Introduction
Polyandrous fertilization is practiced in many fish
hatcheries around the world where pooled milt from multiple males is mixed with
a single female’s eggs (Kekäläinen et al. 2010; Lumley et al. 2016). This fertilization strategy is usually
followed to obtain non-genetic (Squires et al. 2012; Lewis and Pitcher 2017) and genetic benefits (Kekäläinen et
al. 2010; Sagebakken et al. 2011).
In many species of different taxa, polyandrous females produce eggs being
higher in number, smaller in size, greater in viability and larger in yolk
volume (Ward 2000; Omkar 2010; Kawazu et al. 2017) that ensure higher
fertilization and hatching success (Jennions et al. 2007; Byrne and Whiting 2008).
Evidence also shows that polyandrous females produce offspring having
comparatively larger body size (Maklakov and
Lubin 2006) and higher survival rate (Croshaw
et al. 2017) than the
monandrous one.
The underlying mechanisms of these benefits are thought
to be mediated through good genes (Yasui 1997),
sperm competition (Firman and Simmons 2008)
and sperm-egg interaction (Evans and Sherman
2013). Non-genetic benefits are comparatively easy to quantify, while genetic
benefits demonstration faces a lot of challenges that need to consider all the
possible factors influencing offspring fitness. Although many studies in
different taxa have already unveiled that polyandry can enhance offspring
fitness, only a limited number of studies were conducted to explore the
influence of polyandry on the fitness of fish offspring (Kekäläinen et al. 2010;
Sagebakken et al. 2011), and
to date, no result has been found on this issue in a commercially important
aquaculture species. Therefore, this study was carried out to explore whether
polyandrous fertilization strategy could provide any benefit to the fish
breeders of a commercially important Indian major carp, Labeo rohita (Hamilton
1822).
The
Indian major
carp (L. rohita), one of the popular
culture species in the Indian sub-continents, which was produced at 1,843
tonnes (3% of world aquaculture finfish production) in 2016 (FAO 2018). Millions of people are engaged
throughout its production system where a large number of hatcheries are in
operation to produce larvae for the culture of this species. The %20656-660_files/image002.png)
Fig. 1: Experimental design showing total number of broodstocks
(i.e., 30 males and 10 females), and their spawning and larval rearing
processes after diving them into two fertilization groups (e.g., monandry and polyandry). The entire spawning process was
divided into 10 batches in which milt from three males and eggs from a single
female were used during each batch. A total of 10 trials were conducted to
obtain the data from 40 families. Immediately after collection, weights of
total milt and eggs were measured and mixed well to have random samples. 2 mL
of eggs was collected from each female with a new syringe, and simultaneously
1.5 mL milt was collected using another new syringe and mixed with the eggs for
monandrous fertilization, while 0.5 mL milt from each male was collected for
polyandrous group (‘R’ indicates -
replication). 1 mL of eggs was collected in tubes for counting and
imaging later. The fertilized
eggs were shifted to an assigned plastic container of 2 liter capacity,
facilitated with aerated water flow, and incubated them at ambient temperature
until the maximum hatching occurred. 30
larvae from each replication per family were reared for 14 days for the observation
of their fitness (‘n’ indicates the total number of larvae per fertilization
group)
poor quality of eggs and milt, lower rate of
fertilization and hatching, poor larval quality, etc. are the major problems
facing these hatcheries (Mohan 2007; Sahoo et al. 2017). The polyandrous
fertilization technique could be an alternative option together with other
strategies (e.g., broodstock management, genetic selection, etc.) to mitigate these losses.
Materials and
Methods
Experimental
approaches
Sexually mature same sized 30 males (1.59 ± 0.05 kg) and
10 females (1.33 ± 0.02 kg) were sorted up in this study to conduct a full-sib
and half-sib breeding experiment (Fig. 1). Induced spawning was accomplished
following the protocols of Jhingran and Pullin
(1985). The experiment was conducted during the first natural spawning season to
have good quality of gametes (Chattopadhyay
2017), while collection and mixing of milt and eggs in all trials were
done at the same time to avoid sequential effects (Khara 2015).
