Complete Mitochondrial Genome and Phylogenetic Analysis of Gruiformes and Charadriiformes

Peng Chen1,2, Zuhao Huang3, Chaoying Zhu1, Yuqing Han1, Zhifeng Xu1,

Guanglong Sun1, Zhen Zhang1, Dongqin Zhao4, Gang Ge1 and Luzhang Ruan1*

1School of Life Sciences, State Ministry of Education Key Laboratory of Poyang Lake Environment and Resource Utilization, Nanchang University, Nanchang, 330031, P.R. China

2Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment, 8 Jiang wang miao St., Nanjing 210042, China

3School of Life Sciences, Jinggangshan University, Ji’an, Jiangxi Province, China

4Key Laboratory of Animal Resistance of Shandong Province, College of Life Sciences, Shandong Normal University, Jinan 250014, Shandong Province, P.R. China

ABSTRACT

In this study, we used the next-generation sequencing method to obtain mitochondrial DNA (mtDNA) of Porzana paykullii, Rallus aquaticus and Gallirallus striatus in Gruiformes, and Hydrophasianus chirurgus in Charadriiformes, after which we analysed and compared structure, phylogeny, and taxonomic origin of the Gruiformes and Charadriiformes. Based on sequencing, splicing, and annotating the mtDNA of four birds, the results showed that the lengths of mtDNA were 16,955 bp in Porzana paykullii, 17,149 bp in Rallus aquaticus, 17,647 bp in Gallirallus striatus and 16,855 bp in Hydrophasianus chirurgus, respectively. The base compositions were A > C > T > G in 73 species complete mitochondrial sequences in Gruiformes and Charadriiformes. The total AT content in the 73 species was larger than that of GC. The start codons in protein-coding genes (PCGs) included ATG, GTG, ATT, ATC and ATA, while its stop codons included TAA, TAG, AGG, AGA and the incomplete cipher T. In PCGs, the highest frequency of codon was CTA (Leu). The highest frequency of amino acids was Leu, whereas the lowest was Cys. In phylogenetic analyses, Gruiformes included Grui and Ralli, and Charadriformes included Charadrii, Lari and Scolopaci. The genus Porzana was closest to Porphyrio. Gallirallus striatus and Lewinia muelleri consisted a sister group, while Rallus aquaticus was a separate branch. Hydrophasianus chirurgus (Charadriiformes: Jacanidae) was closely related to Rostratulidae. According to the estimation of divergence time corrected by fossil records of related birds and compared with previous studies, the base divergence time of Gruiformes was 46.33 (58.46~25.60) Ma, the emergence time of the suborders Grui was about 17.62 (29.76~4.15) Ma, and the emergence time of the suborders of the Ralli was about 32.18 (46.17~19.69) Ma. The origin time of Charadriiformes was about 45.44 (58.21~24.67) Ma. The origin time of the Charadrii was 44.52 Ma (52.66 ~ 23.55 Ma). The divergence time of Charadrii, Lari and Scolopaci was about 33.46 (43.44~22.92) Ma, 29.10 (40.67~16.43) Ma and 27.06 (34.83 ~ 12.72) Ma, respectively.

Article Information

Received 03 June 2019

Revised 30 July 2019

Accepted 14 September 2019

Available online 10 January 2020

Authors’ Contribution

PC wrote this article. RL and PC planned this research. ZH, CZ, YH, ZX, GS, ZZ and DZ performed the experiments. GG and RL is the corresponding author of the article

Key words

Next-generation sequencing, Mitochondrial genome, Classification status, Phylogenetic, Divergence time

DOI: https://dx.doi.org/10.17582/journal.pjz/20190603010623

* Corresponding author: [email protected]

0030-9923/2020/0002-0425 $ 9.00/0

Copyright 2020 Zoological Society of Pakistan

INTRODUCTION

Aves originated in theropod out of the Jurassic and belongs to Vertebrata. The direct ancestor of Aves was the descendant of a small dinosaur (Ostrom and Mcintosh, 1967; Zheng, 2015) which was the most abundant class of tetrapod vertebrates (Zheng, 2015). In traditional taxonomy, Aves consists Archaeornithes and Neornithes (Gemmell et al., 1994; Jarvis et al., 2014; Prum et al., 2015; Zheng, 2015). Neornithes mainly consists of the Palaiognathae, which has no flying short wings, and all Neognathae that can fly. The study concluded that the Artistic depiction of asteroidal impact occurred approximately 66 million years (Ma) ago. This period serves as the boundary between cretaceous period and tertiary (paleogene) (k-pg) (Avise, 2004), during which a wide range of species extinction events took place, creating conditions for the differentiation of birds and the generation of new species (Jarvis et al., 2014; Prum et al., 2015). After the k-pg period, the ancestors of Neognathae birds differentiated and formed the largest group of existing birds, including Galloanseres and Neoaves (Jarvis et al., 2014; Prum et al., 2015; Zhang et al., 2017). Galloanseres are included in Galliformes and Anseriformes. Neoaves are included in Galliformes, Passeriformes, Charadriiformes and other more than 20 orders (Sibley et al., 1988; Zheng, 2012; Zheng, 2015).

In the face of such a large number of birds with complex kinship, research scholars were keen to explore the systematic classification of birds and the origin and evolution of birds (Zheng, 1979; Cracraft, 1981; Smith and Clarke, 2015). Early classification of bird systems were based on epigenetics, morphological features, geographical distribution, and behavioural differences, but the results were rather confusing (Jarvis et al., 2014; Prum et al., 2015). Especially the phylogenetic analysis for Gruiformes and Charadriiformes is more chaotic (Jarvis et al., 2014; Prum et al., 2015; García–R et al., 2014). The current research of Gruiformes and Charadriiformes mainly focus on macroecology, behavior and histomorphology (Zheng, 1979; Ripley and Beehler, 1985; Avise, 2004; Zheng, 2012; Smith and Clarke, 2015; Huang et al., 2017). The use of molecular means can more effectively solve controversial issues such as the origin and evolution of birds (Jarvis et al., 2014; Prum et al., 2015). According to traditional taxonomy, there are 143 bird species in Gruiformes, including 12 families and 40 genera (Ripley and Beehler, 1985). Most Gruiformes birds have a small distribution range, and many of them are endangered species, such as Grus japonensis and Grus leucogeranus of Gruidae (Ripley and Beehler, 1985). There are great differences in the body types of birds of Gruiformes, with larger body size of Gruidae species if compers with Rallidae (Zheng, 2012). Charadriformes is a complex group, mainly including small and medium-sized waders, curlew and gulls (Sibley et al., 1988; Zheng, 2012). Charadriiformes includes 8 suborders, 19 families, 94 genera and 384 bird species (Hu et al., 2017), which distribute on all continents, from the poles to the tropics (Sibley et al., 1988). Up to now, more than 300 species of mitochondrial genome sequence information of birds have been published in Genbank (Gao et al., 2009; Zhang, 2015; Cheng, 2017), among which 33 species are Gruiformes (García–R et al., 2014; Chen et al., 2017) and 34 species are Charadriiformes (Smith and Clarke, 2015; Hu et al., 2017).

