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\begin{document}

\title{Coping with antagonistic predation risks: Predator-dependent unique
responses are dominant in Ceriodaphnia cornuta}



\author[1]{Lei Gu}%
\author[1]{Shanshan Qin}%
\author[1]{Yunfei Sun}%
\author[1]{Jing Huang}%
\author[1]{Siddiq Akbar}%
\author[1]{Lu Zhang}%
\author[1]{Zhou Yang}%
\affil[1]{Nanjing Normal University - Xianlin Campus}%


\vspace{-1em}



  
  \date{June 18, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Inducible defenses of prey are evolved under diverse and variable
predation risks. However, during the co-evolution of prey and multiple
predators, the responses of prey to antagonistic predation risks, which
may put the prey into a dilemma of responding to predators, remain
unclear. Based on antagonistic predation pressure from an invertebrate
(Chaoborus larvae) and a vertebrate (Rhodeus ocellatus) predator, we
studied the responses of multiple traits and transcriptomes of the
freshwater crustacean Ceriodaphnia cornuta under multiple predation
risks. Chaoborus predation risk altered the expression of genes encoding
cuticle proteins and modulated the biosynthesis of steroid hormones,
cutin, suberine, and wax, leading to the development of horns and
increase in size at the late developmental stage. Meanwhile, fish
predation risk primarily triggered genes encoding ribosomes and those
involved in unsaturated fatty acid biosynthesis and cysteine and
methionine metabolism, resulting in smaller individual size and earlier
reproduction. Inducible responses of both transcriptome and individual
traits revealed that predator-dependent unique responses were dominant
and the dilemma of antagonistic responses was relatively limited.
However, the unique individual traits in response to invertebrate
predation could be significantly impaired by vertebrate predation risk,
even though the unique responses to different predators were extremely
weakly correlated and could be elicited simultaneously. These results
indicate that diverse predator-dependent unique responses are favored by
Ceriodaphnia during its co-evolution with multiple predators.
Nonetheless, Ceriodaphnia is not a generalist that can fully adopt all
predator-dependent unique responses simultaneously under multiple
predation risks.%
\end{abstract}%



\sloppy


\textbf{Coping with antagonistic predation risks: Predator-dependent
unique responses are dominant in\emph{Ceriodaphnia cornuta}}

\textbf{Running title: Inducible responses to multiple predators}

Lei Gu, Shanshan Qin, Yunfei Sun, Jing Huang, Siddiq Akbar, Lu Zhang,
Zhou Yang*

Jiangsu Province Key Laboratory for Biodiversity and Biotechnology,
School of Biological Sciences, Nanjing Normal University, 1 Wenyuan
Road, Nanjing 210023, China

* Corresponding author: Zhou Yang

TEL: +86-25-85891671.

E-mail: \emph{yangzhou@njnu.edu.cn}

Jiangsu Province Key Laboratory for Biodiversity and Biotechnology,
School of Biological Sciences, Nanjing Normal University, 1 Wenyuan
Road, Nanjing 210023, China

\subsection*{Abstract}

{\label{abstract}}

Inducible defenses of prey are evolved under diverse and variable
predation risks. However, during the co-evolution of prey and multiple
predators, the responses of prey to antagonistic predation risks, which
may put the prey into a dilemma of responding to predators, remain
unclear. Based on antagonistic predation pressure from an invertebrate
(\emph{Chaoborus} larvae) and a vertebrate (\emph{Rhodeus ocellatus} )
predator, we studied the responses of multiple traits and transcriptomes
of the freshwater crustacean \emph{Ceriodaphnia cornuta} under multiple
predation risks. \emph{Chaoborus} predation risk altered the expression
of genes encoding cuticle proteins and modulated the biosynthesis of
steroid hormones, cutin, suberine, and wax, leading to the development
of horns and increase in size at the late developmental stage.
Meanwhile, fish predation risk primarily triggered genes encoding
ribosomes and those involved in unsaturated fatty acid biosynthesis and
cysteine and methionine metabolism, resulting in smaller individual size
and earlier reproduction. Inducible responses of both transcriptome and
individual traits revealed that predator-dependent unique responses were
dominant and the dilemma of antagonistic responses was relatively
limited. However, the unique individual traits in response to
invertebrate predation could be significantly impaired by vertebrate
predation risk, even though the unique responses to different predators
were extremely weakly correlated and could be elicited simultaneously.
These results indicate that diverse predator-dependent unique responses
are favored by \emph{Ceriodaphnia} during its co-evolution with multiple
predators. Nonetheless, \emph{Ceriodaphnia} is not a generalist that can
fully adopt all predator-dependent unique responses simultaneously under
multiple predation risks.

\textbf{Keywords:} Cladoceran; \emph{Chaoborus} ; Fish; Inducible
defense; Predation risk

\subsection*{Introduction}

{\label{introduction}}

In the co-evolution of predators and prey, defense is critical for prey
survival. Under variable predation risks, inducible defenses triggered
by predation cues are favored by prey (Tollrian \& Harvell, 1999). For
successful defense against predators, prey organisms adopt various
inducible protective characteristics, including behavioral (De Meester,
1993), morphological (Gu et al., 2021), chemical defense (Selander et
al., 2015), and life history (Kvile, Altin, Thommesen, \& Titelman,
2021) traits. Since diverse predation risks can prevent the stable
expression of an inducible defensive trait (Steiner \& Auld, 2012), the
present study sought to understand the responses of prey to multiple
predation risks, particularly to predators exerting antagonistic
selection pressures.

