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Subspecies in the Sarus Crane Antigone antigone revisited; with particular reference to the Australian population

  • Timothy D. Nevard ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Writing – original draft, Writing – review & editing

    tnevard@woothakata.com

    Affiliations Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia, Atherton Tablelands Foundation, Ravenshoe, Queensland, Australia

  • Martin Haase,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Writing – original draft, Writing – review & editing

    Affiliation AG Vogelwarte, Zoologisches Institut und Museum, Universität Greifswald, Greifswald, Germany

  • George Archibald,

    Roles Conceptualization, Data curation, Methodology, Project administration, Supervision, Writing – review & editing

    Affiliation International Crane Foundation, Baraboo, Wisconsin, United States of America

  • Ian Leiper,

    Roles Conceptualization, Methodology, Resources, Software, Supervision, Visualization, Writing – review & editing

    Affiliation Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia

  • Robert N. Van Zalinge,

    Roles Data curation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing

    Affiliation Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia

  • Nuchjaree Purchkoon,

    Roles Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

    Affiliation Zoological Park Organisation, Dusit Bangkok, Thailand

  • Boripat Siriaroonrat,

    Roles Data curation, Investigation, Writing – original draft, Writing – review & editing

    Affiliation Zoological Park Organisation, Dusit Bangkok, Thailand

  • Tin Nwe Latt,

    Roles Data curation, Formal analysis, Investigation, Resources, Writing – original draft, Writing – review & editing

    Affiliation Faculty of Environmentand Resource Studies, Mahidol University, Salaya, Phutthamonthon Nakhon Pathom, Thailand

  • Michael Wink,

    Roles Conceptualization, Formal analysis, Methodology, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Institut für Pharmazie & Molekulare Biotechnologie (IPMB), Heidelberg, Germany

  • Stephen T. Garnett

    Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Research Institute for the Environment and Livelihoods, Charles Darwin University, Darwin, Northern Territory, Australia

Subspecies in the Sarus Crane Antigone antigone revisited; with particular reference to the Australian population

  • Timothy D. Nevard, 
  • Martin Haase, 
  • George Archibald, 
  • Ian Leiper, 
  • Robert N. Van Zalinge, 
  • Nuchjaree Purchkoon, 
  • Boripat Siriaroonrat, 
  • Tin Nwe Latt, 
  • Michael Wink, 
  • Stephen T. Garnett
PLOS
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Abstract

Subspecies are often less well-defined than species but have become one of the basic units for legal protection. Evidence for the erection or synonymy of subspecies therefore needs to be founded on the best science available. Here we show that there is clear genetic disjunction in the Sarus Crane Antigone antigone, where previously the variation had appeared to be clinal. Based on a total sample of 76 individuals, analysis of 10 microsatellite loci from 67 samples and 49 sequences from the mitochondrial control region, this research establishes that the Australian Sarus Crane A. a. gillae differs significantly from both A. a. antigone (South Asia) and A. a. sharpii (Myanmar and Indochina). A single sample from the extinct Philippine subspecies A. a luzonica clustered with A. a. gillae, hinting at the potential for a more recent separation between them than from A. a. antigone and A. a. sharpii, even though A. a. sharpii is closer geographically. The results demonstrate that failure to detect subspecies through initial genetic profiling does not mean discontinuities are absent and has significance for other cases where subspecies are dismissed based on partial genetic evidence. It could also be potentially important for sourcing birds for reintroduction to the Philippines.

Introduction

Species are defined along a continuum from emphasising phenotypic distinctiveness through to reproductive incompatibility [1] with over 30 definitions currently in use [2]. Subspecies are even less well defined and this is uneven amongst taxa. Broadly, subspecies represent geographically defined populations that are potentially incipient species, diagnosable by at least one heritable trait but still reproductively compatible [3]. While there have also been attempts to define subspecies statistically [4,5], debate continues [6,7] and the expectation that genetic analysis would resolve ambiguities has not eventuated. For example, while cetacean biologists are content to define subspecies quantitatively on the basis of mitochondrial DNA control region sequence data alone [8], this approach has been rejected for birds [9]; not least because there is often discordance between mitochondrial and nuclear DNA [10].

This is not merely an academic debate and definitions matter. A failure to recognise subspecies can mean they might be lost before being recognised as warranting conservation attention [11]; on the other hand, over-splitting increases the probability of genetic problems among the necessarily smaller populations identified [12]. Subspecies are, with species, the common currency of threatened species conservation in most jurisdictions [13] with the erection or synonymy of subspecies having legal, financial and social consequences. For example, had the US Fish and Wildlife Service followed Zink et al. [14] and decided that the California Gnatcatcher (Polioptila c. californica) did not warrant subspecies status, 80,000 ha of its critical coastal sage scrub habitat would have been released to development [15]. In the event they decided otherwise, on the basis that the best available scientific information did not support synonymy [16].

Following extensive fieldwork [17,18,19] involving significant observational and genetic study of Australian Sarus Cranes Antigone a. gillae [20], we hypothesised that further investigation of phylogeographic variation in the full range of Sarus Crane Antigone antigone (Linnaeus 1758) subspecies had the potential to change both the taxonomic treatment of Australian Sarus Cranes and the value given to different populations.

