Supplementary Electronic Material
Methods: Experiment 1
Target colour patterns
Patterns were samples of digital photos of oak tree trunks at 1:1 reproduction (Figure S1). The
images were converted to greyscale, smoothed with a Gaussian filter to remove fine detail,
then thresholded at 50% to create binary (black/white) images. The black and white were then
replaced with shades of brown to create, when printed onto paper, two-tone bark-like dark and
light spatial variation (Figure S1).
Figure S1. Schematic illustration of how the disruptive patterns are created. Left to right: digital photo of oak
bark at 1:1 reproduction, image converted to greyscale, image filtered with Gaussian blur to remove fine detail,
image thresholded to black and white, black and white regions recoloured to two shades of brown.
Colour matching in bird colour space
Similarity of colours was estimated, as in Schaefer et al. (2006), using the photoreceptor noise
limited model of Vorobyev and Osorio (1998; 1998) with spectral sensitivities and cone cell
abundance data from Hart et al. (2000). The 'dry' and 'wet' bark values were the means of 30
samples of each, collected from a haphazard selection of trees in the study site; the 'very dark
brown' represented bark in shadow rather than the reflectance of an object, so was arbitrarily
set at 10% of the value of the wet bark value. Our criterion of a match between the printed
wing colours and the mean of each bark type, and between the pastry and the wing colours
was that they fell within 1 jnd (just noticeable difference) in the Vorobyev and Osorio (1998)
model. An estimate of the match in terms of luminance was, following Stevens et al. (2006)
and Schaefer et al. (2006), based on double cone photon catches and an assumed Weber
fraction of 5%; this is a conservative estimate as avian luminance discrimination may well be
poorer than this (Ghim & Hodos 2006). The calibration check was repeated each time a new
set of stimuli was produced.
It should be stressed immediately that the apparent precision of these colour matches is
illusory. It is a match to the mean wet or dry bark values whereas, because of the large
variation between trees, the match to any one tree that a target is placed on may be much
poorer. We assigned targets to trees at random (see main text) and did not select a target to
match a specific tree. Furthermore, the calculations are based on the blue tit, whereas the
different species of woodland bird are liable to differ in their cone type abundances and retinal
oil droplet characteristics (Hart 2001), both of which affect the noise estimates, and hence
discriminability, in the Vorobyev and Osorio model. We do not feel these deficiencies matter
for the present experiment, for two reasons. First, prior experience suggests that much of the
camouflage benefit of this type of artificial prey arises from disruption of the body shape
rather than a precise match to the background colours (even greyscale targets are hard to
detect; Stevens et al. 2006). Second, the primary aim of our experiment was to assess
potential benefits of disguising a body part (in our case, the edible body) by means of
disruptive coloration coincident with another body part (the wings). Therefore it is the wingbody (paper-pastry) colour match that is most important, factors that were under our
experimental control, rather than a tight match between the combined target and its specific
tree background.
Results: Experiment 2
Figure S2. Survival curves for the targets in experiment 2. Curves are the probability of surviving bird predation
as a function of time, based on Kaplan-Meier estimates to account for censoring due to non-avian predation and
survival to the end of the study period. (a) Experiment conducted in Leigh Woods, where experiment 1 had been
carried out, (b) experiment in Ashton Court, a novel site.
We note that there were block effects in experiment 1 but not 2 (see main text of paper). In
previous experiments of a similar design, we have dismissed significant block effects as
uninterpretable ( Cuthill et al. 2005, 2006a,b; Stevens et al. 2006). This is because, without
replication of specific blocks on different dates, the effects of date and locality are
confounded. Even if we could isolate these, differences in average predation rates could be
due to differences in bird density, habitat structure, light environment, or foraging intensity
(as affected by, e.g., changes in metabolic expenditure with temperature). The lack of a block
effect in experiment 2 (a or b) may be the result of the fact that all prey were conspicuous and,
as it turned out, equally acceptable as prey, so prey were consumed too quickly to detect
differences due to other factors. The lack of treatment effects or a difference between the two
replicates of experiment 2 should not be interpreted as evidence that birds show no effect of
familiarity with artificial prey such as these. Equally, despite the trend, we cannot reliably
ascribe the higher number of targets surviving to 6 h in the novel habitat (compared to the
familiar habitat) to greater neophobia, because the number of differences between these areas
is, strictly speaking, infinite (and certainly includes date, weather conditions, tree density and
composition, and perhaps bird density). Because of the design of experiments 1 and 2 (targets
at low density and blocks taking place in different areas on different dates), we feel it is quite
likely that most of the birds predating the targets in the Leigh Woods replicate of experiment
2, had not encountered any of the prey in experiment 1. Thus the results from the two
different sites could both be considered as coming from naïve birds. Nevertheless, we feel it
was important to conduct the replicate at a novel site to avoid drawing false conclusions about
prey acceptability from birds (in Leigh Woods) that might have encountered the prey before.
