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RESEARCH ARTICLE
Modelling the effects of the repellent scent
marks of pollinators on their foraging
efficiency and the plant-pollinator community
Elise VerrierID*, Emmanuelle Baudry, Carmen Bessa-Gomes
Ecologie Syste
´matique Evolution, UMR 8079, CNRS, AgroParisTech, Universite
´Paris Saclay, Orsay, France
*elise.verrier@laposte.net
Abstract
Pollinator insects forage in complex and unpredictable resource landscapes, often using
social information from congeneric individuals to acquire knowledge about their environ-
ment. It has long been recognized that this process allows them to exploit floral resources
more efficiently and thus increase individual fitness. However, by creating correlations
between the behaviors of pollinators within a population, this could also indirectly influence
the entire plant-pollinator community. One type of social information used by pollinators is
the scent mark left on the corolla of flowers by previous visitors, which can be used as a cue
to avoid recently depleted resources. We developed a spatially explicit agent-based model
to examine the effects, at both individual and community levels, of pollinators using these
scent marks. The model simulates a population of pollinators foraging on flowers in a contin-
uous 2D space in which we can vary the density of pollinators. We showed that the use of
scent marks as a source of information significantly increased the foraging efficiency of polli-
nators except when competition between pollinators was very low. At the community level,
this also resulted in a marked homogenization between floral resources within the land-
scape: in the absence of scent marks, the coefficient of variation of the remaining nectar
quantity per flower strongly increased with greater pollinator competition, but it remained
low at all levels of competition when scent marks were used by the pollinators. Finally, the
use of scent marks markedly decreased the number of pollinator flower visits, especially at
high levels of pollinator competition, which can potentially reduce the pollination service.
1. Introduction
In many species, individuals can increase their knowledge about their environment using the
social information provided by congeneric individuals [1,2]. The social information concept
was initially developed for vertebrates, and it has been evoked in a diversity of context, from
selection of breeding sites in kittiwake to home range formation in wolves and coyotes [3,4].
The concept has since been extended to insects, particularly pollinators foraging in complex
and unpredictable environments [5,6].
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OPEN ACCESS
Citation: Verrier E, Baudry E, Bessa-Gomes C
(2021) Modelling the effects of the repellent scent
marks of pollinators on their foraging efficiency
and the plant-pollinator community. PLoS ONE
16(9): e0256929. https://doi.org/10.1371/journal.
pone.0256929
Editor: Ricardo Martinez-Garcia, International
Center for Theoretical Physics - South American
Institute for Fundamental Research, BRAZIL
Received: April 1, 2021
Accepted: August 18, 2021
Published: September 8, 2021
Peer Review History: PLOS recognizes the
benefits of transparency in the peer review
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responses alongside final, published articles. The
editorial history of this article is available here:
https://doi.org/10.1371/journal.pone.0256929
Copyright: ©2021 Verrier et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which
permits unrestricted use, distribution, and
reproduction in any medium, provided the original
author and source are credited.
Data Availability Statement: All the components
files of the agent based model will be available in a
public GitLab repository.
For an insect pollinator, flowering plants are a rich source of easily accessible reward in the
form of nectar and pollen. Nevertheless, foraging is an extremely complex task, since the small
amount of resource per flower makes it necessary to visit a very large number. For this process
to be efficient, the resource gain must exceed the energy cost of moving between flowers. To
exploit floral resources optimally, pollinators must therefore consider a large number of factors
such as the abundance and location of the flowers, their ease of handling, and the amount of
reward produced by the flowers, which often changes rapidly due to the consumption by other
pollinators.
To obtain information about these different factors, pollinating insects can interact directly
with their environment to acquire personal information (for example the localization of flow-
ers) but they can also interact with or observe other individuals and acquire social information
[7,8]. Social information is often divided between, on the one hand, signals shaped by natural
selection to transmit information and, on the other, inadvertently provided cues consisting of
elements that are inevitably produced by organisms [8]. For example, the well-known waggle
dance of honeybees is a signal that allows other members of the hive to acquire information
about the distance and direction of flower patches with available nectar and pollen [9,10]. By
contrast, bumblebees have been shown to be more effective at selecting flower types with high
rewards using the inadvertent clues provided by conspecifics on these flowers [11,12].