The Sarus Crane has geographically separate populations in southern Asia and Australia (Fig 1) that are believed to be geographically allopatric. As it is extinct in the Philippines and thought to be declining in some of its Asian range, particularly in Myanmar and Indochina [21,22], it is classed as Vulnerable by the IUCN [23]. Intraspecific variation within the species has been the subject of ongoing debate. Blyth and Tegetmeier [24] initially erected the Indian and Myanmar birds as distinct species, based on plumage (the Indian Sarus Crane has a white upper neck and tertials) and body size. Sharpe [25] retained this distinction but shortly afterwards Blanford [26] combined them into one species with two subspecies, Grus antigone antigone and Grus antigone sharpii respectively, a classification which has since endured. Hachisuka [27] described the (then extant) Philippine population as Grus antigone luzonica, sufficiently distinct from both G. a. antigone and G. a. sharpii to warrant subspecies status. Del Hoyo and Collar [28] dispute this and place the Philippine birds in A. a. sharpii. Sarus Cranes were observed in Australia in 1966, [29] and placed in A. a. sharpii but were subsequently described by Schodde [20] as a new subspecies G. a. gillae, on the basis of distinct plumage and a larger ear patch. Archibald (personal observation) noted that A. a. gillae also has different unison calls from both A. a. antigone and A. a. sharpii, helping to differentiate it from the sympatric Brolga A. rubicunda.

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Fig 1. Global distribution of the Sarus Crane A. antigone, showing populations and subspecies [Distribution data derived from BirdLife International and NatureServe Bird Species Distribution Maps of the World [80], the Australian Bird Guide [81] and author contributions].

https://doi.org/10.1371/journal.pone.0230150.g001

These subspecific arrangements, largely indicated by morphology (Fig 2), have not hitherto been strongly supported by genetic analyses. Application of molecular techniques to understand the subspecific arrangements of Sarus Cranes [30,31,32] suggested that colonisation of Australia by Sarus Cranes was relatively recent and there had been little differentiation of populations across their range [32].

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Fig 2. Extant Sarus Crane subspecies.

(a) antigone South Asia (captive bird); (b) sharpii Myanmar and Indochina (wild Cambodian bird); (c) gillae Australia (wild Northeast Queensland bird). [Photographs T, Nevard (a) and (c); Robert van Zalinge (b)].

https://doi.org/10.1371/journal.pone.0230150.g002

Using neutral genetic information as a decisive basis for the recognition of morphologically defined subspecies has been rightly criticized [7]. Morphological variation and variation of standard genetic markers such as mitochondrial DNA or microsatellites do not have to correlate and lack of differentiation at these loci does not disprove taxonomic decisions based on other types of characters. Gavrilets [33] notes that despite gene flow, local selection may be sufficient to maintain differences. However, neutral genetic differentiation among populations that are also morphologically differentiated does indicate limited gene flow among these populations, supporting their taxonomic distinction. It is in this context that we have analysed the genetic differentiation of the morphologically defined subspecies of the Sarus crane, based on the largest sample size available so far.

Potential differentiation among populations is relevant for two reasons. First, as a contribution to the debate about using analyses of neutral genetic markers to synonymise or retain subspecies—particularly as to whether differentiated populations should continue to be treated as separate, valued and taxonomically recognised management units. Second, reintroduction of Sarus Cranes to the Philippines is being considered (J. C. Gonzales, personal communication), so an appropriate potential founding stock needs to be identified.

Materials and methods

Ethics approval

The research was carried out in accordance with Charles Darwin University Animal Ethics Committee’s approval A13019 and Queensland Department of Environment and Heritage Protection Scientific Purposes Permit WISP13984714. Where required, collection and carriage of samples to Greifswald University was undertaken under CITES permits: PWS2014-AU-001240, PWS2015-AU-000119, 000002/FD-2011 and KH1108.

Samples

We secured sample material opportunistically from all four putative subspecies and most range countries (Tables 1& 2). Sample material varied from naturally shed and deliberately plucked feathers, to blood taken from live birds (both wild and captive) and tissue harvested from museum specimens (toe pad samples).

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Table 2. Details of sample collections for Sarus Crane (Ssp: subspecies; M: microsatellites; S: sequences).

https://doi.org/10.1371/journal.pone.0230150.t002

Only samples from captive birds in Germany and Australia (Lemgo Crane Collection and Cairns Tropical Zoo) and feathers from crane flocking sites in Australia and museum specimens in the United States were specifically collected for this project. All other samples assembled were derived from sets previously collected as part of other projects in Myanmar (captive zoo and monastery birds), Thailand (captive zoo birds), and Cambodia (wild-caught birds).

The blood sample collection protocol for captive German and Australian birds was for a three-person restraining team (all with significant experience in crane restraint and sampling) to catch the bird using a landing net; followed by immediate hooding and drawing ≤1mm of blood from the brachial vein (placing this immediately into 100% ethanol). In all cases restraint lasted less than 2 minutes. In Myanmar and Thailand, although sample collection was not part of this project, the collection protocol was consistent. In Cambodia, birds were wild caught using alpha chloralose, as part of a previous project and sampled as above, with an additional oral swab. One tissue (brain) sample from Cambodia was from a bird that had died recently from natural causes.

Shed feathers visibly free of soil and/or faecal contamination were gathered from crane flocking sites in Australia using tweezers and re-sealable plastic bags then refrigerated. Lightly-plucked chest feathers (3 to 5, ≤ 25mm) were obtained from restrained captive birds in Germany and Australia and refrigerated.

Extraction

Nuclear DNA.