After the fertilization, the hatchlings in the incubator
were estimated and stocked family-wise for three days until commencing the
external feeding. Then survival and details of visually deformed hatchlings
were recorded from which 30 good offspring were reared family-wise in a glass
aquarium (50 cm × 29 cm × 30 cm) for two weeks to assess their fitness. The pH
and dissolved oxygen (DO) of water were checked daily. The offspring were fed
to their apparent satiation level twice a day (Rahman
et al. 2020). Finally, the
offspring number was recorded to estimate their mortality rate. Photograph of
each offspring following ice-bathed anaesthetization was taken by a digital
camera for the determination of total length and body area using the Image J software (v. 1.46). The study
was carried out up to this larval stage because most local farmers practice
this system for nursing, larval rearing and marketing purposes (Rahman et al.
2020).
Statistical
analysis
All the analyses were performed using ‘R’ version 3.6.3 (R Development Core Team 2020). The Shapiro-Wilk test
of normality and Levene's tests for homogeneity
of variance were done with ‘one way tests’ package. For any
comparison of a measured trait between two fertilization groups, the ANOVA
model was performed (using ‘car’ package) for normally distributed and homogenous
traits, whereas Kruskal-Wallis (K-W) test
was applied for traits not normally distributed by any transformation but
homogenous, and the Welch test (W-T) was performed (using ‘car’ package) when a
variable was not normally distributed as well as not homogenized.
The linear and nonlinear mixed effects (NLME) models (Pinheiro et al.
2019) were performed using ‘nlme’ package in which the ‘maximum
likelihood (ML)’ method was followed to compare the models. In the model,
fertilization group was included as a ‘fixed factor’ and males’ and females’
body weight and their interaction (males: females body weight) were fixed as
‘covariates’, while the males’ (sire) and females’ (dam) IDs were incorporated
as ‘random effects’. The likelihood ratio test provided the p-values for
the random effects by comparing the full model with a reduced model. To avoid
pitfalls of significance testing, the Cohen’s effect size was calculated (Cohen 1988) using ‘MuMIn’
package. Finally, all other graphs were made using the ‘ggplot2’ package.
Results
The analysis found no significant differences in males’
body weight (ANOVA: F1,38 = 0.001, P = 0.99), standard length (ANOVA: F1,38 = 0.007, P = 0.93) and milt weight (ANOVA: F1,38
= 0.1, P = 0.92) used between
two fertilization groups. Similarly, common females showed no significant
variations in body weight (K-W: χ2 = 0, p = 1.0), standard length (K-W: χ2 = 0, P = 1.0), egg weight (K-W: χ2 =
0, P = 1.0), egg number (ANOVA: F1,38
= 1.34, P = 0.25) and egg
diameter (K-W: χ2 = 0, P =
1.0).
The NLME model revealed no significant variations in
hatching and their deformation rate (Table 1). Interestingly, a significant
difference (t1,35 = 2.08, P < 0.05) was found in offspring survival rate between these two groups
(Fig. 2), while no significant variations were observed in offspring total
length and body area (Table 1). The marginal effect size (R2m = 0.16)
of the model clearly showed the mean difference distribution between two fertilization
groups with a bootstrap of 95% confidence interval (Fig. 3), which is sample
size independent displaying all observed values and avoiding false dichotomy.
