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Zhi-Feng Ding, Chun-Lan Zhang, Wen-Sui Zhang, Qian-Min Yuan, Long-Wu Wang, Gang Ren, En Li, Hui-Jian Hu, Wei Liang. Determining the level of extra-pair paternity in yellow-bellied prinias, a socially monogamous passerine. Zoological Research. doi: 10.24272/j.issn.2095-8137.2020.079
Citation: Zhi-Feng Ding, Chun-Lan Zhang, Wen-Sui Zhang, Qian-Min Yuan, Long-Wu Wang, Gang Ren, En Li, Hui-Jian Hu, Wei Liang. Determining the level of extra-pair paternity in yellow-bellied prinias, a socially monogamous passerine. Zoological Research. doi: 10.24272/j.issn.2095-8137.2020.079

Determining the level of extra-pair paternity in yellow-bellied prinias, a socially monogamous passerine

doi: 10.24272/j.issn.2095-8137.2020.079
Funds:  This work was supported by the National Natural Science Foundation of China (31572257 to H.J.H., 31660617 to L.W.W., 31472013 and 31970427 to W.L.) and Guangdong Academy of Sciences (GDAS) Special Project of Science and Technology Development (2018GDASCX-0107)
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Determining the level of extra-pair paternity in yellow-bellied prinias, a socially monogamous passerine

doi: 10.24272/j.issn.2095-8137.2020.079
Funds:  This work was supported by the National Natural Science Foundation of China (31572257 to H.J.H., 31660617 to L.W.W., 31472013 and 31970427 to W.L.) and Guangdong Academy of Sciences (GDAS) Special Project of Science and Technology Development (2018GDASCX-0107)
Zhi-Feng Ding, Chun-Lan Zhang, Wen-Sui Zhang, Qian-Min Yuan, Long-Wu Wang, Gang Ren, En Li, Hui-Jian Hu, Wei Liang. Determining the level of extra-pair paternity in yellow-bellied prinias, a socially monogamous passerine. Zoological Research. doi: 10.24272/j.issn.2095-8137.2020.079
Citation: Zhi-Feng Ding, Chun-Lan Zhang, Wen-Sui Zhang, Qian-Min Yuan, Long-Wu Wang, Gang Ren, En Li, Hui-Jian Hu, Wei Liang. Determining the level of extra-pair paternity in yellow-bellied prinias, a socially monogamous passerine. Zoological Research. doi: 10.24272/j.issn.2095-8137.2020.079

    Previous work based on molecular evidence has shown that most socially monogamous birds follow a genetic polyandrous mating system. However, our knowledge about avian mating systems is heavily biased toward the north temperate zone, with data on tropical birds remaining relatively scarce. This uneven distribution of both phylogenetic and spatial sampling has hampered our understanding and interpretation of results. In this study, we investigated the frequency of extra-pair paternity (EPP) in a tropical population of yellow-bellied prinias (Prinia flaviventris) in Guangxi, southern China. A total of 129 individuals belonging to 24 nests were sampled, among which 12 out of 83 chicks (14.46%) in seven nests were found to be EPP offspring. In nests in which all nestlings were sampled, only five out of 56 chicks were EPP offspring, accounting for an unbiased EPP rate of 8.93%. This rate is below the average rate of EPP in the family Sylviidae. The possible causes of EPP in prinias and the occurrence of EPP in birds with high resource investment and intensive parental care are discussed. This study highlights the value of genome-wide markers in determining relatedness in a wild bird species without a reference genome.

