An improved understanding of ungulate population dynamics using count data: Insights from western Montana


Autoři: J. Terrill Paterson aff001;  Kelly Proffitt aff002;  Jay Rotella aff001;  Robert Garrott aff001
Působiště autorů: Department of Ecology, Montana State University, Bozeman, Montana, United States of America aff001;  Montana Department of Fish, Wildlife and Parks, Bozeman, Montana, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226492

Souhrn

Understanding the dynamics of ungulate populations is critical given their ecological and economic importance. In particular, the ability to evaluate the evidence for potential drivers of variation in population trajectories is important for informed management. However, the use of age ratio data (e.g., juveniles:adult females) as an index of variation in population dynamics is hindered by a lack of statistical power and difficult interpretation. Here, we show that the use of a population model based on count, classification and harvest data can dramatically improve the understanding of ungulate population dynamics by: 1) providing estimates of vital rates (e.g., per capita recruitment and population growth) that are easier to interpret and more useful to managers than age ratios and 2) increasing the power to assess potential sources of variation in key vital rates. We used a time series of elk (Cervus canadensis) spring count and classification data (2004 to 2016) and fall harvest data from hunting districts in western Montana to construct a population model to estimate vital rates and assess evidence for an association between a series of environmental covariates and indices of predator abundance on per capita recruitment rates of elk calves. Our results suggest that per capita recruitment rates were negatively associated with cold and wet springs, and severe winters, and positively associated with summer precipitation. In contrast, an analysis of the raw age ratio data failed to detect these relationships. Our approach based on a population model provided estimates of the region-wide mean per capita recruitment rate (mean = 0.25, 90% CI = 0.21, 0.29), temporal variation in hunting-district-specific recruitment rates (minimum = 0.09; 90% CI = [0.07, 0.11], maximum = 0.43; 90% CI = [0.38, 0.48]), and annual population growth rates (minimum = 0.83; 90% CI = [0.78, 0.87], maximum = 1.20; 90% CI = [1.11, 1.29]). We recommend using routinely collected population count and classification data and a population modeling approach rather than interpreting estimated age ratios as a substantial improvement in understanding population dynamics.

Klíčová slova:

Bears – Data management – Population dynamics – Population growth – Predation – Snow – Spring – Wolves


Zdroje

1. Gaillard J-M, Festa-Bianchet M, Yoccoz NG, Loison A, Toïgo C. Temporal variation in fitness components and population dynamics of large herbivores. Annu Rev Ecol Syst. 2000;31: 367–393. doi: 10.1146/annurev.ecolsys.31.1.367

2. Griffin KA, Hebblewhite M, Robinson HS, Zager P, Barber‐Meyer SM, Christianson D, et al. Neonatal mortality of elk driven by climate, predator phenology and predator community composition. J Anim Ecol. 2011;80: 1246–1257. doi: 10.1111/j.1365-2656.2011.01856.x 21615401

3. Brodie J, Johnson H, Mitchell M, Zager P, Proffitt K, Hebblewhite M, et al. Relative influence of human harvest, carnivores, and weather on adult female elk survival across western North America. J Appl Ecol. 2013;50: 295–305. doi: 10.1111/1365-2664.12044

4. Gordon IJ, Hester AJ, Festa‐Bianchet M. The management of wild large herbivores to meet economic, conservation and environmental objectives. J Appl Ecol. 2004;41: 1021–1031. doi: 10.1111/j.0021-8901.2004.00985.x

5. Vors LS, Boyce MS. Global declines of caribou and reindeer. Glob Change Biol. 2009;15: 2626–2633. doi: 10.1111/j.1365-2486.2009.01974.x

6. Middleton AD, Kauffman MJ, McWhirter DE, Cook JG, Cook RC, Nelson AA, et al. Animal migration amid shifting patterns of phenology and predation: lessons from a Yellowstone elk herd. Ecology. 2013;94: 1245–1256. doi: 10.1890/11-2298.1 23923485