DNA was extracted using the SDS/salting-out protocol of Miller et al. [34]. Dithiothreitol and Roti-PinkDNA (Carl Roth, Karlsruhe, Germany) were added in order to increase the yield. For the Cambodian blood and tissue samples, QIAGEN’s RNeasy Mini Kit was used, for an oral swab the QIAamp Viral RNA Kit. For the Thailand blood samples, DNA was extracted by using QIAGEN’s RNeasy Mini Kit. We amplified the ten microsatellite loci (Gamμ3, 18, 24,101b; GjM8, 13, 15, 48b; GR22, 25) used in our analysis of hybridization of the Brolga Antigone rubicunda and Australian Sarus Crane A. antigone gillae [19] which have been developed for other crane species [35,36], the Sarus Crane [32] and the Brolga [37], respectively. PCRs conducted in a volume of 10 μl contained 1 μl DNA (10–25 ng), 1 μl of 10 x NH4-based Reaction Buffer, 1.5–2.25 mM MgCl2 Solution (Table 2), 0.25 mM of each primer, 0.2 mM of dNTP, 0.04 μl of BioTaq DNA Polymerase (5 U/μl), 0.6 μl of 1% BSA and sterile ddH2O. If not successful with this first protocol, the MyTaq mix (all products from BIOLINE, London, UK) was used. The PCR profile started with an initial denaturation at 94°C, followed by 36 cycles of denaturation at 94°C, primer specific annealing (Table 2) and extension at 72°C each for 30 s, and a final elongation at 72°C for 30 min. Microsatellite alleles were separated on a 3130xl Genetic Analyzer using the GeneScan 600 LIZ Size Standard 2.0. Fragment sizes were determined manually in GeneMapper 4.0 (all three products from Applied Biosystems, Waltham, USA) as automatic calling with arbitrarily predefined bin width may give inconsistent results. In order to maximize accuracy of size determination, we repeated PCRs of samples with initially weak signals or which had rare variants. Eventually, PCR samples peculiar to different runs had to be loaded on the same plate to improve comparability.

Mitochondrial DNA.

Where DNA quality allowed, we also sequenced large parts of copy 2 of the mitochondrial control region [38] using primers L16707 and H1247 [39] spanning a fragment of c. 1000 bp in A. antigone and 1150 bp in three specimens of A. rubicunda; one from the Gulf plains and two from the Atherton Tablelands (see [19]), which we used as outgroup in the phylogenetic analyses. In some specimens we had to target a shorter fragment using the forward primer L514 instead resulting in lengths of c.610 bp. PCRs were conducted using the MyTaq mix. The temperature profile for the long fragment comprised: 95°C for 3 min, 5 cycles of 95°C/15 s, 65°C/20 s and 72°C/25 s, 5 cycles 95°C/15 s, 60°C/20 s and 72°C/25 s, 30 cycles 95°C/15 s, 55°C/20 s and 72°C/25 s, and a final extension at 72°C for 5 min. For the short fragment the profile was similar and had 4, 4 and 32 cycles with respective annealing temperatures of 60°C, 55°C and 50°C. PCR products were cleaned using an exonuclease I/shrimp alkaline phosphatase mix. Cycle sequencing was then performed in 10 μl using the PCR primers and ABI’s Big Dye Terminator Ready Reaction Mix 3.1 of which 50% were replaced by halfBD (Merck). The thermal cycler profile followed the manufacturer’s suggestions except that the annealing temperature was lowered to 48° C. HighPrep DTR magnetic beads (Biozym) were used for purification of the sequencing reactions. The sequences were read on an ABI 3130xl Genetic Analyzer (Thermo Fisher Scientific Inc., Waltham, MA, USA). Raw sequences were edited in Geneious 10 (www.geneious.com) and BioEdit 7.0.5.3 [40], respectively, and aligned using the web version of MAFFT 7 [41].

Statistical analysis

Nuclear DNA.

FSTAT 2.9.3.2 [42] as well as GenePop 4.2 [43,44] were used to test the microsatellite data for Hardy-Weinberg equilibrium and to calculate gene diversity [45] and allelic richness [46] of subspecies. For population differentiation, the microsatellite data were analysed in two ways, (i) in a divisive approach without a priori designation of subspecies by Bayesian clustering using STRUCTURE 2.3.4 [47,48]; and (ii) by estimating differentiation of the nominal subspecies (except the single individual of A. a. luzonica), calculating pairwise FST values using FSTAT. STRUCTURE was run with K (number of clusters) ranging from one to six and ten replicates assuming the admixture model since the sequence analyses suggested that there had been admixture. We modelled both, uncorrelated and correlated allele frequencies as it was unclear which approach provided a better fit for the biological context. The Markov chains ran for 1 million generations after a burn in of 100,000. Structure Harvester 0.6.94 [49] was used to analyse the data following Evanno et al. [50]. Integrating the results of the replicated runs in STRUCTURE, the most likely assignment of individuals to clusters was inferred in CLUMPAK [51]. K-means clustering was applied to validate the Bayesian approach using GenoDive 2.0b23 [52] because it is free of population genetic assumptions in contrast to STRUCTURE. Individuals were clustered based on their allele frequencies according to the pseudo-F-statistic of Calinski and Harabasz [53] as described in Meirmans [54]. Finally, we estimated gene flow among subspecies based on FST and the private alleles approach of Barton and Slatkin [55] using GenePop (see [56,57]).

Mitochondrial DNA.

