The taxonomic position of the Cape Parrot (Poicephalus robustus robustus) has been the focus of much debate. A number of authors suggest that the Cape Parrot should be viewed as a distinct species separate from the other two P. robustus subspecies (P. r. fuscicollis and P. r. suahelicus). These recommendations were based on morphological, ecological, and behavioural assessments. In this study we investigated the validity of these recommendations using multilocus DNA analyses. We genotyped 138 specimens from five Poicephalus species (P. cryptoxanthus, P. gulielmi, P. meyeri, P. robustus, and P. rueppellii) using 11 microsatellite loci. Additionally, two mitochondrial (cytochrome oxidase I gene and 16S ribosomal RNA) and one nuclear intron (intron 7 of the β-fibrinogen gene) markers were amplified and sequenced. Bayesian clustering analysis and pairwise FST analysis of microsatellite data identified P. r. robustus as genetically distinct from the other P. robustus subspecies. Phylogenetic and molecular clock analyses on sequence data also supported the microsatellite analyses, placing P. r. robustus in a distinct clade separate from the other P. robustus subspecies. Molecular clock analysis places the most recent common ancestor between P. r. robustus and P. r. fuscicollis / P. r. suahelicus at 2.13 to 2.67 million years ago. Our results all support previous recommendations to elevate the Cape Parrot to species level. This will facilitate better planning and implementation of international and local conservation management strategies for the Cape Parrot.
Citation: Coetzer WG, Downs CT, Perrin MR, Willows-Munro S (2015) Molecular Systematics of the Cape Parrot (Poicephalus robustus): Implications for Taxonomy and Conservation. PLoS ONE10(8): e0133376. https://doi.org/10.1371/journal.pone.0133376
Editor: Michael A. Russello, University of British Columbia Okanagan, CANADA
Received: March 13, 2015; Accepted: June 26, 2015; Published: August 12, 2015
Copyright: © 2015 Coetzer et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Data Availability: Sequence data generated in the present study has been deposited in Genbank (accession number(s) KP856835-KP856880).
Funding: South African National Biodiversity Institute (SANBI) provided funding for the running expenses of the project (SWM and MRP). A National Research Foundation Scarce Skills grant, as well as a College of Agriculture, Engineering and Science, University of KwaZulu-Natal Doctoral research grant, was awarded to WGC.
Competing interests: The authors have declared that no competing interests exist.
Accurate species delimitation plays an important role in effective conservation of biodiversity and assisting conservation authorities with the planning and implementation of appropriate conservation strategies. The utility of subspecies in conservation has been a subject of controversy for a long time [1–5]. It has been shown that in some cases subspecies do not form separate phylogenetic clusters and classifying taxa to subspecies rank can be misleading . Subsequently, subspecies are not always given the same conservation consideration as species, especially with less well studied taxa, which can hinder protection of genetically distinct lineages [4, 6]. This is particularly relevant to birds where Phillimore and Owens  estimated that roughly 36% of traditionally defined avian subspecies from North America and Eurasia form distinct phylogenetic clusters. It is therefore important to critically identify such phylogenetically distinct lineages and where appropriate elevate subspecies to species so that they can be given adequate conservation consideration.
The genus Poicephalus (Psittaciformes) is the most species rich and widely distributed parrot genus in Africa. Poicephalus consists of nine species which are divided into two clades [7, 8]. These are the P. robustus clade, including the Cape Parrot (P. robustus) and the Red-fronted Parrot (P. gulielmi); and the P. meyeri clade, consisting of the Yellow-faced Parrot (P. flavifrons), the Senegal Parrot (P. senegalus), the Red-bellied Parrot (P. rufiventrisi), the Niam-niam Parrot (P. crassus), the Brown-headed Parrot (P. cryptoxanthus), Meyer’s Parrot (P. meyeri) and Ruppell’s Parrot (P. rueppellii) . Several of the species (P. robustus, P. gulielmi, P. senegalus, P. flavifrons and P. meyeri) are further divided into subspecies. This study will focus on Poicephalus robustus which is currently divided into three recognised subspecies, namely the Cape Parrot (P. r. robustus), the Grey-headed Parrot (P. r. suahelicus) and the Brown-necked Parrot (P. r. fuscicollis).
The recognition of the South African taxon P. r. robustus as a species separate from P. r. fuscicollis and P. r. suahelicus has been a controversial subject over the last few decades [10–13]. The P. robustus clade exhibits an allopatric geographical distribution, with most species restricted to forest habitats . Within the P. robustus group, the west African P. r. fuscicollis is found in Red Mangrove forests, mature wooded savannah and palm woodlands [10, 12]. This species was once widely distributed from Angola through to West Africa but is now primarily found in Gambia . The subspecies P. r. suahelicus occurs in a wide variety of woodland habitats and is widely distributed in the south-eastern region of the Democratic Republic of Congo, south-western Uganda, Rwanda, north-western Tanzania, Malawi, Zambia, Mozambique, Zimbabwe and the northern Limpopo province of South Africa [7, 14, 15]. The South African subspecies P. r. robustus, however, is a habitat specialist and is almost exclusively restricted to the southern mistbelt (Afrocarpus/Podocarpus) forests of southern Africa (Fig 1) . The distribution of the subspecies P. r. robustus and P. r. suahelicus are reported to overlap in the Limpopo Province of South Africa, but there is strong evidence that the two taxa are ecologically separated by habitat and altitude with P. r. robustus found in mixed Afrocarpus/Podocarpus mistbelt forests above 1000 m and P. r. suahelicus preferring mixed woodland habitats below 800 m .
Morphologically P. r. robustus differs significantly from the other two P. robustus subspecies. In addition to differences in plumage colouration [10, 13, 14], P. r. robustus is the smallest of the three subspecies and has a more lightly structured bill than either P. r. fuscicollis or P. r. suahelicus (Fig 1). Wirminghaus et al.  showed that there are significant differences in skull dimensions between P. r. robustus and P. r. suahelicus. The two taxa also have quite distinct behaviour and show differences in vocalisations [16, 17], feeding and breeding behaviour [18, 19]. P. r. robustus is a dietary specialist, feeding predominantly on Afrocarpus/Podocarpus fruits, they also prefer these trees for nesting during the breeding season (August to February) [18, 20]. P. r. suahelicus, however, is a dietary generalist feeding on a range of forest fruits and seeds, they also prefer to nest in Adansonia trees, and breed from April to August [19, 21].
Given the morphological, ecological and behavioural differentiation of the three Poicephalus subspecies, several authors have proposed full species status for the South African P. r. robustus [10, 11, 13]. However, international conservation organizations, such as BirdLife International, have highlighted the need for genetic data for full species status to be recognized. In particular genetic data is needed to clarify if overlapping populations of P. r. robustus and P. r. suahelicus are reproductively isolated.