7. Lukacs PM, Mitchell MS, Hebblewhite M, Johnson BK, Johnson H, Kauffman M, et al. Factors influencing elk recruitment across ecotypes in the Western United States. J Wildl Manag. 2018;82: 698–710. doi: 10.1002/jwmg.21438

8. Berger J. The last mile: how to sustain long-distance migration in mammals. Conserv Biol. 2004;18: 320–331. doi: 10.1111/j.1523-1739.2004.00548.x

9. Toweill DE, Thomas JW. North American elk: ecology and management. Smithsonian Institution Press; 1982.

10. Barber-Meyer SM, Mech LD, White PJ. Elk calf survival and mortality following wolf restoration to Yellowstone National Park. Wildl Monogr. 2008;169: 1–30. doi: 10.2193/2008-004

11. Morellet N, Gaillard J-M, Hewison AJM, Ballon P, Boscardin Y, Duncan P, et al. Indicators of ecological change: new tools for managing populations of large herbivores. J Appl Ecol. 2007;44: 634–643. doi: 10.1111/j.1365-2664.2007.01307.x

12. Apollonio M, Belkin VV, Borkowski J, Borodin OI, Borowik T, Cagnacci F, et al. Challenges and science-based implications for modern management and conservation of European ungulate populations. Mammal Res. 2017;62: 209–217.

13. Johnson HE, Mills LS, Stephenson TR, Wehausen JD. Population-specific vital rate contributions influence management of an endangered ungulate. Ecol Appl. 2010;20: 1753–1765. doi: 10.1890/09-1107.1 20945773

14. Pfister CA. Patterns of variance in stage-structured populations: Evolutionary predictions and ecological implications. Proc Natl Acad Sci. 1998;95: 213–218. doi: 10.1073/pnas.95.1.213 9419355

15. Gaillard J-M, Yoccoz NG. Temporal variation in survival of mammals: a case of environmental canalization? Ecology. 2003;84: 3294–3306.

16. Péron G, Gaillard J-M, Barbraud C, Bonenfant C, Charmantier A, Choquet R, et al. Evidence of reduced individual heterogeneity in adult survival of long-lived species. Evolution. 2016;70: 2909–2914. doi: 10.1111/evo.13098 27813056

17. Jäkäläniemi A, Ramula S, Tuomi J. Variability of important vital rates challenges the demographic buffering hypothesis. Evol Ecol. 2013;27: 533–545. doi: 10.1007/s10682-012-9606-y

18. Gaillard J-M, Festa-Bianchet M, Yoccoz NG. Population dynamics of large herbivores: variable recruitment with constant adult survival. Trends Ecol Evol. 1998;13: 58–63. doi: 10.1016/s0169-5347(97)01237-8 21238201

19. Raithel JD, Kauffman MJ, Pletscher DH. Impact of spatial and temporal variation in calf survival on the growth of elk populations. J Wildl Manag. 2007;71: 795–804. doi: 10.2193/2005-608

20. Eacker DR, Lukacs PM, Proffitt KM, Hebblewhite M. Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling. Ecol Appl. 2017;27: 1280–1293. doi: 10.1002/eap.1521 28188660

21. White CG, Zager P, Gratson MW. Influence of Predator Harvest, Biological Factors, and Landscape on Elk Calf Survival in Idaho. J Wildl Manag. 2010;74: 355–370. doi: 10.2193/2007-506

22. Harris NC, Kauffman MJ, Mills LS. Inferences about ungulate population dynamics derived from age ratios. J Wildl Manag. 2008;72: 1143–1151. doi: 10.2193/2007-277

23. Caughley G. Interpretation of Age Ratios. J Wildl Manag. 1974;38: 557–562. doi: 10.2307/3800890

24. Bender LC. Uses of herd composition and age ratios in ungulate management. Wildl Soc Bull. 2006;34: 1225–1230. doi: 10.2193/0091-7648(2006)34[1225:UOHCAA]2.0.CO;2

25. Downing RL, Michael ED, Poux RJ. Accuracy of sex and age ratio counts of white-tailed deer. J Wildl Manag. 1977;41: 709–714. doi: 10.2307/3799993

