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\begin{document}

\title{Among-individual behavioural variation in the ornamental red cherry
shrimp, Neocaridina heteropoda}



\author[1]{Rosie Rickward}%
\author[1]{Francesca Santostefano}%
\author[1]{Alastair Wilson}%
\affil[1]{University of Exeter}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\selectlanguage{english}
\begin{abstract}
Personality variation, defined as among-individual differences in
behaviour that are repeatable across time and context, is widely
reported across animal taxa. From an evolutionary perspective,
characterising the amount and structure of this variation is useful
since differences among individuals are the raw material for adaptive
behavioural evolution. However, behavioural variation among-individuals
also has implications for more applied areas of evolution and ecology --
from invasion biology, to ecotoxicology, and selective breeding in
captive systems. Here, we investigate the structure of personality
variation in the red cherry shrimp, Neocaridina heteropoda, a popular
ornamental species that is readily kept and bred under lab conditions
and is emerging as a decapod crustacean model across these fields, but
for which basic biological, ecological, and behavioural data is limited.
Using two assays and a repeated measures approach, we quantify
behaviours putatively indicative of shy-bold variation and test for
sexual dimorphism and/or size-dependent behaviours (as predicted by some
state-dependent models of personality). We find moderate to high
behavioural repeatabilities across traits. Although strong
individual-level correlations across behaviours are consistent with a
major personality axis underlying these observed traits, the
multivariate structure of personality variation does not fully match a
priori expectations of a shy-bold axis. This may reflect our ecological
naivety with respect to what really constitutes bolder, more risk prone,
behaviour in this species. We find no evidence for sexual dimorphism and
only weak support size-dependent behaviour. Our study contributes to the
growing literature describing behavioural variation in aquatic
invertebrates. Furthermore, it lays a foundation for further studies
harnessing the potential of this emerging model system. In particular,
this existing behavioural variation could be functionally linked to
life-history traits, invasive success, and serve as target of artificial
selection or bioassays. It thus holds significant promise in applied
research across ecotoxicology, aquaculture, and invasion biology.%
\end{abstract}%



\sloppy


\textbf{TITLE PAGE}

\textbf{Article Title:} Among-individual behavioural variation in the
ornamental red cherry shrimp, \emph{Neocaridina heteropoda}

\textbf{Authors} : Rickward, Rosie Ann\textsuperscript{1}, Santostefano
Francesca\textsuperscript{12}, Wilson, Alastair
James\textsuperscript{1}\textbf{Affiliations} ~:

\textsuperscript{1}University of Exeter, Centre for Ecology and
Conservation, University of Exeter, Penryn Campus, Cornwall, U.K.

\textsuperscript{2}Universit\selectlanguage{ngerman}é du Québec à Montréal, Département des
Sciences Biologiques, Montréal, Canada

\textbf{Corresponding author contact information:}

Francesca Santostefano, PhD

Centre for Ecology and Conservation, University of Exeter,

Penryn Campus, Penryn, Cornwall

TR10 9FE, UK

F.Santostefano2@Exeter.ac.uk



\textbf{INTRODUCTION}

Within populations, individual animals often exhibit behavioural
differences that are consistent across time and context . Although this
phenomenon, widely referred to as animal personality , has been
demonstrated across very diverse animal taxa , the empirical literature
is dominated by vertebrate studies . In comparison, personality
variation in invertebrates generally, and aquatic invertebrates in
particular, has been less well studied . This omission matters because
understanding personality variation is not only central to fundamental
research on animal behaviour, but also increasingly relevant across more
applied fields such as welfare , ecotoxicology and invasion biology .
Here we investigate the presence and structure of among-individual
variation in the red cherry shrimp (\emph{Neocaridina heteropoda} ).
This is a popular ornamental species that, being easy to maintain and
breed under laboratory conditions, is an emerging model for decapod
research with relevance to ecosystem stability, ecotoxicology, and
aquaculture .

From a fundamental perspective, among-individual variation in behaviour
is widely assumed to have functional importance, impacting fitness via
effects on survival and/or reproduction . While quantitative genetic
studies have also shown that among-individual differences are heritable
, understanding the evolutionary causes and consequences of this
variation remains a challenge. Why does variation persist? Are
among-individual differences adaptive, or do they reflect evolutionary
constraints (e.g. trade-offs)? Why can't all individuals adjust
behaviour plastically to be optimal for the conditions they find
themselves in? Hypothesised answers to these questions often invoke
state-dependence, predicting that personality will covary with other
traits (e.g. metabolic rate, body size) or intrinsic variables (e.g.
age, sex) as a consequence of feedback between behaviour and state . For
example, if small individuals face greater starvation risk, they may
need to be `bolder' (less risk averse) and more explorative to find
resources faster, even if this behaviour increases predation risk .
State-dependence means behaviour is likely to be integrated within
broader life history strategies , and differ systematically between
sexes . For example, in some systems males, on average, need to adopt
riskier behaviour than females in order to obtain mating opportunities .
Conversely, female behaviour may be selected for to reduce costs from
male harassment .

Although evolutionarily motivated studies dominate the literature on
animal personality , this phenomenon is increasingly recognised as
having wider implications and applications. For example, Prentice et al.
(2022) argues how the integration of personality traits with stress
physiology means artificial selection on behavioural biomarkers could be
used to improve welfare in fish aquaculture (see also e.g. .
Ecotoxicology is another applied field in which the potential importance
of among-individual differences in behaviour has been recently
highlighted . For example, great tits (\emph{Parus major} ) with high
levels of lead in their blood and high levels of multiple metals in
their feathers, showed lower explorative behaviours on average , while
insecticide exposure lowered behavioural repeatabilities in spiders ,
reducing the relative importance of among-individual differences.
Furthermore, found long-term fluoxetine exposure in guppies
(\emph{Poecilia reticulata} ) erodes variation in activity levels
between individuals. Ubiquitous contamination may therefore impair
behaviour and future adaptive potential of phenotypic variation to
anthropogenic-induced alterations within both terrestrial and aquatic
landscapes.

