; Ravallec, M.; Tournaire, M.; Cousserans, F.; Bergoin, M.; Dainat, B.; de Miranda, J.R. Viruses
Associated with Ovarian Degeneration in Apis mellifera L. Queens. PLoS ONE
2011
,6, e16217. [CrossRef]
[PubMed]
14.
Alaux, C.; Folschweiller, M.; McDonnell, C.; Beslay, D.; Cousin, M.; Dussaubat, C.; Brunet, J.-L.; Conte, Y.L.
Pathological effects of the microsporidium Nosema ceranae on honey bee queen physiology (Apis mellifera).
J. Invertebr. Pathol. 2011,106, 380–385. [CrossRef] [PubMed]
15.
Chaimanee, V.; Chantawannakul, P.; Chen, Y.; Evans, J.D.; Pettis, J.S. Effects of host age on susceptibility to
infection and immune gene expression in honey bee queens (Apis mellifera) inoculated with Nosema ceranae.
Apidologie 2014,45, 451–463. [CrossRef]
16.
Haarmann, T.; Spivak, M.; Weaver, D.; Weaver, B.; Glenn, T. Effects of Fluvalinate and Coumaphos on Queen
Honey Bees (Hymenoptera: Apidae) in Two Commercial Queen Rearing Operations. J. Econ. Entomol.
2002
,
95, 28–35. [CrossRef] [PubMed]
17.
Pettis, J.S.; Collins, A.M.; Wilbanks, R.; Feldlaufer, M.F. Effects of coumaphos on queen rearing in the honey
bee, Apis mellifera.Apidologie 2004,35, 605–610. [CrossRef]
18.
Burley, L.M.; Fell, R.D.; Saacke, R.G. Survival of Honey Bee (Hymenoptera: Apidae) Spermatozoa Incubated
at Room Temperature from Drones Exposed to Miticides. J. Econ. Entomol.
2008
,101, 1081–1087. [CrossRef]
[PubMed]
19.
Chaimanee, V.; Evans, J.D.; Chen, Y.; Jackson, C.; Pettis, J.S. Sperm viability and gene expression in
honey bee queens (Apis mellifera) following exposure to the neonicotinoid insecticide imidacloprid and the
organophosphate acaricide coumaphos. J. Insect Physiol. 2016,89, 1–8. [CrossRef] [PubMed]
20.
Rangel, J.; Tarpy, D.R. In-Hive Miticides and their Effect on Queen Supersedure and Colony Growth in the
Honey Bee (Apis mellifera). J. Environ. Anal. Toxicol. 2016,6. [CrossRef]
21.
Sandrock, C.; Tanadini, M.; Tanadini, L.G.; Fauser-Misslin, A.; Potts, S.G.; Neumann, P. Impact of Chronic
Neonicotinoid Exposure on Honeybee Colony Performance and Queen Supersedure. PLoS ONE
2014
,9,
e103592. [CrossRef] [PubMed]
22.
Tsvetkov, N.; Samson-Robert, O.; Sood, K.; Patel, H.S.; Malena, D.A.; Gajiwala, P.H.; Maciukiewicz, P.;
Fournier, V.; Zayed, A. Chronic exposure to neonicotinoids reduces honey bee health near corn crops. Science
2017,356, 1395–1397. [CrossRef] [PubMed]
23.
Traynor, K.S.; Pettis, J.S.; Tarpy, D.R.; Mullin, C.A.; Frazier, J.L.; Frazier, M.; vanEngelsdorp, D. In-hive
Pesticide Exposome: Assessing risks to migratory honey bees from in-hive pesticide contamination in the
Eastern United States. Sci. Rep. 2016,6. [CrossRef] [PubMed]
24.
Dussaubat, C.; Maisonnasse, A.; Crauser, D.; Tchamitchian, S.; Bonnet, M.; Cousin, M.; Kretzschmar, A.;
Brunet, J.-L.; Le Conte, Y. Combined neonicotinoid pesticide and parasite stress alter honeybee queens’
physiology and survival. Sci. Rep. 2016,6. [CrossRef] [PubMed]
25.
vanEngelsdorp, D.; Tarpy, D.R.; Lengerich, E.J.; Pettis, J.S. Idiopathic brood disease syndrome and queen
events as precursors of colony mortality in migratory beekeeping operations in the eastern United States.