Several species of pollinators use repellent scent marks left by previous visitors on the
corolla of flowers. This phenomenon has been well studied in social pollinators like honeybees
and bumblebees but has also been described in solitary bees and is likely to be widespread
among the pollinator community [13–18]. These scent marks consist of short-lived odors
found on the cuticula of pollinators. Over time, the scent mark evaporates and fades while the
nectar is replenished until the flowers are no longer avoided by pollinators. The detection of
repellent scent marks is supposed to improve the foraging efficiency of pollinators by allowing
them to identify and avoid depleted flowers. This ability to avoid recently emptied flowers is
supposed to be particularly useful when both flower handling time and pollinator competition
are high [19–21]. Indeed, if most flowers are full of nectar, then it is less necessary to avoid the
few empty flowers, but conversely, if the pollinator density is high and the proportion of
empty flowers is important, the use of scent marks helps pollinators to increase their foraging
profit.
In general, the use of social information leads to correlations between the behaviors of indi-
viduals who produce the information and those who use it [22]. In the case of repellent scent
marks, there is a negative correlation, since the probability that a flower will be foraged at a
given time by a given insect decreases if another insect foraged it shortly before. These correla-
tions are expected to modify the distribution of nectar resources in the landscape. We propose
that the negative correlation induced by repellent scent marks will homogenize the amount of
nectar available from the different flowers in the landscape. Moreover, the use of scent marks
will tend to reduce the number of visits to flowers by pollinators, thus potentially diminishing
the efficiency of the pollination service.
We developed an agent-based model to examine at the individual level how the use of scent
marks affects pollinator foraging efficiency as a function of the level of competition in the pop-
ulation, and at the community level, how it impacts the distribution of resources in the land-
scape and the efficiency of pollination services for flowers.
2. Materials and methods
We developed an agent-based model to examine how the use of repellent scent marks as public
information affects both pollinator foraging efficiency and the flower community. Our model
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Funding: E.V. is funded by the French Ministry of
Research and the SEVE Doctoral School. This work
is part of the “Investments d’Avenir” Programme
overseen by the French National Research Agency
(ANR) (LabEx BASC; ANR-11-LABX-0034). The
funders had no role in study design, data collection
and analysis, decision to publish, or preparation of
the manuscript.
Competing interests: The authors have declared
that no competing interests exist.
was designed to simulate pollinators foraging in a meadow with a specific density of flowers.
The pollinators exploit a stable but unknown environment and compete with conspecifics. At
the end of the simulation, we record the mean nectar quantity collected by pollinators during
the simulation, the mean nectar quantity remaining in the flowers, the mean number of visits
per flower during the simulation, and the pollen transfer (estimated as the number of flowers
receiving the pollen of a focal flower), as well as the coefficient of variation of these four vari-
ables. The model is coded in Python 3.8 [23] and is available on GitLab (https://gitlab.com/
EliseVerrier1/abm-pollinators-scent-mark). A detailed version of the model description based
on the ODD (Overview, Design concepts, Details) protocol [24,25] is available in S1 File along
with a detailed description of the model parameters, variables, and their values (Tables
S1.1-S1.4 in S1 File).
2.1. Model entities and variables
The model is composed of three entities: the meadow (environment occupied by the agents)
and two classes of agents, namely flowers and pollinators. The meadow is a continuous torus
in 2D. The environment is characterized by its topology, i.e., a square with sides set by the user
(size_map). In our model, the environment keeps track of the discrete time during the simula-
tion with the dynamic variable step. The time that the pollinator spends in the flower while vis-
iting it (cost) is also considered to be an environmental variable.
Flowers are immobile agents characterized by the amount of their nectar and the concen-
tration of the scent mark left on their corolla at each step of the simulation. Both variables are
a function of the time since the last pollinator visit, the speed of nectar refill, and the speed of
the disappearance of the scent mark left by the pollinator. In our present analysis, we chose to
consider the scent mark as a reliable signal of nectar refill. Flowers will also record the number
of insects visiting them.
Pollinators are mobile agents characterized by their use or non-use of scent marks as repel-
lent cues. Their foraging performance is examined in light of two variables: the amount of nec-
tar collected, and the number of flowers visited. In our simulations, all pollinators within any
individual simulation are identical regarding these traits.
2.2. Process overview and scheduling
In this discrete time model, one step represents the time required for one action by the pollina-
tor. The foraging duration is 1,000 steps. At the start of the simulation, the flowers are full of
nectar, and the pollinator nectar harvest is set to 0. After initializing the environment, the flow-
ers are randomly positioned. The effect of the number of flowers was examined in the prelimi-
nary sensitivity analysis and was fixed at 250 for the simulation experiments. To model various
levels of competition, our simulation experiments examined a range of values for the number
of pollinators per flower. The pollinators are randomly placed on the map. Due to the random
distribution and stochasticity of the model, the possibility that two agents of the same category
(i.e., two flowers or pollinators) are located at the same place is insignificant. Nevertheless, we
decided that multiple pollinators could not be simultaneously present on the same flowers.
