This video describes Radio-Frequency Identification (RFID) and motion-sensitive video recording methods to monitor choice behavior by bumblebees.
We present two methods for observing bumblebee choice behavior in an enclosed testing space. The first method consists of Radio Frequency Identification (RFID) readers built into artificial flowers that display various visual cues, and RFID tags (i.e., passive transponders) glued to the thorax of bumblebee workers. The novelty in our implementation is that RFID readers are built directly into artificial flowers that are capable of displaying several distinct visual properties such as color, pattern type, spatial frequency (i.e., “busyness” of the pattern), and symmetry (spatial frequency and symmetry were not manipulated in this experiment). Additionally, these visual displays in conjunction with the automated systems are capable of recording unrewarded and untrained choice behavior. The second method consists of recording choice behavior at artificial flowers using motion-sensitive high-definition camcorders. Bumblebees have number tags glued to their thoraces for unique identification. The advantage in this implementation over RFID is that in addition to observing landing behavior, alternate measures of preference such as hovering and antennation may also be observed. Both automation methods increase experimental control, and internal validity by allowing larger scale studies that take into account individual differences. External validity is also improved because bees can freely enter and exit the testing environment without constraints such as the availability of a research assistant on-site. Compared to human observation in real time, the automated methods are more cost-effective and possibly less error-prone.
A key problem in studying unlearned choice behavior by bumblebees and honeybees is that flower-naïve, untrained workers do not readily enter the testing space where preferences can be measured. As a result, many researchers rely on a less than ideal technique: pre-training workers to feed inside the testing space from ostensibly neutral stimuli that researchers deem to be different from the experimental stimuli. However, recent experiments have shown that stimuli that were thought to be neutral (i.e., stimuli that do not influence subsequent choice behavior in a testing session) have influenced preferences in unexpected ways1. Automated systems that include Radio Frequency Identification (RFID)2 and motion-sensitive video recordings may offer an opportunity solve this problem. The aim of the study was twofold: (1) primarily to contribute to the literature on unlearned floral preferences by bumblebees, (2) and secondarily to evaluate two choice measurement systems, as recorded by two different automated recording devices.
Two automated systems3 were implemented in the present study to observe unlearned choice behavior: RFID and motion-sensitive video recordings. Two crucial elements of both systems are that choices are not rewarded, and the display of different visual cues can be manipulated. Motion-sensitive video (high definition, recording at 1 mp resolution) not only allows continuous observation of freely exploring workers in a flight room, but is critical for the efficient observation of relatively rare events4.
The research question in Experiment 1 relates to how different visual properties interact when displayed together. This study seeks to explore the relative importance of pattern positioning in relation to pattern type. Using a 2 x 2 design, radial (i.e., sunburst) and concentric (i.e., bull’s eye) pattern types are placed either centrally or peripherally on an artificial flower (see Figure 1 for examples of stimuli). RFID readers are built into these specially designed artificial flower stimuli, and bumblebees receive RFID enabled tags that allow us to record each tagged worker that enters the artificial flower stimulus. RFID observation works by the reader mechanism (built into the artificial flowers) sending signals at radio frequencies (13.56 MHz in this case), which are modulated by the presence of passive tags. The reader can detect and record these signal modulations, which vary slightly across tags enabling tag unique identification.
The questions of Experiment 2 are threefold. First, are flower-entry, as measured by RFID, and landing, as measured by video recordings equivalent choice criteria? Choice is measured at different points (landing for video, and flower entry for RFID), which may translate into different measures of preference. Second, what is the effect of central vs peripheral positioning? It is not known whether or not workers would choose a central pattern if a combination consisting of two radial patterns in different positions were presented (see Figure 4b). Third, what is the relative importance of pattern position vs pattern type? In other words, will bumblebees land on patterns of the preferred pattern type, or the preferred pattern position? Bees could prefer central-radial to a peripheral-concentric pattern, but the preference could be due to the pattern type or its central positioning. In this experiment, two variables were pitted against each other5 (see Figure 4c, d).
In Experiment 2, we used motion-sensitive video recordings on flower-like stimuli. Artificial flowers were placed inside a flight cage, and motion-sensitive high-definition camcorders were pointed at these flowers from the front and the top. More specifically, two camcorders were positioned so as to capture the frontal view of each of the two stimuli in the testing space. An additional camcorder was positioned between the stimuli to record hovering behavior from above, and captured behavior from both artificial flowers. Bumblebees were identified using number tags that could be read on high-definition video clips. Hovering, antennation and landing behaviors were observed.
The Animal Care Committee of the University of Ottawa has approved our experimental protocol, which delineates safety procedures for personnel working with bees.
