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Honeybees’ foraging choices
for nectar and pollen revealed
by DNA metabarcoding
Matti Leponiemi
1, Dalial Freitak
1, Miguel Moreno‑Torres
2, Eva‑Maria Pferschy‑Wenzig
3,
Antoine Becker‑Scarpitta
4, Mikko Tiusanen
5,7, Eero J. Vesterinen
6 & Helena Wirta
7*
Honeybees are the most widespread managed pollinators of our food crops, and a crucial part of
their well‑being is a suitable diet. Yet, we do not know how they choose owers to collect nectar or
pollen from. Here we studied forty‑three honeybee colonies in six apiaries over a summer, identifying
the oral origins of honey and hive‑stored pollen samples by DNA‑metabarcoding. We recorded the
available owering plants and analyzed the specialized metabolites in honey. Overall, we nd that
honeybees use mostly the same plants for both nectar and pollen, yet per colony less than half of the
plant genera are used for both nectar and pollen at a time. Across samples, on average fewer plant
genera were used for pollen, but the composition was more variable among samples, suggesting
higher selectivity for pollen sources. Of the available owering plants, honeybees used only a fraction
for either nectar or pollen foraging. The time of summer guided the plant choices the most, and the
location impacted both the plants selected and the specialized metabolite composition in honey.
Thus, honeybees are selective for both nectar and pollen, implicating a need of a wide variety of oral
resources to choose an optimal diet from.
Most of the wild and cultivated owering plant species depend on animal pollinators1, 2. Honeybees are the most
abundant pollinators in the world2 and are kept by humans for their production of honey as well as for pollination
they provide. Because of their high abundancy in a variety of environments, honeybees are important pollinators
for crop plants3. At the same time the availability of proper nutrition contributes to the health of honeybees. A
variety of food sources is benecial for bee health4, yet in modern agricultural environments monocultures are
common, which might compromise proper nutrition for the bees5. Furthermore, it has been shown that bees
stressed by pesticides prefer more variable food6, which suggests that a diverse nutrition is not only important
for normal functioning, but could also promote bee health in times of stress.
Honeybees collect nectar and pollen to ll dierent nutritional needs, those of carbohydrates and those of
proteins and lipids. Nectar mostly consists of monosaccharide sugars, namely glucose and fructose. Nectar is
used to support the energetic needs of the colony, such as the costly ight of the foragers and thermoregulation of
the hive7. Honeybees commonly select plants for foraging nectar based on the sugar concentration of the nectar8
and the total sugar content within and between plant species can vary extremely, from 6.3 to 85%9. e amount of
proteins and lipids as well as the composition of dierent amino and fatty acids also vary greatly between pollen
from dierent plant species. Protein content in bee collected pollen varies from 1.5 to 48.4% and lipid content
from 1.2 to 24.6%10. e pollen preferences of the foragers are determined by the requirements of the colony;
preferred pollen sources are inuenced more by the composition of fatty and amino acids of the pollen than by
the total protein content11–13. Like nectar, foraged pollen is stored in the hive, but it is mainly used in feeding the
developing brood, while adults may survive longer without pollen7.
Not only are nectar and pollen used for dierent purposes, but their foraging is also performed by dierent
sets of individuals, as individual forager bees typically only forage either nectar or pollen14, 15. Foraging nectar
and pollen are thus separate processes, also from the perspective of the plants that produce them. As nectar and
pollen dier in terms of nutrients they contain, the nectar and pollen reward plants oer may be very dierent
OPEN
1Institute of Biology, Karl-Franzen University of Graz, Universitätsplatz 2, 8010 Graz, Austria. 2Institute of
Environmental Systems Science, Karl-Franzens-Universität Graz, Merangasse 18/I, 8010 Graz, Austria. 3Institute
of Pharmaceutical Sciences, Pharmacognosy, University of Graz, Beethovenstraße 8, Graz, Austria. 4CIRAD,
UMR PVBMT, 97410 Saint Pierre, La Réunion, France. 5Department of Evolutionary Biology and Environmental
Studies, University of Zurich, Zürich, Switzerland. 6Department of Biology, University of Turku, Vesilinnantie 5,
Turku, Finland. 7Department of Agricultural Sciences, University of Helsinki, Latokartanonkaari 5, P.O. Box 27,
00014 Helsinki, Finland. *email: helena.wirta@helsinki.
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across species. Some plants, such as wind pollinated species, do not produce nectar at all, but may still act as a
source for pollen16. e plant sources may then dier in quantity and quality between the oered nectar and
pollen reward. When also considering the fact that these two resources are used for dierent purposes and are
foraged by dierent individuals, it would be expected that dierent plants are used for nectar and pollen forag-
ing. Yet most research examines the selection of one resource type at a time (e.g., pollen17 or nectar18). However,
some studies have looked how bees select plants for nectar and pollen, yet with either a very restricted number of
colonies19 or at single time point, in the spring19, 20. As the availability of plants changes during the summer due
to dierent owering times, changes in foraging are also likely to happen throughout the season19. us, there
is a lack of thorough understanding of whether honeybees select dierent plants for the two types of resources,
and how selective they are for each resource type from the available owers, as honeybees are known to utilize
only a part of the available resources21.
Previous studies on foraging choices of honeybees have been based on morphological identication of pol-
len grains in honey (melissopalynology)22. Pollen from the nectar source plant may attach to the forager bee
and later end up in the honeycomb. Today, DNA-based methods allow extraction and more precise taxonomic
identication of the plant origin of the honey, not only from pollen but from any plant tissue17. DNA can also
be readily extracted from hive-stored pollen, beebread23. Bees prepare beebread from pollen by mixing it with
glandular secretions and small amounts of nectar. Although some natural cross-contamination of these resources
is inevitable, we can use the DNA in honey to infer the source plants of nectar, while the DNA in beebread can
be used to infer the source plants of pollen17, 22–25.
In addition to the nutrients in nectar and pollen, plants produce a wide variety of specialized metabolites that
also end up in plant provided resources, like nectar. Some specialized metabolites may aect pollinator behavior
and improve pollination success of the owering species26, but some of them also act as deterrents of pollinators,
creating a paradoxical situation, as plants also need to attract pollinators27. It is yet somewhat unclear why deter-
rent compounds end up in nectar, although they could potentially protect the nectar from unwanted visitors or
control microbial activity28. Overall, the role of plant specialized metabolites in honeybee foraging choices and
their eects on honeybee colonies is unresolved29.
Here we use DNA metabarcoding to identify the plant origin of both honey and beebread storages of honeybee
colonies to examine the foraging choices for these two resources simultaneously, at dierent times of the summer
and in dierent locations. By comparing the plants found to be used to the surrounding ower availability, we
determine how selective honeybees are when foraging for nectar and for pollen. We also examine what types of
specialized metabolites are found in the honey samples using ultra-high-performance liquid chromatography-
high resolution mass spectrometry (UHPLC-HRMS). Further, we examine how the foraging choices change
across the owering season for both nectar and pollen and assess how time and location aect the foraging
choices as well as the specialized metabolite composition in the honey.
Results
Summary of methods. We collected honey and beebread samples from 43 beehives, located in six apiaries
within three areas at the beginning of June, July, and August in 2021. From the time of foraging to our sampling,
we assume a similar amount of time for both sample types, as the turnover rate for beebread is about 2weeks
during the summer months30, and as it takes 3–10 days for bees to process nectar into honey31. e three areas
were located in southwestern Finland, approximately ten kilometers apart from each other, thus further apart
than bees typically y for foraging. To identify the plant taxa in the samples we used DNA metabarcoding based
on the gene internal transcribed spacer 2 (ITS2). We also mapped the natural owering plants around the three
apiary areas at the same time points as the sampling, to compare the plants available to the ones detected in the
samples. We collected additional honey samples in August to examine the specialized metabolites in the honey
with UHPLC-HRMS, using two purication methods to achieve wide coverage of compounds.
e analyses were conducted at the taxonomic level of genus using relative read abundances of genera32 and
presence-absence data to ensure the robustness of the results and interpretation, as the DNA metabarcoding
method can generate some biases in relative read abundances among taxa33. Beebread and honey composi-
tion data were graphically compared using nonmetric multidimensional scaling (NMDS), and themultivariate
homogeneity of group dispersions was tested using the PERMDISP procedure.en, we tested the change in
plant composition between the sample types using a permutational analysis of variance (PERMANOVA) with
Hellinger distances. As a complementary honey and beebread composition analysis, we identied indicator
species that characterized each sample type using the IndVal procedure. e richness of genera within a hive in
the two sample types was compared with linear mixed models and the number of shared genera with binomial
generalized linear mixed model. To assess the proportion of available owering plants used for nectar and pollen
foraging, we compared owering plants mapped in the surroundings to the ones detected in the samples using
euler-diagrams. To nd the factors aecting the foraging choices we again applied NMDS’s and used redundancy
analysis (RDA) to assess the inuential variables, with Hellinger-transformed values. To nd whether apiary area
also has an eect on the composition of specialized metabolites in honey, we used multivariate data analysis.
e most abundant specialized metabolites discriminating the three apiary areas as well as the most abundant
ones commonly occurring in all three apiary areas were annotated by comparing their mass spectrometry (MS)
data with literature and databases.
