Published: January 9th, 2019
Measurement of insect emergence patterns requires precision. Existing systems are only semi-automated and sample size is limited. We addressed these issues by designing a system using microcontrollers to precisely measure the time of emergence of large numbers of emerging insects.
Existing systems to measure insect emergence patterns have limitations; they are only partially automated and are limited in the maximum number of emerging insects they can detect. In order to obtain precise measurement of insect emergence, it is necessary for systems to be semi-automated and able to measure large numbers of emerging insects. We addressed these issues by designing and building a system that is automated and can measure emergence of up to 1200 insects. We modified the existing "falling-ball" system using Arduino microcontrollers to automate data collection and expand the sample size through multiple data channels. Multiple data channels enable the user to not only increase their sample size, but also allows for multiple treatments to be run simultaneously in a single experiment. Furthermore, we created an R script to automatically visualize the data as a bubble plot, while also calculating the median day and time of emergence. The current system was designed using 3D printing so the user can modify the system to be adjusted for different species of insects. The goal of this protocol is to investigate important questions in chronobiology and stress physiology, using this precise and automated system to measure insect emergence patterns.
Precisely measuring the timing of terrestrial insect emergence in experimental settings is notoriously difficult and requires some degree of automation. Several mechanisms have been designed in the past, incorporating either a "falling-ball" principle, using falling balls and sensors, or a "bang-box" using a funnel-type system1,2,3. There are two limitations with existing designs: 1) data collection is only partially automated, and 2) sample size or the number of emergent insects that can be detected is limited. These problems decrease the precision of data collection, which is important to studying timing of eclosion and/or emergence patterns. We addressed these problems by designing a system that is automated and not limited by sample size, enabling the user to better visualize emergence rhythms in response to environmental cues.
Our system is an improvement to the falling-ball principle, the most recent version of which used infrared sensors to detect insect emergence in six-minute increments2. Our system still uses infrared sensors, but also incorporates an Arduino microcontroller to record the date and time of each emergence event to the nearest second. Data is automatically stored to a secure digital (SD) card, which can be exported as a comma-delimited file for analysis. The analysis is automated by using a custom R script, which will graph the data as a bubble plot and identify the median time and day of emergence.
Multiple channels enable the user more flexibility in data acquisition. For example, our multiple channel design not only minimizes the impact of a "clogged" sensor, but also can be used to increase sample size. Furthermore, multiple channels allow the user to designate treatments to specific channels so they can run simultaneously in an experiment. Using all six channels allows for approximately 1200 emerging bees to be recorded in a single experiment. To our knowledge, this is the largest sample size of any current system measuring insect emergence and has allowed us to observe fine-scale emergence patterns in response to environmental cues. Finally, our system benefits from the fact that the majority of the parts are 3D printed. This creates precisely sized components, which reduces the likelihood of errors occurring (such as detector clogging) during operations. It also allows for customization for other research systems.
The goal of this protocol is to custom build a precise and automated system to measure insect emergence, to investigate questions in chronobiology and stress physiology. This system has been, and will continue to be, critical in investigating unanswered questions related to insect emergence patterns in response to environmental cues. Here we describe its assembly and use for emergence detection of the alfalfa leaf-cutting bee, Megachile rotundata in lab-based experimental settings. The system is automated using a programmable microcontroller and customizable using 3D printed parts. Printed racks hold in place tubes containing bee nest cells following a metal BB. Upon emergence, the metal BB is freed from the rack, passing through an infrared sensor recording the date and time of emergence to an SD card. The current design is optimized for M. rotundata, but with minor adjustments could be adapted for other species of insects.
1. System Construction
2. System Use
3. Experiment End and Data Analysis
Emergence of M. rotundata is asynchronous without exposure to an environmental cue, with emergence occurring uniformly throughout the day4. However, when exposed to a square-wave thermoperiod (4° C thermoperiod), emergence becomes synchronous to the thermophase4,5. This result is similar to other studies where insects have been found to use thermoperiod cues to regulate emergence, including the flesh fly Sarcophaga crassipalpis6, the onion fly Delia atiqua7 and the boll weevil Anthonomus grandis grandis8. One study has shown that stress during development affects the synchrony of adult emergence in S. crassipaplpis9. Here, we present results from M. rotundata that were exposed to a stress during development, to test the hypothesis that this treatment causes the desynchronization of adult emergence.
