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\begin{document}

\title{Antagonistic selection on body size and sword length in a wild
population of the swordtail fish, \emph{Xiphophorus multilineatus}:
potential for intralocus tactical conflict\emph{}}



\author[1]{Melissa Liotta}%
\author[2]{Jessica Abbott}%
\author[1]{Molly Morris}%
\author[3]{Oscar Rios-Cardenas}%
\affil[1]{Ohio University}%
\affil[2]{Lund University}%
\affil[3]{Instituto de Ecología A.C.}%


\vspace{-1em}



  
  \date{June 03, 2024}


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\selectlanguage{english}
\begin{abstract}
Alternative reproductive tactics (ARTs) have provided valuable insights
into how sexual selection and life history tradeoffs can lead to
variation within a sex. However, the possibility that tactics may
constrain evolution through intralocus tactical conflict (IATC) is
rarely considered. In addition, when IATC has been considered, the focus
has often been on the genetic correlations between the ARTs, while
evidence that the ARTs have different optima for associated traits and
that at least one of the tactics is not at its optima is often missing.
Here we investigate selection on three traits associated with the ARTs
in the swordtail fish \emph{Xiphophorus multilineatus}; body size, body
shape and the sexually selected trait for which these fishes were named,
sword length (elongation of the caudal fin). All three traits are
tactically dimorphic, with courter males being larger, deeper bodied and
having longer swords, and the sneaker males being smaller, more fusiform
and having shorter swords. Using measures of reproductive success in a
wild population we calculated selection differentials, linear and
quadratic gradients, demonstrate that the tactics have different optima
and at least one of the tactics is not at its optima for body size and
sword length. Our results provide the first evidence of selection in the
wild on the sword, an iconic trait for sexual selection. In addition,
given the high probability that these traits are genetically correlated
to some extent between the two tactics, our study suggests that IATC is
constraining both body size and the sword from reaching their phenotypic
optima. We discuss the importance of considering the role of IATC in the
evolution of tactical dimorphism, how this conflict can be present
despite tactical dimorphism, and how it is important to consider this
conflict when explaining not only variation within a species but
differences across species as well.%
\end{abstract}%



\sloppy


\textbf{1 Introduction}

Studies of the evolution of sexual dimorphism have led to many insights
into the evolutionary mechanisms that can produce phenotypic diversity
within a species (Darwin 1871, Andersson 1994, Lande 1980). Sexually
antagonistic selection, different selective optima between males and
females for a shared trait due to their different life histories, is a
main driver in the evolution of sexual dimorphism (Rice 1984; Cox and
Calsbeek 2009; Mank 2009). However, the evolution of sexual dimorphism
can be constrained if the trait is genetically correlated between the
sexes. Intralocus sexual conflict occurs when males and females have
different optima for genotypes at a given locus (Chippindale et al.
2001; Arnqvist and Rowe 2005; Bonduriansky and Rowe 2005; Long and Rice
2007; Bonduriansky and Chenoweth 2009; Abbott et al. 2010). If
unresolved, intralocus sexual conflict can generate gender load,
reduction of the population's mean fitness due to the displacement of
one or both of the sexes from their phenotypic optima (Bedhomme and
Chippindale 2007). While research on intralocus conflict has mainly
focused on the sexes, theory from intralocus sexual conflict is readily
applicable to other polymorphisms. In particular within-sex
polymorphisms such as Alternative Reproductive Tactics (ARTs), have only
recently been considered in the context of intralocus conflict (Abbott
and Svensson 2010; Morris et al. 2013; Buzatto et al. 2015; Pike et al.
2017; Pennell et al. 2018; Abbott et al. 2019a,b).

Alternative Reproductive Tactics (ARTs) are discrete variations in the
reproductive phenotypes of members of the same sex within a species
(Taborsky and Brockmann 2010). ARTs often evolve in response to strong
sexual selection and involve alternate ways to gain reproductive success
through suites of correlated behavioral, morphological, and life history
traits (Oliveira et al. 2008). Analogous to the sexes, the optimal
phenotype for a shared trait often differs between ARTs (Gross 1985;
Emlen 1996; Moczek and Emlen 2000; Taborsky 2001; Engqvist and Taborsky
2016). Antagonistic selection should drive the evolution of tactical
dimorphism, identified as a bimodal distribution of a trait within a
sex; however, if the tactics have a shared genetic architecture then
evolution toward their respective optimum can be constrained by
intralocus ``tactical'' conflict (IATC, Morris et al. 2013). The few
studies on intralocus tactical conflict have mainly focused on genetic
correlations for traits between ARTs (Abbott and Svensson 2010; Buzatto
et al. 2015; Pike et al. 2017; Buzatto et al. 2018), with much less work
on the respective phenotypic optima for shared traits (Abbott and Gosden
2009). Studies of intralocus tactical conflict can help us to better
understand how ARTs evolve, how they are maintained, and the
evolutionary consequences that can arise from the loss of an ART (e.g.
speciation). In addition, it will be important to consider the
possibility of intralocus tactical conflict when examining the evolution
of sexually selected traits across species, in particular if some of the
species have alternative reproductive tactics and some do not.

