Analyses
From the amount of food each individual consumed in the food preference
tests we derived metrics aimed at estimating niche centre, breadth and
overlap (De Cáceres et al. 2011). We computed a distance matrix
between food types based on their nutritional content (hydrocarbons, fat
and proteins) while also taking into account the physical
characteristics of each food type (length and width). These variables,
standardized in order to remove differences due to units of measurement,
were used to calculate the Euclidean distance between pairs of seed
types. The niche centre in this resource space was computed by means of
a principal coordinate analysis (PCoA), averaging the coordinates of the
resources preferred by the individual in each trial (De Cáceres et
al. 2011). The niche centre was defined as the multivariate mean in the
subspace of the two first axes, which together accounted for
> 80% of the observed variation. To describe niche breadth
while accounting for resemblance among resources, we used Rao’s
quadratic entropy as implemented in the ‘indicspecies’ package (De
Cáceres & Legendre 2009). In addition, we also estimated the proportion
of diet variation of each population that was due to either variation
within (WIC) or among individuals (TNW). To this purpose, we modified
the R package “indicspecies” to be able to deal with the fact that
some resources are more similar among them than others (De Cácereset al. 2011). Finally, we estimated the mean pairwise niche
overlap between each pair of individuals as a measure of the overall
similarity among individuals within a population (De Cáceres et
al. 2011).
We used Bayesian general mixed-effects models to estimate the
repeatability of food preferences, using the packages MCMCglmm (Hadfield
2009) and BRMS (Bürkner 2017). For both wild-trapped adults and
captive-bred juveniles, we partitioned the within- vs.
between-individual components of variance in food preferences by fitting
individual identity as a random effect. The among-individual variance
expressed as a proportion of the trait is the repeatability (Gamer et
al., 2010). The confounding effect of sex, age, session, and aviary were
evaluated by including them as fixed effects. Although individuals were
tested in groups of six individuals, we did not include the testing
group as random blocking effect because adding it did not improve model
fit (Table S1). To assess whether morphology influenced food preferences
of pigeons, we conducted a Principal Component Analysis (PCA) based on
the correlation matrix of morphological traits with the function
“prcomp” in R (R core team 2015), and used the scores as predictors in
mixed-effects models.
We disentangled the genetic and environmental components underpinning
variation in food preference using an animal model approach (Kruuk &
Hadfield 2007; Wilson et al. 2010), again based on Bayesian
mixed-effects models. For labile traits, phenotypic variation at the
between-individual level is not necessarily equal to additive genetic
variation because individuals may also experience differing sets of
non-genetic effects on their phenotypes. However, the between-individual
phenotypic variation can be decomposed into genetic and non-genetic (or
so-called ‘permanent environmental’) components using quantitative
genetic models (Kruuk & Hadfield 2007; Wilson et al. 2010).
Thus, we used Bayesian Mixed Models that included individual identity
plus two additional variables as random factors, one relating
individuals to their records in the pedigree and another accounting for
the nest where offspring were raised (as half offspring were
cross-fostered). We estimated heritability by dividing the posterior
distribution of the variance component associated with the pedigree by
total variance.
We also used the “animal model” to analyse diet plasticity by means of
reaction norms, comparing preferences during the short-term assays with
those from the long-term assays.
Following Nussey et al. (2007), we assessed variation in plasticity of
the niche centre among individuals by including the interaction between
period (i.e. short-term vs long-term, coded as a fixed effect) and the
identity of individuals (coded as a random effect). We compared this
model with a model including the pedigree as a random effect to
determine the existence of heritable variation in plasticity (i.e.
adaptive plasticity). We compared models using Waic, the widely
applicable information criterion (Bürkner 2017).