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\begin{document}

\title{Predator-induced shape plasticity in D. pulex}



\author[1]{Sam Paplauskas }%
\author[2]{Oscar Morton}%
\author[2]{Mollie Hunt}%
\author[2]{Ashleigh Courage}%
\author[2]{Stephanie Swanney}%
\author[3]{Stu Dennis}%
\author[2]{Dörthe  Becker}%
\author[1]{Stuart Auld}%
\author[4]{Andrew Beckerman}%
\affil[1]{University of Stirling}%
\affil[2]{The University of Sheffield}%
\affil[3]{Eawag}%
\affil[4]{University of Sheffield}%


\vspace{-1em}



  
  \date{June 14, 2024}


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\selectlanguage{english}
\begin{abstract}
All animals and plants respond to changes in the environment during
their life cycle. This flexibility is known as phenotypic plasticity and
allows organisms to cope with variable environments. A common source of
environmental variation is predation risk, which describes the
likelihood of being attacked and killed by a predator. Some species can
respond to the level of predation risk by producing morphological
defences against predation. A classic example is the production of
pedestals and head spikes in the water flea, Daphnia pulex, which defend
against predation from Chaoborus midge larvae. Previous studies of these
defences have focussed on changes in pedestal size and the number of
spikes along a gradient of predation risk. Although these studies have
provided a model for continuous plasticity, they do not capture the
whole-organism shape response to predation risk. In contrast, studies in
fish and amphibians focus on shape as a complex, multi-faceted trait
made up of different variables. In this study, we analyse how multiple
aspects of shape change in D. pulex along a gradient of predation risk
from C. flavicans. These changes are dominated by the inducible
morphological defence, but there are also changes in the size and shape
of the head and the body. We detected change in specific modules of the
body plan and a level of integration among modules. These results are
indicative of a complex, multi-faceted response to predation and provide
insight into how predation risk drives variation in shape and size at
the level of the whole organism.%
\end{abstract}%



\sloppy


\textbf{INTRODUCTION}

A major challenge facing all organisms is to adapt to environments that
vary within their lifespan. A route to responding to and surviving such
variation is phenotypic plasticity, the ability of individual genotypes
to change phenotype when exposed to different environments during their
life-cycle . One pervasive, natural source of environmental variation is
the risk of mortality from predation, known as predation risk. Predation
risk induces a suite of changes in the behaviour, life history and
morphology of many plants and animals . Of particular interest are
morphological responses to predation risk which range from the
production of spines to changes in the shape of a portion of the body or
the entire body plan of an organism.

Many studies of predator-induced shape change have focussed on linear
assessments of shape, which measure the distance between two points. Key
examples include changes in defensive dorsal spine length , body depth
that affects vulnerability to gape-limited predators and morphological
features associated with behavioural swimming escape responses .
However, this type of analysis only captures a subset of the overall
shape variation.

Measurement of overall shape is a multivariate analysis (i.e. it
involves multiple different variables). A well-established method for
assessing multivariate plasticity in shape is geometric morphometrics ,
which uses anatomical coordinates as shape variables to measure relative
differences in shape. This approach has been used extensively to measure
predator-induced changes in shape for a wide range of organisms, such as
fish , amphibians and snails .

Geometric morphometrics not only allows the measurement of shape
overall, but also the extent of modularity and integration between
different aspects of shape (Klingenberg, 2014). Modularity refers to the
level of covariation between different traits within morphological
structures, or modules, relative to the level of covariation between
these structures. Integration refers to the level of co-variation
between different traits throughout a morphological structure or even a
whole organism (Klingenberg, 2014). Therefore, modularity exists if the
level of integration within modules is strong compared to the
integration between modules (Klingenberg, 2009).

In addition to considering multivariate shape, there has been a shift
from the standard ''two-environment'' approach for assessing phenotypic
plasticity and estimating reaction norms (Roff 1999) to analysing
changes along a gradient. More recently, it has become standard to
evaluate plasticity in multiple traits and along environmental gradients
of more than two environments .

Despite such work, shape has rarely been assessed as a plastic trait in
water fleas (\emph{Daphnia} species), an iconic organism for the study
of size-selective, predator-induced phenotypic change. Instead, research
has largely focused on assessing the production of inducible
morphological defences, such as the head spikes of \emph{Daphnia pulex}
, called `neckteeth', which develop in response to predator cues
(kairomones) released from their midge larvae predators . Although there
has been some research into the dorsal pattern of induced morphology
in\emph{D. pulex} , the question of how overall shape changes in
response to predation risk remains unanswered.