    More than 90% of all bird species are socially monogamous (Lack, 1968). In socially monogamous species, one expects breeding pairs to have low mating success variance due to limited sexual selection forces on males and females, and thus both sexes would play more cooperative roles in parental care (Remeš et al., 2015). However, even for sexually monomorphic species, both males and females can copulate with non-social partners through extra-pair mating, although it is usually difficult to discover such a reproductive strategy in socially monogamous birds without determining parentage using DNA samples. Based on meta-analysis, Brouwer & Griffith (2019) found that nearly 76% of socially monogamous passerine species (255 species) exhibit rates of extra-pair paternity (EPP thereafter). However, despite numerous studies collecting data on EPP rates in many species (a total of 342), this represents less than 4% of bird species and 44% of bird families (Brouwer & Griffith, 2019). This bias in sampling (i.e., uneven distribution of sampling both phylogenetically and geographically) has hampered our understanding and interpretation of results. Thus, to obtain comprehensive insights into variation in overall rates of EPP, more studies across representative bird species from various regions are needed, especially in Asia, where few studies have been reported on this topic (Figure 6 in Brouwer & Griffith, 2019).

    Several factors have been proposed to explain variation in EPP (reviewed in Brouwer & Griffith, 2019; Griffith et al., 2002; Westneat & Stewart, 2003), which can be grouped into adaptive and non-adaptive hypotheses, each including the role of ecology, life history, and/or genetic diversity. Among them, the constrained female hypothesis proposed by Gowaty (1996) is considered a possible explanation for variation in EPP. This hypothesis states that females are constrained in regard to EPP because of limited energy. For example, females are likely constrained by the requirement of resource investment for egg production (Cordero et al., 1999) and by greater investment in parental care (i.e., a trade-off between female parental investment and certainty of EPP, Trivers, 1972). Therefore, a higher level of EPP would be expected under increasing food abundance (Hoi-Leitner et al., 1999).

    The above hypothesis might be explicable for small passerines with certain life-history traits, e.g., short life span and intensive reproductive investment in regard to number of offspring reproduced and parental care (Sæther, 1988). In addition, male parental care is negatively related to paternity, i.e., a low level of EPP will occur when the rates of male parental care are high (Møller & Cuervo, 2000). Based on these assumptions, short-lived species with intensive parental care should exhibit a low rate of EPP. However, recent reviews suggest that a single factor is unlikely to explain all patterns of EPP (Griffith et al., 2002), especially for poorly studied species in tropical zones (Macedo et al., 2008).

    The yellow-bellied prinia (Prinia flaviventris) is a small passerine bird with a monogamous social mating system (Ding et al., 2007, 2016, 2017a). This species demonstrates subtle sexual dimorphism (color difference in upper back), unique seasonal changes in tail length (with shorter tails in the breeding season than in the non-breeding season; Ding et al., 2007), and wide distribution in southern China. Previous work has shown that yellow-bellied prinias display high parental investment during the breeding season and may suffer energy deficits (Ding et al., 2016, 2017b). This species has an average clutch-size of 4.34 ± 0.84 (n=18), and exhibits elaborate biparental care, with both parents participating in all breeding activities such as nest building, egg incubation, and nestling feeding (Ding et al., 2016, 2017b). Specifically, yellow-bellied prinias show enormous overlap between spring molting and breeding activities (both are energy-consumptive behaviors) as well as higher feeding frequencies (compared to sister species with similar body size belonging to the same family) and relatively higher annual productivity than the nine passerine bird species with similar body mass (Ding et al., 2016, 2017b).

    In this study, we determined the level of EPP in yellow-bellied prinias using parental analysis based on genetic variants of genome-wide polymorphic markers, i.e., single-nucleotide polymorphisms (SNPs). Conventionally, genetic-based parental analysis for birds employs polymorphic microsatellite markers (Burke & Bruford, 1987; Gibbs et al., 1990). Given the rapid developments of high-throughput whole-genome sequencing, genome-level SNPs obtained from cost-effective genotype approaches should provide a more powerful and time-effective solution (Flanagan & Jones, 2019; Hauser et al., 2011; Manichaikul et al., 2010; Weinman et al., 2014). Based on known life-history traits of the studied species, we predicted that yellow-bellied prinias would likely display a low level of EPP because they are more energetically limited.

