Statistical analyses
We tested differences in the duration, frequency of push-ups and lateral
compressions, as well as the distance between the lizards when these
behavioral traits were displayed using non-parametric Mann-WhitneyU -test for independent groups. The difference in frequency of
aggressive versus submissive behaviors after each of these focal
events was tested with a χ2. In addition, to analyze
whether self’s (“SELF”) versus rival’s (“RIVAL”) traits were
more informative to explain the behavioral traits summarized with the
factor analysis, we used a corrected information criterion (AICc) to
compare two general mixed models fitted to a Gaussian distribution,
where the trial number was included as random factor. Lower AICc is
indicative of a more informative model (Kletting et al. 2009).
The ‘SELF’ model included SVL, body condition, spectral chroma and area
of blue patch –residual value on SVL–, spectral chroma of yellow patch
in the forelimbs, spectral luminance of the back, spectral hue of the
throat, ectoparasites (presence of mites and number of ticks), and
presence of both hematic and intestinal coccidians of focal individuals.
The ‘RIVAL’ model was built with predictors including the behavioral
traits displayed by the rival lizard (factors 1 to 5), and also the
spectral chroma of the rival’s yellow patch, which correlates with the
body length and condition of the males (i.e. Megía-Palma et al. 2018b).
We also included the rival’s spectral luminance and chroma of the blue
patch because the first correlates with body length and the second is
lower in the males infected by intestinal coccidians (Megía-Palma et al.
2018b). These two spectral variables of the blue patch show low
auto-correlation (r2 = 0.02, P = 0.31). Both
models were controlled by the time of the day when the staged contest
took place.
We based our analyses on information theory to select the best set of
likely models, which is a recommended practice in behavioral ecology
(Hegyi & Garamszegi 2011; Symonds & Moussalli 2011). The multimodel
inference approach uses a corrected criterion for small sample sizes
(i.e. AICc) to estimate the relative importance of each predictor
(Bedrick & Tsai 1994). We considered sufficiently informative all the
models with ΔAICc ≤ 4 in relation to the best model (the one with the
lowest AICc). Model selection was performed using the R-package MuMIn
(Burnham & Anderson 2004; Barton 2018). We summed the AICc weights of
all the models where the predictor appeared (i.e. conditional average)
to calculate the relative importance of each predictor. We calculated
the significance of each effect based on the z-standardized ßcoefficient and standard error. All the analyses were performed in R
version 3.4.3 (R Core Team 2017).