Statistical analysis
All statistical analyses were ran in R v. 3. 4. 3. Cortisol levels and
basal metabolic rate were analysed for the parents using a linear model
with environment (poor vs enriched), and body weight as predictors. We
used GLM with a quassipoisson link to account for overdispersion for the
parental behavioural count data (no. contacts, no. inspections and
activity) and a gaussian link for latency as a function of environment
and body weight.
To test for parental effects on the offspring phenotype, we only
analysed those phenotypes significantly different between parental
environments, using the same model structure as described above but
including also the parental values and environment as predictors. We
used the multi-model approach implemented in the R package glmulti v
1.0.7 (Calcagno & de Mazancourt 2010)
for model selection, which tests all possible models and all
interactions, and considered models within 2 AIC units as being
equivalent. To take into account potential parentage effects, we first
selected the best-fit model (highest Akaike weight) using glmulti and
then ran generalized mixed-models including parent of origin as a random
factor using mlmRev v.1.0-7. Models were tested for overdispersion and
individual observations (fish ID) were also taken into account when
models displayed overdispersion. Outliers were identified using the
function aout.pois in the package alphaOutlier.