2.5 Statistical analyses
To investigate variations in the color and size of plover ornamental
traits, we conducted a principal component analysis (PCA) to reduce the
number of variables. For male plovers, the color and size of the breast
band, eye stripe, and crown were subjected to a PCA. We also included
the color of the head stripe since males have a stripe on their forehead
(see Figure S1 and Tables S2). A negative value for ”lightness” and a
positive value for ”redness” indicated greater color saturation,
resulting in a more colorful appearance (Tables S2). MPC1 captured the
variation in color and was characterized by negative values for the
lightness of the breast band, eye stripe, and head stripe, combined with
a positive value for the redness of the crown. MPC2 captured the
variation in size and was characterized by positive values for the size
of the breast band, eye stripe, and head stripe. Together, MPC1 and MPC2
explained 71.0% of the total variance in male ornament traits (Table
S2). For female plovers, we conducted a PCA on the color and size of the
breast band, eye stripe, and crown (Figure S1). FPC1 captured the
variation in color and was characterized by positive values for the
redness of the breast band, eye stripe, and head stripe. FPC2 captured
the variation in area and was characterized by positive values for the
size of the breast band, eye stripe, and head stripe. Together, FPC1 and
FPC2 explained 65.7% of the total variance in female ornament traits
(Table S3). Finally, we tested the difference in ornamental traits
between male and female plovers in four populations using one-way ANOVA.
To
allow comparisons between Kentish and white-faced plovers, we first
obtained the genetic relatedness among the four populations by
calculating a genome-wide pairwise population differentiation
(F st) using the dataset and methods of . Then, we incorporated
the pairwise F st values in a covariance matrix representing the
amount of shared evolutionary history between the four populations and
performed phylogenetic generalized linear mixed models (PGLMM).
To account for sampling uncertainty in our estimates, we performed a
bootstrapping procedure for all analyses. This involved randomly
resampling the data with replacements for each bootstrap iteration (i),
running 1,000 PGLMM analyses, and reporting the 95% confidence interval
of the estimate. The PGLMM results, including the estimate, z value, and
p value, as well as the 95% CI of the estimate based on the
bootstrapping procedure, are presented throughout the results section.
2.6
Modellin g
the relationships between male body size, ornamental traits, and
temperature
To examine the impact of temperature on the correlation between male
body size and ornamental traits, we employed phylogenetic generalized
linear mixed models (PGLMMs). The models had male body mass as the
dependent variable, and the independent variables were the ornament
PCA
values (coloration: MPC1; size: MPC2) and ambient temperature. By
incorporating the interaction between males PCA values and ambient
temperature, we aimed to investigate the potential moderating role of
temperature in the relationship between male body size and ornamental
traits. Moreover, we included both the breeding season and incubation
duration as fixed effects in the models to control for possible
variation in male body condition. Additionally, population and year were
considered as random effects to account for any unobserved heterogeneity
across different populations and years.
2.7
Modellingthe
relationships between
male