Perspectives 
Corresponding author: Cédric Tentelier ( cedric.tentelier@univpau.fr ) Academic editor: Stephane Boyer
© 2017 Zoé Gauthey, Cédric Tentelier, Olivier Lepais, Arturo Elosegi, Laura Royer, Stéphane Glise, Jacques Labonne.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Gauthey Z, Tentelier C, Lepais O, Elosegi A, Royer L, Glise S, Labonne J (2017) With our powers combined: integrating behavioral and genetic data to estimate mating success and sexual selection. Rethinking Ecology 2: 126. https://doi.org/10.3897/rethinkingecology.2.14956

The analysis of sexual selection classically relies on the regression of individual phenotypes against the marginal sums of a males × females matrix of pairwise reproductive success, assessed by genetic parentage analysis. When the matrix is binarized, the marginal sums give the individual mating success. Because such analysis treats male and female mating/reproductive success independently, it ignores that the success of a male × female sexual interaction can be attributable to the phenotype of both individuals. Also, because it is based on genetic data only, it is oblivious to unproductive matings, which may be documented by behavioral observations. To solve these problems, we propose a statistical approach which combines matrices of offspring numbers and behavioral observations. It models reproduction on each mating occasion of a mating season as three stochastic and interdependent pairwise processes, each potentially affected by the phenotype of both individuals and by random individual effect: visit of a female by a male, concomitant gamete emission, and offspring production. Applied to data from a mating experiment on brown trout, the model yielded different results from the classical regression analysis, with only a negative effect of female body size on the probability of visit and gamete release, while the classical approach based on regression found a positive effect of male size on the number of both visits and offspring, and no effect of female size. Because the general structure of the model can be adapted to other partitioning schemes of the reproductive process, it can be used for a variety of biological systems where behavioral and genetic data are available.
Selection gradient, Bateman; fish, mate choice
Sexual reproduction involves two different individuals which both invest energy in gamete encounter and possibly in offspring survival. The reproductive output of a given mating event is therefore attributable to both partners. In a population, the distribution of the reproductive success RS_{i,j,k} gained by a pair of individuals i and j on a mating occasion k can be summarized by a 3dimension array of number of offspring produced between all possible pairs of males and females for each mating occasion. Here, and throughout this article, “pair” refers to two individuals engaged in a mating interaction, and is applicable to all mating systems, not only to monogamous ones involving stable pairs. Then, summing such array over all mating occasions leads to the socalled "parental table" classically used in studies of sexual selection (
Classical methods in sexual selection use these parental tables to infer the adaptive value of traits in populations by calculating different indices of sexual selection in males and females such as measures of inequality in mating success or reproductive success, selection gradients and selection differentials (
This approach has some pitfalls and shortcomings, and although widely used, its output may often be misinterpreted (
An illustration of the first caveat is the wealth of definitions for individual mating success during one reproductive period (
The second caveat is less evoked in the literature although intuitively simple: in sexual reproduction, reproductive success between two individuals should be attributable to both. Yet, one usually analyzes reproductive success as an individual characteristic, with no regard for the effect of the sexual partner. Classical studies only focus on the marginal sums of the parental table, and therefore cannot control for sexual partner trait or mating success variation (
To solve both matters, we propose an approach that combines genetic data (parental table) and behavioral data (visit matrix and mating matrix) to 1) describe the different components of reproductive success (here visit rate, rate of gamete release, number of offspring produced) for each mating occasion within the reproductive season, and 2) infer the joint effects of both male and female phenotype on each component of the reproductive success. The conditional structure linking the successive components of pairwise reproductive success is the key to extract information from both behavioral and genetic data: presence of offspring from a pair of parents implies the male having visited the female and released his gametes concomitantly with hers, even if these are absent from behavioral data, whereas observation of gamete release despite the absence of common offspring allows distinguishing between zerovalue due precopulatory and postcopulatory mechanisms. We illustrate the model using a reproduction experiment data for Salmo trutta as a case study, with body size as an example of phenotypic covariate as it is known to be involved in sexual selection in salmonids (
