The major role of spring trees in Ohio honey production for bees located in high and low agricultural intensity

Abstract

Nectar is the resource that sustains honey bee colonies through periods of dearth and provides the surplus honey beekeepers harvest for human consumption. While extensive information is available for plants that honey bees visit for pollen and nectar, we lack knowledge on which nectars are stored long-term as honey for harvest and support of colonies through winter. Here, we used citizen science methods and pollen metabarcoding analysis to identify the plants contributing most to honey samples harvested by beekeepers from apiaries with variable intensities of surrounding agriculture. A total of 36 samples were collected from 36 apiaries in Ohio in 2019, with an average of 3 plant genera detected per sample. We found similarity in honey samples collected from all apiaries, regardless of the proportion of agricultural land within a 2-km foraging range, with substantial amounts of honey stored from spring trees, including Salix (willow) and Prunus (cherry). This result suggests the importance of early-season resources regardless of agricultural intensity in the surrounding landscape. This study contributes to a body of work aiming to identify the nectars making it to long-term honey storage and those that are being consumed within the hive shortly after collection.

Full text

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Vol.:(0123456789)
Original article
Apidologie (2023) 54:37
https://doi.org/10.1007/s13592-023-01016-w
© The Author(s), 2023, 2023
The major role ofspring trees inOhio honey production
forbees located inhigh andlow agricultural intensity
HarperMcMinn‑Sauder1 , Chia-HuaLin1, Tylereaton1,and ReedJohnSon1
1Department ofEntomology, The Ohio State University, Columbus, OH, USA
Received 10 February 2023 – Revised 5 June 2023 – Accepted 6 June 2023
Abstract – Nectar is the resource that sustains honey bee colonies through periods of dearth and provides the
surplus honey beekeepers harvest for human consumption. While extensive information is available for plants
that honey bees visit for pollen and nectar, we lack knowledge on which nectars are stored long-term as honey for
harvest and support of colonies through winter. Here, we used citizen science methods and pollen metabarcoding
analysis to identify the plants contributing most to honey samples harvested by beekeepers from apiaries with
variable intensities of surrounding agriculture. A total of 36 samples were collected from 36 apiaries in Ohio
in 2019, with an average of 3 plant genera detected per sample. We found similarity in honey samples collected
from all apiaries, regardless of the proportion of agricultural land within a 2-km foraging range, with substantial
amounts of honey stored from spring trees, including Salix (willow) and Prunus (cherry). This result suggests
the importance of early-season resources regardless of agricultural intensity in the surrounding landscape. This
study contributes to a body of work aiming to identify the nectars making it to long-term honey storage and
those that are being consumed within the hive shortly after collection.
Agriculture / Apis mellifera / Pollen metabarcoding / Prunus / Salix
1. INTRODUCTION
Nectar is the source of carbohydrates for
honey bee colonies; it fuels activities including
foraging, wax production, and thermoregulation
(Haydak 1970; Seeley 1992). Bees source nectar
from flowers on the landscape, making decisions
based on landscape factors including floral prox-
imity and abundance and quality of the reward
(e.g., nectar volume and sugar concentration)
(Nicolson & Thornburg 2007; Corbet etal. 1984;
Goulson 1999). Nectar that is brought back to a
honey bee colony is either consumed immedi-
ately or placed in cells, dehydrated, capped, and
stored as honey (Park 1925; Seeley 1989; Eyer
etal. 2016). Honey bee foragers collect avail-
able nectar, often more than the immediate needs
of the colony, leading to honey hoarding within
the hive (Fewell & Winston 1996; Rinderer &
Baxter 1978). Excess honey is stored for later
consumption during periods when weather con-
ditions are unsuitable for flying or when there is
a nectar dearth. Beekeepers are also dependent
on robust storage of honey by bees to harvest for
human consumption.
During the foraging season, there is a lim-
ited window of time in which bees collect large
quantities of nectar, when flowers that provide
the bulk of seasonal nectar are blooming in abun-
dance. However, the timing is variable depend-
ing on region and climate (Seeley & Visscher
1985; McLellan 1977; Bayir & Albayrak 2016).
Many beekeepers extract this excess and provide
Corresponding author: H.McMinn-Sauder, hmcminn@
clemson.edu
Communicated by Cedric Alaux.

