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RESEARCH ARTICLE
Survey-derived best management practices
for backyard beekeepers improve colony
health and reduce mortality
Kelly KulhanekID
1¤
*, Nathalie Steinhauer
1
, James Wilkes
2
, Michaela Wilson
3
,
Marla Spivak
4
, Ramesh R. Sagili
5
, David R. Tarpy
6
, Erin McDermott
6
, Andrew Garavito
1
,
Karen Rennich
1
, Dennis vanEngelsdorp
1
1Department of Entomology, University of Maryland, College Park, Maryland, United Statesof America,
2Department of Computer Science, Appalachian State University, Boone, North Carolina, United States of
America, 3Department of Entomology and Plant Pathology, University of Tennessee, Knoxville, Tennessee,
United States of America, 4Department of Entomology, University of Minnesota, St. Paul, Minnesota, United
States of America, 5Department of Horticulture, Oregon State University, Corvallis, Oregon, United States of
America, 6Department of Entomology & Plant Pathology, North Carolina State University, Raleigh, North
Carolina, United States of America
¤Current address: Department of Entomology, Washington State University, Pullman, Washington, United
States of America
*Kelly.kulhanek@wsu.edu
Abstract
Honey bee colony losses in the US have exceeded acceptable levels for at least a decade,
leaving beekeepers in need of management practices to improve colony health and survival.
Here, an empirical Best Management Practice (BMP) regimen was tested, comprised of the
top four management practices associated with reduced colony mortality in backyard
beekeeping operations according to Bee Informed Partnership Loss and Management sur-
vey results. Seven study locations were established across the US, and each location con-
sisted of ten colonies treated according to empirical BMPs and ten according to average
beekeeping practice. After 3 years, colonies treated according to empirical BMPs experi-
enced reduced Varroa infestation, viral infection, and mortality compared to colonies man-
aged with Average practices. In addition, BMP colonies produced more new colonies via
splits. The colonies under Average practices were given chemical Varroa treatments only
once per year, and thus spent more months above economic threshold of 3.0 mites/100
bees. Increased time spent above the economic threshold was significantly correlated to
both increased viral infection and colony mortality. This study demonstrates the cumulative
effects of management and colony health stressors over months and years, especially the
dire importance of regular Varroa monitoring and management.
Introduction
Honey bees are the most economically important pollinators in the world, providing billions
of dollars in pollination services [1–3]. However, beekeepers consistently lose more colonies
each year than they deem acceptable [4–9], and the need for pollination units has grown more
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OPEN ACCESS
Citation: Kulhanek K, Steinhauer N, Wilkes J,
Wilson M, Spivak M, Sagili RR, et al. (2021)
Survey-derived best management practices for
backyard beekeepers improve colony health and
reduce mortality. PLoS ONE 16(1): e0245490.
https://doi.org/10.1371/journal.pone.0245490
Editor: Olav Rueppell, University of Alberta,
CANADA
Received: May 29, 2020
Accepted: December 30, 2020
Published: January 15, 2021
Copyright: ©2021 Kulhanek et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
files.
Funding: This project was funded by a Coordinated
Agricultural Project (CAP) grant from US
Department of Agriculture-National Institute of
Food and Agriculture (USDA-NIFA); award # 2016-
68004-24832. The funders had no role in study
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
rapidly than the supply of honey bee colonies [10]. Thus, beekeepers struggle to keep their
operations viable and provide sufficient colonies for crop production.
Research has identified many factors contributing to the taxing rates of colony mortality
[11]. The parasitic mite, Varroa destructor, causes direct damage via feeding wounds [12–14]
and vectors a suite of viruses [15,16]. Prolonged exposure to pesticides reduces a colony’s abil-
ity to combat other stressors [17,18]. Poor nutrition further impacts colony health, particularly
as landscapes are converted to monocultures that provide no or poor food resources [19].
While these factors may not kill colonies in isolation, in concert these stressors can interact to
manifest colony death [11,20]. Over the past decade, substantial research has focused on iden-
tifying these stressors and assessing their impacts. More recently, scientists have begun to
investigate interactions between and among stressors to better understand colony experiences
in real-world settings [21–23].
After identifying risk factors, the logical next step in an epidemiological challenge is to
develop preventative strategies. Beekeepers have an opportunity to mitigate the effects of col-
ony health stressors through the application of good beekeeping management practices. For
example, beekeepers can provide colonies with supplemental food when natural pollen and
nectar sources are scarce [7]. Additionally, interrupting Varroa population growth with vari-
ous control measures is often required to reduce colony mortality [24]. For colonies and apiar-
ies, it can be challenging to determine the effectiveness of these and other management
practices because of multiple interacting health stressors [11]. Science-based management rec-
ommendations can help beekeepers avoid using trial and error to reduce colony mortality.
Multiple groups have conducted surveys on colony losses and beekeeping management
around the world (Germany: [25]; Canada: [26,27] Europe: [28,29]). The Bee Informed Part-
nership (or BIP; beeinformed.org) has conducted an annual Loss and Management Survey of
US beekeepers since 2010. The survey consists of over 80 questions about the number of colo-
nies lost and management practices employed by a given operation over the previous year.
Survey methods and results are published annually (reviewed in [7]). In total, the survey has
collected over 50,000 responses, and it has built the largest database of colony loss and manage-
ment information in the world. These data can be analyzed to assess the effectiveness of man-
agement practices as they relate to reduced colony mortality.
