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Article

Sex-Based Differences in Multilocus Heterozygosity in Wild Boar from Spain

by
Javier Pérez-González
*,
Sebastián J. Hidalgo de Trucios
and
Sebastián P. Hidalgo Toledo
Unidad de Biología y Etología, Grupo de Investigación en Recursos Faunísticos, Cinegéticos y Biodiversidad, Facultad de Veterinaria, Universidad de Extremadura, 10003 Cáceres, Spain
*
Author to whom correspondence should be addressed.
Diversity 2024, 16(10), 610; https://doi.org/10.3390/d16100610
Submission received: 24 July 2024 / Revised: 30 August 2024 / Accepted: 19 September 2024 / Published: 1 October 2024
(This article belongs to the Collection Feature Papers in Animal Diversity)
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Abstract

:
Wild boar (Sus scrofa) populations are increasing worldwide, leading to health, economic and conservation issues. Assessing genetic structure and diversity can aid in the effective monitoring and management of wild boar populations. Processes related to sexual selection and behavioral differences between sexes support the importance of considering sex in measuring genetic diversity. In this study, we investigated the genetic diversity of wild boar in southwestern Spain by comparing multilocus heterozygosity (MLH) in males and females. We collected tissue samples from 142 culled individuals and 146 fetuses during routine hunting activities and used 16 microsatellite markers to quantify MLH. Paternity analyses were conducted to infer the genotypes of reproductive males. Our results indicated that the sampled individuals constituted a unique polygynandrous population without clear genetic structure. We found that males tended to exhibit lower MLH than females, with reproductive males showing significantly lower MLH than females. We discuss the selection and demographic processes that might explain our results. We highlight the importance of sex-balanced culling for population control, as well as the use of sex-balanced samples for monitoring genetic diversities.

1. Introduction

The wild boar (Sus scrofa) is a widely distributed mammal around the world. The natural range of the species extends across Eurasia and north Africa, but it has been introduced to the Americas and Australia [1], where wild boar and feral pigs act as invasive species [2,3,4]. Wild boar populations have increased since the 1980s globally, leading to subsequent effects on natural communities, the risk of spreading infectious diseases, crop damage and vehicle collisions [2,5,6]. Policies to limit wild boar populations, such as increase hunting pressure or implementing fertility control, have been proposed [7,8]. However, these policies have not been sufficient to regulate wild boar populations adequately [6,9,10], so the increasing and expansion of populations, as well as human–wildlife conflicts may continue in the future [6,11]. Monitoring and implementing of management strategies are mandatory due to the high ecological and economic importance of wild boar populations [12,13,14].
The assessment of genetic structure and diversity is a key aim in the monitoring and management of wild boar. On one hand, genetic diversity has been linked to the pathogen resistance and disease progression of wild boar individuals to tuberculosis [15]. Therefore, genetic diversity in wild boar populations may help limit the spread of infectious diseases in nature [16]. Hunting can affect the effective size of populations and, in turn, promote genetic drift, thereby reducing genetic diversity [17]. This highlights the importance of monitoring the genetic diversity of game species like wild boar [16]. On the other hand, measurements related to genetic structure can provide relevant information for managing wild boar populations. For instance, investigating genetic structure can help define genetic clusters and management units, or determine effective population sizes, gene flow and translocations among populations, hybridization levels with domestic pigs and occurrences of inbreeding [1,15,18,19,20,21,22,23,24,25,26,27]. Moreover, genetic structure assessments can shed light on the effects of human activities and Quaternary climatic fluctuations on the genetic composition of wild boar [18,21,28,29].
Human activities can directly influence the genetic diversity of wild boar populations. For instance, translocations can promote hybridization with domestic pigs [30,31] and genetic introgressions into genetically distinct wild boar populations [28,31]. Hybridization is common in areas where wild boar has been translocated and acts as an invasive species [32,33], but it has also been observed in their native range. For instance, Mary et al. [34] found that 3.6% of wild boars in France had over 40% domestic pig ancestry in their genomes. Iacolina et al. [31] found that 10.7% of their European wild boar samples had more than 10% domestic pig ancestry, with this genetic introgression mainly detected in areas of Austria, Bosnia and Herzegovina, Bulgaria and Servia. In contrast, other areas, such as some regions in Spain, did not have hybrids in their wild boar populations [31]. In addition to translocations and hybridization, certain hunting methods might impact population structure and, consequently, influence the mating system and genetic composition of individuals [35,36,37,38,39]. For example, in the Iberian Peninsula, stalking and nocturnal single hunt at bait are common hunting methods focused on adult males, which can bias the population sex ratio [40]. Additionally, it has been shown that culling adult females is more effective in controlling wild boar populations [41,42], and this management action might also bias the sex ratio. These changes in the sex ratio of populations might impact the functioning of reproductive behaviors, with important consequences for genetic diversity. The proportion of females that mate with multiple males during the same cycle (multiple paternity; [43,44]), the mate choice based on genetic similarity [45] or the genetic diversity contributed by males and females to the following generation [46] can significantly impact the genetic diversity of wild boar populations and can be modulated by sex ratio. Other behavioral processes differing between males and females, such as the larger displacements and home ranges of males [47] and the higher response of females to hunting risks [48], support the consideration of sex in the measurement of genetic diversity of wild boar populations.
In this study, we investigated the genetic diversity of wild boar in southwestern Spain by comparing multilocus heterozygosity (MLH) at microsatellite markers between groups of individuals differing in sex. With this assessment, we address the effects of environmental, demographical and behavioral processes on the genetic composition of wild boar. Our results can have implications for the monitoring and the management of increasing wild boar populations.