Other examples of predator-induced changes in \emph{Daphnia} suggest
that overall shape may change in response to predation risk.
Considerable variation exists in many features of the \emph{Daphnia}spp.
body plan including body width , alignment , shoulder shape and carapace
strength . Furthermore, changes in body size are well-documented in the
context of size selective predation theory and empirical assessment of
life history responses to predation risk . Together with associated
research on fish, this suggests that overall shape might increase
survival and therefore provide important fitness benefits to plasticity.

In this study, we evaluate shape plasticity along a gradient of
increasing predation risk in three genotypes of \emph{D. pulex} which
differ in their sensitivity to predator cues. We apply morphometric
landmark-based analysis to photographs of \emph{Daphnia} taken by , in
which \emph{D. pulex} were exposed to six levels of predation risk from
their midge larvae predator, \emph{Chaoborus flavicans} . We combine
geometric morphometrics with phenotypic trajectory analysis to formally
evaluate the multivariate change in shape and estimate measures of both
modularity and integration to evaluate if there are coherent units of
the body plan that respond to predation risk, and whether these units
change independently or together .

In advance of the analysis, we predict narrower bodies and bigger heads
will form part of the predator-induced response in \emph{D. pulex} .
Narrower bodies enhance the predator escape response in fish and
amphibians and we expect that bigger heads are more likely to interfere
with predation linked to the neckteeth defence. Given these two
predictions, we might also predict modularity and / or integration of
these responses. From a modularity perspective, we predict that the head
region and lower body are separated in their response to predation due
to the nature of the induced morphological change in the head region.
This means that the neckteeth defence and associated shape changes of
the head would be localised to that part of the animal and relatively
independent of changes to the body. Furthermore, under the modularity
hypothesis, we might also predict that changes in the dorsal region,
where the neckteeth form, respond independently to the ventral portion
of the daphnid. As for the integration hypothesis, we predict some level
of integration between the head and lower body regions driven by a
negative developmental correlation, where the head gets larger and the
body gets narrower and longer.

\textbf{MATERIALS AND METHODS}

To evaluate these hypotheses, we first analysed the phenotypic
trajectories of three \emph{D. pulex} genotypes along a gradient of
predation risk, focusing on the magnitude, direction and shape of the
change in multivariate trait space. Second, we performed modularity and
integration tests to evaluate the head-body and dorsal-ventral
hypotheses mentioned above. In the following, we introduce the study
system, data and design before details on the above process.

\textbf{Study system}

In this study, we focus on one of the most iconic predator-induced
defences in water fleas (\emph{Daphnia} species), the `neckteeth'
of\emph{D. pulex} (Fig. 1). The neckteeth defence comprises a swollen
area on the back of the head (neck-pedestal) and spikey bits which grow
on top. The defence grows in response to predator cues (kairomones)
released by \emph{Chaoborus} midge larvae . The onset of kairomone
sensitivity begins in the late embryonic stage, when the neck-pedestal
begins to grow, and the spikey bits develop later in the early juvenile
stages . The maintenance of the defence requires consistent exposure to
predator cues and usually lasts until the third instar, after which
the\emph{Daphnia} are large enough to escape size-selective predation .
Also, it is well known that the induction of the defence increases with
the level of predation risk . Although the exact mechanism is unclear,
it has been shown that the neckteeth defence increases prey survival by
up to 50\% .

\textbf{Study design}

We used data originally collected in Dennis \emph{et al.} 2011. This
included photographs of second and third instar \emph{D. pulex} exposed
to six different concentrations of \emph{C. flavicans} kairomone,
including a control (0, 0.1, 0.25, 0.5, 0.75 and 1\selectlanguage{greek}µ\selectlanguage{english}l
ml\textsuperscript{-1}). Animals were photographed at the second and
third instar because this is when the defence is usually expressed the
most . For each treatment, third-generation mothers of at least their
third brood had been exposed to the relevant cue concentration and
offspring of the subsequent brood were transferred to glass jars which
contained 50ml of hard artificial pond water, food (2 x
10\textsuperscript{5} cells ml\textsuperscript{-1} of algae) and the
appropriate concentration of predator cue. Animals were transferred to a
new jar containing fresh media and cue daily, until they reached
maturity. Photographs of the animals were taken at the second and third
instars.