    Data collection from a breeding population of yellow-bellied prinias was carried out in Nonggang National Nature Reserve (N23°39′, E107°04′), Guangxi, southern China, from May to July in the 2013 breeding season (see Ding et al., 2017a; Yang et al., 2014 for details). Birds were banded and blood samples were collected in the field. Blood samples were obtained via brachial venipuncture of the left wing of adults and at least one chick from each clutch following standard protocols. These samples were stored in 96% ethanol and kept in a cool condition in the field before transfer to a –40 °C freezer. All experiments conducted in this study complied with the current laws of China, where they were performed. All experimental procedures were in accordance with the Animal Research Ethics Committee of Hainan Provincial Education Centre for Ecology and Environment, Hainan Normal University (Permit No. HNECEE-2012-002).

    In total, 129 individuals from 24 nests were collected. Among these, one nest (Nest ID: C) lacked data on the female parent, one nest (Nest ID: O) lacked data on the male parent, and 11 nests had incomplete sampling of nestlings (range 1–4 missing individuals). In total, our dataset consisted of 46 social parent birds and 83 nestlings for subsequent sequence analysis (Table S1). Complete broods (n=56 nestlings) were sampled in 13 nests, including one nest with a missing sample from the social mother.

    DNA extraction for all samples was performed with a DNEasy Blood and Tissue Kit (Qiagen, Germany) following the manufacturer’s protocols for blood. We then quantified DNA concentration and quantity using three methods: i.e., (1) bands based on 1% agarose gel electrophoresis; (2) Nanodrop (Wilmington, USA) test for DNA purity (OD260/OD280); and (3) Qubit® DNA Assay Kit with a Qubit® 2.0 Fluorometer (Life Technologies, USA) to measure DNA concentration.

    Genotyping-by-Sequencing (GBS) library preparation and sequencing were performed using the protocols in Elshire et al. (2011) at NovoGene Bioinformatics Technology Co., Ltd., China (www.novogene.cn). In brief, individual genomic DNA was digested with two restriction enzymes, i.e., MseI and HaeIII. The digested DNA was then ligated using one of 96 uniquely barcoded sequencing adaptor pairs (Elshire et al., 2011; Morris et al., 2011). Library amplicons between 250 and 600 bp were extracted from agarose gel and the quality of the libraries was quantified using a Qubit High Sensitivity Assay Kit (Novogene, China). Finally, the libraries were sequenced using the Illumina HiSeq2500-PE125 platform (Illumina, USA), which generates 150 bp paired-end reads. Data with sequenced reads were deposited in Dryad: https://doi.org/10.5061/dryad.73n5tb2v7 (Ding et al., 2020). The raw data obtained by high-throughput sequencing were filtered to remove adapters, missing data, and low-quality reads. After these steps, we obtained 61.27 Gb of clean data, with an average of 474.96 Mb of data (336.11–713.09 Mb) for each individual. We used the standard STACK (v2) program (Catchen et al., 2011, 2013) to align the sequences into matching stacks using the command “Ustacks” with the parameters “-t gzfastq -i -m 3 -M 3 -p 15 -d -r –f –o”. We then built the catalog from this set of samples and created a set of consensus loci, merging alleles together using the command “Cstacks” with the parameters “b 1 –o –s –n 2 –p 15”. In the case of a genetic cross, a catalog was constructed from the parents of the cross to create a set of all possible alleles expected in the progeny of the cross. We searched sets of stacks against a catalog produced by “Cstacks” using the command “Sstacks” with the parameters “-b 1 –c –p 15”. In the case of a genetic map, stacks from progeny were matched against the catalog to determine which progeny contained which parental alleles. We finally corrected genotypes using the command “Sstacks” with the parameters “-b 1 –P -r 1 -c -s -t CP”. After these steps, to exclude SNP calling errors, only high-quality SNPs (coverage depth ≥5 and l, rate of missing data ≤0.5) were selected for further analyses. Genotype data were cleaned using PLINK v1.07 (Purcell et al., 2007).