Prev. Vet. Med. 2013,108, 225–233. [CrossRef] [PubMed]
26.
Wu, J.Y.; Anelli, C.M.; Sheppard, W.S. Sub-Lethal Effects of Pesticide Residues in Brood Comb on Worker
Honey Bee (Apis mellifera) Development and Longevity. PLoS ONE 2011,6, e14720. [CrossRef] [PubMed]
27.
Brodschneider, R.; Crailsheim, K. Nutrition and health in honey bees. Apidologie
2010
,41, 278–294. [CrossRef]
28.
Spivak, M.; Reuter, G.S. Performance of hygienic honey bee colonies in a commercial apiary. Apidologie
1998
,
29, 291–302. [CrossRef]
29.
Harbo, J.R.; Harris, J.W. Responses to Varroa by honey bees with different levels of Varroa Sensitive Hygiene.
J. Apic. Res. 2009,48, 156–161. [CrossRef]
30.
Guzmán-Novoa, E.; Page, R.E. Selective Breeding of Honey Bees (Hymenoptera: Apidae) in Africanized
Areas. J. Econ. Entomol. 1999,92, 521–525. [CrossRef]
Insects 2019,10, 12 16 of 17
31.
Delaplane, K.S.; van der Steen, J.; Guzman-Novoa, E. Standard methods for estimating strength parameters
of Apis mellifera colonies. J. Apic. Res. 2013,52, 1–12. [CrossRef]
32.
Pettis, J.S.; Rice, N.; Joselow, K.; vanEngelsdorp, D.; Chaimanee, V. Colony Failure Linked to Low Sperm
Viability in Honey Bee (Apis mellifera) Queens and an Exploration of Potential Causative Factors. PLoS ONE
2016,11, e0147220. [CrossRef]
33.
Simone-Finstrom, M.; Tarpy, D.R. Honey Bee Queens Do Not Count Mates to Assess their Mating Success.
J. Insect Behav. 2018,31, 200–209. [CrossRef]
34.
Genersch, E.; von der Ohe, W.; Kaatz, H.; Schroeder, A.; Otten, C.; Büchler, R.; Berg, S.; Ritter, W.; Mühlen, W.;
Gisder, S.; et al. The German bee monitoring project: A long term study to understand periodically high
winter losses of honey bee colonies. Apidologie 2010,41, 332–352. [CrossRef]
35. Shimanuki, H.; Calderone, N.W.; Knox, D.A. Parasitic mite syndrome: The symptoms. Am. Bee J. 1994,134,
117–119.
36.
vanEngelsdorp, D.; Evans, J.D.; Donovall, L.; Mullin, C.; Frazier, M.; Frazier, J.; Tarpy, D.R.; Hayes, J.;
Pettis, J.S. “Entombed Pollen”: A new condition in honey bee colonies associated with increased risk of
colony mortality. J. Invertebr. Pathol. 2009,101, 147–149. [CrossRef] [PubMed]
37.
De Jong, D.; De Andrea Roma, D.; Gonçalves, L.S. A comparative analysis of shaking solutions for the
detection of Varroa jacobsoni on adult honeybees. Apidologie 1982,13, 297–306. [CrossRef]
38. Cantwell, G.E. Standard methods for counting nosema spores. Am. Bee J. 1970,110, 222–223.
39.
Traynor, K.S.; Rennich, K.; Forsgren, E.; Rose, R.; Pettis, J.; Kunkel, G.; Madella, S.; Evans, J.; Lopez, D.;
vanEngelsdorp, D. Multiyear survey targeting disease incidence in US honey bees. Apidologie
2016
,47,
325–347. [CrossRef]
40.
Mullin, C.A.; Frazier, M.; Frazier, J.L.; Ashcraft, S.; Simonds, R.; vanEngelsdorp, D.; Pettis, J.S. High Levels of
Miticides and Agrochemicals in North American Apiaries: Implications for Honey Bee Health. PLoS ONE
2010,5, e9754. [CrossRef] [PubMed]
41.
