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.

1. System Construction
<ol>
	<li>Using PLA filament, print the following number of parts for each channel being constructed: 1 collector manifold (collector_manifold.stl), 1 end cap (end_cap.stl), 6 platform supports (platform_support.stl), 4 tube rack base plates (base_plate.stl), and 4 tube rack face plates (face_plate.stl). Make sure printer bed is large enough to print an item before printing. All *.stl files are available in Supplemental data.</li>
	<li>With 3 platform supports and a 33 x 30 cm piece of corrugated plastic, use hot glue to assemble 2 tube rack platforms per channel being constructed, as shown in Figure 2. The corrugated plastic can be scored on one side at each corner to allow for bending.</li>
	<li>Install electronics into the collector manifold.
	<ol>
		<li>Solder a 120 &#937; resistor to the anode (longer leg) of both the infrared emitter and the infrared detector, and a ~5 cm length of 22 GA wire to both cathodes. Use different colors of wires to avoid confusion in later steps.</li>
		<li>Carefully insert the detector into one socket of the collector manifold (highlighted in blue in Figure 3) and the emitter into the second socket (highlighted in red). Both components should fit snugly.</li>
		<li>Feed the detector wires through the cabling channel (highlighted in yellow in Figure 3) 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.</li>
		<li>Solder all four wires to a RJ45 (Ethernet) jack, using the back row of pins. Both anodes should be soldered to the left-most pin, the cathode of the emitter to the right-most pin, and the cathode of the detector to either of the center pins (Figure 4).</li>
		<li>Secure the RJ45 jack over the collector manifold access hole (highlighted in green in Figure 3) with hot glue, ensuring no bare wires are touching inside the manifold.</li>
	</ol>
	</li>
	<li>Construct falling ball collector (1 per channel being constructed) as shown in Figure 5
	<ol>
		<li>With one wired collector manifold, one end cap, and a 24 x 30 cm section of corrugated plastic, use hot glue to connect the base of the unit (red, green, and light grey components of Figure 5).</li>
	</ol>
	</li>
	<li>Use an 8 x 27 cm section of corrugated plastic to add a falling ball ramp to the collector (dark grey component of Figure 5). The end cap and collector manifold designs include ledges to ensure correct placement. Check for a smooth transition from the ramp to the collector to avoid jams during use.</li>
	<li>Construct the central processor for the system (as detailed in Figure 6).
	<ol>
		<li>Print a custom printed circuit board for system construction. All files required for PCB board printing are available in supplemental data.</li>
		<li>Solder female headers on the through-holes labeled for the following installations: Arduino Nano, temp, clock, SD module, and liquid crystal display (LCD) screen (unlabeled 2 x 5 through-hole area in the upper left corner of the PCB board).</li>
		<li>Snap in and solder six RJ45 jacks along the lower edge of the PCB board.</li>
		<li>Solder six 470k ohm pulldown resistors into the through-hole sites located just above the RJ45 jacks.</li>
		<li>Install the Arduino Nano, DHT-temperature and humidity sensor, clock, and SD module onto the PCB board. DHT-temperature and humidity sensor should be tested before use in experiments to ensure accuracy.</li>
		<li>Connect a 10-connector ribbon wire to the LCD screen connector of the PCB board. Solder the other end of the ribbon wire to the LCD screen so that the screen pins correspond to the Arduino pins, as noted in Figure 4. Further details on LCD wiring are available at https://Learn.adafruit.com/character-lcds/wiring-a-character-lcd.</li>
	</ol>
	</li>
	<li>System programming
	<ol>
		<li>Download and install the latest version of the Arduino IDE for the correct operating system from www.arduino.cc.</li>
		<li>On first use, install Arduino libraries for the real time clock (github.com/adafruit/RTClib) and the temperature/humidity sensor (github.com/adafruit/DHT-sensor-library). Set the clock to the current local time by using the ds1307 script included with the library.</li>
		<li>Upload the system Arduino script, available in supplemental data.</li>
	</ol>
	</li>
</ol>
2. System Use
<ol>
	<li>Assemble the system as shown in Figure 7. For each channel being used, one falling-ball collector (assembled in step 1.4) should be flanked on either side by a rack platform (assembled in step 5.1). Use packaging tape to hold together pieces and to create a smooth rounded edge on the rack platform.</li>