In this study we investigated the potential for traits associated with
the male ARTs in the High-backed Pygmy swordtail fish, \emph{Xiphophorus
multilineatus} (Rauchenberger et al. 1990) to be experiencing
antagonistic selection. \emph{Xiphophorus multilineatus} males are
classified into two ARTs: larger courter males (Fig. 1A) that mature
later and are behaviorally fixed (use only a courtship display to
attract females); and smaller sneaker males that mature earlier and are
behaviorally plastic, using courtship and sneak-chases when alone with a
female, and switching almost exclusively to sneak-chases when courter
males are present (Fig. 1B, Zimmerer and Kallman 1989). While the names
of the ARTs in this species are based on the mating behaviors they use,
there are a suite of associated traits that are tactically dimorphic,
thus with a bimodal distribution, (Zimmerer and Kallman 1988; Moretz and
Morris 2003; Morris et al. 2007; Bono et al. 2011; Rios-Cardenas et al.
2018; Liotta et al. 2019) including body size, which is genetically
influenced by the number of copies of the \emph{Mc4r} gene on the
\emph{Y}-chromosome (Lampert et al. 2010). The genetic influence on body
size suggest that this is a good system in which to examine intralocus
tactical conflict. Simulations have been used to demonstrate that for
autosomal, \emph{X}-linked, and \emph{Y}-linked genetically-determined
tactics, the estimated inter-tactical genetic correlations will
generally be high (Abbott et al. 2019a). In addition, the presence of
tactical dimorphism indicates the potential for IATC since dimorphism
between the ARTs is likely evolving due to differing phenotypic optima,
and thus net disruptive selection between ARTs (Morris et al. 2013).
Although, as shown with sexually antagonistic selection and intralocus
sexual conflict (Cox and Calsbeek 2009), the extent of dimorphism may
not indicate the extent of conflict. Also, ARTs may not be dimorphic for
a shared trait that is experiencing IATC, especially if the dimorphism
is slow or difficult to evolve due to genetic correlations.

~~~~~~~~~~~ Using a wild population of \emph{X. multilineatus} we
quantified the strength and mode of selection acting on the ARTs for
three morphological traits, allowing us to determine if the ARTs have
different optima for these traits, as well as if one or both ARTs are
not at their optima. Evidence for these two criteria are necessary for
demonstrating intralocus tactical conflict (Morris et al. 2013).
Estimating the strength of this conflict will require additional studies
that measure the genetic correlation between the ARTs. The first trait
we considered was body size, which has previously been shown to be
tactically dimorphic in this species (Zimmerer and Kallman 1989;
Rios-Cardenas et al. 2018; Liotta et al. 2019). Our measure of selection
on body size will include mating success of individuals that survived to
sexual maturity (Rios-Cardenas et al. 2007; Morris et al. 2010; Lyons et
al. 2014), and not the selection on reaching sexual maturity earlier
which increases smaller males' probability of reaching sexual maturity
(Bono et al. 2011; Morris et al. 2016; Weinstein et al. 2019).
Therefore, our results will allows us to tease apart the influence of
sexual selection and natural selection on this trait. The second trait
we examined was body shape, which has also been shown to be tactically
dimorphic in \emph{X. multilineatus}, as well as correlated with the
propensity to use sneak-chase behavior within the sneaker ART (Liotta et
al. 2019). The relationship between this trait and mating behaviors
within the sneaker ART provides an opportunity to examine the prediction
that behavioral plasticity will reduce selection on correlated
morphological traits (DeWitt et al. 1998; Abbott et al. 2019b). Body
shape in fishes is also well-documented predictor of swimming
performance and locomotive ability (Webb 1984; Blake 2004) and could
directly impact the successful execution of each ARTs' respective mating
behavior, thus influencing fitness. Body shape also has the potential to
function as a sexual signal, with deeper bodies increasing mating
success through female mate preference and male-male competition
(MacLaren et al. 2004). Finally, the third trait we examined was sword
length, an extension of the ventral caudal fin rays, which has not yet
been examined in relation to tactical dimorphism in \emph{X.
multilineatus} (Fig. 1A and B). This trait was first described in the
green swordtail, \emph{X. helleri} (Haeckel 1848), and is considered a
hallmark of sexual selection (Darwin 1871) due to the classical tradeoff
between female mate preference for longer swords (Basolo 1990a,b, 1998)
and costs due to natural selection (Basolo and Alcaraz 2003;
Hernandez-Jimenez and Rios-Cardenas 2012; Oufiero et al. 2014a,b).
However, evidence for selection on this trait in the wild is
surprisingly lacking. The implications for our results are discussed in
relation to how intralocus conflict will influence our understanding of
the evolution of tactical dimorphism, as well as the overall variation
in sexually selected traits between species.

\textbf{2 Materials and Methods}

\emph{2.1 Field Collection and lab conditions}

In November 2014, we exhaustively sampled a side stream of the R\selectlanguage{ngerman}ío
Tambaque, San Luis Potosí, Mexico (Fig. 1C) for \emph{X. multilineatus}
using baited minnow traps and seine nets. The side stream sampled is
blocked off from the main branch of the Tambaque by a strip of land and
has only one entrance and exit. Water flows in from the main branch of
the Tambaque and then passing over a shallow ledge meets back up with
the main branch. It is easy for fish to enter the side stream and most
likely exit out over the ledge, otherwise the stream is relatively
self-contained. All adult males collected (identified by the presence of
a fully developed copulatory organ, i.e., gonopodium) were photographed
and fin-clipped for DNA in the field. Fin-clips were stored in 95\%
ethanol for later DNA extraction. Males were classified as either
sneaker (n = 44) or courter (n = 36) males in the field using standard
length (distance from tip of snout to end of caudal peduncle, Fig. 1D)
and pigmentation patterns, as described by Zimmerer \& Kallman (1989). A
subset of females (n=47) collected in the stream were brought back to
Ohio University to drop fry. Females were isolated in individual 21 L
tanks and kept on a 12 h--12 h light--dark cycle. Fish were fed twice a
day with Ken's Premium Spirulina Max flake (Ken's Fish Inc., Taunton,
MA, USA) in the morning and newly hatched brine shrimp nauplii (Brine
Shrimp Direct, Ogden, UT, USA) in the afternoon. Female
\emph{Xiphophorus multilineatus} can fertilize eggs from stored sperm
for 7 months and can drop a brood every month (Meffe and Snelson 1989).
Females' tanks were checked daily for fry and when found, they were
collected, euthanized, and preserved in 95\% ethanol. Forty-three out of
the forty-seven females gave birth for a total of 526 fry. After females
had exhausted sperm storage, they were fin-clipped for DNA, and
fin-clips were stored in 95\% ethanol.

\emph{2.2 Microsatellite Genotyping~}

In order to determine each males' reproductive success, we conducted a
paternity analysis using five microsatellite loci, \emph{KonT38},
\emph{KonD6}, \emph{KonD21}, \emph{KonT30}, and \emph{KonD15} developed
for the closely related species \emph{Xiphophorus montezumae} (see
Seckinger et al. 2002 for primer sequences and development). These loci
have been shown to provide sufficient polymorphisms for assigning
paternity ~in \emph{X. multilineatus} (Luo et al. 2005). Genomic DNA was
extracted from the tissue samples (fin-clips for adults and half of a
fry's whole body) using the Qiagen DNeasy Kit (Qiagen, Hilden, Germany)
following the manufacturer's protocol, with the exception that fry whole
body tissue was digested overnight and adult fin tissue digested for 3
hours. Fry DNA was eluted at 50 \selectlanguage{greek}µ\selectlanguage{english}l and adult DNA at 100 \selectlanguage{greek}µ\selectlanguage{english}l. DNA was
extracted from and microsatellite loci amplified for a total of 649 fish
(36 courter males, 44 sneaker males, 43 females, and 526 fry).
Microsatellite loci were amplified in two multiplex PCR reactions for
each sample (MIX1= \emph{KonT38}, \emph{KonD6}, \emph{KonD21}, MIX2=
\emph{KonT30}, and \emph{KonD15}). The forward primers were labeled with
the fluorescent tags NED (\emph{KonT38}, \emph{KonT30}), VIC
(\emph{KonD6}), and 6-FAM (\emph{KonD21}, \emph{KonD15}). See Appendix
for additional details. PCR products were run on a DNA Analyzer using
the GeneScan 600 LIZ size standard (Applied Biosystems) at The Ohio
State University Plant-Microbe Genomics Facility for fragment analysis.
Alleles were scored using Geneious ver. 9.1.8 (Kearse et al. 2012,
www.geneious.com).

~~~~~~~~~~~ Microsatellite loci quality was assessed using several
programs. We used the GENEPOP ver. 1.0.5 R package (Rousset 2008) to
calculate allele frequencies using the adult samples, whether loci
deviated from Hardy-Weinberg equilibrium (HWE), linkage disequilibrium
(LD) among pairs of loci, and null allele frequencies. P-values for HWE
and LD were estimated using the Markov chain method with the following
parameters: 1000 dememorization steps, 5000 batches, and 1000 iterations
per batch for all tests. Additionally, MICRO-CHECKER ver 2.2.3 (Van
Oosterhout et al. 2004) was used to check for scoring errors. We used
CERVUS 3.0.7 (Kalinowski et al. 2007) to check for observed vs. expected
heterozygosity and exclusion probabilities. When checking through scored
genotypes we noticed mismatches between known dams and offspring.
Therefore, dams who did not have at least one allele that matched any
offspring and/or offspring in a female's broods that did not have at
least one allele that matched their known dam were checked manually for
scoring errors in Geneious. In most cases this issue occurred in
individuals that were originally assigned homozygous genotypes, but upon
further review a poorly amplified peak was found that was originally
thought to have been a stutter peak. In 1 fry, 3 loci were not
amplified, and in 14 fry one or more loci did not have an allele that
could be matched to their known mother. Thus 15 fry were not included in
subsequent paternity analyses, for a total of 511 out of 526 fry
included.