There have also been a lot of studies on the molecular phylogenetic classification of Gruiformes and Charadriiformes (Fain et al., 2007; Smith and Clarke, 2015; Chen et al., 2017; Hu et al., 2017). But the phylogenetic tree constructed at present was not comprehensive enough to contain species, and the phylogenetic status of many species was controversial (Sibley et al., 1988; Chen et al., 2017). Ruan et al. (2012) used mitochondrial COI and Cytb genes of 15 Gruiformes species to construct phylogenetic trees, Fain et al. (2007) constructed phylogenetic trees and estimated analysis using genes such as Cytb, 12S rRNA and 16S rRNA of 26 Gruiformes species. The time of disagreement mainly includes the Gruidae, Psophiidae, Rallidae and Heliornithidae (Fain et al., 2007). Among them, the Gruidae and Psophiidae were sister group, while Rallidae and Heliornithidae formed sister group. It was believed that the divergence time of Rallidae was 21.8 Ma, Rallidae and Heliornithidae was 42.6 Ma. García–R et al. used 17 PCGs, rRNA and tRNA genes of 17 species mitochondria of Gruiformes to construct a phylogenetic tree and estimate the divergence time. It was believed that the divergence time of Rallidae and Heliornithidae was 52 Ma, and the results were more accurate and reliable (García–R et al., 2014). Gong et al. used a genome-wide sequence of 31 species of Gruiformes (excluding CR sequences) to construct a Bayesian inference (BI) tree. The results showed that Rallidae and Heliornithidae were a sister group, and Gruidae belonged to another branch, but did not estimated the divergence time of Gruiformes (Gong et al., 2017). Gong et al. (2017) was suggested that the genus Gallirallus were sister to Lewinia, and these groups in turn were sister Amaurornis and different from those of García–R (2014). The genus Fulica and the genus Gallinula were one branch, the genus Porphyrio and the genus Coturnicops belong to another branch. while the García–R (2014) considers the genus Porphyrio, genus Fulica and Coturnicops were a branch. While the genus Gallirallus and the genus Lewinia was another branch.

There were also reports on mitochondrial genome sequencing and phylogenetic development in Charadriiformes birds. Based on 12 PCGs markers, Hu et al. (2017) constructed BI and Maximum Likelihood (ML) trees of 40 species to classify Charadriiformes into Charadrii, Lari and Scolopaci, which was more comprehensive, but no divergence time estimate for Charadriiformes. Cheng et al. (2017) studied phylogenetic trees of 18 Charadriiformes birds, and also divided Charadriiformes into the same three suborders. The phylogenetic tree supports the Lari as a suborder, belonging to Charadriiformes. While the suborder of Scolopaci was the newly differentiated Charadriiformes and included Jacanidae and Scolopacidae (Cheng, 2017). It was believed that Charadrii were included Recurvirostridea and Charadriidae, while Recurvirostra avosetta and Haematopus ostralegus was belonged to Recurvirostridea (Chen, 2003). At present, there are few studies on the divergence time of the Charadriiformes. Fain et al. (2007) used the genes of Cytb, 12S rRNA and 16S rRNA of 26 species to construct phylogenetic trees and estimate the divergence time. The results indicated that the base divergence time of Charadriiformes was 74.3 Ma.

Combined with the previous phylogenetic analysis of Gruiformes and Charadriiformes. It was found that due to the lack of molecular data, phylogenetic analysis of Rallidae of Gruiformes was relatively chaotic, the accuracy of estimation of divergence time of Gruiformes was not enough. And the systematic analysis of the divergence time of Charadriiformes was not carried out. Therefore, it was necessary to make a more accurate analysis of phylogenetic development and divergence time of Gruiformes and Charadriiformes. So as to provide favourable reference and sufficient molecular evidence for the improvement of their classification system. This study has collected samples of Gruiformes and Charadriiformes and performed mitochondrial sequencing. Including three species Porzana paykullii, Rallus aquaticus and Gallirallus striatus from Gruiformes, and another bird Hydrophasianus chirurgus from Charadriiformes (Taylor and van Perlo, 1998; Manson and Goldizen, 2000). Combined with mtDNA sequences of four species and data of Gruiformes and Charadriiformes retrieved from GenBank, data of mitochondrial genome of 73 bird species were collected (Table I). In addition, the structure and base ratio of the mitochondrial genome of 73 species of birds of Gruiformes and Charadriiformes were compared, and the use types of starting and ending codons as well as the use frequency of codons and amino acids were compared. Phylogenetic tree was constructed by collecting and sorting mitochondrial genome sequences of Gruiformes and Charadriiformes, analysing the taxonomic status of the four species sequenced in this study, and exploring the relationship between the species of Gruiformes and Charadriiformes. Combined with the known fossil record of birds and the previous research results, this paper estimated the divergence time between the species of Gruiformes and Charadriiformes. So as to provide more molecular data for the study of evolutionary species, conservation aspects and phylogenetic relationships of birds.

 

MATERIALS AND METHODS

Ethics statement

The sample collection was strictly conducted under national ethical guidelines (Regulations for Administration of Affairs Concerning Experimental Animals, China, 1988) for animal husbandry and humane treatment.

Sample collection and DNA extraction

In this study, blood samples from 4 bird species without mitochondrial complete genome sequencing were collected nationwide from Gruiformes and Charadriiformes. Gruiformes including Porzana paykullii, Rallus aquaticus and Gallirallus striatus. The sampling points were Bengbu Anhui, province, Lianyungang Jiangsu, province and Liuzhou, Guangxi province, respectively. Another one species blood samples of Hydrophasianus chirurgus of Charadriiformes collected in Mianyang, Sichuan province. The blood was stored in a 1 ml centrifuge tube, and 20 μL of anticoagulant (0.5% sodium heparin) was added and stored in a refrigerator at – 20 °C. Total DNA was extracted using phenol/chloroform and examined on 1.0% agarose/TBE gel and used as template for PCR reactions.

DNA extraction and sequencing

The mtDNA of four species of birds was extracted and sequenced. First, the blood samples were digested. 10 μL of the sample blood was placed in a 1 mL centrifuge tube, and 375 μL of TE buffer, 20 μL of 10% SDS solution and 5 μL of 20 mg/L protease were added. The total volume of K was 400 μL of mixed digest. Mix the digestive juice, and then bathe in a water bath at 55-65 °C for 10 to 14 hours. Mix the digestive juice at intervals until the blood tissue was completely digested and dissolved.

The total DNA from birds was extracted by phenolic extraction: the cells were first broken up by protease K and SDS, and the proteins were digested. Then the supernatant was extracted by phenolic and phenol-chlorine. And the supernatant liquid was taken after high speed centrifugation (Chen et al., 2017). The centrifuged DNA was washed two to three times with 70% ethanol, dried, dissolved in 100 μL of sterilized double distilled water, and stored in a refrigerator at 4 °C until use. Take 4 μL of total DNA solution, mix with 1 μL of 6×Loading Buffer, and then spot it in agarose gel well. After 120V of constant pressure electrophoresis for 30 minutes, observe total DNA in UV detector or portable UV lamp. Extract the results. The complete sequence of the mitochondrial genome of the three species were sequenced by Beijing Jinnuo Ruijieji Technology Co. Ltd. and the CR partial sequence results of common pheasant and leeches were incomplete and corrected by one generation sequencing. Primers were designed based on the alignment of complete mtDNA sequences of Gallinula chloropus and Rallina eurizonoides by using Primer 5.00 (PREMIER Biosoft International) (Chen et al., 2017) and shown in Supplementary Table I. The complete mitochondrial genome sequence of four species were deposited in Gen-Bank with accession numbers MG200164, MH229988, MH219930 and MH219929, respectively (Table I and Supplementary Tables II, III, IV and V).