Inducible defenses are common in aquatic organisms, such as
phytoplankton (L\selectlanguage{ngerman}ürling, 2020), zooplankton (Diel, Kiene,
Martin-Creuzburg, \& Laforsch, 2020), amphibians (Mitchell,
Bairos-Novak, \& Ferrari, 2017), and fish (Brönmark \& Miner, 1992).
Through the integration of inducible defense research, we classified
responses to multiple predators into two major types. The first type is
the general response, which evolves through diffusion co-evolution and
represents reciprocal adaptation in response to similar predators; for
instance, mayflies adopt the same avoidance behavior under predation
risks from different fish (Alvarez, Landeira-Dabarca, \& Peckarsky,
2014). The second type is the specific response, which evolves through
pairwise co-evolution between specific predators and prey, such as the
immune responses to pathogens (Westra et al., 2015) and the inducible
crests of \emph{Daphnia} in response to \emph{Notonecta} predation
(Grant \& Bayly, 1981). Moreover, the specific responses of different
traits can be further subdivided into antagonistic responses to the same
trait and unique responses to separate traits. Under antagonistic
selection pressures, if unique responses to separate traits are
dominant, the complex defense responses may incur maintenance cost, that
is, energetic costs of the sensory and regulatory mechanisms. Moreover,
if the prey primarily exhibits antagonistic responses of the same
traits, inducible responses to a predator may incur an environmental
cost, that is, vulnerability to other predators (Auld, Agrawal, \&
Relyea, 2010); this environmental cost is further linked to selection
and plays a dual role in the evolution of inducible defenses
(Decaestecker, De Meester, \& Ebert, 2002). Therefore, we hypothesized
that predator-dependent unique responses are dominant, helping, at least
in part, avoid the dilemma of the prey for responding to predators under
multiple predation risks.

In aquatic ecosystems, cladocerans are at the middle of the food chain,
acting as a food resource for insects and fishes (Miner, De Meester,
Pfrender, Lampert, \& Hairston, 2012). These invertebrate and vertebrate
predators constitute antagonistic selection pressures on the size or
habitat selection of waterfleas; for instance, larger plankton are
vulnerable to large visual predators (e.g., fish) but less vulnerable to
small ambush predators (e.g., \emph{Chaoborus} larvae) (Swift, 1992).
Presumably, inducible responses of \emph{Daphnia} to visual predators
primarily include life history changes (Effertz \& Von Elert, 2014),
which are rather different from the defensive traits triggered by
invertebrate predators, such as the development of ``twist'' (Herzog,
Rabus, Ribeiro, \& Laforsch, 2016), neck teeth (Tollrian, 1993), and
horns (Gu, Qin, Zhu, et al., 2020). These reports support our
hypothesis.

Antagonistic inducible traits are commonly expressed by \emph{Daphnia} .
For instance, \emph{Daphnia hyalina} shows completely opposite responses
of size and reproduction under predation pressures by vertebrates and
invertebrates (Stibor \& Lüning, 1994). \emph{Daphnia galeata} prefers
deeper habitats under fish predation risk, while inhabits upper water
layers under \emph{Chaoborus} predation risk (Dodson, 1988). In
addition, general responses, such as the development of an elongated
tail spine, are observed in \emph{Daphnia} in response to
fish,\emph{Triops} , and \emph{Notonecta} (Gu, Qin, Lu, et al., 2020;
Ritschar, Rabus, \& Laforsch, 2020). Consistent with inducible defensive
traits, both general and specific responses appear at the molecular
level; for instance, in \emph{Daphnia magna} , actin and tubulin
expression is decreased under \emph{Chaoborus} larvae or fish predation
risks (Pijanowska \& Kloc, 2004), ribosomal protein and vitellogenin
expression is increased under fish predation risk (Effertz, Mueller, \&
Von Elert, 2015), and cuticle protein expression is increased but
vitellogenin expression is decreased under \emph{Triops} predation risk
(Otte, Fröhlich, Arnold, \& Laforsch, 2014). Overall, in \emph{Daphnia}
, a given species exhibits various types of responses under antagonistic
predation risks. However, from these sporadic studies on
different\emph{Daphnia} species and clones, we cannot determine the
precise type of response preferred by the prey.

\emph{Ceriodaphnia cornuta} is a widely distributed species with
sensitive inducible defensive traits (Gu et al., 2021; Qin et al.,
2021), providing a suitable model for testing our hypothesis. Since some
inducible traits are hidden (Laforsch, Ngwa, Grill, \& Tollrian, 2004),
research on a few traits is insufficient. In recent years, omics
technologies have furthered our understanding of the mechanisms of
inducible defense (Hales et al., 2017; Zhang et al., 2021). Therefore,
to test our hypothesis that predator-dependent unique responses are
dominant, we assessed multiple traits and transcriptomes of \emph{C.
cornuta} in response to predation pressures from \emph{Chaoborus} larvae
and fish, respectively. Additionally, to test the hypothesis that
predator-dependent unique responses help prey avoid the dilemma of
responding to predators, we examined the expression of individual traits
under joint predation risks and explored the correlations between
various inducible responses.

\subsection*{Materials and methods}

{\label{materials-and-methods}}

\subsubsection*{Predation risks}

{\label{predation-risks}}

Predation risks were simulated using different predator-conditioned
medium prepared following the methodology described by Gu, Qin, Zhu, et
al. (2020). We cultured four \emph{Rhodeus ocellatus} or
100\emph{Chaoborus} sp. larvae in aged tap water, fed them \emph{C.
cornuta} for 6 h, and then transferred them into 2 L of COMBO medium
(Kilham, Kreeger, Lynn, Goulden, \& Herrera, 1998) for 18 h. The
predator-conditioned medium stock containing different predator
kairomones (Hahn, Effertz, Bigler, \& von Elert, 2019; Weiss et al.,
2018) was passed through a 0.22 \selectlanguage{greek}μ\selectlanguage{english}m glass fiber filter (Millipore), and
the filtrate was stored in a refrigerator until experiments. The control
(C) included COMBO medium. The fish (F) and \emph{Chaoborus} (CH)
predation risk treatments included respectively 20- (i.e., 1 fish per 10
L) and 2.5-fold (i.e., 20 \emph{Chaoborus} larvae per liter) diluted
filtered medium stocks. Finally, the combination treatment (CH + F),
representing joint predation risks, included both diluted medium stocks.