The conservation of subspecies is a contentious issue, especially given the limited resources currently available for biodiversity conservation. The International Union for Conservation of Nature (IUCN) considers the taxonomic rank of species as the primary unit for conservation  and rarely assesses the status of subspecies . This is problematic for P. r. robustus, given that the population has undergone a dramatic decline in the last century with an estimated current population size of less than 1600 birds in the wild . Up to five decades ago Cape Parrots were recorded in many of the forests of KwaZulu-Natal, but these birds are now only rarely sighted . This population decline has been attributed to various factors, including habitat loss, illegal harvesting of wild birds and psittacine beak and feather disease [17, 23, 24]. Based on previously published morphological, behavioural and ecological data [10, 11, 13], P. r. robustus is considered a distinct conservation unit by the International Ornithologists’ Union and BirdLife South Africa and is accordingly listed for protection under South African legislation . It is also listed as Endangered in the Red Data Book of birds of South Africa, Lesotho and Swaziland . The Cape Parrot is, however, only listed as Least Concern in the IUCN Red List of Threatened Species  as the Red List assessment does not recognize P. r. robustus as a species separate from the more widespread Grey-headed Parrot (P. r. suahelicus) and the Brown-necked Parrot (P. r. fuscicollis) . In accordance with the IUCN Red List and the position taken by BirdLife International, international trade of P. r. robustus under CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora) is regulated at the species level, whereby no distinction is made between the three P. robustus subspecies, effectively ignoring the poor conservation status of this South African endemic. Effective regulation and monitoring of the international trade in P. r. robustus is therefore impossible. To ensure effective conservation of P. r. robustus, both locally and internationally, it is important to provide convincing scientific evidence that P. r. robustus warrants species status. Our main aim was to assess the taxonomic status of P. r. robustus using both multilocus microsatellites and mitochondrial data. We predicted that P. r. robustus would be genetically distinct from P. r. fuscicollis and P. r. suahelicus, thus supporting elevation to full species status that has already been accepted by some authorities on the basis of morphological, ecological and behavioural data [25, 26]. The use of multilocus analyses is a well established method of separating closely related bird species [28–31] and investigating within species relationships [22, 32–34].
Materials and Methods
We obtained ethical approval for this study from the University of KwaZulu-Natal Animal Ethics sub-committee (Ref numbers: 074/13/Animal, 017/14/Animal). The sampling permits for KwaZulu-Natal were obtained through Ezemvelo KZN Wildlife (Permit number: OP 1546/2014). All necessary import permits were obtained through Ezemvelo KZN Wildlife (Permit numbers: OP 1230/2014 and OP 878/2014) or the National Department of Environmental Affairs (Permit Number: 133120).
We included a total of 138 Poicephalus specimens representing the four southern African species (P. cryptoxanthus, P. meyeri, P. robustus and P. rueppellii) and one central African species (P. gulielmi; S1 Table) in our analyses. These species were selected based on their distribution ranges either overlapping or being in close proximity to P robustus ssp. We included representatives of all three P. robustus subspecies (P. r. fuscicollis, P. r. robustus and P. r. suahelicus) and two of the P. gulielmi subspecies (P. g. gulielmi and P. g. massaicus). We had multiple representatives of P. r. robustus (n = 32) drawn from all three of the isolated South African populations (Eastern Cape = 10, KwaZulu-Natal = 13 and Limpopo = 9). We did this to ensure the inclusion of as much genetic variation across the P. r. robustus distribution range as possible.
We used a variety of different tissue types. Whole blood collected from wild trapped and captive bred birds was stored on Whatman FTA Elute or Classic Cards. Clean needles were used for each individual to avoid cross-contamination of blood samples. Feathers were collected from the field, and muscle tissue samples were taken from dead birds. Archival museum toe pad samples were sourced from various local and international museums (S1 Table).
For the whole blood stored on Whatman FTA Elute cards, we followed the standard DNA extraction protocol as suggested by the manufacturer. The DNA was eluted with 30 μl ultrapure water and stored at -20°C. We extracted DNA from the muscle tissue samples using the NucleoSpin Tissue kit (Macherey-Nagel), following the manufacturers standard protocol. Modified extraction protocols were used for the toe pad and feather samples. In order to minimize surface contamination, we performed three washing steps (with 95% ethanol, 70% ethanol and ultrapure water) prior to extraction, followed by a final hydration step where samples were soaked in 1 ml ultrapure water for 60 min. Thereafter we extracted DNA using the NucleoSpin Tissue kit. The lysis step was extended until the samples were completely lysed. The final elution step was also modified, such that after 40 μl of preheated elution buffer was added to the spin column the samples were incubated at 70°C for 10 min. After centrifuging the samples at 11 000 x g the elution buffer was placed back into the same spin column and an additional 40 μl warmed elution buffer added to each tube. We incubated the samples at 70°C for 5 min, after which we centrifuged them at 11 000 x g to obtain the final DNA product.
We chose a panel of 11 microsatellite loci (Prob06, Prob15, Prob18, Prob23, Prob25, Prob26, Prob28, Prob29, Prob30, Prob34 and Prob35), previously described by Pillay et al. , for amplification by polymerase chain reaction (PCR). In each case the forward primers were synthesized with a fluorescent dye on the 5’ end. We divided the microsatellite panel into four multiplex sets (Multiplex 1: Prob06, Prob15 and Prob26; Multiplex 2: Prob29, Prob34 and Prob35; Multiplex3: Prob18 and Prob25; Multplex4: Prob23 and Prob28), with the locus Prob30 in a single reaction. The PCR reactions for the fresh samples (blood and muscle tissue samples) consisted of: ~2–30 ng template DNA, 5 μl KAPA2G Fast Multiplex mix (KAPA Biosystems), 0.2 μM of each primer and dH2O to give a final reaction volume of 10 μl. The PCR reaction mixtures for the feather and archival toe pad samples consisted of: ~20–200 ng template DNA, 5 μl KAPA2G Fast Multiplex mix, 0.2 μM of each primer, 0.3 μl of 1 mg/ml BSA and dH2O to give a final reaction volume of 10 μl. We used identical PCR cycle parameters for all multiplex reactions and included an initial denaturation step at 94°C for 3 min followed by 30 cycles at 94°C for 30 s, 60°C for 30 s, 72°C for 30 s, with a final extension step at 72°C for 5 min. The PCR cycles were increased to 40 cycles for the museum and feather samples to ensure sufficient amplification. PCR setup prior to addition of the DNA was done in a DNA free area to avoid contamination of reagents.
We sent all amplified products to the Central Analytical Facility at Stellenbosch University, South Africa for fragment analysis. The software program Gene Marker v2.4.0 (Soft Genetics) was used for subsequent genotype scoring. To ensure genotyping consistency, we reamplified the archival museum samples and analysed each locus three times. In addition we reamplified 20% of the fresh samples to check for consistency in genotype scoring.
