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Revealing the consequences of male-biased trophy hunting on the maintenance of genetic variation

Abstract

Demographic models accounting for operational sex ratio (OSR) show that male numbers can have a substantial influence on the dynamics of wild populations. We used the Cantabrian capercaillie, a forest bird, as a model to assess the effects of the reduction in the number of breeding males (increased OSR) associated to male-biased hunting, on the genetics of the population. We based our assessment in the comparison of the dynamics of neutral markers transmitted by both parents (microsatellites) versus markers transmitted only by females (mitochondrial DNA—mtDNA). Parallel to the analysis of field data, we ran computer simulations to explore how different levels of OSR and two other important demographic factors, population size and connectivity, might influence the dynamics of genetic variation of microsatellites and mtDNA. We found evidence of a genetic bottleneck and low genetic variability affecting microsatellites but not mtDNA early in our study period, when male-biased hunting was more intense. This was followed by a decline in mtDNA variation around 10–20 years later. Simulations suggested that changes in genetic variation associated with high OSR had the closest similarity to those observed at the beginning of our study, whereas a combination of reduced size and migration rate better resembled the patterns found later on. Our findings indicate that male-biased hunting might have triggered the ongoing decline of the Cantabrian capercaillie, on its own or in combination with habitat configuration, and support the need to incorporate OSR into decision making for the management and conservation of exploited populations.

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References

  1. Aedo C, Ena V, García-Gaona JF, García-Oliva J, Martínez A, Naves J, Palomero G (1986) El urogallo Cantábrico (Tetrao urogallus cantabricus) en Cantabria. Universidad de Cantabria, Santander

    Google Scholar 

  2. Alatalo RV, Hoglund J, Lundberg A, Sutherland WJ (1992) Evolution of black grouse leks: female preferences benefit males in larger leks. Behav Ecol 3:53–59  

  3. Allendorf FW, Luikart G (2007) Conservation and the genetics of populations. Blackwell, Oxford

    Google Scholar 

  4. Allendorf FW, England PR, Luikart G, Ritchie PA, Ryman N (2008) Genetic effects of harvest on wild animal populations. Trends Ecol Evol 23:327–337

    Article  PubMed  Google Scholar 

  5. Bajc M, Čas M, Ballian D, Kunovac S, Zubić G, Grubešić M, Zhelev P, Paule L, Grebenc T, Kraigher H (2011) Genetic differentiation of the western Capercaillie highlights the importance of South-Eastern Europe for understanding the species phylogeography. PLoS One 6:e23602

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  6. Balloux F (2001) EASYPOP (Version 1.7): a computer program for population genetics simulations. J Hered 92:301–302

    CAS  Article  PubMed  Google Scholar 

  7. Bañuelos MJ, Quevedo M, Obeso JR (2008) Habitat partitioning in endangered Cantabrian capercaillie Tetrao urogallus cantabricus. J Ornithol 149:245–252

    Article  Google Scholar 

  8. Brown WM, George M, Wilson AC (1979) Rapid evolution of animal mitochondrial DNA. Proc Natl Acad Sci 76:1967–1971

  9. Caizergues A, Dubois S, Loiseau A, Mondor G, Rasplus JY (2001) Isolation and characterization of microsatellite loci in black grouse (Tetrao tetrix). Mol Ecol Notes 1:36–38

    CAS  Article  Google Scholar 

  10. Caizergues A, Ratti O, Helle P, Rotelli L, Ellison L, Rasplus JY (2003) Population genetic structure of male black grouse (Tetrao tetrix L.) in fragmented vs. continuous landscapes. Mol Ecol 12:2297–2305

  11. Castroviejo J, Delibes M, García Dory MA, Garzón J, Junco E (1974) Censo de urogallos cantábricos (Tetrao urogallus cantabricus). Asturnatura 2:53–74

    Google Scholar 

  12. Caswell H, Weeks DE (1986) Two-sex models: chaos, extinction, and other dynamic consequences of sex. Am Nat 128:707–735

    Article  Google Scholar 

  13. Cornuet JM, Luikart G (1996) Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144:2001–2014

    PubMed Central  CAS  PubMed  Google Scholar 

  14. Corrales C, Pavlovska M, Höglund J (2014) Phylogeography and subspecies status of Black Grouse. J Ornithol 155:13–25

  15. Dickson B, Hutton J, Adams WM (2009) Recreational hunting, conservation and rural livelihoods: science and practice. Wiley, Oxford