Relationships among mitochondrial haplotypes were analysed using statistical parsimony/TCS [58] implemented in PopART [59] and MrBayes 3.2.6 [60], respectively. MrBayes was run using GTR+I+G identified as best fitting substitution model by jModeltest 2.1.4 [61] with default settings over 2 million generations with a 25% burnin. Effective sample sizes were > 700, potential scale reduction factors equalled 1.000 or 1.001, and the standard deviation of split frequencies was < 0.006 indicating convergence of parameter estimates and both parallel runs.

Results

The nominate subspecies A. a. antigone had the highest diversity despite the lowest sample size, while A. a. gillae had comparatively lower diversity than the nominate subspecies (Table 3). A. a. antigone had four private alleles, two of them rare (only in one individual each and only heterozygous), A. a. gillae five, four of them rare (each in not more than 2 specimens and only heterozygous), and A. a. sharpii eight. Of these, five were rare (each in not more than three individuals and four only heterozygous). Three of the private alleles occurred only in Myanmar and another three in both Cambodia and Thailand. The single A. a. luzonica sampled had one allele that did not occur in any other subspecies. Deviations from the Hardy-Weinberg equilibrium at several loci in A. a. antigone and A. a. sharpii suggested that these subspecies are probably not panmictic, although we cannot rule out effects of genetic drift or selection. This was confirmed from the results of analysis using STRUCTURE and K-means clustering.

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Table 3. PCR specifications and diversity of microsatellite loci.

The subspecies are abbreviated by the first three letters (ant: A. a. Antigone; gil: A. a. gillae; sha: A, a. sharpii).

https://doi.org/10.1371/journal.pone.0230150.t003

According to Evanno et al.’s [50] ΔK criterion and assuming correlated allele frequencies, STRUCTURE identified three clusters, modelling independent allele frequencies only two (Fig 3; cluster composition as summarized by CLUMPAK Table 4). In both analyses, all A. a. gillae fell into one cluster together with the A. a. luzonica specimen. Assuming independent allele frequencies, the cluster with these subspecies also contained three specimens of A. a. sharpii and two Indian individuals. For both models, a solution with four clusters had the highest likelihood but the composition of the clusters was less meaningful, apart from grouping all Australian individuals together. K-means clustering also divided the sample set into two clusters (Table 4), one consisting of 23 A. a. gillae, one A. a. sharpii, and the single A. a. luzonica, and the other of two A. a. gillae, all A. a. antigone from India, and the remaining A. a. sharpii. Both Bayesian clustering (assuming admixture and independent allele frequencies) as well as k-means clustering converged to very similar solutions. The STRUCTURE bar plots also reflect the higher genetic diversity in the Asian subspecies as summarized by the standard population genetic parameters above and in Table 2.

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Fig 3. Bayesian cluster analyses of microsatellite data (STRUCTURE/CLUMPAK).

A, B: assuming admixture and correlated allele frequencies, K = 3; y axis, membership coefficient from 0 (bottom) to 100 (top); in A, individuals are sorted by subspecies and country of origin; in B, samples are ordered by cluster, subspecies within clusters and membership coefficient Q (y-axis). For A. a. sharpii, the country of origin is indicated by the first letter (C = Cambodia, M = Myanmar, T = Thailand). C: assuming admixture and independent allele frequencies, K = 2; samples ordered by subspecies and country of origin within A. a. sharpii. Cluster 1 consists of all A. a. gillae as well as specimens labelled with 1.

https://doi.org/10.1371/journal.pone.0230150.g003

Differentiation among subspecies based on FST estimates revealed that A. a. antigone and A. a. sharpii were considerably closer to each other (FST = 0.086) than either were to A. a. gillae (FST = 0.282 and 0.168, respectively). These FST values translated into gene flow estimates of 2.66 migrants per generation between A. a. antigone and A. a. sharpii, 0.64 between the nominate subspecies and A. a. gillae, and 1.24 between A. a. sharpii and A. a. gillae. The private alleles approach estimated 0.71, 0.44, and 0.53 migrants, respectively. This again emphasises the somewhat isolated position of the Australian subspecies.

The alignment of the control region 2 sequences comprised 1155 positions. A major difference between A. rubicunda and A. antigone were two indels comprising 46 and 95 positions, respectively, which were present in the former and absent in the latter species. Apart from these, A. rubicunda differed by at least 28 mutations from A. antigone, rendering the latter monophyletic in the Bayesian tree reconstruction (Fig 4), which is also illustrated by the TCS network (Fig 5). However, both tree and network agreed that no subspecies of A. antigone was monophyletic. Both reconstructions suggested an ancestral polymorphism and/or repeated introgression, meaning that there had been at least limited gene flow among the subspecies. Given the overall low differentiation across A. antigone, resulting in low posterior probabilities (i.e. node support) and the low sample size of the nominate subspecies, inferring evolutionary directions is not possible.

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Fig 4. Phylogenetic tree for three subspecies of the Sarus Crane A. antigone; based on mtDNA sequences resulting from Bayesian analysis.