Data analysis

Data acquisition and analysis

Mitochondrial complete genomes of Gruiformes and

 

Table I. The mitochondrial genome of Gruiformes and Charadriiformes.

Order	Family	Genus	Species	All(bp)	PCGs(bp)	GeneBank
Gruifo-rmes	Gruidae	Anthro-poides	Anthropoides paradiseus	16696	11381	NC_020572
			A. virgo	16541	11381	NC_020573
		Balearica	B. pavonina	16786	11375	NC_020570
			B. regulorum	16802	11375	NC_020569
			Grus carunculatus	16677	11381	NC_020571
		Grus	G. americana	16651	11381	NC_020576
			G. antigone	16549	11381	NC_020581
			G. canadensis	16697	11381	NC_020582
			G. grus	16649	11381	NC_020577
			G. japonensis	16715	11381	NC_020575
			G. leucogeranus	16688	11381	NC_020574
			G. monacha	16650	11381	NC_020578
			G. nigricollis	16646	11381	NC_020579
			G. rubicunda	16693	11381	NC_020580
			G. vipio	16678	11381	NC_021368
	Rallidae	Amaurornis	Amaurornis akool	16950	11296	NC_023982
			A. phoenicurus	17213	11368	NC_024593
		Coturnicops	Coturnicops exquisitus	17136	11368	NC_012143
		Eulabeornis	Eulabeornis castaneoventris	17339	11368	NC_025501
		Fulica	Fulica atra	17029	11368	NC_025500
		Gallicrex	Gallicrex cinerea	17184	11377	NC_028408
		Gallinula	Gallinula chloropus	17027	11365	NC_015236
		Gallirallus	Gallirallus australis	17464	11368	KF701060
			G. okinawae	18404	11368	NC_012140
			G. philippensis	17359	11368	NC_025507
			G. striatus	17647	11368	MH219930
		Lewinia	Lewinia muelleri	17273	11368	NC_025502
		Porphyrio	Porphyrio hochstetteri	16988	11368	NC_010092
			P. porphyrio	17020	11368	NC_025508
		Porzana	Porzana fusca	16935	11368	KY009736
			P. paykullii	16955	11368	MG200164
			P. pusilla	16978	11368	KY009737
		Rallus	Rallus aquaticus	17149	11368	MH229988
		Rallina	Rallina eurizonoides	16942	11377	NC_012142
		Sarothrura	Sarothrura ayresi	16767	11362	NC_034316
	Rhyno-chetidae	Rhynochetos	Rhynochetos jubatus	16937	11396	NC_010091
	Heliorn-ithidae	Heliornis	Heliornis fulica	17008	11367	NC_025499
	Otididae	Otis	Otis tarda	16849	11397	NC_014046
Charadri-iformes	Laridae	Larus	Larus crassirostris	16701	11397	KM507782
			L. dominicanus	16746	11399	AY293619
			L. vegae	16379	11397	NC_029383
			Ichthyaetus relictus	16586	11399	KC760146
		Chroico-cephalus	L. brunnicephalus	16769	11399	JX155863
			L. ridibundus	16807	11391	KM577662
			L. saundersi	16724	11379	JQ071443
		Gelochelidon	Gelochelidon nilotica	16748	11397	NC_036344
	Sternidae	Sterna	Sterna albifrons	16357	11397	KT350612
			S. hirundo	16707	11397	NC_036345
	Alcidae	Synthlibor-amphus	Synthliboramphus antiquus	16730	11402	AP009042
			S. wumizusume	16714	11397	KT592378
		Pinguinus	Pinguinus impennis	16784	11397	NC_031347
	Stercor-ariidae	Stercorarius	Stercorarius maccormicki	16669	11403	KM401546
	Scolop-acidae	Eurynorhy-nchus	Eurynorhynchus pygmeus	16707	11397	KP742478
		Arenaria	Arenaria interpres	16725	11399	AY074885
		Tringa	Tringa erythropus	16683	11397	NC_030585
			T. ochropus	16906	11397	KX668223
			T. semipalmata	16603	11399	NC_036016
		Xenus	Xenus cinereus	16817	11397	KX644890
		Gallinago	Gallinago stenura	16899	11400	KY056596
		Scolopax	Scolopax rusticola	16984	11391	KM434134
		Numenius	Numenius phaeopus	17091	11396	KP308149
		Limosa	Limosa lapponica	16732	11399	KX371106
	Jacanidae	Jacana	Jacana jacana	16975	11394	KJ631049
			J. spinosa	17063	11397	KJ631048
		Hydroph-asianus	Hydrophasianus chirurgus	16855	11397	MH219929
	Charadriidae	Vanellus	Vanellus cinereus	17074	11399	KM404175
			V. vanellus	16795	11397	KM577158
		Pluvialis	Pluvialis fulva	16854	11391	KX639757
		Charadrius	Charadrius placidus	16895	11397	KY419888
	Recurvir-ostridae	Recurvir-ostra	Recurvirostra avosetta	16897	11397	KP757766
		Himantopus	Himantopus himantopus	17378	11397	NC_035423
	Haemat-opodidae	Haematopus	Haematopus ater	16791	11400	AY074886
			H. ostralegus	16798	11394	NC_034237

 

Charadriiformes species were retrieved and downloaded from GenBank, including 35 Gruiformes species and 34 Charadriiformes species (Table I). Combined with the mitochondrial complete genomes of 4 birds determined in this study (Fig. 1), a total of 73 mitochondrial complete genomes of birds were obtained. We firstly used Clustal X (Thompson et al., 1997) and MEGA5.0 (Tamura et al., 2011) and DNASTAR (Burland, 2000) to compare the mitochondrial genome sequences of 73 birds and analysed the base content, start codon and termination of the complete genome and PCGs. Then we also analysed the codon usage and synonymous codon usage of Gruiformes and Charadriiformes PCGs. The calculation formula for base skew was AT skew = (A-T) / (A + T), GC skew = (G-C) / (G + C) (Perna and Kocher, 1995). We used Adobe Illustrator CS6 and OriginPro 8 (Wass, 2008) to draw processing diagrams.

Analysis of phylogeny

A total of 73 birds mitochondrial genome sequences were collected in Gruiformes and Charadriiformes, and the PCG and rRNA genes of 15 species of Charadriiformes were searched in GeneBank (Supplementary Table VI). The sequences of 12 PCGs, 12S rRNA and 16S rRNA genes of mitochondria of 88 species. Both 38 species in 19 genera, 5 families of Gruiformes, and 50 species in 34 genera, 17 families of Charadriiformes were sequenced and what were perform phylogenetic analysis. The construction of the phylogenetic tree requires the use of DAMBE (Xia, 2001; Xia and Lemey, 2009) software to verify the saturation of the selected sequence. The use of the obtained gene sequence substitution does not reach saturation before the phylogenetic analysis can be continued.