Stoner, K.A.; Eitzer, B.D. Using a Hazard Quotient to Evaluate Pesticide Residues Detected in Pollen Trapped
from Honey Bees (Apis mellifera) in Connecticut. PLoS ONE 2013,8, e77550. [CrossRef] [PubMed]
42. US EPA Ecotox Database. Available online: http://cfpub.epa.gov/ecotox/ (accessed on 26 May 2018).
43.
Hertfordshire Pesticide Properties Database. Available online: http://sitem.herts.ac.uk/aeru/ppdb/en/
index.htm (accessed on 26 May 2018).
44.
Sanchez-Bayo, F.; Goka, K. Pesticide Residues and Bees—A Risk Assessment. PLoS ONE
2014
,9, e94482.
[CrossRef] [PubMed]
45.
Alburaki, M.; Chen, D.; Skinner, J.; Meikle, W.; Tarpy, D.; Adamczyk, J.; Stewart, S. Honey Bee Survival and
Pathogen Prevalence: From the Perspective of Landscape and Exposure to Pesticides. Insects
2018
,9, 65.
[CrossRef] [PubMed]
46.
Amdam, G.V.; Omholt, S.W. The Regulatory Anatomy of Honeybee Lifespan. J. Theor. Biol.
2002
,216, 209–228.
[CrossRef] [PubMed]
47.
Corona, M.; Velarde, R.A.; Remolina, S.; Moran-Lauter, A.; Wang, Y.; Hughes, K.A.; Robinson, G.E.
Vitellogenin, juvenile hormone, insulin signaling, and queen honey bee longevity. Proc. Natl. Acad. Sci. USA
2007,104, 7128–7133. [CrossRef] [PubMed]
48.
Nelson, C.M.; Ihle, K.E.; Fondrk, M.K.; Page, R.E.; Amdam, G.V. The Gene vitellogenin Has Multiple
Coordinating Effects on Social Organization. PLoS Biol. 2007,5, e62. [CrossRef] [PubMed]
49.
Seehuus, S.-C.; Norberg, K.; Gimsa, U.; Krekling, T.; Amdam, G.V. Reproductive protein protects functionally
sterile honey bee workers from oxidative stress. Proc. Natl. Acad. Sci. USA
2006
,103, 962–967. [CrossRef]
[PubMed]
50.
Evans, J.D.; Pettis, J.S. Colony-level impacts of immune responsiveness in honey bees, Apis mellifera.Evolution
2005,59, 2270–2274. [CrossRef] [PubMed]
51.
Simone, M.; Evans, J.D.; Spivak, M. Resin collection and social immunity in honey bees. Evolution
2009
,63,
3016–3022. [CrossRef] [PubMed]
52.
Even, N.; Devaud, J.-M.; Barron, A. General Stress Responses in the Honey Bee. Insects
2012
,3, 1271–1298.
[CrossRef] [PubMed]
53.
R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing:
Vienna, Austria, 2017. Available online: https://www.R-project.org/ (accessed on 26 May 2018).
Insects 2019,10, 12 17 of 17
54.
vanEngelsdorp, D.; Lengerich, E.; Spleen, A.; Dainat, B.; Cresswell, J.; Baylis, K.; Nguyen, B.K.; Soroker, V.;
Underwood, R.; Human, H.; et al. Standard epidemiological methods to understand and improve Apis
mellifera health. J. Apic. Res. 2013,52, 1–16. [CrossRef]
55.
Haldane, J.B.S. The mean and variance of the moments of chi-squaredwhen used as a test of homogeneity,
when expectations are small. Biometrika 1940,29, 133–134.
56. Anscombe, F.J. On estimating binomial response relations. Biometrika 1956,4, 461–464. [CrossRef]
57.
Bates, D.; Mächler, M.; Bolker, B.; Walker, S. Fitting Linear Mixed-Effects Models Using lme4. J. Stat. Softw.
2015,67, 1–48. [CrossRef]
58. Woyke, J. Natural and artificial insemination of queen honeybees. Bee World 1962,43, 21–22. [CrossRef]
59.