26. Bonenfant C, Gaillard J-M, Klein F, Hamann J-L. Can we use the young: female ratio to infer ungulate population dynamics? An empirical test using red deer Cervus elaphus as a model. J Appl Ecol. 2005;42: 361–370. doi: 10.1111/j.1365-2664.2005.01008.x

27. de Valpine P, Hastings A. Fitting population models incorporating process noise and observation error. Ecol Monogr. 2002;72: 57–76. doi: 10.2307/3100085

28. Maunder MN, Watters GM. A general framework for integrating environmental time series into stock assessment models: model description, simulation testing, and example. Fish Bull. 2003;101: 89–99.

29. Link WA, Nichols JD. On the importance of sampling variance to investigations of temporal variation in animal population size. Oikos. 1994;69: 539–544. doi: 10.2307/3545869

30. Maunder MN, Deriso RB, Hanson CH. Use of state-space population dynamics models in hypothesis testing: advantages over simple log-linear regressions for modeling survival, illustrated with application to longfin smelt (Spirinchus thaleichthys). Fish Res. 2015;164: 102–111. doi: 10.1016/j.fishres.2014.10.017

31. de Valpine P. Better inferences from population-dynamics experiments using monte carlo state-space likelihood methods. Ecology. 2003;84: 3064–3077. doi: 10.1890/02-0039

32. Kery M, Gardner B, Stoeckle T, Weber D, Royle JA. Use of spatial capture-recapture modeling and DNA data to estimate densities of elusive animals. Conserv Biol. 2011;25: 356–364. doi: 10.1111/j.1523-1739.2010.01616.x 21166714

33. Buckland ST, Newman KB, Thomas L, Koesters NB. State-space models for the dynamics of wild animal populations. Ecol Model. 2004;171: 157–175. doi: 10.1016/j.ecolmodel.2003.08.002

34. Newman KB, Buckland ST, Lindley ST, Thomas L, Fernández C. Hidden process models for animal population dynamics. Ecol Appl. 2006;16: 74–86. doi: 10.1890/04-0592 16705962

35. Kéry M, Schaub M. Bayesian population analysis using WinBUGS: a hierarchical perspective. Academic Press; 2011.

36. Link WA, Royle JA, Hatfield JS. Demographic analysis from summaries of an age-structured population. Biometrics. 2003;59: 778–785. doi: 10.1111/j.0006-341x.2003.00091.x 14969455

37. Bonenfant C, Gaillard J-M, Klein F, Loison A. Sex- and age-dependent effects of population density on life history traits of red deer (Cervus elaphus) in a temperate forest. Ecography. 2002;25: 446–458. doi: 10.1034/j.1600-0587.2002.250407.x

38. Cook JG, Johnson BK, Cook RC, Riggs RA, Delcurto T, Bryant LD, et al. Effects of summer-autumn nutrition and parturition date on reproduction and survival of elk. Wildl Monogr. 2004;155: 1–61. doi: 10.2193/0084-0173(2004)155[1:EOSNAP]2.0.CO;2

39. Bender LC, Carlson E, Schmitt SM, Haufler JB. Production and survival of elk (Cervus elaphus) calves in michigan. Am Midl Nat. 2002;148: 163–171.

40. Garrott RA, Eberhardt LL, White PJ, Rotella J. Climate-induced variation in vital rates of an unharvested large-herbivore population. Can J Zool. 2003;81: 33–45. doi: 10.1139/z02-218

41. Cook JG, Quinlan LJ, Irwin LL, Bryant LD, Riggs RA, Thomas JW. Nutrition-growth relations of elk calves during late summer and fall. J Wildl Manag. 1996;60: 528–541. doi: 10.2307/3802070

42. Hurley MA, Hebblewhite, Mark, Gaillard, Jean-Michel, Dray, Stéphane, Taylor, Kyle A., Smith W. K., et al. Functional analysis of Normalized Difference Vegetation Index curves reveals overwinter mule deer survival is driven by both spring and autumn phenology. Philos Trans R Soc B Biol Sci. 2014;369: 20130196. doi: 10.1098/rstb.2013.0196 24733951