	<li>Configure unused channels to avoid false positive signals. Since the system relies on a low signal to detect an event (infrared detector not receiving a signal from the infrared emitter), unused channels must be configured appropriately to avoid false positive signals. This may be accomplished by one of two ways.
	<ol>
		<li>Deactivate unused channels in the software by commenting out the loops corresponding to the unused channels. In Arduino IDE, this can be accomplished by adding &#8220;/*&#8221; prior to the unneeded loops and &#8220;*/&#8221; at their end.</li>
		<li>Deactivate unused channels through a simple hardware accommodation. Simply solder together wires #6 and #8 (usually the solid brown and solid green wires of a commercially available cat 6 cable) and insert into the empty RJ45 jack on the central processor.</li>
	</ol>
	</li>
	<li>Load and place tube racks immediately prior to running an experiment.
	<ol>
		<li>Ensure that all holes contain a 0.5 mL microcentrifuge tube with the cap removed and that the tubes fit snugly.</li>
		<li>Fill each tube with one insect brood cell, pupal case or cocoon, one airsoft pellet, and finally one metal BB. Make sure the flat edge side (cap) of the brood cell is facing toward the airsoft pellet and metal BB. Affix the tube rack faceplate, with the rounded edge towards the bottom of the rack, using &#188; inch nylon screws.</li>
		<li>Place tube racks on the rack platform, with the opening facing towards the falling-ball collector. Racks should be placed at the very edge of the platform so that a metal BB can fall freely into the collector without bouncing against another portion of the structure (Figure 7). When placing the rack, start with the opening facing upwards and then gently rotate into place to ensure metal BBs are not released. The racks are designed so that the tubes will slant slightly backwards when properly placed, reducing the chance of accidental release of the metal BBs.</li>
	</ol>
	</li>
	<li>Insert a SD card into the adapter and then start the central processor by plugging a micro-USB connector into the Arduino, and the other end into any appropriate USB adapter. The LCD screen will display numbers one through six when ready. Drop a single metal BB into the ball collector of each channel and watch for the corresponding count to appear on the screen and for the correct time to display at the bottom of the screen.
	<ol>
		<li>If the correct time is not displayed, repeat steps 1.6.3 and 1.6.4 to reset the clock.</li>
		<li>If the test metal BB is not recorded, the collector is blocked. Check visually for blockage and restart the system.</li>
		<li>If a channel &#8220;counts&#8221; up one event every second, this indicates that the channel is not properly connected. Check all connections and restart the system.</li>
	</ol>
	</li>
</ol>
3. Experiment End and Data Analysis
<ol>
	<li>After emergence has ended (see Results and Figures 8 and 9 for examples of the time scale), power down the apparatus by unplugging the Arduino. Racks may be disassembled and cleaned for reuse.</li>
	<li>During the experiment, data is stored on the SD card in a comma-delimited file (CSV) accessible by the R programming language. Use the SD card to transfer data to the computer, and RStudio to auto-generate bubble plots of the data.
	<ol>
		<li>Both event and temperature data are saved in the same file for data integrity. Hence, some processing must be completed prior to analysis. Import the comma delimited file into a spreadsheet program. Columns I and J are the date and time of emergence for bees; make them columns A and B by cutting and pasting columns A-E into a second spreadsheet, and save as a separate file, this is the temperature data.</li>
		<li>Title column A with, &#8220;Date&#8221; and column B &#8220;Time,&#8221; and sort the data by column A then by B. Save as a CSV. file.</li>
		<li>Download and install the latest version of the RStudio from https://www.r-project.org/. Help with using RStudio for uploading and analyzing data can be found here at https://cran.r-project.org/doc/manuals/r-release/R-intro.html.</li>
		<li>Using the R script available in the supplemental data, upload the data into RStudio. Change the working destination in the R script to match where the excel *.CSV file is located. Run the script and select the data file to analyze. Type &#8220;plot&#8221; into the R console. The bubble plot will be located in the working destination named &#8220;High-res;&#8221; rename this file to save as a high-resolution tiff (300 dpi) file.</li>
	</ol>
	</li>
</ol>