\subsubsection*{Life history experiment}

{\label{life-history-experiment}}

The \emph{C. cornuta} clone used in this experiment was sampled from
Lake Taihu (31\selectlanguage{ngerman}°22\selectlanguage{english}'13.548\selectlanguage{english}"N, 120deg0\selectlanguage{english}'16\selectlanguage{english}"E), China. \emph{C. cornuta} was
cultured in COMBO medium at 25degC and 500 lx fluorescent light intensity
under a 14:10 h light/dark cycle and fed \emph{Chlorella
pyrenoidosa}(1.5 mg C*L\textsuperscript{-1}). Synchronous \emph{C.
cornuta} at a density of 1 individual per 10 mL, was adapted to the
above conditions for at least two generations. To test the type of
inducible responses of individual traits as well as the response
strategy under joint predation risks, we set up a full factor experiment
containing C, F, CH, and CH + F. We randomly divided newborn individuals
into different treatments within 12 h. Each individual was cultured in
10 mL of medium, with 10 replicates per treatment. The media in
different treatments were refreshed daily. Experimental workflow is
presented in Figure S1.

Body size and horns were measured at maturity and at the late
developmental stage (i.e., at 16th day). Horns were scored following the
method described by Gu et al. (2021): absent (score 0), small (score 5),
and large (score 10). Individual scores were normalized by a maximum
score to define the induction level (0\%--100\%). In addition, time to
the first brood, neonate size, brood number, total offspring number, and
average brood size were recorded.

\subsubsection*{RNA extraction and
sequencing}

{\label{rna-extraction-and-sequencing}}

To accurately evaluate the type of response at the transcriptional
level, we sequenced the transcriptomes of \emph{C. cornuta} under C, F,
and CH treatments. Groups of 250 newborn individuals were cultured in
2.5 L of medium with three replicates per treatment. During cultivation,
responses triggered by different predation risks were verified through
inducible traits, that is, horns and body size at maturity. The medium
was refreshed daily, and samples were collected within 12 h after the
first brood. The samples were frozen in liquid nitrogen and homogenized
in TransZol Up. Total RNA was extracted using the TransZol Up Plus RNA
Kit (ER501, TRANS, China), following the manufacturer's instructions.
RNA quality was assessed using the Agilent 2100 Bioanalyzer (Agilent
Technologies, USA) and verified using agarose gel electrophoresis. The
RNA integrity number of all samples was \textgreater{} 7.0.

The total mRNA of \emph{Ceriodaphnia} was enriched by Oligo (dT) beads.
The enriched mRNA was fragmented into short fragments using
fragmentation buffer and reverse-transcribed into cDNA using random
primers. The cDNA fragments were purified, end-repaired, poly(A)-added,
and ligated to Illumina sequencing adapters. The ligation products were
size-selected and PCR-amplified to develop a cDNA library. The cDNA
library was sequenced using Illumina HiSeq\textsuperscript{TM} 4000 by
Gene Denovo Biotechnology Co. (Guangzhou, China).

\subsubsection*{Transcript assembly and
annotation}

{\label{transcript-assembly-and-annotation}}

Since genomic sequencing of \emph{Ceriodaphnia} has not been conducted
to date, we adopted \emph{de novo} RNA-Seq to analyze the \emph{C.
cornuta} transcriptome. To obtain high-quality clean reads, sequenced
reads were cleaned by removing reads containing adapters, \textgreater{}
10\% unknown nucleotides (N), and low-quality reads (Q-value \selectlanguage{english}[?] 20).
Clean reads were assembled into unigenes using Trinity (Grabherr et al.,
2011). To annotate the unigenes, we used the BLASTx program with an
E-value \textless{} 10\textsuperscript{-5} in the NCBI non-redundant
protein (Nr), SWISS-PROT protein, Kyoto Encyclopedia of Genes and
Genomes (KEGG), and COG/KOG databases. Functional protein annotations
were obtained according to the best alignment results.

\subsubsection*{Gene expression analysis and
RT-qPCR}

{\label{gene-expression-analysis-and-rt-qpcr}}

Unigene expression was quantified and normalized to reads per kb per
million (RPKM) (Mortazavi, Williams, McCue, Schaeffer, \& Wold, 2008).
Differential transcript expression analysis between the control and
different predation risk treatments was performed using DESeq2. Genes
with a false discovery rate (FDR) \textless{} 0.05 and absolute fold
change \selectlanguage{english}[?] 1 were considered differentially expressed genes (DEGs). We
categorized the DEGs into different types according to our
classification and then used KEGG pathways for functional analysis.
Pathways with \emph{P} \selectlanguage{english}[?] 0.05 were considered significantly enriched.
The representative DEGs of various significantly enriched pathways were
selected according to the following precedence conditions: stable
expression, large fold change, and pathways annotated in a closely
related species, that is, \emph{D. magna} and \emph{Daphnia pulex} .

To validate the RNA-Seq data in the \emph{C. cornuta} transcriptome, we
quantified the expression of 18 random DEGs using RT-qPCR.
ddH\textsubscript{2}O was used as the negative control. cDNA was
synthesized from mRNA using Reverse Transcriptase SuperMix (R233,
Vazyme, China), and RT-qPCR was performed using ChamQ Universal SYBR
qPCR Master Mix (Q711, Vazyme, China). All primer sequences are listed
in Table S1. We obtained expression data for four alternative reference
genes (Scoville \& Pfrender, 2010) and calculated their average gene
expression stability using geNorm. Glyceraldehyde-3-phosphate
dehydrogenase (G3PD), RNA polymerase II (RNAP II), and elongation factor
1-alpha genes (EF) were determined to be stably expressed and
geometrically averaged to calculate the gene expression normalization
factor for each sample. Gene expression was quantified using the
2\selectlanguage{greek}\textsuperscript{-ΔΔt}\selectlanguage{english} method. The concordance between RNA-Seq and
RT-qPCR data was assessed using regression analysis.