2. Materials and Methods

2.1. Study Area and Sample Collection

The study was conducted in the Extremadura region, located in southwestern Spain (Figure 1). In the study area, five main habitats can be found: mountain ranges, mountain slopes and riverbanks that are normally covered by Mediterranean forest; flat lands that are mostly covered by dehesas (pasture areas with oak trees) and dry crops; and sedimentary basins that are mostly covered by irrigated crops (Figure 1). Wild boar has traditionally been restricted to areas with Mediterranean forests, which have acted as refuges, but it is now expanding to other types of habitats.
We collected tissue samples from wild boar harvested during ordinary hunting events that took place at 13 sampling points (Figure 1). These were collective hunting events known as Spanish monterias, which use packs of dogs to drive animals towards the hunters’ locations. Although hunters in this type of hunting are primarily interested in adult males with large trophies, they can hardly distinguish wild boar appearance during the hunt, resulting in the culling of animals of both sexes and various age classes. Samples were collected from individuals with a chest girth greater than 80 cm to avoid sampling very young individuals. Tissues were stored at −20 °C until subsequent processing. Harvested individuals were sexed, and we determined which females were pregnant by checking the presence of fetuses. We considered males with chest girths greater than 100 cm and large tusks visible without opening the mouth as adults. Additionally, we collected the fetuses of pregnant females. The fetuses were visually sexed, and we collected a piece of tissue from each. Although we collected tissue from all the fetuses, only those weighing more than 20 g were sexed.

2.2. DNA Purification and Genotyping at Microsatellite Markers

DNA was purified using the Biosprint 96 DNA Blood Kit (Qiagen) in a DNA Biosprint 96 instrument. Individuals and fetuses were genotyped with 18 microsatellite markers recommended by the International Society for Animal Genetics (FAO/ISAG). The microsatellite markers were: IGF1, S0002, S0005, S0090, S0101, S0155, S0226, S0227, S0228, S0355, S0386, SW24, SW240, SW72, SW857, SW911, SW936 and SW951. After the polymerase chain reaction, a 3500xL Dx Genetic Analyzer and GenemapperTM software 5.0 (Applied Biosystems, Foster City, CA, USA) were used to determine allele sizes. DNA purification and microsatellite genotyping were conducted in a specialized lab that is a member of the ISAG (Xenetica Fontao S.A., Lugo, Spain).
In the obtained dataset of genotypes, the proportion of missing genotypes was 0.07. In 99.9% of the genotypes of fetuses, one of the alleles matched at least one allele of their mothers. The S0355 marker was monomorphic and was removed from the analyses. We used Microcheker software 2.2.3. [49] to determine the existence of genotyping errors due to null alleles, large allele dropout and the scoring of stutter peaks. The SW857 locus was removed from the subsequent analyses due to detected problems related to homozygote excess and stuttering.