ML and BI trees were constructed by using the optimal parameter model selected by MrModelTest3.06 software (Klaus-J et al., 2004) and PAUP 4.0b10 software (Swofford, 2003) with Akaike information Criterion (AIC) to select the model with the minimum statistical value. Among them, the BI trees of Gruiformes and Charadriiformes birds were constructed using MrBayes 3.1.2 software (Huelsenbeck and Ronquist, 2001). In the process of BI tree construction, four Monte Carlo Markov chains (MCMC) were established. Start with random number as the starting tree, run 3,000,000 generations in total, and sample once every 100 generations. After discarding the 25% of burning-in samples, the consistent tree can be constructed according to the remaining 75% samples, and the Posterior Probability (PP) of the BI tree can be calculated finally. The ML tree was constructed by using the RaxML software to run the files in Phy format (Stamatakis et al., 2008; Stamatakis, 2014), selected the best model for MrModelTest3.06 software filter, and set the ML thorough bootstrap mode, bootstrap reps to 50 and running 10000 times to get the ML tree. Finally, Treeview32 (Saldanha, 2004) and Figtree 1.4.2 (Rambaut, 2014) software were used to open and annotate the evolutionary tree diagram. The genetic evolution and phylogenetic relationship of different species in the two orders were analysed and the taxonomic status of some birds was discussed.

 

Analysis of divergence time

In this study, the divergence time was calculated based on the ML tree topology constructed by mitochondrial genome, and the accurate fossil record was used as the calibration point. The earliest fossilized Galliformes was recorded as Gallinuloides wyomingensis (Avise, 2004) with a minimum time of 51.58 Ma (Graybeal, 1998; Prum et al., 2015), and the stem sphenisciform Waimanu (Slack et al., 2006) with a normal distribution of 61.5–65.5 Mya. Combining the fossil record and comparing the results of Jarvis et al. (2014) and Prum et al. (2015), the calibration point of the Neognathae was set as 70.00 Ma, and Galloanseres and Neoaves calibration point was 65 Ma (Jarvis et al., 2014; Prum et al., 2015) and set the standard deviation of 0.5. BEAST 1.4.6 was the software that can build phylogenetic trees and estimate the time of species divergence (Drummond and Rambaut, 2007). BEAST 1.4.6 software was used to establish MCMC to estimate the bifurcation time of species. Yule Prior was selected in the branching evolution rate (reference?). The data calculation model setting was consistent with the phylogenetic tree model. MCMC operation parameters were as follows that length of chain was 8 × 106 generations, once every 200 generations, and burn-in 10% of the samples. Tree Annotator 1.6.1 (Drummond and Rambaut, 2007) software was used to construct the tree with the maximum branch credibility. The running results were analysed by Tracer v 1.4 (Rambaut and Drummond, 2007), and then Fig Tree 1.4.2 (Rambaut, 2014) was used to open the evolutionary tree with divergence time and 95% of the highest posterior density (HPD).

 

RESULTS

Genome organization and arrangement

The mitochondrial genome sequences of 38 species (19 genera, 5 families) of Gruiformes and 35 species (35 genera, 9 families) of Charadriformes were analysed in this study (Table I). The structure and gene arrangement of mitochondrial genomes of Gruiformes and Charadriiformes were basically the same, including 37 coding genes and one CR, and the content range of each base was almost the same, and the fluctuation range was less than 3% (Fig. 2).

The mean base composition of mitochondrial genome of Gruiformes was T (23.92±0.56%), C (30.74±0.71%), A (31.82±0.64%) and G (13.52±0.34%). The AT content ranged from 54.70% to 57.53%, with an average value of 55.74 ± 0.70%, higher than GC content (Fig. 2). The sequence of base richness of mitochondrial genome was mostly A>C>T>G, and only Balearica pavonina, Rhynochetos jubatus and Otis tarda had base contents of C>A>T>G. The mean AT skew was 0.14±0.02, and GC skew was -0.39±0.02, indicating that the nucleotide composition of mitochondrial complete genome of Gruiformes had a slight specific bias towards A and C (Fig. 3).

The average base composition of mitochondrial genome of Charadriiformes was T (24.61 ± 0.76%), C (30.48 ± 0.69%), A (31.20 ± 0.47%) and G (13.72 ± 0.38%). The AT contents ranged from 54.39% to 58.35%, with an average value of 55.81 ± 0.86%, slightly higher than that of the mitochondrial whole genome of Gruiformes. The sequence of base abundance of mitochondrial genome was mostly A > C > T > G (Fig. 2). As for Larus crassirostris, Larus dominicanus, Ichthyaetus relictus, Larus brininucephalus, Larus ridibundus, Larus saundersi, Arenaria interpres, and Pluvialis fulva have a base content of C > A > T > G. The mean deviation of AT was 0.12 ± 0.02, and the mean deviation of GC was -0.38 ± 0.01, indicating that the nucleotide composition of the mitochondrial whole genome of Charadriiformes birds was slightly biased towards A and C (Fig. 3).

 

 

Analysis of PCGs

The structure of PCGs of Gruiformes and Charadriiformes were very conservative, including 7 NADH reductase genes, 3 cytochrome oxidase genes, 2 ATP synthase genes (ATPase8 and ATPase6) and a cytochrome oxidase b gene. Among the Gruiformes, the shortest PCGs was Amaurornis akool (11,296 bp), while the longest was Otis tarda (11,397 bp), and the shortest PCGs of Charadriiformes was the Larus saundersi (11,379 bp), whereas the longest PCGs was Stercorarius maccormicki (11,403 bp) (Table I).

Sequence analysis of PCGs

The average base composition of Gruiformes PCGs was T (25.25 ± 0.55%), C (31.60 ± 0.80%), A (29.82 ± 0.68%) and G (13.33 ± 0.36%). The AT content ranged from 53.60% to 57.09%, and the average value was (55.07 ± 0.82%) higher than the GC content (Fig. 4). The AT skew was 0.08 ± 0.02, and the GC skew was − 0.41 ± 0.02, indicating that the nucleotide composition of Gruiformes PCGs has a slight specific bias for A and C (Fig. 3). The AT content of the first codon of PCGs of Gruiformes was 50.51 ± 0.59%, where the AT skew was 0.17 ± 0.01, and the GC skew was −0.13 ± 0.01 The second codon AT content was 58.37 ± 0.18%, where AT skew was −0.37 ± 0.01 and GC skew was −0.39 ± 0.01. Third codon AT content was 56.33 ± 2.02%, where AT skew was 0.47 ± 0.04 and GC skew was −0.74 ± 0.04 (Fig. 3). The AT content of the third codon of PCGs of Gruiformes fluctuated the most, and the AC content of the first and third codon bases was more than the TG content, while the second codon base consisted of the TC content more than the AG content.

 

The base composition of Charadriiformes PCGs was T (26.13 ± 0.91%), C (31.36 ± 0.86%), A (29.15 ± 0.51%) and G (13.35 ± 0.40%). The AT content ranged from 53.75% to 58.31%, and the average value was 55.28 ± 0.99%, which was slightly higher than the AT content of Gruiformes PCGs (Fig. 4). The AT skew was 0.05 ± 0.02 and the GC skew was − 0.40 ± 0.02, indicating that the nucleotide composition of Charadriiformes PCGs has a slight specific bias for A and C. The first codon AT content of Charadriiformes PCGs was 50.55 ± 0.72%, where the AT skew was 0.14 ± 0.01 and the GC skew was − 0.11 ± 0.01. The second codon AT content was 58.45 ± 0.23%, where the AT skew was − 0.37 ± 0.01 and the average value of GC skew was − 0.39 ± 0.01. The AT content of the third codon was 56.85 ± 2.35%, where the AT skew was 0.42 ± 0.05 and the GC skew was − 0.75 ± 0.04. 3.3) (Fig. 3). The AT content of the third codon of the PCGs of Charadriiformes fluctuated the most, and the AC content of the first and third codon bases was more than the TG content, while the second codon base consisted of the TC content more than the AG content.