    Book  Google Scholar 

  16. Emlen ST, Oring LW (1977) Ecology, sexual selection, and the evolution of mating systems. Science 197:215–223

    CAS  Article  PubMed  Google Scholar 

  17. Ena Álvarez V, García-Gaona JF, Martínez Fernández A (1984) Seguimiento en la época de celo de tres cantaderos de Urogallo (Tetrao urogallus) en la Cordillera Cantábrica. Boletín de la Estación Central de Ecología 13:63–71

    Google Scholar 

  18. Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47

    PubMed Central  CAS  Google Scholar 

  19. Festa Bianchet M, Lee R (2009) Guns, sheep, and genes: when and why trophy hunting may be a selective pressure. In: Dickson B, Hutton J, Adams WM (eds) Recreational hunting, conservation and rural livelihoods: science and practice. Wiley, Oxford, pp 94–107

    Chapter  Google Scholar 

  20. Frantz AC, Pope LC, Carpenter PJ, Roper TJ, Wilson GJ, Delahays RJ, Burke T (2003) Reliable microsatellite genotyping of the Eurasian badger (Meles meles) using faecal DNA. Mol Ecol 12:1649–1661

    CAS  Article  PubMed  Google Scholar 

  21. Fu Y (1997) Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics 147:915–925

    PubMed Central  CAS  PubMed  Google Scholar 

  22. Fumihito A, Miyake T, Takada M, Ohno S, Kondo N (1995) The genetic link between the Chinese bamboo partridge (Bombusicola thoracica) and the chicken and junglefowls of the genus Gallus. Proc Natl Acad Sci USA 92:11053–11056

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  23. García D, Quevedo M, Obeso JR, Abajo A (2005) Fragmentation patterns and protection of montane forest in the Cantabrian range (NW Spain). For Ecol Manag 208:29–43

    Article  Google Scholar 

  24. Garza JC, Williamson EG (2001) Detection of reduction in population size using data from microsatellite loci. Mol Ecol 10:305–318

    CAS  Article  PubMed  Google Scholar 

  25. Goldstein DB, Pollock DD (1997) Mutation processes and methods of phylogenetic inference. J Hered 88:335–342

  26. Goudet J (2001) FSTAT, a program to estimate and test gene diversities and fixation indices. http://www.unil.ch/izea/softwares/fstat.html

  27. Harpending HC, Sherry ST, Rogers AR, Stoneking M (1993) The genetic structure of ancient human populations. Curr Anthropol 34:483–496

    Article  Google Scholar 

  28. Helle P, Kurki S, Linden H (1999) Change in the sex ratio of the Finnish capercaillie Tetrao urogallus population. Wildl Biol 5:25–31

  29. Hoglund J, Piertney SB, Alatalo RV, Lindell J, Lundberg A, Rintamaki PT (2002) Inbreeding depression and male fitness in black grouse. Proc R Soc Lond Ser B Biol Sci 269:711–715

    Article  Google Scholar 

  30. Horváth MB, Martínez-Cruz B, Negro JJ, Kalmár L, Godoy JA (2005) An overlooked DNA source for non-invasive genetic analysis in birds. J Avian Biol 36:84–88

    Article  Google Scholar 

  31. Isomursu M, Rätti O, Liukkonen T, Helle P (2012) Susceptibility to intestinal parasites and juvenile survival are correlated with multilocus microsatellite heterozygosity in the capercaillie (Tetrao urogallus). Ornis Fenn 89:109–119

    Google Scholar 

  32. Johnson JA, Dunn PO, Bouzat JL (2007) Effects of recent population bottlenecks on reconstructing the demographic history of prairie-chickens. Mol Ecol 16:2203–2222

    CAS  Article  PubMed  Google Scholar 

  33. Kramer A, Dennis B, Liebhold A, Drake J (2009) The evidence for Allee effects. Popul Ecol 51:341–354

    Article  Google Scholar 

  34. Kuhner MK (2006) LAMARC 2.0: maximum likelihood and Bayesian estimation of population parameters. Bioinformatics 22:768–770

    CAS  Article  PubMed  Google Scholar 

  35. Lee AM, Saether B-E, Engen S (2011) Demographic stochasticity, Allee Effects, and Extinction: the Influence of mating system and sex ratio. Am Nat 177:301–313

    Article  PubMed  Google Scholar 

  36. Liukkonen-Anttila T, Rätti O, Kvist L, Helle P, Orell M (2004) lack of genetic structuring and subspecies differentiation in the capercaillie (Tetrao urogallus) in Finland. Ornis Fennica 41:619–633