2.3. Genetic Analyses

To determine whether wild boar constituted more than one population in our study area, we used Structure 2.3.4. [50] with all individuals. Ten independent runs were performed for K = 1 to K = 7, each with 100,000 iterations following a burn-in period of 100,000 iterations, to determine the most probable number of genetic clusters. We assumed admixture and correlated allele frequencies. The use of independent allele frequencies did not alter the results. For each identified population, we quantified observed and expected heterozygosities using Genetix 4.05 [51]. Genetix software was also used to calculate Fis values for the identified populations, and to assess significant departures from zero for these Fis values using 1000 permutations.
We selected the litters with at least four fetuses to obtain the genotypes of reproductive males at the microsatellite markers. We used Colony 2.0.7.1 [52] to infer the genotypes of the reproductive males. In the program, we included the genotypes of the pregnant females, the genotypes of the fetuses, the genotypes of the males, as well as the known maternal sibships (i.e., which fetuses belonged to each pregnant female). We also analyzed the best configuration output file to determine which fetuses were sired by each inferred reproductive male and to identify the females that mated with more than one male (multiple paternity).
MLH for each individual and each fetus was obtained as the standardized multilocus heterozygosity described by [53]. We used inbreedR package [54] in R [55] to quantify MLH.

2.4. Statistical Analyses

We compared MLH values between different groups based on sex and age class. We used the following groups: male fetuses, female fetuses, females, males, adult males and reproductive males. MLH was compared between female and male fetuses, between females and males, between females and adult males, and between females and reproductive males. The comparisons were conducted using linear mixed models with MLH as the dependent variable, sex as a fixed factor and sampling point (comparisons females vs. males, females vs. adult males and females vs. reproductive males) or mother within sampling point (comparison female fetuses vs. male fetuses) as random effects. We conducted four linear mixed models, so used a threshold of 0.0125 to determine significant differences (Bonferroni correction for multiple comparison: 0.05/4). Mixed models were performed with nlme package [56] in R.

3. Results

We collected tissue samples from 142 culled individuals and 146 fetuses (Table 1). Out of the 84 females, 35 were pregnant (Table 1), resulting in a mean litter size of 4.17 fetuses per pregnant female.

3.1. Genetic Structure and Genetic Diversity at Sampling Points

Structure results indicated that the most probable number of genetic clusters was one (K = 1; Figure 2). When we represented the membership probability of individuals to genetic clusters for K = 2, K = 3 and K = 4, the results did not detect a clear genetic substructure (Figure S1 in the Supplementary Material). Therefore, we did not detect any genetic substructure in our study area, and all individuals can be considered as belonging to a single population.
For the entire population, observed heterozygosity was 0.642 and expected heterozygosity was 0.647. The Fis value was 0.009 and was not significantly different from 0 (1000 permutations, p > 0.7). The Table 1 shows the mean and standard deviation of individual MLH per sampling point. There was no significant effect of sampling point on the MLH of the individuals (ANOVA, F = 1.107, p > 0.3; Tukey tests for all pairs of sampling point, p > 0.3).

3.2. Paternity Analysis

Out of the 35 pregnant females, 26 had at least 4 fetuses. Paternity analysis with Colony was conducted for these 26 pregnant females. Multiple paternity was found in 4 of these pregnant females (Table 1, Figure 3). Among the four pregnant females with multiple paternity, 2 had 4 fetuses each sired by the same male (one from SF sampling point and one from ALC sampling point; see Figure 3). Therefore, we reconstructed the genotypes of the reproductive males that mated with these 24 pregnant females (22 with single paternity + 2 with multiple paternity). We identified 18 different reproductive males that sired at least 4 fetuses produced by the 24 selected pregnant females. Six of the reproductive males (1, 7, 10, 11, 15 and 16 in Figure 3) sired the fetuses of 2 females each. Therefore, we obtained with Colony the genotypes of 18 reproductive males (Table 1, Figure 3).