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37 Page 2 of 10
supplemental carbohydrates to make up for honey
harvested in order to augment honey stores for
winter. In temperate climates, a honey bee colony
can consume over 20kg of honey over winter,
and colonies depend on the energy in stored
honey for thermoregulation in the winter clus-
ter (Seeley & Visscher 1985). Starvation is fre-
quently cited for winter colony failure by bee-
keepers (Steinhauer etal. 2021), and fall weight
has been shown to strongly predict colony winter
survival (Döke etal. 2019). Identifying the flow-
ers that are contributing most to colony honey
storage is important for beekeeper livelihood and
colony winter survival.
Extensive surveys have been done to docu-
ment pollen and nectar plants used by honey bees
worldwide (Crane 1983; Bryant & Jones 2001);
however, we lack knowledge about which nectar
resources contribute most to honey stores which
are most important for honey harvest and winter
survival of colonies. Most existing studies have
relied on observations of floral visitation, pollen
collection, and targeted nectar sampling (Carreck
& Williams 2002; Liolios etal. 2015; Park & Nieh
2017; Requier etal. 2015). Honey bees preferen-
tially consume low-concentration sugar solutions
immediately after collection while nectar with
higher concentrations are stored as honey (Eyer
etal. 2016), though honey storage also depends
on nectar availability (McLellan 1977). Identify-
ing the plants that are key for colony honey pro-
duction is essential for supporting healthy honey
bees. Additionally, assessing the role of land-
scape, including land in agricultural production,
may contribute to the understanding of regional
differences in floral availability or composition.
Here, we aim to determine the plants contrib-
uting most to Ohio honey production and identify
differences in honey composition based on sur-
rounding agricultural intensity. We used citizen
science methods by collaborating with volunteer
beekeepers in Ohio who collected honey from
36 apiaries across the state. Pollen metabarcod-
ing methods were used to identify the taxonomic
composition of honey samples and indicate the
proportional contribution of plant taxa that were
detected. A two-marker approach was used to
increase our confidence in proportional values
of detected plant taxa (Richardson etal. 2015).
Landscape was analyzed around each apiary to
determine the proportion of corn and soybean
fields in a 2-km radius. This value was used to
determine agricultural intensity around each
apiary, classifying apiaries as high (> 50%),
medium (20–50%), or low (< 20%). This met-
ric was compared with honey sample composi-
tion to determine differences in honey related
to agricultural intensity. We hypothesize that
honey collected from apiaries located in areas
with higher agricultural intensity will contain
more pollen from soybeans (Glycine max). Pre-
vious research has identified soybeans as a major
nectar resource for honey bees (Lin etal. 2022;
McMinn-Sauder 2022; St. Clair etal. 2020), sug-
gesting that it should be a large component of
honey collected from highly agricultural sites.
This work will help establish the plants that are
contributing most to honey production in Ohio,
with potential application to other regions with
similar agricultural environments throughout the
Midwestern United States.
2. MATERIALS ANDMETHODS
2.1. Honey identification
Honey samples (50mL) and apiary location
information were solicited from beekeepers at the
annual Ohio State Beekeepers’ Association 2019
meeting in Plain City, Ohio, along with apiary
location information (Fig.1) (IRB study number:
2019E1019, Honey sources in an agricultural
landscape and the impact of soybean fields on
honey production, 2019). To be included in the
study, honey must have been collected in 2019
from apiaries registered with the Ohio Depart-
ment of Agriculture and with an identifiable api-
ary location included in the survey response. A
total of 49 samples were submitted, and 36 sam-
ples fit the study criteria, each collected from a
different apiary site. The dates for honey collec-
tion ranged from May through October, with most
samples collected between July and September.
Samples were stored in air-tight containers at
room temperature until processing.

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Honey samples were heated in a warm water
bath at 65°C to eliminate crystallization and
reduce viscosity. Pollen was isolated from honey
using dilution and centrifugation. First, 3.5g
of honey were added to a 50-mL plastic coni-
cal centrifuge tube and dissolved in 3.5mL of
warm, DI water. The solution was further diluted
in 35mL of 95% ethanol and centrifuged at 2849
RCF for 3min. The supernatant was poured off
and 0.5mL of 95% ethanol was added to the tube
to resuspend the pellet with agitation. Then, the
mixture was transferred to a screw cap microcen-
trifuge tube (Fisherbrand Free-Standing Micro-
centrifuge Tubes; Fisher Scientific, Hampton,
NH, USA), and centrifuged again at 1503 RCF.
The supernatant was poured off, and residual
ethanol was evaporated under a fume hood. To
disrupt the pollen coat, 0.5mL of 0.7-mm diam-
eter zirconia beads (Fisher Scientific, Hampton,
NH, USA) and 200µL of DI water were then
added to each microcentrifuge tube and agitated
vigorously for three minutes with a Mini-Bead-
Beater-16 (BioSpec Products, Bartlesville, OK,
USA). Samples were then prepared for a 3-step
PCR protocol, the product of each step serving
as the template for the subsequent step (PCR
conditions detailed in TableS1) (Richardson
etal. 2015, 2019). Universal primers for plant
ITS2 and rbcL (Kress & Erickson 2007; Chen
etal. 2010; Richardson etal. 2015) were used
for PCR 1 with 1µL of pollen homogenate serv-
ing as the template. In PCR 2 and 3, 1µL of the
Fig. 1 Map of study apiaries. Honey was collected in 2019 from 36 apiaries in Ohio, USA. Honey was brought to the
Ohio State Beekeepers’ Association fall meeting and distributed to the Ohio State University bee lab. The Cropscape–
cropland data layer overlays the study map with corn (yellow) and soybeans (green) representing the dominant crops in
our study region