One practice consistently associated with reduced mortality is Varroa control. Beekeepers
who control Varroa consistently lose fewer colonies annually [24,30]. Despite clear evidence
of their benefits, only 48% of backyard beekeepers (beekeepers with 1–50 colonies) have
reported using Varroa-control measures over the duration of the BIP survey. While more
backyard beekeepers report controlling for Varroa every year (up to 78% of backyard beekeep-
ers in 2018), there are many “treatment-free” beekeepers who do not employ effective mite-
control strategies [31,32]. Further, backyard beekeepers who employ control measures typi-
cally only do so once per year [24], which is likely insufficient to reduce Varroa populations
below economic thresholds. Backyard beekeepers experience the highest levels of colony loss
each year [7], and improved Varroa control likely can reduce this mortality rate.
A full analysis of observational survey data was conducted to identify management practices
that, if adopted, were predicted to have the largest reduction in colony loss rate. The top five of
these empirical best management practices [BMPs; 33] were developed for four different bee-
keeper demographics (southern backyard, northern backyard, stationary professional, and
migratory professional). Four of the top five empirical BMPs were the same for northern and
southern backyard beekeepers. However, before recommending these four practices to bee-
keepers, they needed to be field-tested to assess their effects on colony health and mortality. To
this end, a 3-year study was conducted to assess the effectiveness of these four BMPs. It was
hypothesized that apiaries maintained according to the four empirical BMPs would reach
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Competing interests: The authors have declared
that no competing interests exist.
larger colony sizes, exhibit better brood patterns, and experience fewer queen events. BMP api-
aries were also hypothesized to experience lower Varroa,Nosema, and pathogen loads,
reduced mortality, and produce more honey and colony splits.
Methods
Management practices
This experiment compared two different management regimes (Average vs. BMP; Table 1)
with four categories of management practices: action on deadouts (beekeeping term for colo-
nies that die), Varroa control frequency, method for starting new colonies, and comb-culling
technique. The BMP regime was derived from a combination of expert recommendations and
survey results in Steinhauer et al., 2020. Beekeeper’s survey responses were scored on how well
they aligned with expert recommendations. Beekeepers with higher scores (more aligned with
expert recommendations) experienced significantly reduced winter colony loss, indicating that
the expert’s opinions were correct. Bootstrapped sensitivity analyses were performed to iden-
tify which management practices had the greatest effect on colony loss. The BMP regime in
this study corresponds to the expert recommendation for the top four practices that most
affected colony loss. The “Average practice” regime was derived from BIP Loss and Manage-
ment Survey data as the most common practice employed by backyard beekeepers in the same
four categories.
The only differences in management between Average and BMP groups were in the four
categories of practices being tested, performed as follows. All other apiary management (e.g.,
feeding, requeening, honey harvest) was performed on an as-needed basis according to stan-
dard beekeeping practices and was kept consistent between the two groups.
Action on deadouts refers to how beekeepers respond to dead colonies discovered during
the active season. The Average practice is to remove such equipment from the apiary and store
it for later use, typically the following spring when a new colony is established. The empirical
BMP associated with the lowest winter loss rate is to reuse that equipment immediately, either
by making a new colony (split) using the equipment or by adding the boxes to another colony
that needs more space. In reality, this BMP is difficult to enact because of the seasonality of dis-
covering dead colonies (typically late fall), which does not correspond with the seasonality of
needing equipment for new or expanding colonies (early summer). Additionally, caution
should be exercised when introducing equipment from dead colonies to living ones, as it is
possible to spread disease this way. Regardless, the practice of immediately reusing equipment
was highly associated with lower winter mortality, making it one of the top 4 practices to be
tested in this study. Deadout equipment was reused immediately when colonies in the same
yard were available to receive additional boxes. If no colonies needed additional boxes, combs
were frozen, stored, and frozen again before reuse the following spring.
Table 1. Average practices vs. BMPs to be tested in the field.
Average Practice BMP
Action on deadouts Store equipment for later use Reuse equipment immediately by adding to living
colonies or using for a split
Varroa control
frequency
Apply miticides once in fall Monitor monthly and apply miticides when above 3.0
mites/100 bees
Starting new
colonies
Packages Make splits when possible and buy nucs if splits
impossible
Comb culling
technique
Do not treat old brood comb
before reuse
Freeze old brood comb before reuse
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Varroa control frequency refers to the frequency with which Varroa populations are man-
aged. The Average practice is to apply miticides to the colony once per year in fall (typically in
August or September). The BMP is to monitor Varroa on a monthly basis and employ control
measures whenever a single colony in the apiary exceeds 3.0 mites/100 bees. This practice was
followed strictly throughout the study for the BMP colonies at each location. The choice of spe-
cific miticide was left to the discretion of researchers in each state, as miticides have specific
temperature and brood requirements, and honey contamination risks. Once a colony exceeded
the threshold of 3.0 mites/100 bees, miticides were applied to all colonies within that apiary, in
accordance with expert recommendations.
Starting new colonies refers to the manner by which a new colony is formed at the begin-
ning of the beekeeping season. The average hobbyist beekeeper starts new colonies by purchas-
ing packages. The empirical BMP is to start new colonies by making splits from successfully
overwintered colonies. If insufficient colonies are available to split, then purchasing nucleus
colonies is the next best option. In the spring of 2016, all colonies were started from packages
installed on new plastic foundation to equalize the starting conditions of both management
groups. After initial installation, if a colony died over the course of the year, it was not replaced
until the following spring. In 2017 and 2018, new colonies installed in the spring came from
packages in Average apiaries and splits in BMP apiaries. Apiaries were always replenished to a
size of ten colonies each. If an insufficient number of BMP colonies survived the winter to
make splits to reach ten colonies, local nucleus colonies were purchased.
