Background

Genetic diversity is a prerequisite for the adaptation and survival of species (Schonewald-Cox et al. 1983; Lande 1988). Indeed, the level of genetic diversity exhibited by a species reflects its evolutionary potential (Frankel and Soulé 1981). Therefore, evaluations of genetic diversity are essential for species management and the development of conservation strategies, especially for endangered species (Hedrick 2004). Generally, endangered species exist in small populations and exhibit low rates of genetic diversity (Hinkson and Richter 2016). Small populations are more prone to genetic drift, which can randomly reduce genetic variability and population fitness by increasing homozygosity (Lande 1999; Willi et al. 2007; Freeland et al. 2011). Therefore, species with low genetic diversity may lack the ability to adapt to environmental changes, and their potential for long-term survival may be diminished (Liu et al. 2003). However, relatively ancient endangered species (e.g. those that have become endangered over the last 150 years due to habitat fragmentation) may retain high genetic diversity due to long generation times (Lippé et al. 2006).

The Rufous-backed Bunting (Emberiza jankowskii), a small grassland passerine with strict habitat requirements, was a common species throughout its historical breeding range. Historically, E. jankowskii has bred in Heilongjiang, western and eastern Jilin Province in China, extreme northeastern North Korea, and south of the Russian Far East (Wang et al. 2011). Because of habitat fragmentation, human activities, and low reproductive success, populations of this species have declined dramatically over the last 50 years (Jiang et al. 2008). E. jankowskii has been listed as endangered on the IUCN Red List since 2010 (BirdLife International 2018). From 2011–2016, we surveyed the breeding distribution of E. jankowskii in China and demonstrated that this species has vanished from most of its historic breeding sites (Wang et al. 2011; Han et al. 2018). Fortunately, we discovered 13 new E. jankowskii breeding sites in Inner Mongolia. However, these new breeding sites were seriously degraded because of over-grazing, urbanization, and agriculture (Han et al. 2018). During the course of our survey from 2012 to 2018, the populations of E. jankowskii at the new breeding sites began to decline and become increasingly fragmented (Han et al. 2018).

Experimental studies and field investigations have shown that fragmented populations may lose allelic richness and genetic diversity (Buza et al. 2000; Tomimatsu and Ohara 2003). Previously, major histocompatibility complex (MHC class II B) genes were used to examine the genetic diversity of E. jankowskii, and results showed relatively low levels of MHC polymorphisms (Li et al. 2017). MHC genes were mainly relevant to immune function (Mak and Sanderson 1985; Schad et al. 2004). These findings indicate that MHC polymorphisms are affected by selection. The selection pressures on MHC genes can produce discrepancies between patterns of MHC and neutral variation in natural populations (Alcaide et al. 2010; Sutton et al. 2011). MHC polymorphisms will not provide a precise measure of genome-wide diversity. We suggest the use of neutral markers to evaluate the genetic diversity of E. jankowskii.

To investigate the genetic diversity and structure of E. jankowskii based on neutral markers, we (1) used mitochondrial DNA sequences and microsatellite markers to compare this level of genetic diversity to previously published levels of genetic diversity in other avian species, (2) estimated whether E. jankowskii was inbreeding because of low population sizes, (3) determined the genetic structure among populations of E. jankowskii, and (4) discussed the next stage of E. jankowskii conservation work.

Methods

Sample collection

We used a direct search method to find E. jankowskii nests in Gahaitu (GHT), Aolunhua (ALH), Lubei (LB), Chagantaoligao (CG), and Kundu (KD), in Inner Mongolia, China between May and August of 2012 and 2013 (Fig. 1). Blood samples were taken by venipuncture (brachial vein) under the wings of 7–8 day old nestlings. Samples were mixed with absolute ethyl alcohol in 1.5 mL tubes and preserved at − 20 °C.