<ol><li>Lankinen, P. <a target="_blank" href="https://link.springer.com/article/10.1007/BF00612503">Geographical variation in circadian eclosion rhythm and photoperiodic adult diapause in Drosophila littoralis.</a> Journal of Comparative Physiology A. 159, 123-142 (1986).</li>
<li>Watari, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Comparison+of+the+circadian+eclosion+rhythm+between+non-diapause+and+diapause+pupae+in+the+onion+fly%2C+Delia+antiqua%5BTitle%5D)+AND+%22Journal+of+Insect+Physiology%22%5BJournal%5D)">Comparison of the circadian eclosion rhythm between non-diapause and diapause pupae in the onion fly, Delia antiqua.</a> Journal of Insect Physiology. 48, 83-89 (2002).</li>
<li>Zimmerman, W. F., Pittendrigh, C. S., Pavlidis, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Temperature+compensation+of+the+circadian+oscillation+in+Drosophila+pseudoobscura+and+its+entrainment+by+temperature+cycles%5BTitle%5D)+AND+%22Journal+of+Insect+Physiology%22%5BJournal%5D)">Temperature compensation of the circadian oscillation in Drosophila pseudoobscura and its entrainment by temperature cycles.</a> Journal of Insect Physiology. 14, 669-684 (1968).</li>
<li>Yocum, G. D., Rinehart, J. P., Yocum, I. S., Kemp, W. P., Greenlee, K. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Thermoperiodism+synchronizes+emergence+in+the+alfalfa+leafcutting+bee+%28Hymenoptera%3A+Megachilidae%29%5BTitle%5D)+AND+%22Environmental+Entomology%22%5BJournal%5D)">Thermoperiodism synchronizes emergence in the alfalfa leafcutting bee (Hymenoptera: Megachilidae).</a> Environmental Entomology. 45, 245-251 (2016).</li>
<li>Bennett, M. M., Rinehart, J. P., Yocum, G. D., Doetkott, C., Greenlee, K. J. <a target="_blank" href="http://jeb.biologists.org/content/early/2018/04/09/jeb.175406.abstract">Cues for cavity nesters: Investigating relevant Zeitgebers for emerging leafcutting bees, Megachile rotundata (Hymenoptera: Megachilidae).</a> Journal of Experimental Biology. 221, jeb175406(2018).</li>
<li>Miyazaki, Y., Goto, S. G., Tanaka, K., Saito, O., Watari, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Thermoperiodic+regulation+of+the+circadian+eclosion+rhythm+in+the+flesh+fly%2C+Sarcophaga+crassipalpis%5BTitle%5D)+AND+%22Journal+of+Insect+Physiology%22%5BJournal%5D)">Thermoperiodic regulation of the circadian eclosion rhythm in the flesh fly, Sarcophaga crassipalpis.</a> Journal of Insect Physiology. 57, 1249-1258 (2011).</li>
<li>Watari, Y., Tanaka, K. <a target="_blank" href="https://onlinelibrary.wiley.com/doi/abs/10.1111/ens.12034">Effects of background light conditions on thermoperiodic eclosion rhythm of onion fly Delia antiqua.</a> Entomological Science. 17, 191-197 (2014).</li>
<li>Greenberg, S. M., Armstrong, J. S., Setamou, M., Coleman, R. J., Liu, T. X. <a target="_blank" href="https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1744-7917.2006.00116.x">Circadian rhythms of feeding, oviposition, and emergence of the boll weevil (Coleoptera: Curculionidae).</a> Insect Science. 13, 461-467 (2006).</li>
<li>Yocum, G. D., Zdarek, J., Joplin, K. H., Lee, R. E., Smith, D. C., Manter, K. D., Denlinger, D. L. <a target="_blank" href="https://www.sciencedirect.com/science/article/pii/0022191094901074">Alteration of the eclosion rhythm and eclosion behavior in the flesh fly, Sarcophaga crassipalpis, by low and high temperature stress.</a> Journal of Insect Physiology. 40, 13-21 (1994).</li>
<li>Winfree, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrated+view+of+resetting+a+circadian+clock%5BTitle%5D)+AND+%22Journal+of+Theoretical+Biology%22%5BJournal%5D)">Integrated view of resetting a circadian clock.</a> Journal of Theoretical Biology. 28, 327-374 (1970).</li>
<li>Watari, Y., Tanaka, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Interacting+effect+of+thermoperiod+and+photoperiod+on+the+eclosion+rhythm+in+the+onion+fly%2C+Delia+antiqua+supports+the+two-oscillator+model%5BTitle%5D)+AND+%22Journal+of+Insect+Physiology%22%5BJournal%5D)">Interacting effect of thermoperiod and photoperiod on the eclosion rhythm in the onion fly, Delia antiqua supports the two-oscillator model.</a> Journal of Insect Physiology. 56, 1192-1197 (2010).</li>
<li>Short, C. A., Meuti, M. E., Zhang, Q., Denlinger, D. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Entrainment+of+eclosion+and+preliminary+ontogeny+of+circadian+clock+gene+expression+in+the+flesh+fly%2C+Sarcophaga+crassipalpis%5BTitle%5D)+AND+%22Journal+of+Insect+Physiology%22%5BJournal%5D)">Entrainment of eclosion and preliminary ontogeny of circadian clock gene expression in the flesh fly, Sarcophaga crassipalpis.</a> Journal of Insect Physiology. 93, 28-35 (2016).</li>
</ol>