\emph{2.3 Paternity Analysis}

Paternity was assigned using CERVUS 3.0.7 (Kalinowski et al. 2007).
CERVUS uses maximum likelihood to calculate a log likelihood ratio score
(LOD), which is the likelihood that a male is the true parent of an
offspring given the known genotype of the offspring's mother and the
offspring's own genotype. A positive LOD score indicates that a male is
more likely to be the true sire than not. The program also calculates a
``delta'' score or the difference in LOD scores between the two most
likely candidate sires which helps in assignment when two males have a
positive LOD score for an offspring. In addition, CERVUS uses simulation
to estimate a maximum and minimum confidence level in assignment of the
most likely sire by taking into account the number of candidate sires
(total possible sires in the population both sampled and unsampled),
proportion of possible sires actually sampled, completeness of loci
typed, and the estimated typing error. In our CERVUS simulation we chose
0.95 and 0.80 as the maximum and minimum confidence levels respectively.
Simulation parameters were as follows: 10,000 offspring, 92 candidate
sires, 87\% of sires sampled, 100\% of loci typed, and 0.01 error rate.
We estimated the number of candidate sires as the average number of
males collected in the R\selectlanguage{ngerman}ío Tambaque over 5 sampling years and percentage
of sires sampled represents the total number of males genotyped (n=80).
Assignments with at least an 80\% confidence level were kept for further
analysis.

\emph{2.4 Morphometrics and Tactical Dimorphism}

We examined three traits that are either known or suspected to be
tactically dimorphic in this species: body size, body shape and sword
length. We first considered widely used linear measurements of these
traits (we subsequently refer to these as unidimensional traits). We
measured each fish for standard length (Fig. 1D) and body depth
(pelvic-fin insertion to dorsal-fin origin, Fig. 1D). Sword length was
measured from the insertion point of the ventral caudal fin ray to the
distal tip of the ventral ray extension (Fig. 1D) using the line tool in
ImageJ ver. 1.50i (Schneider et al. 2012). Finally, we also visually
confirmed that there was an extension beyond the edge of the caudal fin
and that there was pigmentation along the dorsal edge of the caudal fin,
given that this aspect of the sword has been identified as important in
relation to female mate preference (Basolo 1995). Unless otherwise
indicated, the relations between these unidimensional variables will be
controlled statistically with a multiple linear regression (see
\emph{Selection Analyses} below).

~~~~~~~~~~~ We then used landmark-based geometric morphometrics
(Zelditch et al. 2012) and principal components analyses to examine
multiple components of body shape, allowing for a more detailed
examination of how selection acts on total body shape. Males were
photographed in the field using a Canon Power Shot (Canon Inc., Tokyo,
Japan), plastic view box with a ruler taped to the front, and a small
hand net with the netting material tightened to create a flat surface.
Fish were gently pressed against one side of the view box using the net
and photographed. The fish's left side was used for landmark placement,
if the left side was not photographed, the right side was mirrored so
that all individuals faced the same way.

~~~~~~~~~~~ We digitized seven landmarks and three semi-landmarks using
tpsDig2 (Rohlf 2015) as per the methods in Liotta et al. (2019): (1) tip
of rostrum; (2) forehead; (3) anterior dorsal ray insertion; (4)
posterior dorsal ray insertion; (5) dorsal caudal fin insertion; (6)
last scale of the midline; (7) ventral caudal fin insertion; (8)
anterior insertion of the gonopodium; (9) abdomen; and (10) ventral
occlusion of the operculum (Fig. 1D, Culumber et al. 2011; Johnson et
al. 2014; Liotta et al. 2019). The three semi-landmarks were used to
characterize the forehead, total body elongation, and the abdomen
(points 2, 6, and 9, respectively). Markers were drawn for the forehead
and abdomen semi-landmarks so that the points could be placed
consistently by drawing a straight line between landmarks 1 and 3 and
between landmarks 8 and 10 in Adobe Illustrator (Adobe Systems Inc., San
Jose, CA, USA) and placing the semi-landmark 90° from the midway point
(Johnson et al. 2014; Liotta et al. 2019). Landmark coordinate files
were obtained using tpsUtil (Rohlf 2015) and imported into R ver 3.6.0
(R Development Core Team, 2019) for analysis using the package
\emph{geomorph} (Adams and Otárola-Castillo 2013; Adams et al. 2018).
Landmark 4 was estimated for 11 individuals and landmark 3 and 4 was
estimated for 1 individual using the thin-plate spline method in the
``estimate missing landmark'' function in \emph{geomorph} due to the
dorsal fin not being extended and obscuring the view of the ray
insertion. All analyses were performed in R unless stated otherwise.

~~~~~~~~~~~ As in Liotta et al. (2019), using all males (both courters
and sneakers) we performed a generalized Procrustes analysis to remove
information unrelated to shape (i.e. size, orientation and position) by
optimal rotation of the coordinates using the least squares criterion,
aligning, and scaling to unit centroid size (Rohlf and Slice 1990).
Semi-landmarks were slid along their tangent directions using the
Procrustes distance criterion (Rohlf 2015). The aligned coordinates were
then projected into tangent space to obtain Kendall's tangent space
coordinates (Rohlf 1999). These shape variables were subjected to a
principal component analysis (PCA) using the covariance matrix. All axes
that explained at least 10\% of the variation were included in further
analyses. Centroid size was calculated as the square root of the summed
squared distances of each individual landmark to the centroid of the
shape (Zelditch et al. 2012).

~~~~~~~~~~~ We determined if the traits standard length, body depth,
sword length, sword index (calculated as sword length/standard length to
correct for body size), centroid size, and body shape (PC axes 1-4) were
tactically dimorphic using a Welch's two-sample independent t-test.
Standard length, body depth sword length and sword index were all log
transformed for normality. We also considered dimorphism in sword length
in relation to extension beyond the caudal fin by comparing the number
of males in each ART with and without a sword extension with a Yates
corrected Chi-square test. In addition, tactical dimorphism in body
shape was examined by visualizing the overlap of the 95\% confidence
ellipses around each group's centroid for each PC axis in relation to
PC1.

\emph{2.5 Selection Analyses~}

We calculated both selection differentials (\emph{s}) and selection
gradients for two sets of traits: three unidimensional traits (body size
measured as standard length, body shape measured as depth, and sword
length), and then the combination of the geometric morphometric trait
for body size (centroid), all PC axes explaining \textgreater{}10\%
variation in body shape, and sword length. Sword length was included in
both sets of traits, allowing us to assess the potential of a
correlation in selection on this trait and the different measures of
body size and body depth.

~~~~~~~~~~~ We calculated each male's absolute fitness (total offspring
sired) using the estimates of reproductive success from the paternity
analysis. Any male that was not assigned offspring was given an absolute
fitness of zero. Relative fitness was calculated as absolute fitness
divided by mean fitness across all males. Then, all independent
variables were standardized to a mean of zero and unit variance (Lande
and Arnold 1983).

