Field Sites and Photography

Images were taken of rock pool and mudflat habitats across six sites, three were rock pool habitats (47 background images) and three were mudflats (47 images). Although there was some variation in features among rock pool sites, in general the background substrate was similar, consisting of large clusters of rocks, forming deep gullies filled with small pebbles and sand, alongside small pools (see9 for habitat assessments; Fig. 1). Conversely, mudflats consist of large expanses of dark brown mud and surface algae, with little shelter other than dispersed rocks or objects. Sites were located on both north and south coasts of Cornwall, Southwest UK and were separated by between six and 50 km. Gyllyngvase beach (50° 8′ 39.42″N, −5° 4′ 5.244″W) in the Falmouth area, Kennack Sands (50° 0′ 23.695″N, −5° 9′ 28.258″W) located further down the southwest coast, and Perranuthnoe (50° 6′ 43.383″N, −5° 26′ 28.142″W) on the south coast were rock pool sites. For mudflats, Penryn (50° 9′ 49.335″N, −5° 5′ 2.124″W) and Helford (50° 5′ 23.1″N, −5° 9′ 58.754″W) were chosen on the south coast, with Hayle (50° 11′ 36.979″N, −5° 25′ 47.973″W) on the north coast. After entry to the focal area of a site, images were taken separated by approximately 3.7 m with 20 images taken at each site. Some images were deselected back in the lab if they were deemed to be too out of focus, resulting in 47 images overall per habitat. All work was conducted under approval from the University of Exeter ethics committee (application number: 2016/1162).

Image acquisition followed standard protocols8,9,13. Images of backgrounds were taken with a Nikon D7000 digital camera modified with a quartz conversion to allow for UV sensitivity (Advanced Camera Services, Norfolk, UK) fitted with a Nikon Nikor 105 mm lens. All photographs were taken in RAW format with fixed aperture settings. The camera was held in position using a tripod and all photographs were taken at the same height (approximately 1 m). Two sets of images were taken, using a visible (Baader UV/IR Cut filter) and UV (Baader Venus U filter) filter, to block UV and infrared light (human-visible images) and allow only UV transmission between 300–400 nm (UV images), respectively. Our camera sensitivities are as follows: UV: 360–400 nm (peak 380 nm), SW: 400–550 nm (peak 460 nm), MW: 420–620 nm (peak 540 nm), LW: 560– 700 nm (peak 625 nm)61. To keep light conditions uniform, images were taken on overcast, cloudy days. A photographic umbrella was also used for each photograph to minimise glare, and a black and white reflectance standard with a scale bar was placed in the corner of each image for subsequent image calibration and standardisation62. The standard was made from 10 × 10 mm sections of zenith diffuse sintered PTFE sheet (Labsphere, Congleton, UK) and reflected 8.2% and 94.8% of all wavelengths respectively63. A scale bar was used to automatically resize all images to the same scale for subsequent pattern and disruption analysis.

For the purposes of this study, we focussed on small juvenile crabs with <15 mm carapace width (CW) at the widest point. While past work has categorised crabs as ‘adults’ when CW > 25 mm, there is a gradual decrease in patterning and a change in the body appearance towards more uniform green as crabs develop2,8,9. The reasons for this likely reflect a switch to a generalist camouflage strategy in adults that are more mobile across sites than juveniles58. We therefore focus on small juvenile crabs that are much more likely to require habitat-specific camouflage and which are likely to incur greater predation risk than adults8,34,63. In total, 97 crabs were sampled and used for the background matching analyses (colour, luminance, and pattern) and 86 of these were used for the disruptive coloration analysis.

Collection of crabs followed past approaches8,9,52,58, with sampling at low tide by systematically searching the substrate, lifting seaweed, rocks, and raking the substrate with fingers to locate individuals in a given area. Crabs were then transported back to the laboratory at the University of Exeter, Penryn Campus in clear tanks containing salt water from the habitat and background substrate to cover the bottom of the tank, providing refuge to avoid inflicting stress during transportation. Individuals were then gently dried with tissue paper and placed underneath a tripod set up in a dark photography room. Each crab was placed on a spectrally flat sheet of 2 mm thick black foam with a reflective white PTFE cylinder surrounding the individual to diffuse the light for photography.

Image analysis and vision modelling

Multispectral images were created using the ‘multispectral image calibration and analysis toolbox’ in Image J61. Images were aligned and the white and black standards selected to allow images to be linearised with regards to radiance and standardised to control for light conditions61,62. Images were resized downwards to the same scale using the scale bar9. Once these images had been calibrated, regions of interest (ROIs) were selected for measurement. Here, the carapace of each crab, excluding appendages, was selected.