Fig. 1
figure 1

Geographical distribution of Emberiza jankowskii in Inner Mongolia, China. Black dots represent sampling sites. Black circles indicate cities. The numbers of individuals sampled are given in parentheses

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According to the population density of Han et al. (2018) and the area of our sample sites (GHT 3.87 km2, ALH 4.74 km2, LB 3.91 km2, CG 1.27 km2, and KD 2.55 km2), we assessed the population sizes of five sampling sites. The population size, sampling nests in each sample site, and the distance among sampling sites are presented in Table 1.

Table 1 Population size, sampling nests, and distance of sample sites
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Mitochondrial DNA amplification and microsatellite genotyping

Total genomic DNA was isolated using an AxyPrep Blood Genomic DNA Miniprep Kit (Aisijin, Hangzhou, China). All nestlings in the same nest have the same parents, we sampled only one randomly selected nestling per nest (a total of 59 individuals). We used polymerase chain reactions (PCRs) to amplify hypervariable region I (HVI) of the mitochondrial control region using the novel primers A629 (5′-GTATTCAACCGACCACTT-3′) and S19 (5′-CTCCGAGACCTACGACCTG-3′). PCR amplifications were performed in 20 μL volumes containing 10 μL of 2 × Taq PCR Master Mix (Sangon Biotech), 0.25 μL of each primer (10 μM), and 1 μL template DNA. The PCR cycling program was as follows: an initial denaturation step at 94 °C for 5 min; followed by 35 cycles of denaturation at 94 °C for 30 s, annealing at 53.7 °C for 30 s, and extension at 72 °C for 30 s; then a final extension at 72 °C for 10 min. All the sequences were deposited in GenBank under accession numbers MH670589–MH670647.

We amplified 10 microsatellite loci from each of the 59 individuals using 10 primer pairs: Ecit3, Dpμ16, Escμ1, Escμ2, Escμ3, Escμ4, LS2, Ecit2, HXW20, and embc30 (Hanotte et al. 1994; Mundy and Woodruff 1996; Dawson et al. 1997; Wonke et al. 2007; Yu et al. 2011; Segelbacher and Schuphan 2014). We optimized the reaction volumes and PCR protocols for each primer pair. Microsatellite loci were scored by Sangon Biotech (Shanghai, China). Final fragment sizes were calculated in GeneMarker v1.91 (SoftGenetics LLC, State College, PA, USA) and scored manually.

Data analysis

Genetic diversity, Hardy–Weinberg, and linkage disequilibrium tests

We calculated various mitochondrial diversity parameters in DnaSP v5.10 (Rozas et al. 2003), including haplotype diversity (Hd), nucleotide diversity (π), and the number of polymorphic and phylogenetically-informative sites (Nei and Tajima 1981; Graur and Li 2000).

We used GENEPOP v4.6 (Rousset 2008) to test for deviations from the Hardy–Weinberg equilibrium and to identify alleles exhibiting linkage disequilibrium among all loci across all populations, using 10,000 dememorizations, 10,000 batches, and 10,000 iterations per batch. Critical significance levels were adjusted for multiple comparisons using the sequential Bonferroni correction (α = p/n; Rice 1989). Loci were checked for the likelihood of null alleles with Micro-checker (Van et al. 2004).

Genetic statistics were used to measure the genetic variability of the microsatellites, observed heterozygosity (Ho) and expected heterozygosity (He) under Hardy–Weinberg were also computed with GENEPOP 4.6. The number of alleles, the number of private alleles, and gene diversity were computed with FSTAT v2.9.3 (Goudet 2001).

Gene flow and inbreeding

We calculated the fixation index (FST) in Arlequin v3.5 and Genetix v4.05 (Nei 1978; Excoffier et al. 1992; Belkhir et al. 1996; Schneider et al. 2000), and gene flow (Nm) was estimated using the equation Nm = 0.25 (1 − FST)/FST (Excoffier et al. 1992; Schneider et al. 2000). The inbreeding coefficient (FIS) was calculated with GENEPOP 4.6.