1. Testing Environment Preparation
Figure 1. RFID Artificial Flower Design. Schematic diagram of the RFID-enabled artificial flower used in Experiment 1. The RFID reader rested on top of the open cylinder through the center of the flower. Stimuli patterns and positions: a. peripheral-concentric, b. central-concentric, c. peripheral-radial and d. central-radial. This figure has been modified from Orbán et al.11.
Figure 2. Stimulus Display Stand. A schematic drawing showing the design of the artificial flower stimulus stand, and attachment clip. The attachment clip is glued to the artificial flower and is used to quickly attach and detach the stimuli to and from the stimuli stands. This configuration was used in the motion-sensitive video experiment. Please click here to view a larger version of this figure.
2. Bumblebee Colony Preparation
3. Preparation for Observation by Radio-frequency Identification
4. Artificial Flower Preparation for RFID Readers
5. Preparation for Observation by Motion-sensitive Video Recording
6. Artificial Flower Preparation for Video Observation
7. Statistical Analysis
8. Stimuli Presentation Sequence
9. Study Termination
Experiment 1: RFID Data
All 375 workers in the colony were RFID tagged, and 318 of these workers (85%) entered the flight-cage at some point during the study. A total of 197 (62% of bees that left the colony) visited at least one of four artificial flower stimuli.
Definition of a choice
A choice was defined as a worker entering into the artificial flower (see Figure 1). We labeled this behavior as “floral exploration.” This definition of a choice is stricter than the ones used in the literature, which, depending on the study, use some combination of hovering, antennation or landing. Floral exploration is a stricter definition of choice because it requires that bees not only attend to a stimulus by hovering, antennating, and landing on it, but also by exploring it.
Data management
RFID techniques have the capacity to collect an enormous amount of data relative to other methods. This experiment produced 310,221 records. A database program such as MySQL is an indispensable tool to store this amount of data and equally important are the SQL queries used to tease out answers to the research questions. For example, one issue is the definition of a choice. The RFID readers were configured to record the presence of a tag every 1 msec, which, in the case of bees that stayed inside the artificial flowers for minutes or even hours, can translate into several thousand recordings. Our definition of a choice was a visit that lasted at least 1 msec, but a new visit would not be counted until the current string of recordings was broken by at least a 1 min break.
Summary of experiment
Four-replicated goodness of fit tests were performed on all choices from a bees’ “naïve session” to compare choice proportions to a theoretical value of chance7. A bee's naïve session refers to the first testing condition in which the bee “participated”. The G-tests reveal a preference for central positioning (see Table 1) and radial pattern type. Figure 4b shows that pattern preference is reversed when the concentric pattern is positioned centrally and the radial pattern is positioned peripherally. However, if positioning is held constant as in Figure 4a and d, pattern preference is towards the radial pattern. Figure 4 shows that the relative proportions of first choices for each pattern for each combination were comparable to the proportions shown for all choices.
Figure 4. RFID Experimental Results. Choice frequencies at the four different flower combinations in Experiment 1. The dark brown bar graphs show all choices from the bee's naïve session (left-side y-axis), and the light brown bars indicate the first choice of each worker (right-side y-axis). “All choices” show comparable patterns to “first choice”, but with greater statistical power. The bar charts show that the positioning of patterns is more important than the type of a pattern. A centrally positioned pattern was preferred even if the pattern type displayed an otherwise less preferable concentric pattern. Asterisks indicate a choice proportion that is significantly different from chance. Note. * p < .05, ** p < .01, *** p < .001. This figure has been modified from Orbán et al.11.
Conditions | Pooled | Heterogeneity | |||||
Gp | df | p | Gh | df | p | ||
Central-Radial vs Central-Concentric | 3.96 | 1 | 0.047 | 197.55 | 41 | 0.000 | |
Peripheral-Radial vs Central-Concentric | 33.77 | 1 | 0.000 | 210.81 | 42 | 0.000 | |
Central-Radial vs Peripheral-Concentric | 508.31 | 1 | 0.000 | 345.78 | 30 | 0.000 | |
Peripheral-Radial vs Peripheral-Concentric | 7.42 | 1 | 0.000 | 84.06 | 24 | 0.000 |
Table 1. Inferential Statistics of RFID Data. Experiment 1. This table has been modified from Orbán et al. (2013)11. Gp refers to significant deviation of a group proportion from chance, and Gh refers to the tests for individual differences (i.e., heterogeneity). Please refer to the manuscript for full details on the statistical tests.
Experiment 2: Video Data
A total of 264 choices were recorded across the four conditions over three testing sessions. Table 2 shows the number of workers and choices contributed from each colony.