Finally, comb culling refers to how brood comb is managed before it is reused in a new col-
ony. Beekeepers often have a stock of old brood combs, typically from colonies that died previ-
ously or shrank in population, allowing a secondary empty brood box to be removed. These
combs are later reused by the beekeeper, either by adding to a growing colony that needs an
additional brood box or installing a new colony into it the following spring. Beekeepers some-
times treat this old comb to kill persistent Nosema spores, small hive beetle, or wax moth adults
or larvae by using chemicals (e.g., paradichlorobenzene crystals or acetic acid), irradiation, or
freezing. Most hobbyist beekeepers do not treat this brood comb before reusing it in a new col-
ony. However, the empirical BMP is to freeze this comb at -20˚C for a minimum of 24 hours
prior to adding it to a new colony. In this study, all brood combs added to BMP apiaries were
frozen prior to use, while combs used in Average apiaries were stored at ambient temperature.
This practice may seem at odds with the BMP of reusing deadout equipment immediately,
and a beekeeper may wonder if it is better to reuse comb immediately or freeze the comb
before reuse. As these practices were 2 of the top 4 practices that most affected winter mortal-
ity, they both had to be performed for this study. To this end, if a dead colony was discovered
in a BMP apiary and the equipment could not be immediately reused, the combs were frozen
immediately and then again before being added to a new colony. In other words, if the comb
left the apiary, it was frozen before being re-introduced.
Apiaries
This experiment was conducted at seven locations in five states across the US: Minnesota,
Maryland, North Carolina, Oregon, and Tennessee (GPS coordinates of study sites can be
found in S1 Table). Apiaries were maintained on university property or author’s personal
property, so no special permits were necessary. Each state represented a different climatic
region as designated by the National Oceanic and Atmospheric Administration (NOAA; [34]
and was chosen to test the effectiveness of empirical BMPs in different climates (Fig 1). Each
location divided 20 colonies into two groups of ten colonies each. One group was treated
according to Average beekeeping practices, and the other was treated according to empirical
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BMPs defined above. The two groups were separated by 10–50 meters to minimize drift of
bees between management groups at each location. Microclimates of the colony groups (e.g.,
hours of shade, direction of colony entrance) were kept as similar as possible. Apiaries were
established in the spring of 2016 and maintained until the spring of 2019. Each colony was
established from packages on new plastic foundation to minimize initial differences in colony
strength.
Sampling
All colonies in this study were monitored from spring 2016 through spring 2019. Each year,
colonies were inspected and sampled once per month for 6 months from spring to fall. The
actual months when colonies were sampled varied somewhat based on weather and climate in
each region. For example, in 2016 Minnesota colonies were sampled from April to September,
and North Carolina colonies were sampled from June to November. In all analyses, only data
from May to October were used for simplicity of comparison between groups.
Each inspection included a colony strength assessment and record of the typical metrics of
frames of bees, queen status, and brood pattern [35]. Frames of bees, a measurement of colony
size, was evaluated according to standard methods [35]; one deep frame completely covered in
adult bees on both sides was counted as one frame of bees. Mediums frames, if used, were
counted as 2/3 of a full deep frame. Brood pattern was evaluated on a scale of 1–5, a 5 being a
frame of continuously capped brood. Brood pattern is a standard colony health metric used by
beekeepers, where better brood patterns are considered indicative of queen and brood health.
Queen status was judged as one of six options: queen seen, queen-right (queen not seen but
fresh eggs observed), virgin queen, drone layer, queen not seen (no queen or fresh eggs seen
but seems otherwise queen right), or queen-less (clearly no queen present). If a colony experi-
enced a queen issue, attempts were made to rectify it (e.g., adding a new queen or frame of
eggs) but occasionally queen issues contributed to colony mortality.
A sample of adult bees was also taken from each colony at each sampling event. Approxi-
mately 300 adult bees were taken from a frame with partially capped brood and placed into a
saltwater bottle. Super-saturated saltwater (1.13 kg salt per 3.79 liters water) was used in lieu of
alcohol for ease of shipping, and all samples were processed before any decay occurred. Each
participating researcher mailed their samples to the bee diagnostics lab at University of Mary-
land, where samples were processed for Varroa (mites/100 bees) by shaking and Nosema (mil-
lions of spores/bee) by microscope according to standard methods [36,37].
Fig 1. Map of apiary locations and corresponding NOAA climate zones.
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A separate sample was taken from each colony for testing of viruses three times per year
(spring, mid-summer, and fall). The precise timing of these samples varied based on regional
climate, and only two samples were taken in the first year (mid-season and fall) as colonies
were not established well enough to support an extra sample in spring. Viral sampling con-
sisted of placing approximately 100 bees from a frame with partially capped brood into a 50
mL Eppendorf tube. The tubes were immediately placed on dry ice and kept at -80˚C until
they could be shipped on dry ice to the North Carolina State University Queen & Disease
Clinic for processing. Samples were tested for copy numbers of the following viruses: Acute
Bee Paralysis Virus (ABPV), Black Queen Cell Virus (BQCV), Chronic Bee Paralysis Virus
(CBPV), Deformed Wing Virus A (DWVA), Deformed Wing Virus B (DWVB), Israeli Acute
Paralysis Virus (IAPV), Lake Sinai Virus (LSV), Trypanosoma spp., and Nosema spp. Reverse
transcription quantitative PCR (RT-qPCR) was performed for detection of all pathogens fol-
lowing previously described methods [38,39].
Honey production and the number of colonies available to split were recorded as metrics of
colony productivity. Honey production was measured by weighing supers as they were
removed from colonies and is presented in total kg and kg/colony. Some splits were made
directly, but the potential for splits was much higher than the actual number made because of
logistical constraints of the experimental design. In order to better quantify split potential, a
metric for splittable colonies was developed. A splittable colony is any colony that survived
winter and had >10 frames of bees in May of the following year.
