Supporting data for: Adaptive cell size, merging, tilting, and layering in Honeybee comb construction

Gharooni Fard, Golnar 1 ; Kavaraganahalli Prasanna, Chethan1 ; Peleg, Orit1 ; López Jiménez, Francisco1

Research facility: University of Colorado Boulder

Published May 17, 2024; Updated Jun 10, 2025 on Dryad. https://doi.org/10.5061/dryad.z8w9ghxmw

Data files

May 17, 2024 version files 19.14 GB

Jun 10, 2025 version files 19.14 GB

Abstract

Honeybees are renowned for their skills in building intricate and adaptive hives that display notable variation in cell size. However, the extent of their adaptability in constructing honeycombs with varied cell sizes has not been investigated thoroughly. We use 3D-printing and X-ray Microscopy to quantify honeybees' capacity in adjusting the comb to different initial conditions. Using the average area of natural worker cells as a reference, our findings suggest three distinct construction modes when faced with foundations of varying cell sizes. For smaller cell size, bees occasionally merge cells to compensate for the reduced space. However, for larger cell sizes, the hive uses adaptive strategies like tilting for cells up to twice the reference size, and layering for cells that are three times larger than the reference cell. Our findings shed light on honeybees’ adaptive comb construction strategies with potential to find applications in additive manufacturing, bio-inspired materials, and entomology.

https://doi.org/10.5061/dryad.z8w9ghxmw

We are sharing the data used to explore the adaptability of the honeycomb structure built on the 3D-printed experimental frames with various cell size foundations.

Description of the data and file structure

All of the files are categorized based on the value of the 3D-printed cell sizes S. The file names in each dataset follow the naming convention s_<cell size>, where <cell size> is the relative 3D-printed cell size of the sample, with a ‘o’ used to represent a decimal point ’.’. For example, for data regarding S=1 or S=1.5, all file names would start with s_1 or s_1o5 respectively. Refer to the paper for more details about S.

s_1_raw_data.zip

A sample of the raw X-ray microscopy data for our control dataset (S = 1) is provided as a stack of TIFF images. Raw X-ray images for the other size categories are available upon request.

2D_images.zip

Contains folders named s_<cell size> which include a set of three JPEG files.

Filenames in each folder: s_<cell size>_<MM>-<DD>-<YY>.jpeg, where <MM>-<DD>-<YY> refers to the month, day and year that the picture is taken.

processed_XRM_data.zip

Stacks of thresholded images for reproducing the 3D volumes, obtained from X-ray Microscopy.

File names: s_<cell size>_histogram_thresholded.zip.

cell_size_data.zip

Cell size data is organized in 7 folders s_<cell size> containing CSV files for more than 1000 cells built on experimental frames.

Filenames in each folder: s_<cell size>_cell_sizes_<n>.csv. All CSV files contain these fields:
cx: the x-coordinate of the cell center
cy: the y-coordinate of the cell center
normalized cell size: the area of the cell in millimeter squared divided by the mean cell area of the reference cell (~12.96)

angle_data.zip

Contains CSV files that show the angle of tilt (calculated in degrees) for each cell on all of the tilted samples.

Filenames: s_<cell size>_cell_tilts.csv. These values are measured, using Dragonfly software, for all the cells imaged with the X-ray.
The contents of the file named natural_drone_cell_tilts.csv are manually measured for 20 individual drone cells.

3D_reconstruction_movies.zip

A movie showing the 3D-reconstructed volume for each sample, made using the thresholded X-ray data (provided in processed_XRM_data.zip files) using Dragonfly software.

Filenames: s_<cell size>_movie.mp4.
All 3D volumes are constructed using the X-ray data from the 5 cm × 5 cm sections of the comb constructed on our experimental frames, segmented to show comb in dark yellow and plastic in blue.

s_o75_covered_cell_data.zip

Contains CSV files showing the number of all covered cells in each row of the three replicates of S=0.75.

Filenames: covered_cells_o75_<n>.csv. All files contain these fields:
Row number
covered: number of covered cells in the row
all: total number of cell in the row

Sharing/access information

All of our experiments are performed using colonies of European honeybees Apis melifera L. at the Peleg lab apiary in Boulder, Colorado, USA.

Fused deposition modeling (FDM) technology was used for 3D-printing of experimental frames using Polylactic Acid (PLA) material, and the designs are made in SolidWorks 2019 CAD design software.

All photographs of honeycomb are captured using a Nikon DSLR camera and controlled lighting and a black background.