The data used in this study were taken from the “constant environment” treatment in
The fish were observed for at least 15 min in the morning and in the evening from the bank, in order to detect behaviors associated to spawning activity. When reproductive behaviors indicating that a female and one/or several male(s) were close to spawning (digging female, chases between males), subaquatic (Bullet camera VB21 EHW, sensor 1/3” Sony 960H Exview HAD CCD) and aerial videocameras (Sony Handycam DCRSR90) were placed in the river or on the bank to record the spawning act (
Previous observations indicate that most interactions between individuals involved in a mating occasion occurred within one meter around the nest, and within the hour before egg laying, although some interactions could occur afterwards (
At the end of the behavioral survey (February 15^{th} 2013), adult fish were electrofished, and under anesthesia (30 mg.L^{1} benzocaine), a small piece of pelvic fin was sampled and stored in 90% ethanol for molecular analysis. The abdomen of females was gently palpated to check whether they still carried eggs, and all individuals were released in the river where they had been captured in the first place. At emergence (800 degree.days: about two months after the last spawning event), juveniles stemming from the reproduction in the experimental channel were collected by either electrofishing or trapping at the downstream end of the experimental reach. They were anesthetized and killed under a lethal dose of 2phenoxyethanol and placed individually in a tube of absolute ethanol (90%) upon molecular analysis. DNA extraction, PCR amplification and genotyping at eight microsatellite loci provided data for parentage analysis run on Cervus software (
Behavioral and genetic data were analyzed using classical methods. We computed the opportunity for selection, as the ratio of variance in the number of offspring genetically assigned on its squared mean. Likewise, opportunity for sexual selection was computed as the ratio of variance in the number of genetic mates on its squared mean. The term “genetic mate” is hereafter used to refer to mates deduced from genetic assignation analysis. Bateman’s gradient (β_{ss}) was measured using a simple linear regression between the number of offspring assigned and number of genetic mates. To quantify selection on individual phenotype, body size was regressed against the number of visits and the number of observed mates on videos, and on the number of offspring and number of genetic mates.
The general philosophy of the model was to consider reproduction between pairs of individuals as a series of K mating occasions, defined as events on which at least one male × female pair mated, i.e. the male visited the female, they emitted gametes simultaneously and produced offspring. So, each mating occasion consisted of three successive processes: visit (a Bernoulli variable indicating if male i visited female j on mating occasion k), gamete release (a Bernoulli variable indicating if male i and female j both emitted their gametes on mating occasion k), and the number of offspring produced (a Poisson variable, including zeros, describing the number of offspring produced by male i and female j on mating occasion k). Any pair could be involved in each process of any mating occasion so the three processes could be modelled as arrays, the dimensions of which were males, females and mating occasions. The effect of male and female body size, as well as random individual effects on each process conditional of the preceding one was then assessed with Bayesian inference.
Although behavioral data stored in matrices of visit and gamete release were only available for the K_{obs} mating occasions that were video recorded, genetic data on the number of offspring produced pool all K mating occasions, because offspring were sampled at the end of the spawning season. Hence, a first challenge to the model was to unfold the parental table (matrix of pairwise reproductive success) N_{i,j} in K sub matrices, with K the total number of mating occasions that occurred in the mating season. We simply assumed that
. However, behavioral data are generally incomplete: here the total number of mating occasions K (K_{obs} ≤ K) as well as the probability p_{o} to observe a male i visiting a female j at each of the K_{obs} known mating occasions must be estimated. For the probability of observation, the occurrence of an observed visit OE_{i,j,k} was modeled as OE_{i.j.k} = E_{i,j,k} × O_{i,j,k}, where E_{i,j,k} and O_{i,j,k} were both binomial variables sampled in Bernoulli distributions of mean p_{e} and p_{o}, respectively the probability that the visit happened and the probability that it was observed. A zero O_{i,j,k} meant we had no direct behavioral data, so visit rate and rate of gamete release could not be directly estimated. In such case, we simply simulated the expected behavioral data using the posterior densities from estimated parameters for the K_{obs} mating occasions where behavioral data were known. The total number of mating occasions, K, could be estimated directly in the model because the posterior distribution revealed the best combination of behavioral and genetic data conditional on the value of K. When behavioral data were resimulated from their posterior distribution, the value of K could therefore be jointly estimated.