Colony mortality was assessed for three time periods per year: summer (April 1
st
–October
31
st
), winter (November 1
st
–March 31
st
), and annual (April 1st–March 31
st
). Dead colonies
included colonies with zero or less than 1 frame of bees remaining, or colonies that were per-
petually queenless.
Analyses
All statistical tests were performed in R (version 3.3.3). All graphs present Average apiary data
in orange and BMP apiary data in blue. All summary statistics are reported as means ±SEM
unless otherwise noted. Time-series data (i.e., those collected at multiple sampling months for
Varroa,Nosema, viruses, frames of bees, and brood pattern) were analyzed with mixed effects
models to account for the pseudo-replication in the data. This study did not aim to describe
how dependent variables changed over time or across locations, rather to describe whether
management had an effect on those changes. Thus location, sampling month, and year were
included as random effects in all models. Location, sampling month, and year were also
included as fixed effects to test for interactions with management.
Binomial response variables (e.g., queen events, colony mortality, splits) were fitted to gen-
eral binomial mixed effect models with sampling month, year, and location as random effects.
When comparing variables at a single time point (e.g., at the start of the experiment) general
linear models were used. Analyses of deviance were used to compare goodness of fit in a step-
wise selection procedure to remove non-significant terms. A relative risk analysis was per-
formed to assess the change in risk of annual colony mortality under a BMP regime using the
following equation, and 95% confidence intervals were calculated based on approximation (R
function “riskratio”, package “fmsb”):
RR ¼BMPdead
BMPdead þBMPalive
=Averagedead
Averagedead þAveragealive
Virus data were analyzed by prevalence (% infected) and load (copy numbers). Prevalence
was analyzed with binomial mixed effects models with season, year, and location as random
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effects. An analysis of deviance was used to eliminate non-significant fixed effects in a stepwise
fashion. Viral copy data is not suited to general linear modeling because it is highly skewed
(contains a high proportion of zeros) and has large variance. Viral copy data was log-trans-
formed to better follow a normal distribution, but the high proportion of zeros in the data still
prevented general linear modelling. Rows containing zeros were then removed for each virus,
and log copy numbers were analyzed for significant differences with mixed effects models.
Year and location were included as random effects. An analysis of deviance was used to com-
pare linear models to null models to generate p-values for the effect of management group.
Where significant differences in viral prevalence or copy number were detected, associations
with other variables including mortality, months exceeding 3.0 mites/100 bees, average yearly
Varroa load were checked with separate mixed effects models.
Results
Colony strength (frames of bees, brood pattern, and queen status)
Over the 3 years, 2,244 colony strength inspections were performed. Colony health metrics
were similar between management groups. The 3-year mean colony size in BMP apiaries was
11.48 ±0.19 and in Average apiaries 11.23 ±0.20 frames of bees. Both groups peaked in colony
size in July and were smallest in October. Although frames of bees varied among years (F
2,1982
= 29.0, p<0.01), months (F
5,1982
= 2.97, p= 0.02) and locations (F
6,1982
= 39.6, p<0.001),
there was no difference between management groups (S1 Fig;F
1,1982
= 0.64, p= 0.41).
Brood pattern was also similar between management groups. The 3-year mean brood pat-
tern rating in BMP colonies was 3.29 ±0.03, and Average colonies 3.26 ±0.04. In both groups,
brood pattern was lowest in fall when brood production slowed and less capped brood was
present. Brood pattern varied among years (F
2,1892
= 0.27, p<0.05), months (F
5,1892
= 10.2,
p<0.001), and locations (F
6,1892
= 11.2, p<0.001), but not between management groups (S2
Fig;F
1,1982
= 0.29, p= 0.51).
Queen status data were subdivided into two categories: colonies that experienced a “queen
event” or no “queen event.” A colony was considered to have experienced a queen event if,
during colony inspection, it was found to be queenless, a drone layer, a virgin queen, or no
queen or eggs were seen [40]. Colonies without queen events either had eggs present or the
queen was seen. Over all 3 years, a total of 79 (39.7%) BMP colonies and a total of 83 (41.7%)
Average colonies had queen events. The number of queen events differed among years (F
2,2003
= 3.48, p= 0.05), months (F
5,2003
= 2.70, p= 0.03), and locations (F
6,2003
= 3.69, p<0.01), but
not between management groups (S3 Fig;F
1,2003
= 0.45, p= 0.43). Some colonies were subject
to repeated queen events, where a colony would become queenless and remain queenless for
several subsequent colony inspections. There was no difference in the number of repeated
queen events between management groups (F
1,396
= 0.13, p= 0.71).
Measures of morbidity (Varroa, Nosema, and pathogens)
Varroa. BMP apiaries exhibited lower Varroa loads than Average apiaries across all sam-
pling months (F
1,5,2017
= 23.4, p<0.001). A post hoc test showed no difference between man-
agement groups in October (F
1,238
= 0.90, p= 0.21), indicating a convergence of Varroa
infestation between groups after Average colonies were treated for Varroa in the fall. Varroa
loads did not differ among years (F
2,2017
= 0.01, p= 0.98). Varroa loads differed among sam-
pling months, and were lowest in May and highest in October (F
5,2017
= 9.25, p<0.001). Var-
roa loads differed between management groups (Fig 2;F
1,2017
= 10.8, p<0.001), and there was
a significant interaction between sampling month and management group (F
1,5,2017
= 4.08,
p<0.01). Varroa also differed among locations (F
6,2017
= 8.60, p<0.001), but there was no
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interaction between location and management group (F
1
,
6,2017
= 0.20, p= 0.40) with BMP api-
aries exhibiting lower Varroa loads at each location. The 3-year average Varroa load in BMP
apiaries was 2.67 ±0.14 and 3.62 ±0.18 in Average apiaries (n = 2,244).