The authors have nothing to disclose.

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.

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 &#34;falling-ball&#34; principle, using falling balls and sensors, or a &#34;bang-box&#34; 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 &#34;clogged&#34; 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.

<table><tbody><tr><td>PLA printer filament</td><td>www.lulzbot.com</td><td>various</td><td>Catalog number varies by color</td></tr><tr><td>0.5 mL microcentrifuge tubes</td><td>www.daigger.com</td><td>EF4254C</td><td></td></tr><tr><td>4.5 mm size &quot;bb&quot; metal pellets</td><td>www.amazon.com</td><td>B00419C1IA</td><td>Daisy 4.5 mm metal size bb pellets</td></tr><tr><td>6.0 mm plastic &quot;softair&quot; pellets</td><td>www.amazon.com</td><td>B003QNELYE</td><td>Crosman 6 mm airsoft pellets</td></tr><tr><td>Plastic corregated sheet</td><td>www.lowes.com</td><td>345710</td><td>Corrugated plastic sheet</td></tr><tr><td>Infrared emmiter/detector pair</td><td>www.amazon.com</td><td>B00XPSIT3O</td><td>5 mm diameter, 940 nm wavelength</td></tr><tr><td>120 ohm resisitors</td><td>www.amazon.com</td><td>B01MSZK8DV</td><td>120 ohm, 1/4 watt</td></tr><tr><td>22 GA hookup wire</td><td>www.adafruit.com</td><td>1311</td><td></td></tr><tr><td>RJ45 jacks</td><td>www.sparkfun.com</td><td>PRT-00643</td><td></td></tr><tr><td>Custom PCB board</td><td>www.pcbexpress.com</td><td>n/a</td><td>Can be printed from files included in the supplimental data</td></tr><tr><td>Arduino Nano v 3.0</td><td>www.roboshop.com</td><td>RB-Gra-01</td><td></td></tr><tr><td>SD card module</td><td>www.amazon.com</td><td>DFR0071</td><td>DFRobot SD card module</td></tr><tr><td>Real Time Clock module</td><td>www.adafruit.com</td><td>264</td><td>DS1307 real time clock breakout board</td></tr><tr><td>Temperature/humidity sensor</td><td>www.tinyosshop.com</td><td>G4F4494F29ED05</td><td>DHT11 temperature/humidity sensor on breakout board</td></tr><tr><td>470k ohm resistors</td><td>www.amazon.com</td><td>B00EV2R39Y</td><td></td></tr><tr><td>Female headers</td><td>www.adafruit.com</td><td>598</td><td>Break off to desired length</td></tr><tr><td>Male headers</td><td>www.adafruit.com</td><td>392</td><td>Break off to desired length</td></tr><tr><td>Ribbon wire</td><td>www.amazon.com</td><td>B00X77964O</td><td>10 wire ribbon wire with connectors</td></tr><tr><td>LCD screen</td><td>www.adafruit.com</td><td>198</td><td></td></tr><tr><td>Cat6 cable</td><td>www.amazon.com</td><td>B00N2VISLW</td><td></td></tr><tr><td>SD card</td><td>www.amazon.com</td><td>B00E9W1URM</td><td></td></tr></tbody></table>