\subsubsection*{Statistical analysis}

{\label{statistical-analysis}}

To test the effects of different predation risks, a MANOVA was performed
on individual traits. When the residuals were normally distributed
(Shapiro--Wilk test) and variances were homogeneous (Levene's test), the
data for each trait were evaluated by two-way ANOVA, followed by
Bonferroni comparisons between different groups. When the normality test
failed, the Scheirer--Ray--Hare test was used, followed by the Wilcoxon
rank-sum test to assess significant differences among the different
treatments. In the present study, statistical significance was set at P
\textless{} 0.05. To simplify the effects of joint predation risks on
different types of responses, we considered our results based on the
conceptual diagram of possible prey responses in the combined predator
treatment (Fig. S2). To test the correlations between different types of
responses, we determined Pearson's correlation coefficients between
traits as well as between representative DEGs in different categories.
The above statistical tests were performed in R (version 3.6.2). In
addition, correlation, principal component, DEGs, and pathway enrichment
analyses were conducted using OmicShare Tools
(\emph{https://www.omicshare.com/tools/}).

\subsection*{Results}

{\label{results}}

\subsubsection*{Morphology and life history
traits}

{\label{morphology-and-life-history-traits}}

The different predation risks significantly triggered various responses
of morphology and life history traits (Table 1). Compared with the
control, the responses induced by fish and \emph{Chaoborus} predation
risks could be classified into the following four categories (Fig. 1).
(1) Unique responses to \emph{Chaoborus} larvae {[}horn expression (at
maturity and 16th day) and total offspring number{]}: \emph{C.
cornuta}developed horns (maturity: \emph{P} \textless{} 0.001; 16th
day:\emph{P} \textless{} 0.001) and produced more offspring (\emph{P} =
0.002) under \emph{Chaoborus} predation risk, although these responses
were not significant under fish predation risk. (2) Unique responses to
fish (time to first brood and neonate size): the neonate size (\emph{P}=
0.006) and time to first brood (\emph{P} \textless{} 0.001) of\emph{C.
cornuta} were significantly decreased under fish predation risk,
although these responses were not significant under\emph{Chaoborus}
predation risk. (3) General responses (size at maturity and brood
number): the size (CH vs. C: \emph{P} = 0.007; F vs. C:\emph{P}
\textless{} 0.001) and brood number (CH vs. C: \emph{P} = 0.006; F vs.
C: \emph{P} = 0.017) were significantly decreased under both fish and
\emph{Chaoborus} predation risks. (4) Antagonistic responses (size at
16th day): \emph{C. cornuta} size increased under\emph{Chaoborus}
predation risk (\emph{P =} 0.034) but decreased under fish predation
risk (\emph{P \textless{}} 0.001). Additionally, no significant
differences were observed in average brood size. Thus, the unique
responses in individual traits of \emph{C. cornuta} to different
predators were dominant: unique responses (five traits) \textgreater{}
general responses (two traits) \textgreater{} antagonistic responses
(one trait).

\textbf{Inducible responses under joint predation risks}

Antagonistic responses would put the prey into a dilemma regarding which
predator to respond to (Fig. S2). In our experiments, size at 16th day
was not significantly expressed under joint predation risks (CH + F vs.
C: \emph{P} = 0.764). Consistently, unique responses to
\emph{Chaoborus}predation risk were significantly affected by the
presence of fish predation risk, that is, horn expression in the
combination treatment was greatly impaired compared with that under the
\emph{Chaoborus}predation risk (maturity: \emph{P} = 0.012; 16th day:
\emph{P}\textless{} 0.001). This effect was highly correlated to
individual development, that is, in the combination treatment, \emph{C.
cornuta}horns were significantly expressed at maturity (CH + F vs. C:
\emph{P} = 0.007) but not at 16th day. Conversely, the effects of joint
predation risks were observed in neither unique responses to fish nor
general responses. As such, the responses of size at maturity (CH + F
vs. C:\emph{P} \textless{} 0.001), time to first brood (CH + F vs. C:
\emph{P}= 0.027), and neonate size (CH + F vs. C: \emph{P} = 0.003) were
significantly altered in the combination treatment but were not
significantly different from those under the fish predation risk
treatment. Thus, various predator-dependent unique responses could be
simultaneously expressed under antagonistic predation risks, although
the prey could not completely avoid the effect of other predation risks,
and the co-expression of unique responses was related to individual
development. These results basically support our hypotheses.

\subsubsection*{Overview of the assembled
transcriptome}

{\label{overview-of-the-assembled-transcriptome}}

We obtained respectively 78,341,484, 72,493,072, and 69,930,191 clean
reads for C, CH, and F. A total of 37,120 unigenes were assembled using
Trinity, and each sample contained \textgreater{} 66.54\% assembled
unigenes (Table S2). Through comparisons against the Nr, KEGG, COG, and
SWISS-PROT databases, 18,343 unigenes (49.4\%) were annotated (Fig. S3).
Regarding species distribution in the Nr database, \emph{C.
cornuta}showed the highest comparison rate with \emph{D. magna}
(26.33\%), followed by \emph{D. pulex} (2.6\%) (Fig. S4A). The unigenes
enriched in the KOG database were classified into transcription,
ribosomal structure, gene replication, recombination, and repair (Fig.
S4B). The annotated GO terms were mainly associated with metabolic
processes, cellular processes, cell parts, and binding (Fig. S4C).

\subsubsection*{DEGs}

{\label{degs}}

Paired samples within the same treatment showed high Pearson's
correlation coefficients ([?]0.90) and were clustered in principal
component analysis (Fig. 2), which conformed to the requirements of
biological repetition. Furthermore, the expression patterns of DEGs
showed significant concordance between RT-qPCR and RNA-Seq (Fig. S5),
indicating that our RNA-Seq analysis of the expression data was
reliable.