There was no difference in Varroa load between management groups at the start of the
experiment (F
1,238
= 2.46, p= 0.12). In the second and third years, Average apiaries started the
season with higher Varroa loads than BMP apiaries in May (1.24 ±0.02 mites/100 bees com-
pared to 0.56 ±0.07, respectively; F
1,238
= 0.93, p= 0.001). This inflated Varroa population per-
sisted through each season, resulting in Average apiaries exceeding 3.0 mites/100 bees 1
sampling month prior to BMP apiaries each year. Additionally, Average apiaries spent more
months above economic threshold: 1.81 ±0.09 compared to 1.34 ±0.08 months in BMP apiar-
ies (F
1,398
= 21.62, p<0.001).
Pathogens. A total of 878 samples were analyzed for pathogens. Prevalence was similar
between management groups, with only Deformed Wing Virus A (DWVA) being significantly
lower in BMP apiaries over all seasons across all years (Fig 3;F
1,869
= 3.38, p<0.001). Fall load
was lower in BMP apiaries for Acute Bee Paralysis Virus (ABPV) (F
1,258
= 6.87, p= 0.01),
DWVA (F
1,258
= 12.89, p<0.001), and DWVB (Fig 3;F
1,258
= 4.30, p<0.05). These metrics
did not differ between BMP and Average apiaries at the start of the experiment (Prevalence:
DWVA F
1,255
= 1.06, p= 0.31; Copy Numbers: DWVA F
1,255
= 2.18, p= 0.09; DWVB F
1,255
=
2.46, p= 0.12; ABPV F
1,255
= 0.03, p= 0.85), indicating that these differences developed after
management practices were employed. Prevalence and loads of Black Queen Cell Virus
(BQCV), Chronic Bee Paralysis Virus (CBPV), Israeli Acute Paralysis Virus (IAPV), Lake
Sinai Virus (LSV), Nosema spp., and Trypanosoma spp. did not differ between management
groups (S4 Fig). For the four viral metrics that significantly differed between BMP and Average
apiaries (prevalence of DWVA and the fall load of ABPV, DWVA, DWVB), separate mixed
effects models were performed to determine if other variables were associated with increased
viral pressure. A colony’s average yearly mite load was positively associated with fall copy
numbers of ABPV, DWVA, and DWVB, as well as the prevalence of DWVA (F
1,867
= 21.5,
p<0.001; F
1,867
= 18.9, p<0.001; F
1,867
= 23.7, p<0.001; F
1,867
= 25.2, p<0.001,
Fig 2. Varroa. Mean Varroa loads +/- standard error for BMP (blue) and Average (orange) apiaries over each
sampling month. This graph represents all 3 years of data together. The red line represents the treatment threshold of
3.0 mites/100 bees. �p<0.05, ��p<0.01.
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respectively). Additionally, the number of months a colony spent above 3.0 mites/100 bees was
also positively associated with these same viral metrics (p<0.05; p<0.001; p<0.01;
p<0.001, respectively).
Nosema. The 3-year average Nosema load in BMP apiaries across all sampling months
was 0.31 ±0.04 million spores/bee and in Average apiaries across all sampling months was
0.32 ±0.04 million spores/bee. Nosema pressure in this experiment was generally low com-
pared to other surveys [41], and averages never exceeded the commonly accepted economic
threshold of 1.0 million spores/bee [42]. Average Nosema load in both experimental treatments
followed typical Nosema seasonal patterns, with loads highest in spring, lowest in summer, and
rising again in fall [41]. Mixed effects models showed differences among locations (F
6,2009
=
7.27, p<0.001) and years (F
2,2009
= 0.92, p= 0.05), but not among sampling month (F
5,2009
=
1.02, p= 0.17) or management groups (S5 Fig;F
1,2009
= 0.03, p= 0.86).
Fig 3. Viruses. Prevalence +/- 95% CI and Average Log Copy Numbers +/- standard error for the 3 viruses which
differed between BMP (blue) and Average (orange) apiaries. These graphs represent all 3 years of data together.
�p<0.05, ���p<0.001.
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Colony outcomes (mortality, honey production, and split production)
Mortality. Total summer mortality for all years in BMP apiaries was 15.2% (95% CI 10.8–
20.8%) and 20.6% (95% CI 15.6–26.6%) in Average apiaries. Summer mortality was highest in
both groups in 2016. Binomial mixed effects models found differences among years (F
2,388
=
4.77, p<0.05) and locations (F
6,388
= 4.42, p<0.01) but no effect of management group on
summer loss (F
1,388
= 1.35, p= 0.13).
Total winter mortality for all years in BMP apiaries was 30.8% (95% CI 24.8–37.6%) and
45.2% (95% CI 38.5–52.2%) in Average apiaries. Binomial mixed effects models found differ-
ences between management groups across all years (F
1,388
= 3.70, p<0.01). Winter loss in
Average apiaries increased each year, while in BMP apiaries winter loss decreased each year.
Post hoc tests of individual years found the main reduction in winter loss in BMP apiaries
occurred in 2018 (F
1,123
= 7.04, p= 0.001). There was no interaction between location and
management group (F
6, 388
= 1.27, p= 0.09), indicating that the effects of management were
similar in all locations.
Total annual mortality for all years in BMP apiaries was 46.0% (95% CI 39.2–53.0%) and
65.8% (95% CI 59.9–72.1%) in Average apiaries. Binomial mixed effects models found no dif-
ferences among locations (F
6,388
= 1.03, p= 0.39) but did find an effect of management across
all years (F
1,388
= 15. 8, p<0.001), with annual loss in BMP apiaries decreasing each year. A
post hoc analysis of individual years found BMP apiaries lost fewer colonies in 2018 (Fig 4;
F
1,123
= 10.94, p<0.01). A relative-risk (RR) analysis of mortality showed that using this set of
best management practices reduced the risk of colony mortality by 30% (RR = 0.70, 95% CI
0.58–0.84, p<0.001).
