Supplementary Methods - Proceedings of the Royal Society B

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Manipulating the appearance of a badge of status causes changes in true badge expression
Cody J. Dey, James Dale and James S. Quinn
SUPPLEMENTARY DATA
(a) Spectrophotometry
Because birds can see into UV wavelengths of light, invisible to humans, we tested whether the paint
manipulations would appear similar to the red shields and the black plumage surrounding the shields
respectively by collecting UV-VIS reflectance values of the dried paint on an actual pukeko shield and
comparing them to reflectance values of the shield and plumage. We used an Ocean Optics (Dunedin,
FL, USA) USB2000 spectrophotometer with a PX-2 pulsed xenon light source to collect reflectance for
wavelengths between 300 and 700 nm (integration time, 100ms; 5 readings averaged per recording;
boxcar 3). The red and black paints exhibited very similar reflectance values to the shield and black
plumage respectively (Figure S1), indicating that our manipulations would appear natural to pukeko.
(b) Further Analysis Methodology
We examined the relationship between social status and frontal shield size using David’s scores [1]. This
dominance index incorporates both the direct dominance interactions involving the focal individual and
also the dominance status of its opponents. Thus, individuals receive high scores if they are dominant
over other individuals that also have high dominance scores. Here, we calculated David’s scores for
each group based on the pooled dominance interactions across the 10 observation periods. Because
variance in David’s scores increases with group size, we normalized the David’s scores by the
procedure outlined in [2]. We built two linear mixed-effect models (using the nlme package in R [3]) to
evaluate the relationship between social dominance and shield size (described in the main text). Group
ID was included in both models as a random effect, and the optimal random effect structure was
determined using Akaike’s Information Criterion, following the recommendations in [4]. In the 11 social
groups considered in this analysis, there were a total of 102 individuals; however, 20 individuals were
banded as juveniles and thus, their adult mass and shield size was unknown. As a result, these
individuals were removed from the analysis. Furthermore, to verify that the relationship between shield
size and dominance was not affected by the date of capture, we visually examined the model residuals
with respect to capture date and confirmed that there was no difference in variation or means between
individuals banded in 2010 and 2012.
To analyze the effects of shield manipulation on dominance behaviours directed towards the focal
individuals, we used three Poisson family generalized linear mixed models (described in the main text).
In each model, focal individual ID was included as a random effect and the fixed effects were centered
(as above). These models were fitted with Laplace approximation in the lme4 package [5], as suggested
in [6] when the mean response value is less than 5.
In all models, the fixed effects were centered to allow for the interpretation of main effects in the
presence of interactions [7]. Model validation was conducted by visual inspection of diagnostic plots
including residuals vs. fitted, quantile-quantile and scale location plots. Additionally, all generalized linear
mixed models were checked for overdispersion [6].
1. David HA. 1987 Ranking from unbalanced paired-comparison data. Biometrika 74, 432-436.
(doi:10.1093/biomet/74.2.432)
2. de Vries H, Stevens JMG, Vervaecke H. 2006 Measuring and testing the steepness of dominance
hierarchies. Anim. Behav. 71, 585–592. (doi:10.1016/j.anbehav.2005.05.015)
3. Pinheiro J, Bates D, DebRoy S, Sarkar D, R Development Core Team 2013 nlme: linear and
nonlinear mixed effects models. R package version 3.1-109.
4. Zuur AF, Ieno EN, Walker NJ, Saveliev AA, Smith GM. 2009 Mixed effects models and extensions in
ecology with R. New York, NY: Springer.
5. Bates D, Maechler M, Bolker B. 2013 lme4: linear mixed-effects models using S4 classes. R package
version 0.999999-2. http://CRAN.R-project.org/package=lme4
6. Bolker BM, Brooks ME, Clark CJ, Geange SW, Poulsen JR, Stevens MHH, White JSS. 2009
Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24,
127-135. (doi: 10.1016/j.tree.2008.10.008)
7. Schielzeth H. 2010 Simple means to improve the interpretability of regression coefficients. Methods
Ecol. Evol. 1, 103–113. (doi:10.1111/j.2041-210X.2010.00012.x)
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