Three genotypes were analysed for shape plasticity from this dataset.
Two (clones Chardonnay and Cletus) showed a characteristically low level
of induced morphology and one (clone Carlos) a characteristically high
level. Animals from second and third instars were pooled so the data
formed a factorial design of six predation risk levels x three
genotypes. There were 5-10 replicate photos (i.e. replicate individuals)
at each level of predation risk for each clone (totalling 518 photos).

\textbf{Digitisation}

We digitised images using geometric morphometrics to create
two-dimensional anatomical co-ordinates (reference points, called
`landmarks') that characterized body shape. First, lateral-view
photographs were uniformly orientated (head at the top) and prepared for
landmarking in Microsoft Paint. This involved drawing a vertical line
from the eye to the base of the tail (a standard measure of daphnid
length) and then two perpendicular lines, one at the midpoint of the
original line and another intersecting the rostrum. These lines provided
a clear method to capture the approximate location of the dorsal and
ventral midpoints, as well as the region on the back of the neck where
defences were induced, consistently between different images (Fig. 2A).
This process identified a total of six landmarks for each photo
including (1) the centre of the eye (near the top of the head), (2) the
neckteeth defence (or the corresponding area in the controls), (3) the
base of the tail, (4) the dorsal (back) midpoint, (5) the ventral
(belly) midpoint and (6) the rostrum (snout). These landmarks were
motivated by a method developed for linear dimensions in two species
of\emph{Daphnia} , \emph{D. dentifera} and \emph{D. mendotae} and were
selected to capture key aspects of the defence, head and body shape.

Images were then digitised by applying landmarks using
the\emph{geomorph} package v4.0.5 in R v4.2.1 . Before these images were
analysed in R, they were standardised using generalised Procrustes
superimposition from the \emph{geomorph} package v4.0.5 . This process
aligned the images to account for differences in the size, position and
orientation of the original specimen so that shape could be compared in
a meaningful way. The resulting Procrustes shape coordinates (Fig. 2D)
were used in the subsequent analyses.

The amount of human (intra-observer) error in selecting landmarks was
measured using two sets of identical photographs. The same landmarks
were applied to 30 photographs of either the defended or undefended
morph, and the standard error of each landmark was measured from the
Procrustes shape coordinates.

\textbf{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 \emph{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 \emph{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.

\textbf{Ethics statement}

We confirm that the methods employed in this study were reviewed and
approved by the institutional review committee and all animals from the
original experiment were cared for in accordance with institutional and
national guidelines.

\textbf{RESULTS}

\textbf{Observer error}

The amount of human (intra-observer) error in selecting landmarks was
very low for both the defended and undefended morphs (defended mean SE =
0.74 x 10\textsuperscript{-3} {[}unitless{]}, undefended mean SE = 0.72
x 10\textsuperscript{-3} {[}unitless{]}). This suggests that any
differences in shape measured in the following analyses are the result
of the treatments applied, rather than any observer error.

\textbf{Predation risk alters shape}

Analysis of \emph{Daphnia} shape using Procrustes ANOVA revealed that
shape varied across all factors, including size, instar, clone and
predation risk (all p \textless{} 0.001). The response to the level of
predation risk did not vary by instar (predation x instar interaction; F
= 1.39, df = 5, p = 0.13), but it did vary by genotype (predation x
genotype interaction; F = 4.92, df = 10, p \textless{} 0.001). The
phenotypic trajectory analysis showed that this interaction was not
based around how much the clones responded to predation cues (path
distances were equivalent, all pairwise differences p \textgreater{}
0.05), but around differences in the direction of these changes in
multivariate trait space (the angle of the change in shape space) and
the shape of these changes (the relative position of the changes in
shape space, all pairwise differences p \textless{} 0.01) along the six
cue concentration gradient (Fig. 3-5). Specifically, the direction of
change in clone `Cletus' differed from both clone `Chardonnay' and
`Carlos' (both p \textless{} 0.05) indicating that the landmarks that
changed in `Cletus` were different than in the other two clones and we
can see the evidence for differences in the shape of the trajectories in
Fig. 5, where each clone follows a slightly different path along the cue
concentration gradient.

\emph{Neckteeth, head height and belly-bulge}

The principal component axes underlying the trajectory analysis
indicated that 55\% of variation in shape was captured by the first PC,
rising to 71\% with PC2 and 83\% with PC3. A visualisation of the
trajectories in 2D PC space, along with detail on what shapes were
associated with the PC-axes, revealed several key insights (Fig. 3-5).
Based on the deformation grids outside the PC-axes, we were able to
define PC1 as neck-change (i.e. the inducible defence), PC2 as head
height and PC3 as belly-bulge.