Separate binomial mixed effects models were used to check for regional differences in the
effect of management on mortality. Considering separate regions is different than considering
separate locations because Maryland represents one region but three locations. Regional analy-
ses were only performed for winter and annual loss, as management had no effect on summer
loss across all regions (F
1,388
= 1.10, p= 0.21). Region did not change the effect of management
on winter (F
4,388
= 1.86, p= 0.18), or annual loss (F
4,388
= 1.36, p= 0.24).
In Minnesota and Oregon, the number of colonies lost in BMP and Average apiaries across
years was similar (S6 Fig), suggesting these management practices may not be as effective in
Fig 4. Colony mortality. Total annual loss +/- 95% CI in each BMP (blue) and Average (orange) apiaries over the
3-year experiment. Summer loss is represented by solid colors, and winter loss by striped colors. Dashed horizontal
lines represent the national total winter loss for backyard beekeepers each year. ��p<0.01.
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northern climates. In Minnesota, a separate set of BMPs was tested in 2018; thus colonies from
that area were not included in the 2018 analysis. Details on the Minnesota best practices and
results will be published separately.
Associations between colony mortality and risk factors that differed between management
groups were also assessed. A colony’s average yearly mite load was positively associated with
colony mortality (p<0.001). Additionally, the number of months a colony was above 3.0
mites/100 bees was positively associated with mortality (p<0.001). Finally, prevalence of
DWVA was positively associated with mortality (p<0.05).
Honey production. In total, 3,699 kg of honey were harvested. Average apiaries produced
a total of 1,541 kg, and BMP apiaries produced a total of 2,158 kg. No honey was harvested in
2016 as colonies had to invest significant energy in wax production in their first year (all colo-
nies were started on foundation). The average honey produced per colony was 21.8 ±4.6 kg
and 27.2 ±7.4 kg in Average and BMP colonies, respectively. Linear mixed effects models
showed no differences between management group in the total honey produced, (F
1,16
= 1.96,
p= 0.23) mean honey produced per colony (F
1,16
= 0.02, p= 0.85) or the proportion of colonies
harvested from (F
1,16
= 1.00, p= 0.22). BMP apiaries did produce 617 kg more honey than
Average apiaries. There was a small number of BMP colonies that produced far above average
honey in 2018, making the total kg produced much higher, but not significantly affecting the
average produced per colony.
Split production. Across all 3 years, BMP apiaries produced 79 splittable colonies and
Average apiaries produced 46. A generalized binomial model found best apiaries produced
more splittable colonies (F
1,388
= 8.14, p<0.01). There was an effect of year (F
2,388
= 6.61,
p<0.05) and separate analyses conducted on each year showed that this trend increased over
time. Best apiaries produce numerically more splits each year, finally producing significantly
more in 2018 (Fig 5;F
1,123
= 4.43, p<0.05).
Discussion
It was hypothesized that BMP apiaries would outperform Average apiaries in colony strength
metrics, productivity, and survival. There were no differences between BMP and Average
Fig 5. Split production. Proportion +/- 95% CI of colonies that survived winter and were splittable the following
spring in BMP (blue) and Average (orange) apiaries. �p<0.05.
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apiaries in colony size, brood pattern, queen status, or Nosema load. However, BMP apiaries
did experience reduced Varroa loads, reaching the threshold of 3.0 mites/100 bees one month
later than Average apiaries and spending fewer months above threshold overall. BMP apiaries
also exhibited reduced infection levels of ABPV, DWVA, and DWVB in the fall. BMP apiaries
produced more splits and experienced lower mortality than Average apiaries.
It was proposed that BMP colonies would reach larger population sizes and exhibit better
queen health and productivity. BMP colonies were started from nucleus colonies or splits,
which in theory should reach larger population sizes my mid-season because of greater estab-
lishment at installation. Further, it was expected that elevated fall Varroa loads in Average api-
aries would reduce adult bee population. It seems that adult bee population during sampling
months was not effected by Varroa load. Rather, adult bee populations may have dwindled
over winter as Varroa loads were left unchecked in late fall, contributing to the elevated rates
of colony mortality in the Average group. The similarity in colony size between management
groups was unexpected but supports the idea that colony size may not represent colony health
or productivity, and that other colony health metrics such as Varroa load and/or viral load
may be better predictors of colony survival [19,43].
The frequency of queen events between management groups was almost identical, indicat-
ing that these management practices did not affect queen issues. Brood pattern, thought to be
an indicator of queen productivity, was also similar between management groups. It is surpris-
ing that Average colonies did not exhibit diminished brood patterns as a result of their elevated
Varroa and viral loads, as these stressors often result in brood not surviving to emergence [44,
45]. However, recent work indicates brood pattern may be a result of some unknown feature
of a colony’s environment as opposed to queen quality or Varroa or viral loads [38].
Regardless of the similarities in colony strength metrics, Varroa loads were significantly
lower in BMP apiaries throughout the season. However, in October, mean Varroa population
appeared to become similar between groups. One potential cause of this occurrence is horizon-
tal transmission of mites among colonies. Horizontal transmission could have occurred if
healthy colonies from BMP apiaries were robbing out weaker colonies in nearby apiaries [46].