Compared with the control, \emph{Chaoborus} and fish predation risks
significantly affected the expression of 1,515 and 846 genes,
respectively (Fig. 3A). Among these, there were 1,399 unique DEGs in CH,
730 unique DEGs in F, 114 general DEGs, and two antagonistic DEGs (Fig.
3B and Table S3). Considering the DEGs triggered by different predators,
we further analyzed the differences in the enriched pathways of \emph{C.
cornuta} (Supplementary Table S4) and identified the major DEGs and
pathways related to inducible defensive traits (Table 2). The DEGs
of\emph{C. cornuta} under \emph{Chaoborus} predation risk, including
cuticle protein, fatty acyl-CoA reductase, and trypsin genes, were
mainly enriched in cutin, suberine, and wax biosynthesis; protein
digestion and absorption; and steroid hormone biosynthesis. Meanwhile,
the DEGs of \emph{C. cornuta} under fish predation risk, including
ribosomal protein, actin, and short-chain type dehydrogenase genes, were
mainly enriched in cysteine and methionine metabolism, ribosome,
phototransduction, and unsaturated fatty acid biosynthesis. The general
DEGs, including cysteine proteinase, HSP70, actin, and alpha-tubulin
genes, were enriched in apoptosis pathways and antigen processing and
presentation. Therefore, specific unique responses to different
predators were dominant at the transcriptional level: unique DEGs (2,129
genes) \textgreater{} general DEGs (114 genes) \textgreater{}
antagonistic DEGs (2 genes).

\subsubsection*{Correlation between different inducible
responses}

{\label{correlation-between-different-inducible-responses}}

Nine individual traits and 40 representative DEGs (Table S5) revealed
strong Pearson's correlations within unique responses to fish
or\emph{Chaoborus} larvae (Fig. 4). For instance, the response of horn
expression at maturity was significantly correlated with changes in
horns at 16th day (\emph{P =} 0.05) and total offspring number
(\emph{P}= 0.05). However, the unique responses to fish and
\emph{Chaoborus}larvae were weakly correlated in both analyses. Only a
few significant correlations appeared on the weak correlation area in
transcriptional expression. For instance, Unigene0008297 in
ribonucleoprotein component protein expression was significantly and
positively correlated with Unigene0027903 (\emph{P=} 0.049) and
Unigene0005204 (\emph{P=} 0.013) in aspartokinase and
adenosylhomocysteinase expression, respectively (Fig. 4). Therefore, the
unique responses triggered by different predators are weakly coupled.

\subsection*{Discussion}

{\label{discussion}}

Our experiments proved that \emph{C. cornuta} responds differently to
antagonistic predation risks of fish and \emph{Chaoborus} larvae. Based
on the classification of inducible responses, our results revealed for
the first time that specific predator-dependent unique responses are
dominant, followed by general responses, whereas the antagonistic
responses are rare. In addition, the unique responses triggered by
different predators were extremely weakly coupled and could be elicited
simultaneously. Overall, the experimental results support our hypothesis
that predator-dependent unique responses are dominant, helping the prey
avoid the dilemma of which predator to respond to. Nonetheless, the
unique responses to \emph{Chaoborus} larvae cannot completely avoid the
effects of fish predation risk, implying the presence of complex costs
and limitations of unique responses to multiple predators.

In \emph{C. cornuta} , horn expression and body enlargement are adaptive
inducible traits to \emph{Chaoborus} larva predation (Gu et al., 2021;
Riessen \& Trevett-Smith, 2009). A larger individual size at late
developmental stages requires rapid growth and high food intake
(Gianuca, Pantel, \& De Meester, 2016), thereby promoting the brood and
total offspring numbers. The horns are formed by the carapace, which
comprises two layers of dermal cells and is covered by chitin in
combination with cuticle proteins (Charles, 2010). Therefore, the
development of morphological defensive traits involves a series of
changes in the expression of chitin, hormone, and epidermal formation
genes at different times (Christjani, Fink, \& Elert, 2016; Miyakawa et
al., 2010) as well as the regulation of epidermal cell growth via
endocrine hormones (Weiss, Leese, Laforsch, \& Tollrian, 2015). In the
present study, significant changes in the expression of genes and
pathways involved in cuticle protein, cutin, suberine, and wax
biosynthesis as well as steroid hormone biosynthesis were noted. The
expression of these genes may promote the synthesis of related
substances (Fig. 5) and regulate individual growth (Edgar, 2006).
However, the growth and development of cladocerans requires continuous
molting and formation of a new carapace; thus, the maintenance of horns
requires continuous substance synthesis, which may result in constant
distribution costs (Auld et al., 2010). Furthermore, in \emph{C.
cornuta} , \emph{Chaoborus} predation risk altered digestion and
absorption by modulating the expression of the trypsin gene, which may
affect the digestion and resource allocation strategies (Von Elert et
al., 2004).

Smaller size, earlier reproduction, and increased brood number are
adaptive responses to fish predation risks, which are similar to the
typical responses of other cladocerans under fish predation (Diel et
al., 2020). In terms of gene expression, our results showed that genes
encoding actin and ribosomal proteins were down-regulated under fish
predation risks. Since actin plays an important role in cytoskeletal
structure, its inhibition may result in a smaller size. Similar results
have been reported in previous studies on inducible defense responses
of\emph{D. magna} (Effertz et al., 2015; Pijanowska \& Kloc, 2004). On
the contrary, Schwarzenberger, Courts, and Von Elert (2009) revealed
that the actin genes in \emph{D. magna} were up-regulated under fish
predation risks. Since gene expression is jointly regulated by
transcriptional regulators and related proteins (Stibor, 2002),
differential expression patterns could be observed at different time
points (Effertz \& Von Elert, 2014). Ribosomal proteins are responsible
for protein assembly and translation; thus, the down-regulation of
ribosomal protein may inhibit the synthesis of proteins essential for
individual growth and development of \emph{C. cornuta} (Zhou, Liao,
Liao, Liao, \& Lu, 2015). In enrichment analysis, some DEGs were found
to be enriched in multiple pathways. Significantly enriched
phototransduction altered the visual perception of \emph{Daphnia}(Mahato
et al., 2014), which may be an adaptation to behavioral responses, such
as habitat selection (Loose \& Dawidowicz, 1994) and escape behavior
(Pietrzak, Pijanowska, \& Dawidowicz, 2017). In\emph{Daphnia} , fish
predation reduced unsaturated fatty acid levels in neonates, rendering
them vulnerable to starvation (Stibor \& Navarra, 2000); therefore, the
significantly enriched unsaturated fatty acid pathways may alter the
distribution of these components. Furthermore, the longevity regulating
pathway was significantly enriched under fish predation risks, which may
incur an opportunity cost, that is, a decline in lifespan (Dawidowicz,
Predki, \& Pietrzak, 2010).