2.2.1. Pollinator process scheduling. The process scheduling for the pollinator is orga-
nized in five major classes of actions (summarized in Fig 1). When the pollinator is on a flower,
its behavior depends on whether it uses the scent mark. If the pollinator uses this source of
public information, the probability of visiting the flower is the outcome of a Bernoulli trial
with the probability set to correspond to the concentration of scent marks on the flower. In the
case of success, the pollinator will visit the flower and take the nectar; otherwise, it will move at
the next time step. If the pollinator does not use social information, it will always visit the
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flower. When a pollinator enters the flower, it will harvest all the available nectar, and this
quantity will be added to the total amount of collected nectar.
In our model, the major cost associated with exploiting a flower is the time involved, a cost
likely to depend on flower complexity. The pollinator will stay in the flower for a fixed period.
Fig 1. Scheduling of the pollinator.
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Fig 2. Scheduling of the flower.
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In our study, all flowers are homogenous, and the time steps spent on flowers are the same for
all individuals on a flower.
When the pollinator is not on a flower, it will move. In the case of a novel free flower within
the pollinator’s detection range (a fixed detection radius) with no other pollinator above or
inside it, the pollinator will move there. If there is more than one such flower, one is selected
randomly. If there is no free flower, it will move in a direction that diverges from the previous
direction by an angle taken from a Gaussian with expected value zero and variance depending
on the degree of inertia.
2.2.2. Flower process scheduling. When all pollinators have completed their action, the
scent mark and nectar quantity of every flower are updated. Both events are linearly dependent
on the time since last visit (Fig 2) [20]. Following the pollinator’s presence within the flower,
the nectar quantity is set to 0, whereas the scent mark is set to 1. As the time since last visit
increases, the amount of nectar accumulates, and the scent mark fades.
2.3. Sensitivity analyses
The impact of each parameter on the quantity of collected nectar was first assessed using sensi-
tivity analysis by simulation that explored a range of parameters (Table 1). We ran 10,000 sim-
ulations, and for each simulation, parameters were assigned a value that was randomly taken
from the variation interval. Their impact was studied by performing a random forest analysis
in R version 4.0.3 [26] using the randomForest package [27]. We built 2,000 regression trees
and examined three variables at each split.
The sensitivity analysis explained more than 95% of the variance. It is presented in detail in
S2 File.
2.4. Simulation experiments
Sensitivity analysis informed our choice of model parameters. The amount of nectar collected
increases linearly with the number of steps. As this parameter markedly influences the dura-
tion of the simulation, it was set to 1,000 steps. Because the coefficient of nectar refill has the
greatest impact on the quantity of nectar collected (Fig S2.1 in S2 File), we chose to examine
three contrasting values (0.001, 0.0025, 0.005), corresponding to 1, 2.5, and 5 refills per simula-
tion, respectively. The time spent probing each individual flower (cost parameter in our
model) will influence the amount of nectar collected and the advantage of using repellent scent
marks. We decided to set the cost to a low value (5-time steps, i.e., the pollinator remains in
Table 1. List of the model parameters and their variation range.
Type Parameters Interval Value
Environment Map size [30–100] 50
Number of steps in the simulation [700–1300] 1000
Flowers Number of flowers (as a function of meadow area) [0.02–2] 0.1 (250 flowers)
Coefficient of nectar refill [0.001–0.005] [0.001, 0.0025, 0.005]
Pollinators Pollinators per flower 0.1–2 [0.1, 0.3, 0.5, 1, 2]
Foraging behavior Use of repellent scent marks True or False True or False
Time spent probing each flower [1–10] 5
Flower detection and movement Detection radius 1 1
Degree of inertia in pollinators’ random movement [0.01–0.4] 0.1
The values exploited during sensitivity analysis appear in the “interval”, while the column “value” presents values exploited in simulation experiments and default values
for the remaining parameters.
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each flower for 0.5% of the simulation) to focus on the consequences of competition on infor-
mation use.
The intensity of competition experienced by pollinators depends on the ratio of pollinators
to flowers, while the time spent locating the flowers depends on the relation between the num-
ber of flowers and the size of the simulated meadow. These parameters increase the mean stan-
dard error of the model by almost 50% (Fig S2.1 in S2 File). We chose to fix the number of
flowers in relation to the meadow size and examine the impact of varying the number of com-
peting pollinators. We set the maximal number of pollinators to 500, with the density of flow-
ers and the size of the map being chosen in consequence.