We tested the additive effects of male and female body size (BS_{i} and BS_{j}) on visit rate (E_{i,j,k}), rate of gamete release (G_{i,j,k}) and offspring number (N_{i,j,k}) as following:
logit(E_{i,j,k}) = e_{1} × BS_{i} + f_{1} × BS_{j} + a_{1,i} + b_{1,j}
logit(G_{i,j,k}) = e_{2} × BS_{i} + f_{2} × BS_{j} + a_{2,i} + b_{2,j}
log(N_{i,j,k}) = e_{3} × BS_{i} + f_{3} × BS_{j} + a_{3,i} + b_{3,j}
where a_{.,i} and b_{.,j} were male and female random effects, which were included to account for the fact that each individual could be involved in several mating occasions during the season. e_{1}, e_{2}, e_{3} are the male body size effects on visit rate, rate of gamete release, and offspring number respectively, and f_{1}, f_{2} and f_{3} are the female body size effects likewise.
Statistical inference was conducted in the Bayesian framework under JAGS 4.1.0 (
Symbol, meaning, prior distribution, and 2.5%, 50% and 97.5% quantiles of posterior distribution for each hyperparameter used in the JAGS model for the analysis of sexual selection in brown trout. For prior distributions, parametrization is: Normal (m=mean, t=precision), Gamma (a=shape, b=rate), Beta (a=number of trials, b=number of successes) and Uniform (a=minimum, b=maximum). √R is Gelman and Rubin (
Parameter  Meaning  Prior distribution  Posterior median [2.5% quantile; 97.5% quantile]  √R 

e _{1}  Effect of male size on visit  Normal (0, 0.001)  0.0009 [0.015 ; 0.008]  1.01 
e _{2}  Effect of male size on gamete release  Normal (0, 0.001)  0.009 [0.005 ; 0.02]  1.01 
e _{2}  Effect of male size on number of offspring  Normal (0, 0.001)  0.001 [0.011 ; 0.007]  1.02 
f _{1}  Effect of female size on visit  Normal (0, 0.001)  2.399E02 [3.450E02 ; 8.921E03]  1.01 
f _{2}  Effect of female size on gamete release  Normal (0, 0.001)  2.041E02 [3.402E02 ; 5.834E03]  1.04 
f _{2}  Effect of female size on number of offspring  Normal (0, 0.001)  1.155E03 [1.124E02 ; 8.798E03]  1.00 
a _{1}  Precision of male random effect on visit  Gamma (0.001, 0.001)  1.61 [0.45 ; 7.53]  1.29 
a _{2}  Precision of male random effect on gamete release  Gamma (0.001, 0.001)  1.57 [0.29 ; 348.1]  1.07 
a _{3}  Precision of male random effect on number of offspring  Gamma (0.001, 0.001)  1.27 [0.42 ; 3.29]  1.10 
b _{1}  Precision of female random effect on visit  Gamma (0.001, 0.001)  34.8 [14.9 ; 79.7]  1.11 
b _{2}  Precision of female random effect on gamete release  Gamma (0.001, 0.001)  0.22 [0.07 ; 0.69]  1.50 
b _{3}  Precision of female random effect on number of offspring  Gamma (0.001, 0.001)  0.89 [0.42 ; 1.88]  1.44 
p_{o}  Probability of observing a mating event  Beta (50,30)  0.66 [0.56 ; 0.75]  1.01 
K  Total number of mating events  Uniform (23, 150)  116 [82 ; 147]  1.00 
Three individuals (2 males and 1 female) were removed from the data set because they escaped from the experimental channel. This event happened during the two first weeks of the experiment when reproductive period just started and these individuals were not observed as sexually active on the videos. These three individuals were therefore discarded from the different analyses.
In total, 22 spawning acts were video recorded (K_{obs} mating occasions) during the reproductive season. Within these K_{obs} occasions, 14 females out of 32 and 12 males out of 17 were observed, totalizing 75 pairwise visits. Thirteen females and 7 males were observed releasing their gametes, totalizing 22 pairwise copulations. No multiple mating (where several males emit their gametes simultaneously) was observed. For five mating occasions, some individuals which did not release their gametes were too far from the camera to be unambiguously identified (1, 1, 2, 2 and 4 unidentified individuals for each occasion, respectively). These individuals were therefore not taken into account for the observations of visits. Abdominal palpation at recapture showed that almost all individuals (especially females) had released their gametes by the end of the experiment (only two females did not lay their eggs), and some nests were detected in places where we did not place our cameras, indicating that a significant proportion of spawning events was not observed.
