Genomic impact of severe population decline in a nomadic songbird


Autoři: Ross Crates aff001;  George Olah aff001;  Marcin Adamski aff002;  Nicola Aitken aff002;  Sam Banks aff001;  Dean Ingwersen aff003;  Louis Ranjard aff002;  Laura Rayner aff001;  Dejan Stojanovic aff001;  Tomasz Suchan aff004;  Brenton von Takach Dukai aff001;  Robert Heinsohn aff001
Působiště autorů: Fenner School of Environment and Society, Australian National University, Canberra, ACT, Australia aff001;  Research School of Biology, Australian National University, Canberra, ACT, Australia aff002;  BirdLife Australia, Carlton, Melbourne, VIC, Australia aff003;  W. Szafer institute of Botany, Polish Academy of Sciences, Krakow, Poland aff004
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223953

Souhrn

Uncovering the population genetic histories of non-model organisms is increasingly possible through advances in next generation sequencing and DNA sampling of museum specimens. This new information can inform conservation of threatened species, particularly those for which historical and contemporary population data are unavailable or challenging to obtain. The critically endangered, nomadic regent honeyeater Anthochaera phrygia was abundant and widespread throughout south-eastern Australia prior to a rapid population decline and range contraction since the 1970s. A current estimated population of 250–400 individuals is distributed sparsely across 600,000 km2 from northern Victoria to southern Queensland. Using hybridization RAD (hyRAD) techniques, we obtained a SNP dataset from 64 museum specimens (date 1879–1960), 102 ‘recent’ (1989–2012) and 52 ‘current’ (2015–2016) wild birds sampled throughout the historical and contemporary range. We aimed to estimate population genetic structure, genetic diversity and population size of the regent honeyeater prior to its rapid decline. We then assessed the impact of the decline on recent and current population size, structure and genetic diversity. Museum sampling showed population structure in regent honeyeaters was historically low, which remains the case despite a severe fragmentation of the breeding range. Population decline has led to minimal loss of genetic diversity since the 1980’s. Capacity to quantify the overall magnitude of both genetic diversity loss and population decline was limited by the poorer quality of genomic data derived from museum specimens. A rapid population decline, coupled with the regent honeyeater’s high mobility, means a detectable genomic impact of this decline has not yet manifested. Extinction may occur in this nomadic species before a detectable genomic impact of small population size is realised. We discuss the implications for genetic management of endangered mobile species and enhancing the value of museum specimens in population genomic studies.

Klíčová slova:

Genomic libraries – Heterozygosity – Molecular genetics – Population genetics – Population size – Species diversity – Bird genomics – Effective population size


Zdroje

1. Tilman D, Fargione J, Wolff B, D’Antonio C, Dobson A, Howarth R, et al. Forecasting agriculturally-driven global environmental change. Science 2001; 292: 281–284. doi: 10.1126/science.1057544 11303102

2. Martinez-Cruz B, Godoy J A, Negro J J. Population fragmentation leads to spatial and temporal genetic structure in the endangered Spanish imperial eagle. Mol. Ecol. 2007; 16: 477–486. doi: 10.1111/j.1365-294X.2007.03147.x 17257107

3. Romiguier J, Gayral P, Ballenghien M, Bernard A, Cahais V, Chenuil A, et al. Comparative population genomics in animals uncovers the determinants of genetic diversity. Nature 2014; 515: 261. doi: 10.1038/nature13685 25141177

4. Frankham R. Genetics and extinction. Biol. Cons. 2005; 126: 131–140.

5. Barr K R, Lindsay D L, Athrey G, Lance R F, Hayden T J, Tweddale S A, et al. Population structure in an endangered songbird: maintenance of genetic differentiation despite high vagility and significant population recovery. Mol. Ecol. 2008; 17: 3628–3639. doi: 10.1111/j.1365-294X.2008.03868.x 18643883

6. Mason N A, Taylor S A. Differentially expressed genes match bill morphology and plumage despite largely undifferentiated genomes in a Holarctic songbird. Mol. Ecol. 2015; 24: 3009–3025. doi: 10.1111/mec.13140 25735539

7. Stojanovic D, Olah G, Webb M, Peakall R, Heinsohn R. Genetic evidence confirms severe extinction risk for critically endangered swift parrots: implications for conservation management. Anim. Cons. 2016; doi: 10.1111/acv.12394