Taxa detected in honey and beebread. Aer bioinformatic ltering, there were 2,592,723 sequencing
reads from the honey samples and 1,724,302 from the beebread samples. Aer ltering out samples with fewer
than 5,000 reads (n = 5), we had 97 honey samples for analyses, with on average 26,656 (SD ± 10,877) reads per
sample. Of these 39 were collected in June, 30 in July and 28 in August. For beebread we had 87 samples aer the
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ltering with on average 19,769 (SD ± 7,249) reads per sample. 36, 28 and 23 of these were collected in June, July
and August, respectively. 95.1% of honey and 91.1% of beebread reads were assigned to a genus. As the propor-
tions assigned to species were far lower (14.9% for honey and 21.8% for beebread), in order to use most of the
data available, we use the genus level assignments for all analyses.
e total number of dierent genera detected in either honey or beebread was 67 (57 in honey, 61 in bee-
bread). In honey samples we found 33, 33 and 42 genera, while in beebread samples we found 39, 20 and 29
genera in June, July and August, respectively (Supplementary TableS1). Over the whole season, almost half of
the sequencing reads in honey came from two genera, 32.8% originating from Brassica and 17.4% from Rubus.
In beebread, most reads also originated from two genera, 27.1% from Brassica and 22.1% from Sorbus. e rela-
tive abundances of genera in the samples at each time point can be found in supplementary tableS1. In terms
of plant diversity specic to the apiary areas, the number of genera found in honey were 17, 25 and 29 in the
apiary area A-B in June, July and August, respectively. For apiary area C–D there were 23, 15 and 20 genera, and
for E–F 26, 23 and 29 genera, in June, July and August. As for beebread, the number of genera in the area A–B
was 24, 15 and 21, for the area C–D 28, 10 and 15, and for the area E–F 28, 10 and 20 genera, in June, July and
August, respectively.
Shared and distinct plant genera found in honey and beebread. Out of the 67 total genera, 51
were detected in both honey and beebread (Supplementary TableS1). Six genera were found only in honey
and ten only in beebread. Regardless of the partial overlap in plant genera composition for both honey and
beebread, as shown by the ordination (Fig.1), the plant communities in honey and beebread samples dier
signicantly in their dispersion (PERMDISP, F = 12.575, p < 0.001, honey = 0.726, beebread = 0.811). We found
a signicant dierence in plant genera composition between honey and beebread (PERMANOVA, F = 24.961,
p = 0.001, R2 = 0.070), although this result is at least partly due to the dierence in group dispersions shown by
the multivariate homogeneity of group dispersion analysis (Supplementary Fig.S1).
To assess how the plant choices change through time in colonies, we analyzed the number of genera in samples
of honey and beebread from individual hives. Honey samples had a larger number of genera in August than at
earlier time points (Fig.2A, Supplementary TableS2) while beebread samples had the lowest number of genera
in July in comparison to the other time points (Fig.2B, Supplementary TableS3). Overall, the number of genera
was signicantly higher in honey samples (mean 10.16) than beebread samples (mean 7.87) (Supplementary
tableS4). To assess the proportion of plant genera shared between the honey and beebread communities in each
hive, we analyzed paired samples of honey and beebread, collected from individual hives at the same time (June
NMDS Stress 0.147
−0.4
−0.2
0.0
0.2
0.4
0.6
−0.40.0 0.
40.8
NMDS1
NMDS2
Sample type
Honey
Beebread
Figure1. Plant genera composition of honey (red, n = 97) and beebread (blue, n = 87) samples collected from 43
hives in 6 apiaries in Finland in 2021. Figure showing non-metric multidimensional scaling (NMDS) based on
Hellinger dissimilarity index. Ellipses show 75% condence limits for each sample type.
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n = 32, July n = 28, August n = 21). e proportion in hives ranged from 0.10 to 0.71 but was on average similar
throughout the summer, ranging from 0.35 to 0.43 between time points (Fig.2C). ere was no signicant dif-
ference in the proportion of shared genera among the time points (GLMM, p = 0.12, Supplementary TableS5).
Based on the indicative species analyses, to identify which plant genera are mostly selected either for nectar
or pollen foraging, twelve genera are signicantly associated with honey samples, compared to eight genera for
beebread (Table1, for results based on presence-absence data see Supplementary TableS6). e genera most
strongly associated with honey were Rubus and Myosotis, whereas for beebread they were x-Amelasorbus (a
hybrid genus) and Pisum.
Selectivity of oral choices from the available owers. During the surveys of owering plants in the
28 plots presenting the six dierent habitat types surrounding the apiary areas, we found 99 species, represent-
ing 73 genera and 27 families (Supplementary TableS7). 39 genera were in ower in June, 43 in July and 50 in
August. e agricultural elds found owering close to the hives were Linum usitatissimum, Brassica sp. and
Solanum tuberosum, all in July. Out of the 73 genera found, less than half were found in the honey (32) or bee-
bread (30) samples. e proportions of owering plant genera that were also found in honey was 40.0%, 32.6%
and 38.0% in June, July and August (Fig.3). In beebread the proportion of owering plants was 40.0%, 23.3%
and 32.0% in June, July and August. Out of the genera found in honey and beebread, 33 genera were not found
during the owering plant survey. e proportion of genera not found owering in the natural habitat types but
found in honey samples in June, July, and August, were 51.5%, 57.6% and 54.8% and in beebread samples 59%,
50% and 44.8%. On the other hand, most of the plants found owering in the natural habitats were not found in
either honey or beebread, being 52.5%, 62.8% and 60.0% in June, July and August, respectively (Fig.3).
Impact of the time of the season and location on the oral choices. e selection of owers by
honeybees strongly varies between sample type and change over time (Table2). In the variation partitioning,
sample type and time explained 30.7% of the total variation, while the variables associated with the experimental
design and spatial eects (i.e., site, apiary and hive) accounted for 1.2% of the total variation, and 62.5% are not
explained by the model (RDA model F = 25.31, df = 5, p-value < = 0.001, adj R2 = 45.7, Table2, Fig.4).
0
5
10
15
20
Month
Number of genera
a) Honey
June July August
a
a
b
0
5
10
15
20
Month
Number of genera
b) Beebread
June July August
a
b
a
0.0
0.2
0.4
0.6
0.8
1.0
Month
Proportion of shared genera
c) Shared
June July August
Figure2. e number of genera in samples collected from beehives in Finland in 2021 at each time point from
6 apiaries, showing genera in honey (a, June n = 39, July n = 30, August n = 28) and beebread (b, June n = 36,
July n = 28, August n = 23), and the proportion of shared genera for paired samples of honey and beebread
from individual hives (c, June n = 32, July n = 28, August n = 21). Signicantly dierent groups in pairwise
comparisons in (a) and (b) are denoted with letters.
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Composition of specialized metabolites in honey. e most abundant specialized metabolites occur-
ring commonly across all apiary areas in the honey samples collected in August were annotated based on the MS
data (Supplementary Text S1, TableS10). Multiple isomeric monoterpene glycosides and tricoumaroyl spermi-
dines, carboxylic and dicarboxylic acids, the plant hormone abscisic acid and the vitamin pantothenic acid were
among the major metabolites found in all three apiary areas (Supplementary TableS9, Figs.S6 and S7).
e impact of the apiary area on the specialized metabolite composition in honey was assessed by multivari-
ate data analysis (Supplementary Text S1, Figs.S4, S5, S8 and S9), showing not only the occurrence but also the
abundance of specic metabolites to dier between the apiary areas. e most abundant discriminant metabolites
Table 1. Statistically supported indicative plant genera associated with either honey (n = 97) and beebread
(n = 87) samples that were collected from 43 hives in 6 apiaries in Finland in 2021, based on relative read
abundances, with component values.