Fig. 3 revealed that the most significant change in shape occurred along
PC1, the inducible defence. The trajectories of all three clones moved
in parallel to PC-axis 1, which showed that the neck region was larger
when there was more cue. Also, there were clear differences among the
genotypes linked to head height (PC2 axis). We suggest that this is
largely representative of clone differences, as Fig. 3 and 4 indicated
relatively large differences between genotypes compared to the effect of
the cue concentration.

\textbf{Head and body shape is modular, but there is integration across
all body regions}

We assessed two hypotheses using modularity and integration tests.
First, we asked whether there was evidence for modularity and
integration between the head (snout -- head top -- neck) and body
regions (belly -- tail base -- back). Second, we asked whether there was
evidence for modularity and integration between the back of the head
(head top -- neck -- back) and the front of the body (snout -- belly --
tail base).

We found evidence for modularity between the head and body (Fig. 6A).
The analysis of modularity showed that the observed CR was significantly
different to the distribution of variables randomly assigned to modules
(CR = 1, CI = 0.00, p \textless{} 0.05). Therefore, this suggested that
there was a high level of coordination between shape variables within
the head and within body regions compared to between them. Our
integration test between the head and body modules suggested that they
also respond to predation risk in an integrated manner (r-PLS: 0.825, p
= 0.001). Fig. 6B showed that, moving from the origin out, as the neck
region enlarged, the back moved higher, the tail was drawn towards the
front of the body and the snout shifted backwards.

We found no evidence for modularity (CR = 1.29, CI = 0.00, p = 0.54;
Fig. 6C) in the back and front regions. However, our integration test
suggested that these two groupings of landmarks do respond to predation
risk in an integrated manner (r-PLS: 0.917; p = 0.001). Fig. 6D showed
similar results to Fig. 6B; moving from the origin out, as the back was
expanded by the inducible defence, the back moved higher, the tail was
pulled towards the front of the body and the snout was pulled towards
the back.

\textbf{DISCUSSION}

Our understanding of how organisms respond to predation risk has
traditionally focused on a small number of specific traits in only a few
environments. Recent advances have moved the standard of plasticity
research to a multi-trait approach including morphology, life history
and behaviour along environmental gradients. This has been complemented
by a growing appreciation that morphometric analyses applied to organism
shape can provide added value to analyses of phenotypic plasticity.

Here, building on this growing use of morphometrics in plasticity
research, we evaluated `shape' plasticity among three genotypes
of\emph{D. pulex} exposed to a gradient of six levels of predation risk.
Our objective was to use morphometric shape as a `summary' trait
affected by responses to predation risk in life history and morphology
to evaluate several hypotheses about the impact of predation risk on
whole organism plasticity. Our motivation was linked to size selective
predation theory and work on the response of fish to predation risk
where size and shape are both linked to survival . We found that
the\emph{D. pulex} response to predation risk involves both modular and
integrated changes, with changes in the inducible neckteeth defence
linked to changes in head and body shape. This suggests that there is a
complex response to predation, including strong developmental
correlations in how \emph{Daphnia} body plans are organised.

Our first set of results from the trajectory analysis showed how
morphology changed in response to a gradient of predation risk. In
accordance with previous studies of the inducible defence and our
initial hypothesis, we found that head width (neck-change) increases
along the gradient of predation risk . Also, we found that head height
varied among clones and body width (belly-bulge) was a non-linear
feature of change. This last result is contrary to our hypothesis
derived from the fish literature; we predicted that body width would
decrease along a rising gradient of predation risk linked to
swimming/evasion, but in reality, body width varies regardless of the
level of predation risk.

Our second set of results from the modularity and integration tests
showed evidence for modularity between the head and the body, as
expected, but also integration across these two body regions. This
suggests that there is a high level of coordination within the head and
the body, but there is also an integrated `trade-off' among aspects of
the body plan under predation risk. Specifically, as we predicted, as
the neck region enlarges, the back moves higher, the tail is drawn
towards the front of the body and the snout shifts backwards. Contrary
to our other hypothesis, we found no evidence for modularity in the back
and front regions. However these two groupings of landmarks are
integrated in their response to predation risk, which is likely to be
derived from the modular and integrated response of the head and lower
body.