Table 1: Results of
the linear and nonlinear mixed effects (NLME) models showing the differences in
reproductive performance between two fertilization groups of Labeo rohita during this study. In the model, DF- degrees of freedom, S.E- standard error, S.D- standard deviation and
L-ratio- likelihood ratio. Significant values are denoted as Italic and
bold at the level of P < 0.05
|
Response trait |
Estimates of variables |
|||||
|
Hatching rate (%) |
Fixed effect |
Estimates |
S.E |
DF |
t-value |
P |
|
Fertilization group |
0.13 |
0.16 |
35 |
0.81 |
0.42 |
|
|
Males body weight (kg) |
-4.74 |
2.38 |
35 |
-1.99 |
0.05 |
|
|
Females body weight (kg) |
-3.20 |
2.06 |
35 |
-1.56 |
0.13 |
|
|
Males: females body weight |
2.49 |
1.32 |
35 |
1.89 |
0.07 |
|
|
Random effect |
Variance |
S.D |
- |
L-ratio |
P |
|
|
Males ID |
0.08 |
0.28 |
|
0.00 |
1 |
|
|
Females ID |
0.08 |
0.28 |
|
0.00 |
1 |
|
|
Residuals |
0.01 |
0.11 |
|
|
|
|
|
Hatchling deformation rate (%) |
Fixed effect |
Estimates |
S.E |
DF |
t-value |
P |
|
Fertilization group |
-0.24 |
0.21 |
35 |
-1.14 |
0.26 |
|
|
Males body weight (kg) |
-0.14 |
3.10 |
35 |
-0.04 |
0.97 |
|
|
Females body weight (kg) |
-1.23 |
2.68 |
35 |
-0.46 |
0.65 |
|
|
Males: females body weight |
0.25 |
1.72 |
35 |
-0.15 |
0.88 |
|
|
Random effect |
Variance |
S.D |
- |
L-ratio |
P |
|
|
Males ID |
0.14 |
0.37 |
|
0.00 |
1 |
|
|
Females ID |
0.14 |
0.37 |
|
0.00 |
1 |
|
|
Residuals |
0.02 |
0.14 |
|
|
|
|
|
Offspring survival rate (%) |
Fixed effect |
Estimates |
S.E |
DF |
t-value |
P |
|
Fertilization group |
0.20 |
0.09 |
35 |
2.08 |
0.04 |
|
|
Males body weight (kg) |
-1.64 |
1.43 |
35 |
-1.15 |
0.26 |
|
|
Females body weight (kg) |
-1.18 |
1.23 |
35 |
-0.96 |
0.34 |
|
|
Males: females body weight |
0.82 |
0.79 |
35 |
1.03 |
0.31 |
|
|
Random effect |
Variance |
S.D |
- |
L-ratio |
P |
|
|
Males ID |
0.03 |
0.17 |
0.00 |
1 |
|
|
|
Females ID |
0.03 |
0.17 |
0.00 |
1 |
|
|
|
Residuals |
0.004 |
0.06 |
|
|
|
|
|
|
Fixed effect |
Estimates |
S.E |
DF |
t-value |
P |
|
Offspring total length (mm) |
Fertilization group |
0.08 |
0.25 |
35 |
0.31 |
0.76 |
|
Males body weight (kg) |
-6.28 |
3.70 |
35 |
-1.69 |
0.09 |
|
|
Females body weight (kg) |
-5.46 |
3.19 |
35 |
-1.71 |
0.09 |
|
|
Males: females body weight |
2.74 |
2.05 |
35 |
1.34 |
0.19 |
|
|
Random effect |
Variance |
S.D |
- |
L-ratio |
P |
|
|
Males ID |
0.19 |
0.44 |
|
0.00 |
1 |
|
|
Females ID |
0.19 |
0.44 |
|
0.00 |
1 |
|
|
Residuals |
0.03 |
0.16 |
|
|
|
|
|
Offspring body area (mm2) |
Fixed effect |
Estimates |
S.E |
DF |
t-value |
P |
|
Fertilization group |
-0.16 |
0.57 |
35 |
-0.28 |
0.78 |
|
|
Males body weight (kg) |
-1.78 |
8.46 |
35 |
-0.21 |
0.83 |
|
|
Females body weight (kg) |
-2.68 |
7.29 |
35 |
-0.37 |
0.72 |
|
|
Males: females body weight |
-0.17 |
4.68 |
35 |
-.0.04 |
0.97 |
|
|
Random effect |
Variance |
S.D |
- |
L-ratio |
P |
|
|
Males ID |
1.005 |
1.0 |
|
0.00 |
1 |
|
|
Females ID |
1.005 |
1.0 |
|
0.00 |
1 |
|
|
Residuals |
0.14 |
0.38 |
|
|
|
|
Discussion
In this study, the size of brood,
quality and quantity of diet, and spawning procedures were
maintained throughly to minimize any variation because of these factors. The
experimental animal was handled cautiously to avoid any physiological
stress. Moreover, the spawning procedures and random selection of the equal
sized parents were tried to minimize their effects. However, parental genetic
quality, egg-sperm interaction, and parental non-genetic materials might be the
plausible reasons for the higher offspring survival in polyandrous group.
In ‘good genes hypothesis’, males vary in their genetic
quality, which is the main interest of females to mate with (Cutrera et al.