We modelled the visual system of both a predatory bird and fish. Among the main predators of shore crabs are shore birds and fish37,64. Most birds are probably tetrachromats, using four cone types in colour vision: longwave (LW), mediumwave (MW), shortwave (SW) and ultraviolet/violet (UV/V). Most shore birds have a ‘violet’ cone type relatively more sensitive to longer violet wavelengths than some other more UV-sensitive birds65. Therefore, for modelling we followed8 and used the visual sensitivity of the peafowl (Pavo cristatus)66, which is widely used as a species for visual modelling of violet birds (Fig. 1). Although other birds that may be relevant predators (e.g. gulls) can have an ‘ultraviolet’ type system more sensitive to UV light, the crabs and backgrounds in our study generally have low ultraviolet reflectance. For fish, the European pollack, Pollachius pollachius67 is thought to be a key predator of crabs and represents a dichromatic fish predator, with LW and SW sensitive cones. While fish in our study site can also be trichromats, past work has showed only minor differences in modelled crab appearance among di- and tetrachromatic systems8, and so other visual systems are unlikely to vary greatly either. We converted standardized images to predicted cone catch data for each species using a widely implemented polynomial mapping technique62. This has been repeatedly shown to provide highly accurate data compared to cone catch modelling with reflectance spectra (see25,61,68).

Background matching: colour and luminance

To quantify colour and luminance match to the background we used a widely employed log version of a model of predator discrimination69. This calculates just noticeable differences (JNDs) between two objects to determine discriminability. The output of the model, JNDs, predicts whether two objects can be discriminated (values < 1.00), with increasing values equating to a reduction in the level of camouflage match. For full details see Supplementary Material. We measured the cone values for each crab ROI for each visual system, and then the same for each background image. Using the above models, we then compared the colour and luminance match of each crab carapace to every background image, followed by calculating average colour and luminance JNDs for each crab to each habitat. We therefore derived an average level of background matching for each crab to each of the two habitat types, across all samples.

Background matching: pattern

To assess background pattern matching between crab carapace and the background for each habitat, a granularity analysis was conducted9,70,71 – see Supplementary Material. We used a modification of this process to make direct comparisons between the body markings of an animal and the substrate (‘pattern energy difference’, PED), giving a measure of background pattern matching that predicts detection by wild predators13 and humans searching for computer targets23. For pattern analysis we used the double cone (luminance) values of the peafowl (Pavo cristatus)66. Any two patterns with similar energy across all spatial scales will produce low pattern difference values, indicative of background matching, whereas deviation in either amplitude or shape of the spectra will produce larger differences. Here, the absolute difference between the spectra of crab carapaces and habitat backgrounds was assessed (both rock pool and mudflat separately). As above, we derived an average level of pattern matching for each crab to each of the two habitat types.

Disruptive coloration

To quantify edge disruption, we used a recently developed method called ‘GabRat’, which uses angle sensitive filters to measure the ratio of false edges to coherent edges around the target outline23 – see Supplementary Material. A high ratio of false edges to coherent edges should be more disruptive, and therefore indicates that prey are more difficult to detect, while lower values suggest salient coherent edges. While we note that GabRat is relatively new and awaits greater testing, especially in natural systems, the metric has been shown to be one of the most important predictors of human detection times of disruptive targets (and superior to other pattern metrics, including those for quantifying disruption based on more conventional edge detection algorithms23). For each image, each crab was randomly placed in 50 different positions that did not overlap with each other or any exclusion zones. This was repeated on all 94 backgrounds (47 rock pool and 47 mudflat), resulting in a total of 4700 edge disruption measurements per individual crab. This process accounted for variation in positioning of crabs in the wild. The average GabRat value of the total 50 positions was calculated for each background, so that one value was generated per crab/image combination. Means per individual were then calculated across both rock pool and mudflat backgrounds, so that each crab had an average edge disruption value for both habitat types.

Statistics

All individuals (from the two habitat types) were placed onto both habitat types, resulting in two mean values per individual. A split plot 2 × 2 repeated measures mixed factorial ANOVA with type III sums of squares was used to assess the match of individuals to rock pool and mudflat images using the R function ezANOVA. Our within subjects factor was image background and the between subjects factor was the collection habitat. Full models including the 2-way interaction were run for each of our dependent variables (the metrics of camouflage): edge disruption data, PED, and colour and luminance data for both avian and fish vision models; so the general form was: (camouflage metric ~ collection.site + background.habitat + collection.site * background.habitat).

Homogeneity of variance was assessed using Levene’s test. Colour JND data for avian vision was non-normal and so a log transformation was applied. Assumptions of normality and homogeneity of variance were met in all analyses, which were conducted in the statistical program R72.

We predict that crabs from rock pool habitat sites will have higher GabRat edge disruption values than crabs from mudflat habitats. There should also be a habitat effect, with the variation in rock pool backgrounds allowing greater disruption regardless of the crab origins. Conversely, for background matching, crabs from mudflat habitats may better match the background for each metric owing to its more simple nature than crabs from rock pool habitats. In addition, mudflat backgrounds may also allow greater background matching owing to their more uniform appearance, whereas rock pools present highly variable environments meaning matching many patches is not possible.