43. Loison A, Langvatn R. Short- and long-term effects of winter and spring weather on growth and survival of red deer in Norway. Oecologia. 1998;116: 489–500. doi: 10.1007/s004420050614 28307518

44. Eacker DR, Hebblewhite M, Proffitt KM, Jimenez BS, Mitchell MS, Robinson HS. Annual elk calf survival in a multiple carnivore system. J Wildl Manag. 2016;80: 1345–1359. doi: 10.1002/jwmg.21133

45. Mech LD, Smith DW, Murphy KM, MacNulty DR. Winter severity and wolf predation on a formerly wolf-free elk herd. J Wildl Manag. 2001; 998–1003.

46. Hebblewhite M. Predation by wolves interacts with the North Pacific Oscillation (NPO) on a western North American elk population. J Anim Ecol. 2005;74: 226–233. doi: 10.1111/j.1365-2656.2004.00909.x

47. Loison A, Festa-Bianchet M, Gaillard J-M, Jorgenson JT, Jullien J-M. Age-specific survival in five populations of ungulates: evidence of senescence. Ecology. 1999;80: 2539–2554.

48. Lubow BC, Smith BL. Population dynamics of the Jackson elk herd. J Wildl Manag. 2004;68: 810–829. doi: 10.2193/0022-541X(2004)068[0810:PDOTJE]2.0.CO;2

49. Harrison XA. Using observation-level random effects to model overdispersion in count data in ecology and evolution. PeerJ. 2014;2: e616. doi: 10.7717/peerj.616 25320683

50. Caughley G. Sampling in aerial survey. J Wildl Manag. 1977;41: 605–615. doi: 10.2307/3799980

51. Nilsen EB, Strand O. Integrating data from multiple sources for insights into demographic processes: Simulation studies and proof of concept for hierarchical change-in-ratio models. PLOS ONE. 2018;13: e0194566. doi: 10.1371/journal.pone.0194566 29596430

52. Daly C, Neilson RP, Phillips DL. A statistical-topographic model for mapping climatological precipitation over mountainous terrain. J Appl Meteorol. 1994;33: 140–158.

53. Pettorelli N, Ryan S, Mueller T, Bunnefeld N, Jędrzejewska B, Lima M, et al. The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Clim Res. 2011;46: 15–27. doi: 10.3354/cr00936

54. MOD09Q1 MODIS/Terra Surface Reflectance 8-Day L3 Global 250m SIN Grid V006. 2015 [cited 2 Aug 2019]. doi: 10.5067/modis/mod09q1.006

55. Chen J, Jönsson P, Tamura M, Gu Z, Matsushita B, Eklundh L. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens Environ. 2004;91: 332–344.

56. Bradley BA, Jacob RW, Hermance JF, Mustard JF. A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sens Environ. 2007;106: 137–145.

57. Ruimy A, Saugier B, Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely sensed data. J Geophys Res Atmospheres. 1994;99: 5263–5283.

58. Jönsson P, Eklundh L. TIMESAT—a program for analyzing time-series of satellite sensor data. Comput Geosci. 2004;30: 833–845. doi: 10.1016/j.cageo.2004.05.006

59. National Operational Hydrologic Remote Sensing Center. Snow Data Assimilation System (SNODAS) Data Products at NSIDC, 2005–2016. Boulder, Colorado, USA; 2004.