3.3. MLH Depending on Sex and Age Class

Male fetuses tended to have lower MLH than female fetuses, but this difference was not significant (Table 2A, Figure 4). Similarly, males generally had lower MLH than females, though the difference was not significant (Table 2B, Figure 4). Adult males had lower MLH than females, but this difference did not reach significance after Bonferroni correction (Table 2C, Figure 4). In contrast, reproductive males had significantly lower MLH than females (Table 2D, Figure 4).

4. Discussion

The wild boar in our study area constitutes a unique polygynandrous population, with observed differences in MLH between some groups of individuals. The largest difference was found between females and reproductive males, with the latter showing significantly lower levels of MLH. Our results might provide insights into factors affecting the genetic diversity of wild boar, which could be used for management purposes.
The wild boar is a large mammal with extensive home ranges and high dispersal distances. For instance, Barasona et al. [57] reported that wild boar in a southern Spanish population had an average home range (95% Kernel method) of 551.33 ha. Laguna et al. [47] found home ranges (95% Kernel method) between 150.56 and 714.47 ha and daily movements between 2278 and 6601 m in southwestern Spanish populations. Moreover, some individuals can travel over 500 km, as demonstrated by a female with three offspring in Slovenia [58]. This high mobility reduces genetic differentiation between sites and increases the likelihood of finding a unique population in relatively large areas. Therefore, wild boar in our study area might be considered as a management unit in which actions taken in one location could impact the entire study area.
The genetic diversity in our study population falls within the range observed in other wild boar populations. For instance, Choi et al. [59] reported observed heterozygosities between 0.422 and 0.836 and expected heterozygosities between 0.506 and 0.859 in wild boars from East Asia. Nikolov et al. [60] found an average Ho of 0.62 and average He of 0.67 in Bulgaria, as well as average Ho of 0.46 and average He of 0.55 in Germany. Rodrigáñez et al. [61] reported average Ho of 0.561 and average He of 0.683 in northeastern Spain. The Fis value close to zero indicates a lack of inbreeding, suggesting that the population does not appear to have genetic diversity issues that would increase susceptibility to infectious diseases [15,16].
We observed cases of multiple paternity, indicating that the studied wild boar population exhibits a polygynandrous mating system. Previous studies also found multiple paternity in wild boar. For instance, Pérez-González et al. [46] reported similar levels of multiple paternity in wild boar from Portugal, Spain and Hungary. High levels of multiple paternity were also found in wild boar from France [43,62] and Germany [63], as well as in feral pigs from the United States [64]. In contrast, low levels of multiple paternity were observed in wild populations from Portugal [65] and France [66]. Multiple paternity in wild boar tends to increase genetic diversity [45] and appears to be situation dependent.
The primary finding related to the aim of this work was that male wild boar tended to present lower MLH than females. We conducted 4 comparisons of MLH (see Table 2), and after adjusting for multiple comparisons, only reproductive males exhibited lower MLH compared to females. The decrease in genetic diversity in wild populations is primarily attributed to genetic drift under low effective population sizes [67]. This might be the case for game species such as wild boar [17]. However, our results show differences in genetic diversity between groups of individuals within the same population. There may be process affecting at the population level with different consequences for males and females. The observed difference in MLH between sexes might be attributed to unintentional selection due to hunting, selection processes acting on males and females or to temporal changes in population demography.
On the one hand, the wild boar is a big game species, and hunting methods such as stalking and nocturnal hunt at bait are focused on males with large trophies. If these hunting methods tend to harvest the most heterozygous individuals [68], then males culled through less selective methods like monterías might exhibit lower multilocus heterozygosities. Despite selective hunting has been shown to alter allele frequencies and may lead to the loss of some rare alleles, its effect over individual heterozygosity is generally weak [17,69]. Furthermore, this unintentional hunting selection does not account for why reproductive males had the lowest MLH.
The cumulative effect of non-excluding sexual selection-related processes might also explain the differences in MLH of male and female wild boar. Sexual dimorphism and the presence of large tusks in wild boar males suggest strong sexual selection pressure on males [36]. Alleles in males that increase the likelihood of siring offspring may lead to increased homozygosity at those loci and adjacent loci [70,71]. This situation might enhance the homozygosity of microsatellite markers that are in linkage disequilibrium with functional regions in the genome. Such ‘local effects’ [72] have been previously observed in several species [73,74], including wild boar [75,76]. However, this explanation requires a strong effect of genes on traits and fitness, but traits in sexually dimorphic species are mainly influenced by many loci with small effects [77,78]. Sexual selection on a trait is likely to impact numerous loci across the genome, each with a low potential to change [79]. Therefore, using a limited number of microsatellite markers (16 in our study) may hardly detect increased homozygosity in males due to differences in sexual selection intensity.