A total of 555 juveniles and 49 parents were genotyped. Among those individuals, 551 juveniles were assigned to 41 pairs of parents (10 males and 22 females) at a confidence level of 95%. Number of offspring varied from 0 to 201 in males (mean ± sd= 32 ± 64) and between 0 and 86 for females (17 ± 24). Only 12 pairs were both seen releasing gametes and assigned offspring, so joint gamete release was assessed for 29 pairs by genetic data only. At the individual level, the number of gamete releases observed on video was correlated to the number of mates inferred from the genetic analysis (Pearson’s r = 0.66, p < 0.0001). From the genetic data, the opportunity for selection was 4.49 for males and 2.34 for females. The opportunity for sexual selection was 2.69 for males and 0.81 for females. Bateman’s gradient was 17.06 for males (t = 4.229 on 15 degrees of freedom, p = 0.0008) and 13.70 for females (t = 4.175 on 30 degrees of freedom, p = 0.0002).
The summary of the regressions of body size on components of reproductive success are given in Table
Linear regressions of brown trout body size against components of reproductive success for males (a, c, e) and females (b, d, f): the number of individuals of the opposite sex which were visited (a, b), the number of mates (c, d) and the number of offspring assigned (e, f). Open symbols and dashed lines are for behavioral data, and filled symbols and solid lines are for genetic data. For c and d, mating success was measured as the number of individuals of the opposite sex with which the focal individual was observed emitting gametes (open symbols, dashed line) and as the number of individuals with which it shared offspring (filled symbols, solid line). Values close to regression lines indicate the Pearson’s correlation coefficient of the corresponding regression line, with asterisk indicating p < 0.05.
Summary for the linear regressions of male and female brown trout body size on the number visits to or from individuals of the opposite sex observed on video recordings, number of gamete releases observed on video recordings, number of mates inferred from genetic assignation of offspring, and number of offspring genetically assigned.
Males  Estimate  Standard error  t value  p  

Visits  Intercept  2.718  6.077  0.447  0.661 
Body size  0.030  0.025  1.195  0.251  
Gamete releases  Intercept  5.804  2.213  2.623  0.019 
Body size  0.029  0.009  3.268  0.005  
Genetic mates  Intercept  8.170  2.612  3.127  0.007 
Body size  0.041  0.011  3.851  0.002  
Offspring  Intercept  187.096  63.152  2.963  0.010 
Body size  0.914  0.260  3.511  0.003  
Females  
Visits  Intercept  1.895  6.260  0.303  0.764 
Body size  0.002  0.028  0.072  0.943  
Gamete releases  Intercept  1.156  1.551  0.746  0.462 
Body size  0.002  0.007  0.304  0.763  
Genetic mates  Intercept  0.068  1.756  0.039  0.969 
Body size  0.005  0.008  0.659  0.515  
Offspring  Intercept  24.096  39.949  0.603  0.551 
Body size  0.035  0.178  0.198  0.845 
The posterior of all parameters for the model are provided in Table
Male body size had no effect on the probability of visit or on the number of offspring produced at each mating occasion, and had a slight nonsignificant tendency to increase the probability of gamete release (Fig.
Random effects were more variable for females than for males for the probability of visit and the probability of gamete release, while male random effects were more variable than female’s for the number of offspring (Fig.
Joint posterior probability distributions were used to predict the number of visits, gamete releases and offspring for each individual and these predictions were plotted against the number of visits and gamete releases observed on videos and number of offspring genetically assigned (Fig.
Posterior probability distributions of model parameters associated to the effect of brown trout body size on a the probability of visit b the probability of gamete release and c the number of offspring produced on each mating occasion. Dashed and solid lines are for females and males, respectively
Random individual effects on the probability of visit, the probability of gamete release and the number of offspring produced by brown trout on each mating occasion. The diagonal indicates the posterior probability distribution of the standard deviation of the Gaussian distribution in which random effects for the three components of reproductive success were drawn (dashed and solid lines are for females and males, respectively). Plots above the diagonal show the pairwise relations between random individual effects on each process, for females (one circle per female). Plots below the diagonal show the same thing for males (one triangle per male).