Population structure

For our phylogenetic analyses, the D-loop sequences of E. schoeniclus (AJ243929), E. cirlus (AJ243928), and Miliaria calandra (AJ243925) were downloaded from GenBank and used as outgroups. The optimal models of nucleotide substitution were determined using the Akaike Information Criterion (AIC) in jModelTest v2.1.10 (Posada and Thomas 2004). The best model of nucleotide substitution, as selected by the AIC, was TVM + G. We used maximum likelihood (ML) in PHYML (Guindon et al. 2005), maximum parsimony (MP) and neighbor-joining (NJ) in PAUP* v4.0 (Swofford 2001), and Bayesian inference (BI) in MrBayes v3.1.2 (Huelsenbeck and Ronquist 2001; Ronquist and Huelsenbeck 2003) to construct phylogenies. For the ML analysis, we set the parameters of the custom model based on the AIC results. Networks were constructed by median-joining in Network 5.0 (Bandelt et al. 1999).

We assessed population structure based on a Bayesian analysis of microsatellites. We used STRUCTURE v2.3.3 (Pritchard et al. 2000) to assign individuals to K = 1–5 data clusters, with the higher number of clusters corresponding to the number of populations assessed (Flanders et al. 2009; Mao et al. 2010). We performed 10 replicate STRUCTURE runs for each K value. Monte Carlo Markov chains (MCMCs) were admixed for 2,000,000 cycles using independent models of allele frequencies, with the first 20% of the cycles discarded as ‘burn-in’. The best K value was defined using the ΔK statistic as described by Evanno et al. (2005).

We used the Bayesian method of MIGRATE-N to assess the microsatellite migration rates (Beerli 2006). Five long chains of 1.0 × 106 generations were used for all runs of MIGRATE-N. The same three-step process was conducted on the actual data to form the test statistic. To ensure adequate mixing of Markov chains, we used Markov-coupling, where 4 independent chains were allowed to mix according to an adaptive heating scheme (1.0, 1.2, 1.5, 6.0).

Demographic estimates

To reconstruct the demographic history of E. jankowskii, the historical demographic dynamics of E. jankowskii were inferred from Bayesian skyline analyses implemented in BEAST 2.6.2 (Bouckaert et al. 2019). We used D-loop sequences for this analysis and selected the mean substitution rate at 1.0 to estimate time in units of substitution/site. Independent MCMC analyses were run for 1.0 × 108 steps, sampling every 105 steps, and discarding 1000 samples as burn-in. The results visualized skyline plots with TRACER 1.5 (Drummond and Rambaut 2007). Sequence divergence rate of 2%/Myr was applied to the mtDNA D-loop region (Shields and Wilson 1987).

Results

Genetic diversity

The genetic diversity of E. jankowskii based on mitochondrial DNA and microsatellites is shown in Table 2. Amplification of the control regions (544 bp) from 59 individuals yielded 16 haplotypes with 14 polymorphic sites and 8 parsimony informative sites. The total haplotype diversity (Hd) was 0.874 (standard deviation (SD) = 0.023), and the overall nucleotide diversity (π) was 0.0032 (SD = 0.0003).

Table 2 Genetic diversity parameters of E. jankowskii
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We detected no significant departures from the Hardy–Weinberg equilibrium after correcting for the number of pairwise comparisons (n = 49; α = 0.001), with the following exceptions: locus Ecit3 in GHT, LB, and ALH; locus Escμ4 in GHT; locus HXW20 in GHT and CG; and locus embc30 in GHT and ALH. After Bonferroni correction, Fisher’s exact test identified no significantly linked pairs of loci (n = 225, α = 0.0002). The micro-checker analysis did not suggest any scoring errors due to stuttering, nor did it suggest any large allelic dropout in any loci across all populations. Null alleles may have been present in locus Ecit3 in GHT, LB, and ALH; locus Escμ4 in GHT; locus LS2 in LB; locus HXW20 in GHT, ALH, and CG; and locus embc30 in GHT and ALH. Because we identified departures from the Hardy–Weinberg equilibrium and null alleles in the loci Ecit3, HXW20, and embc30 in two or more populations, we deleted these three loci from subsequent analyses.