    Google Scholar 

  37. Llano Ad (1928) Bellezas de Asturias de oriente a occidente. Excelentisima Diputación Provincial de Oviedo, Oviedo

    Google Scholar 

  38. Mackenzie A, Reynolds JD, Brown VJ, Sutherland WJ (1995) Variation in male mating success on leks. Am Nat 145:633–652

  39. Marjakangas A, Kiviniemi S (2005) Dispersal and migration of female Black Grouse Tetrao tetrix in eastern central Finland. Ornis Fenn 82:107–116

  40. McKelvey KS, Schwartz MK (2005) DROPOUT: a program to identify problem loci and samples for noninvasive genetic samples in a capture-mark-recapture framework. Mol Ecol Notes 5:716–718

    CAS  Article  Google Scholar 

  41. Miller SA, Dykes DD, Polesky HF (1988) A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res 16:1215

    PubMed Central  CAS  Article  PubMed  Google Scholar 

  42. Milner-Gulland EJ, Bukreeva OM, Coulson T, Lushchekina AA, Kholodova MV, Bekenov AB, Grachev IA (2003) Reproductive collapse in saiga antelope harems. Nature 422:135

    CAS  Article  PubMed  Google Scholar 

  43. Muñoz Sobrino C, Ramil-Rego P, Rodríguez Guitián M (1997) Upland vegetation in the north-west Iberian peninsula after the last glaciation: Forest history and deforestation dynamics. Veg Hist Archaeobot 6:215–233

  44. Nei M (1987) Molecular evolutionary genetics. Columbia University Press, NY

    Google Scholar 

  45. Peery MZ, Kirby R, Reid BN, Stoelting R, Doucet-Bëer E, Robinson S, Vásquez-Carrillo C, Pauli JN, Palsbøll PJ (2012) Reliability of genetic bottleneck tests for detecting recent population declines. Mol Ecol 21:3403–3418

    Article  PubMed  Google Scholar 

  46. Piertney SB, Höglund J (2001) Polymorphic microsatellite DNA markers in Black Grouse (Tetrao tetrix). Mol Ecol 1:303–304

    CAS  Article  Google Scholar 

  47. Piry S, Luikart G, Cornuet JM (1999) Computer note. BOTTLENECK: a computer program for detecting recent reductions in the effective size using allele frequency data. J Hered 90:502

    Article  Google Scholar 

  48. Posada D, Crandall KA (2001) Intraspecific gene genealogies: trees grafting into networks. Trends Ecol Evol 16:37–45

    Article  PubMed  Google Scholar 

  49. Quevedo M, Banuelos MJ, Obeso JR (2006a) The decline of Cantabrian capercaillie: how much does habitat configuration matter? Biol Conserv 127:190–200

    Article  Google Scholar 

  50. Quevedo M, Bañuelos MJ, Saez O, Obeso JR (2006b) Habitat selection by Cantabrian capercaillie Tetrao urogallus cantabricus at the edge of the species’ distribution. Wildl Biol 12:267–276

    Article  Google Scholar 

  51. Randi E, Lucchini V (1998) Organization and evolution of the mitochondrial DNA control region in the avian genus Alectoris. J Mol Evol 47:449–462

    CAS  Article  PubMed  Google Scholar 

  52. Rankin DJ, Kokko H (2007) Do males matter? The role of males in population dynamics. Oikos 116:335–348

    Article  Google Scholar 

  53. Raymond M, Rousset F (1995) GenePop (Ver. 1.2): a population genetics software for exact test and ecumenicism. J Hered 86:248–249

    Google Scholar 

  54. Robles L, Ballesteros F, Canut J (2006) El urogallo en España Andorra y Pirineos franceses. Situación actual (2005). SEO/BirdLife, Madrid

    Google Scholar 

  55. Rodriguez-Muñoz R, Mirol PM, Segelbacher G, Fernandez A, Tregenza T (2007) Genetic differentiation of an endangered capercaillie (Tetrao urogallus) population at the Southern edge of the species range. Conserv Genet 8:659–670

    Article  Google Scholar 

  56. Rolstad Jr, Wegge P, Sivkov AV, Hjeljord O, Storaunet KO (2009) Size and spacing of grouse leks: comparing capercaillie (Tetrao urogallus) and black grouse (Tetrao tetrix) in two contrasting Eurasian boreal forest landscapes. Can J Zool 87:1032–1043