 

 

Codon usage of PCGs

Most of Gruiformes and Charadriiformes PCGs were used ATG, GTG, ATT, ATC and ATA as the start codon, while TAA, TAG, AGG, AGA and incomplete codon T were used as the stop codons (Fig. 5 and Fig. 6). Incomplete stop codons were frequently found in the COIII gene in birds and can be complemented by the acidification of adenosine or poly (A) at the mRNA3’ during transcriptional processing Stop codon (TAA). The initiation codons of Gruiformes and Charadriiformes ATPase8, COIII, Nd4L, Nd4, Nd6 and Cytb of Gruiformes and Charadriiformes were ATG, whereas the codons of COI and Nd5 were ATG and GTG. The starting codons of Gruiformes and Charadriiformes other PCGs genes were somewhat different. The stop codons of the mitochondrial genome of Gruiformes and Charadriiformes were more complicated. Among them, only the stop codons of ATPase8, ATPase6 and Nd4L were TAA, and the stop codons of COI were AGG. The stop codons of Gruiformes and Charadriiformes the other 9 PCGs were different.

In the PCGs encoding process of Gruiformes, the four codons with the highest frequency average were CTA (Leu), ATC (Ile), ACA (Thr) and TTC (Phe). The average number of occurrences of these four codons was separately 312.80, 201.00, 155.80, and 155.30, while the average value of the Synonymous Codon Usage (RSCU) was 2.85, 1.36, 1.71, and 1.45, respectively. In the PCGs encoding process of Charadriiformes, codons having a relatively high frequency average were CTA (Leu), ATC (Ile), TTC (Phe) and ACA (Thr). The average number of occurrences of these five codons was separately 301.20, 200.90, 160.60, and 149.50, while the average RSCU was 2.73, 1.38, 1.44, and 1.69, respectively. By counting the frequency of amino acid use of Gruiformes PCGs, we found that the highest frequency of use was Leu (17.44%) whereas the lowest was Cys (0.70%). The results for Charadriiformes PCGs were similar, with the highest average frequency of use being Leu (17.48%) and the lowest being Cys (0.76%) (Fig. 7).

 

Analysis of phylogeny and divergence time

By arranging the gene sequences of 12 PCGs, 12S rRNA and 16S rRNA of mitochondria (Table I; Supplementary Table VI), the base substitution saturation was detected based on the F84 model in the DAMBE software. We also analysed the ratio of nucleotide conversion number (×s) and nucleotide transversion number (Δv) to the genetic distance of nucleotide sequences of different species. The results showed that the sequence ratio was increased with the increase of genetic distance. The conversion and

 

transversion of nucleotides increased linearly (Supplementary Fig. 1), which was a linear regression model. The results also indicated that the substitution of gene sequences was not saturated, so phylogenetic analysis could continue. The sequence was screened by Modeltest and Mrmodeltest. The best model was GTR+G+I (-lnL = 322120.5625, K = 10, AIC = 644261.1250) which was then used to construct BI and ML trees. Since the constructed BI and ML tree topologies were identical, only the ML tree (the number on the branch represents the support rate of the BI and ML trees) was presented to show the phylogenetic relationship between Gruiformes and Charadriiformes.

The selected model (GTR+G+I) was the same as the phylogenetic analysis of the divergence Gruiformes and Charadriiformes, and based on 12 PCGs, 12S rRNA and 16S rRNA gene of 88 species mitochondria sequence of Gruiformes and Charadriiformes (Fig. 8 and Fig. 9). The estimating of the divergence time was performed ML tree harvested from BEAST software. The results showed that the differentiation of Neognathae was about 70.22 (71.21 ~ 69.22) Ma (Fig. 9), belonging to the Late Cretaceous period. The boundary between the Cretaceous and Tertiary (Paleo) strata (K-Pg) was about 66 Ma (Jarvis et al., 2014; Prum et al., 2015), and the species of the Neoaves was beginning to differentiate at this time, with a divergence time of 64.84 (65.83 ~ 63.82) Ma (Fig. 9). The birds of Galloanseres were emerged slightly earlier than the birds of Neoaves, and the time of divergence was 65.91 (66.89 ~ 64.94) Ma. This result was basically consistent with the results of studies by Jarvis et al. (2014) and Prum et al. (2015).

 

Phylogeny and divergent time of Gruiformes

In phylogenetic analyses, Gruiformes include Grui and Ralli, Gruidae and Otididae were included in the branch of Grui, while Rallidae and Heliornithidae were included in the branch of Ralli (Fig. 8). Rhynochetidae was a separate one and had a long relationship with other Gruiformes birds. Grui branches were dominated by Gruidae, the genus Grus and genus Bugeranus belonged to one branch, while the genus Balearica was the other. Ralli were dominated by Rallidae, among which Porzana fusca, Porzana paykullii, Porzana pusilla sequenced in this study and all belonged to the genus Porzana. The genus Porzana and genus Porphyrio belonged to one branch, and then the genus Amaurornis, genus Fulica and genus Gallinula belong to another branch (81% support for ML trees and 1.00 support for BI trees). In addition, Gallicrex cinerea was also included in the genus Amaurornis. Gallirallus striatus was sister to Lewinia muelleri, and this groups belonged to a branch with the genus Gallirallus. Whereas Rallus aquaticus belonged to the genus Rallus for another branch. (100% support for ML trees and 1.00 support for BI trees) (Fig. 8). In addition, Heliornis fulica and Sarothrura ayresi were a sister group and were separated from other birds of Rallidae to form a single one (100% support for ML trees and 1 support for BI trees).

The basal divergence time of Gruiformes was 46.33 (58.46 ~ 25.60) Ma, and the divergence time of Grui and Ralli was 41.36 (53.10 ~ 24.18) Ma (Fig. 9). The divergence time of Grui was about 17.62 (29.76 ~ 4.15) Ma, and the divergence time of Ralli was about 32.18 (46.17 ~ 19.69) Ma. The divergence time of Porzana fusca, Porzana paykullii, Porzana pusilla of the genus Porzana was 9.60 (14.81 ~ 5.83) Ma, which were sequenced in this study. The divergence time of Rallus aquaticus of the genus Rallus was 18.32 (24.99 ~ 8.98) Ma. The divergence time of Gallirallus striatus of the genus Gallirallus was 4.98 (8.87 ~ 1.93) Ma.

Phylogeny and divergence time of Charadriiformes

In this study, Charadriformes included Charadrii, Lari and Scolopaci (Fig. 8). The branch of Charadrii mainly included Charadriidae and Recurvirostridea. The branch of Lari mainly included Alcidae, Sternidae and Laridae. The branch of Scolopaci mainly included Jacanidae and Scolopacidae. While Larus vegae was a separate branch. Among Charadrii, Charadriidae, Haematopodidae and Recurvirostridea were one branch (100% support for ML trees and 1.00 support for BI trees). Chionididae and Burhinidae was a sister group (100% support for ML trees and 1.00 support for BI trees). In the Lari, Laridae, Sternidae and Rhynchopidae belonged to a branch (100% support for ML trees and 1.00 support for BI trees), and Alcidae and Stercorariidae was a sister group (81% support for ML trees and 1.00 support for BI trees). While Glareolidae and Turnicidae were in separate branches. In Scolopaci, Scolopacidae belonged to a branch (100% support for ML trees and 1.00 support for BI trees), while the relative relationship between Jacanidae, Rostratulidae, Pedionomidae and Thinocoridae was close (100% support for ML trees and 1.00 support for BI trees). In addition, Gelochelidon nilotica in Laridae was closed relationship with Sternidae (100% support for ML trees and 1.00 support for BI trees). Pluvianellus socialis in Charadriidae and Chionis minor was a sister group (100% support for ML trees and 1.00 support for BI trees) and closed relationship with Chionididae.