~~~~~~~~~~~ The selection differentials were estimated from linear
models with relative fitness regressed on each trait separately,
including ART and an interaction between ART and the trait. A
significant overall result suggests that at least one of the ARTs is not
at its optima, and a significant interaction with ART suggests the ARTs
have different optima. As selection differentials include both direct
and indirect selection, we determined if the selection on the trait was
direct by estimating the linear selection gradients (\emph{ß)} from the
standardized partial regression coefficients of multiple linear
regressions. The models included relative fitness as the dependent
variable, the respective standardized independent variables measuring
body size, body shape and sword length, a male's ART classification, and
two-way interactions between each trait and ART. A significant \emph{ß}
coefficient suggests that significant selection detected in the
differential analysis is direct, and not due to correlation with another
trait in the analysis, while a significant coefficient for the quadratic
gradient (\emph{y}\textsubscript{ii}) suggests either disruptive or
stabilizing selection. The linear selection gradients \emph{(ß,}
directional selection) were estimated from multiple regressions that
included only linear terms, since if the data violates multivariate
normality the linear and quadratic coefficients will be correlated
(Brodie 1992). The quadratic selection gradients
(\emph{y}\textsubscript{\emph{ii}}, stabilizing or disruptive selection)
were estimated from models that included both linear and quadratic terms
(Brodie et al. 1995). Quadratic coefficients were doubled as linear
models underestimate stabilizing/disruptive selection by half
(Stinchcombe et al. 2008). We used marginal effects plots to visualize
the significant linear and quadratic selection gradients using the
Effects package (Fox and Weisburg 2018, 2019). We also calculated and
reported the selection coefficients for both of the ARTs from each of
the full models. For all models, if the interaction term between a trait
and ART was significant, subsequent separate models were performed for
each ART in order to determine if the ARTs' slope differed from zero for
that trait. Relative fitness was always calculated from the whole male
population.

~~~~~~~~~~~ Violation of the assumption that model residuals are
normally distributed is not problematic for estimating the linear and
quadratic coefficients; however, violation of this assumption is
problematic for estimating statistical significance (Lande and Arnold
1983). As this was the case for our models, significance values for the
selection differentials and gradients were calculated using a
re-sampling procedure in which relative fitness was randomly shuffled
across individuals to obtain a null distribution for each gradient where
there is no relationship between trait and fitness. Thus, probabilities
were the number of times (out of 9999 permutations) in which the
gradient pseudo-estimate was greater than (if the original gradient was
positive) or less than (if the original gradient was negative) the
original estimated gradient (Lewis et al. 2011). All statistics were
performed in R ver 3.6.0 (R Core Development Team, 2019) unless stated
otherwise.

\textbf{3 Results}

\emph{3.1 Microsatellite Genotyping}

After fixing dam-offspring mismatches when possible, 4 out 5 loci were
within expected population genetic parameters in the adult population,
i.e. within the expectations of HWE, exhibited genetic independence (no
significant LD), and had low null allele frequencies. In addition,
MICRO-CHECKER reported no evidence of scoring errors for all 5 loci.
However, \emph{KonT38} was problematic for all population genetic
parameters. \emph{KonT38} was not within HWE (P \textless{} 0.001), it
exhibited significant linkage disequilibrium with \emph{KonD15} and
\emph{KonT30} (P = 0.007, P = 0.032 respectively), and the estimation
for a null allele frequency was considerable (0.171). Therefore, we ran
subsequent paternity analyses both with and without \emph{KonT38}. In
the absence of \emph{KonT38} the other 4 loci again were within expected
population genetic parameters.

\emph{3.2 Paternity Analysis}

The paternity analysis run with all 5 loci assigned paternity to 334 out
of 511 fry to 51 out of 80 males (28 sneaker, 23 courters) at the
minimum 80\% confidence level. This is lower than the expected
assignment of 416 fry (81\% assignment rate) from the simulation,
although this is likely due to the null allele increasing mismatches
between fry and candidate sires. Runs excluding \emph{KonT38} with four
loci were only able to assign 139 fry. Dakin and Avise (2004) report
that loci with a null allele frequency equal to or below 0.2 will not
greatly affect exclusion probability (in our case it was 0.171). Given
that a true parent may be falsely excluded in this case, estimates of
reproductive success will likely be underestimated. Therefore, we
decided to use the run with all five loci as this is a conservative
estimate of paternity. Courter males had higher reproductive success
(mean \# of fry sired ± SE, 6 ± 1.44) than sneaker males (3 ± 0.64). The
mean number of fry sired among all males was 4 ± 0.76 fry, with a
maximum of 38 fry sired (see Fig. Appendix 1 for the distribution of
paternity).

\emph{3.3 Morphometrics and Tactical Dimorphism}

Courters and sneakers were dimorphic for standard length (SL) and body
depth (BD), with courters on average 11.2 mm longer and 4.5 mm deeper
than sneakers (SL: \emph{t =} 18.43, \emph{df =} 54.56, \emph{p}
\textless{} 0.001, BD: \emph{t =} 11.18, \emph{df =} 64.35, \emph{p}
\textless{} 0.001). The swords were 8.6 mm longer on average for courter
males as compared to sneaker males (\emph{t} = 12.94, \emph{df} = 61.27,
\emph{p} \textless{} 0.001). The sword index, sword length relative to
standard length, was also dimorphic, with courter males on average
having a 39.71\% longer sword per body size than sneaker males (\emph{t}
= 6.99, \emph{df} = 57.57, \emph{p} \textless{} 0.001). The swords were
also tactically dimorphic in the extent to which they extended beyond
the caudal fin: there were more adults with swords that did not extend
beyond the caudal fin for sneaker males (30 of 44) than for courter
males (0 of 36; Yates Chi-square 36.4, \emph{p} = \textless{} 0.0001).
Swords continue to grow after sexual maturity (Basolo 1990b), and
ideally to compare sword length within and across ARTs, models should
include total male age as well as size. In wild caught males it is only
possible to assess age up until sexual maturity (using otolith rings)
and therefore we were unable to include total age as a fixed effect in
our models. However, assessment of sword length is still informative
given we sampled exhaustively for males in this population which should
yield a random distribution of ages.

~~~~~~~~~~~ Courters and sneakers were tactically dimorphic for centroid
size, with courter males being 35.23\% larger than sneaker males
(\emph{t} = 15.93, \emph{df} = 44.37, \emph{p} \textless{} 0.001). As
for body shape, the first four PC axes each explained at least 10\% of
the variation (PC1 = 28.99\%, PC2 = 22.80\%, PC3 = 14.81\%, PC4 =
11.40\%). Courters and sneakers are dimorphic along the first major axis
of variation PC1 (t = 10.31, df = 75.25, \emph{p} \textless{} 0.001),
with courter and sneaker males occupying separate areas of morphospace,
with no overlap in the 95\% confidence ellipses along this axis (Fig.
2). Wire-frame models generated for each PC axis represent the extreme
minimum and maximum shape changes along each axis, and for PC1 suggest
sneaker males were overall more fusiform than courter males which were
comparatively more dorsoventrally expanded in their body shape,
consistent with findings from Liotta et al. (2019). The ARTs are also
dimorphic along PC2 (t = -2.68, df = 77.64, \emph{p} = 0.009) which
described the variation in the distance between the anterior and
posterior dorsal fin insertion points, the slope of the forehead (steep
or shallow), deepening in the abdomen, and slight elongation or
shortening of the caudal peduncle (Fig. 2). However, courters and
sneakers are not dimorphic along PC3 (t = -1.15, df = 65.20, \emph{p} =
0.254) and PC4 (t = -0.906, df = 77.89, \emph{p} = 0.368).