It is known that drifting of mites and bees across colonies increases in the fall, concurrent with
an increase in Varroa population [47]. This phenomenon may also help explain why, on occa-
sion after miticide application, BMP apiaries reached Varroa loads above the economic thresh-
old of 3.0 mites/100 bees the following month. It is unlikely that the cause of these high post-
treatment mite loads is ineffective products. All experimenters used products with well dem-
onstrated rates of mite mortality and no documented resistance. Consequently, miticides may
have been effective immediately after application but the intense mite pressures within the
adjacent landscape caused rapid re-infestations before the next sampling event. These re-infes-
tations may have inflated Varroa measurements, so the fact that significant differences were
observed in spite of this shows the effect of management is quite robust. Further, this finding
emphasizes the importance of monitoring for mites as often as possible, especially after imple-
menting control measures to ensure management effectiveness.
Despite comparable mean fall Varroa loads, BMP apiaries exhibited reduced winter mortal-
ity compared to Average apiaries. While post hoc tests revealed the biggest reduction in winter
loss occurred in 2018, there was a significant main effect of management across all years. BMP
apiaries experienced reductions in winter and annual loss in each year of the study, while
Average apiaries experienced increases in winter loss and no change in annual loss. This indi-
cates that if beekeepers adopted BMPs, they are likely to experience reduced winter losses, but
these differences may not be observable until the third year.
The reduction in colony mortality may be because BMP apiaries exceeding 3.0 mites/100
bees in October would receive critical pre-winter Varroa management in November or
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December, which likely reduced mite loads below damaging thresholds. However, weather
conditions did not permit sampling for Varroa late in the season to confirm this supposition.
Still, the Average beekeeping practice of applying a single Varroa treatment in late summer is
insufficient to adequately control mite populations in overwintering colonies.
Another consequence of insufficient Varroa control was demonstrated in the viral results.
Prevalence of most pathogens was similar between management groups; only DWVA was less
prevalent in BMP apiaries. However, the intensity of the Varroa-vectored viruses (ABPV,
DWVA, and DVWB) in the fall was higher in Average apiaries. This suggests that Average col-
onies were more likely to succumb to these infections than BMP apiaries. It is possible that the
elevated mite populations in Average colonies were more effective at transmitting viruses at
higher rates. Models of Varroa-virus interactions support the hypothesis that increased mite
numbers lead to increased viral load in a colony [16,48].
Furthermore, after the first year, Average apiaries began each spring with a higher Varroa
load than BMP apiaries, suggesting that high fall infestations from the prior year persist in a
colony over winter. These Varroa populations remained inflated throughout the season, result-
ing in Average apiaries exceeding 3.0 mites/100 bees one month earlier than BMP apiaries.
The number of months spent above threshold and average Varroa load were positively associ-
ated to viral infection and mortality. Time spent above threshold is therefore a good predictor
of mortality, presumably because it is also related to viral infection. The longer a colony is
above threshold, the higher the risk of experiencing Varroa-vectored viruses and at higher lev-
els. This relationship can likely explain much of the mortality exhibited in Average apiaries.
An example of the effect of time spent above threshold was illustrated in Minnesota in
2017. In the first year, mite levels remained below 3.0 mites/ 100 bees in both treatment groups
until mid-September when all were treated, and by the following spring, 2017, 80% of colonies
in both groups survived. By July 2017, many colonies were above 3.0 mites/ 100 bees, and miti-
cide application was delayed due to long sample processing times. As a consequence, only one
colony from both management groups survived winter. To test whether the results from Min-
nesota were affecting our conclusions, all statistical tests above were performed with Minne-
sota removed from the data set. None of the significant differences detected were changed by
this exclusion, indicating that the results and conclusions put forth in this study are valid
regardless of the outcome in Minnesota. Another set of BMPs designed specifically for Minne-
sota was tested in 2018, and those results will be presented separately. The present study dem-
onstrates the strong effect of time spent above threshold suggests that there is a cumulative
effect of management and its impact on colony health. While a beekeeper can conceivably con-
trol their mite load in the fall after significant mite population build up, the damage incurred
from viruses is much harder to rectify. Although monitoring all colonies every month may
seem an excessive amount of work, monitoring as often as possible is just as critical early in
the season as it is when preparing for winter.
The cumulative effect of management can also be seen over multiple years. The amount of
honey and the number of splits produced in BMP apiaries increased each year. Winter mortal-
ity in Average apiaries increased each year, while in BMP apiaries it decreased, becoming 30
percentage points lower by the third study year. One explanation for these cumulative effects
may be that new BMP colonies were started from nucs or splits in 2017 and 2018. Survey
results demonstrate that nucs and splits are less likely to die than packages [30]. It is also possi-
ble that the brood break resulting from splitting overwintered BMP colonies provided extra
Varroa control by reducing initial mite populations in parent colonies, resulting in reduced
Varroa population growth over entire seasons [24,49]. Another important cumulative factor
is likely the elevated residual mite populations left in Average colonies in the spring of 2017
and 2018. Although mite populations in overwintered Average colonies were low enough to
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avoid immediate colony mortality, the overwintered mite populations negatively impacted col-
ony health for months afterward. The resulting elevated viral loads still increased colony mor-
tality, just over a longer time period. These results indicate that the effects of management and
of colony health stressors occur over longer time periods than previously documented.
Likely as a result of reduced Varroa and viral pressure, the BMP apiaries outperformed
Average apiaries in split production, as well as winter survival, most notably by the third year
of the study. While these results seem to indicate that Varroa is the main driver of colony loss,
and thus Varroa control is the only important BMP, it must be noted that the other BMPs
could have contributed to colony health in subtle ways. Future tests of individual BMPs are
needed to parse out their effects on colony health.
Honey production did not differ between management groups in this study. While BMP
apiaries may have been expected to produce more honey, it is beneficial to confirm that these
BMPs do not result in decreased honey production compared to average beekeeping practice.