a precise and autonomous system for the detection of insect emergence patterns

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 &#34;falling-ball&#34; 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.

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&#176; 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.
Successful run
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&#176; 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). 
Complication
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.
Analysis
Analyzing data for presence of synchronization can be done by calculating &#34;parameter R,&#34; 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; 60 are considered rhythmic emergence, 60 &#60; R &#60; 90 are weakly rhythmic, and R &#62; 90 are arrhythmic. R values &#62; 150 indicate uniform distribution of emergence10. Figure 8 shows that emergence is rhythmic with parameter R = 20.21 &#60; 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 &#39;circular&#39;- CRAN.R-project.org).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/58362/58362fig1.jpg" /> 
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). <a href="https://www.jove.com/files/ftp_upload/58362/58362fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/58362/58362fig2.jpg" /> 
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). <a href="https://www.jove.com/files/ftp_upload/58362/58362fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/58362/58362fig3.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/58362/58362fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/58362/58362fig4.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/58362/58362fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/58362/58362fig5.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/58362/58362fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/58362/58362fig6.jpg" /> 
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). <a href="https://www.jove.com/files/ftp_upload/58362/58362fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/58362/58362fig7.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/58362/58362fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/58362/58362fig8.jpg" /> 
Figure 8: Graph of a typical experimental run after processing in R. <a href="https://www.jove.com/files/ftp_upload/58362/58362fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/58362/58362fig9.jpg" /> 
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. <a href="https://www.jove.com/files/ftp_upload/58362/58362fig9large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about A Precise and Autonomous System for the Detection of Insect Emergence Patterns at JoVE.com

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

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 ...

a-precise-autonomous-system-for-detection-insect-emergence

Research

JoVE Journal

Environment

5.6K Views.  North Dakota State University. 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.

Video: A Precise and Autonomous System for the Detection of Insect Emergence Patterns

Methods for Performing Crosses in Setaria viridis, a New Model System for the Grasses

Setaria viridis is an emerging model system for C4 grasses. It is closely related to the bioenergy feed stock switchgrass and the grain crop foxtail millet. Recently, the 510 Mb genome of foxtail millet, S. italica, has been sequenced 1,2 and a 25x coverage genome sequence of the weedy relative S. viridis is in progress. S. viridis has a number of characteristics that make it a potentially excellent model genetic system including a rapid generation time, small stature, simple growth requirements, prolific seed production 3 and developed systems for both transient and stable transformation 4. However, the genetics of S. viridis is largely unexplored, in part, due to the lack of detailed methods for performing crosses. To date, no standard protocol has been adopted that will permit rapid production of seeds from controlled crosses.
The protocol presented here is optimized for performing genetic crosses in S. viridis, accession A10.1. We have employed a simple heat treatment with warm water for emasculation after pruning the panicle to retain 20-30 florets and labeling of flowers to eliminate seeds resulting from newly developed flowers after emasculation. After testing a series of heat treatments at permissive temperatures and varying the duration of dipping, we have established an optimum temperature and time range of 48 &deg;C for 3-6 min. By using this method, a minimum of 15 crosses can be performed by a single worker per day and an average of 3-5 outcross progeny per panicle can be recovered. Therefore, an average of 45-75 outcross progeny can be produced by one person in a single day. Broad implementation of this technique will facilitate the development of recombinant inbred line populations of S. viridis X S. viridis or S. viridis X S. italica, mapping mutations through bulk segregant analysis and creating higher order mutants for genetic analysis.