8. Runge C A, Martin T G, Possingham H P, Willis S G, Fuller R A. Conserving mobile species. Front. Ecol. Environ. 2014; 12: 395–402. doi: 10.1890/130237

9. Hung C-M, Shaner P-J L, Zink R M, Liu W-C, Chu T-C, Huang W-S, et al. Drastic population fluctuations explain the rapid extinction of the passenger pigeon. Proc. Nat. Acad. Sci. 2014; 111: 10636–10641. doi: 10.1073/pnas.1401526111 24979776

10. Kamp J, Oppel S, Ananin A A, Durnev Y A, Gashev S N, Hölzel N, et al. Global population collapse in a superabundant migratory bird and illegal trapping in China. Cons. Biol. 2015; 29: 1684–1694.

11. Crnokrak P, Barrett S H C. Perspective: purging the genetic load: a review of the experimental evidence. Evolution 2002; 56: 2347–2358. doi: 10.1111/j.0014-3820.2002.tb00160.x 12583575

12. Bi K, Linderoth T, Vanderpool D, Good J M, Nielsen R, Moritz C. Unlocking the vault: next generation museum population genomics. Mol. Ecol. 2013; 22: 6018–6032. doi: 10.1111/mec.12516 24118668

13. Kvistad L, Ingwersen D, Pavlova A, Bull J K, Sunnucks P. Very low population structure in a highly mobile and wide-ranging bird species. PLoS ONE 2015; e0143746. doi: 10.1371/journal.pone.0143746 26649426

14. Diez-del-Molino D, Sanchez-Barreiro F, Barnes I, Gilbert M T B, Dalen L. Quantifying temporal genomic erosion in endangered species. Trends Ecol. Evol. 2018; doi: 10.1016/j.tree.2017.12.002 29289355

15. Palstra F P, Ruzzante D E. Genetic estimates of contemporary effective population size: what can they tell us about the importance of genetic stochasticity for wild population persistence? Mol. Ecol. 2008; 17: 3428–3447. doi: 10.1111/j.1365-294x.2008.03842.x 19160474

16. Athrey G, Barr K R, Lance R F, Leberg P L. Birds in space and time: genetic changes accompanying habitat fragmentation in the endangered black-capped vireo (Vireo atricapilla). Evol Appl. 2012; 5: 540–552. doi: 10.1111/j.1752-4571.2011.00233.x 23028396

17. Spurgin L G, Wright D J, van der Velde M, Collar N J, Komdeur J, Burke T, et al. Museum DNA reveals the demographic history of the endangered Seychelles warbler. Evol. Appl. 2014; 7: 1134–1143. doi: 10.1111/eva.12191 25553073

18. Harrison K A, Pavlova A, Telonis-Scott M, Sunnucks P. Using genomics to characterize evolutionary potential for conservation of wild populations. Evol. Appl. 2014; 7: 1008–1025. doi: 10.1111/eva.12149 25553064

19. Moritz C. Defining ‘Evolutionarily Significant Units’ for conservation. Trends Ecol. Evol. 1994; 9: 373–375. doi: 10.1016/0169-5347(94)90057-4 21236896

20. Mikheyev A, Zwick A, Magrath M J L, Grau M L, Qui L, Su Y N. Museum genomics confirms the Lord Howe Island stick insect survived extinction. Curr. Biol. 2017; 270: 3157–3161.

21. Ralls K., Ballou J.D., Dudash M.R., Eldridge M.D., Fenster C.B., Lacy R.C, et al. Call for a paradigm shift in the genetic management of fragmented populations. Conservation Letters 2017; 11: e12412.

22. Ellgren H. Genome sequencing and population genomics in non-model organisms. Trends Ecol. Evol. 2014; 29: 51–63. doi: 10.1016/j.tree.2013.09.008 24139972

23. Suchan T, Pitteloud C, Gerasimova N S, Kostikova A, Schmid S, Arrigo N, et al. Hybridization capture using RAD probes (hyRAD), a new tool for performing genomic analyses on collection specimens. PLoS ONE 2016; 11: e0151651. doi: 10.1371/journal.pone.0151651 26999359

24. Stronen A V, Lacolina L, Pertoldi C, Kusza S, Hulva P, Dykyy I, et al. The use of museum skins for genomic analyses of temporal genetic diversity in wild species. Cons. Genet. Res. 2018; doi: 10.1007/s12686-018-1036-x

25. BirdLife International. Species factsheet: Anthochaera phrygia. 2019. Downloaded from http://www.birdlife.org on 10/05/2019.