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37 Page 4 of 10
previous reaction served as the template, with a
linking sequence added to primers in PCR 2 and
unique Illumina index sequences added in PCR
3. Gel electrophoresis was conducted on a subset
of samples following PCR 3 for sample quality
control. Samples were then combined and puri-
fied with a SequalPrep Normalization Plate kit
(Thermo Fisher Scientific, Waltham, MA, USA).
Libraries were sequenced at the Molecular and
Cellular Imaging Center in Wooster, Ohio on a
15 million read, paired end 300 base-pair stand-
ard Illumina MiSeq Flow Cell.
The MetaClassifier protocol for taxonomic
identification (Sponsler etal. 2020) was used to
calculate proportional abundances of plant taxa
detected in each honey sample using computing
resources provided by the Ohio Supercomputer
Center. Sequence paired-end reads were first
merged and converted into FASTA format. Each
sample sequence was then compared to library
databases curated with the MetaCurator method
(Richardson etal. 2020) and Taxonomizr (https://
CRAN.R- proje ct. org/ packa ge= taxonomizr)
for each locus to identify sample taxonomy at
the genus level. Sample alignment parameters
were assigned as 92.5% sequence identity for
ITS2 and 96% sequence identity for rbcL and
sample coverage of 80% for both markers. Since
each marker exhibits biases towards detection
of certain taxa, a median value for each taxon
was used. Proportional abundances were used
for detected plant taxa by calculating the pro-
portion of reads per marker for rbcL and ITS2
and calculating the median of those values. Sam-
ple taxonomy was visualized using the ggplot2
package (Wickham etal.2016; R Development
Core Team 2022) in R studio (version 4.0.3).
Genera detected at 1% proportional abundance
or greater were retained for further analysis.
Sequences are available in GenBank (accession:
PRJNA924028).
Honey sample richness and evenness and sam-
ple diversity, calculated with the Shannon–Wiener
diversity index, were assessed. Diversity values
were normally distributed and analyzed using a
generalized linear model with agricultural intensity
as the independent model effect and plant diversity
as the dependent variable. Sample evenness and
richness were nonnormally distributed and ana-
lyzed using a Kruskal–Wallis test and Wilcoxon
rank sums test. Nonmetric multidimensional scal-
ing (NMDS) was used to visualize dissimilarity
between the floral composition of samples col-
lected from apiaries with high, medium, and low
surrounding agricultural intensity (Minchin 1987).
We used the Bray–Curtis index distance metric,
2-dimensional scaling (k = 2), and defined the
upper limit for stress at 0.2. The metaMDS (vegan,
version 1.8–6) and ggplot2 R packages were used
to perform the analysis and visualization. A per-
mutational multivariate analysis of variance was
performed using the adonis function in the vegan
package (Oksanen etal. 2020) to assess differences
in composition of honey samples collected from
sites with variable surrounding agriculture.
2.2. Landscape analysis
The landscape surrounding each apiary was
characterized at a 2-km radius, as colonies typi-
cally forage within 2km during the summer
(Couvillon etal.2015). The amount of land
in agriculture, development, forest, pasture,
and roadside was quantified using the USDA
CropScape 2019 Cropland Data Layer (USDA-
NASS-RDD-2015). Apiaries were grouped by
agricultural intensity for analysis, calculated
by the proportion of row crop (primarily corn,
soybean, and wheat) agriculture in the surround-
ing 2-km radius. Apiaries with greater than 50%
agriculture were classified as high agriculture,
those with 20–50% surrounding agriculture were
classified as medium agriculture, and apiaries
with less than 20% surrounding agriculture were
classified as low agriculture.
3. RESULTS
A total of 45 honey samples were collected,
yielding an average of 258,715 raw reads per
sample. Nine samples did not contain location
information, excluding them from further analy-
sis, leaving a total of 36 samples. Twelve samples
were collected from apiaries with high (> 50%)