{\emph{3.4 Selection Analyses}}

The differential selection coefficients for the traits male size
(standard length), body depth and sword length were all significant
(Table 1A). In addition, there were significant interactions with ART
for all three traits (Table 1A). When males from each ART were analyzed
separately, selection differentials for all three traits for courter
males were significant (Table 1A), suggesting selection for larger size,
deeper bodies and longer swords (Fig. 3). However, the selection
differentials for sneaker males were only significant for sword length
(Table 1A), suggesting selection on sneakers for shorter swords (Fig.
3). Together, the results suggest that at least one of the ARTs is not
at its optima and that the ARTs have different optima for all three of
the unidimensional traits. In addition, these results suggest that
selection due to mating alone (what our data measured) is stronger in
courter males than in sneaker males.

~~~~~~~~~~~ In the analyses of the linear (\emph{ß}) and quadradic
(\emph{\emph{y}}\textsubscript{ii}) selection gradients, (\emph{ß})
coefficient was significant for standard length, but not for the
quadradic (\emph{\emph{y}}\textsubscript{ii}) selection coefficients or
interactions with ART (Table 1B-C). This suggests that selection on
standard length detected from the differentials (see above) is direct.
Inspection of the marginal effects plots suggests that the directional
selection for this trait may be driven primarily by courters (see Fig.
Appendix 2A), similar to what was detected for the differential
coefficients (see above). In the analysis of body depth, the linear
(\emph{ß}) and quadradic (\emph{\emph{y}}\textsubscript{ii}) selection
gradients were not significant (Table 1B-C). This could suggest that
selection detected by the selection differential on this trait is
indirect, potentially due to a correlation with standard length.
Interestingly, both the linear selection gradient (\emph{ß}) as well as
its interaction with ART were significant for sword length (Table 1B).
These results suggest that there is direct selection on sword length,
and that it is not in the same direction across the ARTs. Further
analysis of the ARTs separately for this trait detected significant
positive selection on courter males for longer swords, and a marginally
nonsignificant negative selection coefficient (\emph{s}) for sneaker
males (Table 1B, see Fig. Appendix 2B). Finally, as indicated by the
significant quadradic selection gradient
(\emph{\emph{y}}\textsubscript{ii} Table 1C) and the marginal effects
plots, there may also be disruptive selection on the sword for both ARTs
(Fig. Appendix 2C).