Honey A (specicity) B (delity) stat p value
Rubus 0.936 0.845 0.889 0.001
Myosotis 0.971 0.474 0.678 0.001
Salix 0.647 0.505 0.572 0.016
Vicia 0.628 0.485 0.551 0.004
Taraxacum 0.888 0.268 0.488 0.001
Prunus 0.754 0.309 0.483 0.002
Malus 0.654 0.289 0.434 0.031
Chamaenerion 0.796 0.165 0.362 0.014
Rosa 1.000 0.124 0.352 0.001
Medicago 0.925 0.082 0.276 0.016
Populus 1.000 0.062 0.249 0.028
Comarum 1.000 0.062 0.249 0.031
Beebread A (specicity) B (delity) stat p value
x-Amelasorbus 0.757 0.379 0.536 0.001
Pisum 0.724 0.391 0.532 0.026
Calluna 0.856 0.322 0.525 0.001
Rhododendron 0.690 0.253 0.418 0.024
Syringa 0.808 0.161 0.361 0.009
Cirsium 0.771 0.161 0.352 0.035
Crataegus 1.000 0.092 0.303 0.002
Aronia 1.000 0.069 0.263 0.010
a) June b) July c) August
Honey
5Beebread
11
Flowering
21
12
3
3
13
Honey
13
Beebread
4
Flowering
27
6
6
2
8
Honey
13 Beebread
3
Flowering
30
10
4
1
15
Figure3. Number of shared and unique plant genera found in samples of honey (June n = 39, July n = 30,
August n = 28) and beebread (June n = 36, July n = 28, August n = 23), collected in 43 beehives in six apiaries in
Finland in 2021, and the number of owering plants surrounding the hives. Honey shown in pink, beebread in
green and owering plants in blue at dierent times; (a) June, (b) July and (c) August.
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were deduced from OPLS-DA models using the apiary areas as classiers, and annotated (TableS11, S12). eir
distribution among the three apiary areas was found to vary strongly (Supplementary Figs.S8 and S9). Using
NMDS the specialized metabolites from both datasets seem to cluster in similar fashion, while the plant genera
in honey samples from the same time points overlap more in apiary sites CD and EF (Fig.5).
Discussion
Nectar and pollen constitute the whole diet of honeybees, yet the nutritional contents, as well as quantities of
nectar and pollen dier greatly among plant species. us, the choices honeybees make, when selecting owers
to collect nectar or pollen from, are very important for their diet. Here we found that honeybees largely choose
the same plants for both nectar and pollen, when considering all the hives across the whole summer. Yet, when
focusing at individual colonies at a time point the plants chosen for nectar or pollen dier substantially. We also
show that honeybees use only a fraction of the available owers in the surrounding natural habitats, for either
nectar or pollen foraging. In our study the time of the summer was the largest determinant on which plants were
foraged on, but the foraged resource type and location also played a signicant role. A large variety of specialized
Table 2. Results from the partial canonical model including spatial variables to control pseudoreplication
linked to experimental design (see statistical methods). Variation partitioning quanties the proportion of
variation explained by sample type, sampling time and variables (site, apriary, hive) on the plant genera found
in samples of honey (n = 97) and beebread (n = 87), collected from 43 hives in 6 apiaries in Finland in 2021.
Signicance values are [bold].
Variables Df Variance Fp-value
sample_type 1 0.043 25.535 0.001
time 2 0.145 42.636 0.001
sample_type:time 2 0.052 15.204 0.001
Residual 135 0.229
Fractions Df Adj. R2
X1 = Sample_type & Time 3 0.307
X2 = Area, apiary & hive 42 0.012
X1 + X2 45 0.375
Residuals 0.625
NMDS Stress 0.147
−0.4
−0.2
0.0
0.2
0.4
0.6
−0.4 0.00.4 0.8
NMDS1
NMDS2
Sample − time
Beebread June
Beebread July
Beebread August
Honey June
Honey July
Honey August
Figure4. Plant genera composition of beebread (triangles, dashed ellipses, June n = 36, July n = 28, August
n = 23) and honey (circles, solid ellipses, June n = 39, July n = 30, August n = 28) at dierent time points in
samples collected from 43 hives in 6 apiaries in Finland in 2021. Figure showing non-metric multidimensional
scaling (NMDS) based on Hellinger dissimilarity index, ellipses showing 75% condence limits.
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metabolites was found in the honey samples, showing dierences among the apiary areas. Below we discuss all
these ndings in turn.
Honeybees are more selective for pollen. We found that honeybee colonies use dierent plants for
nectar and for pollen, as on average less than half of the plant genera found in a hive at a time were found in both
honey and beebread samples. ese dierences are not surprising, as nectar and pollen are collected for dier-
ent needs, by dierent specialized foragers, and nectar and pollen nutritional qualities as well as quantities vary
widely among plants34. e average number of genera in honey samples in the colonies was higher than in bee-
bread. However, the composition of genera in beebread samples varied signicantly more. erefore, while fewer
genera were used for pollen foraging, the genera varied more between hives and time points, making the overall
number of plant genera used for pollen higher. is result corresponds to previous studies nding that honey-
bees forage on fewer species for nectar than for pollen19, 20, 35. is together with our results suggests that same
plants are more consistently used for nectar, while the foraging choices of pollen change more frequently. is
could mean that pollen foraging sources are more variable to maintain the ow of important nutrients required
by the colony, as honeybee colonies can remedy deciencies in essential fatty and amino acids by preferring pol-
len that complement the deciencies11, 12. We further detected a number of genera being strongly associated with
one resource type, suggesting the properties of nectar or pollen of these plants to be favorable19, 20, 35.
Although about half of the genera detected in colonies at a time was dierent for nectar and pollen, we found
that as a whole largely the same plant genera were utilized for both nectar and pollen foraging. is36 indicates
that many plant genera are suitable for both pollen an nectar foraging, but the resource itself is important, as we
see clear dierences in the choices for the two resource types.
Limited use of the available oral resources. roughout the summer fewer than half of the available
owering natural plants were found to have been foraged on for either nectar or pollen. Previously, DNA based
comparisons of what honeybees forage on from the surrounding oral resources have similarly found that only
a fraction of the available owers is used by honeybees21, 37. For example, a study in a botanic garden in Wales
found that honeybees used only 11% of the available owering taxa, preferring native or near-native plants21. In
our study we similarly found that horticultural plants were not majorly used by bees. Only two genera had rela-
tive abundance greater than 1% at any point in either honey or beebread, Hydrangea (2.2%) and Phacelia (2.3%),
which is sometimes planted as a resource for honeybees.
On the other hand, we found that honeybees had used many genera that we had not detected in our surveys
of owering plants in natural habitats. Eight of such genera were ornamental or other garden plants, such as
Rosa and Paeonia, and six were cultivated plants, such as Coriandrum (coriander) and Raphanus (radish), show-
ing that honeybees forage also in gardens and on elds in our study area. is is expected, as also in previous
studies in the UK honeybees have been shown to use garden plants extensively38, 39. Ten genera found in honey
and beebread samples, but not recorded during our survey of owering understory plants, were typical Finnish
trees. Yet, a few native Finnish owering genera were found commonly in the honey samples, such as Persicaria
(knotweeds), Fallopia (e.g., black bindweed) and Convallaria (lily-of-the-valley), although we had not recorded
them in the survey. is means our owering plant surveys were not thorough enough to give a full picture of
NMDS Stress 0.125
−0.2
0.0
0.2
0.4
0.6
−0.2 0.00.2 0.4
NMDS1
NMDS2
AB
CD
EF
a) Metabolite dataset 1
NMDS Stress 0.149
−0.2
0.0
0.2
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0.6
−0.4 −0.2 0.00.2 0.4
NMDS1
NMDS2
AB
CD
EF
b) Metabolite dataset 2
NMDS Stress 0.105
−0.3
0.0
0.3
0.6
−0.75−0.50 −0.250.000.250.50
NMDS1
NMDS2
AB
CD
EF
c) Plant genera
Figure5. Non-metric multidimensional scaling (NMDS) plots for specialized metabolites in honey with two
separation techniques producing dataset 1 (a, n = 27), dataset 2 (b, n = 27) and plant genera composition in
honey samples at the same time point (c, n = 30), grouped by apiary sites. Ellipses show 75% condence limits
for each site.
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the available oral resources, even though we assessed all the habitat types in the area. Yet, honeybees only used
a fraction of the owers that we did nd owering, indicating honeybees are selective in choosing plants to for-
age on, in support of other studies showing honeybees to be selective21, 37.
Choices for nectar and pollen change according to time of the summer dierently. e time of
the summer was the strongest determining factor of plant genera found in honey and beebread samples. is is
an expected result considering the dierent owering times of plants, changing the availability during the year,
and in line with previous research nding honeybees’ ower usage to clearly follow plant phenologies38, 40. Loca-
tion also played a role in the usage of plants, as the spatial eects accounted for almost one fourth of the varia-
tion. is was anticipated since the owering plant pool within reach of a colony would be dened by the site.
e diversity of plants used for nectar and pollen foraging, detected in honey and beebread samples, had
dierent dynamics through the summer. e number of genera in honey was highest in August, which was
also when most owering plants were detected in the environment, but aer the main nectar ow in Finland41.