In addition to the microsatellite analysis, we amplified and sequenced two mitochondrial (mtDNA) markers and one nuclear intron (nucDNA) marker: cytochrome oxidase I (COI using the primers BirdF1/BirdR1; ), 16S ribosomal RNA (16S rRNA using the primers 16Sa/16SB; ) and a nuclear intron of the β-fibrinogen gene (β-fib using the primers FIB-BI7U/FIB-BI7L; ). Where possible, these three markers were amplified for five representative P. r. robustus samples (Eastern Cape, KwaZulu-Natal and Limpopo) and two samples for each of the other species and subspecies included in the microsatellite analysis (See S1 Table). PCR reactions for COI and 16S rRNA consisted of: ~20–150 ng template DNA, 2.5 μl 10 x KAPA buffer, 1 U KAPA Taq DNA polymerase, 200 μM dNTPs, 0.2 μM of each primer and 18.4 μl dH2O to give a final reaction volume of 25 μl. We added an additional 0.5 mM MgCl2 to the reaction mixture for β-fib. A touchdown PCR protocol was used. The PCR cycle parameters for COI and 16S rRNA included an initial denaturation step at 95°C for 3 min followed by 10 cycles at 95°C for 30 s, 60–50°C for 30 s, 72°C for 30 s, 25 cycles at 95°C for 30 s, 50°C for 30 s, 72°C for 30 s with a final extension step at 72°C for 5 min. The touchdown temperature range and annealing temperature for β-fib was 65–55°C and 55°C.
We sent all PCR products which showed positive amplification for sequencing. Cycle sequencing was performed using the BigDye Chemistry, v3.1 and sequencing products were analyzed on an Applied Biosystems 3730xl Genetic Analyzer (Applied Biosystematics, Perkin Elmer). All heterozygous sites in the nuclear intron were coded using the International Union of Biochemistry (IUB) codes. All raw sequence data were viewed and edited in BioEdit v7.1.11 . The edited sequences were aligned using ClustalW  as implemented in MEGA v6  and then checked manually to ensure homology. We deposited all new sequences in GenBank (S2 Table). Psittacus erithacus, the Grey Parrot, was included as an outgroup with sequences downloaded from GenBank (S2 Table).
We estimated null allele frequencies for each marker using the software program FreeNA  using the Expectation Maximization algorithm (EM) . We compared the null allele corrected and uncorrected global FST values using the excluding null alleles (ENA) method . Summary statistics (average number of alleles, observed and expected heterozygosity and the number of private alleles), polymorphic information content (PIC), pairwise FST and analysis of molecular variance (AMOVA) were estimated using GenAlEx v6.5  and Cervus v3.0.7 . Two AMOVA analyses were conducted. One grouping individuals into five species (P. cryptoxanthus, P. gulielmi, P. meyeri, P. robustus and P. rueppellii). The three P. robustus subspecies and the two P. gulielmi subspecies were placed into P. robustus and P. gulielmi respectively. A second AMOVA was conducted in which the subspecies (P. g. gulielmi, P. g. massaicus, P. r. fuscicollis, P. r. robustus and P. r. suahelicus) were placed into individual groups. We used the program XLSTAT 2014  to generate a 3D principal coordinate analysis (PCoA) figure using pairwise FST values. Arlequin v3.5  was used to test for linkage disequilibrium and deviation from Hardy–Weinberg equilibrium. We performed Bayesian clustering analysis in STRUCTURE v2.3.4 . Ten independent runs were performed. Each STRUCTURE run consisted of 1,000,000 Markov chain Monte Carlo (MCMC) replicates with a burn-in of 100,000 with the proposed number of clusters (K) ranging from 2 to 10. The no admixture model with correlated allele frequencies was selected for all runs. Sampling locality information was incorporated using the LOCPRIOR model. We used the program STRUCTURE harvester  to estimate the most probable number of genetic clusters using the method implemented by Evanno et al. . The STRUCTURE figure and the membership probabilities (Q-values) for each individual and for each cluster were estimated using ClumpAK (http://clumpak.tau.ac.il).
DNA sequence analysis.
We initially analyzed the three gene regions (COI, 16S rRNA and β-fib) separately and then combined them into a single data matrix. In addition, we analyzed the sequence data from each gene according to origin of marker (mtDNA or nucDNA). Number of variable sites, number of observed transitions, number of observed transvertions and number of observed indels were estimated using MEGA and Arlequin. Phylogenies were constructed using both maximum likelihood (ML) conducted in Garli v2 ; and Bayesian inference (BI) using MrBayes v3.2 . For these analyses the optimal model of nucleotide substitution for each gene region was used. This was selected using the Akaike information criterion (AIC)  in jModelTest v.2.1 . In the combined analyses, data were partitioned by gene with model parameters unlinked across partitions. In ML analyses branch support was assessed using 1000 bootstraps replicates with consensus topologies generated using PHYLIP v3.695 [55, 56]. Each Bayesian run consisted of three heated chains at default temperature of 0.2 and one cold chain and was run for 10 million generations with the sampling frequency of 1000 and a burn-in of 0.25 (25,000 trees). To ensure that MCMC chains had reached convergence, Tracer v1.5  was used to verify that the appropriate estimated sample sizes (ESS) for all parameters were above 200 . A 50% majority rule consensus tree was constructed in PHYLIP after burn-in was removed. We viewed trees in FigTree v1.4.0 . We calculated pairwise genetic distances in RAxMLGUI v.1.3.1  using the general time reversible nucleotide substitution model with gamma distribution and invariant sites (GTRGAMMAI).
Molecular clock analysis.
There are no fossil calibration points available within the genus Poicephalus. Molecular clock analysis was performed using secondary calibration dates from two other studies [61, 62] to estimate divergence times of Poicephalus species. Schweizer et al. , using three nuclear genes, used three avian fossil records outside Psittaciformes as calibration points to estimate diversification times. These authors estimated that the separation of Strigopidae from the rest of the parrot taxa occurred ~58.6 million years ago (Mya). White et al.  used full mitochondrial genomes and six avian fossil records as calibration points to study the evolutionary history of the Cacatuidae and estimated Strigopidae and the other parrot taxa split ~47.4 Mya.
Given that the divergence dates estimated by Schweizer et al.  and White et al.  are quite different, we used the calibration points from these two studies in separate analyses. Five calibration points were used from Schweizer et al.  and included the split between Nestor and the rest of the parrot taxa (58.59 Mya; SD: 8.2), the split between the Australasian Cacatuidae and Psittacidae (47.38 Mya; SD: 7), the split between Psittacus-Poicephalus and Arini (35.16 Mya; SD: 5.6), the split between Amazona/Pionus and Ara/Deroptyus (25.26 Mya; SD: 5) and the split between Psittacus and Poicephalus (12.92 Mya; SD: 3.5). We used three calibration points from White et al.  and included the split between Nestor and the rest of the parrot taxa (47.4 Mya; SD: 7), the split between the Australasian Cacatuidae and Psittacidae (40.7; SD: 7) and the split between Cacatuninae and Calyptorhynchinae (27.9 Mya; SD: 6).
We downloaded sequences from 27 parrot species covering 21 genera, used by Schweizer et al. , from GenBank (S2 Table) and included them in the molecular clock analyses. Divergence times were calculated using BEAST v1.8 . We conducted analyses on two datasets, one containing sequences from all three gene regions (COI, 16S rRNA and β-fib) and then to limit the inclusion of missing data we also analyzed a dataset containing only the mtDNA genes. In all analyses we partitioned the data by gene with the parameters of the substitution models unlinked. The GTR + Γ + I substitution model was used for COI and the GTR + I model was used for β-fib as the best-fit models suggested by jModelTest (TPM2uf + Γ + I and TPM1uf + I) are not currently implemented in BEAST. The GTR + Γ + I model was, however, identified as the best fit model for 16S rRNA. A lognormal relaxed-clock approach was implemented following Schweizer et al.  with a Yule speciation model set as tree prior.