The analyses were performed with R 4.0.3 [26] using the Rmisc [28] and plyr [29] packages.
The graphs were created with the ggplot2 package [30].
3. Results
3.1 Efficiency of nectar collection by pollinators
3.1.1 Impact of the cost. We first study the influence of the amount of time necessary to
exploit a flower on pollinator efficiency. This time represents an exploitation cost that affects
the amount of nectar collected and can be linked to the flower complexity. Our results showed
that when the cost increases, it gives an advantage to individuals using the scent mark, even in
a low competition context (Fig 3).
To keep the focus on the consequences of the competition, we decided to set the cost to the
relatively low value of 5 (0.5% of the simulation time) in the rest of this study.
3.1.2 Impact of the competition. Then, we analyzed the effect of scent mark use on the
efficiency of pollinators’ nectar collection for three flower nectar secretion rates and for relative
Fig 3. Mean amount of nectar collected by pollinators during the simulation as a function of the cost. The relative number of pollinators per flower was set to 0.1
(low pollinator density). The vertical dotted line represents the default value chosen for our analyses.
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abundances of pollinators compared to flowers varying from 0.1 to 2 pollinators per flower
(Fig 4).
Fig 4. Pollinators foraging efficiency as a function of scent mark use and pollinators’ relative abundance (here measured in relation to the number of flowers, i.e.,
pollinators per flower) (A) Mean amount of nectar collected by the pollinators during the simulation. (B) Ratio between the amount of nectar collected by pollinators
using the scent mark in relation to that collected by pollinators not using the scent mark. The red dashed line represents the threshold where the amount of nectar
collected is equal for both strategies. (C) Mean amount of nectar collected by pollinators at each flower visit. (D) Coefficient of variation of the mean amount of nectar
collected by pollinators during the simulation. In each figure, the x-axis represents the relative abundance of pollinators compared to flowers.
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We observed that the relative abundance of pollinators, which is directly related to the level
of competition for nectar, strongly reduces the average amount of nectar harvested by pollina-
tors, regardless of whether they use the scent mark (Fig 4A). The amount of nectar harvested
increases with the level of nectar secretion. To better visualize the effect of using the scent
mark for a given level of competition, we plotted the average amount of nectar collected by
pollinators using the scent mark divided by the amount collected by those not using it (Fig
4B). This figure highlights the interaction between the effect of scent mark use and competi-
tion: when pollinator abundance is low, that is, with less than one pollinator for two flowers,
scent mark use decreases the average amount of nectar collected. Nevertheless, for higher val-
ues of pollinator abundance, scent mark use results in the better efficiency of nectar collection,
with the difference between the two strategies increasing with the level of competition.
Another aspect of the efficiency is to maximize the nectar quantity harvested per flower and
avoid losing time visiting unrewarding flowers. As expected, the mean nectar quantity col-
lected per visit is higher when using the scient mark, regardless of competition level or nectar
refill coefficient (Fig 4C).
The use of the scent mark also affects the coefficient of variation of the nectar collected (Fig
4D). On the one hand, the coefficient of variation of the amount of nectar collected increases
with the level of competition, and this increase is greater in individuals that do not use the scent
mark: when pollinator abundance is low, that is, with less than one pollinator per flower, using
the scent mark leads to higher coefficients of variation. On the other hand, for higher values of
pollinator abundance, the use of scent marks results in less variability in the amount of nectar
collected, with the difference between the two strategies increasing with the level of competition.
3.2 Amount of nectar in the flowers
We also examined the distribution of nectar within the flowers. Fig 5 shows the average
amount of nectar remaining in the flowers at the end of the simulation and its coefficient of
variation for the same nectar secretion values and relative abundances of pollinators as
described above. As expected, the amount of nectar remaining in the flowers is lower when the
abundance of pollinators increases (Fig 5A). The amount of nectar remaining at the end of the
simulation is higher when pollinators use the scent mark than when they do not, with the ratio
between the two increasing with the abundance of pollinators (Fig 5B).
Interestingly, when pollinators do not use the scent mark, the coefficient of variation of the
remaining nectar increases sharply with the level of competition. On the contrary, when polli-
nators use the scent mark, the coefficient of variation is much lower and very similar for every
value of competition and nectar coefficient (Fig 5C). When pollinators do not use the scent
mark, competition increases the heterogeneity among the flowers, but when they use scent
marks, this social information leads to a homogenization of the nectar distribution among the
flowers.