    Because of incomplete sampling of parents and nestlings in some nests, we were not able to apply the conventional parentage program, CERVUS (Kalinowski et al., 2007; Marshall et al., 1998), which assigns parents to offspring based on the possibility that the genotypes of parents and offspring match. Instead, we used two methods with different principles to infer relatedness within and between nests, and thus identify potential EPP nestlings.

    First, to visualize the genetic relationships among individuals within nests, we reconstructed a neighbor-joining tree using the program “fneighbor” in PHYLIP v3.5 (Felsenstein, 1993). This method is based on the uncorrected genetic P-distance (Nei & Kumar, 2000) between each pair of individuals (dyad) using individual SNP genotypes. The resulting phylogenetic tree was visualized in MEGA v6 (Tamura et al., 2013). Because genetic relationships between nestlings could be either full-siblings (no EPP offspring) or half-siblings (EPP offspring), we expected individuals from the same nest to have shorter genetic distances than individuals among nests, and that nestlings would be closely related to their mother. If nestlings in a nest were not closely related to their social father, they were considered EPP offspring sired by another male. Because the sampling of all possible males in tropical habitats is difficult, it is highly unlikely we would be able to find the genetic father of EPP offspring. Overall, this method offers rapid and qualitative detection of EPP offspring when sampling is not perfect but is not a quantitative means to offer relatedness estimates.

    Second, we estimated pairwise relatedness using a population genetic method, in which identical-by-descent (IBD) statistics between dyads are determined based on the estimation of allele frequencies at each SNP under Hardy-Weinberg Equilibrium (HWE) assumptions. Because population substructures and high levels of inbreeding deviate from the HWE and thus affect parental analysis (Huang et al., 2018), we ascertained several population genetic estimates, such as observed and expected heterozygosity of genotypes, inbreeding coefficients (FIS), and population substructures for the study population using POLYRELATEDNESS v1.4 (Huang et al., 2016). We estimated the relatedness of all samples in all nests using KING v2.1.3 (Manichaikul et al., 2010). This method estimates the kinship coefficient using genome-wide SNPs with no missing sites. The analytical framework of KING accurately estimates genetic distance between a pair of individuals as a function of their allele frequencies and kinship coefficients with or without population substructure. According to the method description and program recommendations (Manichaikul et al., 2010), a kinship coefficient threshold between 0.177 and 0.354 for a pair of individuals (a dyad) indicates a full-sibling or parent-offspring relationship (first-degree relative). Half-siblings (second-degree relative) have kinship coefficients between 0.088 and 0.177 and third-degree relatives have kinship coefficients between 0.022 and 0.088. Unrelated individuals have a negative kinship coefficient value or a value that never exceeds 0.022 (Manichaikul et al., 2010). These threshold values are based on simulations using a high density of SNPs. For smaller datasets, missing data, heterozygosity rates, and rare alleles may influence the estimation of kinship coefficients. Thus, we further checked the values between individuals within a nest and plotted kinship coefficients using the R program (R Core Team, 2018). We considered an individual to be an EPP offspring if it was not closely related to one of its parents (usually the male) or did not show a full-sibling relationship (half-sibling or unrelated) with at least one other nestling within the same nest.

    We successfully sequenced 129 individuals using the GBS approach based on the Illumina short-read sequencing platform. In total, 12 nests had complete data, with the remaining nests lacking information on some nestlings and/or parent birds (Supplementary Table S1). Because there is currently no genome available for closely related species of prinias, we generated genetic maps using the STACK pipeline and acquired a total of 532 411 SNPs. For each individual, we obtained an average of 2 738.85±1 676.07 (mean±SD, same below) SNPs, with an average heterozygosity of 1 115±792.33 (heterozygosity rate of 39.33%±6.23%) (Figure 1A). After all filtering and correction steps, 210 388 SNPs were retained for further analysis.

