Fig. 2 Number of events that were detected correctly or with an
error by the Bee Tracker software in the 15 videos that were
checked visually
Discussion
The Bee Tracker software is a helpful tool to collect large
amounts of data on the nesting and foraging behavior of solitary,
cavity-nesting bees in an automated way. It identifies individual
nesting females and assigns them to their nests. This permits to obtain
robust data on per female reproductive success, if nesting progress
within nests is additionally recorded. Moreover, the software counts the
number of cavities a female probes until it finds its nest, collects
information on the flight duration and allows to assess flight activity.
Once the software is trained for the experimental setup in use, the
method requires low labor input but can generate large data sets with a
high measurement precision. Here we showed that a precision of 96% can
be achieved with a relatively low training effort of about 30 working
hours. Minor adaptations may further improve the performance of the
software.
The software is designed to achieve a high precision at the expenses of
the recall (fraction of events that was retrieved), which is of minor
interest in this type of analysis as it only affects the sample sizes
but not the extracted measurements themselves. The precision of theBee Tracker therefore exceeds precision values typically found in
automated image analysis software (Eikelboom et al., 2019; Gallmann et
al., 2020). The software may, however, only achieve the here reported
precision of 96% in experiments with a similar setup, with respect to
light conditions during video recording, hues and digits of bee IDs as
well as the shape, size and location of the nest cavities in the nesting
units. For variant setups, the training of the software may need to be
repeated to achieve a comparable measurement precision of the software
analysis. While errors by bee ID swapping cannot be entirely avoided due
to the limitations of the centroid object tracking algorithm used by the
software, errors caused by color misclassifications between green and
yellow were probably caused by the convergence of spectra under
different light conditions and could likely be reduced by choosing
colors for ID tags with more distinct spectra. Thus, while an increased
training effort may reduce the error rate, replacing either green or
yellow by e.g. blue or red ID tags may completely eliminate color
misclassifiactions, which would increase the precision to 98% in our
data set.
Direct observations of the nesting activity of individually marked bees
is very challenging and nearly impossible in most experimental setups,
as several bees frequently aggregate in front of the nesting unit and
the bee IDs are small for human vision while the bees move quickly.
Researchers therefore used visual analysis of videos for the assessments
of individual nesting and foraging behavior in solitary bees (McKinney
& Park, 2012). In comparison to direct observations, a main advantage
of the Bee Tracker is the large data sets that can be collected
with relatively low time and labor input. Despite these advantages, the
method also has some limitations. The main disadvantage of the software
is its restriction to relatively large bee species that allow fixing ID
tags on the bees’ thorax (e.g. tags produced for the marking of honey
bee queens). Furthermore, the current version of the Bee Trackersoftware was trained on the model solitary bee species Osmia
bicornis. Although bee recognition and the classification of movement
(entering or leaving a cavity) seemed to work equally precise when
tested on the closely related species O. cornuta (Knauer A.,
personal observation), further training may be required when working
with other solitary bee species to obtain full precision of the
software. Furthermore, the current version of the software can only
analyze the above described 24 unique color-digit based bee IDs and
identify cavities with a certain size and shape that are arranged in the
nesting unit as described (Fig. 1). These limits can, however be adapted
by training the software to additional bee IDs (with more digits or
colors) and different nesting units. After such additional training, the
software could be used in various experimental setups to study the
behavior of solitary, cavity-nesting bees that can be established in
standardized nesting units.
In social bee species, the number of adult bees, brood cells and the
amount of food stores (honey and pollen) are used as indicators of
colony strength and vitality (Dainat et al., 2020; Hernandez et al.,
2020), while in solitary bees reproductive success measured by brood
cell or offspring production is the most important proxy of fitness
(Rundlöf et al., 2015; Stuligross & Williams, 2020; Zurbuchen et al.,
2010). RFID technology has furthermore been used for the monitoring of
foraging behavior in social species. RFID can automatically perform
individual bee recognition and detect the inbound and outbound movements
of tagged bees at the nest entrance where the antenna and reader are
placed (Nunes-Silva et al., 2019). With this technology, flight
activity, homing ability and flight duration of social bees can be
studied (Henry et al., 2012; Schneider et al., 2012; Stanley et al.,
2016; Tenczar et al., 2014). Such behavioral data can contribute to the
understanding of behavior mediated impacts of environmental stressors on
colony development (Henry et al., 2012). In addition, the measurements
of behavior can be a powerful tool to assess the impact of specific
stressors in (semi-)field experiments, especially as colony strength and
development can be biased by various confounding factors (Oldroyd et
al., 1992; Sandrock et al., 2014; Schmid‐Hempel & Schmid‐Hempel, 1998).
Similarly, the Bee Tracker software can be used to collect large
amounts of behavioral data to supplement and better understand
measurements of reproductive success and fitness in solitary,
cavity-nesting bees.
To our knowledge, the Bee Tracker software is the first automated
tool that allows to efficiently collect large amounts of behavioral data
on cavity-nesting solitary bee species. Foraging behavior can respond to
various environmental stressors. Pesticide exposure for example, can
impair orientation and memory in bees (Siviter et al., 2018) and
increase flight duration or cause a reduction in homing or foraging
activity (Artz & Pitts-Singer, 2015; Henry et al., 2012; Stanley et
al., 2016). Flight duration may also be increased by habitat degradation
or food competition, which can cause increased flight distances to food
sources (Leonhardt et al., 2016; Thomson, 2004). Pathogens can reduce
homing ability in honey bees (Li et al., 2013) or cause a premature
onset of foraging and reduce the total activity span of foragers
(Benaets et al., 2017). Overall, understanding bees’ foraging and flight
activities can provide valuable information for evaluating the impact of
a wide range of environmental stressors on bees. For example, behavioral
data collected with RFID contributed to the detection of sublethal
adverse effects of neonicotinoids which finally led to the ban of
several compounds from this class of insecticides in the European Union
(Gross, 2013).
The effect of different stressors can vary between species and depend on
their functional traits such as body size, sociality or mode of nesting
(Brittain & Potts, 2011; Sgolastra et al., 2019). A range of solitary
bee species are therefore increasingly studied for the assessment and
monitoring of stressors on pollinators (Boff et al., 2020; Ganser et
al., 2020; Klaus et al., 2021; Stuligross & Williams, 2020; Zurbuchen
et al., 2010). The Bee Tracker software can be a helpful tool to
efficiently collect robust data on individual nesting and foraging
behavior, of cavity-nesting solitary bees.
Data availability
The data associated with this manuscript and the software including the
underlying Python code will be made available on dryad upon acceptance
of the paper.
Author contribution
A.C.K. and M.A. conceived the study. J.G. developed the software. A.C.K.
collected the data. A.C. conducted the statistical analysis. A.C.K.
wrote the first version of the manuscript and all authors contributed to
the writing of subsequent drafts.
Acknowledgements
We thank Jonas Winizki for technical support and advice. This project
has received funding from from the European Union’s Horizon 2020
research and innovation programme under grant agreement No. 773921,
PoshBee Project (www.poshbee.eu).
Competing interests
The authors declare no competing interests.
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