For each dataset (three gene and mtDNA only), we conducted two independent simulations for each set of calibration points. Each BEAST run consisted of 400 million generations, with a sampling frequency of 10000 trees. The program Tracer was used to confirm that MCMC chains had reached stationarity and ESS of all parameters were greater than 200. We used TreeAnnotator v1.8.1 (in Beast v1.8.1) to estimate the maximum clade probability tree which we viewed in TreeGraph v2 .
In this study we genotyped 138 individuals using 11 microsatellite loci. Individuals in this data set had minimal missing data, with only 3.03% missing data included. Mean null allele frequencies ranged from 0.4% -14.6% across species (Na; S3 Table). In particular the error rate in the data collected from four loci in the P. rueppellii dataset (Prob18, Na = 23.3%; Prob26, Na = 22.9%; Prob29, Na = 30.2%; Prob34, Na = 26.0%) was high, although below values reported in other studies . No loci showed null allele frequencies higher than 30.2%. The detection of null alleles can be biased in natural populations which deviate from Hardy-Weinberg Equilibrium (HWE), as is the case in this study where all loci except Prob35 deviated from Hardy-Weinberg equilibrium in at least one species/subspecies. The presence of null alleles can inflate FST values , but we found that there was little difference between the global FST values using the ENA corrected (FST = 0.25) and the uncorrected (FST = 0.26) data. The effects of any null alleles present in the Poicephalus data set are likely minimal and we performed all further analysis using data from all loci.
Estimates of the mean number of alleles, private alleles, observed and expected heterozygosity are reported in Table 1. All loci were polymorphic in all species/subspecies with the exception of Prob18 and Prob30 which were monomorphic in P. g. gulielmi and Prob25 and Prob35 which were monomorphic in P. g. massaicus (S3 Table). Ascertainment bias could influence genetic diversity analyses, as allele numbers might be higher in the focal species from which the markers were developed . Noticeably lower allele numbers were only observed in two species (P. rueppellii and P. gulielmi). One locus in P. rueppellii (Prob6), two loci in P. g. massaicus (Prob30 and Prob25), and four loci in P. g. gulielmi (Prob6, Prob15, Prob18 and Prob30) showed allele numbers < 50% of that observed in P. robustus ssp. Low sample number is also a consideration in the case of P. g. gulielmi (n = 4; S3 Table). Nine of the eleven loci used were highly informative with PIC values > 0.7. The PIC values for each locus range from 0.513 (Prob35) to 0.895 (Prob23) with a mean PIC value of 0.794. (S3 Table). The highest mean number of alleles was recorded for P. r. suahelicus (NA = 7.091). Private alleles (PA) were identified for all species and subspecies. The most distinct species P. rueppellii has nine private alleles. The number of private alleles observed in the P. robustus subspecies ranged from one to six alleles, with P. r. suahelicus possessing the highest number of private alleles (PA = 6). The observed heterozygosity (HO) ranged from 0.368 to 0.632, and the expected heterozygosity (HE) range from 0.457 to 0.705 over all species and subspecies. Of the three P. robustus subspecies, P. r. suahelicus showed the highest level of genetic diversity (HE = 0.701; Table 1) and is comparable to previous observations (HE = 0.76) . P. r. fuscicollis has a relatively small and fragmented distribution range, and comparatively has the lowest level of genetic diversity among the P. robustus subspecies (HE = 0.557). This lower genetic diversity estimate might be an artefact of sample size or that the majority of these samples were from captive bred birds, given that Taylor  observed a higher level of genetic diversity for P. r. fuscicollis (HE = 0.77). The observed heterozygosity of P. r. robustus (HO = 0.622) was comparable to previous estimates (HO = 0.63) .
The Bayesian clustering analysis identified seven genetic clusters (K = 7, mean LnP(K) = -4222.4; Fig 2) as the most likely number of clusters following Evanno et al. . These clusters corresponds to the species P. cryptoxanthus, P. gulielmi (with P. g. gulielmi and P. g. massaicus clustering together; Q = 1), P. meyeri and P. rueppelli. The three P. robustus subspecies were assigned to separate clusters with only two P. r. fuscicollis individuals assigned to the P. r. suahelicus cluster with high probability (Q = 0.99).
Each individual is represented by a vertical line, and coloured according to each individual’s estimated membership probability (Q-values). Average Q-values for each cluster is depicted above the figure.
The STRUCTURE clustering was supported by pairwise FST values which were highly significant between all species and subspecies (0.13 ≤ FST ≤ 0.41; P-value < 0.05). The pairwise FST values between P. r. robustus and P. r. suahelicus (FST = 0.14; P-value = 0.001), and P. r. robustus and P. r. fuscicollis (FST = 0.22: P-value = 0.001) were comparable to the pairwise values between the other Poicephalus species, for example between P. cryptoxanthus and P. meyeri (FST = 0.14; P-value = 0.001) and P. cryptoxanthus and P. rueppelli (FST = 0.21; P-value = 0.001; S4 Table). These relationships can also be clearly seen in the 3D PCoA drawn from the pairwise FST values (Fig 3). The global FST value (subspecies assigned to species) was significantly different from zero (FST = 0.21; P-value = 0.001) and the analysis of molecular variance (AMOVA) indicated that 21% of the observed genetic variation occurred between species with 58% occurring within individuals and 21% among individuals that belong to the same species/subspecies. High FST values were also recovered when individuals were assigned to subspecies (FST = 0.25; P-value = 0.001). The AMOVA analysis indicated that 25% of the variation occurs between species and subspecies, with 14% variation among individuals. The majority of the genetic variation occurred within individuals (61%).
The two mtDNA markers were successfully amplified for all 18 specimens; unfortunately β-fib was only successfully sequenced from 10 specimens (S5 Table). The data matrices for each marker included (S5 Table): COI (592 bp; 66 variable sites), 16S rRNA (707 bp; 32 variable sites) and β-fib (707 bp; 4 variable sites). To reduce the effects of missing data we conducted two analyses. Firstly, the data from all three markers including missing data were concatenated and analysed. Secondly, only data from the two mtDNA markers were analysed (no missing data included). There was no significant conflict among the topologies produced when each marker was analysed independently and the data were concatenated (concatenated: 1834 bp, 102 variable characters; mtDNA only: 1127 bp, 98 variable characters). The P. robustus clade formed a distinct monophyletic group separate from the P. meyeri clade in both the concatenated (ML bootstrap, 87; Bayes’ posterior probability, 1.00) and mtDNA topologies (ML bootstrap, 94; Bayes’ posterior probability, 1.00), supporting hypotheses proposed by Forshaw  and Fry et al. .