In decapod crustaceans, such as the species we investigate here, several
applications of personality variation have been suggested. First, just
as in fishes, personality traits may be relevant to welfare outcomes in
captivity, which are under increasing scrutiny following recognition of
sentience . Second, decapod behavioural change following sub-lethal
exposure to environmental pollutants could contribute to bioassays
relevant for monitoring ecosystem health and susceptibility of benthic
and/or sediment dwelling invertebrates to pollutants . Third, since
decapods demonstrate trait-biased dispersal, with bolder individuals
outcompeting conspecifics, variation in boldness and activity may link
to invasive success . Finally, many billions of decapod crustaceans are
harvested from wild fisheries and raised in aquaculture systems for
human consumption annually . Personality differences have been shown to
predict trappability in decapods just as in vertebrates , and are also
expected to be integrated with life-history traits important for
production in aquaculture .

Here we characterise personality variation in the red cherry
shrimp\emph{Neocaridina heteropoda} (syn. \emph{N. davidi} ), a small
(\textless{}30mm) caridean species that is emerging as a model for
pharmaceutical and ecotoxicological research . A short generation time
and fast development also make it amenable to genetic studies and
potentially a convenient model system for decapod aquaculture . Cherry
shrimp are of commercial aquaculture importance themselves as an
ornamental species . Unfortunately, release by aquarists combined with a
wide tolerance of water and temperature parameters means they have
become invasive outside their native range . Despite this, basic
biological information on this species is scarce (but see e.g. . In
particular there is a lack of baseline behavioural data that may, for
example, impede use of `behavioural endpoints' in ecotoxicology . Very
little is currently known about the amount or structure of behavioural
variation among-individuals. Nor is it known whether state-dependent
behavioural variation, if present, is linked to intrinsic variables such
as size or sex.

We focus specifically on `shy-bold' variation , an aspect of personality
that describes differences in behavioural response to (perceived) risk .
We use two simple testing paradigms, Open Field Trials (OFT) and Food
and Shelter trials (FST), coupled with multivariate behavioural
phenotyping and a repeated measures design. Our specific aims are to (i)
test for repeatable among-individual differences of the behavioural
measures of boldness across the OFT and FST, (ii) determine whether the
structure of multivariate behavioural variation observed is consistent
with expectations given an underlying shy-bold among-individual axis,
and (iii) determine whether size and/sex explains behavioural variation
among-individuals.

\textbf{METHODS}

\textbf{Study animals and husbandry}

All shrimp used were from a captive-bred colony sourced from the
aquarium pet trade and subsequently maintained in the Freshwater
Laboratory of the Animal Facility in Penryn, Cornwall. On arrival in the
laboratory, adult shrimp were housed in large breeding stock tanks (28cm
x 19.5cm x 18.5cm) for several months to establish the breeding colony.
Behavioural data for this study were collected during October and
November 2022 under the local ethical approval (University of Exeter
approval ID 517031). Forty-eight individuals were taken from stock tanks
and moved to individual housing containers (22cm x 8.5cm x 15cm)
connected to a shared recirculating water supply. Each individual tank
contained a short piece of black plastic (3.5cm x 3.5cm) and a plastic
plant to provide refuge. Shrimp moved to these containers were sampled
haphazardly from stock, but with the condition that they needed to have
a body length of at least 6mm. These were presumed to be adult females
and males since individuals with a total length of \textgreater{}7mm can
be sexed ensure the effective tracking of behaviour in the experimental
set-up (described below). Water temperature was maintained at 25 °C and
shrimps were fed every two days on commercial ORGANIX granulate shrimp
pellets. A constant light:dark cycle was enforced (lighting hours
07:00-19:00).

\textbf{Overview of experimental design}

To test for and characterise among-individual variation in \emph{N.
heteropoda} we aimed to subject each of the 48 individuals to three
repeats of two separate behavioural assays: an Open field trial (OFT)
and a Food and shelter trial (FST). The order of assays was held
constant, with all individuals completing 3 x OFT followed by 3 x FST
over a five-week period, with a minimum of 48 hours between any two
successive trials. This design would have yielded 288 trials (48
individuals x3 repeats x2 assays). However, some mortalities occurred
during the investigation period. In some cases, we opportunistically
replaced mortalities with new stock shrimp such that our final data set
analysed actually comprised 273 trials on 53 shrimp, with a mean of 2.5
OFT and 2.6 FST per individual.

Trials were run between 0900 and 1300 hours with individuals tested in a
random order. On any given day, all trials conducted in the laboratory
were of a single assay type, with duplicate experimental arena tanks
allowing two shrimp to be tested simultaneously. These experimental
tanks (30 cm x 20.5 cm x 21cm) were filled to 5cm\textsuperscript{3}with
water from the sump of the recirculating water system to which all
individual housing units were connected. The water was changed every 6
trials to reduce any influence of conspecific cues that might be
produced. Each tank was filmed from above using a Sunkwang C160 video
camera mounted with a 5-50mm manual focus lens, and the tracking
software Viewer II (BiObserve) used to measure behaviours putatively
linked to shy-bold variation (described below for each assay). The
experimental tanks were surrounded by cardboard screens to exclude
external visual stimuli that might otherwise impact behaviour.