Methods: Experiment 3
Examples of the stimuli used in the human visual search experiment.
DD
Dark body on dark wings
DL
Dark body on light wings
LD
Light body on dark wings
LL
Light body on light wings
TTC
Two-tone body on two-tone wings, with body and wing colours coincident
TTN
Two-tone body on two-tone wings, with body and wing colours non-coincident
TD
Two-tone body on dark wings
TL
Two-tone body on light wings
DT
Dark body on two-tone wings
LT
Light body on two-tone wings
ZL
Light wings (no body)
ZD
Dark wings (no body)
ZT
Two-tone wings (no body)
Tree (no moth)
Table S1. Experiment 3. Mean pair-wise differences between treatments, and statistical
significance.
Tree
ZD
ZL
ZT
TTC
DD
LL
DT
LT
TD
TL
TTN
LD
DL
Tree
X
-0.054
-0.057
-0.053
-0.029
-0.150
-0.143
-0.298
-0.282
-0.231
-0.226
-0.248
-0.557
-0.588
ZD
ZL
ZT
TTC
DD
LL
DT
LT
TD
TL
TTN
LD
DL
0.105
X
-0.003
0.001
0.026
-0.096
-0.089
-0.244
-0.228
-0.176
-0.171
-0.193
-0.503
-0.534
0.057
-0.048
X
0.004
0.029
-0.093
-0.086
-0.241
-0.225
-0.174
-0.168
-0.191
-0.500
-0.531
0.063
-0.042
0.006
X
0.025
-0.097
-0.090
-0.245
-0.229
-0.178
-0.172
-0.195
-0.504
-0.535
0.257
0.152
0.200
0.194
X
-0.122
-0.115
-0.270
-0.254
-0.202
-0.197
-0.219
-0.529
-0.560
0.014
-0.091
-0.043
-0.049
-0.243
X
0.007
-0.148
-0.132
-0.080
-0.075
-0.097
-0.407
-0.438
0.067
-0.038
0.010
0.004
-0.190
0.053
X
-0.155
-0.139
-0.088
-0.082
-0.105
-0.414
-0.445
-0.145
-0.250
-0.202
-0.208
-0.402
-0.159
-0.212
X
0.016
0.068
0.073
0.051
-0.259
-0.290
0.096
-0.010
0.038
0.033
-0.161
0.082
0.028
0.240
X
0.051
0.056
0.034
-0.275
-0.306
0.007
-0.098
-0.050
-0.056
-0.250
-0.007
-0.060
0.152
-0.088
X
0.005
-0.017
-0.326
-0.357
-0.203
-0.309
-0.260
-0.266
-0.460
-0.217
-0.271
-0.059
-0.299
-0.210
X
-0.022
-0.332
-0.363
-0.185
-0.291
-0.242
-0.248
-0.442
-0.199
-0.253
-0.041
-0.281
-0.193
0.018
X
-0.309
-0.340
-0.564
-0.669
-0.621
-0.627
-0.820
-0.578
-0.631
-0.419
-0.659
-0.571
-0.360
-0.378
X
-0.031
-0.381
-0.487
-0.439
-0.445
-0.638
-0.396
-0.449
-0.237
-0.477
-0.389
-0.178
-0.196
0.182
X
The bottom left half cells contain the mean pair-wise differences in log10(Response time); the
top right cells contain the mean pair-wise differences in arc-sine(square-root(proportion of
errors)). Significant differences at p = 1 – 0.951/91, where 91 is the number of tests for each
variable, are indicated in bold. To calculate t-tests, use a pooled standard error of 0.0259 for
response times and 0.0770 for errors. The overall univariate ANOVA results were F13,247 =
99.65, P < 0.001 for log10(Response time) and F13,247 = 15.95, P < 0.001 for arc-sine(squareroot(proportion of errors)).
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