The number of alleles per locus ranged from 4 to 16 across all populations (mean ± SD: 9.00 ± 5.03), with the most alleles found in the GHT population (7.00 ± 3.87) and the least found in the KD population (3.29 ± 1.11). Private alleles with a frequency over 10% were observed in the KD (PA = 2) and LB (PA = 1) populations. The gene diversity per population was 0.71 ± 0.19 in the GHT population, 0.72 ± 0.14 in the LB population, 0.67 ± 0.15 in the ALH population, 0.75 ± 0.13 in the CG population, and 0.58 ± 0.21 in the KD population. The highest observed heterozygosity (Ho) and expected heterozygosity (He) were observed in the CG population (0.7959 and 0.7551, respectively), while observed heterozygosity was lowest in the ALH population (0.5929) and expected heterozygosity was lowest in the KD population (0.5970) (Table 2).

Average inbreeding coefficient (FIS) of E. jankowskii in Inner Mongolia was estimated as 6.92%. Within population, FIS ranged from − 0.1633 (KD) to 0.1545 (LB) (Table 2).

Based on mitochondrial DNA and microsatellite data, the fixation index (FST) was highest between the CG and LB populations (0.18462) and the CG and KD populations (0.06296), and lowest between the GHT and LB populations (0.00546) and CG and LB populations (0.00411). Thus, gene flow was highest between the GHT and LB populations (45.5376) and CG and LB populations (60.5779), and lowest between the CG and LB populations (1.1041) and CG and KD populations (3.7206) (Tables 3, 4).

Table 3 Pairwise fixation index (FST) (lower triangle) and gene flow (Nm) (upper triangle) of five E. jankowskii populations based on D-loop sequences
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Table 4 Pairwise fixation index (FST) (lower triangle) and gene flow (Nm) (upper triangle) of five E. jankowskii populations based on microsatellite data
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Population structure

The NJ, MP, ML, and BI phylogenies were highly congruent. All E. jankowskii haplotypes clustered together, in a clade marked by shallow sequence divergences with no well-supported basal haplotype (Fig. 2). Additional analyses were conducted to assess the genetic structure of five populations by building a median-joining network. The network showed three different second levels (hap 1, 2 and 7), with some significant associations. Based on expectations from the coalescent theory, hap 2 was the haplotype with the highest frequency and most connections, and thus was the most likely ancestral haplotype (Fig. 3).

Fig. 2
figure 2

Phylogenetic tree of E. jankowskii recovered by maximum likelihood analysis. Bootstrap support and posterior probabilities are shown only for nodes highly supported by at least two phylogenetic reconstruction methods (i.e., ≥ 60% maximum likelihood, maximum parsimony or neighbor-joining bootstrap support or ≥ 0.60 Bayesian posterior probability). Outgroups (obtained from GenBank) are indicated by their species names. Colored squares represent different populations. Details of the populations are given in the text

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Fig. 3
figure 3

Median-joining network of 16 mtDNA haplotypes of E. jankowskii. In the network, different colors represent different populations

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Although the best separation was obtained at K = 2 (ΔK = 1.68), our STRUCTURE simulation indicated that homogenization was occurring across all E. jankowskii populations in Inner Mongolia; clear signs of admixture were observed in all subpopulations (Fig. 4, showing K = 2 and 3 only).

Fig. 4
figure 4

Structure clustering of E. jankowskii individuals based on microsatellite genotypes. Clusters are shown for K = 2 and K = 3. Population acronyms are shown on the X-axis. Details of the populations are given in the text

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Our analysis of molecular variance (AMOVA), based on the distance matrices among individuals, indicated that the overall variation could be partitioned into two levels (Table 5). The proportion of variation attributable to within-population differences was particularly high (mtDNA: 96.76%; microsatellites: 98.71%). The fixation indices (FST) were 0.03 based on mtDNA, and 0.01 based on microsatellites (P > 0.05). All estimates of genetic differentiation indicated a relatively low level of genetic differentiation across all populations.

Table 5 Analysis of molecular variance (AMOVA) among E. jankowskii mitochondrial D-loop sequences and microsatellite data across five populations
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The results of Migrate-N show that there were significant gene flows between different populations. There are migrations in and out among the five populations (Table 6).