The user should watch the LCD screen before opening the incubator to make sure insects are no longer emerging. Once the experiment is completed, the SD card is removed and the data can be exported into RStudio as a comma-delimited file to be visualized as a bubble plot, as described earlier. Figure 8 displays bee emergence under a 4° C thermoperiod after exposure to a cold stress during development. The red crosshairs indicate the median time and day of emergence and the file name is the title. This R script should be used to visualize the data, but should not serve as the sole analysis. To analyze emergence response to an environmental cue, the data can be analyzed for rhythmicity (see Analysis).
When a sensor is clogged with metal BBs, the lack of a signal is repeatedly counted, giving rise to multiple false data points. Figure 9 demonstrates the same dataset presented in Figure 8, but with one of the six channels clogged with BBs, therefore creating the large bubble on the graph. In the event of a clogged sensor, data from this channel can be easily removed from the analysis. Incorporating multiple channels in an experiment is beneficial in minimizing the impact of a clogged sensor.
Analyzing data for presence of synchronization can be done by calculating "parameter R," a scalar statistic that identifies if emergence is rhythmic or arrhythmic10,11,12. This is done by calculating the highest number of emerging adults in an 8-hour window, dividing this number by the number of adults emerging outside the 8-hour window, then multiplying by 100. All individuals that emerged should be pooled to calculate the number of emerging adults for each hour of the day. The theoretical range of parameter R is from 0 (all emergence occurs within the gate) to 200 (emergence is distributed uniformly throughout the day)10. R values < 60 are considered rhythmic emergence, 60 < R < 90 are weakly rhythmic, and R > 90 are arrhythmic. R values > 150 indicate uniform distribution of emergence10. Figure 8 shows that emergence is rhythmic with parameter R = 20.21 < 60. Due to the fact that this type of data is distributed around a repeating 24-hour clock, circular statistics must be employed for a more robust analysis (described in detail in Bennett et al., 20185). This can be accomplished via circular statistics packages available for RStudio (Package 'circular'- CRAN.R-project.org).
Figure 1: Additive manufactured components. Using PLA filament, 3D print the required parts for the system. For each channel being constructed, parts needed are 1 collector manifold (green), 1 end cap (red), 6 platform supports (orange), 4 tube rack base plates (purple), and 4 tube rack face plates (yellow). Please click here to view a larger version of this figure.
Figure 2: Tube rack platform assembly. Use hot glue to assemble two tube rack platforms per channel being constructed. Use three platform supports (shown in orange) with a section of corrugated plastic (shown in grey). Please click here to view a larger version of this figure.
Figure 3: Collector manifold x-ray. Insert one infrared detector into one socket of the collector (shown in blue) and the emitter into the second socket (shown in red). Feed the detector wires through the cabling channel (shown in yellow) and pull all four wires through the access hole (highlighted in green). Ensure no bare wires are touching, using hot glue to secure them in place. Please click here to view a larger version of this figure.
Figure 4: Connector wiring. Wiring diagram for the RJ45 jack prior to affixing to the collector manifold, as seen from the bottom of the jack and wiring table for connecting the LCD screen to the central processor. Please click here to view a larger version of this figure.
Figure 5: Ball collector assembly. Using one end cap (shown in red), one collector manifold (shown in green) and a 24 x 30 cm piece of corrugated plastic (shown in light grey) assemble the shell of the ball collector assembly. Use an 8 x 27 cm piece of corrugated plastic (shown in dark grey) to add a ramp. Please click here to view a larger version of this figure.
Figure 6: Central Processor PCB board. The PCB board for the central processor consists of a bottom layer (depicted in green), a top layer (depicted in red), and a silkscreen layer (depicted in blue). Solder female headers to all pass-through holes, except for those for the RJ45 jacks (along the bottom) and for the pull-down resistors (directly above the RJ45 pads). Please click here to view a larger version of this figure.
Figure 7: Final assembly. When in use, the apparatus should be assembled with a tube rack platform on either side of each ball collector being used. Tube racks with attached faceplates should be positioned so that they are at the very edge of the Tube Rack Platform, reducing the possibility of falling BBs bouncing off of the apparatus. The footprint of the assembled apparatus is approximately 25 cm x 35 cm, with a height of 20 cm. Please click here to view a larger version of this figure.
Figure 8: Graph of a typical experimental run after processing in R. Please click here to view a larger version of this figure.