Definition of a choice
Video data allows the recording of three types of choice behavior: hovering, antennation and landing. While all three types of behaviors can be observed, hovering and antennation are difficult to associate with a tag number due to quick motions that camcorders with poor resolution, or low speed cannot record. It is crucial to use a high-definition camcorder (though this was not available to us, ideally a high-frame rate camcorder should be used to minimize blurring) to ensure tag numbers that may appear only on a small number of frames can be read. This method was also used to compare choice patterns with the RFID technique, which detects floral exploration.
Motion sensitivity considerations
One of the key issues in producing a successful experiment is the configuration of the motion-sensitive camcorders. A camcorder that is too sensitive will record too much data that is impractical and can become very expensive to process. For example, initially our camcorder was triggered by regular vibrations in the building (e.g., people passing by on the hallway, air conditioner, etc.), which resulted in 1–2 valid data points for every 150–200 recorded video clips. On the other hand, an even more serious error is a low sensitivity configuration, which can miss key data. It is crucial to configure all camcorders in the same way, otherwise, sampling errors can skew the results.
Summary of experiment
Four replicated goodness of fit tests found three group proportions that deviated significantly from chance, and one non-significant overall proportion (see Table 3 and Figure 5). (1) Pattern is important: a significant preference for the central-radial over the central-concentric pattern was found (see Table 3). (2) Position of the radial pattern is less important: the presentation of the central-radial and peripheral radial combination showed no significant difference from chance. (3) The central-radial and peripheral-concentric combination resulted in a strong preference towards the central-radial pattern. The central-concentric and peripheral-radial combination elicited significant preference towards the peripheral-radial pattern. Pattern trumped location. Individual differences were non-significant in all four combinations (see Table 3).
Figure 5. Motion-Sensitive Video Results. Choice frequencies at the four different flower combinations in Experiment 2. The results show the importance of pattern type over pattern positioning: Radial patterns were preferred even if the patterns were positioned peripherally. Values indicate the number of choices of the displayed pattern. Asterisks indicate a choice proportion that is significantly different from chance. Note. ** p < 0.01, *** p < 0.001. This figure has been modified from Orbán11.
Conditions | Session 1 | Session 2 | Session 3 | ||
Colony 1 | Colony 2 | Colony 3 | Colony 4 | Colony 5 | |
No. of Workers | 45 | 7 | 2 | 8 | 23 |
No. of Choices | 151 | 25 | 2 | 20 | 65 |
Table 2. Descriptive Statistics of Motion-Sensitive Video Data. Total number of choices recorded at the artificial flowers in Experiment 2 for each colony, and the number of workers making these choices. This table has been modified from Orbán et al.11. Please refer to the manuscript for full details.
Conditions | Pooled | Heterogeneity | ||||
Gp | df | p | Gh | df | p | |
Central-Radial vs Central-Concentric | 17.98 | 1 | 0.000 | 40.72 | 29 | 0.073 |
Central-Radial vs Peripheral Radial | 1.85 | 1 | 0.173 | 53.63 | 39 | 0.060 |
Peripheral Radial vs Central Concentric | 6.57 | 1 | 0.010 | 26.31 | 27 | 0.500 |
Central Radial vs Peripheral Concentric | 18.18 | 1 | 0.000 | 41.92 | 37 | 0.256 |
Table 3. Inferential Statistics Motion-Sensitive Video Data. Experiment 2. This table has been modified from Orbán et al.11. Gp refers to significant deviation of a group proportion from chance, and Gh refers to the tests for individual differences (i.e., heterogeneity). Please refer to the manuscript for full details on the statistical tests.
RFID technology enables studying hundreds of individual workers with ease and high precision, but the characteristics of the recorded behavior is different from observations by humans and video recordings. The choice behavior recorded by RFID can be described as floral exploration. This is a very strict criterion of preference compared to criteria used in other studies, such as approach8, entry into a maze-arm9,10, antennal reaction8, or landing on a pattern11,12. In order to compare the validity of choice behavior definitions, and to validate the new RFID method for unrewarded behavior, video recordings of landing were observed in Experiment 2. All choice measures are not equal: the RFID criterion as measured by floral entry, indicated that the visual property of pattern positioning is more important to bee choice, while the video data indicated that the visual property of pattern type is more important to bee choice.
One of the general challenges in studying unlearned choice behavior is that it is very difficult to attract flower naïve, untrained bees to artificial flowers that do not offer any pollen or nectar. Indeed, many of the previous experiments resorted to training bees in the testing environment on stimuli that are thought to be irrelevant to choice behavior at testing stimuli. RFID and motion-sensitive video recordings overcome this obstacle by allowing continuous recording, 24 hr a day, without the constant supervision of the researcher, and by increasing the sample size from 15–20 bees to several hundred bees. While unrewarded choices by untrained bees remain a rare occurrence, these new experimental design parameters make the observation possible.