2012). Unfortunately, females are unable to assess these genes directly (Neff 2000) and therefore, they prefer to mate
with multiple males to achieve the highest benefits from the superior males (Jennions and Petrie 2000). Evidence shows that
superior males produce good quality sperm, which have higher paternity success
through sperm competition (Gage et al. 2004) as well as increase
the offspring fitness (Eilertsen et al. 2009). In the present
study, the higher offspring survival in polyandrous group could be because of
sperm competition in which superior males might fertilize the maximum number of
eggs. Unfortunately, the present study failed to assess the sperm traits due to
the very remote location of hatchery that has very limited laboratory
facilities. Moreover, sperm concentration was not possible to count because of
high fat contents. At this point, total milt volume was considered only to be
an indicator of male’s quality following the suggestions of some previous
studies (Kowalski and Cejko 2019; Rahman et al. 2020).
%20656-660_files/image004.png)
Fig. 2: The offspring
survival rate (%) between two fertilization groups where ‘M’ (M1-M30) on the
top of each bar denotes the respective male ID and ‘F’ (F1-F10) indicates the
common female ID, while the number at the bottom of each bar is the family ID
(1-40)
%20656-660_files/image006.png)
Fig. 3: The estimation plot of offspring survival rate model displaying the
marginal effect size with a mean difference between two fertilization groups of
L. rohita
Evidence has shown that polyandrous strategy can ensure
inbreeding avoidance (Michalczyk et al. 2011) and increase
outbreeding (Burdfield-Steel et al. 2015), which are usually
the outcomes of sperm-by-eggs interactions (Evans
and Marshall 2005; Alonzo et al. 2016).
Studies
have revealed that ovarian fluid and gamete-recognition proteins can modulate
fertilization success of genetically compatible males (Evans and Sherman 2013). Thus, egg-sperm interaction during
fertilization could be responsible for higher offspring survival in polyandrous
group.
Parents can transfer non-genetic information (e.g.,
chromatin
modifications, RNAs and proteins) to offspring through gametes (Giesing et al. 2011;
Casas and Vavouri 2014), which play important roles in offspring fitness
and development. In European whitefish, offspring, fertilized from low
temperature treated sperm, acquired larger body size and showed higher swimming
performance than those of high temperature group (Kekäläinen et al. 2018).
In three-spined
sticklebacks, offspring of predator-exposed mothers exhibited tighter shoaling
behavior than those of non-predator exposed mothers (Giesing et al. 2011). Thus, it
could be possible in the present study that parental non-genetic information
might influence the offspring survival. However, further studies are needed to
explore how (underlying mechanisms) and why (genetic or non-genetic purposes)
they prefer polyandrous rather than monandrous reproductive tactics.
Conclusion
Overall, this study provides an important information to
the spawners of this species about how to obtain good quality larvae by
following the polyandrous fertilization.
Acknowledgments
The authors
are indebted to MH Rahman of FMRT Discipline, Khulna University for ensuring
required facilities for brood stock maintenance and husbandry. This study was
conducted by a grant-aid offered by Bangladesh Bureau of Educational
Information & Statistics (grant no.: LS2016107), Ministry of Education,
Bangladesh.
Author Contributions
Md. Moshiur Rahman, Muhammad Abdur Rouf, and Sk.
Mustafizur Rahman designed the experiment. Soma Kundu and Md. Shahin
Parvez conducted the experiment and collected the data. Md. Moshiur Rahman, Md. Shahin Parvez, and Muhammad Abdur Rouf performed the analysis. Md. Moshiur Rahman, Md. Asaduzzaman, Md. Mostafizur Rahman, Roshmon Thomas Mathew,
Yousef Ahmed Alkhamis and Sheikh. Mustafizur Rahman
prepared the draft manuscript, and also provided extensive support and feedback on further data
analysis and finalized the manuscript. All authors commented on the manuscript
drafts.
Conflicts of Interest
The authors declare no conflicts of interest.
Data
Availability
We hereby declare that the data related to this study
are available with the corresponding author and will be produced on demand.
Ethics
Approval
This work was carried out under the School of Life
Science of Khulna University’s Animal Ethics approval (KUAEC-2019/07/8).
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