3.3 Pollination service
The last aspect examined here was the pollination service, with an analysis of the number of
visits per flower and the number of flowers potentially reached by pollen of each individual
(Fig 6).
3.3.1 Number of pollinator visits per flower. Concerning the number of visits per flower,
we can see that, as expected, when the abundance of pollinators increases, the number of
flower visits also increases (Fig 6A). However, the number of visits is much lower when the
pollinators use the scent mark. This number can be divided by 2 to 10 depending on the refill
rate and the abundance of pollinators.
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Fig 6. (A) Mean number of visits per flower during the simulation. (B) Mean number of flowers visited by a pollinator after he leaves the focal flower. In each figure, the
x-axis represents the relative abundance of pollinators compared to flowers. The effect on the coefficient of variation can be found in the S3 File.
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Fig 5. (A) Mean amount of nectar remaining in the flowers at the end of the simulation. (B) Ratio of the amount of nectar remaining in the flowers when pollinators do
and do not use the scent mark. The red dashed line represents the threshold where the nectar quantity remaining is equal for both strategies. (C) Coefficient of variation
of the nectar remaining in the flowers at the end of simulation. In each figure, the x-axis represents the relative abundance of pollinators compared to flowers.
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3.3.2 Number of flowers reached by the pollen. To complete our study of the pollination
services, we analyzed how many flowers may be reached by the pollen of any given flower. Fig
6B presents the mean number of flowers visited by a pollinator after he leaves the focal flower.
In line with the previous results, the pollen is less dispersed among the flower community
when the pollinators used the scent-mark.
4. Discussion
Many insects can detect and recognize conspecifics by scent, an ability that can be found across
taxonomic groups and ecological functions [31]. This ubiquitous ability to detect the scent of
other insects and exploit odorant information is likely to have evolved before the existence of
flowers [32]. In our agent-based model, pollinators can collect inadvertently provided cues in
the form of repellent scent marks. Our simulations show that pollinators foraging in competi-
tive contexts can collect more nectar when using scent marks as repellent cues, which is in
accordance with experimental and observational studies [19,33–35]. As expected, by using the
scent mark to avoid recently visited flowers, pollinators no longer spend time in flowers with
little or no nectar, leaving them more time to search elsewhere.
The relative advantage of using scent marks to avoid recently visited flowers depends on
both the intensity of competition, which determines the average amount of nectar remaining
in the flowers, and the relative cost of visiting depleted flowers. Pollinators face competition by
exploitation, as their congeners deplete the floral resources. When the number of competitors
is low, pollinators that do not use the scent mark may fare better than their “informed” coun-
terparts, but as the relative abundance of competitors increases, the use of scent marks as repel-
lent cues allows them to collect a greater amount of nectar.
In our model, the relative cost of visiting depleted flowers modulates the relationship
between the intensity of competition and the advantage of using the scent mark, resulting in
the relative advantage of foregoing the scent mark when the cost is low. Indeed, as the handling
time of a given flower is likely to depend on its complexity, insects visiting simple flowers
where nectar reward can be quickly assessed may forego the use of scent marks [32]. Saleh
et al. [21] tested this hypothesis in a laboratory setting using bumblebees foraging on simple or
complex artificial flowers. As predicted, bumblebees are more than twice as likely to reject
complex scent-marked flowers compared to simple flowers. The handling time of a given
flower is also likely to depend on the characteristics of the visiting insects. The co-evolution of
pollinator feeding apparatus and flower traits results in visiting costs and rewards that may
vary in relation to the visiting insect. Our model focused on scent mark use in homogeneous
communities, but plant-pollinator communities are heterogeneous with networks character-
ized by varying degrees of both insect and flower generalism [36]. It is possible that the degree
of generalism will affect the benefit of using repellent scent marks. We expect that the scent
mark can be useful when foraging on generalist flowers but not when visiting flowers exploited
by a small fraction of pollinators.
In our study, we were also interested in the effect of using repellent scent marks on the nec-
tar resources. As we hypothesized, the use of social information correlates the behavior of pol-
linators and modifies the distribution of nectar resources. When the individuals use the scent
mark as an inadvertent source of social information, the repartition of resources is more
homogenous. Because the social information is a repellent, the correlation between visits will
be negative, and flowers will not be overexploited. Moreover, because pollinators using scent
marks no longer exploit flowers with little or no nectar, flowers should have enough time to
produce a high amount of nectar between two visits. Our results corroborate this hypothesis,
as the mean amount of nectar collected per flower is higher when pollinators use the scent-
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mark. Consequently, the pollinators can detect a higher number of flowers, while the flowers
are not underexploited, resulting in optimal use of nectar resources.