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surrounding agriculture, 10 samples were col-
lected from apiaries with medium (20–50%)
surrounding agriculture, and 14 samples were
collected from apiaries with low (< 20%) sur-
rounding agriculture.
Twenty-four genera were detected by each
the rbcL and ITS2 marker, with 17 genera
in rbcL and 16 genera in ITS2 above the 1%
threshold (Tables S3 and S4). When median val-
ues were calculated for both markers, eighteen
plant genera were detected at greater than 1%
proportional abundance in samples, and 14 gen-
era were detected at proportional abundances
greater than 5% (TableS2). Salix (willow)
was the genus detected at highest proportional
abundance in averaged samples taken from all
levels of agricultural intensity (Fig.2). In addi-
tion, Malus (apple) and Prunus (cherry) were
detected in high proportional abundances in
samples collected from apiaries with high sur-
rounding agriculture. In honey collected from
apiaries with medium surrounding agriculture,
Prunus, Pyrus (pear), and Trifolium (clover)
were detected in proportional abundances
greater than 10%. Samples collected from api-
aries with low surrounding agriculture were
largely (> 10%) composed of Prunus and Tri-
folium. Results of the NMDS ordination show
high similarity in genus-level nectar composi-
tion of honey samples collected from apiaries
Fig. 2 Composition of honey samples collected from beekeepers in 2019. Pollen metabarcoding analysis was used
to identify the proportional abundance of nectar from plant species contributing to samples. Average proportional
abundance of combined reads from rbcL and ITS2 markers were used, as each marker displays individual biases for
detection of different taxa. Study apiaries are classified as high (> 50%), medium (20–50%), or low (< 20%) sur-
rounding agriculture

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37 Page 6 of 10
with high, medium, and low surrounding agri-
culture (Fig.3); however, the permutational
multivariate analysis indicates differences in the
taxonomic composition of honey samples col-
lected from sites with low, moderate, and high
surrounding agriculture (p < 0.001, F = 35.3,
r2 = 0.68, df = 1). No significant effect of agri-
cultural intensity was detected on the diversity,
richness, or evenness of plants contributing to
honey (p > 0.05) (TablesI and II).
4. DISCUSSION
Honey harvested by beekeepers over the
summer of 2019 was predominantly composed
of spring blooming trees, largely Salix (willow)
and Prunus (cherry). These results imply that
soybean and clover nectar either were not col-
lected by bees in 2019 or that nectar collected
from these plants was consumed by the colonies
prior to honey harvest. This result differs from
findings of previous studies conducted in Ohio,
which detected large quantities of soybean in
summer honey and nectar. However, this may
be due to differences in methods between the
studies. A previous study found that Glycine
(soybeans) and Trifolium (clover) were major
components of nectar collected by colonies in
highly agricultural areas during summer months
(McMinn-Sauder 2022). However, these samples
were collected from uncapped nectar rather than
end-of-season honey. Uncapped nectar is likely
to represent recent foraging efforts by the colony.
The composition of nectar collected from cells
and extracted honey has shown to differ in plant
Fig. 3 Nonmetric multidimensional scaling to assess similarity of sample composition with variable agriculture.
Samples were collected from apiaries with high (> 50%, red), medium (20–50%, yellow), or low (< 20%, blue) sur-
rounding agriculture (K = 2, stress = 0.18)

