Data from: Innate visual attraction before, during and after escape from adverse substrates in carpenter ants
Data files
Jul 16, 2025 version files 71.97 MB
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dataset.zip
71.96 MB
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README.md
9.63 KB
Abstract
Many animals exhibit an innate attraction to dark areas or objects, driving orientation behaviours such as beacon aiming. In ants, some species do not appear to display beacon aiming. Here, we show that in one such species, Camponotus japonicus, the behaviour is triggered when crossing liquid-covered surfaces, regardless of locomotor pattern and the presence of water in the liquid. Once initiated, beacon aiming persisted even after the ants transitioned from water to dry substrates, as evidenced by their reorientation towards a displaced beacon. Beacon aiming could be observed before the ants fully transitioned from a dry substrate to a liquid-covered surface: when the ants were isolated on a water-surrounded platform, attraction to a beacon emerged while they were contacting the water, before finally deciding to swim towards the beacon. Adverse substrate conditions in general appear to be a factor triggering beacon aiming as we also identified one condition (so far) in which even liquid immersion was not required for beacon aiming, namely upside-down walking. These results indicate that beacon aiming in C. japonicus is performed before, during and after escape from adverse substrates. Evidence that substrate conditions can alter seemingly hardwired responses suggests that insects may adjust even simple behaviours in response to environmental conditions in a more sensitive way than commonly assumed.
https://doi.org/10.5061/dryad.cnp5hqcfs
Description of the data and file structure
We investigated beacon-aiming in the ant Camponotus japonicus under various substrate conditions. This experiment allowed us to probe the decision-making dynamics in ants before, during, and after the transition between substrates.
Files and variables
File: dataset.zip
Description of the data and file structure
The tracking data obtained using DeepLabCut or UMATracker is stored in CSV format.
The code for statistical analysis is provided in Python (.py) or R format (.R).
dataset.zip
|- rawdata/
| |- experiment1/ # single substrate conditions
| | |- land/ # ‘Land-walking’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- water/ # ‘Water-swimming’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- shallow_water/ # ‘Water-wading’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- glycerol/ # ‘Glycerol-swimming’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + _ + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + trial index + _path_full)
| | |- tylose/ # ‘Tylose-swimming’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + _ + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + trial index + _path_full)
| | |- upsidedown/ # ‘Upside-down-walking’ conditions
| | | |- umatracker/ # XY coordinate data from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + trial index + _path_full)
| | |- readme.txt # Correspondence between the analysis edge radius (cm) and the unit circle radius (r=1) in polar coordinates
| |- experiment2/
| | |- walking_without_transition # ‘Walking-without-transition’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- beacon_fixed # ‘Beacon Fixed’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- full/ # complete trajectories
| | | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | | |- land/ # trajectories after reaching shore
| | | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | | |- pool/ # trajectories before reaching shore
| | | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- beacon_displaced # ‘Beacon Displaced’ conditions
| | | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | | |- full/ # complete trajectories
| | | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | | |- land/ # trajectories after reaching shore
| | | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | | |- pool/ # trajectories before reaching shore
| | | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- readme.txt # Correspondence between the analysis edge radius (cm) and the unit circle radius (r=1) in polar coordinates
| |- experiment3/
| | |- deeplabcut/ # XY coordinate data and the likelihood for each body part (head, thorax, petiole, gaster) from DeepLabCut
| | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index)
| | |- r_theta/ # Normalized polar coordinates (r, theta) with the center of the beacon as radian 0
| | | |- full # complete trajectories
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | |- before_1st_contact_water # trajectories before first contacting with water
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | |- before_1st_immersion_after_1st_contact_water # trajectories before first immersion after first contacting with water
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | |- before_start_swim_after_1st_immersion # trajectories before starting swimming after first immersion
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | | |- after_start_swim # trajectories after starting swimming
| | | | |- *.csv # each trial (species name + individual index + _ + condition index + trial index + _path_full)
| | |- readme.txt # Correspondence between the analysis edge radius (cm) and the unit circle radius (r=1) in polar coordinates
|- statistical_analysis/ # scripts for statistical analysis
| |- calculate_straightness.py # Python code for calculation path straightness
| |- circular_test.R # R code for circular tests to perform one-sample and two-sample tests
| |- mann_whitney_u_test.R # R code for Mann–Whitney U test
File naming
The name of each directory is set according to the experiment number and the name of the condition. First, directories are divided by experiment and experimental condition names, and then by the type of tracking data.
- “deeplabcut”: XY coordinates and likelihood of each body part obtained using DeepLabCut.
- “umatracker”: XY coordinates of the tracking data obtained using UMATracker.
- “r_theta”: Polar coordinate data derived from the tracking data, where the defined edge is treated as the unit circle, and the centre of the beacon is arranged at radian = 0. The radius of the defined edge for each condition is described in the readme.txt file.
If there are additional directories within “r_theta”, the “full” directory contains all trajectory data, while the other directories contain subdivided data for each behavioural phase.
Code/software
Set up the Python environment
To set up the Python environment, install the required libraries using pip or conda. Ensure you have Python installed (version 3.x is recommended). For example:
pip install numpy pandas
Set up the R environment
To set up the R environment, install the required packages using the install.packages() function. For example:
install.packages("circular") install.packages("exactRankTests", repos="http://cran.ism.ac.jp/")
Load the libraries:
library(circular) library(exactRankTests)
We investigated beacon-aiming in the ant Camponotus japonicus under various substrate conditions. All experiments were conducted inside an opaque cylindrical arena. A vertical black rectangle (beacon) was placed on the inner wall of the arena. To minimise the potential influence of external cues, the beacon was placed in one of three possible positions, each separated by 120°, with trials evenly distributed across these positions. Each trial began with the ant placed at the centre of the floor. The behavioural sequence was recorded at 59.94 fps, until the ants reached the edge of the circular area used for analysis, as defined for each experiment.
Experiment (i): To investigate whether C. japonicus exhibits different orientation responses on land and water surfaces, three conditions were prepared: ‘Land-walking’ – walking on a dry substrate; and ‘Water-swimming’ – forced swimming in water; ‘Water-wading’ – walking (wading) in shallow water. Furthermore, the involvement of water reception in beacon-aiming was assessed by replacing water with two kinds of liquids: ‘Glycerol-swimming’ – forced swimming in a mixture of 80% (v/v) glycerol and 20% (v/v) 2-propanol; ‘Tylose-swimming’ – forced swimming in a 1.0% (w/w) tylose aqueous solution. In addition, as an adverse substrate condition that does not employ liquids at all, we forced ants to walk upside-down on sandpaper.
Experiment (ii): Building on the results of experiment (i), we examined whether the directional decision persisted after a transition from water to a dry substrate. The experiment was conducted under three conditions: ‘Beacon Fixed’ – the beacon remained stationary on the arena wall; ‘Beacon Displaced’ – the beacon was rapidly shifted by 60° upon the transition of the ants from water to dry substrate; and ‘Walking-without-transition’ – ants walked on a dry arena without a pool to account for differences in floor height and texture compared to the standard arena used in experiment (i).
Experiment (iii): The ants were provided with an island surrounded by water, and they were isolated on the island. We investigated whether the ants orient towards a beacon prior to starting to swim, thereby examining whether the decision-making process occurs independently of immersion.
Analysis: Trajectories of the ants were obtained using DeepLabCut, except for Upside-down-walking, where UMATracker was instead used. Statistical analyses were conducted using Python and the package ‘circular’ implemented in R.