Giacobino, A.; Molineri, A.; Cagnolo, N.B.; Merke, J.; Orellano, E.; Bertozzi, E.; Masciángelo, G.;
Pietronave, H.; Pacini, A.; Salto, C.; et al. Risk factors associated with failures of Varroa treatments in
honey bee colonies without broodless period. Apidologie 2015,46, 573–582. [CrossRef]
60.
Tarpy, D.R.; vanEngelsdorp, D.; Pettis, J.S. Genetic diversity affects colony survivorship in commercial honey
bee colonies. Naturwissenschaften 2013,100, 723–728. [CrossRef] [PubMed]
61.
Niño, E.L.; Malka, O.; Hefetz, A.; Tarpy, D.R.; Grozinger, C.M. Chemical Profiles of Two Pheromone Glands
Are Differentially Regulated by Distinct Mating Factors in Honey Bee Queens (Apis mellifera L.). PLoS ONE
2013,8, e78637. [CrossRef] [PubMed]
62.
Kocher, S.D.; Grozinger, C.M. Cooperation, Conflict, and the Evolution of Queen Pheromones. J. Chem. Ecol.
2011,37, 1263–1275. [CrossRef] [PubMed]
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2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
... This poses considerable challenges. Five studies included in this review link colony health outcomes to HQ values: Traynor et al. (2016) in the eastern United States, Traynor et al. (2021a) in the United States, Lee et al. (2019) in the United States, Smart et al. (2016) in the northern great plains, and El Agrebi et al. (2019) in Belgium. Of these five, only one study found clear association of colony health with HQ and two found a weak association. ...
... These co-occurrences, while interesting, do not directly indicate synergism is occurring, however they do point to the potential for synergistic toxicity to occur if each pesticide is present in a high enough concentration. Lee et al. (2019) analyzed the relationship between complete and unbroken brood pattern and patchy brood pattern and found that HQ was not correlated with brood pattern. However, the number of pesticides detected was significantly correlated with brood pattern in at least 1 year. ...
... However, the number of pesticides detected was significantly correlated with brood pattern in at least 1 year. Notably, Lee et al. (2019) found much lower HQ values in wax throughout the study compared to Traynor et al. (2016) which may explain the lack of connection with brood pattern. ...
Article
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Estimates of pesticide application hazards have grown to be one of the most common methodologies for evaluating the impact of pest management practices on honey bees. Typically, hazards are estimated by calculating a Hazard Quotient (HQ), which is based on acute toxicity data for different pesticides and the quantity of those pesticides applied to a field or detected on bees and matrices associated with their hive (honey, wax, pollen, and/or bee bread). Although use of HQ is widespread, there have been few reviews of this methodology, particularly with focus on how effective this method is at predicting effects of pesticides on hives. We evaluated 36 relevant papers, containing calculations of HQ to estimate hazards to honey bees. We observed that HQ was primarily calculated using two different approaches: (1) from the concentration of pesticides in the food, hive, or tissues of honey bees or (2) using the field application rate of the active ingredient as the estimation of pesticide hazard. Within and between HQ calculation methods, thresholds vary widely with some HQ thresholds set below 1 and others set at 10,000. Based on our review we identify key weakness with current HQ methodology and how studies relate HQ to honey bee health endpoints. First, HQ thresholds from studies of pesticides in hives are not based on the same pesticide consumption models from the EPA, potentially overestimating the risk of impacts to colonies. Conversely, HQ estimates calculated from field application rates are not based on eco-toxicological estimates of field exposure, resulting in an overestimation of pesticide reaching colonies. We suggest it is for these reasons that there is poor correspondence between HQ and field-level honey bee health endpoints. Considering these challenges, HQ calculations should be used cautiously in future studies and more research should be dedicated to field level exposure models.
... Unlike worker bees that typically survive for roughly 4 weeks during the active spring, summer, and fall months (Winston 1991), honey bee queens can live for multiple years, during which time they are solely responsible for producing fertilized eggs inside a honey bee colony (Page and Peng 2001). In commercial operations, beekeepers typically requeen annually as a prophylactic measure or when brood pattern becomes noticeably irregular (Lee et al. 2019). However, the indicators of queen failure could easily be missed, particularly in large-scale operations where beekeepers cannot thoroughly inspect brood frames in every colony, and often the indications come too late, when colony populations decline precipitously due to a shortage of emerging adult bees to replace the aging forager population. ...