Posterior probabilities are given if > 0.70 (for sample codes see Table 2; outgroup (A. rubicunda) pruned off.).

https://doi.org/10.1371/journal.pone.0230150.g004

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Fig 5. TCS network for five populations of the Sarus Crane A. antigone based on mtDNA sequences compared to the Brolga A. rubicunda (for sample codes see Table 2).

https://doi.org/10.1371/journal.pone.0230150.g005

Discussion

Our analyses differ from earlier work [31,32] by having a larger sample size and in sequencing a highly variable part of the mitochondrial control region [39] instead of protein coding genes [31], thereby providing better phylogenetic resolution. Similarly to [31], we found that Sarus Crane subspecies and populations were not monophyletic (probably due to an ancestral polymorphism and/or introgression) and microsatellite variation in A. a. antigone and A. a. sharpii overlapped significantly [32]. However, we have established that A. a. gillae is far more distinct from A. a. antigone and A. a. sharpii than previously thought, irrespective of the clustering method and the model assumptions used in Bayesian clustering. This was also confirmed by F-statistics and gene flow estimates. We have also shown that the single A. a. luzonica specimen we have hitherto been able to sample was more similar to A. a. gillae than the geographically closer A. a. sharpii. The first finding has potential implications for definitions of subspecies, the second in relation to better understanding the phylogeography of the species and potential sourcing of birds for any Philippine reintroduction. We are well aware how problematic any conclusions based on a single specimen might be but given that Philippine Sarus Cranes are extinct and the scarcity of museum material, no alternative approach is available.

Given there are now attempts to define subspecies under law [62], there is a need for far greater understanding of just how much weight should be given to genetic data, particularly where genetic variation appears to be lacking. While patterns of crane morphological variation have not been reflected in marked genetic differences between populations [63,64,65], the findings of Jones et al. [32] could have been used to suggest that the variation in Sarus Cranes is clinal without distinct breaks in genetic variability. Our results suggest that simply by looking at a slightly different part of the genome with a larger sample size a different conclusion would have been drawn. This is relevant for current Australian policy, which has not been consistent. For example, Schodde and Mason [66] diagnosed new subspecies of Southern Emu-wren Stipiturus malachurus and Eastern Bristlebird Dasyornis brachypterus on the basis of morphological discontinuities. Despite genetic differences in the emu-wren failing to match morphology [67], threatened subspecies continue to be recognised under legislation [68]. A similar level of variation in the Eastern Bristlebird [69] has meant that there has been no recognition of the northern form of Eastern Bristlebird D. b. monoides [66], of which 40 individuals are thought to survive [70] but for which conservation effort has been inconsistent [71]. Were rigid definitions of subspecies enforced by law, as now being argued in the USA [62], with the level of knowledge previously available from Jones et al. [32], Sarus Crane subspecies might not have been eligible for conservation as separate subspecies. Our results confirm the position of Patten and Remsen [7] that synonymising subspecies can be highly problematic without testing hypotheses using multiple data sources, as advocated by integrative taxonomy [72]. We therefore wish to stress that our findings are not a final verdict on phylogeographic differentiation in the Sarus Crane, which requires further morphological and genetic work to complement our analyses.

Although first formally noted in Australia in the 1960s [29], Sarus Cranes have been in the country long enough to have been given a Wik (Cape York Aboriginal language group) name, meaning ‘the Brolga that dipped its head in blood’ [73]. Wood and Krajewski [31] have suggested that Sarus Cranes first arrived in Australia 37,500 years ago, when sea levels were 40 m lower than today [74], permitting development of a savanna corridor that extended both north and south of the equator across the Sunda plain [75], much of which would have been lost by rising sea levels at the start of the Holocene (about 10,000 years ago). In an Australian context, this corridor [76] would have ended not far north of the Pleistocene Lake Carpentaria, around which it is thought there would have been savannas structurally similar to those used by Sarus Cranes in northern Australia. It is also true that lower sea levels would have narrowed the distance between the Philippines, Borneo/Peninsula Malaya (via Palawan) and Indochina [77] and hence potentially also brought A. a luzonica and A. a. sharpii into closer proximity. However, especially given the shifting of the courses of the Mekong [78], it is likely that facing coastal regions of both Indochina and the Philippines would have been mainly comprised of closed forest, making sub-specific contact more difficult.

Conclusions and recommendations

We have shown that A. a. gillae differs significantly from the A. a. antigone and A. a. sharpii genetic cline described by others. Where once A. a gillae might have been considered part of this cline, more detailed analysis has revealed greater structure. This has relevance to the wider debate about subspecies, suggesting that the level of genetic analysis required before subspecies are dismissed needs to be carefully considered, and wherever feasible triangulated with information gleaned from other character traits.

That the single sample from A. a luzonica clustered with A. a. gillae hints at the potential for a close evolutionary relationship. Should reintroduction of Sarus Cranes to the Philippines be deemed desirable and viable, subject to further research on the genetic affinities of A. a. luzonica, Australia might be an appropriate source of birds.

Whilst Hachisuka [27] found that Philippine birds were significantly smaller than those on the south-Asian mainland, the general case for insular dwarfism is equivocal [79]. As we had access to only one individual of A. a. luzonica, further genetic work on samples from Philippine museum specimens could help to clarify the status of this subspecies and its potential to shed further light on the phylogeography of Sarus Cranes.