Methods for Performing Crosses in Setaria viridis, a New Model System for the Grasses

We have developed a methodology for performing crosses in Setaria viridis (S. viridis). The method involves pruning the panicle prior to a hot water treatment to kill viable pollen. Crosses are performed following a well-controlled growth regime and typically result in the recovery of 1 to 7 cross-pollinated seed/s per panicle.

Setaria viridis is an emerging model system for C4 grasses. It is closely related to the bioenergy feed stock switchgrass and the grain crop foxtail ...

A Technique to Screen American Beech for Resistance to the Beech Scale Insect (Cryptococcus fagisuga Lind.)

Beech bark disease (BBD) results in high levels of initial mortality, leaving behind survivor trees that are greatly weakened and deformed.&#160;The disease is initiated by feeding activities of the invasive beech scale insect, Cryptococcus fagisuga, which creates entry points for infection by one of the Neonectria species of fungus.&#160;Without scale infestation, there is little opportunity for fungal infection. Using scale eggs to artificially infest healthy trees in heavily BBD impacted stands demonstrated that these trees were resistant to the scale insect portion of the disease complex1.&#160;Here we present a protocol that we have developed, based on the artificial infestation technique by Houston2, which can be used to screen for scale-resistant trees in the field and in smaller potted seedlings and grafts.&#160;The identification of scale-resistant trees is an important component of management of BBD through tree improvement programs and silvicultural manipulation.

A Technique to Screen American Beech for Resistance to the Beech Scale Insect Cryptococcus fagisuga Lind.

Beech bark disease is initiated by feeding activities of the beech scale insect that create fungal entry points in the bark.&#160;Trees that are resistant to the scale insect are also disease resistant.&#160;Here we present the protocol we have developed to screen individual beech trees for beech scale resistance.

Beech bark disease (BBD) results in high levels of initial mortality, leaving behind survivor trees that are greatly weakened and deformed.&#160;The ...

Electrophysiological Measurements from a Moth Olfactory System

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively. These approaches have been exploited for getting a better understanding of insect chemical communication. The identified stimuli can then be used as either attractants or repellents in management strategies for insect pests.

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single sensillum recordings (SSR), which are summed responses from all odorant receptor neurons (ORNs) located on the antenna and from those housed in individual sensilla, respectively.

Insect olfactory systems provide unique opportunities for recording odorant-induced responses in the forms of electroantennograms (EAG) and single ...

Neuroscience

Flying Insect Detection and Classification with Inexpensive Sensors

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow for the development of many useful applications in vector and pest control for both medical and agricultural entomology. Given this, the last sixty years have seen many research efforts devoted to this task. To date, however, none of this research has had a lasting impact. In this work, we show that pseudo-acoustic optical sensors can produce superior data; that additional features, both intrinsic and extrinsic to the insect&#8217;s flight behavior, can be exploited to improve insect classification; that a Bayesian classification approach allows to efficiently learn classification models that are very robust to over-fitting, and a general classification framework allows to easily incorporate arbitrary number of features. We demonstrate the findings with large-scale experiments that dwarf all previous works combined, as measured by the number of insects and the number of species considered.

We proposed a system that uses inexpensive, noninvasive pseudo-acoustic optical sensors to automatically and accurately detect, count, and classify flying insects based on their flying sound.

An inexpensive, noninvasive system that could accurately classify flying insects would have important implications for entomological research, and allow ...

Bioengineering

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Sampling methods are selected depending on the targeted species or the spatial and temporal requirements of the study. However, most methods for passive sampling of flying insects have a poor temporal resolution because it is time-consuming, costly and/or logistically difficult to perform. Effective sampling of flying insects attracted to artificial light at night (ALAN) requires sampling at user-defined time points (nighttime only) across well-replicated sites resulting in major time and labor-intensive survey effort or expensive automated technologies. Described here is a low-cost automated intercept trap that requires no specialist equipment or skills to construct and operate, making it a viable option for studies that require temporal sub-sampling across multiple sites. The trap can be used to address a wide range of other ecological questions that require a greater temporal and spatial scale than is feasible with previous trap technology.

To study the impacts of artificial light at night (ALAN) on nocturnal flying insects, sampling needs to be confined to nighttime. The protocol describes a low-cost automated flight intercept trap that allows researchers to sample at user-defined periods with increased replication.