Under different predation risks, \emph{C. cornuta} showed general
responses, such as the expression of cysteine protease, heat shock
protein, actin, and tubulin genes. The cDNA sequence of crustacean
cysteine is similar to that of insect cathepsin L, which regulates the
molting cycle and promotes cell death during development (Agrawal,
Bagchi, \& Bagchi, 2005). Thus, cysteine protease likely affected
molting and increased brood number in the present study. The
up-regulation of heat shock proteins is an adaptive response to various
environmental stresses, including predation risks (Pijanowska \& Kloc,
2004). As this response is rapid and returns to the previous state after
long-term treatment (Pauwels, Stoks, \& De Meester, 2005; Pauwels,
Stoks, Decaestecker, \& De Meester, 2007), the down-regulation of heat
shock protein genes likely promoted the recovery of heat shock proteins
in the present study. Similarly, general responses of the actin and
tubulin genes, which are involved in cytoskeleton formation and other
life activities have been observed in \emph{Daphnia} (Pijanowska \&
Kloc, 2004), although their specific functions warrant further research
(Chen et al., 2018).

Based on the classification of inducible responses, we further analyzed
the early transcriptional data of \emph{Daphnia magna} in response to
vertebrate (sticklebacks) and invertebrate (\emph{Triops} ) predation
risks (Orsini et al., 2016). The results are consistent with our finding
that unique responses to different predators are dominant, while
antagonistic responses are rare (Fig. 6 and Table S6). Moreover,
previous weighted gene co-expression network analysis revealed that the
most significantly co-expressed gene networks under vertebrate and
invertebrate predation risks were unique (Orsini et al., 2018). Thus,
diverse predator-dependent unique responses are favored by cladocerans
during their co-evolution with multiple predators. For successful
evolution of diverse predator-dependent unique responses, genotype,
selection, and cost are important. First, the genotypes of cladocerans
in ponds or lakes are highly diverse and the inducible traits of
different clones are uncoupled (Boersma, Spaak, \& De Meester, 1998;
Decaestecker, De Meester, \& Mergeay, 2009; Stoks, Govaert, Pauwels,
Jansen, \& De Meester, 2016). Second, multiple predators produce
variable selection effects, which contribute to predator-dependent
unique defensive traits (Herzog \& Laforsch, 2013; Heynen, Bunnefeld, \&
Borcherding, 2017). Finally, environmental costs, such as altered
predator regimes, may exceed maintenance costs (Decaestecker et al.,
2002; Tollrian, 1995; Yin, Laforsch, Lohr, \& Wolinska, 2011).
Therefore, diverse predator-dependent unique responses are favored by
prey.

In the present study, prey exhibited coupled responses to the same
predator but extremely weakly coupled predator-dependent unique
responses to antagonistic predation risks. Evidently, prey can alter
resource allocation strategies under a single type of predation risk,
resulting in an array of adaptive responses (Reede, 1995). In addition,
the co-expression of uncoupled unique inducible responses can help prey
avoid the dilemma of which predator to respond to, thus improving their
survival rate under antagonistic predation risks. For instance,
smaller\emph{C. cornuta} individuals are less likely to be found by fish
(O'Brien, 1987). Simultaneously, horn expression renders \emph{C.
cornuta} less vulnerable to predation by \emph{Chaoborus} larvae (Gu et
al., 2021). However, the effects of joint predation risks on the
expression of predator-dependent unique responses are prominent and
could be influenced by development, indicating a complex trade-off
underlying adaptation to multiple predation risks (Riessen \& Gilbert,
2019). Therefore, further studies are warranted to elucidate the
mechanisms of the phenomenon that diverse inducible responses of prey
can be elicited simultaneously under joint predation risks.

\subsection*{Conclusions}

{\label{conclusions}}

Through responses of individual traits and transcriptome, the present
study revealed the inducible responses of \emph{C. cornuta} to predation
by \emph{Chaoborus} larvae and fish. To cope with such antagonistic
predation risks, \emph{C. cornuta} mainly altered cuticle gene
expression and developed horns under \emph{Chaoborus} predation risk,
while it altered ribosome gene expression and reduced body size under
fish predation risk. Our analysis of these inducible responses revealed
for the first time that predator-dependent unique responses are dominant
and antagonistic responses are rare, implying that predator-dependent
unique responses are favored by cladocerans during their long-term
co-evolution with multiple predators, thereby lowering the environmental
cost of inducible defenses. However, unique responses to one predator
cannot completely avoid their dilemma of responding to other predators,
although this dilemma is relatively limited. The present study expands
our understanding of the evolution and expression of inducible defenses
under multiple predation risks.

\textbf{Acknowledgments}

We thank Katie Lotterhos and three anonymous reviewers for their
constructive comments. The study was financially supported by the Key
Project of National Natural Science Foundation of China (31730105), the
National Natural Science Foundation of China (32101312), and the Project
funded by China Postdoctoral Science Foundation (2021M691627), the
Priority Academic Program Development of Jiangsu Higher Education
Institutions of China.