Table 6 Estimates of mutation-scaled effective population size (θ) and mutation-scaled effective immigration rate (M) for the five populations of E. jankowskii using MIGRATE-N
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Demographic history

Due to the existence of gene flow and the absence of genetic structure, we placed E. jankowskii in Inner Mongolia into one group for BSP analysis. The Bayesian skyline plots (BSP) revealed that the population size of E. jankowskii in Inner Mongolia is stable (Fig. 5).

Fig. 5
figure 5

Historical demography of E. jankowskii inferred from mtDNA D-loop sequences. Bayesian skyline plots for E. jankowskii in Inner Mongolia, showing effective population size as a function of time. The upper and lower limits of light blue trend represent the 95% confidence intervals of HPD analysis

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Discussion

High-level genetic diversity and inbreeding

Surveys of genetic diversity in endangered species are essential for the evaluation of the evolutionary and adaptive potential of these species, such surveys are also vital for the development of effective conservation strategies to protect threatened populations, especially those that are fragmented (Zhang et al. 2017; Stevens et al. 2018). In this study, we investigated the genetic diversity of E. jankowskii for the first time, employing mitochondrial DNA sequences and microsatellite data. E. jankowskii had a higher level of haplotype diversity (0.874) and nucleotide diversity (0.0032) in its D-loop gene sequence than other endangered species, such as Nipponia nippon (Hd = 0.386, π = 0.0007) (Zhang et al. 2004) and Megulus palustris sinensis (Hd = 0.772, π = 0.0014) (Zhang 2007). Interestingly, the haplotype and nucleotide diversity of E. jankowskii was also higher than one species of least concern, Parus varius (Hd = 0.5328, π = 0.0017) (Du 2011). The microsatellite results also indicated that E. jankowskii had a higher degree of genetic diversity than all passerines birds (He = 0.24‒0.59), which were listed in the article of Jamieson et al. (2006), and also higher than N. nippon (He = 0.50, Ho = 0.47) (He 2007), and M. palustris sinensis (He = 0.53, Ho = 0.38) (Zhao 2012). The observed heterozygosity of E. jankowskii was higher than Gallus gallus (Ho = 0.37–0.41) and Perdix dauuricae (Ho = 0.58), but the expected heterozygosity of E. jankowskii was a little lower than Gallus gallus (He = 0.72–0.77) and Perdix dauuricae (He = 0.72) (Cao et al. 2010; Masroor et al. 2012).

It is well established that decreases in genetic variation may reduce fitness and adaptability (Allentoft and O’Brien 2010). Generally, endangered species tend to have small population sizes and low levels of genetic diversity due to historical reasons, and reduce fitness (Matocq and Villablanca 2001). Unexpectedly, E. jankowskii exhibited high levels of genetic variability. This indicated that habitat fragmentation and degradation have not led to the rapid loss of genetic diversity in this species. There may be several explanations for this result. First, it is possible that E. jankowskii populations were historically very large (Fu and Chen 1966). Second, the observed reduction in E. jankowskii population sizes occurred during past 50 years (Jiang et al. 2008). This means that due to the small number of generations, E. jankowskii has not experienced a rapid decline in genetic diversity (Welch et al. 2012). Third, birds with higher dispersal abilities may therefore maintain high levels of genetic diversity due to gene flow. Fourth, hybridization and introgression with closely related species can also contribute to the maintenance of high levels of genetic diversity (Mallet 2005). However, whether hybridization and introgression had occurred between E. jankowskii and E. cioides requires further investigation.

Although there are some methods available to minimize inbreeding (Pusey 1987), inbreeding may be inevitable in small, isolated populations and play a role in determining the probability of local extinction events (Frankham 2005). In this study, the average inbreeding coefficient of E. jankowskii in Inner Mongolia was estimated at 0.069 (Table 2), higher than other Passerine birds (FIS = 0.024–0.054) and the captive panda population at the Chengdu (n = 37, FIS = 0.045) (Sun et al. 2010; Abalaka et al. 2015). This indicated that a high inbreeding risk existed for E. jankowskii in Inner Mongolia or may have occurred to some extent. However, high levels of genetic diversity within E. jankowskii populations indicated that inbreeding depression did not occur.