Acknowledgments

The authors would like to acknowledge the assistance of many people who have made this work possible, including: Elinor Scambler, who has shared her insights on cranes on the Atherton Tablelands and elsewhere; Silke Fregin, who managed our samples and lab work at the University of Greifswald; Adam Miller of Deakin University, who provided insights into the genetics of Brolgas; Emily Imhoff of Cincinnati Museum and Chris Milensky, the Collections Manager of the Division of Birds at the Smithsonian Institution, who both provided specimen samples from their collections; Annabelle Olsson, who facilitated veterinary health clearance for our samples; Betsy Didrickson of the International Crane Foundation library who tracked down some key references; Gopi Sundar, Barry Hartup, Claire Mirande and other colleagues at the International Crane Foundation who provided much valued advice; Yulia and Kuni Momose of the Red-crowned Crane Foundation, who advised on crane capture; Harry, Ruairidh & Bronwen Nevard, Dominic & Vera von Schwertzell, Inka Veltheim, Jess Harris, John Grant and others who assisted with sample collection in Australia and Germany; Hans Rehme of Lemgo, Germany who provided access to his global collection of crane species; Tom and Tanya Arnold, who provided access to Miranda Downs cattle station; Terry Trantor and Nick Reynolds, who provided access to their land on the Atherton Tablelands; U Win Naing Thaw (Director of the Myanmar Nature and Wildlife Conservation Division, Ministry of Environmental Conservation and Forestry) who facilitated the collection of Myanmar samples: Jeb Barzen and Kit Sokny who assisted with collecting field samples in Cambodia; and Mathieu Pruvot of the Wildlife Conservation Society Cambodia Program and Michael Meyerhoff and Christel Griffioen of the Angkor Centre for Conservation of Biodiversity, who provided additional samples from their collections.

Data deposits

DNA sequences have been deposited in NCBI GenBank under accession numbers MN577986-MN578037.