... However, the indicators of queen failure could easily be missed, particularly in large-scale operations where beekeepers cannot thoroughly inspect brood frames in every colony, and often the indications come too late, when colony populations decline precipitously due to a shortage of emerging adult bees to replace the aging forager population. One further complication is the recent finding that brood pattern is not always a reliable indicator of queen quality (Lee et al. 2019), highlighting the need for more research to investigate the factors that contribute to queen performance and identify more reliable diagnostic metrics. ...
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Honey bees are valued pollinators of agricultural crops, and heavy losses reported by beekeepers have spurred efforts to identify causes. As social insects, threats to honey bees should be assessed by evaluating the effects of stress on the long-term health and productivity of the entire colony. Insect growth disruptors are a class of pesticides encountered by honey bees that target pathways involved in insect development, reproduction, and behavior, and they have been shown to affect critical aspects of all three in honey bees. Therefore, it is imperative that their risks to honey bees be thoroughly evaluated. This review describes the effects of insect growth disruptors on honey bees at the individual and colony levels, highlighting hazards associated with different chemistries, and addresses their potential impacts on the longevity of colonies. Finally, recommendations for the direction of future research to identify strategies to mitigate effects are prescribed.
... Although queen failure is described as a leading contributor to losses (Amiri et al., 2017;Steinhauer et al., 2018;Bee Informed Team, 2021), the cause of "poor queen quality" is not well understood, partly due to a lack of reliable methods to study queen phenotypes (Lee et al., 2019), and partly because measuring effects on queens requires colony level experiments that are logistically difficult, expensive, and, most importantly, hard to control (i.e., colonies are exposed to a wide variety of environmental variables). In many cases, combined factors, all of which have the potential to disrupt queen health, contribute to poor overall colony function (Maini et al., 2010;Vanengelsdorp and Meixner, 2010;Hristov et al., 2020). ...
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Full-text available
Beekeepers experience high annual losses of colonies, with environmental stressors like pathogens, reduced forage, and pesticides as contributors. Some factors, like nutritional stress from reduced flower abundance or diversity, are more pronounced in agricultural landscapes where extensive farming limits pollen availability. In addition to affecting other aspects of colony health, quantity and quality of pollen available are important for colony brood production and likely for queen egg laying. While some US beekeepers report >50% of colony loss due to queen failure, the causes of poor-quality queens are poorly understood. Access to resources from native prairie habitat is suggested as a valuable late-season resource for honey bees that can reverse colony growth declines, but it is not clear how prairie forage influences queen egg laying. We hypothesized that the pollen resources present in an extensive Midwestern corn/soybean agroecosystem during the critical late season period affect honey bee queen egg laying and that access to native prairies can increase queen productivity. To test this, we designed a field experiment in Iowa, keeping colonies in either soybean or prairie landscapes during a critical period of forage dearth, and we quantified queen egg laying as well as pollen collection (quantity and species). Then, using pollen collected in the field experiments, we created representative dietary mixtures, which we fed to bees using highly controlled laboratory cages to test how consumption of these diets affected the egg laying of naive queens. In two out of three years, queens in prairies laid more eggs compared to those in soybean fields. Pollen quantity did not vary between the two landscapes, but composition of species did, and was primarily driven by collection of evening primrose (Oenothera biennis). When pollen representative of the two landscapes was fed to caged bees in the laboratory queens fed prairie pollen laid more eggs, suggesting that pollen from this landscape plays an important role in queen productivity. More work is needed to tease apart the drivers of these differences, but understanding how egg laying is regulated is useful for designing landscapes for sustainable pollinator management and can inform feeding regimes for beekeepers.
... State University using methods as previously described 73 with minor adjustments. For this sample set, we analyzed thoraxes because we have observed that other components of the head can sometimes interfere with PCR. ...