The homogenization of nectar resources is likely to affect the selective value of social infor-
mation. Stout and Goulson [20] used a theoretical model to examine the consequences of nec-
tar secretion rates on bumblebee’s response to previously visited flowers. Their model
predicted that the optimum frequency between visits was open to “cheating” by indiscriminate
foragers that do not reject any flowers and would benefit from a homogeneous resource land-
scape when in low frequency. Likewise, we expect that the use of scent marks in our model is
frequency-dependent and that at low frequencies, foragers foregoing the use of social informa-
tion would fare better than their informed counterparts.
In addition to the homogenization of nectar resources, the use of repellent scent marks also
impacts the number of visits per flower and the subsequent pollen dispersal among the com-
munity. Indeed, even if the overall number of flowers detected increases, the number of visits
per flower decreases when pollinators use repellent scent marks. The relation between visita-
tion rate and reproductive success will depends on a myriad of factors, including flower mor-
phology, flower phenology, and pollinator behavior [37–39]. Our results suggest that the use of
the scent-mark can decrease the pollination by decreasing the overall visitation rate, which has
been shown to be correlate to pollen receipt [40], but also by limiting the pollen transfer to
other flowers.
The mismatch between pollinator foraging success and pollination does not necessarily
entail a deficient pollination service, particularly when we take diversity into account. Higher
pollinator abundance and diversity have been shown to increase yield in pollinator-dependent
crops in several independent studies [41]. More diverse communities should contain species
with variable degrees of generalism, life span, and mobility, thus differing in their ability to use
social information, which might increase pollination services and ensure resilience. The diver-
sity in social information use among the pollinators’ community is likely to lead to the co-exis-
tence of pollinators that use the scent-mark with some that do not. This is particularly likely
when we consider possibility of frequency dependent selection of the scent-mark use [20] as
discussed above.
To better understand how foraging strategies impact pollination services, it would be inter-
esting to further explore the diversity of pollinators and plants and the associated foraging
strategies. According to the optimal foraging theory, adaptation promotes pollinators’ general-
ism when competition between pollinators is strong [42], which consequently facilitates plant
diversity. Social foraging might further contribute to our understanding of the link between
foraging strategies and diversity in plant-pollinator communities, as differential exploitation
costs might maintain the diversity of pollinators.
Supporting information
S1 File. Detailed model description. Model description is based on the ODD (Overview,
Design concepts, Details) protocol.
(DOCX)
S2 File. Sensitivity analysis. Sensitivity analysis.
(DOCX)
S3 File. Extra figures. Coefficient of variation of the number of visits per flower during the
simulation and the Coefficient of variation of the number of flowers which receive a visit of a
pollinator after he leaves the focal one.
(DOCX)
PLOS ONE
Effects of repellent scent marks on pollinator foraging efficiency and plant-pollinator community
PLOS ONE | https://doi.org/10.1371/journal.pone.0256929 September 8, 2021 11 / 14
Acknowledgments
We thank Pau Capera-Aragones and an anonymous reviewer whose suggestions greatly
improved the quality of this article.
Author Contributions
Conceptualization: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
Formal analysis: Elise Verrier.
Funding acquisition: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
Investigation: Elise Verrier.
Methodology: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
Project administration: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
Software: Elise Verrier.
Supervision: Emmanuelle Baudry, Carmen Bessa-Gomes.
Validation: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
Visualization: Elise Verrier.
Writing – original draft: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
Writing – review & editing: Elise Verrier, Emmanuelle Baudry, Carmen Bessa-Gomes.
References
1. Galef BG, Laland KN. Social Learning in Animals: Empirical Studies and Theoretical Models. BioSci-
ence. 2005; 55(6):489.
2. Danchin E, Giraldeau LA, Valone TJ, Wagner RH. Public Information: From Nosy Neighbors to Cultural
Evolution. Science. 2004; 305(5683):487–91. https://doi.org/10.1126/science.1098254 PMID:
15273386
3. Boulinier T, Danchin E, Monnat J-Y, Doutrelant C, Cadiou B. Timing of Prospecting and the Value of
Information in a Colonial Breeding Bird. J Avian Biol. 1996 Sep; 27(3):252.