~~~~~~~~~~~ In our second set of analyses that considered the geometric
morphometric traits, the results were similar to those that considered
only the unidimensional traits. The selection differentials were
significant for centroid size, the first PC axis describing body shape
(PC1), and sword length (Table 2A). These results suggest that at least
one of the ARTs is not at its optima for all three of these traits.
Interactions between a trait and ART were significant for body shape
(PC1 and PC3) and sword length, while marginally nonsignificant for
centroid size (Table 2A, Fig. 4). When the ARTs were analyzed separately
for body shape (PC1) we detected significant selection for deeper bodies
in courter males, but not for more narrow bodies in sneaker males. For
the sword, selection was for longer swords in courter males and shorter
swords in sneaker males (Table 2A, Fig. 4).

~~~~~~~~~~~ Further consideration of the linear (\emph{ß}) and quadratic
(\emph{\emph{\emph{y}}\textsubscript{i}}\textsubscript{ii}) selection
gradients detected significant linear and quadratic selection on
centroid size but no significant interactions with ART (Table 2B). These
results suggest that the significant selection differential for centroid
size is due to direct selection, that this selection is directional, but
that differences between the ARTs were not detected (Fig. Appendix 3A).
Examination of the marginal effects plot for the quadratic selection
gradient within each ART also suggests disruptive selection on centroid
size for both ARTS, but again, differences between the ARTs were not
detected (see Fig. Appendix 3B). As for body shape described by PC1, the
linear selection gradient was not significant, which suggests that the
selection detected on this trait with the differential may be indirect.
As with the unidimensional measurement of body shape (depth), PC1 may be
correlated with body size. The significant linear selection gradients
(\emph{ß}) as well as interactions with ART for body shape as described
by axes PC2 and PC3 are interesting, given that only PC2 is tactically
dimorphic (see above). Inspection of the marginal effects plots suggest
that selection is in the opposite direction for the ARTs on these
components of body shape (Fig. Appendix 3C and D). Further study of the
potential function of the variation in these components of body shape,
and their ability to evolve differences in relation to the ARTs, would
be interesting to examine. Finally, we detected significant linear
selection gradient (\emph{ß}) on sword length as well as an interaction
with ART (Table 2B), similar to the results for this trait when it was
analyzed with the other unidimensional traits. Therefore, selection on
the sword appears to be direct, regardless of the measures used for body
size and body shape in the analyses. In addition, analyses of the ARTs
separately detected selection for longer swords in courter males and
shorter swords in sneaker males (Table 2B, Fig. Appendix 3E).

\textbf{4 Discussion}

Alternative Reproductive Tactics (ARTs), which involve the use of
alternative mating behaviors as well as a suite of other traits, evolve
due to intense intrasexual competition that imposes strong sexual
selection (Oliveria et al. 2008). If selection can optimize reproductive
success by alternative suites of traits, then we expect tactical
dimorphism to evolve (Gross 1985; Moczek and Emlen 2000; Radwan and
Klimas 2001; Sato et al. 2004). However, little is known about whether
the evolutionary constraints due to intralocus tactical conflict (IATC)
can prevent traits reaching their optima within an ART. Our results
suggest that body size and sword length, both tactically dimorphic
traits in \emph{Xiphophorus multilineatus}, have different optima, and
one or more of the ARTs is not at its optima for both traits, providing
strong evidence of the potential for Intralocus Tactical Conflict. Body
shape was also experiencing selection that was different between the
ARTs, but our data did not allow us to demonstrate that the selection
was direct. Finally, the selection we measured appears to be stronger
for courter males than sneaker males for all of the traits except for
sword length. We discuss the implications of our results in relation to
the selection of these traits in this system, as well as the
implications for considering intralocus tactical conflict in relation to
the evolution of ARTs in general below.

Body size is the morphological trait that has been studied most
extensively in relation to the ARTS in \emph{X. multilineatus}. The ARTS
are dimorphic for body size (Liotta et al. 2019, current study), and yet
our results suggest that intralocus tactical conflict could be
constraining the courter males from reaching their optimal size.
Variation in body size has a strong genetic influence due to variation
in both alleles and copy number of the \emph{Mc4r} gene on the \emph{Y}
chromosome (Lampert et al. 2010). However, for a trait like male body
size, it is unlikely that variation in the \emph{Mc4r} gene is
explaining all of the variation, such that numerous autosomal genes
could lead to IATC. The genetic correlation across the ARTs for this
trait is currently being examined and will allow us to determine the
extent to which IATC is constraining this trait. It is also interesting
to note that because the selection we measured did not account for the
invisible fraction (males that did not survive to reproduce, Grafen
1988) the antagonistic selection on body size we measured is not due to
the differences between the ARTs in relation to the benefits and costs
of reaching sexual maturity earlier (Rios-Cardenas et al. 2018,
Weinstein et al. 2019). Instead, what we measured is due to influence of
sexual selection alone. This is important as it suggests that intralocus
tactical conflict is an additional evolutionary mechanism beyond
survival costs of maturing later that can limit the evolution of this
influential trait. Finally, when body size was measured as centroid size
we detected disruptive selection, selection that was not different
across the ARTs. Further examination of female mate preference functions
as well as male-male interactions has the potential to reveal a more
complex role for centroid size in this system.

The results for selection on body shape were not as clear as those for
body size. The selection differentials on both the unidimensional
measure of body shape (body depth) and the first PC axis for the
geometric morphometric measure (PC1) were significant, but the selection
gradients for both measures of this trait were marginally
nonsignificant. One possible explanation is that the selection on this
aspect of body shape is indirect, due to correlation with another trait.
We have shown previously that body size and shape are phenotypically
correlated across both courters and sneakers (Liotta et al. 2019).
However, we also presented two theoretical juvenile development pathways
for body shape (Liotta et al. 2019, Fig.5) that could both explain this
correlation. In one, body shape differences between the ARTs is
attributable to differences in ages at sexual maturity alone. In the
other theoretical pathway, the differences between the ARTs in body
shape are due to different developmental paths, in which case body shape
could potentially evolve independently of size. To tease apart these two
scenarios, future studies of the developmental trajectories of both body
shape and body size across the ARTs are planned.

Another interesting result from the analyses of selection on body shape
is that similar to the results for body size, the selection
differentials for body depth and PC1 were significant for courter males
but not sneaker males. Given that deeper bodies increased the size of
the signal to female sail-fin mollies (MacLaren et al. 2004), selection
for deeper bodied courter males in \emph{X. multilineatus} is not too
surprising, as this trait could have increased mate attraction. We had,
however, expected selection for more fusiform shapes in the sneaker
males to allow for less drag and improved prolonged swimming for males
using the sneak-chase mating behavior. One possible explanation for the
apparent lack of selection on body shape in the sneaker males is their
behavioral plasticity, which could buffer selection for a body shape
that is optimal for use with either behavior (Abbott et al. 2019b). The
correlation between body shape and propensity to use sneak-chase we
detected previously (Liotta et al. 2019) supports this interpretation.
More recent work detected this same relationship in lab reared males
with limited experience in addition to wild caught males, providing
further evidence to suggest that the relationship is genetically
influenced and not learned (Liotta et al., in prep). In addition, the
fact that we could detect selection for an overall deeper body shape in
the courter males, which are fixed for courtship, lends further support
to this hypothesis. Finally, there is the interesting possibility of
using a comparative study to further test the hypothesis that behavioral
plasticity is buffering selection on body shape in this system. Two
closely related species have evolutionarily lost large males size and
are fixed for sneak-chase behavior (\emph{X. continens} and \emph{X.
pygmaeus}, Ryan and Causey 1989, Morris et al. 2005). We predict that
body shape in both of these species will be experiencing stronger
selection than what we detected in the \emph{X. multilineatus} sneaker
males.

Given that body shape is a multidimensional trait, and the PC axes that
we examined are a composite of multiple characteristics of, in this
case, body shape, they are not independent characters with specific
biological meanings (Klingenberg and Monteiro 2005; Mitteroecker and
Bookstein 2011; Kuchta and Svensson 2014). While this can complicate the
interpretation of selection on individual PC axes, the benefit is that
each axis reflects linear combinations of traits and therefore includes
correlational selection between the different elements of shape
described by each PC axis (Kuchta and Svensson 2014). As for direct
selection on PC2, it would be interesting to further explore the
potential function of a more upturned head and the elongation of the
caudal peduncle. Variation in these aspects of body shape has been
previously detected in relation to habitat differences (e.g. Franssen et
al. 2013, Piñeros et al. 2015), but to our knowledge has not been
examined in relation to mating behaviors. However, these morphological
variations seem to be in accordance with the use of unsteady swimming
(Langerhans 2009, Langerhans and Reznick 2010), predominantly used by
courters, as opposed to a more steady swimming favored by the
sneak-chase behavior used by sneakers. And finally, aspects of body
shape may have aided courter males in maneuverability or even fast
starts (Webb 1994; Blake 2004; Langerhans 2009; Oufiero et al. 2014a),
allowing the execution of rapid back and forth turns used in both
courtship display and aggressive interactions with other males.

The sword on \emph{Xiphophorus} fishes has long been a trait of interest
in evolutionary biology (Darwin 1871) as it illustrates the importance
of trade-offs between sexual selection and natural selection (Darwin
1871, Basolo 1990a,b; Hernandez-Jimenez and Rios-Cardenas 2012; Oufiero
et al. 2014b). We provide the first evidence of direct selection on this
trait in a wild population, in addition to the potential for IATC to be
constraining its evolution. Across many \emph{Xiphophorus} species,
females are known to prefer males with longer swords (Basolo 1990a,b,
1995; Rios-Cardenas and Morris 2011, although see Rosenthal et al.
2002), which would explain the direct positive selection on sword length
in the courter males. Selection on the sword for sneaker males was also
significant, but negative. The potential for locomotor and metabolic
costs of possessing a sword (Basolo and Alcaraz 2003; Royle et al. 2006;
although see Oufiero et al. 2014a) could explain the negative selection
in males that primarily use coercive mating behaviors\emph{.} The
differences in selection on the sword across the ARTs suggests that
Intralocus Tactical Conflict could be constraining the courter males
from evolving longer swords, while at the same time constraining the
sneaker males from evolving shorter swords. Therefore, even though sword
length is dimorphic for the ARTs, neither ART is at its optima for this
trait.

Further evidence that Intralocus tactical conflict may be constraining
the evolution of the sword comes from comparative studies of sword
length across species of Norther Swordtails fishes. There are two
independent losses of the large male morphs in this clade (\emph{X.
continens} and \emph{X. pygmaeus}, Morris et al. 2005), which would
reduce or eliminate Intralocus tactical conflict. As predicted based on
the selection on sword length in sneaker males we detected, loss of the
larger males would reduce or eliminate Intralocus Tactical Conflict,
leading to the evolution of shorter swords. In both species all males
have evolutionarily lost the sword (Rauchenberger et al. 1990, Ryan and
Causey 1989; Morris et al. 2005). An additional comparative study of
swordtail fishes could determine if the presence of more distinct
alternative mating tactics and antagonistic selection, such as detected
here for the sword in \emph{X. multilineatus}, could help explain
variation in sword length across species. The sword index for \emph{X.
multilineatus} males, whether considering the average across all males
(0.4 ± 0.02 SE, N=80), or for just the courter (0.5 ± 0.02 ), or sneaker
males (0.32 ± 0.01) in the current study, is less than the average for
\emph{X. nezahualcoyotl} (0.56) and \emph{X. montezumae} (1.0; lab
reared males; Rauchenberger et al. 1990), species without discrete ARTs.
We suggest that the evolution of this hallmark trait for sexual
selection may be influenced by variation across species in intralocus
tactical conflict, in addition to variation in the strength of female
mate preference (Rosenthal et al. 2002) and predation (Basolo and Wagner
2004; Hernandez-Jimenez and Rios-Cardenas 2012).

~~~~~~~~~~~ Understanding tactically antagonistic selection based on
data from a wild population adds to the growing body of research
suggesting that IATC is constraining ARTs from reaching their fixed
phenotypic optima. ARTs are ubiquitous across taxa and a better
understanding of IATC will ultimately elucidate evolutionary processes
and patterns we see across species with ARTs and other polymorphisms. It
is also clear that IATC needs to be considered when explaining variation
in sexually selected traits both within and across taxa. Finally, given
that loss of an ART is associated with speciation across a range of
species (ant species, Oettler et al. 2010; the side-blotched lizards,
Corl et al. 2010; northern swordtail fishes, Morris et al. 2013),
further study of how these losses release traits from intralocus
tactical conflict could help explain the rapid evolution of the
remaining ART (West-Eberhard 1986), which could lead to a speciation
event (Bonduriansky and Chenoweth 2009; Bonduriansky 2011; Abbott et al.
2019b).