The basal divergence time of Charadriiformes was 45.44 (58.21 ~ 24.67) Ma, the divergence time of Charadrii, Lari and Scolopaci was about 33.46 (43.44 ~ 22.92) Ma, 29.10 (40.67 ~ 16.43) Ma and 27.06 (34.83 ~ 12.72) Ma, respectively (Fig. 9). The divergence time of Charadriidae and Scolopacidae was 21.27 (33.19 ~ 8.54) Ma and the divergence time of Laridae and Sternidae was 17.41 (24.29 ~ 6.78) Ma, while the divergence time of Alcidae and Stercorariidae was 18.31 (27.29 ~ 7.10) Ma. The divergence time of Scolopacidae was 25.24 (30.96 ~ 13.38) Ma, and the divergence time of Jacanidae was 6.80 (11.87 ~ 3.39) Ma (Fig. 9).

 

DISCUSSION

Complete genome sequence

Studies have shown that the length of the mitochondrial genome of birds was 16,300 bp to 23,500 bp (Gao et al., 2009; Zheng, 2015), and in this study the shortest mitochondrial genome of Gruiformes was Anthropoides virgo (16,541 bp), while the longest was Gallirallus okinawae. (18,404 bp). The shortest mitochondrial genome of Charadriiformes was Sternula albifrons (16,357 bp), whereas the longest was Himantopus himantopus (17,378 bp). The structure and gene arrangement of the mitochondrial genome of Gruiformes and Charadriiformes were basically the same as those of most birds (Gao et al., 2009; Zheng, 2015), also including 37 coding genes and a CR (Zheng, 2015). The AT content of the mitochondrial genome sequence of Gruiformes and Charadriiformes mitochondria were greater than the GC content, and the highest AT content was Gallinago stenura, which reached 58.35% and was higher than the AT content of the general birds (50.5% to 57.7%) (Gao et al., 2009; Cheng et al., 2017). PCGs of Gruiformes and Charadriiformes mitochondria were very conservative, and their structure and arrangement were consistent with previous studies of bird mitochondrial PCGs (Gao et al., 2009; Zheng, 2015). Studies have shown that most birds have a first codon AT content of 47.50% to 52.10%, a second codon AT content of 50.0% to 60.0%, and a third codon AT content of 42.1% to 62.0% (Gao et al., 2009). The results of the AT content of each codon of PCGs in Gruiformes and Charadriiformes were similar to this result, and the AT content of the third codon fluctuated the most. Because the first and second codons face greater selection pressures, while the third codon and non-coding regions were subject to the least selection pressure. This difference can itself cause an imbalance in base distribution (Zhong et al., 2002). Moreover, the restriction of amino acids on bases and the frequency of corresponding codon usage were also an important reason for the local base imbalance in the genome (Frank and Lobry, 1999). This group of genomic local bases was uneven, showing differences in nucleotide base composition in different regions (Belozersky and Spirin, 1958). Compared with the base composition of the first two digits of the codon, the third base of the codon has a higher mutation rate (Daubin and Perrière, 2003). Whether this situation was the result of choice or neutral change was still inconclusive (Necşulea and Lobry, 2006). In addition, differences in AT and GC content were also considered to be closely related to genomic features such as repetitive element distribution, methylation patterns, and gene density. Most of the PCGs of the genus and the genus PCGs use ATG, GTG, ATT, ATC and ATA as the start codon, and TAA, TAG, AGG, AGA and incomplete codon T as stop codons. In the process of coding mitochondrial proteins in the order of Gruiformes and Charadriiformes, the codons with the highest frequency average were CTA (Leu), and the highest frequency of amino acid use was also Leu. The frequency of amino acid use was similar to that of other birds, such as Emberiza chrysophrys (Ren et al., 2014), but it was very different from Invertebrates, which may be related to the function of mitochondria and the utilization efficiency of mitochondria in different species (Zhao et al., 2018).

Phylogeny and divergence time

At present, the classification of the species of Gruiformes and Charadriiformes was based on the characteristics of the bird’s morphology, sound, and tracheal evolution (Taylor and van Perlo, 1998; Yang and Wang, 2004; Zheng, 2012), as well as the molecular phylogenetic analysis (Ruan et al., 2012). The species of Gruiformes and Charadriiformes were relatively clear in the classification level of the family, but they were very complicated and controversial at the classification level of the genus (García–R et al., 2014; Hu et al., 2017).

Most of the studies have concluded that Rallidae and Heliornithidae were closely related, while Gruidae belongs to another branch (Fain et al., 2007; García–R et al., 2014). The classification of birds in Rallidae was more complicated. The genus Grus and the genus Bugeranus of Gruidae was sister group, while the genus Balearica was another branch (Yang and Wang, 2004). In this study, the evolutionary analysis of Gruidae in Gruiformes was generally consistent with previous studies (Yang and Wang, 2004; Gong et al., 2017), but the genetic relationship between the species of Rallidae was slightly different (García–R et al., 2014; Chen et al., 2017; Gong et al., 2017). For the classification of Rallidae birds, the results of García–R et al. (2014) were consistent with this study, and they were believed that the genus Porphyrio, the genus Amaurornis, the genus Fulica and the genus Gallinula were belong to a branch. The genus Gallirallus and the genus Lewinia was more closely related and belonged to another branch. Gong et al. (2017) thought that the genus Gallirallus was closely related to the genus Lewinia, and then that with the genus Amaurornis, Fulica and Gallinula were belonged to branch, while the genus Porphyrio and Coturnicops belonged to another branch. The differences in these classifications were related to different molecular markers. García–R et al. (2014) used 12 PCGs, tRNA and rRNA gene sequences to construct phylogenetic trees, while Gong et al. (2017) used the complete sequences of mitochondrial genome (The CR sequence was removed) to construct a phylogenetic tree. Therefore, the accuracy of the phylogenetic tree can only be compared by the support rate and the posterior probability, and there was no more acceptable definition (Zheng, 2015). However, the species data contained in this study was more comprehensive, and the sequence information also contains a higher posterior probability value, which has a higher degree of credibility. In addition, according to the morphological classification, Gallicrex cinerea was considered to be a bird of the genus Gallicrex, and Gallirallus striatus belonged to the genus Gallirallus (Zheng, 2012). However, this study believes that Gallicrex cinerea and Amaurornis phoenicurus were sisters and should be classified as the genus Amaurornis. In addition, Gallirallus striatus and Lewinia muelleri belong to sister group, preferring the genus Lewinia. Regarding the classification of the genus Gallicrex, Ruan et al. (2012) and Gong et al. (2017) also believed that it should be classified as the genus Amaurornis, rather than the traditionally believed the genus Gallicrex. The classification results of Gallirallus striatus need further research and demonstration.