4. Briscoe B, Lewis MA, Parrish SE. Home Range Formation in Wolves Due to Scent Marking. Bull Math
Biol. 2002 Mar; 64(2):261–84. https://doi.org/10.1006/bulm.2001.0273 PMID: 11926117
5. Leadbeater E, Chittka L. Social learning in insects—from miniature brains to consensus building. Curr
Biol. 2007; 17(16):R703–13. https://doi.org/10.1016/j.cub.2007.06.012 PMID: 17714664
6. Leadbeater E, Chittka L. A new mode of information transfer in foraging bumblebees? Curr Biol. 2005
Jun; 15(12):R447–8. https://doi.org/10.1016/j.cub.2005.06.011 PMID: 15964260
7. Gru¨ter C, Leadbeater E. Insights from insects about adaptive social information use. Trends Ecol Evol.
2014; 29(3):177–84. https://doi.org/10.1016/j.tree.2014.01.004 PMID: 24560544
8. Wagner RH, Danchin E
´. A taxonomy of biological information. Oikos. 2010; 119(2):203–9.
9. Von Frisch K. The dance language and orientation of bees. Harv Univ Press. 1967. PMID: 5601830
10. Riley JR, Greggers U, Smith AD, Reynolds DR, Menzel R. The flight paths of honeybees recruited by
the waggle dance. Nature. 2005; 435(7039):205–7. https://doi.org/10.1038/nature03526 PMID:
15889092
11. Baude M, Dajoz I, Danchin E
´. Inadvertent social information in foraging bumblebees: effects of flower
distribution and implications for pollination. Anim Behav. 2008 Dec; 76(6):1863–73.
12. Baude M, Danchin E
´, Mugabo M, Dajoz I. Conspecifics as informers and competitors: an experimental
study in foraging bumble-bees. Proc R Soc B Biol Sci. 2011; 278(1719):2806–13. https://doi.org/10.
1098/rspb.2010.2659 PMID: 21288951
13. Goulson D, Stout JC, Langley J, Hughes WOH. Identity and Function of Scent Marks Deposited by For-
aging Bumblebees. J Chem Ecol. 2000; 26(12):2897–911.
PLOS ONE
Effects of repellent scent marks on pollinator foraging efficiency and plant-pollinator community
PLOS ONE | https://doi.org/10.1371/journal.pone.0256929 September 8, 2021 12 / 14
14. Wilms J, Eltz T. Foraging scent marks of bumblebees: footprint cues rather than pheromone signals.
Naturwissenschaften. 2008; 95(2):149–53. https://doi.org/10.1007/s00114-007-0298-z PMID:
17724572
15. Saleh N, Scott AG, Bryning GP, Chittka L. Distinguishing signals and cues: bumblebees use general
footprints to generate adaptive behaviour at flowers and nest. Arthropod-Plant Interact. 2007; 1
(2):119–27.
16. Stout JC, Goulson D, Allen JA. Repellent scent-marking of fowers by a guild of foraging bumblebees
(Bombus spp.). Behav Ecol Sociobiol. 1998; 43:317–26.
17. Yokoi T, Fujisaki K. Recognition of scent marks in solitary bees to avoid previously visited flowers. Ecol
Res. 2009 Jul; 24(4):803–9.
18. Ballantyne G. Ants as flower visitors: floral ant-repellence and the impact of ant scent-marks on pollina-
tor behaviour. 2011.
19. Williams CS. The identity of the previous visitor influences flower rejection by nectar-collecting bees.
Anim Behav. 1998; 56(3):673–81. https://doi.org/10.1006/anbe.1998.0794 PMID: 9784216
20. Stout J, Goulson D. The influence of nectar secretion rates on the responses of bumblebees (Bombus
spp.) to previously visited flowers. Behav Ecol Sociobiol. 2002; 52(3):239–46.
21. Saleh N, Ohashi K, Thomson JD, Chittka L. Facultative use of the repellent scent mark in foraging bum-
blebees: complex versus simple flowers. Anim Behav. 2006; 71(4):847–54.
22. Gil MA, Hein AM, Spiegel O, Baskett ML, Sih A. Social Information Links Individual Behavior to Popula-
tion and Community Dynamics. Trends Ecol Evol. 2018; 33(7):535–48. https://doi.org/10.1016/j.tree.
2018.04.010 PMID: 29748042
23. Python Software Foundation. Python Language Reference, version 3.8, Available at http://www.python.
org. 2019.
24. Grimm V, Berger U, DeAngelis DL, Polhill JG, Giske J, Railsback SF. The ODD protocol: A review and
first update. Ecol Model. 2010; 221(23):2760–8.