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\textbf{Figure Legends}

Figure 1. \textbf{} Courter (A) and sneaker (B) males of
\emph{Xiphophorus multilineatus}. a, vertical body bars; a*, no vertical
body bars present on this sneaker male; b, the sword, extension of the
ventral caudal fin rays; C) Sampling \textbf{} for DNA and morphology
took place \textbf{} in a side stream of the Río Tambaque located just
outside of Aquismón, San Luis Potosí, Mexico. D) Red filled circles =
landmarks; red open circles = semi-landmarks; blue line = semi-landmark
markers or linear measurements; numbers correspond to landmark locations
(see Material and Methods). (A, B photos by Luke Weinstein; C by Melissa
Liotta)

Figure 2. Principal components analysis of the variation in body shape
along the first two principal component (PC) axes. Orange triangles =
sneaker males; Blue circles = courter males. Wire-frame models
representing the extreme minimal and maximal changes in shape along PC1
and PC2 are shown (red = consensus shape, i.e. average body shape of all
males; black = shape change, i.e. the change in body shape from the
average body shape). Small filled ellipses are the 95\% confidence
regions around the respective group centroids.

Figure 3. Selection differentials for each unidimensional trait and
trait distribution by ART. \textbf{} Solid line is the differential
(\emph{s}) and shaded regions are the 95\% confidence intervals.
Vertical dashed lines show the mean trait value for each ART. Histograms
show the distribution of the trait values. Large purple asterisks
indicate a significant difference between the slopes. Blue and orange
asterisks indicate a significant slope. Orange = sneaker males, blue =
courter males. *P \selectlanguage{english}[?] 0.05, **P \selectlanguage{english}[?] 0.01 ***P\selectlanguage{english}[?] 0.001

Figure 4. Selection differentials for each geomorphometric trait and
trait distribution by ART. \textbf{} Solid line is the differential
(\emph{s}) and shaded regions are the 95\% confidence intervals.
Vertical dashed lines show the mean trait value for each ART. Histograms
show the distribution of the trait values. Large purple asterisks
indicate a significant difference between the slopes. Blue and orange
asterisks indicate a significant slope. Wireframes as explained in
Figure 2. Orange = sneaker males, blue = courter males. \selectlanguage{english}+ Average slope
between the ARTs is significant, we did not perform subsequent separate
models in these cases because the interaction between the trait and ART
was not significant (see Table 2A). *P \selectlanguage{english}[?] 0.05, **P \selectlanguage{english}[?] 0.01 ***P\selectlanguage{english}[?] 0.001\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCCCC}
\selectlanguage{ngerman} A &  & Differential (\emph{s})
 &  &  &  &  &  \\
 & SS & F\textsubscript{3, 76}
 & P & Courter (s) & P* & Sneaker (s) & P* \\
Standard Length (size) & 18.05 & 7.83 & 0.01 & 1.38 & 0.02 & -0.53 & 0.13 \\
ART:Standard Length & 10.49 & 4.55 & 0.02 &  &  &  &  \\
Body Depth & 13.55 & 5.7 & 0.02 & 1.3 & 0.04 & -0.18 & 0.32 \\
ART:Body Depth & 7.29 & 3.07 & 0.05 &  &  &  &  \\
Sword Length & 37.16 & 18.41 & <0.0 & 1.58 & 0.0 & -0.64 & 0.03 \\
ART:Sword Length & 28.07 & 13.91 & 0.0 &  &  &  &  \\
B &  & Linear (\selectlanguage{ngerman}ß) &  &  &  &  &  \\
 & SS & F\textsubscript{7, 72}
 & P & Courter (\selectlanguage{ngerman}ß) & P* & Sneaker (\selectlanguage{ngerman}ß) & P* \\
Standard Length (size) & 8.77 & 4.38 & 0.04 & 2.1 & - & -0.09 & - \\
ART:Standard Length & 4.06 & 2.03 & 0.11 &  &  &  &  \\
Body Depth & 5.13 & 2.57 & 0.08 & -1.88 & - & 0.24 & - \\
ART:Body Depth & 4.27 & 2.13 & 0.1 &  &  &  &  \\
Sword Length & 28.21 & 14.1 & 0.0 & 1.64 & 0.01 & -0.68 & 0.06 \\
ART:Sword Length & 19.53 & 9.77 & 0.01 &  &  &  &  \\
C &  & Quadradic (y\textsubscript{ii})
 &  &  &  &  &  \\
 & SS & F\textsubscript{13, 66}
 & P & Courter (y\textsubscript{ii}\emph{)}
 &  & Sneaker (y\textsubscript{ii}\emph{)}
 &  \\
Standard Length (size) & 0.39 & 0.22 & 0.32 & 1.51 &  & 0.47 &  \\
ART:Standard Length & 0.06 & 0.04 & 0.44 &  &  &  &  \\
Body Depth & 0.4 & 0.23 & 0.34 & 1.92 &  & 0.71 &  \\
ART:Body Depth & 0.1 & 0.06 & 0.42 &  &  &  &  \\
Sword Length & 10.06 & 5.74 & 0.05 & 2.39 &  & 1.72 &  \\
ART:Sword Length & 0.36 & 0.21 & 0.34 &  &  &  &  \\
\end{tabulary}
\caption{{Selection analyses on the unidimensional traits. (A) differential
selection model with both ARTs, in addition to analyses of ARTs
separately for traits that had a significant interaction with ART; (B)
linear and (C) quadratic selection models including all traits and
interactions with ART. ART = alternative reproductive tactic, SS = sums
of squares, F = ANOVA F statistic, P = p-value. P-values calculated
using a resampling procedure (Lewis et al. 2011). Significant P-values
highlighted in bold. P* were calculated from separate analyses by ART in
cases where interactions were significant.
{\label{919199}}%
}}
\end{table}\textbf{}\selectlanguage{english}
\begin{table}[H]
\centering
\normalsize\begin{tabulary}{1.0\textwidth}{CCCCCCCC}
\selectlanguage{ngerman} A &  & Differential (\emph{s})
 &  &  &  &  &  \\
 & SS & F\textsubscript{3, 76}
 & P & Courter (s) & P* & Sneaker (s) & P* \\
Centroid Size & 21.26 & 9.35 & 0.01 & 1.24 & - & -0.27 & - \\
ART:Centroid size & 5.19 & 2.28 & 0.07 &  &  &  &  \\
Body shape (PC1) & 17.39 & 7.48 & 0.01 & 1.08 & 0.02 & -0.09 & 0.33 \\
ART:PC1 & 11.3 & 4.86 & 0.02 &  &  &  &  \\
Body shape (PC2) & 3.55 & 1.41 & 0.11 & 0.39 & - & 0.04 & - \\
ART:PC2 & 1.97 & 0.78 & 0.17 &  &  &  &  \\
Body shape (PC3) & 3.16 & 1.31 & 0.11 & 0.27 & 0.17 & -0.48 & 0.01 \\
ART:PC3 & 10.63 & 4.4 & 0.02 &  &  &  &  \\
Body shape (PC4) & 0.0 & 0 & 0.49 & -0.0 & - & 0.2 & - \\
ART:PC4 & 0.74 & 0.29 & 0.29 &  &  &  &  \\
Sword length & 37.16 & 18.41 & <0.0 & 1.58 & 0.0 & -0.64 & 0.03 \\
ART:Sword & 28.07 & 13.91 & 0.0 &  &  &  &  \\
B &  & Directional (\selectlanguage{ngerman}ß) &  &  &  &  &  \\
 & SS & F\textsubscript{13, 66}
 & P & Courter \selectlanguage{ngerman}\emph{(ß)}
 & P* & Sneaker \selectlanguage{ngerman}\emph{(ß)}
 & P* \\
Centroid Size & 11.01 & 6.21 & 0.03 & 1.14 & - & 0.42 & - \\
ART:Centroid size & 0.74 & 0.42 & 0.29 &  &  &  &  \\
Body shape (PC1) & 5.23 & 2.95 & 0.07 & -0.93 & - & 0.03 & - \\
ART:PC1 & 3.77 & 2.13 & 0.11 &  &  &  &  \\
Body shape (PC2) & 14.72 & 8.3 & 0.02 & 0.86 & 0.04 & 0.09 & 0.29 \\
ART:PC2 & 7.54 & 4.26 & 0.05 &  &  &  &  \\
Body shape (PC3) & 9.55 & 5.39 & 0.02 & 0.5 & 0.05 & -0.45 & 0.01 \\
ART:PC3 & 15.53 & 8.76 & 0.01 &  &  &  &  \\
Body shape (PC4) & 1.79 & 1.01 & 0.19 & -0.26 & - & 0.18 & - \\
ART:PC4 & 3.36 & 1.9 & 0.13 &  &  &  &  \\
Sword length & 29.55 & 16.67 & 0.0 & 1.95 & 0.01 & -0.74 & 0.04 \\
ART:Sword & 23.84 & 13.45 & 0.0 &  &  &  &  \\
C &  & Quadratic (y\textsubscript{ii})
 &  &  &  &  &  \\
 & SS & F\textsubscript{25, 54}
 & P & Courter (y\textsubscript{ii}\emph{)}
 &  & Sneaker (y\textsubscript{ii}\emph{)}
 &  \\
Centroid Size & 14.31 & 8.93 & 0.02 & 3.32 &  & 2.37 &  \\
ART:Centroid size & 0.05 & 0.03 & 0.47 &  &  &  &  \\
Body shape (PC1) & 0.14 & 0.09 & 0.37 & 0.33 &  & -0.65 &  \\
ART:PC1 & 0.74 & 0.46 & 0.29 &  &  &  &  \\
Body shape (PC2) & 3.07 & 1.91 & 0.11 & -0.49 &  & -0.17 &  \\
ART:PC2 & 0.61 & 0.38 & 0.31 &  &  &  &  \\
Body shape (PC3) & 1.49 & 0.93 & 0.19 & 0.18 &  & 0.97 &  \\
ART:PC3 & 2.9 & 1.81 & 0.14 &  &  &  &  \\
Body shape (PC4) & 0.08 & 0.05 & 0.39 & 0.14 &  & -0.06 &  \\
ART:PC4 & 0.13 & 0.08 & 0.4 &  &  &  &  \\
Sword length & 1.05 & 0.66 & 0.24 & 1.05 &  & 1.03 &  \\
ART:Sword & 0.0 & 0.0 & 0.49 &  &  &  &  \\
\end{tabulary}
\caption{{\emph{Selection analyses for the geomorphometric traits, including sword
length. (A) differential selection model with both ARTs, in addition to
analyses of ARTs separately for traits that had a significant
interaction with ART; (B) linear and (C) quadratic selection models
including all traits and interactions with ART; separate analyses by ART
if interaction for trait was significant. ART = alternative reproductive
tactic, SS = sums of squares, F = ANOVA F statistic, P = p-value, DF =
degrees of freedom. P-values calculated using a resampling procedure
(Lewis et al. 2011). Significant P-values highlighted in bold. P* were
calculated from separate analyses by ART in cases where interactions
were significant. ~}
{\label{819427}}%
}}
\end{table}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-1/Figure-1-revised}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-2/Figure-2-revised}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-3/Figure-3-revised}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-4/Figure-4-revised}