The phylogenetic analysis confirmed the monophyly of all species with the exception of P. robustus (Fig 4). Phylogenetic analysis of the mtDNA markers cluster together the subspecies P r. fuscicollis and P. r. suahelicus (ML bootstrap, 54; Bayes’ posterior probability, 0.92). The phylogenetic position of P. r. robustus is not well resolved. In the concatenated analysis P. r. robustus is placed sister to a clade containing the two P. gulielmi subspecies although this association is only weakly supported (ML bootstrap, 58; Bayes’ posterior probability, 0.62). In the mtDNA phylogeny the three P. robustus subspecies are clustered together (ML bootstrap, 68; Bayes’ posterior probability, 0.89).
The analyses were performed using (left) concatenated data (COI, 16S rRNA and β-fib) and (right) mitochondrial DNA data. Values given above the branches represent maximum likelihood bootstrap values and Bayesian posterior probabilities (in that order). Dated nodes are indicated by numbers next to each node in b) and correspond to Table 2. Species/subspecies were highlighted according to the microsatellite clustering analyses. Psittacus erithacus was used as an outgroup.
The mean estimated values and the 95% highest posterior density (HPD) ranges are given for the two dataset partitions. The node numbers correspond to Fig 4B. See S1 Fig for the corresponding maximum clade probability trees.
The COI sequence differentiation among the P. robustus subspecies was comparable to that observed among other well-established parrot species. For example, the average pairwise genetic distance for P. r. robustus vs. P. r. fuscicollis (D = 4.5%) and P. r. robustus vs. P. r. suahelicus (D = 4.9%; Table 3) was greater than the genetic difference between three well-established cockatoo species : Calyptorhynchus funereus vs. C. latirostris (D = 3.0%) and C. funereus vs. C. baudinii (D = 3.6%; S6 Table). Comparable genetic distance values were observed by Rocha et al.  using COI sequences to investigate the taxonomic relationship between two closely related Amazon parrot species, Amazona pretrei and A. tucumana (D = 2.2%).
Molecular clock analysis.
The molecular clock analyses conducted with the concatenated (COI, 16S rRNA and β-fib) and mtDNA (COI and 16S rRNA) datasets produced similar maximum clade probability trees, which suggests that the β-fib missing data did not negatively bias the molecular clock analysis. The estimated divergence dates obtained from the Schweizer et al.  and White et al.  calibration points were similar (all fall within the 95% highest posterior density (HPD) range of each other; Table 2). The most recent common ancestor of the Poicephalus species included in the present study is 10.27 to 10.63 Mya. The origin of the P. robustus clade is estimated at 6.16 to 6.72 Mya. The maximum clade probability tree suggests that the P. r. fuscicollis and P. r. suahelicus lineage (0.57 to 0.69 Mya) is younger than the most recent common ancestor of the three P. r. robustus populations (Eastern Cape, KwaZulu-Natal and Limpopo; 1.16 to 1.44 Mya). It is clear that P. r. robustus represents a distinct evolutionary lineage. Having diverged from the other species during the late Pliocene to early Pleistocene (2.13 to 2.67 Mya; Table 2). Similar divergence dates were estimated for two recognised cockatoo species, Calyptorhynchus funereus and Calyptorhynchus latirostris (2.49 to 2.83 Mya; S1 Fig).
The multilocus nuclear and mtDNA results obtained from the current study along with previous morphological, ecological and behavioural data [10–13] provide strong support for the classification of P. r. robustus as a distinct species separate from P. r. fuscicollis and P. r. suahelicus, namely P. robustus sensu stricto. Our results showed no hybrids or signs of genetic introgression among P. r. robustus and P. r. suahelicus, even in the Limpopo Province of South Africa where these subspecies occur in close proximity.
Multilocus molecular data is often used to investigate taxonomic issues within Psittaciformes. Wenner et al.  performed a taxonomic analysis of the Amazona farinose species complex using four mtDNA and two non-coding nuclear intron fragments. The authors found support for distinct Central and South American Mealy Amazon clades. It was suggested that these clades should be split into separate species to allow for the implementation of appropriate conservation planning . In another study, the phylogenetic relationships within the Ampazona ochrocephala species complex were investigated by Eberhard et al.  using four mtDNA markers. The authors found no support for the division of the complex into three species as proposed by others [32, 71–73]. Molecular data have also been used in the past to resolve taxonomic problems in other avian species. For example, microsatellite and mtDNA data were used to assess taxonomic questions within the widespread plover species, Charadrius alexandrinus . The authors confirmed the recommendations by Küpper et al.  that C. nivosus should be considered a separate species. Vilaça and Santos  investigated the taxonomic status of the Basileuterus culicivorus species complex using an mtDNA (cytochrome b), a nuclear intron (β-fibrinogen intron 5) and six microsatellite markers. The two species from the species complex were found to be genetically indistinguishable and it was recommended that these taxa should be grouped into a single species, namely Basileuterus culicivorus.
Evolution of the Cape Parrot
The divergence of P. robustus senso stricto from P. r. fuscicollis and P. r. suahelicus coincides with the start of the Quaternary Period (early Pleistocene) about 2.4 Mya. The Quaternary period consisted of a series of ice ages which led to major global climatic changes resulting in drastic vegetation and habitat changes. These climatic changes led to numerous cycles of grassland expansions and forest contractions and vice versa. The expansion of grasslands in Africa and subsequent forest contraction happened at around 2.4 Mya . It has been estimated that the Afromontane forests of southern Africa have been expanding and contracting over the last 100 000 years in accordance with glacial cycles . South African Afromontane forest is the oldest of the two major forest types found in southern Africa, and has been present prior to the last glacial maximum (~18000 BP) . The discovery of parrot fossils in the Western Cape Province, South Africa, dating to the early Pliocene indicates the presence of woodlands in this area and signifies the substantial changes in habitat during the Plio-Pleistocene . The contraction of forests during the arid glacial periods would have driven ancestral forest dwelling species, for example the P. robustus ancestor, into forest refugia . These fragmented subpopulations would have started to differentiate under adaptive pressures (such as dietary constraints), leading to speciation events [9, 11]. It is proposed that about 1–2 million years (Myr) is sufficient time for speciation to occur . Using Cytochrome-b sequence data, Kundu et al.  estimated that speciation events occurred within the Afro-Asian parakeet genus Psittacula about every 1–2 Myr, and our data suggests that Poicephalus show similar short periods of cladogenesis. Comparable molecular clock estimates were obtained for the two extant New Zealand Nestor species (Nestor meridionalis and Nestor notabili; ). The authors suggest that the separation between N. meridionalis and N. notabili occurred between 2.3 to 2.5 Mya, using a multilocus dataset and calibration points from Schweizer et al.  and White et al. .