XRM data collection was performed at MIMIC facility, at CU Boulder (RRID:SCR_019307).

All tomography scans are performed using Micro CT ZEISS Xradia 520 Versa (Carl Zeiss X-ray Microscopy Inc, Pleasanton, CA, USA).

Image processing and segmentation on the X-ray data is conducted using Python and Dragonfly (Object Research Systems, Montreal, QC, Canada).

Code/Software

The code that is used for this study can be found on the Peleg Lab’s GitHub page, along with detailed instruction of how to use this data to run it.

Change Log

Jun 10, 2025: We made some changes to the repository based on the reviewers’ request to include all the data used for reproducing the plots in our paper and SI. The changes are as follows:

Added the folder “s_o75_covered_cell_data.zip”
Updated the “cell_size_data.zip” files to reflect the values for the normalized cell sizes rather than raw values

Experimental data Collection

All of our experiments are performed using colonies of European honeybees Apis melifera L. at the Peleg lab apiary in Boulder, Colorado, USA.

Fused deposition modeling (FDM) technology was used for 3D-printing of experimental frames using Polylactic Acid (PLA) material, and the designs are made in SolidWorks 2019 CAD design software. Each frame consists of a pair of 3D-printed plates that are placed opposite to each other. For our control set with S=1, each hexagonal element on the 3D-printed plates has a side-length of 2.7 mm. All the other configurations are designed relative to the area of our control set.

All photographs of honeycomb are captured using a Nikon DSLR camera and controlled lighting and a black background.

XRM data collection was performed at MIMIC facility, at CU Boulder (RRID:SCR_019307).

All tomography scans are performed using Micro CT ZEISS Xradia 520 Versa (Carl Zeiss X-ray Microscopy Inc, Pleasanton, CA, USA).

Image processing and segmentation on the X-ray data is conducted using Python and Dragonfly (Object Research Systems, Montreal, QC, Canada).

Dataset Contents

The raw data obtained from X-ray microscopy of our control dataset, S=1, in the form of a stack of tiff images.

2D images of honeycomb that captured using a Nikon DSLR camera and controlled lighting, and a black background.

Stacks of thresholded tiff images for reproducing the 3D volumes, obtained from X-ray Microscopy.

CSV files that show the angle of tilt (in degrees) for each cell on the X-rayed sample.

CSV files that contain the xy coordinates of cell centers, and their area.

Movies showing the 3D-reconstruction of the sample, made using the thresholded X-ray data in Dragonfly software.

X-ray data Processing

X-ray tomography scanning of our experimental samples generate stacks of cross-sectional tomography images which form a 3D model of the samples when digitally stitched together. Each tomography image comprises of the following elements-- air, honeycomb, and the plastic base, each captured with a different pixel intensity which is a function of the radiodensity of the element. We develop an image processing pipeline that converts the stack of tomography images to filter out unwanted parts of the raw X-ray image data and segment particular regions of interest. We mainly used computer vision techniques, with OpenCV [1] library in Python 3.0, to conduct all our image analyses. For creating clear 3D visualizations, we use a combination of convolution with a specially designed kernel and morphological operations to segment the plastic cell edges. We use the second derivative of the image histogram to find the optimum threshold value for the effective segmentation of air from data (i.e., honeycomb and plastic). Next, we identify pixels that mark the boundary of the plastic base and air using convolution with a specially designed kernel that detects changes in pixel intensities.Using this we create a kernel which is convolved over the image as a sliding window and at each row, the result of convolution is maximized precisely when the kernel is at the boundary of the plastic base. As a result, this process generates a mask that marks the boundary pixels. With this mask as a seed, we use morphological operations to generate an enhanced mask that covers the plastic base entirely.

Tilt angle calculation

For the comb built on the 3D-printed frames with size S=1, 1.25, 1.5, 1.75, and 2 we compare and quantify the cell tilt values relative to the plastic base. We achieve this by creating two parallel planes intersecting the honeycomb at the base and the upper layer. Then, in each plane we identify the intersecting hexagonal cross-sections (components) corresponding to the hexagonal cavities of individual cells. Subsequently, we identify the centroids of each of these hexagonal components and their centroids by running Connected Component Analysis on each plane in the Dragonfly software [2]. Once these contours are established, the angle of tilt for each honeycomb cell is computed through simple geometric analysis using the displacement value of the cell centroids.

References

[1] G. Bradski, Dr. Dobb’s Journal of Software Tools (2000).

[2] Comet Technologies Canada Inc., Montreal, Canada, Dragonfly.