Population dynamics of foxes during restricted-area culling in Britain: Advancing understanding through state-space modelling of culling records


Autoři: Tom A. Porteus aff001;  Jonathan C. Reynolds aff002;  Murdoch K. McAllister aff003
Působiště autorů: Department of Zoology, University of British Columbia, Vancouver, BC, Canada aff001;  Game & Wildlife Conservation Trust, Fordingbridge, Hampshire, United Kingdom aff002;  Institute for the Oceans and Fisheries, University of British Columbia, Vancouver, BC, Canada aff003
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225201

Souhrn

Lethal control is widely employed to suppress the numbers of target wildlife species within restricted management areas. The success of such measures is expected to vary with local circumstances affecting rates of removal and replacement. There is a need both to evaluate success in individual cases and to understand variability and its causes. In Britain, red fox (Vulpes vulpes) populations are culled within the confines of shooting estates to benefit game and wildlife prey species. We developed a Bayesian state-space model for within-year fox population dynamics within such restricted areas and fitted it to data on culling effort and success obtained from gamekeepers on 22 shooting estates of 2 to 36 km2. We used informative priors for key population processes—immigration, cub recruitment and non-culling mortality–that could not be quantified in the field. Using simulated datasets we showed that the model reliably estimated fox density and demographic parameters, and we showed that conclusions drawn from real data were robust to alternative model assumptions. All estates achieved suppression of the fox population, with pre-breeding fox density on average 47% (range 20%–90%) of estimated carrying capacity. As expected, the number of foxes killed was a poor indicator of effectiveness. Estimated rates of immigration were variable among estates, but in most cases indicated rapid replacement of culled foxes so that intensive culling efforts were required to maintain low fox densities. Due to this short-term impact, control effort focussed on the spring and summer period may be essential to achieve management goals for prey species. During the critical March-July breeding period, mean fox densities on all estates were suppressed below carrying capacity, and some maintained consistently low fox densities throughout this period. A similar model will be useful in other situations to quantify the effectiveness of lethal control on restricted areas.

Klíčová slova:

Death rates – Foxes – Humoral immune response – Population density – Population dynamics – Predation – Probability distribution – United Kingdom


Zdroje

1. Fall MW, Jackson WB. The tools and techniques of wildlife damage management—changing needs: an introduction. Int Biodeterior Biodegrad. 2002;49: 87–91. doi: 10.1016/S0964-8305(01)00107-X

2. Lennox RJ, Gallagher AJ, Ritchie EG, Cooke SJ. Evaluating the efficacy of predator removal in a conflict-prone world. Biol Conserv. 2018;224: 277–289. doi: 10.1016/j.biocon.2018.05.003

3. Tapper SC, Potts GR, Brockless MH. The effect of an experimental reduction in predation pressure on the breeding success and population density of grey partridges Perdix perdix. J Appl Ecol. 1996;33: 965–978.

4. Fletcher K, Aebischer NJ, Baines D, Foster R, Hoodless AN. Changes in breeding success and abundance of ground-nesting moorland birds in relation to the experimental deployment of legal predator control. J Appl Ecol. 2010;47: 263–272.

5. Baker PJ, Harris S. Does culling reduce fox (Vulpes vulpes) density in commercial forests in Wales, UK? Eur J Wildl Res. 2006;52: 99–108.

6. Newsome TM, Crowther MS, Dickman CR. Rapid recolonisation by the European red fox: how effective are uncoordinated and isolated control programs? Eur J Wildl Res. 2014;60: 749–757. doi: 10.1007/s10344-014-0844-x

7. Lieury N, Ruette S, Devillard S, Albaret M, Drouyer F, Baudoux B, et al. Compensatory immigration challenges predator control: An experimental evidence-based approach improves management. J Wildl Manag. 2015;79: 425–434. doi: 10.1002/jwmg.850

8. Gervasi V, Brøseth H, Nilsen EB, Ellegren H, Flagstad Ø, Linnell JDC. Compensatory immigration counteracts contrasting conservation strategies of wolverines (Gulo gulo) within Scandinavia. Biol Conserv. 2015;191: 632–639. doi: 10.1016/j.biocon.2015.07.024

