Data analysis
To test the statistical significance of variation in shape across
different factors, including size (a commonly used factor in
morphometric analyses, measured as the mean position of all the
landmarks for an individual specimen), instar, genotype and predation
risk, we performed Procrustes ANOVA using the procD.lm() function from
the geomorph package v4.0.5 . The Procrustes ANOVA used a
permutation procedure of 10,000 iterations to assess the importance of
variation in shape across the different factors for our set of
Procrustes-aligned coordinates.
To further understand the relationship between different factors,
specifically genotype and predation risk, we performed trajectory
analysis using the trajectory.analysis() function in RRPP package
v1.3.1 for R . The phenotypic trajectory analysis measured morphological
variation between treatments in terms of its magnitude (distance moved
in shape space), direction (angle of the change in shape space) and
shape (relative position of the change in shape space). The mean
phenotypic trajectories were visualised using principal component
analysis and were connected in order of increasing predation risk.
Thin-plate spline deformation grids were used to describe the principal
component axes by indicating the departure from the mean shape of the
sample to the lower and upper bounds of the sample (see .
We evaluated modularity and integration of morphological (co)variation
using the covariance ratio and partial least-squares (PLS) analysis .The
CR is a ratio of the overall covariation between modules relative to the
overall covariation within modules. The significance of the CR is tested
by comparison to a distribution of values obtained by randomly assigning
landmarks into subsets. A significant result, which indicates
modularity, is found when the observed CR is small relative to this
distribution.
When used with landmark data, PLS analysis is referred to as singular
warps analysis . The analysis calculates normalized composite scores
(linear combinations), one from the X-variables and one from the
Y-variables, that have the greatest mutual linear predictive power.
Similar to the test for modularity, the observed PLS value is compared
to a distribution of values obtained by randomly permuting the
individuals (rows) in one set relative to those in the other. A
significant result, which indicates integration, is found when the
observed PLS correlation is large relative to this distribution.
We applied the CR and singular warps analyses with 999 iterations to
test for two non-mutually exclusive potential patterns of modularity and
integration between 1) the head (snout, head top, neck) and lower body
(belly, tail base, back) regions, and 2) the dorsal (head top, neck,
back) and ventral (snout, belly, tail base) regions. It is important to
note that these tests do not represent two ends of a continuum. Both
modularity and integration can co-occur and it is entirely possible to
find modules (by rejecting a null model of no covariation within
modules) and detect integration between these modules (by rejecting a
null model of no covariation between modules). We performed these tests
across clones and predation risk levels.