According to the analysis of this study, the divergence time of Neoaves was 64.84 (65.83 ~ 63.82) Ma, while the divergence time of Galloanseres was 65.91 (66.89 ~ 64.94) Ma and diverged about the same time as Neoaves. This result was basically consistent with the findings of Jarvis et al. (2014) and Prum et al. (2015). The basal divergence time of Gruiformes was 46.33 (58.46 ~ 25.60) Ma, which was less than that of Jarvis et al. (2014) (65.00 Ma) and Fain et al. (2007) (66.40 Ma). The diverging time of 41.36 (53.10 ~ 24.18) Ma of Ralli was similar to that of García–R et al. (2014) (40.10 Ma), and was consistent with the fossil record of two ancient birds of Ralli (Mayr, 2009; García–R et al., 2014). The reasons for the differences in the divergence time of Gruiformes include the difference in the amount of data used and the reference point used for the estimation of the divergence time. Prum et al. (2015) and Jarvis et al. (2014) used relatively little data on Gruiformes, and even used only one species instead of the whole order to estimate the time difference, which may lead to some deviation in their results. Fain et al. (2007) used the fossil records of Rallidae and Heliornithidae and compared the results of previous studies (Krajewski and Fetzner, 1994; Houde, 2009), and set 43 Ma as the reference base point for the estimation of Rallidae and Heliornithidae as the reference base point, which was not authoritative and accurate enough (García–R et al., 2014).

The evolutionary analysis results of Charadriiformes were basically consistent with previous studies (Smith and Clarke, 2015; Cheng, 2017; Hu et al., 2017), including the three branches of Charadrii, Lari and Scolopaci. Lari and Scolopaci form a sister group, while Charadrii was a separate branche. Among them, Hydrophasianus chirurgus of Jacanidae, which was closely related to Rostratulidae. Cheng et al. (2017) studied the phylogenetic tree of 18 species of Charadriiforme, which were also divided into Charadrii, Lari and Scolopaci, and analysed the phylogenetic evolution of 12 species of Charadriiformes, then concluded that Recurvirostridea and Charadriidae belonged to Charadrii (Chen, 2003). The results of the evolution analysis of Charadriiformes were basically the same as those of the traditional classification, but there were differences between Gelochelidon nilotica and Pluvianellus socialis. Traditionally, Gelochelidon nilotica belonged to Laridae, and Pluvianellus socialis belonged to Charadriidae (Zheng, 2012). However, this study suggested that Gelochelidon nilotica belonged to Sternidae as a sister group to Thalasseus bengalensis and Sterna hirundo. Pluvianellus socialis were sisters of Chionis minor, belonging to Chionididae. There were no other molecular methods for classifying the two species, and the results need to be further confirmed.

The estimated divergence time for Charadriiformes was 45.44 (58.21 ~ 24.67) Ma, lower than that of Jarvis et al. (2014) (65 Ma) and Fain et al. (2007) (74.3 Ma), but consistent with that of Prum et al. (2015) (55 Ma). According to the time difference of each suborder, Charadrii species first began to differentiate, followed by Lari, and finally Scolopaci, which also confirmed the prediction of Chen et al. (2003) on the differentiation of the suborder of Charadriiformes. This study ensured accurate and reliable estimation of the divergence time of Gruiformes and Charadriiformes species through fossil record calibration (Prum et al., 2015) and sufficient molecular data support

 

ACKNOWLEDGEMENTS

This study was supported by the National Science Foundation of China (NSFC no. 31260510, no. 30960052 and no. 31960107) and Water Resources Department of Jiangxi Province Science and Technological Project (no. KT201537).

 

Supplementary material

There is supplementary material associated with this article. Access the material online at: https://dx.doi.org/10.17582/journal.pjz/20190603010623

 

Conflict of interest statement

The authors report no conflicts of interest and were alone responsible for the content and writing of the paper.

 

REFERENCES

Avise, J.C., 2004. Molecular markers natural history and evolution. Sinauer Associates.

Belozersky, A.N. and Spirin, A.S., 1958. A correlation between the compositions of deoxyribonucleic and ribonucleic acids. Nature, 182: 111. https://doi.org/10.1038/182111a0

Burland, T.G., 2000. Dnastar’s lasergene sequence analysis software. Meth. mol. Biol., 132: 71-91. https://doi.org/10.1385/1-59259-192-2:71

Chen, P., Han, Y., Zhu, C., Gao, B. and Ruan, L., 2017. Complete mitochondrial genome of Porzana fusca and Porzana pusilla and phylogenetic relationship of 16 Rallidae species. Genetica, 145: 1-15. https://doi.org/10.1007/s10709-017-9982-x

Chen, X., 2003. Mitochondrial DNA sequence differences and their phylogenetic relationships in Charadriiformes birds. Liaoning Normal University.

Cheng, Y., 2017. The complete mitochondrial genome and molecular phylogeny of three shorebirds. Anhui University.

Cracraft, J., 1981. Toward a phylogenetic classification of the recent birds of the world (class aves). The Auk, 98: 681-714.

Daubin, V. and Perrière, G., 2003. G+C3 structuring along the genome: a common feature in prokaryotes. Mol. Biol. Evolut., 20: 471-483. https://doi.org/10.1093/molbev/msg022

Drummond, A.J. and Rambaut, A., 2007. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evolut. Biol., 7: 214. https://doi.org/10.1186/1471-2148-7-214

Fain, M.G., Krajewski, C. and Houde, P., 2007. Phylogeny of “core Gruiformes” (Aves: Grues) and resolution of the Limpkin-Sungrebe problem. Mol. Phylogenet. Evolut., 43: 515-529. https://doi.org/10.1016/j.ympev.2007.02.015

Frank, A.C. and Lobry, J.R., 1999. Asymmetric substitution patterns: a review of possible underlying mutational or selective mechanisms. Gene, 238: 65-77. https://doi.org/10.1016/S0378-1119(99)00297-8

Gao, Y., Miao, Y., Su, X., Chi, Z., Yu, Y. and Jiang, F., 2009. A comprehensive analysis genome base on 74 avian mit0ch0ndrial compositions. J. Yunnan Agric. Univ., 24: 51-58.

García–R, J.C., Gibb, G.C. and Trewick, S.A., 2014. Eocene diversification of crown group rails (Aves: Gruiformes: Rallidae). PLoS One, 9: e109635. https://doi.org/10.1371/journal.pone.0109635

Gemmell, N.J., Janke, A., Western, P.S., Watson, J.M., Pääbo, S. and Graves, J.A., 1994. Cloning and characterization of the platypus mitochondrial genome. J. mol. Evolut., 39: 200.

Gong, J., Zhao, R., Huang, Q., Sun, X., Huang, L. and Jing, M., 2017. Two mitogenomes in Gruiformes (Amaurornis akool/A. phoenicurus) and the phylogenetic placement of Rallidae. Genes Genom., 39: 1-9. https://doi.org/10.1007/s13258-017-0562-3

Graybeal, A., 1998. Is it better to add taxa or characters to a difficult phylogenetic problem? System. Biol., 47: 9-17. https://doi.org/10.1080/106351598260996

Houde, P., 2009. Cranes, rails, and allies (Gruiformes), In: The timetree of life (eds. S.B. Hedges and S. Kumar). Oxford University Press.

Hu, C., Zhang, C., Lei, S., Yi, Z., Xie, W., Zhang, B. and Chang, Q., 2017. The mitochondrial genome of pin-tailed snipe Gallinago stenura, and its implications for the phylogeny of Charadriiformes. PLoS One, 12: 244. https://doi.org/10.1371/journal.pone.0175244

Huang, Z., Tu, F. and Ke, D., 2017. Complete mitochondrial genome of blue-throated bee-eater Merops viridi (Coraciiformes:Meropidae) with its taxonomic consideration. Pakistan J. Zool., 49: 81-86. http://dx.doi.org/10.17582/journal.pjz/2017.49.1.81.86

Huelsenbeck, J.P. and Ronquist, F., 2001. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics, 17: 754-755. https://doi.org/10.1093/bioinformatics/17.8.754

Jarvis, E.D., Mirarab, S., Aberer, A.J., Li, B., Houde, P., Li, C., Ho, S.Y.W., Faircloth, B.C., Nabholz, B. and Howard, J.T., 2014. Whole-genome analyses resolve early branches in the tree of life of modern birds. Science, 346: 1320-1331.