    Figure 1.  Summary of SNPs and genetic relationships among yellow-bellied prinia individuals

    The neighbor-joining tree based on uncorrected genetic P-distances using individual SNP genotypes of the yellow-bellied prinia showed that individuals within the same nest formed an inclusive cluster. We identified 24 clusters, representing the 24 sampled nests. In all cases, nestlings were non-exclusively clustered together with their female parent (Figure 1C). Most male parents were also clustered with nestlings of the same nests, except for two males (S3 and TT1), suggesting that they were not the genetic father of their specific nestlings. In the nest CC, two nestlings were clustered with the male (CC7), whereas the other three offspring were clustered with the female (CC6). Therefore, these three nests likely contained EPP offspring.

    Using the program POLYRELATEDNESS, we determined certain population genetics of our study population. The observed and expected heterozygosities of genotypes were 0.1012 and 0.1277, respectively. The estimated inbreeding coefficient (FIS) was 0.166, suggesting a low level of inbreeding, which may have a neglected effect on parental analysis (Huang et al., 2018). In addition, we did not detect any population substructure signatures (global Fst=0), meaning that the overall samples could be treated as a single population.

    We obtained a matrix of pairwise kinship coefficients based on 642 non-missing SNPs with a minimum allele frequency ≥0.05 using KING. As expected, individuals among nests had negative values, indicating unrelated relationships (data not shown). We obtained pairwise kinship coefficients between dyads within each nest (Supplementary Table S2) and plotted these values based on thresholds of different relative degree (Figure 1B). The average kinship coefficient of this population was 0.1747±0.09. We found parents of each nest were usually unrelated individuals, except in nests X and G, where parents had a positive kinship value, indicating the cause of inbreeding in this population. In three nests (CC, TT, and S), we found evidence of unrelated relationships between males and nestlings (n=3, 4, 1, respectively). We also detected other candidate EPP individuals (Table 1), which showed consistent patterns of kinship coefficients, as described above. In a few cases, although we detected a lower degree of relatedness within a nest, e.g., AA, D, G, and q, we were able to determine the occurrence of EPP for certain individuals. We assumed that these low kinship coefficients may be an artefact of SNP data due to the low rate of heterozygosity.

    Nest ID.No. of nestlings and parentsNo. of relationships1st degree relative2nd degree relative3rd degree relativeUnrelatedEPPFull brood

    Table 1.  Summary of degree of kinship coefficients and number of EPP offspring in each nest of yellow-bellied prinia

    Based on the above results, 12 out of 83 chicks (14.46%, 95% CI=6.89%–22.02%, Rohlf & Sokal, 1981) from seven nests were found to be the offspring of extra-pair copulations. If we only consider fully sampled nests (13), five out of 56 chicks from three nests were EPP offspring, accounting for an unbiased EPP rate of 8.93% (95% CI=1.46%–16.40%) and a rate per nest of 23.07% (95% CI=5.04%–53.79%). Notably, we did not find EPP offspring in the two nests (G and X) with inbred parents.

    In this study, we found EPP rates ranging from 8.93% to 14.46%. In addition, the sampled prinia population was characterized by a relatively low level of genetic diversity and low level of inbreeding. Overall, using two methods with different principles, we showed that the likelihood of EEP was fairly low in the yellow-bellied prinia population.

    Bird species within a family tend to exhibit similar rates of EPP (Brouwer & Griffith, 2019). In Sylviidae birds, EPP rates are about 10% (Brouwer & Griffith, 2019), which is slightly higher than that observed for the yellow-bellied prinias (8.93%, unbiased EPP rate). Nevertheless, family-level variation in EPP is still striking, even when considering monogamous passerine bird species with biparental care (Brouwer & Griffith, 2019). Our observations, in contrast to those reported by meta-analysis, may be due to differences in phylogeny and geographical variation. For example, previous research has reported that EPP rates are quite different across continents, with average EPP levels of 11% in Europe, 20% in Africa, 23% in Australasia, 17% in South America, and 20% in North America (Brouwer & Griffith, 2019). In addition, variation in EPP may be related to different study methodologies and/or estimate quality. As few studies have reported on EPP rates in China, whether the low level of EPP presented in this study is a general or unique phenomenon needs further investigation.