Figure 9: Graph of an experiment that suffered from a clogged detector, as shown by the relatively large bubble on day 4. The clogged channel can be removed from the analysis, thereby preserving the remaining data points. Please click here to view a larger version of this figure.
We present a protocol for the assembly and use of a system that allows for precise measurement of the timing of insect emergence. This system solves two problems, which limited previous designs: partial automation and limited sample size. We solved these problems by automating data collection using microcontrollers, which also enabled us to increase sample size by using multiple channels. The current design has six channels that can hold a total of 1200 bees. Additional channels can be added or subtracted if needed, allowing not only for increased sample size, but also for simultaneously investigating the effects of multiple treatments. Critical steps, modifications, limitations and future applications are discussed below.
The only part of the system that is not automated is loading the racks with brood cells, metal BBs, and airsoft pellets at the beginning of the experiment. Although the racks are designed so they lean back slightly to prevent metal BBs from falling when the racks are standing upright, care must be taken when placing the racks to prevent the accidental release of metal BBs. Also, make sure the racks are flush with the edge of the shelf, so the falling trajectory of the metal BB aligns with the runway. Finally, leaf debris should be wiped clear from the runway, and the undercarriage holding the metal BBs from previous experiments should be cleared to prevent blocking of the sensor. Data is automatically recorded to an SD card as a CSV file, and the script is written so that the Arduino will not run unless an SD card is present. The data file is manually imported into RStudio and visualized using the previously mentioned R script. This script will automatically graph the data as a bubble plot and identify the median time and day of emergence. The Arduino script is written to append event data to the end of the file, which prevents data loss in the event of a power failure. However, this also means that once data is extracted from the SD card, all files should be cleared prior to the next experiment.
Modifications to the SketchUp files can be made to adjust the size of the racks for insects of different sizes, with different sized tubes being used in the modified racks. Furthermore, the size of the airsoft pellet is important because it prevents the insect from leaving the tube, and pellets of different sizes may be needed as well. A wide variety of alterations can be made to the R script to change the appearance of the bubble plots, and other graphic parameters.
We reduced the risk of false positives by writing a debounce code that disables any given channel for one second after a metal BB is detected thereby preventing a single metal BB from being counted as multiple data points. Although, this creates the possibility of a data point being missed if many bees emerge at once, but the fact that the channels are independent reduces this risk. Another limitation of the current system is that individual data points are not discernable, i.e., a falling metal BB cannot be traced back to a specific individual. Furthermore, the current system measures emergence but not eclosion rhythms in M. rotundata, but would measure eclosion rhythms in species where emergence and eclosion are synonymous. Finally, the current design is not weatherproof, limiting its use to controlled environments.
Future applications include examining the effects of other abiotic and biotic environmental cues for timing emergence of M. rotundata. Furthermore, because insects occupy diverse environments, relevant environmental cues vary between species. Thus, incorporation of more insect species is important to investigating how circadian systems evolved across taxa. Little is known about how developmental conditions affect the timing of adult emergence; therefore, our system can be used to decipher the effects of treatments on emergence. Furthermore, combinations of environmental cues can affect insect responses, thus future experiments should incorporate multiple environmental cues to understand their relative effects on emergence. Lastly, deployment in the field to observe how natural settings mediate emergence rhythms is of interest. The ease of use of this system, and its unique combination of additive manufacturing, open-source programming, and observable biological traits, make it a candidate for use in an educational setting.
The authors have nothing to disclose.
We would like to acknowledge the Insect Cryobiology and Ecophysiology working group in Fargo, ND for their helpful feedback on experiments using the described system.
|PLA printer filament
|Catalog number varies by color
|0.5 mL microcentrifuge tubes
|4.5 mm size "bb" metal pellets
|Daisy 4.5 mm metal size bb pellets
|6.0 mm plastic "softair" pellets
|Crosman 6 mm airsoft pellets
|Plastic corregated sheet
|Corrugated plastic sheet
|Infrared emmiter/detector pair
|5 mm diameter, 940 nm wavelength
|120 ohm resisitors
|120 ohm, 1/4 watt
|22 GA hookup wire
|Custom PCB board
|Can be printed from files included in the supplimental data
|Arduino Nano v 3.0
|SD card module
|DFRobot SD card module
|Real Time Clock module
|DS1307 real time clock breakout board
|DHT11 temperature/humidity sensor on breakout board
|470k ohm resistors
|Break off to desired length
|Break off to desired length
|10 wire ribbon wire with connectors
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