  57. Rutkowski R, Nieweglowski H, Dziedzic R, Kmiec M, Gozdziewski J (2005) Genetic variability of Polish population of the Capercaillie Tetrao urogallus. Acta Ornithologica 40:27–34

    Article  Google Scholar 

  58. Segelbacher G, Paxton RJ, Steinbruck G, Trontelj P, Storch I (2000) Characterization of microsatellites in capercaillie Tetrao urogallus (AVES). Mol Ecol 9:1934–1935

    CAS  Article  PubMed  Google Scholar 

  59. Segelbacher G, Höglund J, Storch I (2003) From connectivity to isolation: genetic consequences of population fragmentation in capercaillie across Europe. Mol Ecol 12:1773–1780

    CAS  Article  PubMed  Google Scholar 

  60. Slatkin M, Hudson RR (1991) Pairwise comparisons of mitochondrial DNA sequences in stable and exponentially growing populations. Genetics 129:555–562

    PubMed Central  CAS  PubMed  Google Scholar 

  61. Storch I, Bañuelos MJ, Fernández-Gil A, Obeso JR, Quevedo M, Rodríguez-Muñoz R (2006) Subspecies Cantabrian capercaillie Tetrao urogallus cantabricus endangered according to IUCN criteria. J Ornithol 147:653–655

    Article  Google Scholar 

  62. Tajima F (1989) Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123:585–595

    PubMed Central  CAS  PubMed  Google Scholar 

  63. Valière N (2002) GIMLET: a computer program for analysing genetic individual identification data. Mol Ecol Notes 2:377–379

    Article  Google Scholar 

  64. Van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P (2004) MICRO-CHECKER: software for identifying and correcting genotyping errors in microsatellite data. Mol Ecol Notes 4:535–538

    Article  Google Scholar 

  65. Vázquez JF, Pérez T, Quirós F, Obeso JR, Albornoz J, Domínguez A (2012) Population genetic structure and diversity of the endangered Cantabrian capercaillie. J Wildl Manag 76:957–965

    Article  Google Scholar 

  66. Westemeier RL, Brawn JD, Simpson SA, Esker TL, Jansen RW, Walk JW, Kershner EL, Bouzat JL, Paige KN (1998) Tracking the long-term decline and recovery of an isolated population. Science 282:1695–1698

    CAS  Article  PubMed  Google Scholar 

  67. Wiley RH (1991) Lekking in birds and mammals: behavioral and evolutionary issues. Adv Study Behav 20:201–291

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Acknowledgments

We are very grateful to K. Pérez, D. Ramos, J.R. Jiménez, E. González, E. de la Calzada, an anonymous taxidermist, and several hunters and small public and private collections for providing access to many of the preserved birds and old feather samples. Many people collaborated in the collection of shed feathers, we are especially grateful to the Environmental Administration of Asturias and their rangers, as well as B. Blanco, A. Fernández, M. González, M. Quevedo and L. Robles. J. Cabot, helped us with the specimens from the Estación Biológica de Doñana, and J. Barrerio with those from the Museo de Ciencias Naturales de Madrid. Thanks also to the people of the Museo de Ciencias de Santiago de Compostela, to J.C. Illera, who made possible all Mac based analyses and to M. Quevedo, who made useful comments on the final version of the manuscript. This work has been supported by the Natural Environment Research Council (Grant: 244 NE/E005403/1), FICYT (IB08-158 and POST10-41), MICINN (CGL2010-15990) and Agencia Nacional de Promoción Científica y Tecnológica (PME 2151).

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Correspondence to Patricia Mirol.

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Appendix: Computer simulations

Appendix: Computer simulations

Background

Starting around 3000 years ago, the Cantabrian Mountains have undergone intensive human driven deforestation and forest fragmentation (Muñoz Sobrino et al. 1997), a process that intensified over the last 1000 years. Woodlands lost and fragmentation are prevalent factors associated to the decline of forest fauna by reducing population size and connectivity (Allendorf and Luikart 2007). Therefore, together with male hunting, habitat loss and fragmentation are potential factors that could explain the ongoing population decline in this area, leading to a reduction in effective (male and female) population size and/or a reduction in population connectivity. We used Easypop 2.0.1 (Balloux 2001) to model the dynamics of number of haplotypes (H), haplotype diversity (h), number of alleles (Ao), expected and observed heterozygosity (He and Ho) and inbreeding coefficient (F IS ) under various levels of: (1) population size, (2) population fragmentation (i.e. variation in genetic flow by using migration rate as a proxy) and (3) operational sex ratio. We did not intend to accurately recreate the patterns of genetic variation that we found in the field through time, but to get some insights on how different levels of each of the three parameters could influence genetic variation in mitochondrial versus nuclear DNA.