Taxonomic and conservation considerations
Multiple data sources, including morphological, ecological, behavioural and now molecular, provide convincing scientific evidence that P. r. robustus is a distinct taxonomic unit separate from P. r. fuscicollis and P. r. suahelicus. This lineage fulfills the criteria for at least four methods of species delimitation including Biological , Morphological [85, 86], Genotypic  and Phylogenetic [88, 89] species concepts. Reproductive isolation is a key criteria for the Biological Species Concept. Behavioural studies have reported that the two taxa whose distributions overlap in South Africa, P. r. robustus and P. r. suahelicus, breed at different times of the year, with P. r. robustus breeding from August to February (mainly utilizing Afrocacarpus/Podocarpus trees), while P. r. suahelicus breeds from April to August (preferring Adansonia trees for nesting) [20, 21]. In addition, the genetic data presented in this study found no signs of introgression between these two taxa, providing additional evidence for reproductive isolation. Morphologically, P. r. robustus can be easily seperated from the other subspecies, with distinctive colouration [10, 13, 14], small body size and much smaller, narrower bill . These unique diagnostic morphological characters support the reclassification of this taxon using the Morphological Species Concept. The elevation of P. r. robustus to species is also supported by the genetic differentiation of this taxon from other Poicephalus species using both microsatellite and sequence data. The mutilocus genotype data unambiguously assigned P. r. robustus individuals to a single genetic cluster separate from other Poicephalus species and subspecies. This finding is further strengthened by phylogenetic analysis of both mtDNA and nuclear sequences, with P. r. robustus recovered as monophyletic on both the maximum likelihood and Bayesian trees. The observed molecular divergence between P. r. robustus and other subspecies is congruent with that seperating other well-established parrot species. The P. r. robustus lineage can also be diagnosed by fixed molecular characters (three synapomophic and eleven autapomorphic). Molecular clock analysis suggests that the P. r. robustus lineage diverged from P. r. fuscicollis and P. r. suahelicus approximately 2.4 Mya (early Pleistocene).
We propose that P. r. robustus should be elevated to species status, namely P. robustus sensu stricto following Gmelin . Given that our molecular data support a close relationship between P. r. fuscicollis and P. r. suahelicus, we recommend that these two taxa remain as subspecies under P. fuscicollis, namely P. f. fuscicollis stat. nova.  and P. f. suahelicus stat. nova.  following previous authors [10, 11, 13]. The reclassification of P. robustus will have considerable implications for conservation management. Given that the South African Cape Parrot (P. robustus sensu stricto) has a population size less than 10 000 mature individuals with no subpopulation containing more than 1000 mature individuals [23, 93] P. robustus sensu stricto meets criterion C2a(i) for a Vulnerable listing in the IUCN Red List of Threatened Species. The Cape Parrot also meets all of the biological criteria for a CITES Appendix I listing . The recognition of P. robustus sensu stricto will allow for the effective regulation and monitoring of any international trade under CITES.
Our study is the most comprehensive analysis of the taxonomic relationships within the P. robustus clade using molecular data. The clear genetic differentiation of P. r. robustus from P. r. fuscicollis and P. r. suahelicus coupled with the differences in morphology, habitat and dietary needs provides strong scientific evidence for the elevation of P. r. robustus to P. robustus sensu stricto. Our results are sufficient to provide conservation authorities with strong evidence that the South African endemic Cape Parrot should be viewed as a Vulnerable species of conservation priority. This recognition will in turn assist the biodiversity conservation sector to prioritize, plan and implement conservation strategies.
S1 Fig. The maximum clade probability trees generated using the concatenated and mtDNA only data sets, with two sets of calibration points each.
S1 Table. The Poicephalus specimens included in the present study.
S2 Table. Genbank accession numbers for Poicephalus sequences generated in the present study and accession numbers for the 27 additional parrot sequences used for molecular clock analysis.
S3 Table. The polymorphic information content and null allele frequencies of each locus over all Poicephalus specimens analysed.
S4 Table. The pairwise FST values for all Poicephalus specimens used in this study.
S5 Table. Summary statistics of the COI, 16S rRNA and β-fib sequences generated for the Poicephalus specimens analysed in the current study.
The authors would like to thank a number of researchers, private breeders and organisations for providing samples; East London Museum (East London, South Africa), Ditsong National Museum of Natural History (Pretoria, South Africa), Durban Natural Sciences Museum (Durban, South Africa), the British Natural History Museum at Tring (United Kingdom), the National Zoological Gardens (Pretoria, South Africa), the Umgeni River Bird Park (Durban, South Africa), the Loro Parque Foundation (Canary Islands, Spain), Dr. Craig Symes, Mr. William Horsfield and Dr. Luke Arnot. We would also like to acknowledge the following individuals for their assistance during field sampling: Dr. Riddhika Kalle, Dr. T. Ramesh and Dr. Lindy Thompson. We thank Cyril Laubscher for use of his photographs.
Conceived and designed the experiments: WGC CTD MRP SW-M. Performed the experiments: WGC. Analyzed the data: WGC. Contributed reagents/materials/analysis tools: CTD MRP SW-M. Wrote the paper: WGC CTD MRP SW-M.
- 1. Phillimore AB, Owens IPF. Are subspecies useful in evolutionary and conservation biology? Proc R Soc B. 2006; 273(1590): 1049–53. pmid:16600880
- 2. Zink RM. The role of subspecies in obscuring avian biological diversity and misleading conservation policy. Proc R Soc B. 2004;271(1539): 561–4. pmid:15156912
- 3. Mayr E. Of what use are subspecies? Auk. 1982;99: 593–5.
- 4. Gippoliti S, Amori G. The problem of subspecies and biased taxonomy in conservation lists: the case of mammals. Folia Zool Brno. 2007;56(2): 113–7.
- 5. Barrowclough GF. Genetic and phenotypic differentiation in a wood warbler (genus Dendroica) hybrid zone. Auk. 1980;97: 655–88.
- 6. Hazevoet C. Conservation and species lists: taxonomic neglect promotes the extinction of endemic birds, as exemplified by taxa from eastern Atlantic islands. Bird Conserv Int. 1996;6(2): 181–96.
- 7. Fry CH, Keith S, Urban EK. The Birds of Africa, Vol. 3. New York, London: Academic Press;1988.
- 8. Forshaw JM. Parrots of the world. Newton Abbot, London: David & Charles; 1978.
- 9. Massa R, Sara M, Piazza M, Di Gaetano C, Randazzo M, Cognetti G. A molecular approach to the taxonomy and biogeography of African parrots. Ital J Zool (Modena). 2000;67(3): 313–7.
- 10. Clancey PA. The Cape Parrot: an additional valid species. Honeyguide. 1997;43: 61–2.
- 11. Perrin MR. A review of the taxonomic status and biology of the Cape Parrot Poicephalus robustus, with reference to the Brown-necked Parrot P. fuscicollis fuscicollis and the Grey-headed Parrot P. f. suahelicus. Ostrich. 2005;76(3–4): 195–205.
- 12. Perrin MR. Parrots of Africa, Madagascar and the Mascarene Islands. Johannesburg: Wits University Press; 2012.
- 13. Wirminghaus JO, Downs CT, Perrin MR, Symes CT. Taxonomic relationships of the subspecies of the Cape Parrot Poicephalus robustus (Gmelin). J Nat Hist. 2002;36(3): 361–78.
- 14. Forshaw JM. Parrots of the world. Willoughby: Lansdowne Books; 1989.
- 15. Snow DW. An Atlas of Speciation of African Non-passerine Birds. London: British Museum (Natural History); 1978.
- 16. Symes CT, Perrin MR. Behaviour and some vocalisations of the Grey-headed Parrot Poicephalus fuscicollis suahelicus. Durban Mus Nov. 2004;29: 5–13.