Another process related to selection that might also be associated with increased homozygosity in males involves female mate choice. Females might choose mates based on good genes that can be transmitted to their offspring [36,80], or based on genetic dissimilarity to avoid inbreeding [72,81,82]. Since both criteria are mutually exclusive, females might use one criterion or the other depending on the circumstances [82]. Carranza et al. [83] proposed that the selection criterium might vary depending on the sex of the offspring. In sexually dimorphic species with high variance in male reproductive success, females might select mates with good genes when producing sons and prioritize genetic dissimilarity when producing daughters. As a result, daughters might exhibit higher levels of heterozygosity than sons [83]. These processes of sexual selection might contribute to the tendency of higher homozygosity in male fetuses and might help explain why reproductive males have higher homozygosity compared to females. However, given the variation in litter size and sex ratio influenced by different environmental and evolutionary processes [84,85], as well as the limitations of using microsatellite markers to measure inbreeding [86,87,88], we must be cautious with these considerations.
Finally, demographic changes in an increasing population might also provide explanations for our results. Despite the trend, there was no significant difference in MLH between male and female fetuses or between male and female individuals. Moreover, after the correction for multiple comparisons, adult males and females did not exhibit different MLH. The only significant difference was that reproductive males had lower MLH than females. We collected tissues from a random sample of females than can span a range of ages, while all reproductive males are expected to be relatively old. The genotypes of the reproductive males might have been produced several years ago, whereas the genotypes of some females might be produced more recently, and for other, several years ago. In an increasing population, the genotypes of individuals produced in recent years might result from the contact of genetically different individuals that might proceed from distant areas [89,90]. Conversely, the genotypes of individuals produced several years ago might result from the contact of genetically more similar individuals that might proceed from nearby areas. Therefore, the genotypes of the reproductive males might have been produced in a more stable situation, promoting homozygosity, while at least some females might have been produced in a situation with higher individual exchange between areas. This scenario in an increasing population might also explain the gradual decrease of MLH of males across the age classes (Figure 4).
Determining the process behind the observed sexual differences in MLH is important for designing wild boar-management strategies. Wild boars in our study area, as well as in most of their range [6,10], need to be controlled to mitigate health, economic, safety and conservation issues [2,5,6]. Since females produce the new generations, culling adult females is generally more effective to population control [41,42]. If our results are due to demographic variations in an increasing population, the recommendation of culling mainly adult females does not change. This is because there is no significant difference in MLH between males and females, and the lower MLH in reproductive males would be attributed to biases raised by demography. In contrast, if selective processes that decrease MLH in males are at play, the recommendation of primary cull females should be reconsidered. Maintaining genetic diversity tends to reduce the risk of disease spread [15], and females would harbor higher genetic diversity than males. Therefore, for population control, the culling strategy might need to be sexually balanced. This means removing females to reduce the reproduction capacity of the population while also culling males to help maintain genetic diversity. Removing males may also limit the spread of infectious diseases, as males tend to have larger home ranges than females.
Future studies focusing on sexual selection process and utilizing a significantly larger number of individuals and genetic markers could confirm whether males exhibit significantly lower genetic diversity than females in wild boar. Based on the results of this study, we propose the following key conclusion. If the wild boar population poses a serious risk to humans or human activities, reducing the population should be the primary management objective, with culling focused on adult females. However, if the population control is a medium-long term action, culling should be sexually balanced because males and females are subject to different selection processes [36] and might have different genetic composition. In this case, maintaining appropriate sex ratios in wild boar populations would favor population viability and effective management [39,66]. Additionally, given the potential differences in genetic composition between males and females, we recommend using sexually balanced samples to monitor wild boar genetic diversity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d16100610/s1, Figure S1: Structure results. Membership coefficient of probability for wild boar individuals.