\end{center}
\end{figure}

\textbf{APPENDIX}

\emph{Microsatellite Genotyping}

Primer stock was made to include a 2 \selectlanguage{greek}µ\selectlanguage{english}M concentration of each primer
(forward and reverse) for each multiplex, except for \emph{KonD6} in
MIX1, which had a 1 \selectlanguage{greek}µ\selectlanguage{english}M concentration due to its intense peaks exhibited
in test plates. The PCR cocktail for each sample consisted of a final
volume of 10 \selectlanguage{greek}µ\selectlanguage{english}l and the following: 2.55 \selectlanguage{greek}µ\selectlanguage{english}l of RNAse-Free Water (Type-it
Microsatellite PCR kit, Qiagen, Hilden, Germany), 5.0 \selectlanguage{greek}µ\selectlanguage{english}l of 2x Type-it
Microsatellite PCR Master Mix (Qiagen, Hilden, Germany, final
concentration 0.06x), 1.45 \selectlanguage{greek}µ\selectlanguage{english}l of the primer stock (final concentration
0.29 \selectlanguage{greek}µ\selectlanguage{english}M), and 1 \selectlanguage{greek}µ\selectlanguage{english}l of DNA.

\par\null\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-Appendix-1/Figure-Appendix-1}

\end{center}
\end{figure}

Figure Appendix 1. Frequency of how many males in each ART (courters =
blue, sneakers = orange) sired a certain number of fry. Numbers within
each bin correspond to the number of sire\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-Appendix-2/Figure-Appendix-2}

\end{center}
\end{figure}

Figure Appendix 2. Marginal effects plots for traits in the
unidimensional analysis showing the effect of each response variable on
the predicated values (prd) of relative fitness when all other traits
are held constant. Solid line is the selection gradient (\emph{b} or
g\textsubscript{ii}) and shaded regions are the 95\% confidence
intervals. Large purple asterisks indicate a significant difference
between the slopes. Blue and orange asterisks indicate a significant
slope. Orange = sneaker males, blue = courter males. + Average slope
between the ARTs is significant, we did not perform subsequent separate
models in these cases because the interaction between the trait and ART
was not significant (see Table 1B-C). ++ Quadratic coefficients
visualized here, are on the original scale but are reported in Table 1
multiplied by 2 as suggested by Stinchcombe et al. 2008.
\textsuperscript{*} P = 0.06, *P [?] 0.05, **P [?] 0.01 ***P[?] 0.001\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/Figure-Appendix-3/Figure-Appendix-3}

\end{center}
\end{figure}

Figure Appendix 3. Marginal effects plots for traits in the
geomorphometric analysis showing the effect of each response variable on
the predicated values (prd) of relative fitness when all other traits
are held constant. Solid line is the selection gradient (b or g) and
shaded regions are the 95\% confidence intervals. Large purple asterisks
indicate a significant difference between the slopes. Blue and orange
asterisks indicate a significant slope. Orange = sneaker males, blue =
courter males. Wire-frame models as explained in Figure 2. + Average
slope between the ARTs is significant, we did not perform subsequent
separate models in these cases because the interaction between the trait
and ART were not significant (see Table 2B-C). ++ Quadratic coefficients
visualized here, are on the original scale but are reported in Table 2
multiplied by 2 as suggested by Stinchcombe et al.
2008.~\textsuperscript{*} P = 0.06, *P [?] 0.05, **P [?] 0.01 ***P[?] 0.001

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