However, in beebread samples we found fewer genera in July, when the rapeseed (Brassica) owers. A similar
eect in reduced pollen richness coinciding with the mass owering of rapeseed has been seen in other studies
as well42. is has been explained by optimal foraging theory, predicting that when a preferred food source is
abundantly available, foragers should utilize that, and when the preferred resource becomes limited, the number
of utilized species would increase40. is is supported by our observations, as in July rapeseed also dominated
the read abundance in beebread, and the diversity of genera in beebread was at its lowest. As rapeseed appears to
be a good source of nutrition for honeybees43 a preference could be expected, yet such preference has oen not
been shown18, 35. Interestingly in honey samples the overall number of genera did not dip in July. It was slightly
lower in apiary area E–F, and clearly lower in area C-D, which is surrounded by the lowest amount of agricultural
landscape. ese dierences could result from the dierent landscape imposing dierent resource availability
between the areas, shaping the breadth of foraged plants40. Nevertheless, the result shows the availability of an
abundant source aects the choices of pollen foragers dierently from nectar foragers.
e lower number of genera in beebread in July may appear alarming, because the diversity of the pollen
diet has been linked to honeybee health44. However, lower diversity of pollen does not always cause problems,
as the nutritional quality of pollen is more important4. For example, the mass owering of maize is detrimental
to honeybee health, because maize pollen is of low quality4, but a pollen diet of similar low diversity does not
cause detrimental health eects when composed of better-quality sources4. Diverse sources of pollen during the
mass owering are thus especially important in areas where the hives are close to crops that produce pollen with
low nutritional content, and at times of resource limitations40.
Natural DNA contaminations between honey and beebread in the hive. When honeybees for-
age, pollen from the ower attaches to them, and some of it may also enter the combs as honeybees are process-
ing nectar into honey. Also, honeybees add small amounts of nectar and glandular secretions to pollen as they
prepare it as beebread7. us, the beebread samples could contain traces of DNA from the plants used as nectar
sources and vice versa. Such possible natural contaminations will make the plant composition of the two sample
types more similar, making dierences detected more conservative. We nevertheless nd dierences in plant
genera in honey and beebread samples, both based on relative read abundances and occurrences, and suspect
that due to the mentioned biases the actual foraging choice dierences are stronger.
Specialized metabolite composition in honey is inuenced by apiary location. Like the plant
genus composition, the specialized metabolite proles of honey samples were inuenced by location, suggesting
that the availability of the dierent plant taxa in each apiary area contributed to the observed dierences. It was
consistent with the perceived similarity of the surrounding landscape, areas A–B and E–F having a similar land
use composition. Interestingly, the specialized metabolites appear to cluster more distinctly when compared
with the plant genera composition, although it could be explained by the slightly dierent sampling methods.
Many of the annotated compounds have previously been detected in honey samples. For example, numerous
hydroxcinnamoylamines, known pollen constituents with varying levels and substitution patterns between plant
species45–47, were annotated. Some of the detected isomers with obvious oral origins consistently occurred in
samples from all three areas (e.g. tricoumaroylspermidine isomers are known to occur in rapeseed beebread48),
while others obviously were derived from more specically occurring plant taxa. Commonly occurring were also
e.g., pantothenic acid, a vitamin, and abscisic acid, a plant hormone possessing diverse and important regulatory
roles in plants. Interestingly, abscisic acid seems to have a benecial impact on bee health. Abscisic acid supple-
mentation has been shown to enhance the immune response in honeybees and to contribute to colony tness49,
and it was able to enhance cold stress tolerance in in-vitro reared honeybee larvae50, thus it could have guided
the oral choices.
Two carboxylic acids commonly occurring across apiary areas are traced back to royal jelly, the larval feed of
the honeybee, which is known to contain decene- and decanedioic acids identied herein51, 52. ese compounds
have been previously detected in various honey accessions, and their occurrence in royal jelly suggests that they
originate from the bee itself, and not be playing a role in the foraging choices.
Among the metabolites that discriminate among the areas, the avonoids chrysin, tectochrysin and pin-
obanksin have frequently been detected in honey53, 54. Since they are known as typical propolis constituents,
their abundance in honey may rather depend on its propolis content than on its oral origin55. Vomifoliol has
previously been detected as major constituent in honey produced from Salix nectar56 and as minor constituent in
Trifolium pratense honey57. In line, Salix and Trifolium were among the relatively most abundantly found genera
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in the DNA of these honey samples. Salicin has only occasionally been detected in honey, but is also known as
constituent of Salix species58.
Conclusions
We found honeybees to be clearly selective for owers overall, using only a fraction of the plants available. e
mass-owering of rapeseed seemed to alter pollen foraging more than nectar. e time of the summer and the
surroundings of the hives determine ower availability, and although many plant species could provide both
suitable nectar and pollen, we found each colony to select largely dierent plants for nectar and for pollen.
ese together tell that honeybees base their foraging choices on multiple factors, and that they actively choose
the plants they forage on. Further research on honeybee nectar and pollen requirements and foraging choices
simultaneously could aid bee conservation eorts. As we know that a diversity of plants is important to fulll
the nutrient needs of honeybees, we need to assure a wide variety of plants are available for them throughout
the summer, so that they can select the right plants to ll their resource needs.
Materials and methods
Description of habitat and sampling. To study the foraging choices of honeybees for nectar and pollen,
we studied 43 honeybee colonies and the surrounding owering plants in Southern Finland in 2021. e hives
were established in 2020 and maintained by two experienced beekeepers using conventional Finnish beekeeping
practices41. e hives were located in six apiaries, with two apiaries less than 2 km apart and 10–15 km between
each pair of apiaries (Fig.6). In each apiary there were two to eight hives. e hives were also used in an experi-
ment on trans-generational immune priming59. e priming treatment had no eect on foraging behavior (Sup-
plementary Fig.S2) and is thus not further considered in this study.
As honeybees mainly forage within a few kilometers of their hive60 and rarely go beyond ten kilometers61, we
consider each pair of apiaries to partly share the oral resources, while between the pairs of apiaries the studied
honeybees would mostly be too far from each other to use the same ower resources. e apiary area consists of
a mosaic of cultivated elds and managed forests, with dierences in the ratios of these between the vicinity of
each apiary, as determined by Corine land cover database (version 20b2, 2018, European 100 m raster database62;
Supplementary Fig.S3).
We mapped the owering plants and from the bee hives we collected beebread and honey samples at three
time points during the summer 2021. e rst sampling from the hives and ower counts were done in June
(8.–13.6.), second in July (9.–14.7.), and third in August (10.–13.8.2021). For the sampling, which was only
from the hives, we had the permission of the owners of the hives and thus it complied with all national rules
and legislation in Finland, as no other permits are required. e owering plants were assessed within 3km dis-
tance of the hives, in sites selected by stratied (random) sampling with arbitrary allocation, in dierent habitat
types indicated by Corine land cover database. Selected habitat types to map were mixed forests, conifer forests
and broadleaf forests, roadsides, riversides, and natural pastures. Five of the habitat types were found close to
each pair of apiaries, while broad-leaf forest was close to only two (apiaries A-B and C-D) and in the third area
Figure6. e study area location in Finland (rectangle on map not to scale) with relative locations of the
apiaries with hive numbers each month (June, July and August of 2021).
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(apiaries E–F) the only riverside vegetation was present. In total owers were identied and counted in 24 sites,
as six types of vegetation close to each pair of apiaries and then a site next to each apiary. In each site an area of
200 m2 was established (14.5 m × 14.5 m or 100 m × 2 m). Additionally, we assessed the owering plants close
to each apiary, along the edge of a eld if the apiary was placed next to a eld, or along a road if the apiary was
located in forest. For these we established areas of 100 m × 2 m (200 m2). All owering plants within the area
were identied based on literature on site63.
To assess the plants used for nectar foraging we collected a spoonful (approximately 15 g) of newly covered
honey from three frames from each hive, to cover the diversity of honey at a time. e honey was collected by a
DNA-clean spoon into one DNA-free 50 ml tube (Sarstedt AG & Co. KG, Germany), pooling the sample from
the three frames. DNA-free spoons were prepared by washing with detergent and incubation for 5h in + 200°C.
During the sampling in August another honey sample was collected to assess plant metabolites in honey. For
this the pooled sample was collected the same way except sampling the spoonful from one newly covered honey
frame and two frames with older honey, to get a sample representing the latter part of season more comprehen-
sively. To identify which owering species honeybees collect pollen from, in comparison to nectar, we collected
a beebread sample from each hive at the same time points. Twenty cells of beebread were sampled from three
frames, resulting in pooled sample of sixty beebread cells, to cover the diversity of stored pollen in a hive at a
time. e beebread from a cell was sampled by pushing a plastic straw to the bottom of a cell and twisting, thus
including all or most of the beebread in a cell. e samples were frozen immediately over dry ice in the eld. All
the samples were stored frozen in − 20°C before further processing.