Author Contributions

Conceptualization, J.P.-G. and S.J.H.d.T.; methodology, J.P.-G.; validation, J.P.-G.; formal analysis, J.P.-G.; investigation, J.P.-G., S.J.H.d.T. and S.P.H.T.; resources, J.P.-G.; data curation, J.P.-G.; writing—original draft preparation, J.P.-G.; writing—review and editing, S.J.H.d.T. and S.P.H.T.; visualization, J.P.-G.; supervision, J.P.-G. and S.J.H.d.T.; project administration, S.J.H.d.T.; funding acquisition, S.J.H.d.T. All authors have read and agreed to the published version of the manuscript.

Funding

European Regional Development Fund: Complementary Plan for Biodiversity from MICIU and Junta de Extremadura.

Institutional Review Board Statement

No animal was harvested for the purpose of this study. All samples were collected from carcasses of individuals hunted during ordinary hunting activities, based on hunting plans approved by autonomous government of Extremadura.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to ongoing research.

Acknowledgments

We thank Juan Carlos Caldera, Ignacio Grande and the Official Veterinarian Service of the Junta de Extremadura for their help during sample collection. We also thank all managers and estate owners for their facilities. Two anonymous reviewers made valuable comments that contributed to improve de manuscript. Xenetica Fontao S.L. conducted the works related to DNA purification and the genotyping at microsatellite markers. Antonio Flores and Montaña Caballero assisted with tissue collection. Pilar Gonçalves provided us the tissue samples from CC1 sampling point.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Study area in Extremadura region (Spain), and location of the sampling points (monterías). The figure also shows the distribution of the different types of habitats in the study area. CN: Cañaveral, JA: Jaraicejo, MO: Monroy, CC1: Cáceres 1, CC2: Cáceres 2, SF: Sierra de Fuentes, TO: Torremocha, ALI: Aliseda, MP: Malpartida de Cáceres, CL: Cordobilla de Lácara; ALC: Alcuéscar, TR: Trujillanos, OM: Oliva de Mérida.
Figure 1. Study area in Extremadura region (Spain), and location of the sampling points (monterías). The figure also shows the distribution of the different types of habitats in the study area. CN: Cañaveral, JA: Jaraicejo, MO: Monroy, CC1: Cáceres 1, CC2: Cáceres 2, SF: Sierra de Fuentes, TO: Torremocha, ALI: Aliseda, MP: Malpartida de Cáceres, CL: Cordobilla de Lácara; ALC: Alcuéscar, TR: Trujillanos, OM: Oliva de Mérida.
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Figure 2. Structure results. Mean and standard deviation of log-likelihood values (ten independent runs) of the assessed K values.
Figure 2. Structure results. Mean and standard deviation of log-likelihood values (ten independent runs) of the assessed K values.
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Figure 3. Paternity results from Colony. The figure illustrates which fetuses were sired by each inferred reproductive male (fetus ID on the x-axis and reproductive male ID on the y-axis). Fetuses enclosed within continuous vertical lines belonged to the same litter. Litters from the same sampling point are coded with the same color (refer to Figure 1 for spatial location of each sampling point). Horizontal dotted lines connect fetuses sired by the same reproductive male. Genotypes were reconstructed for reproductive males that sired at least 4 fetuses from the same litter (indicated by reproductive males with black dotted lines).
Figure 3. Paternity results from Colony. The figure illustrates which fetuses were sired by each inferred reproductive male (fetus ID on the x-axis and reproductive male ID on the y-axis). Fetuses enclosed within continuous vertical lines belonged to the same litter. Litters from the same sampling point are coded with the same color (refer to Figure 1 for spatial location of each sampling point). Horizontal dotted lines connect fetuses sired by the same reproductive male. Genotypes were reconstructed for reproductive males that sired at least 4 fetuses from the same litter (indicated by reproductive males with black dotted lines).
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Figure 4. Multilocus heterozygosity (MLH) of wild boar depending on sex and age class (Female Fet.: female fetuses; Females; Male Fet.: male fetuses; Males; Ad. Males: adult males; and Rep. Males: reproductive males). The figure shows means and standard errors of values. Dotted lines represent the four comparisons in Table 2. Letters A, B, C and D in the figure correspond to the comparisons A, B, C and D in Table 2. * Significant difference after Bonferroni correction.
