Data analysis
We used the proportion of time spent on each of the saplings for the
analysis. We used the glmmTMB package (Brooks et al. 2017) within
R 3.6.1 (R Core Team 2019) to build a generalised linear mixed model
with a beta error structure and a logit link. Models that follow beta
distribution only allow proportion values between 0 and 1. Therefore, 0
and 1 values in our study were converted into 0.0001 and 0.9999,
respectively. We used the mixed model to determine the effect of
experiment (sapling combination), bird training and their interaction on
the proportion of time spent by an individual bird in proximity of each
sapling. We used Anova function from ‘car’ package to calculate
P-values for each variable using Wald chisquare test (Fox & Weisberg
2018). We then made pairwise comparisons between the five experiments
within each of the training types using the emmeans functions in
‘emmeans’ package that adjusts P-values (Padj) following
the Tukey method (Lenth 2007).
The centroided GC‐MS data in NetCDF format were further processed using
XCMS online (version 2.7.2) The peaks in each sample were detected with
the cent Wave algorithm (peak width 2–20 s; signal to noise threshold
10). Peak grouping across samples was restricted to peaks present in at
least 50% of the samples in at least one treatment group
(minfrac = 0.5). Retention time correction was accomplished with the
symmetric method and nonlinear loess‐smoothing and iterated three times
with decreasing bandwidth parameter for the grouping from 10 to 0.2 s.
The extracted ion species were grouped according to their parent
molecule into pseudospectra with the Bioconductor package camera (Kuhlet al. 2012). This resulted in a final feature table containing
mass‐to‐charge ratio (m/z), retention time, peak area and the
pseudospectra group for each detected ion species. Groups containing
only a single ion species were considered to be artefacts and removed
from the final feature table. The feature table was uploaded to
MetaboAnalyst for further statistical analyses. We performed quantile
normalization and Log transformation. Pareto Scaling was chosen for
Principal Component Analysis. The annotation of the most significant
features was done by spectral library search (NIST) and MS spectra and
Kovats Index comparison with standard compounds.