Sample numbers. At the beginning of the sampling there were 43 hives from which we sampled, but unfor-
tunately nearly one third of the colonies were le without a queen (queen had died or swarmed) before the
second sampling, so 30 hives remained in July and 29 in August. In total we collected 99 honey samples and 90
beebread samples, as we were not able to get a proper sample. At the rst sampling in June, eleven of the hives
had no covered honey yet, so instead we sampled partially processed nectar as the honey sample.
Sample preprocessing for DNA extraction. Before extracting the DNA, the samples were preproc-
essed. For honey, the sample collected from three frames was mixed and 10g of honey was diluted to 30ml of
DNA clean water in a 50ml tube. e honey was let to dissolve into the water for 30min in 60°. e samples
were then centrifuged at 8000G for 60min, aer which most of the supernatant was discarded and the pellet
was transferred to a 2ml tube. e 2 ml tube was further centrifuged at 11,000 G for 5min and the remaining
supernatant was removed.
For beebread, the beebread was rst extracted from the straw and weighed with a precision scale. e sample
was then diluted in double distilled water with a 2:1 water-beebread weight ratio and mixed with magnetic stirrer
for 10min to produce a homogenized suspension. 100µl of beebread suspension per sample was collected into a
2ml microcentrifuge tube. e 2ml tube was centrifuged at 16,873 G for 3 min and the supernatant was removed.
All the preprocessed samples were stored in freezer until DNA extraction.
DNA extraction, amplications, and sequencing. QIAamp DNeasy plant mini kit (Qiagen, Neth-
erlands) was used to extract DNA with adapted manufacturer protocols. For the honey samples, the pellet was
resuspended in 400 µl of buer AP1, and then 4 µl RNase, 4 µl proteinase K (20mg/ml) and one 3 mm tungsten
carbide bead was added to each sample tube. e sample was disrupted 2 × 2 min 30 Hz (Mixer Mill MM 400,
Retsch, Germany). DNA extraction then followed the protocol with the exception of skipping the QIAshredder
column step and nally the DNA was eluted to 50 μl of elution buer.
For beebread samples, the pellet was resuspended in 400 µl of buer AP1 with two 5mm metal beads and
disrupted 2 × 2 min 30 Hz (TissueLyser II, Qiagen, Netherlands). Incubation with buer AP1 and 4 µl RNase A
was done in 65°C for 30 min, inverting tubes twice during incubation. Manufacturer protocol was thereaer
followed, except 100 µl nal elution volume was used with a single centrifugation step. With the extraction of
each sample type, 2–3 DNA extract controls were included. We only used DNA-free tubes, pipet tips and PCR
plates as well as DNA-free water.
e initial amplications were done with a total volume of 10 μl, each containing 5 μl MyTaq Red Mix (Bio-
line, London, UK), 1.3 μl DNA-free water, 0.3 μM of each primer and 3 μl of DNA extract. To amplify a partial
ITS2 region from both honey and beebread samples, we used the plant-targeted primers with a tag to attach the
index in the second PCR, tagF_ITS2-F and tagR_ITS2-R (tcgtcggcagcgtcagatgtgtataagagacagATG CGA TAC TTG
GTG TGA AT and gtctcgtgggctcggagatgtgtataagagacagTCC TCC GCT TAT TGA TAT GC, respectively, tag shown
in lower case and annealing primer in upper case64, 65). PCR cycling conditions were as follows. e initial dena-
turation was for 3 min at 95°C, followed by 28 cycles of 30 s 95°C (denaturation), 30 s 55°C (annealing), 30 s
72°C (extension), and ending with nal extension for 7 min at 72°C. To minimize initial bias of amplication,
each reaction was carried out as two replicates. All the amplicons were checked on agarose gel and imaged to
check the reaction had worked and the DNA and PCR controls were clean. e PCR replicates were combined
before library-PCR as 1.3 μl of each PCR product replicate. Illumina‐specic adapters and unique dual‐index
combinations for each sample was used 66. e library PCR had a total volume of 10 μl, each containing 5 μl
MyTaq Red Mix (Bioline, London, UK), 0.3 μM of reverse primer, 0.3 μM of forward primer and 2.6 μl of the
locus-specic combined 1st PCR product. PCR cycling conditions were as follows, the same for all gene regions
for the library PCR. Starting with 4 min 95°C to denature, followed by 15 cycles of 20 s 98°C, 15 s 60°C and 30
s 72°C, and ending with 3 min 72°C. DNA libraries were pooled per gene region and per 96 samples, and con-
centrated using a SPRI bead protocol. e concentrated pooled sample was loaded on 1% agarose gel (Agarose
tablets + TAE) and run with 90 V for 120 min. e target bands were cut on UV light and the pooled sample
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was cleaned from gel with the PCR and Gel CleanUp Kit (Macherey–Nagel), diluted in 2 × 20 μl of the elution
buer provided in the kit. e DNA concentration of the cleaned pools were measured with Qubit 2.0 (dsHS
DNA Kit, ermoFisher Scientic).
e pools of 96 samples were combined in equimolar ratios and sequenced in two MiSeq sequencing runs
(including other libraries also) with v3 chemistry with 600 cycles and 2 × 300 bp paired-end read length.
Bioinformatics. e bioinformatic processing (following67, 68) rstly involved truncating the reads to 240
bp. is was done to cut o lower quality ends before merging the paired ends for each gene region using
VSEARCH69 with a maximum of 80 dierences allowed for overlap and a minimum assembly length of 150 bp.
e merged reads were quality controlled by fastq_maxee, with maxee = 3. Primers were removed using cutadapt
with a maximum of 0.2 error rate for primers, and reads were kept with minimum length of 100 bp aer primer
removal. e merged and quality-controlled reads were only retained if they contained the expected primers at
each end. e reads were then dereplicated into uniques and singletons were removed. e reads were denoised
to zero-radius operational taxonomic units (ZOTU) using with unoise3 with USEARCH70. A ZOTU table was
built and the taxonomic assignation of ZOTUs was done by comparison against an ITS2 reference database from
PLANTiTS71, accessed 21.3.2022 with VSEARCH. We consider here the taxonomic assignments as they resulted
from the analyses without correcting based on species distributions, although some of the genus assignments
might not be correct, e.g., x-Amelasorbus is a hybrid genus, and most likely the sequences assigned to it would
originate from Sorbus in Finland.
To remove possible misassigned reads and false positives, due to contamination, we further ltered the reads
in ZOTUs (following e.g.72, 73). As small numbers of reads were found in all controls, reads were removed for each
ZOTU, from each sample if they were less reads in the sample than the maximum number of reads from the DNA
extraction or PCR negative controls for the ZOTU. Aer taxonomic assignation, taxa with less than 0.05% of the
total read number of that sample were removed, as well as taxa with less than 10 reads were removed. Samples
with fewer than 5000 reads were removed to omit samples with shallow sequencing.
Statistical methods. We calculated the relative read abundances (RRA) of each plant genus per sample 32,
and RRA data was used for the analyses. We ran the analyses also using presence-absence (PA) data. Results from
PA-analyses are in general agreement with the RRA-results and are available in the supplementary information.
Statistical methods were implemented and gures generated in R version 4.2.2 74, except for the specialized
metabolite analyses (see below). P values of < 0.05 were considered statistically signicant.
To identify the dierence in the selection of oral resources by bees to produce honey or beebread, we rst
tested the multivariate homogeneity of the dispersion of groups between honey and beebread (PERMDISP, R
function “betadisper” from the vegan package75, using the Hellinger-transformed data at genus-level). Second, we
quantify the dierence in plant genus composition between beebread and honey using a permutational analysis
of variance (PERMANOVA, with the “adonis2” function of the vegan package75). As adonis2 does not allow
random eects, the terms site, apiary, and hive were included in the model formula as xed eects to account
for pseudoreplication following the structure:
To describe how honey and beebread samples dier and group into distinct clusters based on their composi-
tion of plant genera, we applied nonmetric multidimensional scaling (NMDS, with function “metaMDS” from
package vegan75). NMDS was further used to illustrate the temporal eects on the samples as well as comparison
with metabolite datasets, for which pooled honey sample data from July and August were used. NMDS analyses
were performed with Hellinger transformed data.