Kahl, M. P., 2009. A revision of the family ciconiidae (aves). J. Zool., 167: 451-461. https://doi.org/10.1111/j.1469-7998.1972.tb01736.x

Klaus, J., Ouml, R. and Rster, U., 2004. Bayesian phylogenetic analysis of combined data. System. Biol., 53: 47-67. https://doi.org/10.1080/10635150490264699

Krajewski, C. and Fetzner, J.W., 1994. Phylogeny of cranes (Gruiformes: Gruidae) based on cytochrome-B DNA sequences. Auk, 111: 351-365. https://doi.org/10.2307/4088599

Manson, F. and Goldizen, A., 2000. Rails: A guide to the rails, crakes, gallinules and coots of the World by B. Taylor and B. van Perlo. Emu, 100: 79-80. https://doi.org/10.1071/MU00906

Mayr, G., 2009. Paleogene fossil birds. Springer Berlin, pp. 139-152. https://doi.org/10.1007/978-3-540-89628-9_13

Necşulea, A. and Lobry, J.R., 2006. Revisiting the directional mutation pressure theory: The analysis of a particular genomic structure in Leishmania major. Gene, 385: 28-40. https://doi.org/10.1016/j.gene.2006.04.031

Ostrom, J.H. and Mcintosh, J.S., 1967. Records of a paleontological enterprise. (Book Reviews: Marsh’s Dinosaurs. The Collections from Como Bluff). Science, 155: 309.

Perna, N.T. and Kocher, T.D., 1995. Patterns of nucleotide composition at fourfold degenerate sites of animal mitochondrial genomes. J. mol. Evolut., 41: 353-358. https://doi.org/10.1007/BF01215182

Prum, R.O., Berv, J.S., Dornburg, A., Field, D.J., Townsend, J.P., Lemmon, E.M. and Lemmon, A.R., 2015. A comprehensive phylogeny of birds (Aves) using targeted next-generation DNA sequencing. Nature, 526: 569-573. https://doi.org/10.1038/nature15697

Rambaut, A., 2014. FigTree v1.4.2 [Internet]. University of Edinburgh, Edinburgh.

Rambaut, A. and Drummond, A.J., 2007. Tracer v1.4. Encyclopedia of atmospheric sciences. 141: 2297–2305.

Ren, Q., Yuan, J., Ren, L., Zhang, L., Zhang, L., Jiang, L., Chen, D., Kan, X. and Zhang, B., 2014. The complete mitochondrial genome of the yellow-browed bunting, Emberiza chrysophrys (Passeriformes: Emberizidae), and phylogenetic relationships within the genus Emberiza. J. Genet., 93: 699-707. https://doi.org/10.1007/s12041-014-0428-2

Ripley, S.D. and Beehler, B.M., 1985. Rails of the world: A compilation of new information, 1975-1983 (Aves: Rallidae). Smithsonian Libraries I-IV. https://doi.org/10.5479/si.00810282.417

Ruan, L., Wang, Y., Hu, J. and Ouyang, Y., 2012. Polyphyletic origin of the genus Amaurornis inferred from molecular phylogenetic analysis of rails. Biochem. Genet., 50: 959. https://doi.org/10.1007/s10528-012-9535-z

Saldanha, A.J., 2004. Java Treeview--extensible visualization of microarray data. Bioinformatics, 20: 3246-3248. https://doi.org/10.1093/bioinformatics/bth349

Sibley, C.G., Ahlquist, J.E. and Monroe, B.L., 1988. A classification of the living birds of the world based on DNA-DNA hybridization studies. Auk, 105: 409-423. https://doi.org/10.1093/auk/105.3.409

Slack, K., Jones, C., Ando, T., Harrison, G., Fordyce, R., Arnason, U. and Penny, D., 2006. Early penguin fossils, plus mitochondrial genomes, calibrate avian evolution. Mol. Biol. Evolut., 23: 1144-1155. https://doi.org/10.1093/molbev/msj124

Smith, N.A. and Clarke, J.A., 2015. Systematics and evolution of the Pan-Alcidae (Aves, Charadriiformes). J. Avian Biol., 46: 125-140. https://doi.org/10.1111/jav.00487

Stamatakis, A., 2014. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics, 30: 1312-1313. https://doi.org/10.1093/bioinformatics/btu033

Stamatakis, A., Hoover, P., Rougemont, J. and Renner, S., 2008. A Rapid Bootstrap Algorithm for the RAxML Web Servers. System. Biol., 57: 758. https://doi.org/10.1080/10635150802429642

Swofford, D.L., 2003. PAUP*: Phylogenetic analysis using parsimony (*and other methods), version 4.0 [computer program]. Sinauer Associates Sunderland, (Massachusetts).

Tamura, K., Peterson, D., Peterson, N., Stecher, G., Nei, M. and Kumar, S., 2011. MEGA5: Molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol. Biol. Evolut., 28: 2731-2739. https://doi.org/10.1093/molbev/msr121

Taylor, B. and van Perlo, B., 1998. Rails: a guide to the rails, crakes, gallinules and coots of the world.

Thompson, J.D., Gibson, T.J., Plewniak, F., Jeanmougin, F. and Higgins, D.G., 1997. The CLUSTAL_X Windows Interface: Flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucl. Acids Res., 25: 4876-4882. https://doi.org/10.1093/nar/25.24.4876

Wass, J.A., 2008. OriginPro 8 - Not just for graphics anymore. Scientific Computing.

Xia, X., 2001. DAMBE data analysis in molecular biology and evolution, DBLP. https://doi.org/10.1093/jhered/92.4.371

Xia, X. and Lemey, P., 2009. Assessing substitution saturation with DAMBE.

Yang, Z. and Wang, Q., 2004. The Classification And Evolution Of Trachea In Gruidae Birds. Wuyi Sci. J., 20: 130-135.

Zhang, H., 2015. The complete mitochondrial genomes of six Passeriforms birds and their phylogenetic relationship. Anhui University.

Zhang, Y., Zhou, X., Luo, L. and Zhang, L., 2017. Construction and analysis of the avian phylogenetic tree. Hans J. Comput. Biol., 7: 1-11. https://doi.org/10.12677/HJCB.2017.71001

Zhao, L., Li, X. and Huang, Y., 2018. Characterization of the mitochondrial genomics and phylogeny of Orthoptera (Insecta: Arthropoda). Chinese Bull. Life Sci., 30:113-123.

Zheng, G., 2012. Ornithology. 2nd edition, Beijing Normal University Publishing House.

Zheng, L., 2015. Advances on the mitochondrial genome and phylogeny of Aves. J. Wuhu Vocat. Inst. Technol., (Volume): 90-92.

Zheng, Z., 1979. Chinese animal records: Avian. Volume I, Science Press.

Zhong, D., Zhao, G., Zhang, Z. and Xu, A., 2002. Advance in the entire balance and local unbalance of base distribution in genome. Hereditas (Beijing), 24: 351-355.