At the end of each trial, size and sex data were recorded and the shrimp
was then returned to individual housing. For size we measured both
length (from the tip of the rostrum to the posterior end of the telson
using digital callipers to the nearest 0.01mm) and mass (using a digital
balance after dabbing the animal with a tissue to remove water droplets
to the nearest 0.001g). However, length and mass measures were highly
correlated across observations (r=0.717, t \textsubscript{268} = 16.843,
P\textless{}0.001), while mass was slightly more repeatable at the
individual level (R\textsubscript{mass}= 0.768 versus,
R\textsubscript{length}= 0.581). Assuming size was (approximately)
constant for individuals over study period then this suggests mass has a
lower measurement error. We therefore used mass as our measure of size
in all analyses. Sex was estimated from external morphology after each
trial, and shrimp were scored as a male, female, or of unknown sex. On
average, females are larger and more opaque than males, and have more
rounded bellies and body plates . Females can sometimes be seen carrying
eggs in their swimmerets and may show a distinctive `saddle' marking .
Given uncertainty in sex determination, we elected to score it after
each trial blind to any previous assessments of the same individual.
After all observations were complete, we assign a single sex
determination of a male or female if \selectlanguage{english}[?]5/6 trial-specific assessments
were in agreement. We assigned sex as `unknown' if this criterion was
not met.

\textbf{Open field trials (OFT)}

The OFT is a generic and simple assay widely used across taxa to measure
shy-bold variation related aspects of personality (e.g. exploration in a
novel environment, anxiety-like behaviour, stress coping-style) \selectlanguage{english}. In
this assay, a shrimp was placed within a tube positioned in the centre
of the tank (Figure 1a) and allowed to acclimate for 120 seconds. The
tube was then lifted out and movement tracked for a subsequent 240
seconds using Viewer. We extracted four behavioural traits from tracking
data: \emph{Track Length, Area Covered, Wall Distance}
and\emph{Freezings} (see Table 1 for definitions). Based on OFT
behaviour in other taxa, our prediction is that bolder individuals will
tend to show higher values for the first three traits (i.e. more active
exploration of the arena and less thigmotaxis) but lower values for
freezing . The latter prediction stems from the fact that freezing
behaviour under perceived risk is a common component of behavioural
stress response (e.g. the `flight-fight-freeze' response) . These
behaviours are widely used in studies of shy-bold variation based on
similar assays applied in fishes . We consider this an appropriate
starting point, but fully acknowledge that \emph{a priori} predictions
are naive with respect to decapod biology in general (and \emph{N.
heteropoda} specifically).~~Summary statistics for the original
variables measured in the OFT assay are presented in Table S1.

\textbf{Food and Shelter Trial (FST)}

The FST used a modified arena into which we added (i) a shelter made of
black-plastic (18cm x 5cm x 5.5cm), positioned at one end of the tank,
and (ii) a food zone, comprising a black-plastic square (5cm x 5cm) with
food glued to it at the other end (Figure 1b). The food was a mixture of
Repashy Solient Green premix powder and ORGANIX shrimp granulate pellets
(approximately 10) glued to the black-plastic square using aquarium safe
glue (see Figure 1b). Shrimp were introduced via the black acclimation
tube at the centre of the tank for 120 seconds, then tracked for 240
seconds after being released. Four traits were measured: \emph{Track
Length, Time in Open, Food Latency,} and \emph{Freezings.} Predictions
for \emph{Track Length} and \emph{Freezings} are as described above but
note that movement of shrimp could not be tracked once they were within
the shelter and food zones, so these traits are recorded only for the
portion of the observation period that they are outside these zones. We
predict that bolder individuals will spend more time in the open (i.e.
outside the shelter zone) and have a short latency to enter the food
zone (used as a willingness to feed). Summary statistics for the
original variables measured in the FST assay are presented in Table S1.

\textbf{Statistical analyses}

We used mixed effect models fitted with ASReml-R implemented in R
version 4.1.1 . We applied log-transformations (OFT \emph{Wall
Distance;} FST \emph{Food Latency} ) and square root transformations
(OFT\emph{Track Length, Freezings} ; FST \emph{Track Length, Time in
Open}and \emph{Freezings} ) to improve Gaussian assumptions, before
scaling to standard deviation units which facilitates multivariate
modelling. Finally, we also multiplied the transformed and scaled data
for\emph{Freezings} (both assays), and \emph{Food Latency} (FST) by -1.
This sign reversal was to simplify biological interpretation of results
by making high values correspond to \emph{a priori} expectation of
`bolder' behaviour in all cases. Following these transformations, model
residuals were (approximately) Gaussian with the exception
of\emph{-(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.

\textbf{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),
trial\emph{repeat} 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 \emph{Track Length} and \emph{-(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 V\textsubscript{I.} For each
trait, we then estimated repeatability (R) conditional on fixed effects
as the proportion of phenotypic variance (V\textsubscript{P} ) explained
by individual differences. Thus
R=V\textsubscript{P}/(V\textsubscript{I}+ V\textsubscript{R}) where
V\textsubscript{R} 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 V\textsubscript{I}. For testing
a single variance component, we assumed twice the difference in
log-likelihoods is distributed at a 50:50 mix of~\selectlanguage{greek}\emph{χ}
\selectlanguage{english}\textsuperscript{2} on 0 and 1 DF following

\textbf{Among individual covariance in behavioural traits}

Next, we fitted a multivariate mixed model to estimate the
among-individual behavioural co-variance matrix (\textbf{ID} ) for the
full set of 8 traits. Fixed and random effects on each trait were as
described above for the univariate models. \textbf{ID} contains
estimates of V\textsubscript{I} for each trait on the diagonal, with
off-diagonal elements corresponding to COV\textsubscript{I}, the
among-individual covariance for each pair of traits. Residual
within-individual (co)variance was partitioned to the corresponding
matrix \textbf{R} . However residual covariance (COV\textsubscript{R})
is only identifiable between trait pairs observed simultaneously (i.e.
in the same trial), so was fixed to zero between OFT and FST traits. To
test the presence of among-trait covariance in \textbf{ID} , we compared
the full model to one in which all COV\textsubscript{I} were fixed to
zero by LRT assuming twice the difference in model log-likelihoods as
distributed as \selectlanguage{greek}\emph{χ} \selectlanguage{english}\textsuperscript{2}\textsubscript{28}.