We analyzed the number of genera in each sample type (honey or beebread) with linear models, using time
point as explanatory variable and hive as random variable, with function “lmer” from package lme476. Pairwise
comparisons for time points were made using emmeans-package77 with Tukey p-value adjustment. To analyze
the proportion of shared and non-shared genera within the paired honey-beebread comparisons a binomial
generalized linear model with logit-link function was used (using “glmer” function from package lme476), again
using time point as explanatory variable and hive as random variable. Model assumptions were checked visually
and with the R package DHARMa78. To identify which plant genera are the most associated with the honey and
beebread samples, we used the Indicator Species Analysis (IndVal, with function “multipatt” from the package
indicspecies79, using 999 permutations).
To test the contribution of variables aecting oral choices, we used partial redundancy analysis (RDA) using
Hellinger distances. e model follows the structure:
As with the PERMANOVA model, the RDA controls the design variables experimentally to account for
pseudo-replication. e model was also tested with the sequencing read depth, which appeared to decrease the
observed conditional variation by approximately 5% (Supplementary TableS8). Constrained ordination was
tested by ANOVA-type permutation test in the vegan package. e variation associated with temporal, spatial
and methodological variables were quantied by variation partitioning with the function “varpart” from the
vegan package75.
Extraction of plant specialized metabolites. Specialized metabolites were puried from honey sam-
ples from 27 hives. First, ve grams of honey was measured into 50 ml centrifuge tubes in three replicates. Two
adonis
(
Hellinger_aboundance
∼
site
/
apiary
/
hive
+
sample_type
)
.
RDA
(Hellinger_abundance ∼sample type ∗time +conditional
site ∗apiary ∗hive
.
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dierent purication methods were employed to remove sugars and to recover as wide a variety of specialized
metabolites as possible. Purication method 1 followed a method mainly directed on the enrichment of avo-
noids, i.e., medium-polar metabolites80, with slight modications. 15ml of water, adjusted to pH 2 with HCl,
was mixed with the honey and stirred with magnetic stirrer for 15 min until completely uid. Samples were
then centrifuged 3220 G, 25°C, 1 min to remove particles. e supernatant was loaded on an Oasis 500 mg
HLB cartridge (Waters, USA), preconditioned with 10 ml methanol, followed by 10 ml water (pH 2), allowed
to equilibrate for 10 min, and washed with 10 ml pure water. Analytes were eluted with 5 ml of methanol into a
15 ml centrifuge tube. e eluent was removed under a stream of nitrogen. Prior to measurement, samples were
reconstituted in 1ml of 0.1% formic acid/acetonitrile (70/30) containing 0.025 mg/ml indomethacin as internal
standard (ISTD) and ltered through 0.45 µm PTFE lters.
Since purication method 1 would lead to the loss of alkaloids and very polar compounds during the washing
step, samples were puried with method 2 which is based on Quick Easy Cheap Eective Rugged Safe (QuECh-
ERS) protocol. is has been widely applied for enrichment of various trace compounds, among them for the
analysis of alkaloids in honey81. First, 10 ml of water and 10 ml of acetonitrile was mixed in the 5 g honey sample
and thoroughly shaken until mixed. Subsequently 8.2 g of MgSO4.7H2O and 1 g of NaCl were added. e mixture
was shaken vigorously for 1 min and then centrifuged for 5 min at 2465 G, 25°C, 5 min. e upper layer was
ltered through 0.45µm cellulose acetate lter, and 1ml was dried under a stream of nitrogen and redissolved
in 100 µl of MeOH/H2O 1/1 containing 0.01 mg/ml benzanilide as ISTD.
Analyses were performed on a Dionex Ultimate 3000 UHPLC hyphenated with a ermo QExactive Hybrid
Quadrupole Orbitrap mass spectrometer that was equipped with an H-ESI II probe (ermo Fisher Scientic).
As a stationary phase, an Acquity UPLC® HSS T3 1.8 µm, 100 × 2 mm column protected by an Acquity UPLC®
HSS T3 1.8 µm, 2.1 × 5 mm guard column (Waters) was used. Two dierent separation methods were applied
for the sample sets prepared by the two dierent purication methods:
For samples puried with method 1, the mobile phase consisted of water + 0.1% HCOOH (solvent A) and
acetonitrile + 0.1% HCOOH (solvent B). e column temperature was 40 °C, the ow rate was 0.45 ml/min, and
the gradient was as follows: 0–15 min, 5–25% B in A; 15–22 min, 25–70% B in A; 22–25 min, 70–100% B in A;
25–26 min, 100% B; 26–26.3 min, 100–5% B in A; 26.3–32 min, 5% B in A. For samples puried with method
2, the mobile phase consisted of water (solvent A) and acetonitrile (solvent B). e column temperature was
40 °C and the ow rate was 0.4 ml/min. e gradient was as follows: 0–22 min, 10–72% B in A; 22–22.5 min,
72–100% B in A; 22.5–25 min, 100% B in A; 25–25.5 min, 100–10% B in A; 25.5–30 min, 10% B in A. Injection
volume for both methods was 3 µl.
e mass spectrometer was run in the ESI negative mode for separation method 1 and in the positive mode
for separation method 2. e MS parameters were as follows: Probe heater temperature was 350°C, capillary
temperature was 330°C, sheath gas ow was 50 arbitrary units, auxiliary gas ow was 10 arbitrary units, capillary
voltage was 3 kV in the negative mode and 3.5 kV in the positive mode, and S- lens RF level was 60. Scan range
was m/z 100–1,500, and resolution was 70,000 (FWHM) for full MS and 17,500 (FWHM) for data dependent MS2
scans. During the rst 1.0 min (separation method 1) and 0.9 min (separation method 2) of elution, the eluent
bypassed the mass spectrometer, and no data were recorded in order to avoid contamination of the MS with high
levels of carbohydrates that were expected to be still present in the samples despite the purication measures.
As blank samples, the solvents used for preparation of the samples were injected, and as QC samples, pooled
samples were prepared from both sample types (purication method 1 and 2) by mixing 10 µl of replicate 1 of
each sample. Blank samples were injected at the beginning, in the middle and at the end of each sequence, and
the QC samples were injected at intervals of 10 runs.
Data processing and evaluation for specialized metabolites. Raw analytical data were processed
with Compound Discoverer 3.2 using the following parameters: Retention time window for spectra selection
was 1–32 min for dataset 1 (acquired in the ESI negative mode with separation method 1) and 0.9–26 min for
dataset 2 (acquired in the ESI positive mode with separation method 2). Retention time alignment was per-
formed with adaptive curve model (maximum RT shi 2 min, maximum mass tolerance 10 ppm). For detecting
and grouping unknown compounds, S/N threshold was 3, minimum intensity threshold was 5,000,000 for data-
set 1 and 10,000,000 for dataset 2, and RT tolerance was 1min. S/N threshold for gap lling was 20. e output, a
data matrix consisting of retention time and intensity of every feature in every sample, was exported to MS Excel
for further treatment. In both datasets, rst, features derived from the analytical background were removed, and
the peak areas of the ISTD in all samples were graphically compared in order to inspect the dataset for samples
that had not been injected properly. On this basis, sample 23_2 was excluded from dataset 1, and sample 5_2 was
excluded from dataset 2.
en, all remaining peak areas were normalized to the peak area of the ISTD in the respective run. In order to
remove unreliable features, means and relative standard deviation were calculated for all features detected in the
pooled quality control samples. Features with a relative standard deviation above 33% were considered unreliable
and removed from the datasets. ese pretreated datasets were subjected to multivariate data analysis (MVDA)
using SIMCA 17 (Sartorius). Prior to MVDA, data were log-transformed and pareto-scaled. For unsupervised
MVDA, principal component analysis (HCA) and hierarchical clustering analysis (HCA) were used. For super-
vised MDVA, OPLS-DA models with three classes that correspond to the three apiary areas were constructed.
For tracking metabolites occurring at high level across all apiary areas, average peak areas per apiary area were
calculated for each metabolite in the two datasets. ose peaks occurring with average peak areas > 200,000,000
(dataset 1) and > 750,000,000 (dataset 2) in all three apiary areas were subjected to peak annotation.
Annotation of discriminant and common metabolites was performed either comparing retention time or MS
data with authentic reference compounds (ID level 1), or by comparing calculated molecular formula and MS/
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MS fragmentation pattern with literature or database data (ID level 2), or in case that no literature or database
data were available, by theoretical interpretation of these data (ID level 3).
Data availability
e sequence datasets generated during the current study are available in the Sequence Read Archive reposi-
tory, in the BioProject PRJNA889252 https:// datav iew. ncbi. nlm. nih. gov/ object/ PRJNA 889252? revie wer= g7m
n7q3m t60s7 0onkm bf0m for the review, once accepted, in https:// www. ncbi. nlm. nih. gov/ sra/ PRJNA 889252.