9. Minnie L, Gaylard A, Kerley GIH. Compensatory life-history responses of a mesopredator may undermine carnivore management efforts. J Appl Ecol. 2016;53: 379–387. doi: 10.1111/1365-2664.12581

10. Reynolds JC, Richardson SM, Rodgers BJE, Rodgers ORK. Effective control of non-native American mink by strategic trapping in a river catchment in mainland Britain. J Wildl Manag. 2013;77: 545–554. doi: 10.1002/jwmg.500 23761940

11. Reynolds JC, Tapper SC. Control of mammalian predators in game management and conservation. Mammal Rev. 1996;26: 127–156.

12. Defra. Determining the extent of use and humaneness of snares in England and Wales. Report submitted to Defra. 2012. Available: http://randd.defra.gov.uk/Document.aspx?Document_9872_wm0315-humaneness-snares.pdf

13. Heydon MJ, Reynolds JC. Demography of rural foxes (Vulpes vulpes) in relation to cull intensity in three contrasting regions of Britain. J Zool. 2000;251: 265–276.

14. Heydon MJ, Reynolds JC, Short MJ. Variation in abundance of foxes (Vulpes vulpes) between three regions of rural Britain, in relation to landscape and other variables. J Zool. 2000;251: 253–264.

15. Baker PJ, Harris S. The fox. 4th ed. In: Harris S, Yalden DW, editors. Mammals of the British Isles: Handbook. 4th ed. Southampton, UK: The Mammal Society; 2008. pp. 407–423.

16. Devenish-Nelson ES, Harris S, Soulsbury CD, Richards SA, Stephens PA. Demography of a carnivore, the red fox, Vulpes vulpes: what have we learnt from 70 years of published studies? Oikos. 2013;122: 705–716.

17. Porteus TA, Reynolds JC, McAllister MK. Quantifying the rate of replacement by immigration during restricted-area control of red fox in different landscapes. Wildl Biol. 2018;2018: wlb.00416. doi: 10.2981/wlb.00416

18. Reynolds JC. Fox control in the countryside. Fordingbridge, UK: The Game Conservancy Trust; 2000.

19. Kämmerle J-L, Corlatti L, Harms L, Storch I. Methods for assessing small-scale variation in the abundance of a generalist mesopredator. PLOS ONE. 2018;13: e0207545. doi: 10.1371/journal.pone.0207545 30462707

20. Sollmann R, Mohamed A, Samejima H, Wilting A. Risky business or simple solution–Relative abundance indices from camera-trapping. Biol Conserv. 2013;159: 405–412. doi: 10.1016/j.biocon.2012.12.025

21. Rowcliffe JM, Field J, Turvey ST, Carbone C. Estimating animal density using camera traps without the need for individual recognition. J Appl Ecol. 2008;45: 1228–1236.

22. Arnold SF. Mathematical statistics. Englewood Cliffs, N.J: Prentice-Hall; 1990.

23. Harding EK, Doak DF, Albertson JD. Evaluating the effectiveness of predator control: the non-native red fox as a case study. Conserv Biol. 2001;15: 1114–1122.

24. McLeod SR, Saunders G. Fertility control is much less effective than lethal baiting for controlling foxes. Ecol Model. 2014;273: 1–10. doi: 10.1016/j.ecolmodel.2013.10.016

25. Kéry M, Schaub M. State-Space Models for Population Counts. In: Kéry M, Schaub M, editors. Bayesian Population Analysis using WinBUGS: A Hierarchical Perspective. Boston, MA, USA: Academic Press; 2012. pp. 115–132.

26. Newman KB, Buckland ST, Morgan BJT, King R, Borchers DL, Cole DJ, et al. Modelling population dynamics: model formulation, fitting and assessment using state-space methods. 2014. Available: http://dx.doi.org/10.1007/978-1-4939-0977-3

27. Bolker BM. Ecological Models and Data in R. Princeton, NJ, USA: Princeton University Press; 2008.

28. Lebreton J-D, Gimenez O. Detecting and estimating density dependence in wildlife populations. J Wildl Manag. 2013;77: 12–23. doi: 10.1002/jwmg.425

29. Buckland ST, Newman KB, Fernández C, Thomas L, Harwood J. Embedding population dynamics models in inference. Stat Sci. 2007;22: 44–58.