Predictions based on the joint posterior distributions of model parameters, against values observed in the raw data for the number of visits (a, b), the number of gamete releases (c, d) and the number of offspring (e, f) for brown trout males (a, c, e) and females (b, d, f). For each panel, solid lines indicate the mean of each variable, and the dashed line has intercept zero and slope one, which would correspond to a perfect correspondence between observed and predicted values.
In this study, we combined behavioral observations with genetic assignation of offspring to estimate the effect of a phenotypic trait (here body size as an example in brown trout) on different components of sexual selection. On the one hand, we applied classical analyses on data pulled out from the marginal sums of each male × female matrix: number of visits and gamete releases observed on videos, and number of offspring and mates inferred from genetic assignation. There we found that body size, in males only, would correlate positively with mating success and offspring number, but not with visit rate. Because of the strong skew in mating data, and in particular the wealth of zeros, the classical regression approach probably suffers bias due to high leverage of a few successful individuals. On the other hand, we developed a statistical framework combining all these data, thereby enabling information to circulate through the successive processes of visit, gamete release and offspring production. This approach accounted for the threedimensional structure of the data: males, females and mating occasions. This allowed a qualified definition of mating success, a more rigorous modelling of the many zeros in the dataset, and disentangling the joint effects of male and female phenotypes on the different components of reproductive success. There we found that body size, in females only, would correlate negatively with visit rate and mating success, but not with offspring number.
The multiple definitions of mating success have been shaped by a dichotomy of approaches, which our model aimed at overcoming. On the one hand, because the classical approach based on the genetic parental table is oblivious to both ineffective mating acts and multiple inseminations between the same pair of individuals, it has constrained the definition of mating success to the number of individuals with which the focal individual produces offspring that are alive at sampling (
At the scale of the reproductive group, our behavioral observations showed 75 male × female visits and 22 pairwise gamete releases, whereas the parental table based on genetic assignation indicated that 41 broods were produced. Given that only 12 pairs both were observed copulating and had their offspring sampled, a rough estimate of the probability that a pair was observed mating would be 12/41 = 0.29, and a rough estimate of the probability of a pair having its offspring sampled would be 12/22 = 0.54. This would mean that 12/(0.29*0.54) = 76 mating events had occurred, 10 of which were video recorded only, 29 of which were detected genetically only, 12 of which were detected both on video and by the genetic analysis, and 25 were missed by both methods. In our model, the parameter K, called the number of mating occasions, was estimated to be 117, meaning that each pair had 117 occasions to mate. This concept of mating occasion, defined as an event on which any male × female pair may visit, emit gametes and produce offspring, was much broader than mating, defined as an event on which a male does visit a female, emit gamete with her and produce offspring with her. By splitting individual mating success in a number of mating occasions (trials), our modelling approach considered mating success as the result of a Bernoulli process, with inferences made on the probability of success. Moreover, this success of joint gamete release was conditioned on the success of visit on each occasion, and conditioned in turn the number of offspring produced. This conditional structure is in line with the concepts of “sexual networks” and “sexual niche” (
At the individual scale, the number of visits and gamete releases predicted by the model were higher than those observed in the raw data. This was expected, because it was one aim of the model to combine behavioral and genetic data to distinguish unobserved mating events from genuine nonmating. The model output indicates that males and females had approximately ten times as many visits and gamete releases as observed. On average, the number of offspring estimated by the model was the same as the number observed. However, they were not correlated at the individual level. A possible explanation for this lack of fit may be the failure of the Markov chains to converge towards stable estimates for the hyperparameters controlling the precision of the Normal distribution in which random effects were drawn (a_{1}, a_{2}, a_{3}, b_{1}, b_{2}, b_{3}). This was probably due to the sparsity of the data matrices, in which most rows and columns only had one or very few nonzero elements, meaning that we lack withinindividual replication to estimate random effects properly. In polygynandrous systems such as brown trout’s, this only could be solved by a stronger sampling effort. In other systems where individuals interact with very few mates, random effects are probably not worth modelling. Individual variance in mating success is the fuel for sexual selection, and the opportunity for sexual selection is computed as the variance in number of mates on the squared mean of number of mates. Based on classical treatment of genetic data, opportunity for sexual selection was higher for males (2.69) than for females (0.81) as usually expected (
Sexual selection on phenotypic traits is classically quantified for each sex separately, by regressing the number of mates against phenotypic trait in a separate model for each sex (
Applying classical linear regressions to our data indicated that larger males tended to have more visits, and had significantly more gamete releases, more genetic mates and more offspring, while female body size affected none of the behavioral or genetic indicators of reproductive success. However, our model accounting for the size of both males and females as well as individual random effects on each reproductive process indicated that larger females had a lower probability of being visited by males and a lower probability of gamete release, whereas male size affected neither visit, gamete release nor number of offspring. Hence the output of the two analyses differed greatly.