Having estimated \textbf{ID} , we then wanted to assess whether it was
qualitatively consistent with a dominant underlying axis of shy-bold
variation as we predicted. To do this we (i) standardised
among-individual covariance terms to the more intuitive correlation
scale (where, for any pair of traits x,y the among-individual
correlation  \emph{r}
\textsubscript{I(x,y)} = COV\textsubscript{I(x,y)} / [?](\emph{V}
\textsubscript{Ix} \selectlanguage{ngerman}×\selectlanguage{english} \emph{V} \textsubscript{Iy})); and (ii) subjected
our estimated matrix to eigen decomposition (principal component
analysis). Since all traits were transformed such that high values
indicated bolder behaviour, we predict correlations should be uniformly
positive. We also predict that the leading eigen vector of \textbf{ID}
(subsequently referred to as\textbf{id\textsubscript{max}} ) will
explain a large proportion of among-individual variance and have
same-sign loadings on all traits. We used a parametric bootstrap
approach, following with a bootstrap sample size of 1000, to generate
approximate 95\% CI on the eigen values of\textbf{ID} and trait loadings
on \textbf{id\textsubscript{max.}}

\textbf{Testing whether sex and size contribute to among-individual
variation}

To assess the extent of the contribution of sex and/or size to the
among-individual variation in behaviour, we refitted all models
described above but with additional fixed effects of sex (a 3 level
factor: male, female, unknown), size (mass as a covariate) and their
interaction sex:size. Using univariate models and conditional F-tests,
we tested the significance of these effects on each trait. We compared
estimates of V\textsubscript{I} and R from these expanded models to
those obtained above. We then refitted our multivariate model with the
additional fixed effects and
estimated\textbf{ID\textsubscript{sex:mass}} , the among individual
(co)variance matrix conditional on sex and mass. We scaled covariances
to correlations, and subjected \textbf{ID\textsubscript{sex:mass}} to
eigen decomposition as described above, allowing us to compare its
structure to \textbf{ID} . We also checked for any clustering of
personality visually by sex and/or size in a multivariate personality
space, by predicting and plotting individual scores
on\textbf{id\textsubscript{max}} for each shrimp.

\textbf{RESULTS}

\textbf{Among-individual behavioural variation}

Our shrimp population shows significant among-individual differences in
all behavioural traits measured in OFT and FST assays bar \emph{-(Food
Latency)} (Table 2). Thus, behaviours are repeatable with a median of
R=0.353 (range 0.168 -- 0.626) conditional on fixed effects (Figure 2,
blue points). Fixed effect estimates from these models are not relevant
to biological hypotheses but are presented in the Supplementary Material
(Table S2).

\textbf{Among individual (co) variation}

The multivariate model provides statistical support for individual-level
covariance among the traits tested (LRT comparison of full model to one
in which all COV\textsubscript{I} terms fixed to zero;
X\textsuperscript{2}\textsubscript{28}=104.87, P\textless{}0.001).
Pairwise correlations between traits in \textbf{ID} are generally strong
(Figure 3a) with a median absolute magnitude of 0.736 (range -0.95 to
0.963). Furthermore, correlations among traits within- and across-
assays are of similar magnitude. However, contrary to our prediction of
a simple shy-bold axis of variation, the among-individual correlations
between (transformed) trait pairs are not uniformly positive (Figure
3a). This result is also reflected in the eigen decomposition
where\textbf{id\textsubscript{max}} captures 61\% (95\% CI,
46.82\%-72.23\%) of the among-individual (co)variance, consistent with a
strong axis of personality underpinning the observed behaviours, but
loads antagonistically on some traits (Figure 4, blue points). These
loadings show that individuals appearing bolder than average as measured
by most traits (e.g., OFT \emph{Track Length} , OFT \emph{-(Freezings)}
), tend to appear less bold than average as measured by OFT \emph{Wall
Distance} , FST \emph{-(Food Latency)} , and FST \emph{Time in Open} .
Of these traits, only FST \emph{Time in Open} clearly loads
significantly on\textbf{id\textsubscript{max}} (based on 95\% CI not
overlapping zero), but this is particularly notable as it indicates
shrimp we might view as bolder based on most other criteria actually
spend more time than average in the shelter during FST. We present the
full \textbf{ID}variance-covariance matrix from which the correlations
are derived in Table S3.

\textbf{Effects of sex and mass}

We find limited support for the hypothesis that among-individual
behavioural differences are explained by sex and/or size (mass).
Refitted univariate models provide no statistical support for
differences in behavioural means between assigned sexes (Table 3), nor
did we detect any significant sex:size interactions. However, we did
detect significant main effects of size (mass) on OFT \emph{Track
Length} and OFT \emph{Area Covered} . Both coefficients are positive
meaning heavier individuals on average cover longer distances and a
larger area in OFT trials. Estimates of V\textsubscript{I} and R from
these refitted univariate models were very similar to those estimated
unconditional on the extra fixed effects (Figure 2), providing further
confirmation that neither sex nor size affects considerable
among-individual behavioural variation in single traits. This same
result also holds for the multivariate phenotype in that the correlation
structure in \textbf{ID\textsubscript{sex:mass}} is very similar to that
in \textbf{ID} (Figure 3). Moreover, the first vector
of\textbf{ID\textsubscript{sex:mass}} captures 65\% (95\% CI
47.96\%-73.26\%) of multivariate variance and has trait loadings almost
identical to those of \textbf{id\textsubscript{max}} (Figure
4).~Finally, plotting individual behavioural scores
on\textbf{id\textsubscript{max}} confirms the absence of clustering by
sex (Figure 5). While a net positive association between mean weight
and\textbf{id\textsubscript{max}} can be seen, it is also clear that
size accounts for only a small fraction of the variation present. We
present the full table of fixed effects estimates in Table S4 and the
full\textbf{ID\textsubscript{sex:mass}} \textsubscript{.}
variance-covariance matrix from which the correlations are derived in
Table S5.

