DOI: 10.5061/dryad.tqjq2bw99
title: An analysis of silk density in spider webs

description: This study explores the structural complexity of spider webs through
 information-theoretic and harmonicity-based frameworks to quantify spatial
 patterns in silk density across different web regions and reveal the
 underlying resource allocation strategies. By analyzing the entropy of
 silk density distributions across different species, we observed that the
 entropy values follow a normal distribution with a mean of 1.24 bits and a
 standard deviation of 0.22 bits when using 10 quantization levels. In the
 second part of the paper, by measuring the harmonicity of the silk
 density, we reveal that the silk density at a given point can be inferred
 from its neighbors, with an average harmonicity value of 0.0039 (fraction
 of total points in point cloud data). The harmonic behavior is notable for
 its maximum principle, suggesting that the strongest parts of the web
 appear at the boundaries, aligning with existing knowledge of spider web
 construction. These findings provide a new technique for quantifying
 web-building strategies and offer new insights into spider behavior and
 evolution.
description: Collected spiders were kept in small containers for 1-2 weeks
 without food prior to introduction to experimental space to ensure
 relative uniformity of physiological state. Specimens were placed inside
 10.16 x 10.16 x 10.16 cm (4” x 4” x 4”) acrylic boxes with clear sides, a
 black base, and no lid. Vaseline was coated on the inside top 2.54 cm
 (1”)of each side to prevent specimens from escaping. Each spider was given
 1-2 weeks in ambient conditions to complete a web. Linyphiids construct
 webs over the course of multiple days and weeks, and web completion was
 defined as the point at which no additional silk was added to the web
 after several consecutive days. Completed webs were
 placed into an enclosed experimental scanning apparatus blocking all
 outside light. Scanning was conducted by a laser (Quarton Inc. VLM-520-56)
 and camera (Canon EOS R5C mirrorless camera using a Canon RF 100mm F 2.8
 Macro lens) mounted on a track at a fixed distance (0.67 meter). A small
 motor (Habow Technic Power-Functions XL-Motor-Set 8882) was used to
 advance the laser and camera simultaneously to scan the length of the
 experimental chamber from front to back, recording at 120 FPS for 20-30
 seconds. An infrared laser distance sensor (DFROBOT DFRduin Uno v3.0[R3])
 monitored the location of the camera relative to its start position to
 provide z-axis information for 3D
 reconstruction.<br><br>Individual video frames representing
 two dimensional slices of the web were extracted for cleaning and analysis
 using a custom Python script (Github
 Repository).  For each video, a set cropped region was
 identified in each frame that excluded the box edges and background and
 accounted for any minute changes in elevation across the length of the
 box. Red and blue color values were reset to the minimum value and the
 image converted to grayscale. Background noise was removed using a minimum
 threshold filter of 0.55 max RGB value.  Three-dimensional Point Cloud
 Data (PCD) representations of each web were created using the filtered
 data.
description: # From local interactions to global patterns: An analysis of silk density
 in spider webs [Access this dataset on
 Dryad](https://doi.org/10.5061/dryad.tqjq2bw99) This collection comprises
 high-resolution video scans of spider webs recorded under controlled laser
 illumination, paired with synchronized distance sensor measurements. Each
 web was grown in a fixed arena and periodically scanned by sweeping a
 laser sheet across its plane; the resulting video captures the laser line
 intersecting the silk strands. The accompanying CSV files record the raw
 distance readings from the sensor, which can be mapped back onto the video
 timestamps after appropriate post-processing. These data support
 quantitative studies of web geometry, silk deposition dynamics, and the
 temporal evolution of web-building behavior. ## Description of the data
 and file structure All scans are provided in pairs of files, one video and
 one sensor log: ```
 \_(sample-number)_(light-level)_(date-of-scan)_(time-of-scan).mp4
 \_(sample-number) (light-level) distance_data (date-of-scan)
 (time-of-scan).csv (included in distance_records.zip) ``` *
 **sample-number**: Unique integer identifier for each spider/web. *
 **light-level**: Integer 0–255 indicating laser brightness; only level 255
 (maximum detail) is provided. * **date-of-scan**: ISO format `YYYY-MM-DD`.
 * **time-of-scan**: 24-hour format `HH-MM-SS`. ### Video files *
 **Format**: MP4, 1920×1080 or 1280×720 pixels at 30 fps. * **Content**:
 Laser line sweeping across the web; silk strands appear as bright ridges.
 ### CSV files Each CSV has one row per sensor reading with two columns: |
 Column | Type | Description | | ------------ | ----- |
 ------------------------------------------------------ | | **Time** |
 Float | Timestamp in seconds since CSV recording started. | | **Distance**
 | Float | Distance in meters from the sensor to the laser sheet. | ##
 Sharing/Access information * **Primary access**: Dryad repository at
 [https://doi.org/10.5061/dryad.tqjq2bw99](https://doi.org/10.5061/dryad.tqjq2bw99) * **Derived from**: Original experiments conducted in the GeoDes Lab under Dr. Norman Sheu, University of California, Berkeley. ## Code/Software We provide scripts to process these scans: * **Repository**: [https://github.com/normansheu/spiderweb](https://github.com/normansheu/spiderweb) * **Scripts**: * `pcd_generation_improved.py`: Takes a video-csv pair and outputs the PCD representation of the spiderweb scan.