Sampling methods are selected depending on the targeted species or the spatial and temporal requirements of the study. However, most methods for passive ...

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Robots designed to track chemical leaks in hazardous industrial facilities1 or explosive traces in landmine fields2 face the same problem as insects foraging for food or searching for mates3: the olfactory search is constrained by the physics of turbulent transport4. The concentration landscape of wind borne odors is discontinuous and consists of sporadically located patches. A pre-requisite to olfactory search is that intermittent odor patches are detected. Because of its high speed and sensitivity5-6, the olfactory organ of insects provides a unique opportunity for detection. Insect antennae have been used in the past to detect not only sex pheromones7 but also chemicals that are relevant to humans, e.g., volatile compounds emanating from cancer cells8 or toxic and illicit substances9-11. We describe here a protocol for using insect antennae on autonomous robots and present a proof of concept for tracking odor plumes to their source. The global response of olfactory neurons is recorded in situ in the form of electroantennograms (EAGs). Our experimental design, based on a whole insect preparation, allows stable recordings within a working day. In comparison, EAGs on excised antennae have a lifetime of 2 hr. A custom hardware/software interface was developed between the EAG electrodes and a robot. The measurement system resolves individual odor patches up to 10 Hz, which exceeds the time scale of artificial chemical sensors12. The efficiency of EAG sensors for olfactory searches is further demonstrated in driving the robot toward a source of pheromone. By using identical olfactory stimuli and sensors as in real animals, our robotic platform provides a direct means for testing biological hypotheses about olfactory coding and search strategies13. It may also prove beneficial for detecting other odorants of interests by combining EAGs from different insect species in a bioelectronic nose configuration14 or using nanostructured gas sensors that mimic insect antennae15.

We describe a protocol for using insect antennae in the form of electroantennograms (EAGs) on autonomous robots. Our experimental design allows stable recordings within a day and resolves individual odor patches up to 10 Hz. The efficiency of EAG sensors for olfactory searches is demonstrated in driving a robot toward an odor source.

Robots designed to track chemical leaks in hazardous industrial facilities1 or explosive traces in landmine fields2 face the same problem as insects ...

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their applicability in environmental and security monitoring and search-and-rescue operations. Small drones with commercial metal-oxide-semiconductor (MOX) gas sensors have been developed for odor source localization; however, their real-time-odor-detection performance has proven inadequate. However, biosensing technologies based on insect olfactory systems exhibit relatively high sensitivity, selectivity, and real-time response with respect to odorant molecules compared to commercial MOX gas sensors. In such devices, excised insect antennae function as portable odorant biosensor elements and have been found to deliver excellent sensing performance. This study presents experimental protocols for odorant-molecule detection in the air using a small autonomous bio-hybrid drone based on a mountable electroantennography (EAG) device incorporating silkmoth antennae.
We developed a mountable EAG device including sensing/processing parts with a Wi-Fi module. The device was equipped with a simple sensor enclosure to enhance the sensor directivity. Thus, odor source localization was conducted using the spiral-surge algorithm, which does not assume an upwind direction. The experimental bio-hybrid odor-detecting drone identified real-time odorant-concentration differences in a pseudo-open environment (outside a wind tunnel) and localized the source. The developed drone and associated system can serve as an efficient odorant molecule-detection tool and a suitable flight platform for developing odor source localization algorithms owing to its high programmability.

This study introduces experimental protocols for a bio-hybrid odor-detecting drone based on silkmoth antennae. The operation of an experimental electroantennogram device with silkmoth antennae is presented, in addition to the structure of a bio-hybrid drone designed for odor source localization using the spiral-surge algorithm.

Small drones with chemical or biosensor devices that can detect airborne odorant molecules have attracted considerable attention owing to their ...