\textbf{DISCUSSION}

Our repeated measure design provided strong statistical support for
consistent among-individual differences in behaviour across the Open
field (OFT) and Food and shelter (FST) assays. As expected, multivariate
analyses also yielded evidence of strong individual-level correlations
among the specific traits assayed. However, in asking whether the
structure of multivariate behavioural variation was consistent
with\emph{a priori} expectations of an underlying axis of shy-bold
personality variation, our results provided a somewhat mixed picture.
Specifically, we did find a dominant leading eigen vector of
\textbf{ID}that explains most (61\%) of individual level variation in,
and covariation-among, traits. However, several of the behaviours
measured load antagonistically on this axis, a pattern that does not
match our\emph{a priori} predictions for a simple shy-bold axis. Further
analyses provided little statistical support for major contributions of
size- or sex-effects to the personality variation described (though some
size effects were found). Below we discuss each of these main results in
the context of the existing literature and highlight some important
caveats to our conclusions.

Behavioural repeatabilities were statistically significant in seven of
the eight behavioural traits tested. This confirms the presence of
personality variation in red cherry shrimp and shows it is readily
detected using simple testing paradigms adapted from fish models.
Although personality has yet to be widely tested in decapods, our
results align with the conclusions of ) who demonstrated
among-individual variation in startle response duration (a proxy of
boldness) in male hermit crabs. Repeatability in traits associated with
shy-bold and/or exploratory personality variation has also been
demonstrated in the European crayfish (\emph{Astacus astacus} ; , and
the rock pool shrimp (\emph{Palaemon elegans} ; . In these latter
examples, boldness was also negatively correlated with resource holding
potential, suggesting a trade-off whereby bolder individuals may be
better at finding resources but less able to defend them in competition
(. We do not yet know if similar relationships hold in \emph{N.
heteropoda} but investigating the functional significance of personality
in relation to competition and other ecological processes ) in this
species would be useful. More generally, testing for associations of
behavioural profiles with individual life histories would permit
scrutiny of whether personality variation is maintained through adaptive
processes (e.g., resource allocation trade-offs ), and/or arises through
differences in resource acquisition coupled to state-dependent behaviour
.

While estimates clearly varied among traits, some repeatabilities were
notably high in comparison to the wider animal personality literature.
For example, estimates of R=0.63 and R=0.56 for OFT \emph{Track
Length}and \emph{Area Covered} are high compared to a median behavioural
repeatability of 0.37 reported by . This may, at least in part, reflect
the use of a short inter-observation interval; R is known to decline as
the time between observations increases (e.g., ). However, high
repeatabilities also have some implications worth noting. First, R sets
an upper limit for heritability (and so potential rate of selection
response) and facilitates more accurate selection

. Selection on behaviour has been suggested as a way to improve growth
and other production traits in commercial shrimp cultivation and in this
context, high repeatability of the selection target would be
advantageous. In a study of Pacific white shrimp (\emph{Litopenaeus
vannamei} ), found that individuals interacting more with feed had a
lower latency of approach and consumed food more rapidly. In our FST
assay, latency to enter the zone containing food in the FST was actually
not repeatable (and so not heritable). However, if simple behavioural
biomarkers of improved feeding could be identified, then selecting on
these could be valuable in an industry where food waste is a major
source of economic inefficiency .

Although the implications are perhaps less clear at present, personality
variation has been widely linked to the likelihood of establishment
and/or invasive spreading of species following accidental introduction
For instance in the American signal crayfish (\emph{Pacifastacus
leniusculu} s), found repeatable differences in shy-bold type behaviour
that predicted individual motivation to disperse by passing through
weirs (though not successful at doing so). Invasion processes could
therefore be considered a `selective filter' whereby only individuals
with appropriate combinations of personality and other traits can invade
a novel environment successfully Working on the same species of
crayfish, , found invasive populations were bolder and more aggressive
on average than populations within their native range. However, this was
only true where invasive populations were allopatric to native crayfish,
and the extent to which differences were present prior to invasions, as
opposed to emerging after as adaptations to a new environment is unclear
. Moreover, whether population level repeatability to shy-bold type
behaviours (as opposed to individual or population mean) influences
invasive potential is unresolved. Low repeatability implies high
plasticity- at least relative to `fixed' individual differences- and
there is a long-standing hypothesis that plasticity plays an important
role in adaptation to novel environments following dispersal . A recent
study found differences in behavioural repeatability between native and
invasive species of nudibranchs that is consistent with this idea , but
more empirical studies are needed to see if low behavioural
repeatabilities reliably predict risk of invasive spread.

Although we found strong support for personality in red cherry shrimp,
multivariate analyses show the structure of behavioural variation
differs somewhat from our initial predictions. Our eigen decomposition
of the \textbf{ID} matrix is consistent with the presence of a latent
personality axis that describes the majority of among-individual
(co)variation in the measured behavioural traits . This was expected:
all traits analysed were chosen precisely because they are putatively
measures of the same underlying shy-bold personality axis. Thus, the
important result here is not the presence of correlation structure
in\textbf{ID} , but the overall `shape' of that structure. Specifically,
because traits were scaled such that higher numbers denoted putatively
bolder phenotypes, we had also predicted that covariances in\textbf{ID}
, would be uniformly positive, and all traits would load on the main
eigenvector with concordant signs. Some, but not all, relationships were
as expected. For example, individuals that travelled further than
average in the OFT, also have higher track length in the FST, cover more
area (OFT), and freeze less (both assays). These behavioural
characteristics meet \emph{a priori} expectations for the bolder end of
the shy-bold continuum indicating that exploratory individuals are
associated with the propensity to take greater risks . However, the same
individuals also tend to swim closer to the tank walls in the OFT (i.e.,
be more thigmotaxic) and take longer to visit the food zone in the FST,
which are characteristics typically associated with shy personality
types. Neither of these traits load significantly
on\textbf{id\textsubscript{max}} (based on 95\% CI not overlapping zero)
and among-individual variance for FST \emph{-(Food Latency)} was not
statistically supported in the univariate model. However, FST \emph{Time
in Open} loads significantly on \textbf{id\textsubscript{max}} with a
negative sign and is also moderately repeatable. This actually means
individuals considered bolder and/or more exploratory in the OFT spend
longer durations than average in the shelter during FST, a result that
is counterintuitive. Very speculatively, it is possible that the shelter
provided may have been perceived as a risky environment (rather than a
safe one as intended). This could arise if, for instance, our shelter
mimicked the type of structure used by drift-feeding and opportunistic
foraging predatory fish . With this considered, it would be interesting
to investigate whether a refuge with greater structural complexity
and/or smaller open spaces may be preferred (as demonstrated in mud
crabs \emph{Scylla serrata} ; .

