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Electronic Supplementary Material
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Additional methods
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1. Study species and housing
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Seven wild male European starlings were caught under license from English Nature (License
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Nos. 20040489), and transferred to a free-flight aviary in laboratories at Newcastle University.
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We used only male European starlings in order to reduce variance in behavior arising from sex
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differences in mass and mass regulation strategies. Therefore, birds could have different
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strategies of diurnal mass regulation and toxin ingestion. The birds were taken from the free-
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flight aviary and housed individually in wire mesh cages measuring 450 x 750 x 450 mm (h x w x
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d) with a metal drawer at the bottom, which could be removed for cleaning. Cages contained two
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dowel perches and two water bottles, and birds were provided bathing water every 2-3 days.
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The birds had no prior experience with unpalatable prey in the laboratory nor the stimuli or
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experimental protocol used in this experiment. The birds were kept on a 14:10 hour light/dark
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cycle (lights on at 0900 h; lights off at 2300 h). Differences in ambient temperature can have a
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significant effect on the birds’ consumption of chemically defended prey [S1]. However, room
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temperatures during the experiment ranged between 14-16 °C and so this would unlikely have
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been the cause of differences in consumption observed within sessions, days, or prey types.
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During training, birds were fed ad libitum pheasant breeder pellets and were given a piece of
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fruit daily and water was available continuously. During the experiment, birds were fed ad libitum
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pheasant breeder pellets except while the daily sessions were taking place when their food was
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removed from their cages 5-min prior to the start of daily experiments and placed back in cages
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immediately after the daily session finished. After the experiment, birds were returned to the
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free-flight aviary before being released back into the wild at their site of capture in 2006.
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2. Training procedure
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Five minutes before the start of every session, we removed a bird's food bowl and moved their
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cage to a new position in visual isolation from the experimenter and other birds in the room. In
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order to observe the birds’ behaviour we used a video camera connected to a television monitor.
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Birds were initially trained to flip white lids off Petri dishes to find unmanipulated and palatable
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mealworms underneath [S2]. A Petri dish containing a single mealworm was presented every
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three minutes, and the bird had 60 seconds to attack and eat the mealworm before it was
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removed. Birds were given 18 presentations in a session. Once the birds had learned to flip the
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white lids off the Petri dishes (up to three training sessions), we trained birds to recognise
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differences among the three prey types (undefended, mixed-defence and fixed-defence prey) by
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changing the white lids for coloured lids that corresponded with prey type. The prey lid colours
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(pastel colours of pink, yellow, green, and blue) were assigned for each bird’s prey using a Latin-
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square procedure to ensure no lid colour was over represented with a particular prey type. Each
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half-session (nine presentations) contained three of each prey type presented in a random order.
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Birds took between 3 and 11 sessions (mean ± s.e. = 6.6 ± 1.46 sessions) to eat significantly
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more undefended than defended prey when summed over three consecutive days (total number
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of each prey eaten summed over three days: mean ± SE; undefended = 17.4 ± 0.30 [from a
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maximum of 18 prey], defended (i.e. mixed and fixed-defence prey) = 24.4 ± 1.60 [from a
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maximum of 36 prey]; all 𝜒12 ≥ 4.021, all P ≤ 0.045). Once they had each reached this criterion,
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we recorded their responses for a further 10 days (see Results).
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3. The amount of quinine ingested from eating mixed- and fixed-defence prey
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An important assumption of the experiment was that birds experienced the same mean level of
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defences for the mixed-defence and fixed-defence prey (i.e., the defences for the mixed and
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fixed-defence prey should both equal 2% quinine). To test this assumption, for the mixed-
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defence prey, we compared the numbers of 4% prey eaten with the numbers of 0% prey eaten.
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We ran a generalised additive mixed model on the data obtained from the 10 daily sessions
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comparing the numbers of water-injected (0%) and quinine-injected (4%) mixed-defence prey
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type were eaten. We included prey type (water-injected and quinine-injected) and statistically
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controlled for the effects of day, prey number, and bird by including them as random factors in
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the model. We found that there was no significant difference in the numbers of water-injected
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and 4% quinine-injected mixed-defence prey that were eaten (mean proportion of eats ± SE
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[total prey eaten by all birds/total prey available for all birds]: 4% quinine injected = 0.5238 ±
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0.035 [110/210], 0% water-injected = 0.5 ± 0.035 [105/210]; GAMM: 𝜒12 = 0.4809, P = 0.488).
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Therefore, we conclude the difference in the number of fixed and mixed-defence prey eaten was
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not the result of a difference in the average amount of quinine ingested from each prey type.
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4. Data handling and statistics
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We conducted all analyses in this study using generalised additive mixed models (GAMMs).
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GAMMs are a powerful mixed-modeling technique that allow continuous variables to be included
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in mixed-models as factors expressed as smoothed terms (similar to splines) along with
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categorical fixed-factors and random factors [S3]. We excluded the undefended prey from
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analyses because they were almost universally eaten by birds (mean consumption rate ± SE =
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0.988 ± 0.005). We used whether birds ate the prey (0 or 1) or not as the dependent variable in
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our models. We included prey type (fixed or mixed) as a fixed factor and presentation number
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within session (1-18) as a smoothed factor. In the original model, there was a significant day
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effect (GAMM: 𝜒12 = 5.066, P = 0.0244, Fig S1). While there did not appear to be any biologically
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important trend in the variation among days (Fig S1), we included session number (1-10) as a
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random factor in the model to account for the possibility that these differences were due to
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differences among days of an unmeasured variable (e.g., energetic state [S4]). Bird ID (1-7) was
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also included as a random intercept factor to control for differences among subjects. Our
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dependent variable was binary meaning that we fitted a binary distribution with a log-link function.
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We ran models using the GAMM command in the mgcv package [S3] to run models using R
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(version 3.0.1) [S5]. All tests were two-tailed, and the level of significance was primarily set as 
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= 0.05. We used log-likelihood tests to test for significance of factors throughout the paper.
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References
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S1. Chatelain M, Halpin CG, Rowe C. 2013 Ambient temperature influences birds’ decisions to
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eat toxic prey. Anim. Behav. 86, 733–740.
S2. Skelhorn J, Rowe C. 2006 Predator avoidance learning of prey with secreted or stored
defences and the evolution of insect defences. Anim. Behav. 72, 827–834.
S3. Wood S. 2013 Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation.
R package version: 1.7-24. http://CRAN.R-project.org/package=mgcv.
S4. Barnett CA, Bateson M, Rowe C. 2007 State-dependent decision making: educated
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predators strategically trade off the costs and benefits of consuming aposematic prey. Behav.
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Ecol. 18, 645–651.
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S5. R Core Team. 2013 R: A Language and Environment for Statistical Computing (version:
3.0.1). R Foundation for Statistical Computing, Vienna, Austria.
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Electronic supplementary figures
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Proportion eaten
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Figure S1. The combined means ±SE of the proportions of mixed and fixed prey eaten in
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different daily sessions.
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