Although our aim is to get insights about what would be the most plausible explanation for the genetic patterns found in the field for the Cantabrian capercaillie, our hypotheses should apply to a more general context. Thus, we hypothesize that in any similar species (in terms of social structure and reproductive system), a decrease in the number of reproductive males, would reduce microsatellite variability but had limited or no effect on mtDNA variation; conversely, any factor reducing both the number of reproductive females and males (overall drop in population size), should result in a sharper drop in mtDNA than microsatellite variation. A severe limitation in connectivity, as a result of habitat loss and fragmentation, would also have limited or no effect in mtDNA, but would lead to a more inbred population, when compared to the effects of a decrease in the number of reproductive males.

There is a possibility that the simulation results obtained for our study population are constrained in some way, due to the use of genetic parameters coming from a declining population. In order to check the generality of our hypotheses, we have also explored these alternative scenarios for the black grouse (Tetrao tetrix), a polygynous lekking bird and a close relative of capercaillie. To run the simulations, we used demographic parameters extracted from the available literature on the large black grouse populations of Scandinavia. We did not aim to develop a detailed demographic model, but to explore how different levels of population size, population connectivity or operational sex-ratio (OSR), might affect nuclear verus mitochondrial genetic variation in a demographically healthy population of a lekking bird.

Cantabrian capercaillie

Simulation settings

All the simulations are based in a metapopulation made by 16 identical populations. The setting of most of the genetic and demographic parameters required by Easypop, are shared by all the simulations regardless of what factor is being modeled. These “fixed” parameters include the proportion of copulations by subordinate males, the migration model, the number of loci, the mutation scheme, rate and model and the number of possible allelic states. All other parameters varied according to the factor being modeled.

For the shared parameters, we started the simulation with the maximal variability achievable with the actual overall values of number of alleles and haplotypes that we found in our study population (average of 6 alleles per microsatellite for 11 locus and 9 haplotypes for the mtDNA control region). We set basic simulation parameters (population density and breeding success) according to figures reported for large capercaillie populations living in Finland (Helle et al. 1999), Norway and Russia (Rolstad et al. 2009). There are no data available about the proportion of males that contributes to mating in capercaillie, although a strong skew in male mating success in lekking species has been repeatedly reported, so that 10–20 % of the males get 70–80 % of the matings of a lek (Wiley 1991; Mackenzie et al. 1995). We set this parameter to a conservative 0.5, i.e. one male in each population gets half of the copulations. We followed a 2-dimension stepping stone migration model, with 4 × 4 populations (see Fig. 4). These ‘populations’ would resemble the groups of neighbouring lekking sites located in the same forest or group of nearby forests as they are found in the field. We set mutation rate to 5 × 10−4 for mtDNA (Brown et al. 1979) and 10−3 for microsatellites (Goldstein and Pollock 1997) and adopted the same mutation model we used for the estimation of effective population size from our field data, a SMM mutation model with a 0.1 proportion of KAM (K-Allele model) for the microsatellites (this is similar to the TPM model used in the bottleneck analysis), and a pure SMM mutation model for the mtDNA. We run five replicates per simulation.

Fig. 4
figure4

Diagram representing the two-dimension stepping stone migration model used in Easypop. Each black dot represents a lek. Leks are grouped in batches of 3–4 (encircled) that correspond to the populations in the simulation. Migration rates correspond to movements among circles, each circle being connected to the four closest circles around it, with the exception of the borders, where circles are connected to two (circles at the corners) or three (circles at the sides) other circles

The “variable” parameters, i.e., those that were different depending on what factor was being modeled, included male and female population size and the proportion of male and female migration. To explore the effect of population size, we run three simulations with three levels of population size and a 1:1 sex-ratio. We set the highest level to 800 individuals (400 females), the maximum effective population size that we estimate could be supported bearing in mind the area occupied by the western Cantabrian capercaillie subpopulation and the densities found in the healthy populations living in Finland. For the lower levels we halved the preceding one each time, so we set 384 and 192 (we did not use 400 and 200 because we needed numbers that could be divided by the number of populations—16). These simulations were run with a migration proportion of 0.5 for each sex. To model the effect of migration rate, we set population size to the highest level just described (800) and 1:1 sex-ratio, and run four simulations with one of four levels of proportion of migration each (identical values for each sex), no migration (0), low migration (0.1), moderate migration (0.2) and high migration (0.5). Finally, to explore the effect of operational sex-ratio, we set the same proportion of migration for each sex to 0.5, and the female population size to 400, and then run four levels of OSR, 2, 4, 12 and 25, by setting different male numbers (see Table 7).