Received: 12 May 2023; Accepted: 5 September 2023
References
1. Ollerton, J., Winfree, R. & Tarrant, S. How many owering plants are pollinated by animals?. Oikos 120, 321–326 (2011).
2. Klein, A.-M. et al. Importance of pollinators in changing landscapes for world crops. Proc. R. Soc. B Biol. Sci. 274, 303–313 (2007).
3. Klein, A. M., Boreux, V., Forno, F., Mupepele, A. C. & Pufal, G. Relevance of wild and managed bees for human well-being. Curr.
Opin. Insect Sci. 26, 82–88 (2018).
4. Di Pasquale, G. et al. Variations in the availability of pollen resources aect honey bee health. PLoS One 11, e0162818 (2016).
5. Naug, D. Nutritional stress due to habitat loss may explain recent honeybee colony collapses. Biol. Conserv. 142, 2369–2372 (2009).
6. Vodovnik, C., Borshagovski, A. M., Hakala, S. M., Leponiemi, M. & Freitak, D. Coeects of diet and neonicotinoid exposure on
honeybee mobility and food choice. Apidologie 52, 1–10 (2021).
7. Brodschneider, R. & Crailsheim, K. Nutrition and health in honey bees. Apidologie 41, 278–294 (2010).
8. Waller, G. D. Evaluating responses of honey bees to sugar solutions using an articial-ower feeder. Ann. Entomol. Soc. Am. 65,
857–862 (1972).
9. Pamminger, T., Becker, R., Himmelreich, S., Schneider, C. W. & Bergtold, M. e nectar report: Quantitative review of nectar sugar
concentrations oered by bee visited owers in agricultural and non-agricultural landscapes. PeerJ 2019, e6329 (2019).
10. Vaudo, A. D. et al. Pollen protein: Lipid macronutrient ratios may guide broad patterns of bee species oral preferences. Insects
11, 132 (2020).
11. Hendriksma, H. P. & Shar, S. Honey bee foragers balance colony nutritional deciencies. Behav. Ecol. Sociobiol. 70, 509–517
(2016).
12. Zarchin, S., Dag, A., Salomon, M., Hendriksma, H. P. & Shar, S. Honey bees dance faster for pollen that complements colony
essential fatty acid deciency. Behav. Ecol. Sociobiol. https:// doi. org/ 10. 1007/ s00265- 017- 2394-1 (2017).
13. Cook, S. M., Awmack, C. S., Murray, D. A. & Williams, I. H. Are honey bees’ foraging preferences aected by pollen amino acid
composition?. Ecol. Entomol. 28, 622–627 (2003).
14. Rotjan, R. D., Calderone, N. W. & Seeley, T. D. How a honey bee colony mustered additional labor for the task of pollen foraging.
Apidologie 33, 367–373 (2002).
15 Page, R. E., Scheiner, R., Erber, J. & Amdam, G. V. e development and evolution of division of labor and foraging specialization
in a social insect (Apis mellifera L.). Curr. Top. Dev. Biol. 74, 253–286 (2006).
16. Saunders, M. E. Insect pollinators collect pollen from wind-pollinated plants: Implications for pollination ecology and sustainable
agriculture. Insect. Conserv. Divers. 11, 13–31 (2018).
17. Hawkins, J. et al. Using DNA metabarcoding to identify the oral composition of honey: A new tool for investigating honey bee
foraging preferences. PLoS ONE 10, e0134735 (2015).
18. Danner, N., Molitor, A. M., Schiele, S., Härtel, S. & Stean-Dewenter, I. Season and landscape composition aect pollen foraging
distances and habitat use of Honey bees. Ecol. Appl. 26, 1920–1929 (2016).
19. Coey, M. F. & Breen, J. Seasonal variation in pollen and nectar sources of honey bees in Ireland. J. Apic. Res. 36, 63–76 (1997).
20. McMinn-Sauder, H., Lin, C. H., Eaton, T. & Johnson, R. A comparison of springtime pollen and nectar foraging in honey bees
kept in urban and agricultural environments. Front. Sustain. Food Syst. 6, 66 (2022).
21. De Vere, N. et al. Using DNA metabarcoding to investigate honey bee foraging reveals limited ower use despite high oral avail-
ability. Sci. Rep. 7, 1–10 (2017).
22. Von Der Ohe, W., PersanoOddo, L., Piana, M. L., Morlot, M. & Martin, P. Harmonized methods of melissopalynology. Apidologie
35, 18–25 (2004).
23. Cannizzaro, C. et al. Forest landscapes increase diversity of honeybee diets in the tropics. For. Ecol. Manag. 504, 119869 (2022).
24 Wirta, H. K., Bahram, M., Miller, K., Roslin, T. & Vesterinen, E. Reconstructing the ecosystem context of a species: Honey-borne
DNA reveals the roles of the honeybee. PLoS One 17, e0268250 (2022).
25. Wirta, H., Abrego, N., Miller, K., Roslin, T. & Vesterinen, E. DNA traces the origin of honey by identifying plants, bacteria and
fungi. Sci. Rep. 11, 4798 (2021).
26. omson, J. D., Draguleasa, M. A. & Tan, M. G. Flowers with caeinated nectar receive more pollination. Arthropod. Plant Interact.
9, 1–7 (2015).
27. Köhler, A., Pirk, C. W. W. & Nicolson, S. W. Honeybees and nectar nicotine: Deterrence and reduced survival versus potential
health benets. J. Insect Physiol. 58, 286–292 (2012).
28. Rivest, S. & Forrest, J. R. K. Defence compounds in pollen: Why do they occur and how do they aect the ecology and evolution
of bees?. New Phytol. 225, 1053–1064 (2020).
29. Stevenson, P. C., Nicolson, S. W. & Wright, G. A. Plant secondary metabolites in nectar: Impacts on pollinators and ecological
functions. Funct. Ecol. 31, 65–75 (2017).
30. Roessink, I. & van der Steen, J. J. M. Beebread consumption by honey bees is fast: Results of a 6-week eld study. J. Apic. Res. 60,
659–664 (2021).
31. Eyer, M., Neumann, P. & Dietemann, V. A look into the cell: Honey storage in honey bees Apis mellifera. PLoS One 11, e0161059
(2016).
32. Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol.
Ecol. 28, 391–406 (2019).
33. Bell, K. L. et al. Quantitative and qualitative assessment of pollen DNA metabarcoding using constructed species mixtures. Mol.
Ecol. 28, 431–455 (2019).
34. Filipiak, M., Walczyńska, A., Denisow, B., Petanidou, T. & Ziółkowska, E. Phenology and production of pollen, nectar, and sugar
in 1612 plant species from various environments. Ecology 103, e3705 (2022).
35. Requier, F. et al. Honey bee diet in intensive farmland habitats reveals an unexpectedly high ower richness and a major role of
weeds. Ecol. Appl. 25, 881–890 (2015).
36. Latty, T. & Trueblood, J. S. How do insects choose owers? A review of multi-attribute ower choice and decoy eects in ower-
visiting insects. J. Anim. Ecol. 89, 2750–2762 (2020).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
14
Vol:.(1234567890)
Scientic Reports | (2023) 13:14753 | https://doi.org/10.1038/s41598-023-42102-4
www.nature.com/scientificreports/
37 Milla, L., Schmidt-Lebuhn, A., Bovill, J. & Encinas-Viso, F. Monitoring of honey bee oral resources with pollen DNA metabarcod-
ing as a complementary tool to vegetation surveys. Ecol. Solut. Evid. 3, 10. https:// doi. org/ 10. 1002/ 2688- 8319. 12120 (2022).
38. Jones, L. et al. Temporal patterns of honeybee foraging in a diverse oral landscape revealed using pollen DNA metabarcoding of
honey. Integr. Comp. Biol. 62, 199–210 (2022).
39. Lowe, A., Jones, L., Brennan, G., Creer, S. & de Vere, N. Seasonal progression and dierences in major oral resource use by bees
and hoveries in a diverse horticultural and agricultural landscape revealed by DNA metabarcoding. J. Appl. Ecol. 59, 1484–1495
(2022).
40. Lowe, A. et al. Temporal change in oral availability leads to periods of resource limitation and aects diet specicity in a generalist
pollinator. Mol. Ecol. https:// doi. org/ 10. 1111/ MEC. 16719 (2022).
41 Ruottinen, L., Ollikka, T., Vartiainen, H. & Seppälä, A. Mehiläishoitoa käytännössä: osa 1 (Suomen mehiläishoitajain liitto SML,
2003).
42. Danner, N., Keller, A., Härtel, S. & Stean-Dewenter, I. Honey bee foraging ecology: Season but not landscape diversity shapes
the amount and diversity of collected pollen. PLoS ONE 12, e0183716 (2017).