Absence of genetic structure and high levels of gene flow

Genetic population structure imparts important information about species ecology and evolutionary history, including dispersal ability, adaptation, and speciation (Rousset 2004). Genetic population structure can also help to determine important species conservation units in patchy, fragmented habitats (Paetkau 1999). Here, no clear phylogeographic structure was observed across the five E. jankowskii populations in either the phylogenetic tree or the cluster analyses. The FST values of the five populations were relatively low (mtDNA: 0.00546–0.18462; microsatellites: 0.00411–0.06296). In addition, the FST values between populations ALH and LB; KD and LB; ALH and KD; GHT and LB; GHT and CG were negative (Tables 2, 3). Negative values suggest a lack of support for genetic differentiation between these population pairs (Foster et al. 2006).

Gene flow among populations is an important mechanism that maintains intraspecific genetic diversity (Slatkin 1994). Species with low levels of gene flow show more genetic differentiation among populations than do species with high levels of gene flow (Hamrick et al. 1991). In E. jankowskii, various levels of gene flow were calculated among populations (Tables 3, 4). This was consistent with the AMOVA results, which identified more genetic variation within populations than between populations (Table 5). There was an asymmetric gene flow between certain populations (Table 6).

Conservation implications

The five populations of E. jankowskii sampled here exhibited an absence of genetic structure and various levels of gene flow. Thus, E. jankowskii in Inner Mongolia is a metapopulation. We therefore suggest that, for the purposes of species management, E. jankowskii in Inner Mongolia should be defined as one protected unit.

Although E. jankowskii showed high genetic diversity, and E. jankowskii population sizes ranged between 9800 and 12,500 individuals (Han et al. 2018), but the number of individuals in every single patch is small (Table 1) and with high inbreeding risk. The local population in each patch might vanish due to habitat loss (Wang et al. 2011). Therefore, conservation policies limiting habitat destruction are necessary to mitigate the threat of species extinction.

The E. jankowskii in Inner Mongolia is still at risk of human disturbances, habitat loss, and habitat degradation (Han et al. 2018). The extremely limited habitat of this species (similar to the habitat requirements of Prunus sibirica and Stipa baicalensis on the Mongolian steppes) might make E. jankowskii very sensitive to habitat fragmentation, water pollution, global warming, and the degradation of habitats suitable for P. sibirica and S. baicalensis. Thus, habitat conservation and population management are urgently needed for this avian species. Indeed, the high genetic variability of E. jankowskii indicates the ability to recover if provided with effective protection (Evans and Sheldon 2008). Therefore, we suggest that management efforts for this species focus on the protection of the current breeding habitats and elimination of over-grazing. Establishing specific natural areas to preserve the breeding habitats of E. jankowskii and publicizing the importance of protecting this avian species to local residents might also be effective and should be considered. We hope that our work contributes to the establishment of a management and protection system for E. jankowskii. Our laboratory will continue to carry out the long-term monitoring of this species.

Future prospects

This study has shown that E. jankowskii exhibits a high level of genetic diversity. We found that E. cioides, a closely related species (Alström et al. 2008), had overlapping distributions with E. jankowskii in Inner Mongolia. Several studies have indicated that species in nature are often incompletely isolated for millions of years after speciation. There are many cases of hybridization and introgression occurred among closely related sympatric species (Mallet 2005). Whether hybridization and introgression had occurred between E. jankowskii and E. cioides and whether this contributes to the high level of genetic diversity of E. jankowskii require further investigation.

Conclusions

E. jankowskii in Inner Mongolia is a metapopulation, so we suggest that the E. jankowskii in Inner Mongolia should be considered one protected unit for management purposes. The conservation efforts should concentrate on protecting the current breeding habitats and forbidding over-grazing.