Data from: Camouflage using surface disruption: the importance of corners versus edges
Data files
Sep 22, 2025 version files 214.89 KB
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2D_Image_data_no_outliers.csv
68.10 KB
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BOTHEXPS.csv
20.08 KB
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Jen_survival_both_years_no_nas.csv
21.36 KB
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LumResultsPooledLASTCHECK_Outlier.csv
36.84 KB
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README.md
5.94 KB
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Ruby_jen_both_experiments.csv
41.78 KB
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Supporting_R_code_2D_survival.R
8.36 KB
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Supporting_R_code_3D_2D_combined.R
1.98 KB
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Supporting_R_code_3D_survival.R
10.46 KB
Abstract
Disruptive colouration is a common mode of camouflage used by predators and prey to conceal their body contours. Several studies have noted the importance of edge disruption in breaking up two-dimensional (2D) body outlines, but less is known about how disruptive colouration hinders the detection and recognition of three-dimensional (3D) body shapes. Specifically, in the context of animal camouflage, it is not clear how the location of three-dimensional (3D) surface disruption affects prey detection. In human visual systems, it is known that corners, not edges, of an object are critical for shape processing and contour completion. However, whether corners are also critical for 3D shape recognition in non-human animals has not been investigated. To test this, in Experiment 1, we presented 3D-printed moth-like targets with either corner disruption, edge disruption, or no disruption to wild, free-living birds. We repeated this in Experiment 2 with 2D targets, comprising images matched to Experiment 1, to determine if pictorial cues (patterns that produce an impression of depth) had the same effect. Contrary to our predictions, we found no effect of surface disruption location on survival probability for either 3D or 2D moth targets. Contrary to previous work, we found that targets with disrupted surfaces, irrespective of the disruption location, did not have higher survival than those with continuous surfaces. However, when the data from both experiments were combined, we found that 3D targets had higher survival than 2D targets. Although the disruptive and non-disruptive targets differed in contrast, this did not account for the variation in survival. Indeed, most of the variation in our data was explained by the spatial and temporal structure of the data, owing to different predator communities and seasonal variation in predation risk. Given that corners are critical for shape recognition in humans, developing an appropriate system to test visual perception in non-human animals will provide key insights into the role of visual perception in the predator-prey arms race.
https://doi.org/10.1002/ece3.72052
Description of the data and file structure
The attached data (.csv files) and code (R scripts) are for three separate analyses:
- Analysis of 3D targets (Experiment 1)
- Analysis of 2D targets (Experiment 2)
- Analysis of both Experiments 1 & 2 combined
Files and variables
File: Supporting_R_code_3D_survival.R
Description: R code for analysing the results of experiment 1 (3D targets).
File: BOTHEXPS.csv
Description: Survival data for Experiment 1 (3D targets)
Variables
- Block: Replicate ID, each block was carried out in a separate part of the woodland
- Treat: Target treatment code (6 treatments)
- Surv_time: Survival time in hours
- Cens: Target censored (1 = taken by bird predator) or not (0 = target present at last check or taken by non-bird predator)
- Year: Whether the experiment was performed in November 2023 or April 2024
File: Supporting_R_code_2D_survival.R
Description: R code for analysing the results of experiment 2 (2D targets).
File: 2D_Image_data_no_outliers.csv
Description: Image data for Experiment 2 (data from photographs of targets on trees)
Variables
- EXPERIMENT: Location of fieldwork in Noggerup (November 2023) or Wilga (April 2024), Western Australia.
- BLOCK: Replicate ID, each block was carried out in a separate part of the woodland. Coded separately by location.
- Block2: Replicate ID, each block was carried out in a separate part of the woodland. Coded continuously.
- Label: Name of image used to collect data
- photoCheck: Additional code of another image of the target (typically two photographs were taken).
- Cens: Target censored (1 = taken by bird predator) or not (0 = target present at last check or taken by non-bird predator)
- Surv_Time: Survival time in hours
- Treat: Target treatment code (6 treatments)
- lumMean: Mean luminance of target (modelled for bird vision)
- lumSD: Standard deviation of luminance (modelled for bird vision)
- area: Measurement area in pixels
- lwMean: Mean cone catch for long wavelength cones (modelled for bird vision)
- lwSD: Standard deviation of cone catch for long wavelength cones (modelled for bird vision)
- mwMean: Mean cone catch for medium wavelength cones (modelled for bird vision)
- mwSD: Standard deviation of cone catch for medium wavelength cones (modelled for bird vision)
- swMean: Mean cone catch for short wavelength cones (modelled for bird vision)
- swSD: Standard deviation of cone catch for short wavelength cones (modelled for bird vision)
- dblMean: Mean cone catch for double cones (modelled for bird vision)
- dblSD: Standard deviation of cone catch for double cones (modelled for bird vision)
File: Supporting_R_code_3D_2D_combined.R
Description: R code for analysing the results of Experiments 1 & 2 combined
File: Ruby_jen_both_experiments.csv
Description: Survival data for Experiments 1 and 2 combined
Variables
- year: Whether the experiment was performed in 2023 or 2024
- Experiment: Whether target type was 3D (Experiment 1) or 2D (Experiment 2)
- Tree: Tree ID (each target was pinned on 60 trees per block)
- Block: Replicate ID, each block was carried out in a separate part of the woodland
- Treat: Target treatment code (6 treatments)
- Cens: Target censored (1 = taken by bird predator) or not (0 = target present at last check or taken by non-bird predator)
- Surv_time: Survival time in hours
File: LumResultsPooledLASTCHECK_Outlier.csv
Description: Image data for Experiment 1 (data from photographs of targets on trees)
Variables
- EXPERIMENT: Whether the experiment was performed in November or April
- BLOCK: Replicate ID, each block was carried out in a separate part of the woodland
- Label: Name of image used to collect data
- Treatment: Target treatment code (6 treatments)
- Surv_Time: Survival time in hours
- Cens: Target censored (1 = taken by bird predator) or not (0 = target present at last check or taken by non-bird predator)
- lumMean: Mean luminance of target (modelled for bird vision)
- lumSD: Standard deviation of luminance (modelled for bird vision)
- area: Measurement area in pixels
- lwMean: Mean cone catch for long wavelength cones (modelled for bird vision)
- lwSD: Standard deviation of cone catch for long wavelength cones (modelled for bird vision)
- mwMean: Mean cone catch for medium wavelength cones (modelled for bird vision)
- mwSD: Standard deviation of cone catch for medium wavelength cones (modelled for bird vision)
- swMean: Mean cone catch for short wavelength cones (modelled for bird vision)
- swSD: Standard deviation of cone catch for short wavelength cones (modelled for bird vision)
- dblMean: Mean cone catch for double cones (modelled for bird vision)
- dblSD: Standard deviation of cone catch for double cones (modelled for bird vision)
File: Jen_survival_both_years_no_nas.csv
Description: Survival data for Experiment 2 (2D targets)
Variables
- year: Whether the experiment was performed in 2023 or 2024
- Tree: Tree ID (each target was pinned on 60 trees per block)
- Block: Replicate ID, each block was carried out in a separate part of the woodland
- Treat: Target treatment code (6 treatments)
- Cens: Target censored (1 = taken by bird predator) or not (0 = target present at last check or taken by non-bird predator)
- Surv_time: Survival time in hours
Code/software
The scripts are for R and were run using RStudio software (version 4.4.2). The following R packages were used:
coxme, dplyr, ggplot2, nlme, lme4, survival, survminer
Access information
No publicly accessible locations of the data or data sources. Please contact the authors for access to the images of targets.