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Westra, Edze~R., van~Houte, S., Oyesiku-Blakemore, S., Makin, B.,
Broniewski, Jenny~M., Best, A., . . . Buckling, A. (2015). Parasite
exposure drives selective evolution of constitutive versus inducible
defense. \emph{Current Biology, 25} (8), 1043-1049.
doi:10.1016/j.cub.2015.01.065

Yin, M., Laforsch, C., Lohr, J. N., \& Wolinska, J. (2011).
Predator-induced defense makes \emph{Daphnia} more vulnerable to
parasites. \emph{Evolution, 65} (5), 1482-1488.
doi:10.1111/j.1558-5646.2011.01240.x

Zhang, C., Jones, M., Govaert, L., Viant, M., De Meester, L., \& Stoks,
R. (2021). Resurrecting the metabolome: Rapid evolution magnifies the
metabolomic plasticity to predation in a natural
\emph{Daphnia}population. \emph{Molecular ecology, 30} , 2285-2297.
doi:10.1111/mec.15886

Zhou, X., Liao, W., Liao, J., Liao, P., \& Lu, H. (2015). Ribosomal
proteins: functions beyond the ribosome. \emph{Journal of Molecular Cell
Biology, 7} (2), 92-104. doi:10.1093/jmcb/mjv014

\subsection*{Data Accessibility~}

{\label{data-accessibility}}

The raw data was uploaded to the NCBI Sequence Read Archive (SRA) and
the BioProject ID is PRJNA735795.

\subsection*{Author contributions}

{\label{author-contributions}}

LG, SA, LZ, and ZY designed the experiment. LG, SQ, YS, and JH performed
the experiment and analyzed the data. LG and ZY wrote the first draft of
the manuscript. All authors participated in discussions and editing of
the manuscript.

\subsection*{Tables and Figures}

{\label{tables-and-figures}}

\textbf{Tables}

\textbf{Table 1.} Results of MANOVA, two-way ANOVA, and
Scheirer--Ray--Hare test on different traits of \emph{C. cornuta} .
Significant results (\emph{P} \textless{} 0.05) are indicated in bold.\selectlanguage{english}
\begin{longtable}[]{@{}llllllllllllllll@{}}
\toprule
MANOVA & MANOVA & \emph{Chaoborus} larvae (CH) & \emph{Chaoborus} larvae
(CH) & \emph{Chaoborus} larvae (CH) & \emph{Chaoborus} larvae (CH) & &
Fish (F) & Fish (F) & Fish (F) & Fish (F) & & CH \selectlanguage{ngerman}× F & CH \selectlanguage{ngerman}× F & CH \selectlanguage{ngerman}× F &
CH \selectlanguage{ngerman}× F\tabularnewline
\midrule
\endhead
& & \emph{F} & \emph{df} & Pillai & \emph{P} & \emph{} & \emph{F} &
\emph{df} & Pillai & \emph{P} & \emph{} & \emph{F} & \emph{df} & Pillai
& \emph{P}\tabularnewline
All traits & All traits & 26.615 & 9 & 0.952 & \textbf{\textless{}0.001}
& & 20.324 & 9 & 0.938 & \textbf{\textless{}0.001} & & 9.966 & 9 & 0.882
& \textbf{\textless{}0.001}\tabularnewline
Two-way ANOVA & Two-way ANOVA & \emph{F} & \emph{df} & \emph{P} &
\emph{P} & & \emph{F} & \emph{df} & \emph{P} & \emph{P} & & \emph{F} &
\emph{df} & \emph{P} & \emph{P}\tabularnewline
Size at maturity & Size at maturity & 3.811 & 1, 35 & 0.059 & 0.059 & &
78.308 & 1, 35 & \textbf{\textless{}0.001} & \textbf{\textless{}0.001} &
& 6.867 & 1, 35 & \textbf{0.013} & \textbf{0.013}\tabularnewline
Size at 16th day & Size at 16th day & 4586 & 1, 34 &
\textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 11065 & 1, 34
& \textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 9 & 1, 34 &
0.858 & 0.858\tabularnewline
Neonate size & Neonate size & 0.368 & 1, 20 & 0.551 & 0.551 & & 26.9 &
1, 20 & \textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 0.06 &
1, 20 & 0.809 & 0.809\tabularnewline
Time to first brood & Time to first brood & 1.041 & 1, 35 & 0.315 &
0.315 & & 11.12 & 1, 35 & \textbf{0.002} & \textbf{0.002} & & 6.57 & 1,
35 & \textbf{0.015} & \textbf{0.015}\tabularnewline
Total offspring number & Total offspring number & 15.158 & 1, 34 &
\textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 2.521 & 1, 34
& 0.122 & 0.122 & & 3.181 & 1, 34 & 0.083 & 0.083\tabularnewline
Brood number & Brood number & 2.269 & 1, 34 & 0.141 & 0.141 & & 0.721 &
1, 34 & 0.402 & 0.402 & & 12.871 & 1, 34 & \textbf{0.001} &
\textbf{0.001}\tabularnewline
Average brood size & Average brood size & 10.626 & 1, 34 &
\textbf{0.003} & \textbf{0.003} & & 5.167 & 1, 34 & \textbf{0.029} &
\textbf{0.029} & & 0.396 & 1, 34 & 0.533 & 0.533\tabularnewline
Scheirer-Ray-Hare & Scheirer-Ray-Hare & \emph{H} & \emph{df} & \emph{P}
& \emph{P} & & \emph{H} & \emph{df} & \emph{P} & \emph{P} & & \emph{H} &
\emph{df} & \emph{P} & \emph{P}\tabularnewline
Horns at maturity & Horns at maturity & 24.743 & 1, 34 &
\textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 2.687 & 1, 34
& 0.101 & 0.101 & & 2.986 & 1, 34 & 0.084 & 0.084\tabularnewline
Horns at 16th day & Horns at 16th day & 12.46 & 1, 34 &
\textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 11.214 & 1, 34
& \textbf{\textless{}0.001} & \textbf{\textless{}0.001} & & 12.46 & 1,
34 & \textbf{\textless{}0.001} &
\textbf{\textless{}0.001}\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Key genes involved in significant functional pathways
in response to fish and \emph{Chaoborus} predation risks.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
\begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Function pathway\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\emph{P} value\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
\emph{P} value\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
ID\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Gene name\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Log\textsubscript{2} (Change ratio) by transcriptomics\strut
\end{minipage} & \begin{minipage}[b]{0.10\columnwidth}\raggedright\strut
Log\textsubscript{2} (Change ratio) by transcriptomics\strut
\end{minipage}\tabularnewline
\midrule
\endhead
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
C vs. CH\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
C vs. F\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
CH/C\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
F/C\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Unique responses to \emph{Chaoborus} predation risk\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Steroid hormone biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
Unigene0036707\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
UDP-glucuronosyltransferase 2A1\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
1.358\strut
\end{minipage} & \begin{minipage}[t]{0.12\columnwidth}\raggedright\strut
-0.025\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0033497\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Estradiol 17-beta-dehydrogenase 12\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.330\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.097\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Protein digestion and absorption\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.001\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0036164\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Trypsin\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.379\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.744\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0029770\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Monocarboxylate transporter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-2.637\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.207\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0033452\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Proton-coupled amino acid transporter\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.816\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.333\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cutin, suberine and wax biosynthesis\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.018\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0030892\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Fatty acyl-CoA reductase 1\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
1.776\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.270\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Others\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0000377\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cuticle protein\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-2.935\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.277\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unique responses to fish predation risk\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Cysteine and methionine metabolism\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\textless{}0.001\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0005204\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
adenosylhomocysteinase\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.993\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.084\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0027903\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
aspartokinase\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.041\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.196\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Ribosome\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.001\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0031920\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
60S ribosomal protein L18a\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.501\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-2.000\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0015781\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
60S ribosomal protein L23a\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.241\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-3.459\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0033039\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
40S ribosomal protein S3a\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.481\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.445\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0021976\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
ribosomal protein L13\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.716\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.414\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Phototransduction\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.004\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0000385\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
actin\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.626\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.363\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0009756\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Calmodulin\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.400\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-3.985\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Biosynthesis of unsaturated fatty acids\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.022\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0026611\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
short-chain type dehydrogenase\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.776\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-4.644\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Longevity regulating pathway\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.028\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0032764\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Glutathione S-transferase\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-0.501\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-5.066\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
General responses\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Apoptosis\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.002\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.003\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0019346\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
cysteine proteinase\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.389\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.506\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0016049\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
HSP70\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.302\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.404\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Antigen processing and presentation\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.008\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
0.007\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0000044\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
actin\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.522\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.237\strut
\end{minipage}\tabularnewline
\begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
Unigene0031360\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
alpha-tubulin\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.107\strut
\end{minipage} & \begin{minipage}[t]{0.10\columnwidth}\raggedright\strut
-1.477\strut
\end{minipage}\tabularnewline
\bottomrule
\end{longtable}