References

  1. 1. de Quieroz K. Species concepts and species delimitation. Syst. Biol. 2007;56:879–886. pmid:18027281
  2. 2. Zachos FE. Species Concepts in Biology: Historical Development, Theoretical Foundations and Practical Relevance. Switzerland, Springer Nature. 2018.
  3. 3. Wallin H, Kvamme T, Bergsten J. To be or not to be a subspecies: description of Saperda populnea lapponica ssp. (Coleoptera, Cerambycidae) developing in Downy Willow (Salix lapponum L.). ZooKeys. 2017;69:103–148.
  4. 4. Baker AN, Smith ANH, Pichler FB. Geographical variation in Hector’s dolphin: recognition of new subspecies of Cephalorhynchus hectori. J. R. Soc. N.Z. 2002;32:713–727.
  5. 5. Patten MA, Unitt P. Diagnosability versus mean differences of Sage Sparrow subspecies. The Auk. 2002;119:26–35.
  6. 6. Winker K. Reuniting phenotype and genotype in biodiversity research. BioScience. 2009;59:657–665.
  7. 7. Patten MA, Remsen JV Jr. Complementary roles of phenotype and genotype in subspecies delimitation. J. Hered. 2017;108:462–464. pmid:28498988
  8. 8. Martien KK, Leslie MS, Taylor BL, Morin PA, Archer FI, Hancock-Hanser BL, et al. Analytical approaches to subspecies delimitation with genetic data. Mar. Mammal Sci. 2017;33(S1):27–55.
  9. 9. McCormack JE, Maley JM. Interpreting negative results with taxonomic and conservation implications: Another look at the distinctness of coastal California Gnatcatchers. The Auk. 2015;132(2):380–388.
  10. 10. Toews DP, Brelsford A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 2012;21(16):3907–3930. pmid:22738314
  11. 11. Gippoliti S, Cotterill FP, Zinner D, Groves CP. Impacts of taxonomic inertia for the conservation of African ungulate diversity: an overview. Biol. Rev. 2018;93(1):115–130. pmid:28429851
  12. 12. Frankham R, Ballou JD, Dudash MR, Eldridge MD, Fenster CB, Lacy RC, et al. Implications of different species concepts for conserving biodiversity. Biol. Conserv. 2012;153:25–31.
  13. 13. Garnett ST, Christidis L. Implications of changing species definitions for conservation purposes. Bird Conserv. Int. 2007;17:187–195.
  14. 14. Zink RM, Groth JG, Vázquez-Miranda H, Barrowclough GF. Phylogeography of the California Gnatcatcher (Polioptila californica) using multilocus DNA sequences and ecological niche modelling: Implications for conservation. The Auk. 2013;130(3):449–458.
  15. 15. Frost G. Rare coastal sage scrub habitat provides a home for threatened gnatcatcher and many other species. http://ca.audubon.org/news/rare-coastal-sage-scrub-habitat-provides-home-threatened-gnatcatcher-and-many-other-species. 2015.
  16. 16. US Fish & Wildlife Service. Service Determines Coastal California Gnatcatcher is a Subspecies and Remains Threatened under Endangered Species Act. https://www.fws.gov/news/ShowNews.cfm?ref=service-determines-coastal-california-gnatcatcher-is-a-subspecies-and-&_ID=35782. 2016. Cited 14 May 2019.
  17. 17. Nevard TD, Leiper I, Archibald G, Garnett ST. Farming and cranes on the Atherton Tablelands, Australia. Pac. Conserv. Biol. 2018;25:184–192.
  18. 18. Nevard TD, Franklin DC, Leiper I, Archibald G, Garnett ST. Agriculture, brolgas and Australian sarus cranes on the Atherton Tablelands, Australia. Pac. Conserv. Biol. 2019;25:377–385.
  19. 19. Nevard TD, Haase M, Archibald G, Leiper I, Garnett ST. The Sarolga: conservation implications of genetic and visual evidence for hybridization between the Brolga Antigone rubicunda and the Australian Sarus Crane Antigone antigone gillae. Oryx. 2020;54:40–51.
  20. 20. Schodde R. New sub-species of Australian birds. Canberra Bird Notes. 1988;13:1
  21. 21. Meine CD, Archibald GW. The Cranes: Status Survey and Conservation Action Plan. IUCN, Gland, Switzerland, and Cambridge, UK. 1996.
  22. 22. Archibald GW, Sundar KSG, Barzen J. A review of the three subspecies of Sarus Cranes Grus antigone. Ecology. 2003;16:5–15.
  23. 23. BirdLife International. Antigone antigone. The IUCN Red List of Threatened Species 2016: http://dx.doi.org/10.2305/IUCN.UK.2016-3.RLTS.T22692064A93335364.en. Cited 27 September 2018.
  24. 24. Blyth E, Tegetmeier WG. The Natural History of the Cranes. Horace Cox, London. 1881
  25. 25. Sharpe RB. Catalogue of Birds in the British Museum, vol. 23. British Museum of Natural History, London. 1894.
  26. 26. Blanford W. Grus (antigone) sharpei. Bull.B.O.C. 1895;5:7.
  27. 27. Hachisuka M. Further contributions to the ornithology of the Philippine Island. Tori. 1941;11:61–89.
  28. 28. del Hoyo J, Collar NJ. HBW and BirdLife International Illustrated Checklist of Birds of the World (Volume 1 Non-Passerines). Lynx Edicions, in association with BirdLife International. Barcelona. 2014
  29. 29. Gill HB. The first record of the Sarus Crane in Australia. Emu. 1967;69:49–52.
  30. 30. Dessauer HC, Gee GF, Rogers JS. Allozyme evidence for crane systematics and polymorphisms within populations of Sandhill, Sarus, Siberian, and Whooping cranes. Mol. Phylogenet. Evol. 1992;1:279–288. pmid:1342943
  31. 31. Wood TC, Krajewski C. Mitochondrial sequence variation among the subspecies of Sarus Crane (Grus antigone). The Auk. 1996;113:655–633.
  32. 32. Jones KL, Barzen JA, Ashley MV. Geographical partitioning of microsatellite variation in the Sarus Crane. Anim. Conserv. 2005;8:1–8.
  33. 33. Gavrilets S. Models of speciation: what have we learned in 40 years? Evolution. 2003;57:2197–2215. pmid:14628909
  34. 34. Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988;16:1215. pmid:3344216
  35. 35. Hasegawa O, Ishibashi Y, Abe S. Isolation and characterisation of microsatellite loci in the red-crowned crane Grus japonensis. Mol. Ecol. 2000;9:1677–1678. pmid:11050571
  36. 36. Meares K, Dawson DA, Horsburgh GJ, Glenn TC, Jones KL, Braun MJ, et al. Microsatellite loci characterized in three African crane species (Gruidae, Aves). Mol. Ecol. Resour. 2009;9:308–311. pmid:21564635
  37. 37. Miller A, Veltheim I, Nevard TD, Gan HM, Haase M. Microsatellite loci and the complete mitochondrial DNA sequence characterized through next generation sequencing and de novo genome assembly, and a preliminary assessment of population genetic structure for the Australian crane, Antigone rubicunda. Avian Biol. Res. https://doi.org/10.1177/17581559198321422019. 2019
  38. 38. Akiyama T, Nishida C, Momose K, Onuma M, Takami K, Masuda R. Gene duplication and concerted evolution of mitochondrial DNA in crane species. Mol. Phylogent. Evol. 2017;106:158–163.
  39. 39. Krajewski C, Sipiorski JT, Anderson FE. Complete Mitochondrial Genome Sequences and the Phylogeny of Cranes (Gruiformes: Gruidae). The Auk. 2010;127:440–452.