We find limited evidence for sex and size effects on behaviour and
conclude that these aspects of state do not make a major contribution to
personality (co)variation in our population. The absence of sex effects
is perhaps somewhat surprising given the extensive evidence of
behavioural sexual dimorphism in decapods. For example, male rock pool
prawns (\emph{Palaemon elegans} ) are bolder and more active than
females , while aggression is sexually dimorphic in white shrimp
(\emph{Penaeus vannamei} ; , rock shrimp (\emph{Rhynchocinetes typus} ;)
and American lobsters (\emph{Homarus americanus} ;). In these species,
males tend to be more aggressive and able to monopolise food for longer
durations relative to females. In finding an absence of sexual
dimorphism here we acknowledge that uncertainty in sexing the shrimp
reduces statistical power. Of the 53 shrimp tested, we ultimately
classified 22 as being of unknown sex. These were, on average, smaller
individuals than those assigned to male or female categories and likely
to be younger (and potentially sexually immature). We therefore cannot
exclude the possibility that our study partially conflates sex, age and
maturation status in ways that mask any dimorphism. Nevertheless, we
also note that several other decapod studies have reported an absence of
sex effects on shy-bold type traits. For example, found no difference in
mean boldness or activity between male and female Chinese mitten crabs
\emph{Eriocheir sinensis,} while sex did not predict startle response
duration in hermit crabs \emph{Pagurus bernhardus} .

We did find some evidence for size-dependent behaviour. Statistical
support was limited to two traits in the OFT, with larger (heavier)
individuals traveling slightly further and covering more area on
average. These trait-specific effects drive a trend towards larger
individuals having higher behaviour scores
on\textbf{id\textsubscript{max}} . This could potentially be explained
by links between behavioural type and life-history strategy as proposed
under heuristic frameworks such as the `Pace of Life Syndrome' . For
instance, high metabolic rate may be associated with bolder behaviour,
increased resource acquisition, and faster growth leading to increased
size , albeit at the likely cost of higher mortality risk (e.g. from
predation; Wolf et al., 2007). At present we lack individual level data
on life histories to test these hypothesised relationships. We also lack
sufficient ecological data to explore (arguably) simpler explanations
for size-dependence. For instance, argued size-dependent behaviour of
mud crabs (\emph{Panopeus herbstii} ) was linked to size-dependent
predation risk. Small crabs used refuges more than large crabs and also
increased use in the presence of predators. Other mechanisms proposed
for generating size-behaviour relationships in decapods are related to
mating traits. For instance, male-male competition can drive
size-dependent mating tactics with larger males engaging more in mate
guarding and contest behaviour while smaller rivals adopt exploratory
mate searching tactics . Although we cannot yet rule out similar
processes in cherry shrimp, it is notable we found no sex by size
interactions on the behaviours assayed here (i.e., both larger males and
females have higher \emph{Track Length} and \emph{Area Covered} in the
OFT).

In conclusion, this is the first study to our knowledge that describes
patterns of among-individual variation in putative measures of shy-bold
variation in the cherry shrimp, \emph{N. heteropoda.} Using simple
assays widely applied to small fish models, our results are consistent
with the verbal model of a single major personality axis underlying
among-individual differences observed. However, the structure of this
axis does not fully match \emph{a priori} expectations of shy-bold. This
could be because our initial assumptions of what constitutes `riskier'
behaviour in this species are incorrect. We also found no evidence for
sexual dimorphism in behaviour and only limited support for
size-behaviour relationships. Our description of personality variation
in red cherry shrimp adds to the growing picture of this phenomenon in
invertebrates generally and decapod crustaceans specifically . We
therefore hope this study will set the foundations for future
investigations of mechanism and functional significance in this emerging
model system, as well as providing baseline data for more applied
research across ecotoxicology, aquaculture, and invasion biology.

\textbf{LITERATURE CITED}

\textbf{DATA ACCESSIBILITY STATEMENT}

The dataset and associated metadata used for the analyses presented in
this paper are available in the Dryad repository
https://doi.org/10.5061/dryad.2547d7wz2 which will be made public upon
acceptance. R Scripts used for data analysis will also be made available
there. This is the temporary link for the review process:
https://datadryad.org/stash/share/F8BBNQJcARM6jgWsJubB7rRWUGs955oTMcm6f2Bno7M

\textbf{COMPETING INTERESTS STATEMENT}

The authors declare no competing interests.

\textbf{AUTHORS CONTRIBUTION}

RAR: Conceptualization (Equal); Data curation (Lead); Formal analysis
(Lead); Investigation (Lead); Methodology (Lead); Writing -- original
draft (Lead); Writing -- review \& editing (Lead).

FS: Conceptualization (Equal); Data curation (Supporting); Formal
analysis (Supporting); Investigation (Supporting); Methodology
(Supporting); Supervision (Supporting); Validation (Supporting);
Visualization (Lead); Writing -- original draft (Supporting); Writing --
review \& editing (Supporting).