BMP apiaries produced 33 more splittable colonies than Average apiaries. This is likely mainly
due to the larger number of BMP colonies remaining alive each spring. When factoring in the
average cost that a backyard beekeeper would pay to replace a dead colony, or the price at
which a beekeeper could sell a nucleus colony, these splits are worth $175 each for a total of
$5,775. Furthermore, BMP practices lowered the relative risk of mortality by 30%. This repre-
sents a substantial reduction in the labor and cost of replacing dead colonies each year, assum-
ing a beekeeper would have to replace 1/3 fewer colonies.
It is important to emphasize that this set of BMPs was specifically designed for backyard
beekeepers. While elements of the results can apply to commercial operations, the logistics of
such aggressive monitoring and management may only be realistic in a backyard setting.
Although BMPs improved colony productivity and reduced mortality in a backyard setting,
after 3 years the total loss in BMP apiaries still exceeded 30%. This is still well above the level of
colony loss that beekeepers report as acceptable (~20% in 2019; [30]). This study demonstrates
that while management can help inhibit some colony health stressors, it cannot prevent all col-
ony mortality. There are environmental factors that management cannot control, such as
other heavily Varroa infested colonies nearby, landscape nutritional quality, and pesticide
exposure [17,19,39,50,51]. Even with an aggressive Varroa-monitoring and control strategy,
BMP apiaries faced significant Varroa pressure and frequently exceeded economic threshold,
likely as a consequence of other heavily infested colonies nearby. Indeed, supplemental feeding
of carbohydrates and protein was often required and protein supplements are not as nutritious
as resources from flowers [52]. Pesticide exposure could have interacted with other colony
health stressors to inhibit the effects of management [53–55]. While management alone cannot
prevent all colony losses, the BMPs tested in this study are meant to act as additional tools for
beekeepers to bolster their colony health. This study focused on aspects of colony health that
beekeepers can control, in an attempt to arm them with practical methods that can be readily
integrated into their current practices to further improve colony health and reduce colony
mortality across the US.
Supporting information
S1 Table. GPS coordinates of study sites.
(DOCX)
S1 Fig. Frames of bees. Mean frames of bee s+/- standard error for BMP (blue) and Average
(orange) apiaries over each sampling month. This graph represents all 3 years of data together.
(DOCX)
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S2 Fig. Brood pattern. Mean brood pattern +/- standard error for BMP (blue) and Average
(orange) apiaries over each sampling month. This graph represents all 3 years of data together.
(DOCX)
S3 Fig. Queen events. Proportion of colonies that had a queen event, and the average number
of queen events colonies had once they became queenless +/- 95% CI in BMP (blue) and Aver-
age (orange) apiaries.
(DOCX)
S4 Fig. Pathogens. Prevalence +/- 95% CI and average log copy numbers +/- standard error
over the season (all years combined) for viruses, Trypanosome spp. and Nosema spp. that did
not significantly differ between BMP (blue) and Average (orange) apiaries.
(DOCX)
S5 Fig. Nosema. Mean Nosema loads +/- standard error for BMP (blue) and Average (orange)
apiaries over each sampling month. This graph represents all 3 years of data together. The red
line represents the recommended economic threshold of 1.0 million spores/ bee.
(DOCX)
S6 Fig. Colony mortality by region. Total annual loss +/- 95% CI in average (orange) and
BMP (blue) apiaries (all years combined) by region. Summer loss is represented by solid col-
ors, and winter loss by striped colors.
(DOCX)
S1 Data.
(XLSX)
S2 Data.
(XLSX)
S3 Data.
(XLSX)
S4 Data.
(XLSX)
Acknowledgments
The authors would like to thank Hollie Dalenberg, Cora Demler, Hannah Lucas, Carolyn
Breece, Max O’Grady, Michael Gladchuk and Sullivan Wilkes for their tireless performing col-
ony inspections and taking samples. We also thank vanEngelsdorp bee lab staff members
Heather Eversole, Rachel Kuipers, and Dan Reynolds for processing samples. Our endless grat-
itude goes out to every beekeeper who has spent their valuable time taking the Loss and Man-
agement Survey.
Author Contributions
Conceptualization: Kelly Kulhanek, Nathalie Steinhauer, James Wilkes, Michaela Wilson,
Marla Spivak, Ramesh R. Sagili, David R. Tarpy, Erin McDermott, Andrew Garavito, Karen
Rennich, Dennis vanEngelsdorp.
Data curation: Kelly Kulhanek, James Wilkes, Michaela Wilson, Marla Spivak, Ramesh R.
Sagili, David R. Tarpy, Erin McDermott, Andrew Garavito.
Formal analysis: Kelly Kulhanek, Nathalie Steinhauer, David R. Tarpy, Erin McDermott.
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Funding acquisition: Nathalie Steinhauer, Karen Rennich, Dennis vanEngelsdorp.
Investigation: Kelly Kulhanek.
Methodology: Nathalie Steinhauer, Andrew Garavito, Karen Rennich, Dennis vanEngelsdorp.
Project administration: Kelly Kulhanek, Karen Rennich, Dennis vanEngelsdorp.
Supervision: Kelly Kulhanek, Dennis vanEngelsdorp.
Validation: Kelly Kulhanek.
Visualization: Kelly Kulhanek.
Writing – original draft: Kelly Kulhanek.
Writing – review & editing: Kelly Kulhanek, Nathalie Steinhauer, James Wilkes, Michaela
Wilson, Marla Spivak, Ramesh R. Sagili, David R. Tarpy, Erin McDermott, Andrew Gara-
vito, Karen Rennich, Dennis vanEngelsdorp.
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