26. Franklin D C, Menkhorst P W, Robinson P W. Ecology of the regent honeyeater. Emu. 1989; 89: 140–154.

27. Bradshaw C J A. Little left to lose: deforestation and forest degradation in Australia since European colonisation. J. Plant Ecol. 2012; 5: 109–120.

28. Peters D E. Some evidence of a decline in population status of the regent honeyeater. Aust. Bird Watch. 1979; 8: 117–123.

29. Franklin D C, Menkhorst P W. A history of the regent honeyeater in South Australia. South Austr. Ornith. 1988; 30: 141–145.

30. Commonwealth of Australia. National recovery plan for the regent honeyeater (Anthochaera phrygia). Department of Environment, Commonwealth of Australia. 2016; http://www.environment.gov.au/biodiversity/threatened/recovery-plans/national-recovery-plan-regent-honeyeateranthochaera-phrygia-2016.

31. Crates R A, Rayner L, Stojanovic D, Webb M, Terauds A, Heinsohn R. Contemporary breeding biology of critically endangered regent honeyeaters: implications for conservation. Ibis. 2018. doi: 10.1111/ibi.12659

32. Olah G, Heinsohn R G, Brightsmith D J, Espinoza J R, Peakall R. Validation of non-invasive genetic tagging in two large macaw species (Ara macao and A. chloropterus) of the Peruvian Amazon. Cons. Genet. Res. 2016; 8: 499–509. doi: 10.1007/s12686-016-0573-4

33. Miller S A, Dykes D D, Polesky H F. A simple salting out procedure for extracting DNA from human nucleated cells. Nucl. Acid. Res. 1988; 16: 1215.

34. Lepais O, Weir J T. SimRAD: an R package for simulation-based prediction of the number of loci expected in RADseq and similar genotyping by sequencing approaches. Mol. Ecol. Res. 2014; 14: 1314–1321. doi: 10.1111/1755-0998.12273 24806844

35. Aronesty E. Ea-utils. Command-line tools for processing biological sequencing data. 2011; https://github.com/ExpressionAnalysis/ea-utils.

36. Babraham Bioinformatics. FastQC: a quality control tool for high throughput sequence data. 2011.

37. Bolger A M, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014; 30: 2114–2120. doi: 10.1093/bioinformatics/btu170 24695404

38. Luo R, Liu B, Xie Y, Li Z, Huang W, Yuan J, et al. SOAPdenovo2: an empirically improved memory-efficient short-read de novo assembler. Gigascience. 2012: 1: 18. doi: 10.1186/2047-217X-1-18 23587118

39. Huang S, Kang M, Xu A. HaploMerger2: rebuilding both haploid sub-assemblies from high-heterozygosity diploid genome assembly. Bioinformatics. 2017; 33: 2577–2579. doi: 10.1093/bioinformatics/btx220 28407147

40. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint, arXiv 1303.3997. 2013.

41. Jónsson H, Ginolhac A, Schubert M, Johnson P L F, Orlando L. MapDamage2.0: fast approximate Bayesian estimates of ancient DNA damage parameters. Bioinformatics 2013; 29: 1682–1684. doi: 10.1093/bioinformatics/btt193 23613487

42. Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint, arXiv 1207.3907. 2012.

43. Danecek P, Auton A, Abecasis G, Albers C A, Banks E, dePristo M A, et al. The variant call format and VCFtools. Bioinformatics. 2011; 27: 2156–2158. doi: 10.1093/bioinformatics/btr330 21653522

44. Gruber B, Georges A. Package ‘dartR:’ Importing and analysing SNP and silicodart data generated by genome-wide restriction fragment analysis. 2017; https://cran.r-project.org/web/packages/dartR/dartR.pdf.

45. R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2018; Available at: http://www.R-project.org.

46. Raj A, Stephens M, Pritchard J K. FastSTRUCTURE: Variational inference of population structure in large SNP data sets. Genetics 2014; 197: 573–589. doi: 10.1534/genetics.114.164350 24700103

47. Jombart T. Adegenet: Exploratory analysis of genetic and genomic data. 2018; https://cran.r-project.org/web/packages/adegenet/adegenet.pdf.

