Statistical analyses
We used mixed effect models fitted with ASReml-R implemented in R
version 4.1.1 . We applied log-transformations (OFT Wall
Distance; FST Food Latency ) and square root transformations (OFTTrack Length, Freezings ; FST Track Length, Time in Openand Freezings ) to improve Gaussian assumptions, before scaling to
standard deviation units which facilitates multivariate modelling.
Finally, we also multiplied the transformed and scaled data forFreezings (both assays), and Food Latency (FST) by -1.
This sign reversal was to simplify biological interpretation of results
by making high values correspond to a priori expectation of
‘bolder’ behaviour in all cases. Following these transformations, model
residuals were (approximately) Gaussian with the exception of-(Food Latency) , which showed major departures from residual
normality that could not be resolved. While analyses applied are broadly
robust to deviations from normality , we nonetheless suggest statistical
inferences for this trait should therefore be interpreted with some
caution.
Among-individual variance in behavioural traits
We tested for among-individual variation in each of the OFT and FST
traits using a series of univariate linear mixed models. For each trait,
we fitted a model with fixed effects of: order (from 1-6 reflecting the
order of individuals tested between experimental water changes), trialrepeat number for the individual (from 1-3), time of day (in
minutes after midnight) and experimental arena used (tank A versus B).
The FST traits of Track Length and -(Freezings) are
analogous to OFT traits but were only recorded for the portion of the
observation period while shrimp were trackable outside the food and
shelter zones. Since both traits were square root transformed for
analysis, we included the square root of time spent in the trackable
part of the arena as an additional fixed effect in the model of these
traits. All these fixed effects were included simply to control for
potential nuisance variables unrelated to our hypotheses. Each model
also contained a random effect of individual identity (ID), allowing us
to estimate among-individual variance VI. For each
trait, we then estimated repeatability (R) conditional on fixed effects
as the proportion of phenotypic variance (VP ) explained
by individual differences. Thus R=VP/(VI+ VR) where VR is the residual
(within-individual) variance. For each trait we compared our model to a
reduced version of the same model without the random effect of
individual identity by likelihood ratio test (LRT) to assess the
significance of VI. For testing a single variance
component, we assumed twice the difference in log-likelihoods is
distributed at a 50:50 mix of χ 2 on 0 and 1 DF
following