30. Walsh AL, Harris S. Foraging habitat preferences of vespertilionid bats in Britain. J Appl Ecol. 1996;33: 508–518.

31. Reynolds JC, Goddard HN, Brockless MH. The impact of local fox (Vulpes vulpes) removal on fox populations at two sites in southern England. Gibier Faune Sauvage. 1993;10: 319–334.

32. Leslie PH, Davis DHS. An attempt to determine the absolute number of rats on a given area. J Anim Ecol. 1939;8: 94–113.

33. DeLury DB. On the estimation of biological populations. Biometrics. 1947;3: 145–167. 18902271

34. Hilborn R, Walters CJ. Quantitative fisheries stock assessment: choice, dynamics & uncertainty. New York, NY, USA: Chapman & Hall Inc.; 1992.

35. Robert M, Faraj A, McAllister MK, Rivot E. Bayesian state-space modelling of the De Lury depletion model: strengths and limitations of the method, and application to the Moroccan octopus fishery. ICES J Mar Sci J Cons. 2010;67: 1272–1290. doi: 10.1093/icesjms/fsq020

36. Macdonald DW, Reynolds JC. Red Fox Vulpes vulpes. In: Sillero-Zubiri C, Hoffmann M, Macdonald DW, editors. Canids: Foxes, Wolves, Jackals and Dogs Status Survey and Conservation Action Plan. Gland, Switzerland, and Cambridge, UK: IUCN/SSC Canid Specialist Group; 2004. pp. 129–136.

37. Lloyd HG. The Red Fox. London, UK: Batsford; 1980.

38. Bacon PJ. Discrete time temporal models of rabies. In: Bacon PJ, editor. Population Dynamics of Rabies in Wildlife. London, UK: Academic Press; 1985. pp. 147–196.

39. Smith DW. A continuous time deterministic model of temporal rabies. In: Bacon PJ, editor. Population Dynamics of Rabies in Wildlife. London, UK: Academic Press; 1985. pp. 131–146.

40. Hostetler JA, Chandler RB. Improved state-space models for inference about spatial and temporal variation in abundance from count data. Ecology. 2015;96: 1713–1723. doi: 10.1890/14-1487.1

41. Hobbs NT, Hooten MB. Bayesian Models: A Statistical Primer for Ecologists. Princeton, NJ, USA: Princeton University Press; 2015.

42. Thorson JT, Ono K, Munch SB. A Bayesian approach to identifying and compensating for model misspecification in population models. Ecology. 2014;95: 329–341. doi: 10.1890/13-0187.1 24669727

43. Holling CS. Some characteristics of simple types of predation and parasitism. Can Entomol. 1959;91: 385–398.

44. Porteus TA, Reynolds JC, McAllister MK. Modelling the rate of successful search of red foxes during population control. Wildl Res. 2019;46: 285–295. doi: 10.1071/WR18025

45. Knape J, Jonzén N, Sköld M. On observation distributions for state space models of population survey data. J Anim Ecol. 2011;80: 1269–1277. doi: 10.1111/j.1365-2656.2011.01868.x 21635251

46. Porteus TA, Reynolds JC, McAllister MK. Establishing Bayesian priors for natural mortality rate in carnivore populations. J Wildl Manag. 2018;82: 1645–1657. doi: 10.1002/jwmg.21543

47. Soulsbury CD, Iossa G, Baker PJ, Cole NC, Funk SM, Harris S. The impact of sarcoptic mange Sarcoptes scabiei on the British fox Vulpes vulpes population. Mammal Rev. 2007;37: 278–296.