Table 7 Parameter values used to set up the 12 Easypop simulations for a model metapopulation including 16 populations of Cantabrian capercaillie, each one containing 3–4 leks. Simulations include three levels of population size and four of migration rate and operational sex ratio (OSR). The table includes only those parameters having some difference among simulations

Results

Reducing population size decreased the amount of variation retained for nuclear and mitochondrial markers. This decrease was much stronger and faster on mitochondrial than on microsatellite variability regardless of the parameter used to assess genetic variation (Figs. 5a and 6). The difference between observed and expected heterozygosities increased as population size decreased, resulting in an increase in inbreeding coefficient due to heterozygote deficit. The loss of haplotypes (H) and haplotype diversity (h) was particularly strong for the smallest population size, with a reduction in the number of haplotypes of 30 % after 20 generations (Figs. 5a and 6). In turn, the reduction in the number of alleles (Ao) for the smallest population size was around 2 % (Fig. 6).

Fig. 5
figure5

Effect of different levels of population size (a), migration rate (b) and operational sex ratio (c) on inbreeding coefficient and the proportion of genetic variation (number of haplotypes, haplotypic diversity and expected and observed heterozygosities) retained after 20 generations of computer simulation using EasyPop (Balloux 2001). Simulations started with maximum diversity for a sequence of mtDNA with nine haplotypes and 11 microsatellite loci with six alleles each (microsatellites). Number of locus and alleles resemble values found in the Cantabrian capercaillie. Lines represent trends based on the mean of five independent simulation replicates. Simulations of population size were performed with a constant migration rate of 0.5 and an even sex-ratio. For migration rate, we used a constant population size of 800 and an even sex-ratio. For operational sex ratio, we used a constant migration rate of 0.5 and a female population size of 400. Sex ratio was increased by changing male population size

Fig. 6
figure6

Effect of different levels of population size, migration rate and operational sex ratio (OSR), on the proportion of variation retained for the number of haplotypes (H), haplotypic diversity (h), number of alleles (Ao), genetic diversity (expected heterozygosity, He), observed heterozygosity (Ho) and inbreeding coefficient (Fis), over 20 generations. Graphs are based on the means of five replicates for each of 16 computer simulations using Easypop (see Supplementary Table S1 for parameter setting). Simulations for different population sizes (left column) were calculated for an even OSR, an equal migration rate of 0.5 for both sexes. Simulations for different migration rates were calculated for a female effective population of 400 and an even OSR. Simulations of OSR were calculated for a female population of 400 and an equal migration rate of 0.5 for both sexes. Increasing values of OSR are based on reducing male population size. All simulations started with maximum variability based on the genetic parameters found for the Cantabrian capercaillie (9 haplotypes and 11 loci with 6 alleles each)

Genetic drift associated to low migration rates, contributed to retain overall genetic variation for mitochondrial and microsatellites at the cost of a decrease in observed (actual) heterozygosity. Drift favours the fixation of different alleles at different populations, increasing the genetic diversity when pooling all the populations together. Thus, the number of haplotypes, haplotype diversity and expected heterozygosity remained nearly unchanged in simulations with no migration after 20 generations. However, the effect was opposite for observed heterozygosity, for which an increase in population subdivision favoured inbreeding and so reduced sharply the actual proportion of heterozygotes. This divergence between observed and expected heterozygosities led to a rapid increase of the inbreeding coefficient due to heterozygote deficit, particularly when migration rate was set to zero (Fig. 5b and 6).

Because mitochondrial DNA is only inherited from females, haplotype number and diversity are insensitive to increasing levels of OSR associated to a reduction in the number of reproductive males (the simulations assume no effect of OSR on a female’s chances to mate). However, both heterozygosities (expected and observed) decreased with the reduction of male effective population (Figs. 5c and 6). The faster decrease of observed versus expected heterozygosity caused an increase of the inbreeding coefficient over the first 20 generations, although not as strong as the increase caused by severe population fragmentation.