Preprint
Declining insect populations emphasize the importance of understanding the drivers underlying reductions in insect fitness. Here, we investigated viruses as a threat to social insect reproduction, using honey bees as a model species. We report that in a sample of N = 93 honey bee ( Apis mellifera ) queens from nine beekeeping operations across a wide geographic range, high levels of natural viral infection are associated with decreased ovary mass. We confirmed this finding in an independent sample of N = 54 queens. Failed (poor quality) queens displayed higher levels of viral infection, reduced sperm viability, smaller ovaries, and altered ovary protein composition compared to healthy queens. We experimentally infected queens with Israeli acute paralysis virus (IAPV) and found that the ovary masses of IAPV-injected queens were significantly smaller than control queens, demonstrating a causal relationship between viral infection and ovary size. Queens injected with IAPV also had significantly lower expression of vitellogenin, the main source of nutrition deposited into developing oocytes, and higher levels of heat-shock proteins (HSPs), which are part of the honey bee’s antiviral response. This work together shows that viral infections occurring naturally in the field are compromising queen reproductive success.
... This is in part because she is the sole reproductive member of the colony (usually) but also because in many cases she is a product produced outside of the control of the beekeeper, and therefore the subject of contention when colonies fail. Therefore, much attention has been paid to assess and understand the quality of commercial queens and the extent to which colony-level phenotypes commonly attributed to queens are in fact their 'fault' , Lee et al. 2019. A particular finding of the research into queen quality is that the major factors leading to her heading a productive colony or those related to her mating (e.g., number of partners, sperm count, and sperm viability) are correlated with colony growth and survival (Collins 2004, Tarpy et al. 2013, Pettis et al. 2016. ...
Article
Full-text available
Exploration into reproductive quality in honey bees (Apis mellifera Linneaus (Hymenoptera: Apidae) largely focuses on factors that affect queens, with drones primarily being considered insofar as they pass on effects of environmental stressors to the queen and subsequent offspring. In those studies that consider drone quality explicitly, a primary focus has been on the dimorphic nature of drones laid in worker cells (either through rare queen error or worker reproduction) as compared to drones laid by the queen in the slightly larger drone cells. The implication from these studies is that that there exists a bimodality of drone morphological quality that is related to reproductive quality and competitive ability during mating. Our study quantifies the presence of such small drones in commercial populations, finding that rates of 'low-quality' drones are far higher than theoretically predicted under optimum conditions. Observations from commercial colonies also show significant inter-colony variation among the size and fecundity of drones produced, prompting speculation as to the mechanisms inducing such variation and the potential use of drone-quality variation for the colony- or apiary-level exposure to nutrition, agrichemical, or parasitic stressors.
... et al.,2010). The mechanism for the occurrence of CCD in honey bee is still unclear, but poor queen quality could be a major contributor to the mass death of honey bee (vanEngelsdorp et al.,2011; Lee et al.,2019). High-quality honey bee queen is the key to bee colony development and high-production of bee products. ...
Preprint
Full-text available
Queen is arguably the most important member of a honey bee colony, and queen quality is crucial for honey bee colony growth and development. In this study, queens were reared with eggs laid in queen cells (QE), eggs laid in worker cells (WE) and 2-day old larvae in worker cells (L). Those physiological indexes (the weight, thorax size and number of ovarioles) of newly reared queens in each group were measured. Moreover, the reproductive potential of the newly reared queens and foraging ability of worker bees laid by the newly reared queens in each group were further explored. In addition, we also examined whether maternal effects would be transmitted to the offspring queens in honey bee. We found that the weight, number of ovarioles and thorax weight of newly emerged queens in QE were significantly higher than those in WE and L, suggesting the reproductive potential was stronger in QE group than WE and L group. Furthermore, offspring worker bees and queens of QE queens had higher weight at emergence than those from the other two groups. This study proved profound honey bee maternal effects on queen quality, which can be transmitted to their offspring. Our results of the present study were important for improving queen quality and promoting the development of beekeeping and agriculture.