The difference between the linear regression approach and ours is due to three features of our model which lack in the classical approach: 1) conditioning of each process (visit, gamete release and offspring production) on the preceding one, 2) simultaneous estimation of the effect of male and female phenotype, and 3) random individual effects. First, the conditional structure of the model allowed to infer the effect of individual phenotype on each process independently, whereas regression made on all individuals may confound them. For instance, our analysis indicated that larger males tended to have a higher probability of releasing gametes with the females they visited, but once mated they did not tend to sire more offspring. According to the regression analysis, though, the number of offspring by males was positively related to their body size, but this relationship was indirect and mediated by the positive relation between number of mates and number of offspring a male could gain (Bateman gradient). Although reproductive success may be split into multiplicative components on each of which individual phenotype can be regressed, such analysis requires as many regressions as components (e.g.
Now as to why female body size, for instance, had a negative effect on visit and gamete release probability, and no positive effect on offspring number, we must turn to the behavioral knowledge of the species. In particular, assortative or disassortative visit and mating, be it the result of mate choice, intrasexual competition or chance, is possible in brown trout (
The experimental design and the quantity of data we used to illustrate our model indubitably constrained the analysis we carried out, and one can wonder how the approach can be transposed to other systems, with other types of data on either the components of reproductive success or traits affecting them. In fact, we hope the approach presented in this study will encourage empiricists interested in sexual selection to collect data of different nature on different stages of the reproduction process, and combine them in ad hoc models, capitalizing on ours. For instance, because we sampled all offspring at the end of the experiment, the genetic data did not inform much on the number of offspring produced at each mating occasion. However, in other systems where clutches are well separated in time or space, even within a reproductive season, the parental table of genetic data would also be threedimensional (male × female × occasion) and inferences on each component of reproductive success would probably be more accurate. Also, depending on the system studied, reproductive success may be further decomposed, and inference might be done on individual or environmental features affecting the additional components. For example, one may disentangle copulation from gamete fertilization by combining behavioral data and singlemolecule PCR and genotyping of zygotes just after copulation. Here, an additional threedimension matrix containing gamete fertilization of each malefemale pair at each occasion would be built, and fertilization success would be included in the model, conditioned by copulation success, and conditioning the number of offspring. This would disentangle fertilization success from zygote survival, something we were not able to do in our case study on brown trout, and which would be useful in polygynandrous or promiscuous systems.
Regarding traits affecting components of reproductive success, we illustrated our approach with body size only, a trait which is known to affect intrasexual competition and intersexual preference in brown trout and other salmonids (
Beyond the analysis of experimental data, the parameters estimated in a model such as the one presented here can readily be included in individual based models of sexual interaction, which implement mating as a stochastic process the success of which may be influenced by the phenotype of both individuals involved (
ZG, CT and JL designed the experiment, collected and analyzed the data, and wrote the manuscript, OL analyzed the genetic data, AE designed the experiment and revised the manuscript, LR analyzed the behavioral data, SG ran the experimental facility. ZG: 25%, CT: 20%, OL: 10%, AE: 5%, LR: 10%, SG: 5%, JL: 25%.
Authors  Contribution  ACI 
ZG  0.25  2 
CT  0.2  1.5 
OL  0.1  0.67 
AE  0.05  0.32 
LR  0.1  0.67 
SG  0.05  0.32 
JL  0.25  2 
This study was funded by the INTERREG Atlantic Aquatic Resource Conservation program (AARC) funded by the European Union. The experimental procedures were approved by the Service for Forest Fauna and Flora of the Guipuzkoa Deputation and the French National Office for Water and Aquatic Environments (authorization n° 6420120923005). The experiment was done in the experimental facility Ecologie Comportementale des Poissons (ECP) of the UMR INRA/UPPA 1224 ECOBIOP, Bordeaux INRA Center. We thank five anonymous peers who reviewed the manuscript through the Peerage of Science platform (peerageofscience.org).