Black grouse

Parameter settings

According to Alatalo et al. (1992), the mean distance between neighboring black grouse leks is 2.1 km, and the average number of males per lek is 7.1. The operational sex ratio is quite high due the strong skew in male mating success. Around 55 % of the males in a lek are 2 years old or older, and these males get over 90 % of the copulations. At each lek a single dominant male gets between 50 and 100 % of the copulations, with an average of around 60 %. There is a strong relationship between the number of males that get copulations in a lek and the number of males present at that lek (Fig. 7). In most of the leks with less than 10 males, only 1–2 males mate, and only leks with more than 16 males have more than 6 mating males. Dispersal has a strong sex-skew. Males are philopatric, moving mainly around neighboring leks, whereas the median dispersal distance of yearling females is 9.2 km, and 75 % of them move farther than 4 km (Marjakangas and Kiviniemi 2005). Ten different haplotypes have been described for the mtDNA Control Region of the Scandinavian black grouse populations (Corrales et al. 2014), and the average number of alleles found for 14 microsatellite loci was 12 (Caizergues et al. 2003).

Fig. 7
figure7

Relationship between number of males getting copulations in a lek and total number of males present at the same lek. Each dot represents a lek-year after Alatalo et al. (1992)

We run 52 simulations combining different population sizes, dispersal rates and operational sex-ratios as shown in Table 8. All simulations were run using a 2-dimension stepping stone migration model with 16 populations. This would be equivalent to a metapopulation of 56 leks grouped in 16 blocks of 3–4 neighboring leks (each block being one of the populations in the simulation, see Fig. 4). We started the simulation with the maximal variability achievable with the values of allele and haplotype richness described in the literature, an average of 12 alleles per microsatellite locus (14 loci) and 10 haplotypes for the mtDNA control region. We set mutation rate to 5 × 10−4 for mtDNA (Brown et al. 1979) and 10−3 for microsatellites (Goldstein and Pollock 1997) and adopted the same mutation model used for the capercaillie simulations, a SMM mutation model with a 0.1 proportion of KAM (K-Allele model) for the microsatellites, and a pure SMM mutation model for the mtDNA. We run 100 replicates of each simulation for a total of 20 generations.

Table 8 Values used to set up the 52 Easypop simulations run for a metapopulation including 16 populations of black grouse, each one containing 3–4 leks. Simulations include all possible interactions among four levels of migration rate, four of population size and four of operational sex ratio (OSR), with the only exceptions of those where the number of males per population is smaller than 1 (Easypop does not allow populations with 0 individuals of either sex). Each cell shows the OSR value corresponding to each of the simulations for the different combinations of total female population size and male and female migration rates. Number of males per simulation can be calculated dividing the number of females by OSR (numbers have been rounded to the nearest integer for female numbers of 80 and 160)

The simulations covered all combinations among the different levels of each factor (see Table 8). They included four levels of metapopulation size, with a female effective population of: 80, 160, 400 and 800 birds (these numbers correspond to 5, 10, 25 and 50 females per population including 3–4 leks). We used also four levels for migration rates (female-male): 0–0 (no dispersal), 0.4–0 (moderate female dispersal), 0.75–0 (high female dispersal) and 0.75–0.3 (high female dispersal with moderate-low male dispersal). Finally, we tested four levels of OSR (by decreasing the number of males in relation to the number of females), using values of 2, 4, 12 and 25 females per male. Assuming an even sex-ratio, values of OSR range in nature from 2 to 9, with an average of 4. Values of 1–4 mating males per lek are found for leks sizes of up to 17 males (Fig. 7), and there are leks with up to four males that get no copulations.

Results and discussion

The patterns obtained were very similar to those found for the Cantabrian capercaillie, with the differences being attributable to variation found in a bottlenecked versus a healthy population. The relationship between population size and genetic variation was positive for both nuclear and mitochondrial DNA, i.e. a decrease in population size caused a loss of genetic variation. With population sizes of 400 and over, the loss of variation after 20 generations was very small for both types of markers, although smaller for microsatellites than mtDNA. However, with less than 400 females breeding females, genetic variation started to decrease more quickly, and this decrease was more pronounced for mtDNA than microsatellites (Figs. 8a and 9). The faster decrease of mtDNA variation for populations smaller than 400 females, did happen regardless of migration rate and OSR, with the only exception of zero migration, when trends were very similar to those found for microsatellites (Fig. 10). This shows that any relevant decrease in genetic variation associated to a reduction in effective population size would have a stronger effect for mtDNA than microsatellites.