AJW: Conceptualization (Equal); Formal analysis (Equal); Investigation
(Supporting); Methodology (Supporting); Project administration (Lead);
Resources (Lead); Supervision (Lead); Validation (Equal); Writing --
original draft (Supporting); Writing -- review \& editing (Supporting)

\textbf{ACKNOWLEDGEMENTS}

We thank technical staff in the CEC aquatic facility for their support
with animal husbandry, Sam Greene for assistance with the videotracking
software, and James Chatfield, Jake Chenoweth, Seren Sharland-Kiersch
who contributed to data collection.

\textbf{FUNDING}

FS was supported by the European Union's Horizon 2020 research and
innovation programme under the Marie Sklodowska-Curie Individual
Fellowship (grant agreement no. 101023262).

\textbf{TABLES}

\textbf{Table 1} : Behavioural traits recorded in Open field trial (OFT)
and Food and shelter trials (FST)\selectlanguage{english}
\begin{longtable}[]{@{}lll@{}}
\toprule
\textbf{Assay} & \textbf{Trait} & \textbf{Definition of measured
behaviour (before transformation)-}\tabularnewline
\midrule
\endhead
OFT & \emph{Track Length} & Total distance travelled (cm)\tabularnewline
& \emph{Area Covered} & Proportion of total arena (\%)\tabularnewline
& \emph{Wall Distance} & Average distance away from tank walls
(cm)\tabularnewline
& \emph{-(Freezings)} & Number of `freezes' defined as speed dropping
\textless{}4cm. \emph{s\textsuperscript{-1~}} for [?]25s\tabularnewline
FST & \emph{Track Length} & Distance travelled while outside shelter and
food zones (cm)\tabularnewline
& \emph{Time in Open} & Time spent outside the shelter zone
(s)\tabularnewline
& \emph{-(Food Latency)} & Time to first entry of the food zone
(s)\tabularnewline
& \emph{-(Freezings)} & Number of `freezes' (defined above) outside
shelter and food zones\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2:} Estimated variance components and repeatabilities (R)
from univariate mixed-effect models of OFT and FST traits.
V\textsubscript{I} and V\textsubscript{R} denote among-individual and
residual variances respectively and standard errors are shown in
parentheses. Also shown are X\textsuperscript{2} and P derived from LRT
to test the significance of V\textsubscript{I} for each trait.\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
\textbf{Assay} & \textbf{Trait} & \textbf{V\textsubscript{I}} &
\textbf{V\textsubscript{R}} & \textbf{R} &
\textbf{X\textsuperscript{2}\textsubscript{0,1}} &
\textbf{P}\tabularnewline
\midrule
\endhead
OFT & \emph{Track Length} & 0.616 (0.158) & 0.368 (0.058) & 0.626
(0.074) & 48.884 & \textless{}0.001\tabularnewline
& \emph{Area Covered} & 0.529 (0.146) & 0.413 (0.065) & 0.562 (0.085) &
33.988 & \textless{}0.001\tabularnewline
& \emph{Wall Distance} & 0.153 (0.100) & 0.759 (0.118) & 0.168 (0.102) &
3.040 & 0.041\tabularnewline
& \emph{-(Freezings)} & 0.456 (0.141) & 0.546 (0.086) & 0.455 (0.094) &
22.633 & \textless{}0.001\tabularnewline
FST & \emph{Track Length} & 0.102 (0.06) & 0.458 (0.069) & 0.183 (0.098)
& 4.032 & 0.022\tabularnewline
& \emph{Time in Open} & 0.365 (0.13) & 0.668 (0.102) & 0.353 (0.097) &
14.207 & \textless{}0.001\tabularnewline
& \emph{-(Food Latency)} & 0.000* & 0.995 (0.122) & 0.000* & 0.000 &
0.500\tabularnewline
& \emph{-(Freezings)} & 0.109 (0.048) & 0.306 (0.046) & 0.263 (0.099) &
8.056 & 0.002\tabularnewline
\bottomrule
\end{longtable}

*For this trait the estimate of V\textsubscript{I} was bound to zero and
no SE is estimable.