48. Trewby ID, Wilson GJ, Delahay RJ, Walker N, Young RP, Davison J, et al. Experimental evidence of competitive release in sympatric carnivores. Biol Lett. 2008;4: 170–172. doi: 10.1098/rsbl.2007.0516 18089523

49. Spiegelhalter DJ, Thomas A, Best NG, Lunn DJ. WinBUGS. Cambridge, UK: Medical Research Council Biostatistics Unit; 2007. Available: https://www.mrc-bsu.cam.ac.uk/software/bugs/the-bugs-project-winbugs/

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

51. Sturtz S, Ligges U, Gelman A. R2WinBUGS: a package for running WinBUGS from R. J Stat Softw. 2005;12: 1–16.

52. Meyer R, Millar RB. BUGS in Bayesian stock assessments. Can J Fish Aquat Sci. 1999;56: 1078–1086.

53. Plummer M, Best N, Cowles K, Vines K. CODA: Convergence Diagnosis and Output Analysis for MCMC. R News. 2006;6: 7–11.

54. Gelman A, Carlin JB, Stern HS, Rubin DB. Bayesian Data Analysis. 2nd ed. London, UK: Chapman & Hall; 2004.

55. McAllister MK. A generalized Bayesian surplus production stock assessment software (BSP2). ICCAT Int Comm Conserv Atl Tunas Collect Vol Sci Pap. 2014;70: 1725–1757.

56. Gimenez O, Viallefont A, Catchpole EA, Choquet R, Morgan BJT. Methods for investigating parameter redundancy. Anim Biodivers Conserv. 2004;27: 561–572.

57. Trenkel VM. A two-stage biomass random effects model for stock assessment without catches: What can be estimated using only biomass survey indices? Can J Fish Aquat Sci. 2008;65: 1024–1035. doi: 10.1139/F08-028

58. Schnute JT. A general framework for developing sequential fisheries models. Can J Fish Aquat Sci. 1994;51: 1676–1688. doi: 10.1139/f94-168

59. Auger-Méthé M, Field C, Albertsen CM, Derocher AE, Lewis MA, Jonsen ID, et al. State-space models’ dirty little secrets: even simple linear Gaussian models can have estimation problems. Sci Rep. 2016;6: 26677. doi: 10.1038/srep26677 27220686

60. Magnusson A, Hilborn R. What makes fisheries data informative? Fish Fish. 2007;8: 337–358.

61. McAllister MK, Starr PJ, Restrepo VR, Kirkwood GP. Formulating quantitative methods to evaluate fishery-management systems: what fishery processes should be modelled and what trade-offs should be made? ICES J Mar Sci. 1999;56: 900–916.

62. Rademeyer RA, Plagányi ÉE, Butterworth DS. Tips and tricks in designing management procedures. ICES J Mar Sci J Cons. 2007;64: 618–625. doi: 10.1093/icesjms/fsm050

63. Harris S, Trewhella WJ. An analysis of some of the factors affecting dispersal in an urban fox (Vulpes vulpes) population. J Appl Ecol. 1988;25: 409–422.

64. McAllister MK, Kirkwood GP. Using a Bayesian decision analysis to help achieve a precautionary approach for managing developing fisheries. Can J Fish Aquat Sci. 1998;55: 2642–2661.

65. Boatman ND, Brockless MH. The Allerton Project: farmland management for partridges (Perdix perdix, Alectoris rufa) and pheasants (Phasianus colchicus). Gibier Faune Sauvage. 1998;15: 563–574.

66. Kämmerle J-L, Niekrenz S, Storch I. No evidence for spatial variation in predation risk following restricted-area fox culling. BMC Ecol. 2019;19: 17. doi: 10.1186/s12898-019-0235-y 31023268

67. Dunham K, Grand JB. Effects of model complexity and priors on estimation using sequential importance sampling/resampling for species conservation. Ecol Model. 2016;340: 28–36. doi: 10.1016/j.ecolmodel.2016.08.010

68. Conn PB, Diefenbach DR, Laake JL, Ternent MA, White GC. Bayesian Analysis of Wildlife Age-at-Harvest Data. Biometrics. 2008;64: 1170–1177. doi: 10.1111/j.1541-0420.2008.00987.x 18266894

69. Gauthier G, Besbeas P, Lebreton J-D, Morgan BJT. Population growth in snow geese: a modeling approach integrating demographic and survey information. Ecology. 2007;88: 1420–1429. doi: 10.1890/06-0953 17601135