Engineering

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor sources is an essential task for organism survival. Insects are well-studied organisms with regard to odor tracking, and their behavioral strategies have been applied to mobile robots for evaluation. This "bottom-up" approach is a fundamental way to develop biomimetic robots; however, the biological analyses and the modeling of behavioral mechanisms are still ongoing. Therefore, it is still unknown how such a biological system actually works as the controller of a robotic platform. To answer this question, we have developed an insect-controlled robot in which a male adult silkmoth (Bombyx mori) drives a robot car in response to odor stimuli; this can be regarded as a prototype of a future insect-mimetic robot. In the cockpit of the robot, a tethered silkmoth walked on an air-supported ball and an optical sensor measured the ball rotations. These rotations were translated into the movement of the two-wheeled robot. The advantage of this "hybrid" approach is that experimenters can manipulate any parameter of the robot, which enables the evaluation of the odor-tracking capability of insects and provides useful suggestions for robotic odor-tracking. Furthermore, these manipulations are non-invasive ways to alter the sensory-motor relationship of a pilot insect and will be a useful technique for understanding adaptive behaviors.

The capability to localize an odor source is necessary for insect survival and is expected to be applicable to artificial odor-tracking. The insect-controlled robot is driven by an actual silkmoth and enables us to evaluate the odor-tracking capability of insects through a robotic platform.

Robotic odor source localization has been a challenging area and one to which biological knowledge has been expected to contribute, as finding odor ...

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis during feeding. The ability to rapidly obtain high-resolution measurements of natural antenna and proboscis movements and assess how they change in response to chemical, developmental, and genetic manipulations can aid the understanding of insect behavior. By extending our previous work on assessing aggregate insect swarm or animal group movements from natural and laboratory videos using the video analysis software SwarmSight, we developed a novel, free, and open-source software module, SwarmSight Appendage Tracking (SwarmSight.org) for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The software processes frames about 120 times faster than humans, performs at better than human accuracy, and, using 30 frames per second (fps) videos, can capture antennal dynamics up to 15 Hz. The software was used to track the antennal response of honey bees to two odors and found significant mean antennal retractions away from the odor source about 1 s after odor presentation. We observed antenna position density heat map cluster formation and cluster and mean angle dependence on odor concentration.

This protocol describes steps for using the novel software, SwarmSight, for frame-by-frame tracking of insect antenna and proboscis positions from conventional web camera videos using conventional computers. The free, open-source software processes frames about 120 times faster than humans and performs at better than human accuracy.

Many scientifically and agriculturally important insects use antennae to detect the presence of volatile chemical compounds and extend their proboscis ...

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible

Using tracking devices to follow animal movements has long been a standard approach for studying the behavior and ecology of vertebrates. Until relatively recently, these tracking methods have been unavailable to entomologists due to the prohibitively large size and mass of the tracking devices that must be attached. Technologies applied to insect tracking include radio telemetry, Bluetooth, RFID, and harmonic radar. Among these, radio telemetry is the most used method to track larger insects. It offers a longer detection range and unique signals for each tracked insect but requires expensive tags that have limited shelf and field life and are heavy, generally at least 150 milligrams. By contrast, harmonic radar tags, first used in 1986, are lighter by at least an order of magnitude, inexpensive, and have detection ranges that allow field tracking of many smaller insects. Here, we demonstrate the use of an off-the-shelf transceiver marketed for backcountry rescue. Together, inexpensive tags and off-the-shelf transceivers make this insect-tracking technique highly accessible to entomologists studying insect behavior and ecology. In this article, we will touch on creating tags, attaching tags to insects, tracking these tagged insects, and present&#160;some representative tracking results.

This protocol presents the methods for creating, attaching, and tracking harmonic radar tags for insects.

Using tracking devices to follow animal movements has long been a standard approach for studying the behavior and ecology of vertebrates. Until relatively ...

A Precise and Autonomous System for the Detection of Insect Emergence Patterns

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Methods for Performing Crosses in Setaria viridis, a New Model System for the Grasses

A Technique to Screen American Beech for Resistance to the Beech Scale Insect (Cryptococcus fagisuga Lind.)

Electrophysiological Measurements from a Moth Olfactory System

Flying Insect Detection and Classification with Inexpensive Sensors

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

SwarmSight: Real-time Tracking of Insect Antenna Movements and Proboscis Extension Reflex Using a Common Preparation and Conventional Hardware

Harmonic Radar Tags for Insect Tracking: Lightweight, Low-cost, and Accessible