\textbf{Table 3:} Estimated effects of sex and size on OFT and FST
behavioural traits. Sex was fitted as 3 level factor (female, male,
unknown) with unknown treated as the reference level. Size was measured
as live mass (g). Estimates are from univariate models with significance
tested using conditional F tests.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\textbf{Assay} & \textbf{Trait} & \textbf{Effect} & \textbf{Level} &
\textbf{Coefficient (SE)} & \textbf{F} & \textbf{DF} &
\textbf{P}\tabularnewline
\midrule
\endhead
OFT & \emph{Track Length} & sex & female & 1.523 (0.862) & 0.271 &
2,44.7 & 0.76\tabularnewline
& & & male & 1.246 (0.774) & 1.246 (0.774) & 1.246 (0.774) & 1.246
(0.774)\tabularnewline
& & mass & & 12.38 (6.131) & 7.049 & 1,111.6 & 0.009\tabularnewline
& & sex:mass & female & -3.00 (10.14) & 0.058 & 2,115.8 &
0.944\tabularnewline
& & & male & 0.792(11.61) & 0.792(11.61) & 0.792(11.61) &
0.792(11.61)\tabularnewline
& \emph{Area Covered} & sex & female & -1.913 (0.876) & 0.072 & 2,45.8 &
0.931\tabularnewline
& & & male & -1.870 (0.788) & -1.870 (0.788) & -1.870 (0.788) & -1.870
(0.788)\tabularnewline
& & mass & & 18.40 (6.232) & 4.608 & 1,110.4 & 0.034\tabularnewline
& & sex:mass & female & -17.09 (10.31) & 2.073 & 2,115 &
0.130\tabularnewline
& & & male & -19.43 (11.84) & -19.43 (11.84) & -19.43 (11.84) & -19.43
(11.84)\tabularnewline
& \emph{Wall Distance} & sex & female & -0.327 (0.885) & 0.251 & 2,44.8
& 0.779\tabularnewline
& & & male & -0.907 (0.853) & -0.907 (0.853) & -0.907 (0.853) & -0.907
(0.853)\tabularnewline
& & mass & & 1.985 (6.303) & 0.044 & 1,74.9 & 0.834\tabularnewline
& & sex:mass & female & -9.506 (10.49) & 0.437 & 2,84.3 &
0.648\tabularnewline
& & & male & -0.510 (13.02) & -0.510 (13.02) & -0.510 (13.02) & -0.510
(13.02)\tabularnewline
& \emph{-(Freezings)} & sex & female & 2.735 (0.939) & 0.192 & 2,46.5 &
0.826\tabularnewline
& & & male & 2.852 (0.860) & 2.852 (0.860) & 2.852 (0.860) & 2.852
(0.860)\tabularnewline
& & mass & & 12.195 (6.697) & 0.764 & 1,101.1 & 0.384\tabularnewline
& & sex:mass & female & -13.75 (11.09) & 1.612 & 2,108.1 &
0.204\tabularnewline
& & & male & -21.00 (13.00) & -21.00 (13.00) & -21.00 (13.00) & -21.00
(13.00)\tabularnewline
FST & \emph{Time in Open} & sex & female & -0.717 (0.913) & 2.076 &
2,49.1 & 0.136\tabularnewline
& & & male & 1.306 (0.996) & 1.306 (0.996) & 1.306 (0.996) & 1.306
(0.996)\tabularnewline
& & mass & & -0.608 (5.973) & 0.081 & 1,90.6 & 0.776\tabularnewline
& & sex:mass & female & 11.77 (10.40) & 1.528 & 2,95.6 &
0.222\tabularnewline
& & & male & -15.54 (15.26) & -15.54 (15.26) & -15.54 (15.26) & -15.54
(15.26)\tabularnewline
& \emph{-(Food Latency)} & sex & female & -2.088 (0.775) & 0.766 & 2,127
& 0.297\tabularnewline
& & & male & 0.343 (0.937) & 0.343 (0.937) & 0.343 (0.937) & 0.343
(0.937)\tabularnewline
& & mass & & -0.939 (5.300) & 0.025 & 1,127 & 0.874\tabularnewline
& & sex:mass & female & 10.698 (8.915) & 2.586 & 2,127 &
0.079\tabularnewline
& & & male & -23.32 (14.52) & -23.32 (14.52) & -23.32 (14.52) & -23.32
(14.52)\tabularnewline
& \emph{Track Length} & sex & female & -3.449 (0.666) & 0.157 & 2,49.3 &
0.855\tabularnewline
& & & male & -2.915 (0.756) & -2.915 (0.756) & -2.915 (0.756) & -2.915
(0.756)\tabularnewline
& & mass & & 5.909 (4.441) & 0.267 & 1,75.6 & 0.607\tabularnewline
& & sex:mass & female & -7.836 (7.673) & 1.465 & 2,85.5 &
0.237\tabularnewline
& & & male & -18.36 (11.65) & -18.36 (11.65) & -18.36 (11.65) & -18.36
(11.65)\tabularnewline
& \emph{-(Freezings)} & sex & female & 0.187 (0.573) & 0.818 & 2, 48.8 &
0.447\tabularnewline
& & & male & 0.028 (0.645) & 0.028 (0.645) & 0.028 (0.645) & 0.028
(0.645)\tabularnewline
& & mass & & 1.587 (3.804) & 0.937 & 1,77.5 & 0.336\tabularnewline
& & sex:mass & female & 2.223 (6.591) & 0.203 & 2,87 &
0.817\tabularnewline
& & & male & 6.008 (9.93) & 6.008 (9.93) & 6.008 (9.93) & 6.008
(9.93)\tabularnewline
\bottomrule
\end{longtable}

\textbf{FIGURES}

\textbf{Figure 1:} The tank set-ups used for (a) Open field trials (OFT)
and (b) Food and shelter trials (FST) showing a side view on the left
and an overhead view on the right. The starting set up for both assays
has the shrimp to be tested placed inside the black acclimation tube.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

(a)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

(b)

\textbf{Figure 2:} Repeatabilities for OFT and FST traits estimated from
initial univariate models (blue points) and refitted models that
condition on additional fixed effects of size and sex (red points).
Error bars depict estimates \selectlanguage{ngerman}±1SE.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}

\textbf{Figure 3:} Among-individual correlation structure between traits
as determined from multivariate model estimates of (a) \textbf{ID} the
variance covariance matrix and \textbf{ID\textsubscript{sex:mass}} , the
corresponding matrix conditional on sex and size. Ellipse shape and
colour denote the strength and sign of each correlation. Light grey
background shading indicates sets of correlations among-traits measured
within each assay type (FST, OFT), while across-assay correlations have
white backgrounds.

(b)\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image4/image4}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image5/image5}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image6/image6}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image7/image7}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image8/image8}

\end{center}
\end{figure}

\textbf{Figure 4:} Trait loadings on the leading vectors of \textbf{ID}
(blue points) and \textbf{ID\textsubscript{sex:mass}} (red points).
Error bars denote 95\% confidence intervals obtained using a parametric
bootstrap (n=5000) and loadings can be considered nominally significant
if CI do not cross zero (solid vertical line).

\textbf{Figure 5:} A graphical illustration of the relationship between
multivariate personality, mass, and sex. Each point represents an
individual's predicted behavioural score
\textbf{id\textsubscript{max}}plotted against mean weight with colours
denoting assigned sex (female, male, unknown). Behavioural scores are
calculated for each individual as\textbf{id\textsubscript{max}.i}
\textsuperscript{T} where \textbf{i} is a column vector containing the
best linear unbiased predictions (BLUPs) of individual deviations from
each trait mean. Also shown for illustrative purpose are overall (grey
dashed) and sex-specific regressions of behavioural score on mean
weight. Behavioural scores are (uncertain) model predictions and no
statistical inference is intended.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image9/image9}

\end{center}
\end{figure}

\selectlanguage{english}
\FloatBarrier
\end{document}