70. Scroggie MP, Forsyth DM, McPhee SR, Matthews J, Stuart IG, Stamation KA, et al. Invasive prey controlling invasive predators? European rabbit abundance does not determine red fox population dynamics. J Appl Ecol. 2018;55: 2621–2631. doi: 10.1111/1365-2664.13253

71. McAllister MK, Hill SL, Agnew DJ, Kirkwood GP, Beddington JR. A Bayesian hierarchical formulation of the De Lury stock assessment model for abundance estimation of Falkland Islands’ squid (Loligo gahi). Can J Fish Aquat Sci. 2004;61: 1048–1059.

72. Hilborn R, Liermann M. Standing on the shoulders of giants: learning from experience in fisheries. Rev Fish Biol Fish. 1998;8: 273–283.

73. McDonald RA, Harris S. The use of trapping records to monitor populations of stoats Mustela erminea and weasels M. nivalis: the importance of trapping effort. J Appl Ecol. 1999;36: 679–688.

74. Sadlier LMJ, Webbon CC, Baker PJ, Harris S. Methods of monitoring red foxes Vulpes vulpes and badgers Meles meles: are field signs the answer? Mammal Rev. 2004;34: 75–98.

75. Stoate C, Brockless MH, Boatman ND. A multifunctional approach to bird conservation on farmland: a ten-year appraisal. Asp Appl Biol. 2002;67: 191–196.

76. Stoate C, Leake AR. Where the Birds Sing. The Allerton Project: 10 Years of Conservation on Farmland. Fordingbridge, UK: The Game Conservancy Trust with Allerton Research & Educational Trust; 2002.

77. Reynolds JC, Stoate C, Brockless MH, Aebischer NJ, Tapper SC. The consequences of predator control for brown hares (Lepus europaeus) on UK farmland. Eur J Wildl Res. 2010;56: 541–549.

78. White PJC, Stoate C, Szczur J, Norris K. Predator reduction with habitat management can improve songbird nest success. J Wildl Manag. 2014;78: 402–412. doi: 10.1002/jwmg.687

79. Webbon CC, Baker PJ, Harris S. Faecal density counts for monitoring changes in red fox numbers in rural Britain. J Appl Ecol. 2004;41: 768–779.

80. Lloyd HG, Englund J. The reproductive cycle of the red fox in Europe. J Reprod Fertil Suppl. 1973;19: 119–130. 4522367

81. Kolb HH, Hewson R. A study of fox populations in Scotland from 1971 to 1976. J Appl Ecol. 1980;17: 7–19.

82. Ahrestani FS, Hebblewhite M, Post E. The importance of observation versus process error in analyses of global ungulate populations. Sci Rep. 2013;3: 3125. doi: 10.1038/srep03125 24201239

83. Guthery FS, Shaw JH. Density dependence: applications in wildlife management. J Wildl Manag. 2013;77: 33–38. doi: 10.1002/jwmg.450

84. Gilpin ME, Ayala FJ. Global models of growth and competition. Proc Natl Acad Sci. 1973;70: 3590–3593. doi: 10.1073/pnas.70.12.3590 4519647

85. Sibly RM, Barker D, Denham MC, Hone J, Pagel M. On the regulation of populations of mammals, birds, fish and insects. Science. 2005;309: 607–610. doi: 10.1126/science.1110760 16040705

86. Clark F, Brook BW, Delean S, Reşit Akçakaya H, Bradshaw CJA. The theta-logistic is unreliable for modelling most census data. Methods Ecol Evol. 2010;1: 253–262.

87. Abadi F, Gimenez O, Arlettaz R, Schaub M. An assessment of integrated population models: bias, accuracy, and violation of the assumption of independence. Ecology. 2010;91: 7–14. doi: 10.1890/08-2235.1 20380189

88. Schaub M, Abadi F. Integrated population models: a novel analysis framework for deeper insights into population dynamics. J Ornithol. 2011;152: 227–237. doi: 10.1007/s10336-010-0632-7


Článek vyšel v časopise

PLOS One


2019 Číslo 11