Statistical Analyses
Statistical analyses were conducted in R version 4.2.0 (R Core Team
2022). All data were checked for normality and homoscedasticity. To
determine the effect of diet treatment and time (day 0, day 8) on body
mass, fat score, corticosterone concentrations, and complement (CH50)
activity in experiment 1, we ran GLMMs using the glmmTMB package
in R (Brooks et al. 2017). For body mass, we ran a GLMM fitted with a
gaussian distribution, while the models for the remaining endpoints (fat
score, corticosterone, and complement activity) were fitted with a
tweedie distribution. All models included treatment, time (day 0, day
8), and the interaction between treatment and time as fixed effects with
bird ID as a random effect. When testing the effect of diet treatment
and time on corticosterone responses, we also included sample type
(baseline or stress-induced) as a fixed effect in the model. To test
whether the average grams consumed per day varied based on diet
treatment, we ran a generalized linear model (GLM) fitted with a
gaussian distribution with grams consumed as the response variable and
treatment as a fixed effect. Because physiology can both mediate and
respond to shifts in gut microbiota composition
(Noguera et al. 2018,
Williams et al. 2020, Yoshiya et al. 2011) and diet might influence
these interactions, we also ran a separate GLMM for each alpha diversity
metric (observed richness, Shannon diversity) which considered the
interactions between diet treatment and time (day 0, day 8) with each
physiology metric (complement activity, baseline corticosterone,
stress-induced corticosterone). P-values from these models were adjusted
for false discovery rate with a Benjamini–Hochberg correction where
significance was determined as Padj < 0.05. We
used the DHARMa package (Hartig 2019) to plot residuals and
confirm the suitability of each model and the Anova function in thecar package (Fox and Weisberg 2018) to determine significance.
To examine how feeding behavior and diet preference changed over time
with treatment in experiment 2 (diet selection), we ran separate GLMs
fitted with a gaussian distribution to test for differences in the
stimulus (LPS, saline) and focal (LPS-focal, saline-focal) groups,
respectively. Because birds were housed in pairs for this experiment
(Figure 1), pre- and post-treatment feeding behavior was analyzed by
cage rather than by individual. To examine how baseline corticosterone
concentrations and hemolytic complement activity varied in the focal
birds, we ran GLMMs for each response variable where we included
treatment (LPS-focal or saline-focal), time, an interaction between
treatment and time, and sex as predictors. Testosterone samples were
collected from males only, so sex was not included in this model. For
body mass and fat score, we ran a GLMM fitted with a gaussian
distribution, while the models for the remaining endpoints (baseline
corticosterone, complement activity, and testosterone) were fitted with
a tweedie distribution. To investigate whether differences in feeding
behavior affected the gut microbiota, we also ran a separate GLMM for
each alpha diversity metric (observed richness, Shannon diversity),
which considered the interactions between treatment and time (day 0, day
5) as well as the average amount of grams of each diet type (fat diet,
protein diet) consumed per day pre- and post- treatment. P-values from
these models were adjusted for false discovery rate with a
Benjamini–Hochberg correction where significance was determined as
Padj < 0.05. Due to power limitations
associated with low sample sizes for focal birds that had both
physiological and gut microbiota samples for days 0 and 5 (n=2-9
depending on the physiological metric), we were unable investigate
interactions between physiology (complement, baseline corticosterone,
testosterone), treatment, and time for gut microbial metrics as we did
in the first experiment.