Fig. 8
figure8

Effect of different levels of population size (a), migration rate (b) and operational sex ratio (c) on inbreeding coefficient and the proportion of genetic variation retained after 20 generations of computer simulation using EasyPop (Balloux 2001) for several genetic parameters of a black grouse metapopulation. Simulations started with maximum diversity for a sequence of mtDNA with 10 haplotypes and 11 microsatellite loci with 12 alleles each. Number of locus and alleles were taken from the literature about the Scandinavian populations. Lines represent trends based on the mean of 100 simulation replicates. Simulations of population size were performed with a constant female migration rate of 0.75, no male dispersal and an OSR of 2. For migration rate, we used a constant female population size of 400 and an OSR of 2. For operational sex ratio, we used a constant female migration rate of 0.75 with no male dispersal and a female population size of 400. Sex ratio was increased by changing male population size

Fig. 9
figure9

Effect of different levels of population size, migration rate and operational sex ratio, on the proportion of variation retained for the number of haplotypes (H), haplotypic diversity (h), number of alleles (Ao), genetic diversity (expected heterozygosity, He) and observed heterozygosity (Ho) and inbreeding coefficient (Fis), after 20 generations. Graphs represent the mean of 100 replicates per each of 12 computer simulations using Easypop (see Supplementary Table S2 above). Simulations for different population sizes (left column) were calculated for an OSR of two, a female migration rate of 0.75 and no male dispersal. Simulations for different migration rates were calculated for a female effective population of 400 and an OSR of 2. Simulations of OSR were calculated for a female population of 400, a female migration rate of 0.75 and no male dispersal. Settings are based on demographic parameters reported for the black grouse in Scandinavia

Fig. 10
figure10

Effect of different levels of population size, migration rate and operational sex ratio, on the proportion of variation retained for the number of haplotypes (H), haplotypic diversity (h), number of alleles (Ao), genetic diversity (expected heterozygosity, He) and observed heterozygosity (Ho) and inbreeding coefficient (Fis), after 20 generations (different parameters represented on different series or rows). Graphs represent the mean of 100 replicates per each of 52 computer simulations using Easypop. Simulations include all combinations among four different population sizes (x-axis), four levels of migration rate (columns) and four levels of OSR (series within each graph) (see Table S2 above). General parameter settings are based on demographic parameters reported for the black grouse in Scandinavia. Note that different to Fig. S5 above, the x-axis does not represent generations but population sizes

Connectivity among populations had a similar effect for the two types of genetic markers. Decreasing migration rates increased metapopulation genetic diversity and decreased observed heterozygosity as a consequence of genetic drift among populations. The effect was small for moderate to high migration rates, but strong when populations were completely isolated. Thus, under zero dispersal, variability within populations was very small but remained high for the metapopulation as a whole (Figs. 4B and 5). For all the different combinations of population size and migration rate where a relevant decrease in genetic variation was observed, this was always much stronger for mtDNA than microsatellites (Fig. 10).

Due to maternal inheritance, mtDNA variation is not sensitive to changes in OSR, so this factor had no effect on it. For microsatellites, genetic diversity decreased with increasing OSR values (Figs. 8c and 9). This decrease did happen regardless of the levels of population size and migration rate (Fig. 6). This decrease was relevant in all cases except for genetic diversity when migration rate was set to zero. This exception was a consequence of the fixation of different alleles at different populations due to genetic drift. The effect was particularly strong for OSR levels of 12 or higher.

Conclusion

Population size and fragmentation are the two most influential factors reported in the literature in relation to the maintenance of genetic variation. Mithocondrial DNA is four times more sensitive than nuclear DNA to a decrease in population size and both are similarly sensitive to changes in migration rate. Our simulations for Cantabrian capercaillie and black grouse are consistent with that, showing also that for any combination of size and migration rate, relevant decreases in genetic variation are always stronger for mitochondrial than microsatellite DNA. We simulated a third factor, the operational sex ratio, and found that it has also a relevant effect on the maintenance of genetic variation, although because of its maternal inheritance, mtDNA is insensitive to OSR.

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Rodríguez-Muñoz, R., del Valle, C.R., Bañuelos, M.J. et al. Revealing the consequences of male-biased trophy hunting on the maintenance of genetic variation. Conserv Genet 16, 1375–1394 (2015). https://doi.org/10.1007/s10592-015-0747-8

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Keywords

  • Operational sex ratio
  • Population bottleneck
  • Demography
  • Male-biased hunting
  • Cantabrian capercaillie