Other improvements offered by these two techniques include the elimination of sample bias, the improvement of external validity, and tracking of individual differences. Sample bias may be introduced when only studying a dozen or so bees in a colony. There are significant differences in behavioral idiosyncrasies across individual workers even within the same colony that are likely missed because only those workers are observed that happen to “cooperate” with the researcher at a given time. Studying 15–20 bees in a colony of 300 or more bees, represents as little as 5% of the total colony, in which case sampling bias may be significant. Tagging and observing the behavior of all workers eliminates this issue altogether. The number of simultaneous stimuli choices can also be manipulated. We offered binary choices in our experiment for technical reasons, but single choice or several choice designs are also feasible.
In terms of external validity, studying bees in a laboratory environment has traditionally been highly artificial, which has impeded the generalizability of results. For example, researchers had to be present for data collection, bees had to forage in a testing environment one by one, and testing was restricted to a small time window. The new techniques described in this paper remove these artificial limitations by making the observation unsupervised and unrestricted. Finally, individual differences of behavior can be documented because we can ascertain whether these were repeated choices by a single bee or by several bees.
Motion-sensitive, high-spatial resolution camcorders have the edge over RFID techniques in terms of the flexibility of stimuli designs: the appearance of a visual stimulus can be almost any shape or form as long as the subject’s identification can be captured on at least a few frames. Processing videos is a little more time-consuming than processing RFID data because the identification needs to be read by the researcher, which requires manual inspection of each video clip. If the visual stimulus design can meet the constraints of RFID reader (i.e., the RFID tags on the bee must come to at least 3–4 mm of the RFID reader), then RFID technology has the edge over automated large-scale data collection. Qualitative research would likely continue to be favored by video analysis. As shown in this experiment, RFID readers can amass very large data sets that requires no manual coding. The slightly different advantages associated with each technique suggest that in the future they could be used in a complementary way.
The future of both technologies may lie in the precise quantification of rarely occurring behaviors. For example, one distinct possibility for future applications is to employ these techniques in greenhouses and other more naturalistic environments. The combination of naturalism and experimental control would allow addressing questions that were not possible to answer before. Broadly speaking, these techniques offer two new ways of observing behavior in a rigorous and efficient manner. RFID and motion-sensitive video are a significant step forward not just for researchers studying pollinators or insects, but these techniques may also appeal to other behavioral scientists.
The authors have nothing to disclose.
The experiments were supported by a grant from the Natural Sciences and Engineering Research Council of Canada to CMSP. We thank Koppert Canada for their bumblebee colony donations. Portions of this manuscript, including some figures and tables have been published in Naturwissenschaften11, and reproduced here with permission from Springer.
Name of Material/ Equipment | Company | Catalog Number | Comments/Description |
Miniaturized mic3 tags | Microsensys | mic3 TAG 64 bit RO | RFID tags to glue to bee |
RFID reader 2k6 head | Microsensys | 2k6 | RFID readers built into artificial flowers |
IP camcorders | Vivotek | IP8161 | Motion-sensitive video recorders |
Opalith Plattchen number tags and non-toxic glue | Beeworks.com | n/a | Number tags to glue to bees |
Bumblebee Colony for Research | Koppert Canada | ||
Artificial flowers | N/A | Developed by campus biology shop | |
Artificial flower stand | N/A | Developed by campus biology shop | |
Flight room | N/A | Developed by campus biology shop | |
Laptop with Windows | Generic hardware / Microsoft software | Used to download RFID data | |
RS 232 to USB converter | Generic | Connect RFID reader to computer | |
Desktop | IBM | Used to transmit video data | |
Second NIC | Generic | 10/100M NIC PCI | Used to transmit video data |
Network hub | Generic | 4-port | Used to transmit video data |
High precision tweezer | SPI | Used to glue number and RFID tags to bees | |
Sugar | Generic | Used to mix with water to create sugar-water | |
Pollen | Any local apiarist | Fed to bumblebees | |
Marking cage with plunger | Beeworks.com | Aids tagging process | |
Honey | Generic | Used to mix with water ot create pollen paste | |
Bake clay | Sculpey | Stimulus for RFID | |
Clay shaping tools | Generic | Stimulus for RFID | |
White paper | Generic | Stimulus for Video | |
Laser printer | Generic | Stimulus for Video | |
Wood | Generic | Stimulus for Video — attachment clip |