\subsection*{Figures}

{\label{figures}}

\textbf{Hosted file}

\verb`image1.emf`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

\textbf{Figure 1} . Morphology and life history responses of \emph{C.
cornuta} under fish and \emph{Chaoborus} predation risks. The dark line
with asterisks indicates significant differences between treatments with
and without fish predation risk (*\emph{P} \textless{} 0.05,
**\emph{P}\textless{} 0.01, and ***\emph{P} \textless{} 0.001). The
upper- and lowercase letters indicate differences among treatments
without and with\emph{Chaoborus} predation risk, respectively. The error
bar indicates standard error.

\textbf{Hosted file}

\verb`image2.emf`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

\textbf{Figure 2} . Correlation analysis (A) and principal component
analysis (B) of gene expression in \emph{C. cornuta} under different
predation risks. C: control; CH: \emph{Chaoborus} predation risk; F:
fish predation risk.

\textbf{Hosted file}

\verb`image3.emf`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

\textbf{Figure 3} . Quantitative analysis of differentially expressed
genes of \emph{C. cornuta} under different treatments. Abbreviations of
different treatments are the same as in Figure 2.

\textbf{Hosted file}

\verb`image4.emf`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

\textbf{Figure 4} . Correlations of different responses of individual
traits (A) and transcriptional expression (B). Asterisks indicate
significant Pearson's correlations between two traits or genes
(\emph{P}\textless{} 0.05). The correlations between unique responses to
different predators are in squares. HM: horns at maturity; H16D: horns
at 16th day; TON: total offspring number; TFB: time to first brood; NS:
neonate size; SM: size at maturity; BN: brood number; S16D: size at 16th
day; ABS: average brood size.

\textbf{Hosted file}

\verb`image5.emf`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

\textbf{Figure 5} . Diagram of \emph{C. cornuta} pathways in response to
fish and \emph{Chaoborus} predation risks.

\textbf{Hosted file}

\verb`image6.emf`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

\textbf{Figure 6.} Quantitative analysis of differentially expressed
genes of \emph{Daphnia magna} under fish (sticklebacks) and\emph{Triops}
predation risks. The data are from the work of Orsini et al. (2016).
Detailed results are presented in Table S6. Clones I and X were
collected from a system of ephemeral rock pools in Southwest Finland and
a fish-rearing pond in Southern Germany, respectively. CO: control; FI:
fish predation risk; TR: \emph{Triops} predation risk.

\textbf{Hosted file}

\verb`MEC-21-0732R1 with changes marked.docx`  available at \url{https://authorea.com/users/424411/articles/568457-coping-with-antagonistic-predation-risks-predator-dependent-unique-responses-are-dominant-in-ceriodaphnia-cornuta}

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