Figure 4. Multilocus heterozygosity (MLH) of wild boar depending on sex and age class (Female Fet.: female fetuses; Females; Male Fet.: male fetuses; Males; Ad. Males: adult males; and Rep. Males: reproductive males). The figure shows means and standard errors of values. Dotted lines represent the four comparisons in Table 2. Letters A, B, C and D in the figure correspond to the comparisons A, B, C and D in Table 2. * Significant difference after Bonferroni correction.
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Table 1. Sample sizes for each sampling point. The table shows the total number of sampled individuals (N), the number of sampled females (N females), the number of adult males (see text for details), the number of pregnant females and the number of sampled fetuses (N fetuses). The table also includes the number of females that mated with more than one male (multiple paternity, MP females), the number of inferred reproductive males (Rep. males) from which we obtained the genotypes (see text for details) and the mean and standard deviation (sd) values for multilocus heterozygosity (MLH).
Table 1. Sample sizes for each sampling point. The table shows the total number of sampled individuals (N), the number of sampled females (N females), the number of adult males (see text for details), the number of pregnant females and the number of sampled fetuses (N fetuses). The table also includes the number of females that mated with more than one male (multiple paternity, MP females), the number of inferred reproductive males (Rep. males) from which we obtained the genotypes (see text for details) and the mean and standard deviation (sd) values for multilocus heterozygosity (MLH).
Sam. PointNN
Females		Adult MalesPregnant FemalesN
Fetuses		MP
Females		Rep.
Males		MLH (Mean)MLH (sd)
CN52215011.0720.182
JA1264416020.9260.206
MO31000000.8770.000
CC154100001.1740.259
CC221133522030.9890.180
SF1282626140.9830.134
TO742310011.0020.175
ALI53226100.9360.111
MC54100000.9740.182
CL1794314021.0660.160
ALC271651147251.0250.197
TR2012300000.9700.172
OM32100001.0400.148
Total142843035146418
Table 2. Results of the linear mixed models for the comparison of MLH between groups of wild boars based on sex and age class. (A) Comparison between female and male fetuses. (B) Between females and males. (C) Between females and adult males. (D) Between females and reproductive males whose genotypes were inferred with Colony. In all models, Female as reference for the Sex fixed factor. DF: degrees of freedom.
Table 2. Results of the linear mixed models for the comparison of MLH between groups of wild boars based on sex and age class. (A) Comparison between female and male fetuses. (B) Between females and males. (C) Between females and adult males. (D) Between females and reproductive males whose genotypes were inferred with Colony. In all models, Female as reference for the Sex fixed factor. DF: degrees of freedom.
ValueStandard ErrorDFtp
(A)Intercept1.0400.0317933.341<0.001
Female fetuses vs. male fetuses Sex (Male)−0.0440.03179−1.4210.159
(B)Intercept1.0250.02012852.061<0.001
Females vs. malesSex (Male)−0.0530.030128−1.7410.084
(C)Intercept1.0250.01910052.683<0.001
Females vs. adult males Sex (Male)−0.0780.038100−2.0680.041
(D)Intercept1.0250.0198852.825<0.001
Females vs. sires Sex (Male)−0.1470.04588-3.2360.002
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Pérez-González, J.; Hidalgo de Trucios, S.J.; Hidalgo Toledo, S.P. Sex-Based Differences in Multilocus Heterozygosity in Wild Boar from Spain. Diversity 2024, 16, 610. https://doi.org/10.3390/d16100610

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Pérez-González J, Hidalgo de Trucios SJ, Hidalgo Toledo SP. Sex-Based Differences in Multilocus Heterozygosity in Wild Boar from Spain. Diversity. 2024; 16(10):610. https://doi.org/10.3390/d16100610

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Pérez-González, Javier, Sebastián J. Hidalgo de Trucios, and Sebastián P. Hidalgo Toledo. 2024. "Sex-Based Differences in Multilocus Heterozygosity in Wild Boar from Spain" Diversity 16, no. 10: 610. https://doi.org/10.3390/d16100610

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