48. Rosenberg N A. Distruct: a program for the graphical display of population structure. Mol. Ecol. Not. 2004; 4: 137–138. doi: 10.1046/j.1471-8286.2003.00566.x

49. Pembleton L W, Cogan N O I, Forster J W. StAMPP: an R package for calculation of genetic differentiation and structure of mixed ploidy level populations. Mol. Ecol. Res. 2013; 13: 946–952.

50. Rousset F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 1997; 145: 1219–1228. 9093870

51. Kamvar Z N, Tabima J F, Brooks J C. Package ‘poppr:’ Genetic analysis of populations with mixed reproduction. 2018; https://cran.rproject.org/web/packages/poppr/poppr.pdf.

52. Smouse P E, Banks S, Peakall R: Converting quadratic entropy to biodiversity: Both animals and alleles are diverse, but some are more diverse than others. PLoS ONE 2017; 12: e0185499. doi: 10.1371/journal.pone.0185499 29088229

53. Goudet J, Jombart T. Package ‘hierfstat:’ Estimation and tests of hierarchical F-statistics. 2015; https://cran.r-project.org/web/packages/hierfstat/hierfstat.pdf.

54. Stoffel M. inbreedR: an R package for the analysis of inbreeding based on genetic markers. Meth. Ecol. Evol. 2016; 7: 1331–1339.

55. Do C, Waples R S, Peel D, Macbeth G M, Tillet B J, Ovenden J R. NeEstimator v2: re‐implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Res. 2014; 1: 209–14.

56. Waples R S. A bias correction for estimates of effective population size based on linkage disequilibrium at unlinked gene loci. Cons. Genet. 2006; 7: 167.

57. Drummond A J, Rambaut A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 2007; 7: 214. doi: 10.1186/1471-2148-7-214 17996036

58. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu C-H, Xie D, et al. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS Comp. Biol. 2014; 10: e1003537.

59. Rambaut A, Drummond A J, Xie D, Baele G, Suchard M A. Posterior summarisation in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. syy032. 2018; doi: 10.1093/sysbio/syy03

60. Mills L S, Allendorf F W. The one migrant-per-generation rule in conservation and management. Cons. Biol. 1996; 10: 1509–1518.

61. Powys V. Regent honeyeaters: mapping their movements through song. Corella 2010; 94: 92–102.

62. Taberlet P, Griffin S, Goossens B, Questiau S, Manceau V, Escaravage N, et al. Reliable genotyping of samples with very low DNA quantities using PCR. Nucl. Acid. Res. 1996; 24: 3189–3194. https://doi.org/10.1093/nar/24.16.3189.

63. Wandeler P, Hoeck P E A, Keller L F. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 2007; 22: 634–642. doi: 10.1016/j.tree.2007.08.017 17988758

64. Wang J, Santiago E, Caballero A. Prediction and estimation of effective population size. Heredity 2016; 117: 193–206. doi: 10.1038/hdy.2016.43 27353047

65. Heller R, Chikhi L, Siegismund H R. The confounding effect of population structure on Bayesian skyline plot inferences of demographic history. PLoS ONE. 2013; 8: e62992. doi: 10.1371/journal.pone.0062992 23667558

66. Crates R A, Rayner L, Stojanovic D, Webb M, Heinsohn R. Undetected Allee effects in Australia’s threatened birds: implications for conservation. Emu. 2017; 117: 1–15.

67. Ryman N, Palm S, André C, Carvalho G R, Dahlgren T G, Jorde P E, et al. Power for detecting genetic divergence: Differences between statistical methods and marker loci. Mol. Ecol. 2006; 15: 2031–2045. doi: 10.1111/j.1365-294X.2006.02839.x 16780422

68. Schmid S, Neuenschwander S, Pitteloud C, Heckel G, Pajkovic M, Arlettaz R, et al. Spatial and temporal genetic dynamics of the grasshopper Oedaleus decorus revealed by museum genomics. Ecol. Evol. 2018; 8:1480–95. doi: 10.1002/ece3.3699 29435226

69. O’Leary S J, Puritz J B, Willis S C, Hollenbeck C M, Portnoy D S. These aren’t the loci you’re looking for: principles of effective SNP filtering for molecular ecologists. Mol. Ecol. 2018; doi: 10.1111/mec.14792 29987880


Článek vyšel v časopise

PLOS One


2019 Číslo 10