- 17. Wirminghaus JO, Downs CT, Symes CT, Perrin MR, Dempster ER. Vocalisations, and some behaviours of the Cape Parrot Poicephalus robustus. Durban Mus Nov. 2000;25: 12–7.
- 18. Wirminghaus JO, Downs CT, Symes CT, Perrin MR. Diet of the Cape Parrot, Poicephalus robustus, in Afromontane forests in KwaZulu-Natal, South Africa. Ostrich. 2002;73(1–2): 20–5.
- 19. Symes CT, Perrin MR. Feeding biology of the Grey-headed Parrot, Poicephalus fuscicollis suahelicus (Reichenow), in northern province, South Africa. Emu. 2003;103(1): 49–58.
- 20. Wirminghaus JO, Downs CT, Perrin MR, Symes CT. Breeding biology of the Cape Parrot, Poicephalus robustus. Ostrich. 2001;72(3–4): 159–64.
- 21. Symes CT, Perrin MR. Breeding biology of the Grey-headed Parrot (Poicephalus fuscicollis suahelicus) in the wild. Emu. 2004;104(1): 45–57.
- 22. Wenner TJ, Russello MA, Wright TF. Cryptic species in a Neotropical parrot: genetic variation within the Amazona farinosa species complex and its conservation implications. Conserv Genet. 2012;13(5): 1427–32.
- 23. Downs CT, Pfeiffer M, Hart LA. Fifteen years of annual Cape Parrot Poicephalus robustus censuses: current population trends and conservation contributions. Ostrich. 2014;85(3): 273–80.
- 24. Wirminghaus JO, Downs CT, Symes CT, Perrin MR. Conservation of the Cape Parrot in southern Africa. S Afr J Wlildl Res. 1999;29(4): 118–29.
- 25. Minister of Environmental Affairs and Tourism. National Environmental Management: Biodiversity Act (ACT 10 of 2004): Publication of Lists of Critically Endangered, Endangered, Vulnerable and Protected Species. In: DEAT, editor. Cape Town, South Africa; 2007.
- 26. Taylor M. Eskom Red data book of birds of South Africa, Lesotho and Swaziland. Birdlife South Africa, Johannesburg; 2014.
- 27. BirdLife International. Poicephalus robustus. 2012 [09 December 2014]. Available from: http://www.iucnredlist.org.
- 28. Russello MA, Stahala C, Lalonde D, Schmidt KL, Amato G. Cryptic diversity and conservation units in the Bahama parrot. Conserv Genet. 2010;11(5): 1809–21.
- 29. Tavares ES, Baker AJ, Pereira SL, Miyaki CY. Phylogenetic relationships and historical biogeography of neotropical parrots (Psittaciformes: Psittacidae: Arini) inferred from mitochondrial and nuclear DNA sequences. Syst Biol. 2006;55(3): 454–70. pmid:16861209
- 30. Calderón L, Quintana F, Cabanne GS, Lougheed SC, Tubaro PL. Phylogeography and genetic structure of two Patagonian shag species (Aves: Phalacrocoracidae). Mol Phylogenet Evol. 2014;72: 42–53. pmid:24418531
- 31. Eberhard JR, Bermingham E. Phylogeny and comparative biogeography of Pionopsitta parrots and Pteroglossus toucans. Mol Phylogenet Evol. 2005;36(2): 288–304. pmid:15955511
- 32. Eberhard JR, Bermingham E, Zink R. Phylogeny and biogeography of the Amazona ochrocephala (Aves: Psittacidae) complex. Auk. 2004;121(2): 318–32.
- 33. Masello JF, Quillfeldt P, Munimanda GK, Klauke N, Segelbacher G, Schaefer HM, et al. The high Andes, gene flow and a stable hybrid zone shape the genetic structure of a wide-ranging South American parrot. Front Zool. 2011;8: 16. pmid:21672266. Pubmed Central PMCID: 3142489.
- 34. Faria P, Guedes NR, Yamashita C, Martuscelli P, Miyaki C. Genetic variation and population structure of the endangered Hyacinth Macaw (Anodorhynchus hyacinthinus): implications for conservation. Biodivers Conserv. 2008;17(4): 765–79.
- 35. Pillay K, Dawson DA, Horsburgh GJ, Perrin MR, Burke T, Taylor TD. Twenty-two polymorphic microsatellite loci aimed at detecting illegal trade in the Cape parrot, Poicephalus robustus (Psittacidae, AVES). Mol Ecol Resour. 2010;10(1): 142–9. pmid:21564999
- 36. Hebert PDN, Stoeckle MY, Zemlak TS, Francis CM. Identification of birds through DNA barcodes. PLoS Biol. 2004;2(10): e312. pmid:15455034
- 37. Palumbi S, Martin A, Romano S, McMillan W, Stice L, Grabowski G. The Simple Fool's Guide to PCR, Version 2.0. Honolulu: Department of Zoology and Kewalo Marine Laboratory, University of Hawaii; 1991.
- 38. Prychitko TM, Moore WS. The utility of DNA sequences of an intron from the β-fibrinogen gene in phylogenetic analysis of woodpeckers (Aves: Picidae). Mol Phylogenet Evol. 1997;8(2): 193–204. pmid:9299224
- 39. Hall TA. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucl Acids Symp Ser. 1999;41: 95–8.
- 40. Thompson JD, Higgins DG, Gibson TJ. CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Res. 1994;22(22): 4673–80. pmid:7984417
- 41. Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30(12): 2725–9. pmid:24132122
- 42. Chapuis M-P, Estoup A. Microsatellite null alleles and estimation of population differentiation. Mol Biol Evol. 2007;24(3): 621–31. pmid:17150975
- 43. Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J Roy Stat Soc B. 1977;39: 1–38.
- 44. Peakall R, Smouse PE. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research-an update. Bioinformatics. 2012;28: 2537–9. pmid:22820204
- 45. Kalinowski ST, Taper ML, Marshall TC. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol Ecol. 2007;16: 1099–106. pmid:17305863
- 46. XLSTAT. XLSTAT Version 2014.1.01. Addinsoft 1995–2014; 2014.
- 47. Excoffier L, Laval G, Schneider S. Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evol Bioinform Online. 2005;1: 4750.
- 48. Pritchard JK, Wen X, Falush D. Structure, version 2.3. 2010 [20 November 2013]. Available from: http://pritch.bsd.uchicago.edu/software.
- 49. Earl D. STRUCTURE Harvester, Version 0.3. 2009 [01 May 2013]. Available from: http://users.soe.ucsc.edu/~dearl/software/struct_harvest/.
- 50. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol. 2005;14(8): 2611–20. pmid:15969739
- 51. Zwickl DJ. Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion. PhD dissertation; 2006.
- 52. Ronquist F, Teslenko M, van der Mark P, Ayres DL, Darling A, Höhna S, et al. MrBayes 3.2: Efficient Bayesian phylogenetic inference and model choice across a large model space. Syst Biol. 2012;61(3): 539–42. pmid:22357727
- 53. Akaike H. A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974;19(6): 716–23.