43 Schmidt, L. S., Schmidt, J. O., Hima, Rao, Weiyi, W. & Ligen, Xu. Feeding preference and survival of young worker honey bees
(Hymenoptera: Apidae) fed rape, sesame, and sunower pollen. J. Econ. Entomol. 88, 1591–1595 (1995).
44. Di Pasquale, G. et al. Inuence of pollen nutrition on honey bee health: Do pollen quality and diversity matter?. PLoS ONE 8,
e72016 (2013).
45. Nasti, R. et al. An analytical investigation of hydroxylated cinnamoyl polyamines as biomarkers of commercial bee pollen botanical
origin. Int. J. Food Sci. Technol. 57, 7787–7796 (2022).
46. Qiao, J. et al. Phenolamide and avonoid glycoside proles of 20 types of monooral bee pollen. Food Chem. 405, 134800 (2023).
47 . Palmer-Young, E. C. et al. Chemistry of oral rewards: intra- and interspecic variability of nectar and pollen secondary metabolites
across taxa. Ecol. Monogr. 89, e01335 (2019).
48. Zhang, H., Liu, R. & Lu, Q. Separation and characterization of phenolamines and avonoids from rape bee pollen, and comparison
of their antioxidant activities and protective eects against oxidative stress. Molecules 25, 1264 (2020).
49. Negri, P. et al. Abscisic acid enhances the immune response in Apis mellifera and contributes to the colony tness. Apidologie 46,
542–557 (2015).
50. Ramirez, L. et al. Abscisic acid enhances cold tolerance in honeybee larvae. Proc. R. Soc. B Biol. Sci. 284, 20162140 (2017).
51. Isidorov, V. A., Czyzewska, U., Jankowska, E. & Bakier, S. Determination of royal jelly acids in honey. Food Chem. 124, 387–391
(2011).
52. Kokotou, M. G., Mantzourani, C., Babaiti, R. & Kokotos, G. Study of the royal jelly free fatty acids by liquid chromatography-high
resolution mass spectrometry (LC-HRMS). Metabolites 10, 40 (2020).
53. Cheung, Y., Meenu, M., Yu, X. & Xu, B. Phenolic acids and avonoids proles of commercial honey from dierent oral sources
and geographic sources. Int. J. Food Prop. 22, 290–308 (2019).
54. Koulis, G. A. et al. orough investigation of the phenolic prole of reputable Greek honey varieties: Varietal discrimination and
oral markers identication using liquid chromatography–high-resolution mass spectrometry. Molecules 27, 4444 (2022).
55 Tomás-Barberán, F. A., Martos, I., Ferreres, F., Radovic, B. S. & Anklam, E. HPLC avonoid proles as markers for the botanical
origin of European unioral honeys: HPLC avonoid proles as unioral honey markers. J. Sci. Food Agric. 81(5), 485–496. https://
doi. org/ 10. 1002/ jsfa. 836 (2001).
56 Jerković, I., Kuś, P. M., Tuberoso, C. I. G. & Šarolić, M. Phytochemical and physical–chemical analysis of Polish willow (Salix spp.)
honey: Identication of the marker compounds. Food Chem. 145, 8–14. https:// doi. org/ 10. 1016/j. foodc hem. 2013. 08. 004 (2014).
57. Jerković, I. et al. Red clover (Trifolium pratense L.) honey: Volatiles chemical-proling and unlocking antioxidant and anticorrosion
capacity. Chem. Pap. 70, i–xi (2016).
58. McLoone, P. et al. Qualitative phytochemical analysis and invitro investigation of the immunomodulatory properties of honeys
produced in Kazakhstan. Nat. Prod. Res. 37, 996–1001 (2023).
59. Leponiemi, M., Wirta, H. & Freitak, D. Trans-generational immune priming against American Foulbrood does not aect the
performance of honeybee colonies. Front. Vet. Sci. 10, 202 (2023).
60. Visscher, P. K. & Seeley, T. D. Foraging strategy of honeybee colonies in a temperate deciduous forest. Ecology 63, 1790–1801
(1982).
61. Beekman, M. & Ratnieks, F. L. W. Long-range foraging by the honey-bee Apis mellifera L. Funct. Ecol. 14, 490–496 (2000).
62. European Environment Agency (EEA). European Union, Copernicus Land Monitoring Service. (2018).
63. Mossberg, B. & Stenberg, L. Maastokasvio (Tammi, 2007).
64. Chen, S. et al. Validation of the ITS2 region as a novel DNA barcode for identifying medicinal plant species. PLoS ONE 5, 1–8
(2010).
65. White, T., Bruns, T., Lee, S. & Taylor, J. Amplication and direct sequencing of fungal ribosomal RNA genes for phylogenetics.
PCR Protoc. Guide Methods Appl. 18, 315–322 (1990).
66. Vesterinen, E. J., Puisto, A. I. E., Blomberg, A. S. & Lille y, T. M. Table for ve, please: Dietary partitioning in boreal bats. Ecol. Evol.
8, 10914–10937 (2018).
67 Vesterinen, E. J., Kaunisto, K. M. & Lilley, T. M. A global class reunion with multiple groups feasting on the declining insect
smorgasbord. Sci. Rep. https:// doi. org/ 10. 1038/ s41598- 020- 73609-9 (2020).
68. Kaunisto, K. M. et al. reats from the air: Damsely predation on diverse prey taxa. J Anim Ecol 89, 1365–1374 (2020).
69. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ https://
doi. org/ 10. 7717/ peerj. 2584 (2016).
70. Edgar, R. C. & Bateman, A. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
71. Banchi, E. et al. PLANiTS: A curated sequence reference dataset for plant ITS DNA metabarcoding. Database 2020, 155 (2020).
72. Lee, T., Alemseged, Y. & Mitchell, A. Dropping Hints: Estimating the diets of livestock in rangelands using DNA metabarcoding
of faeces. Metabarcod. Metagenom. 2, e22467 (2018).
73. Alberdi, A., Garin, I., Aizpurua, O. & Aihartza, J. e foraging ecology of the Mountain Long-eared bat Plecotus macrobullaris
revealed with DNA mini-barcodes. PLoS One 7, e35692 (2012).
74. R Core Team. in R: A Language and Environment for Statistical Computing. https:// www.r- proje ct. org/ (2022).
75. Oksanen, J. et al. in vegan: Community Ecology Package. https:// CRAN.R- proje ct. org/ packa ge= vegan (2022).
76. Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-eects models using lme4. J. Stat. Sow. 67, 1–48 (2015).
77. Lenth, R. V. in emmeans: Estimated Marginal Means, aka Least-Squares Means. https:// cran.r- proje ct. org/ packa g e= emmea n s (2021).
78. Hartig, F. in DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. https:// cran.r- proje ct. org/
packa ge= DHARMa (2020).
79 De Caceres, M. & Legendre, P. Associations between species and groups of sites: indices and statistical inference. Ecology 90,
3566–3574 (2009).
80. Zhou, J. et al. Floral classication of honey using liquid chromatography–diode array detection–tandem mass spectrometry and
chemometric analysis. Food Chem. 145, 941–949 (2014).
81. Sixto, A., Niell, S. & Heinzen, H. Straightforward determination of pyrrolizidine alkaloids in honey through simplied methanol
extraction (QuPPE) and LC–MS/MS modes. ACS Omega 4, 22632–22637 (2019).
Content courtesy of Springer Nature, terms of use apply. Rights reserved
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Vol.:(0123456789)
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www.nature.com/scientificreports/
Acknowledgements
We would like to thank Jason Rissanen, Tristan Ubaldi and Jaakko Kuurne for the help with eldwork and
Marjo Kilpinen and Eija Hakala for assisting in the laboratory work. We also thank the two beekeepers for their
expertise and maintaining the hives during the study. e study was funded by a grant to HW from Kone foun-
dation, a grant to ML from Emil Aaltonen Foundation (220131) and a grant to DF from the Finnish Cultural
Foundation (00180246). NAWI Graz is thanked for supporting Central Lab Environmental, Plant & Microbial
Metabolomics.Open access funded by Helsinki University Library.
Author contributions
M.L., D.F., M.M.-T. and H.W. designed the study. M.L., M.M.-T., M.T. and H.W. conducted the eldwork. M.L.
and H.W. did the DNA laboratory analyses and E.P.-W. the secondary metabolite laboratory analyses. E.V. and
H.W. did the bioinformatic analyses and M.L., M.M.-T., A.B.-S., E.P.-W. and H.W. the statistical analyses. M.L.
and H.W. wrote the rst dra of the manuscript. All authors contributed to and approved the manuscript.
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 023- 42102-4.
Correspondence and requests for materials should be addressed to H.W.
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