... Oral pesticide exposure has been shown to influence queen nutrition during development 21 , and it is unknown how pesticide exposure may interact with the queen pheromone production and their perception by workers. Furthermore, the most visible phenotypes under investigation as indicators of queen health (e.g., brood pattern and colony population) are not solely under queen control, and brood viability can be impacted by other stressors not relating to queen quality 54 . Finally, in this study, we did not investigate potential indirect effects of queen fertility via mating with pesticide-exposed drones. ...
Article
Full-text available
Honey bee queen health is crucial for colony health and productivity, and pesticides have been previously associated with queen loss and premature supersedure. Prior research has investigated the effects of indirect pesticide exposure on queens via workers, as well as direct effects on queens during development. However, as adults, queens are in constant contact with wax as they walk on comb and lay eggs; therefore, direct pesticide contact with adult queens is a relevant but seldom investigated exposure route. Here, we conducted laboratory and field experiments to investigate the impacts of topical pesticide exposure on adult queens. We tested six pesticides commonly found in wax: coumaphos, tau-fluvalinate, atrazine, 2,4-DMPF, chlorpyriphos, chlorothalonil, and a cocktail of all six, each administered at 1, 4, 8, 16, and 32 times the concentrations typically found in wax. We found no effect of any treatment on queen mass, sperm viability, or fat body protein expression. In a field trial testing queen topical exposure of a pesticide cocktail, we found no impact on egg-laying pattern, queen mass, emergence mass of daughter workers, and no proteins in the spermathecal fluid were differentially expressed. These experiments consistently show that pesticides commonly found in wax have no direct impact on queen performance, reproduction, or quality metrics at the doses tested. We suggest that previously reported associations between high levels of pesticide residues in wax and queen failure are most likely driven by indirect effects of worker exposure (either through wax or other hive products) on queen care or queen perception.
... Long-term colony sociometry. To capture the long-term sociometric dynamics [53][54][55][56] of bee colonies, we devised a segmentationbased method for the detection of bees and brood in dense configurations within a 2D hive 21,49 . We reported the honey bee (but not brood) detection in an earlier conference proceedings 21 and we review that approach here for clarity. ...
Article
Full-text available
From cells in tissue, to bird flocks, to human crowds, living systems display a stunning variety of collective behaviors. Yet quantifying such phenomena first requires tracking a significant fraction of the group members in natural conditions, a substantial and ongoing challenge. We present a comprehensive, computational method for tracking an entire colony of the honey bee Apis mellifera using high-resolution video on a natural honeycomb background. We adapt a convolutional neural network (CNN) segmentation architecture to automatically identify bee and brood cell positions, body orientations and within-cell states. We achieve high accuracy (~10% body width error in position, ~10° error in orientation, and true positive rate > 90%) and demonstrate months-long monitoring of sociometric colony fluctuations. These fluctuations include ~24 h cycles in the counted detections, negative correlation between bee and brood, and nightly enhancement of bees inside comb cells. We combine detected positions with visual features of organism-centered images to track individuals over time and through challenging occluding events, recovering ~79% of bee trajectories from five observation hives over 5 min timespans. The trajectories reveal important individual behaviors, including waggle dances and crawling inside comb cells. Our results provide opportunities for the quantitative study of collective bee behavior and for advancing tracking techniques of crowded systems.
... The quality of queens affects many genetic and environmental factors (Hatjina et al., 2014;Amiri et al., 2017;Köseoglu et al., 2017;Okuyan & Akyol, 2018;Lee et al., 2019). Genetic properties are transferred to the colonies through a breeder queen and drone production colonies (Güler, 2008;Czekonska et al., 2015;Metz & Tarpy, 2019). ...
Article
Full-text available
The influence of different commercial queen producers on the quality of Apis mellifera queens was assessed. It was aimed to determine the quality characteristics of queens reared by commercial queen producers located in the province of Antalya, which is an important region in queens production due to its climatic characteristics. For this purpose, the quality characteristics of a total of 105 queen bees obtained from 21 enterprises were determined. Differences between the enterprises in terms of the number of spermatozoa (P<0.01) were determined. In terms of the diameter of spermatheca, spermatheca volume and live weight, statistical differences between the enterprises were also observed (P<0.05). When the relationships between the measured characteristics were examined, significant values were obtained statistically between live weight and diameter of spermathecae (0.268) and spermatheca volume (0.258). It was also determined that there is a significant correlation between spermatheca diameter and spermatheca volume (0.995). The spermatheca diameter of a good quality queen bee should not be less than 1.2 mm, spermatheca volume 0.90 mm³ and live weight not less than 200 mg. Only live weight was found to be within the normal quality standard values when the average results of the quality criteria are taken into consideration. Other characters such as spermathecae diameter, spermathecae volume and number of spermatozoa in spermathecae seem to be below quality standard values.