- 54. Darriba D, Taboada GL, Doallo R, Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat Meth. 2012;9(8): 772.
- 55. Felsenstein J. PHYLIP–Phylogeny Inference Package, Version 3.2. Cladistics. 1989;5: 164–6.
- 56. Felsenstein J. PHYLIP–phylogeny inference package, Version 3.69. 2009. Epub 3.69.
- 57. Rambaut A, Drummond AJ. Molecular evolution, phylogenetics and epidemiology, Tracer v.1.5. 2007 [12 December 2014]. Available from: http://tree.bio.ed.ac.uk/software/tracer/.
- 58. Drummond AJ, Ho SYW, Phillips MJ, Rambaut A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 2006;4(5): e88. pmid:16683862
- 59. Rambaut A. FigTree v1.4.0. 2012 [12 December 2014]. Available from: http://tree.bio.ed.ac.uk/software/figtree/.
- 60. Silvestro D, Michalak I. raxmlGUI: a graphical front-end for RAxML. Org Divers Evol. 2012;12(4): 335–7.
- 61. Schweizer M, Seehausen O, Hertwig ST. Macroevolutionary patterns in the diversicification of parrots: effects of climate change, geological events and key innovations. J Biogeogr. 2011;38: 2176–94.
- 62. White NE, Phillips MJ, Gilbert MT, Alfaro-Nunez A, Willerslev E, Mawson PR, et al. The evolutionary history of cockatoos (Aves: Psittaciformes: Cacatuidae). Mol Phylogenet Evol. 2011;59(3): 615–22. pmid:21419232
- 63. Drummond AJ, Suchard MA, Xie D, Rambaut A. Bayesian phylogenetics with BEAUti and the BEAST 1.7. Mol Biol Evol. 2012;29(8): 1969–73. pmid:22367748
- 64. Stover B, Muller K. TreeGraph 2: Combining and visualizing evidence from different phylogenetic analyses. BMC Bioinformatics. 2010;11(1): 7.
- 65. Dakin EE, Avise JC. Microsatellite null alleles in parentage analysis. Heredity (Edinb). 2004;93(5): 504–9.
- 66. Carlsson J. Effects of microsatellite null alleles on assignment testing. J Hered. 2008;99(6): 616–23. pmid:18535000
- 67. Brandström M, Ellegren H. Genome-wide analysis of microsatellite polymorphism in chicken circumventing the ascertainment bias. Genome research. 2008;18(6): 881–7. pmid:18356314
- 68. Taylor TD. Cross-utility of microsatellite markers across species of the African Poicephalus parrots to encourage conservation research. Ostrich. 2011;82(1): 65–70.
- 69. Saunders DA, Mawson PR, Dawson R. One fledgling or two in the endangered Carnaby's Cockatoo (Calyptorhynchus latirostris): a strategy for survival or legacy from a bygone era? Conserv Physiol. 2014;2(1): cou001.
- 70. Rocha AV, Rivera LO, Martinez J, Prestes NP, Caparroz R. Biogeography of speciation of two sister species of neotropical Amazona (Aves, Psittaciformes) based on mitochondrial sequence data. PLoS One. 2014;9(9): e108096. pmid:25251765
- 71. Sibley CG, Monroe BL. Distribution and taxonomy of birds of the world. New Haven, Connecticut: Yale University Press; 1990.
- 72. American Ornithologists' Union. Checklist of North American Birds, 7th ed. Washington, D.C.: American Ornithologists' Union; 1998.
- 73. Juniper T, Parr M. Parrots: A guide to parrots of the world. New Haven, Connecticut: Yale University Press; 1998.
- 74. Rheindt FE, Székely T, Edwards SV, Lee PLM, Burke T, Kennerley PR, et al. Conflict between genetic and phenotypic differentiation: the evolutionary history of a ‘Lost and Rediscovered’ shorebird. PLoS One. 2011;6(11): e26995. pmid:22096515
- 75. Küpper C, Augustin J, Kosztolányi A, Burke T, Figuerola J, Székely T. Kentish versus Snowy Plover: Phenotypic and genetic analyses of Charadrius alexandrinus reveal divergence of Eurasian and American subspecies. Auk. 2009;126: 839–52.
- 76. Vilaça ST, Santos FR. Biogeographic history of the species complex Basileuterus culicivorus (Aves, Parulidae) in the Neotropics. Mol Phylogenet Evol. 2010;57: 585–97. pmid:20659571
- 77. Bobe R, Behrensmeyer A. The expansion of grassland exosystems in Africa in ralation to mammalian evolution and the origin of the genus Homo. Palaeogeogr Palaeocl. 2003;207: 399–420.
- 78. Lawes M. The distribution of the samango monkey (Cercopithecus mitis erythrarchus Peters, 1852 and Cercopithecus mitis labiatus I. Geoffroy, 1843) and forest history in southern Africa. J Biogeogr. 1990: 669–80.
- 79. Moreau RE. Vicissitudes of the African biomes in the late Pleistocene. P Zool Soc Lond. 1963;141(2): 395–421.
- 80. Manegold A. Two new parrot species (Psittaciformes) from the early Pliocene of Langebaanweg, South Africa, and their palaeoecological implications. Ibis. 2013;155(1): 127–39.
- 81. Hewitt G. The genetic legacy of the Quaternary ice ages. Nature. 2000;405: 907–13. pmid:10879524
- 82. Kundu S, Jones CG, Prys-Jones RP, Groombridge JJ. The evolution of the Indian Ocean parrots (Psittaciformes): extinction, adaptive radiation and eustacy. Mol Phylogenet Evol. 2012;62(1): 296–305. pmid:22019932
- 83. Rheindt FE, Christidis L, Kuhn S, de Kloet S, Norman JA, Fidler A. The timing of diversification within the most divergent parrot clade. J Avian Biol. 2014;45(2): 140–8.
- 84. Mayr E. Systematics and the origin of species. New York, NY, USA: Columbia University Press; 1942.
- 85. Nelson G, Plantick N. Systematics and biogeograph: cladistics and vicariance. New York: Columbia Universiy Press; 1981.
- 86. Mishler BD. The morphological, developmental, and phylogenetic basis of species concepts in bryophytes. Bryologist. 1985;88: 207–17.
- 87. Mallet J. A species definition for the modern synthesis. TREE. 1995;10: 294–9. pmid:21237047
- 88. Cracraft J. Species concepts and speciation analysis. In: Jonhnston RF, editor. Current ornithology. Vol. I. New York: Plenum Press; 1983. p. 159–87.
- 89. Eldredge N, Cracraft J. Phylogenetic patterns and the evolutionary process. New York: Columbio University Press; 1980.
- 90. Gmelin JF. Systema Naturae. 1788. p. 334.
- 91. Kuhl. Nova Acta Academiae Caesareae Leopoldino-Carolinae Germanicae Naturae Curiosorum. 1820. p. 93.
- 92. Reichenow ANT. Bericht uber die Februar-Sitzung. Journal für Ornithologie. 1898;46: 314.
- 93. IUCN. IUCN Red List Categories and Criteria: Version 3.1. 2nd ed. Gland, Switzerland and Cambridge, UK: IUCN; 2012.