It is well-documented that the inducible defence of \emph{D.
pulex}increases in response to higher levels of predation risk . This
morphological defence has long been hypothesized to form part of a
whole-organism response to predation that includes changes in size and
life-history traits linked to the allocation of energy to growth,
defence morphology and reproduction .

In comparison, there has been some work on body width, which is another
key aspect of shape that we analysed, but it is unclear what role this
plays in the response to predation risk. Previous studies have used
linear morphometrics to show a relatively small increase in body width
in defended compared to undefended morphs of \emph{D. pulex} and\emph{D.
magna} which suggests that body width may play a minor role in the
response to predation. In support of this, it has been shown that body
width is a better predictor for prey size range in \emph{Chaoborus}than
body length . \emph{Chaoborus} usually swallows prey that cannot be
deformed only if its diameter is not wider than the larva's head capsule
diameter . The increase in body depth might additionally make prey
handling for the larvae more difficult. Alternatively, the increased
body width in defended compared to undefended morphs may result from the
increase in strength, and possibly thickness, of the carapace . Similar
to the changes in body width, variation in head height, something
revealed in our analysis, has not been well-documented, but may be
linked to hydrodynamics .

In this study, there was both a modular and whole-organism, integrated
response to predation (\emph{sensu} . This is entirely possible, as
modularity and integration are not two ends of a continuum, but
represent two mutually compatible destinations of selection. It is
commonly thought that the evolution of modularity and integration is
linked to trait functionality. In the case of modularity, there can be
selection for `variational adaptation', where traits that often respond
together to environmental pressures, such as predation risk, are
integrated into one module, and traits that rarely need to be changed at
the same time are packed into another module . This may explain why
changes in the head of \emph{D. pulex} , which form the main response to
predation risk, are relatively independent of changes in the body.

Possible functions of the observed integration could be for carapace
stability or an improved escape response . Alternatively, integration
could increase fitness by co-ordinating the response of multiple traits
to spatial and/or temporal variation in the environment. Regardless, our
results deliver strong support for the existence of developmental
constraints (correlations) that are worth investigating to understand
the nature of predator induced phenotypic plasticity.

Shape variation in response to predation risk has not previously been
studied in \emph{D. pulex} . In this study, we exposed \emph{D. pulex}
to six levels of predation risk and evaluated shape plasticity using
geometric morphometrics and phenoptypic trajectory analysis. We now have
a better understanding that \emph{D. pulex} shape is a multivariate
response to predation risk, that there is genetic variation in this
response and that the responses can be both modular and integrated, with
associated adaptive and non-adaptive (constraint) hypotheses in need of
further evaluation. Thus, there are two clear `next-steps'. The first is
to establish the adaptive benefit/costs of shape change and variation.
The second is the molecular ecology of the developmental constraints.
The availability of genomic tools, the clonal nature of \emph{Daphnia} ,
well-established experimental protocols and recent high throughput image
analysis are an outstanding platform for future research.

\textbf{REFERENCES}

Table 1. Observer error for each landmark in the defended and undefended
morphs.\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Intra-observer error\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Intra-observer error\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Landmark\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Defended, standard error (unitless)\strut
\end{minipage} & \begin{minipage}[t]{0.32\columnwidth}\raggedright\strut
Undefended, standard error (unitless)\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
1 -- eye\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.74 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.70 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
2 -- neckteeth defence (or corresponding area)\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.61 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.64 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
3 -- base of the tail\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.81 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.69 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
4 -- dorsal midpoint\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.59 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.61 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
5 -- ventral midpoint\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.77 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.76 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
6 -- rostrum\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.92 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.92 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Mean\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.74 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.72 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Maximum\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.92 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.92 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
Minimum\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.59 x 10\textsuperscript{-3}\strut
\end{minipage} & \begin{minipage}[t]{0.30\columnwidth}\raggedright\strut
0.61 x 10\textsuperscript{-3}\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\textbf{FIGURE LEGENDS}

Figure 1. (A) Defended and (B) undefended morphs of \emph{D. pulex} .
Animals are third instar juveniles exposed to either 1\selectlanguage{greek}µ\selectlanguage{english}l
ml\textsuperscript{-1} or 0\selectlanguage{greek}µ\selectlanguage{english}l ml\textsuperscript{-1} (control)
of\emph{C. flavicans} predator cue. The enlarged image shows a close-up
of the neckteeth defence.