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composition (Layek etal. 2020). Honey collected
in 2014 from colonies in agricultural landscapes
consistently included soybean pollen (Lin etal.
2022). Differences in taxonomic abundance
could be explained in part by method selection
(Corby-Harris etal. 2018), with soybean pol-
len potentially being underrepresented in the
metabarcoding analysis relative to microscopic
analysis. The high abundance of Salix detected in
honey samples is consistent with previous stud-
ies, identifying it as a key nectar resource for
honey bees in spring, and a pollen resource for
various specialist bees (McMinn-Sauder etal.
2022; Jones etal. 2022; de Vere etal. 2017;
Richardson etal. 2015; Ostaff etal. 2015).
This region routinely experiences a nectar dearth
period during August, resulting in colony weight
loss (Couvillon etal. 2015; McMinn-Sauder etal.,
unpubl. data), following soybean bloom. It is possi-
ble that soybean nectar was collected by these colo-
nies and consumed by bees in the hive either imme-
diately, or during this dearth period and, therefore,
absent in most of the stored honey. While soybean
was detected in high abundance in one sample, it is
possible that soybean pollen was present inother
samples in trace amounts that fell below the 1%
threshold for inclusion in analysis.
This study contributes to a body of research
identifying which resources are consumed
immediately and which are capped and stored
for later consumptionand for honey harvest by
beekeepers (Park 1925; Seeley 1989; Eyer etal.
2016). The abundance of spring-collected nec-
tar in summer honey demonstrates that much of
the honey harvested throughout the summer and
fall of 2019 was composed of spring-blooming
plants. The absence of abundant pollen from
plants flowering in summer suggests that bees
did not visit them for nectar, or the nectar was
consumed in the hive prior to honey extrac-
tion. This finding suggests that spring nectar
resources may be important for colony survival
in the subsequent winter, as the honey present at
the end of the season sustains colonies through
to the following spring.
An alternative explanation for this finding is
related to colony management. Samples in this
study were collected largely by hobbyist bee-
keepers. It is possible that inexperienced bee-
keepers did not provide sufficient space thereby
limiting the quantity of nectar collected. If the
available space was filled with abundant spring-
collected nectar, there may not have been room
for colonies to store resources from the summer
soybean and clover nectar flow. Another poten-
tial explanation is the erratic weather conditions
experienced in 2019 that resulted in late planting
and a relatively poor soybean harvest that year,
suggesting that soybean resource availability
may have been different than in other years. It is
Table I. Diversity, richness, and evenness of beekeeper collected honey. Honey sample diversity, richness,
and evenness were calculated. Samples were categorized as high (> 50%), medium (20–50%), or low (< 20%)
by the intensity of agriculture surrounding the study apiary
Agriculture Samples Richness Richness Evenness Evenness Shannon
diversity
index
Shannon
diversity
index
Mean STDEV Mean STDEV Mean STDEV
High 12 3.58 1.68 0.66 0.27 0.83 0.46
Medium 10 3.2 1.03 0.54 0.26 0.67 0.37
Low 14 3 1.57 0.56 0.31 0.65 0.46
Table II. Kruskal–Wallis test summary for honey
evenness and richness and summary of GLM fit for
sample diversity
Factor ChiSquare df Prob > ChiSq
Evenness 1.51 2 0.47
Richness 1.05 2 0.59
Diversity 1.32 2 0.52

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37 Page 8 of 10
also important to note the limitations of a single
year of data collection. Honey floral composition
collected from different years may vary due to
annual differences in weather and floral avail-
ability. To better understand the resources most
important for Ohio honey production, additional
data are necessary.
A significant difference in genus-level nectar
composition was found for colonies surrounded
by high, medium, and low-intensity agriculture
using a PERMANOVA test (p < 0.001). While
the same plants were detected as major com-
ponents of honey samples, differences related
to agricultural intensity were present in minor
nectar resources.
Using citizen science methods, we collected
honey from a broad range of locations with dif-
ferent levels of agricultural intensity across Ohio.
We worked with Ohio beekeepers to identify the
plants contributing most to their honey. Though
there was a wide range of sampling dates and
levels of surrounding agriculture, the floral com-
position of honey was strikingly similar and was
largely composed of nectar from spring trees,
primarily willow, and cherry. In addition, we
found that soybeans played a relatively small role
in seasonal honey production in 2019. Access to
these early-season resources may be crucial for
maintaining colony strength during periods of
resource scarcity. This highlights the importance
of spring trees for seasonal colony performance,
emphasizing the role of trees in the nectar diet
of Ohio honey bees.
ACKNOWLEDGEMENTS
The authors would like to thank the Ohio beekeepers
who donated honey samples for this project, as well as
the Ohio State Beekeepers’ Association for allowing us
to solicit samples at their annual meeting. Thank you to
Isabel Nazarian for your work in processing these honey
samples. We would like to thank the Ohio Supercomputer
Center for their computational resources.
AUTHOR CONTRIBUTIONS
HMS, CHL, and RJ designed experiments. TE and HMS
performed experiments and analysis. HMS, CHL, TE, and
RJ wrote and revised the manuscript. All authors read and
approved the final manuscript.
FUNDING
This work was supported by the US Department of
Agriculture National Institute of Food and Agriculture
(#2019–67013-29297 and #2022–67019-36437) and state
and federal funds appropriated to the Ohio Agricultural
Research and Development Center (OHO01355-MRF and
OHO01277).
DATA AVAILABILITY
The datasets generated during the current study are avail-
able in the GenBank repository (accession: PRJNA924028).
CODE AVAILABILITY
The code used to analyze data in the current study is
available in the GitHub repository (https:// github. com/
ewafu la/ MetaC lassi fier).
DECLARATIONS
Ethics approval This is not applicable.
Consent to participate This is not applicable.
Consent for publication This is not applicable.
Competing interests The authors declare no competing
interests.
Open Access This article is licensed under a Creative
Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and repro-
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provide a link to the Creative Commons licence, and
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