25. Grimm V, Railsback SF, Vincenot CE, Berger U, Gallagher C, DeAngelis DL, et al. The ODD Protocol
for Describing Agent-Based and Other Simulation Models: A Second Update to Improve Clarity, Repli-
cation, and Structural Realism. J Artif Soc Soc Simul. 2020; 23(2):7.
26. R Core Team. R: A language and environment for statistical computing [Internet]. Vienna, Austria;
2020. Available from: https://www.R-project.org/
27. Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002; 2(3):18–22.
28. Hope RM. Rmisc: Rmisc: Ryan Miscellaneous. [Internet]. 2013. (R package). Available from: https://
CRAN.R-project.org/package=Rmisc
29. Wickham H. The Split-Apply-Combine Strategy for Data Analysis. J Stat Softw [Internet]. 2011 [cited
2021 Mar 1]; 40(1). Available from: http://www.jstatsoft.org/v40/i01/
30. Wickham H, Chang W, Henry L, Pedersen T, Takahashi K, Wilke C, et al. ggplot2: Elegant Graphics for
Data Analysis. Springer-Verl N Y. 2016.
31. Renou M. Pheromones and general odor perception in insects. Neurobiol Chem Commun. 2014; 1:23–
56. PMID: 24830044
32. Goulson D. The use of scent marks by foraging bumble bees. In: Food Exploitation by Social Insects:
Ecological, Behavioral, and Theoretical Approaches. CRC Press; 2009. p. 251–60.
33. Kato M. Bumblebee visits to Impatiens spp.: pattern and efficiency. Oecologia. 1988; 76(3):364–70.
https://doi.org/10.1007/BF00377030 PMID: 28312015
34. Schmitt U, Bertsch A. Do foraging bumblebees scent-mark food sources and does it matter? Oecologia.
1990; 82(1):137–44. https://doi.org/10.1007/BF00318545 PMID: 28313149
35. Goulson D, Hawson SA, Stout JC. Foraging bumblebees avoid flowers already visited by conspecifics
or by other bumblebee species. Anim Behav. 1998 Jan; 55(1):199–206. https://doi.org/10.1006/anbe.
1997.0570 PMID: 9480686
36. Poisot T, Ke
´fi S, Morand S, Stanko M, Marquet PA, Hochberg ME. A Continuum of Specialists and Gen-
eralists in Empirical Communities. Heil M, editor. PLOS ONE. 2015; 10(5):e0114674. https://doi.org/10.
1371/journal.pone.0114674 PMID: 25992798
37. La
´zaro A, Jakobsson A, Totland Ø. How do pollinator visitation rate and seed set relate to species’ floral
traits and community context? Oecologia. 2013; 173(3):881–93. https://doi.org/10.1007/s00442-013-
2652-5 PMID: 23579571
38. King C, Ballantyne G, Willmer PG. Why flower visitation is a poor proxy for pollination: measuring sin-
gle-visit pollen deposition, with implications for pollination networks and conservation. FreckletonR, edi-
tor. Methods Ecol Evol. 2013; 4(9):811–8.
PLOS ONE
Effects of repellent scent marks on pollinator foraging efficiency and plant-pollinator community
PLOS ONE | https://doi.org/10.1371/journal.pone.0256929 September 8, 2021 13 / 14
39. Gallagher MK, Campbell DR. Pollinator visitation rate and effectiveness vary with flowering phenology.
Am J Bot. 2020; 107(3):445–55. https://doi.org/10.1002/ajb2.1439 PMID: 32086803
40. Cayenne Engel E, Irwin RE. Linking pollinator visitation rate and pollen receipt. Am J Bot. 2003; 90
(11):1612–8. https://doi.org/10.3732/ajb.90.11.1612 PMID: 21653336
41. Garibaldi LA, Carvalheiro LG, Vaissiere BE, Gemmill-Herren B, Hipolito J, Freitas BM, et al. Mutually
beneficial pollinator diversity and crop yield outcomes in small and large farms. Science. 2016; 351
(6271):388–91. https://doi.org/10.1126/science.aac7287 PMID: 26798016
42. Revilla TA, Křivan V. Pollinator Foraging Adaptation and Coexistence of Competing Plants. PLOS
ONE. 2016; 11(8):e0160076. https://doi.org/10.1371/journal.pone.0160076 PMID: 27505254
PLOS ONE
Effects of repellent scent marks on pollinator foraging efficiency and plant-pollinator community
PLOS ONE | https://doi.org/10.1371/journal.pone.0256929 September 8, 2021 14 / 14