Figure 2. Image digitisation and Procrustes superimposition. (A) First,
images were prepared for landmarking in Microsoft Paint. This process
identified a total of six landmarks for each photo (open circles)
including (1) the centre of the eye (head top), (2) the neckteeth
defence (or the corresponding area in the controls), (3) the base of the
tail, (4) the dorsal (back) midpoint, (5) the ventral (belly) midpoint
and (6) the rostrum (snout). (B) Second, the landmarks (closed circles)
were applied in R to create digitised images that captured key aspects
of shape (links). (C) Third, Procrustes superimposition was performed to
align digitised images in terms of size, position and orientation. The
digitised images in grey differ from the image in black in terms of
size, position and orientation. (D) The final set of digitised images.

Figure 3. The first set of three \emph{D. pulex} phenotypic trajectories
along a gradient of predation risk. The principal components summarise
changes in neck shape (PC1) and head height (PC2), which are visualised
by the deformation grids along the axes. Individual data points for each
specimen are shown by the small points, whereas the large points
correspond to the mean phenotype for each treatment. The colour
indicates the level of risk (0, 0.1, 0.25, 0.5, 0.75 and 1 \selectlanguage{greek}µ\selectlanguage{english}l
ml\textsuperscript{-1}) and the lines connect each treatment in order of
increasing predation risk. The shape of the points refers to the
specific clone used in the experiment (circles -- Carlos, squares --
Cletus, triangles -- Chardonnay).

Figure 4. The second set of three \emph{D. pulex} phenotypic
trajectories along a gradient of predation risk. The principal
components summarise changes in belly bulge (PC3) and head height (PC2).
See the description of figure 3 for the legend.

Figure 5. The third set of three \emph{D. pulex} phenotypic trajectories
along a gradient of predation risk. The principal components summarise
changes in neck shape (PC1) and belly bulge (PC3). See the description
of figure 3 for the legend.

Figure 6. Modularity and integration tests. (A) Histogram of CR
coefficients from simulations of random partitions of the head and body,
with the observed CR coefficient indicated by the arrow. (B) PLS scores
for the head and body, with the line of best fit and deformation grids
which visualise the shape identities and how they change along the axes.
(C) The same as (A), but for the front and back. (D) The same as (B),
but for the front and back. The specific landmarks involved in the tests
are represented by the diagrams above the plots. If there was
modularity, the observed CR coefficient would be significantly less than
one, and if there was no integration, the line of best fit would look
like either a horizontal or vertical line.

\textbf{AUTHOR CONTRIBUTIONS}

\textbf{Sam Paplauskas} : Conceptualisation (supporting), investigation
(equal), data curation (lead), formal analysis (equal), funding
acquisition (lead), investigation (equal), validation (lead),
visualisation (lead), writing -- original draft preparation (lead),
writing -- review and editing (equal). \textbf{Oscar Morton} :
Conceptualisation (supporting), investigation (equal). \textbf{Mollie
Hunt} : Conceptualisation (supporting), investigation
(equal).\textbf{Ashleigh Courage} : Conceptualisation (supporting),
investigation (equal)\textsuperscript{.} \textbf{Stephanie Swanney} :
Conceptualisation (supporting), preliminary (equal)\textsuperscript{.}
\textbf{Stuart R. Dennis} : Writing -- review and editing (supporting).
\selectlanguage{ngerman}\textbf{Dörthe Becker} : Writing -- review and editing (supporting).
\textbf{Stuart K. J. R. Auld} : Writing -- review and editing
(supporting). \textbf{Andrew P. Beckerman} : Supervision (lead),
conceptualisation (lead), formal analysis (equal), investigation
(equal), methodology (lead), visualisation (supporting), writing --
review and editing (equal)

\textbf{ACKNOWLEDGEMENTS}

Dean C. Adams and Brad A. Duthie helped with the analysis. Comments and
discussion were provided by Matthew C. Tinsely, Luc F. Bussière and
Rebecca A. Boulton.

\textbf{FUNDING INFORMATION}

The PhD student who carried out this research was funded by the Natural
Environment Research Council through the IAPETUS doctoral training
programme (S154).

\textbf{DISCLOSURE STATEMENT}

The authors have no conflict of interest to declare.

\textbf{DATA ACCESSIBILITY STATEMENT}

The data that support the findings of this study are openly available in
Dryad at https://doi.org/10.5061/dryad.zkh1893fx.\selectlanguage{english}
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