  40. 40. Hall TA. BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/ 98/NT. Nucleic Acids Symp. Ser. 1999;41:95–98.
  41. 41. Katoh K, Rozewick J, Yamada KD. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief. Bioinform. https://doi.org/10.1093/bib/bbx108: 2017;108.
  42. 42. Goudet J. FSTAT (version 1.2): a computer program to calculate F-statistics. J. Hered. 1995;86:485–486.
  43. 43. Raymond M, Rousset F. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. J. Hered. 1995;86:248–249.
  44. 44. Rousset F. Genepop'007: a complete reimplementation of the Genepop software for Windows and Linux. Mol. Ecol. Res. 2008;8:103–106.
  45. 45. Nei M. Molecular Evolutionary Genetics. Columbia University Press, New York. 1987.
  46. 46. Petit RJ, El Mousadik A, Pons O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 1998;12:844–855.
  47. 47. Pritchard JK, Stephens M, Donnelly P. Inference of population structure using multilocus genotype data. Genetics. 2000;155:945–959. pmid:10835412
  48. 48. Falush D, Stephens M, Pritchard JK. Inference of population structure using multilocus genotype data: dominant markers and null alleles. Mol. Ecol. Notes. 2007;7:574–578. pmid:18784791
  49. 49. Earl DA, von Holdt BM. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. 2012;4:359–361.
  50. 50. Evanno G, Regnaut S, Goudet J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 2005;14:2611–2620. pmid:15969739
  51. 51. Kopelman NM, Mayzel J, Jakobsson M, Rosenberg NA, Mayrose I. CLUMPAK: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 2015;15:1179–1191. pmid:25684545
  52. 52. Meirmans PG, Van Tienderen PH. GENOTYPE and GENODIVE: two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes. 2004;4:792–794.
  53. 53. Calinski RB, Harabasz J. A dendrite method for cluster analysis. Commun. Stat. Simul-Comput. 1974;3:1–27.
  54. 54. Meirmans PG. AMOVA-based clustering of population genetic data. J. Hered. 2012;103:744–750. pmid:22896561
  55. 55. Barton NH, Slatkin M. A quasi-equilibrium theory of the distribution of rare alleles in a subdivided population. Heredity. 1986;56:409–415. pmid:3733460
  56. 56. Slatkin M, Barton NH. A comparison of three indirect methods for estimating average levels of gene flow. Evolution. 1989;43:1349–1368. pmid:28564250
  57. 57. Whitlock MC, McCauley DE. Indirect measures of gene flow and migration: FST ≠ 1/(4 Nm + 1). Heredity. 1999;82:117–125. pmid:10098262
  58. 58. Clement M, Snell Q, Walke P, Posada D, Crandall K. TCS: estimating gene genealogies. Proc. 16th Int. Parallel Distrib. Process Symp. 2002;2:184.
  59. 59. Leigh JW, Bryant D. PopART: Full-feature software for haplotype network construction. Methods Ecol. Evol. 2015;6(9):1110–1116.
  60. 60. 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):539–542. pmid:22357727
  61. 61. Darriba D, Taboada GL, Doallo R, Posada D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods. 2012;9:772.
  62. 62. Mallet J, Ehrlich P, Gill F, McCormack J, Raven P. In Comments on Petition of Pacific Legal Foundation, et al., for Rule-Making Under the Administrative Procedure Act (Which Aimed to Promulgate New Regulatory Definitions of “Species” and “Subspecies” Under the Endangered Species Act). http://nrs.harvard.edu/urn-3:HUL.InstRepos:37568375. 2018.
  63. 63. Rhymer JM, Simberloff D. Extinction by hybridization and introgression. Annu. Rev. Ecol. Syst. 1996;27:83–109.
  64. 64. Haase M, Ilyashenko V. A Glimpse on Mitochondrial Differentiation among four Currently Recognized Subspecies of the Common Crane Grus grus. Ardeola. 2012;59(1):131–135.
  65. 65. Mudrik EA, Kashentseva TA, Redchuk PS, Politov DV. Microsatellite variability data confirm low genetic differentiation of western and eastern subspecies of common crane Grus grus L. (Gruidae, Aves). Mol. Biol. 2015;49(2):260–266.
  66. 66. Schodde R, Mason IJ. Directory of Australian Birds: Passerines. CSIRO Publishing, Melbourne. 1999.
  67. 67. Donnellan SC, Armstrong J, Pickett M, Milne T, Baulderstone J, Hollfelder T, et al. Systematic and conservation implications of mitochondrial DNA diversity in emu-wrens, Stipiturus (Aves: Maluridae). Emu. 2009;109(2):143–152.
  68. 68. Department of Environment and Energy. EPBC Act List of Threatened Fauna. http://www.environment.gov.au/cgi-bin/sprat/public/publicthreatenedlist.pl#birds_extinct 2016. Cited 27 September 2018.
  69. 69. Roberts DG, Baker J, Perrin C. Population genetic structure of the endangered Eastern Bristlebird, Dasyornis brachypterus; implications for conservation. Conserv. Genet. 2011;12(4):1075–1085.
  70. 70. Stone ZL, Tasker E, Maron M. Grassy patch size and structure are important for northern Eastern Bristlebird persistence in a dynamic ecosystem. Emu. 2018;118:269–280.
  71. 71. Guerrero AM, McKenna R, Woinarski JCZ, Pannell DJ, Wilson KA, Garnett ST. Threatened species recovery planning in Australia: Learning from two case studies. National Environmental Science Programme, Threatened Species Recovery Hub. 2017.
  72. 72. Dayrat B. Towards Integrative Taxonomy. Biol. J. Linnean Soc. 2005;83:407–415.
  73. 73. Reardon M. Brolga Country: Travels in Wild Australia. Allen & Unwin. Crows Nest, Australia. 2007.
  74. 74. Zong Y, Huang G, Li XY, Sun YY. Late Quaternary tectonics, sea-level change and lithostratigraphy along the northern coast of the South China Sea. Geological Society, London, Special Publications. 2016;429(1):123–136.
  75. 75. Bird MI, Taylor D, Hunt C. Palaeoenvironments of insular Southeast Asia during the Last Glacial Period: a savanna corridor in Sundaland? Quat. Sci. Rev. 2005;24:2228–2242.
  76. 76. Joseph L, Bishop KD, Wilson AC, Edwards SV, Iova B, Campbell CD, et al. Review of evolutionary research on birds of the New Guinean savannas and closely associated habitats of riparian rainforests, mangroves and grasslands. Emu.
  77. 77. Voris HK. Maps of Pleistocene sea levels in Southeast Asia: Shorelines, river systems and time durations. J. Biogeogr. 2000;27:1153–1167.
  78. 78. Attwood SW, Johnston DA. Nucleotide sequence differences reveal genetic variation in Neotricula aperta (Gastropoda: Pomatiopsidae), the snail host of schistosomiasis in the Lower Mekong Basin. Biol. J. Linnean Soc. 2001;73:23–41.
  79. 79. Van der Geer A, Lyras G, de Vos J, Dermitzakis M. Evolution of Island Mammals, Adaption and Extinction of Placental Mammals on Islands. Wiley-Blackwell, Oxford. 2010.
  80. 80. BirdLife International and NatureServe. Bird Species Distribution Maps of the World. 2014. http://datazone.birdlife.org/site/search. Cited 08 August 2018.
  81. 81. Menkhorst P, Rogers D, Clarke R, Davies J, Marsack P, Franklin K. The Australian Bird Guide. CSIRO Publishing, Clayton South, Australia. 2017.