    Nevertheless, we suggest that the low EPP rate in yellow-bellied prinias may be related to energy constraints, as both parents exhibit high energy-consuming behavior during the breeding season (Ding et al., 2007, 2016, 2017b). Indeed, a negative relationship between female and/or male parental investment and certainty of EPP has been reported in other studies (e.g., Bonier et al., 2014; Dixon et al., 1994; Perlut et al., 2012; Petrie & Kempenaers, 1998). During breeding, both parents engage in nest-building, which is regarded as an energetically and temporally expensive activity (Mainwaring & Hartley, 2013). Furthermore, partner and nestling feeding are frequently observed for both parents (Ding et al., 2016). Tail molting, which is another energy-demanding behavior (Hoye & Buttemer, 2011), also occurs during the breeding period. Taken together, these energetically costly activities in breeding yellow-bellied prinias may prevent extra-pair copulations, leading to a low level of EPP. Indeed, it is important to note that the low level of inbreeding in yellow-bellied prinias may indicate that females engage in extra-pair copulation to reduce inbreeding depression. However, this needs further confirmation. In addition, the relatively low population genetic diversity may also contribute to the low level of EPP as females obtain little scope for good genes (different males with similarly low genetic variability would be expected to engage in EPP rarely, Møller & Cuervo, 2003; Petrie et al., 1998). Finally, evidence shows that male parental care is negatively related to the frequency of EPP (Møller & Cuervo, 2000), and the high rate of male parental care, as seen in yellow-bellied prinias, may prevent male extra-pair copulations because they not only participate in nest building and egg incubation, but also in nestling feeding.

    In conclusion, the present study showed that EPP rates are low in yellow-bellied prinias, which may be related to energy constraints (i.e., female constraint hypothesis). However, caution is needed when interpreting the results due to several study limitations, namely the lack of experimental work, consideration of a single population, and interpretation using a single hypothesis (Brouwer et al., 2017; Brouwer & Griffith, 2019). In addition, the incomplete broods included in analysis may have affected the rates of EPP (Sardell et al., 2010). That is, incomplete sampling of offspring and males could underestimate or overestimate the frequency of EPP and restrict the use of genotype-matching approaches like CERVUS. Nevertheless, we applied an analytical framework using both phylogenetic and population genetic approaches, which were based on thousands of genome-wide SNP makers. Therefore, the results of this study are more powerful than results using tens of microsatellites (Weinman et al., 2014). Taken together, the merits and limitations of this study highlight the necessity for collecting more data at multiple temporal and spatial scales on this species and other prinia birds to gain insight into the underlying mechanisms of low-level EPP.

  • Fieldwork, including capturing and sampling birds, was carried out under permission from the Nonggang National Nature Reserve, Guangxi, China.

  • Supplementary data to this article can be found online.

  • We declare that the authors have no competing interests.

  • H.H. and W.L. conceived and designed the study. L.W. sampled and collected data in the field. Z.D., C.Z., W.Z., Q.Y., G.R., and E.L. performed laboratory work and data analyses. Z.D. wrote the draft manuscript. H.H. and W.L. improved the manuscript. All authors read and approved the final version of the manuscript.

  • We are grateful to Yang Liu and Zhe-Chun Zhang from Sun Yat-Sen University, and Kang Huang from Northwest University for their kind help with parental analysis and constructive comments. We would like to thank Jun-Tao Hu for assistance in figure preparation. We also thank Ian Will from the University of California, Berkeley, USA, and Ai-Wu Jiang, De-Meng Jiang, Qiu-Li Huang, and Yun-Gao Hu for their assistance with fieldwork.

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