Article
Full-text available
Numerous honeybee (Apis mellifera) products, such as honey, propolis, and bee venom, are used in traditional medicine to prevent illness and promote healing. Therefore, this insect has a huge impact on humans’ way of life and the environment. While the population of A. mellifera is large, there is concern that widespread commercialization of beekeeping, combined with environmental pollution and the action of bee pathogens, has caused significant problems for the health of honeybee populations. One of the strategies to preserve the welfare of honeybees is to better understand and protect their natural microbiota. This paper provides a unique overview of the latest research on the features and functioning of A. mellifera. Honeybee microbiome analysis focuses on both the function and numerous factors affecting it. In addition, we present the characteristics of lactic acid bacteria (LAB) as an important part of the gut community and their special beneficial activities for honeybee health. The idea of probiotics for honeybees as a promising tool to improve their health is widely discussed. Knowledge of the natural gut microbiota provides an opportunity to create a broad strategy for honeybee vitality, including the development of modern probiotic preparations to use instead of conventional antibiotics, environmentally friendly biocides, and biological control agents.
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In order to study the in situ effects of the agricultural landscape and exposure to pesticides on honey bee health, sixteen honey bee colonies were placed in four different agricultural landscapes. Those landscapes were three agricultural areas with varying levels of agricultural intensity (AG areas) and one non-agricultural area (NAG area). Colonies were monitored for different pathogen prevalence and pesticide residues over a period of one year. RT-qPCR was used to study the prevalence of seven different honey bee viruses as well as Nosema apis in colonies located in different agricultural systems with various intensities of soybean, corn, sorghum and cotton production. Populations of the parasitic mite Varroa destructor were also extensively monitored. Comprehensive MS-LC pesticide residue analyses were performed on samples of wax, honey, foragers, winter bees, dead bees and crop flowers for each apiary and location. Significantly higher level of varroa loads were recorded in colonies of the AG areas, but this at least partly correlated with increased colony size and did not necessarily result from exposure to pesticides. Infections of two viruses (DWVa, ABPV) and Nosema apis varied among the four studied locations. The urban location significantly elevated colony pathogen loads, while AG locations significantly benefited and increased the colony weight gain. Cotton and sorghum flowers contained high concentrations of insecticide including neonicotinoids, while soybean and corn had less pesticide residues. Several events of pesticide toxicity were recorded in the AG areas, and high concentrations of neonicotinoid insecticides were detected in dead bees.
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The mating system of honey bees (genus Apis) is extremely polyandrous, where reproductive females (queens) typically mate with 12 or more males (drones) during their mating flight(s). The evolutionary implications for hyperpolyandry have been subject to considerable debate and empirical testing because of the need to understand the proximate mechanisms that drive such extreme mating behavior despite the potential costs. The ability of queens to gauge and adjust their reproductive success is therefore important for selection to act on queen mating number at both the evolutionary (colony-level) and proximate (individual-level) timescales. We observed the mating flight activities of 80 queens in their respective mating nucleus hives each with a modified entrance that restricts flight attempts. We also attached a small weight (0, 16, or 38 mg) onto each queen’s thorax as a means of imposing additional flight costs. We then compared queens that were restricted from taking multiple mating flights to those that started oviposition after a single flight for their mating numbers as quantified by microsatellite analyses of their respective worker offspring. We found that neither additional weight nor restricted mating attempts had any significant effect on the effective mating frequencies of the experimental queens during their single mating flight. This observation suggests that queens are not adjusting their nuptial flight activity according to their precise mating number during their flight. These findings provide insights into the proximate regulation of honey bee queen mating behavior and the fitness consequences of hyperpolyandry at the colony level.
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