60. White PJ, Garrott RA. Northern Yellowstone elk after wolf restoration. Wildl Soc Bull. 2005;33: 942–955. doi: 10.2193/0091-7648(2005)33[942:NYEAWR]2.0.CO;2

61. Hooten MB, Hobbs NT. A guide to Bayesian model selection for ecologists. Ecol Monogr. 2015;85: 3–28. doi: 10.1890/14-0661.1

62. Plummer M. JAGS version 4.3. 0 user manual [Software manual]. 2017.

63. Denwood MJ. runjags: an R Package providing interface utilities, model templates, parallel computing methods and additional distributions for MCMC models in JAGS. J Stat Softw. 2016;71: 1–25. doi: 10.18637/jss.v071.i09

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

65. Gelman A, Shalizi CR. Philosophy and the practice of Bayesian statistics. Br J Math Stat Psychol. 2013;66: 8–38. doi: 10.1111/j.2044-8317.2011.02037.x 22364575

66. Proffitt KM, Cunningham JA, Hamlin KL, Garrott RA. Bottom-up and top-down influences on pregnancy rates and recruitment of northern Yellowstone elk. J Wildl Manag. 2014;78: 1383–1393. doi: 10.1002/jwmg.792

67. Adams LG, Singer FJ, Dale BW. Caribou calf mortality in Denali National Park, Alaska. J Wildl Manag. 1995;59: 584–594. doi: 10.2307/3802467

68. Tveraa T, Fauchald P, Henaug C, Yoccoz NG. An examination of a compensatory relationship between food limitation and predation in semi-domestic reindeer. Oecologia. 2003;137: 370–376. doi: 10.1007/s00442-003-1373-6 12955491

69. Onillon B, Durand J-L, Gastal F, Tournebize R. Drought effects on growth and carbon partitioning in a tall fescue sward grown at different rates of nitrogen fertilization. Eur J Agron. 1995;4: 91–99. doi: 10.1016/S1161-0301(14)80020-8

70. MacKlon AES, MacKie-Dawson LA, Shand CA, Sim A. Soil water effects on growth and nutrition in upland pastures. J Range Manag. 1996;49: 251–256. doi: 10.2307/4002887

71. Yang J, Zhang J, Wang Z, Zhu Q, Liu L. Water deficit–induced senescence and its relationship to the remobilization of pre-stored carbon in wheat during grain filling. Agron J. 2001;93: 196–206. doi: 10.2134/agronj2001.931196x

72. Blanchard P, Festa-Bianchet M, Gaillard J-M, Jorgenson JT. A test of long-term fecal nitrogen monitoring to evaluate nutritional status in bighorn sheep. J Wildl Manag. 2003;67: 477. doi: 10.2307/3802705

73. Tollefson TN, Shipley LA, Myers WL, Dasgupta N. Forage quality’s influence on mule deer fawns. J Wildl Manag. 2011;75: 919–928. doi: 10.1002/jwmg.113

74. Fryxell JM. Forage quality and aggregation by large herbivores. Am Nat. 1991;138: 478–498. doi: 10.1086/285227

75. Hebblewhite M, Merrill E, McDermid G. A multi-scale test of the forage maturation hypothesis in a partially migratory ungulate population. Ecol Monogr. 2008;78: 141–166. doi: 10.1890/06-1708.1

76. Johnson HE, Gustine DD, Golden TS, Adams LG, Parrett LS, Lenart EA, et al. NDVI exhibits mixed success in predicting spatiotemporal variation in caribou summer forage quality and quantity. Ecosphere. 2018;9: e02461. doi: 10.1002/ecs2.2461

77. Hamel S, Garel M, Festa‐Bianchet M, Gaillard J-M, Côté SD. Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates. J Appl Ecol. 2009;46: 582–589. doi: 10.1111/j.1365-2664.2009.01643.x

78. Merkle JA, Monteith KL, Aikens EO, Hayes MM, Hersey, Middleton AD, et al. Large herbivores surf waves of green-up during spring. Proc R Soc B Biol Sci. 2016;283: 20160456. doi: 10.1098/rspb.2016.0456 27335416

79. Singer FJ, Harting A, Symonds KK, Coughenour MB. Density dependence, compensation, and environmental effects on elk calf mortality in Yellowstone National Park. J Wildl Manag. 1997;61: 12–25. doi: 10.2307/3802410

80. Boutin S. Predation and moose population dynamics: a critique. J Wildl Manag. 1992; 116–127.

81. Wolfe ML, Gese EM, Terletzky P, Stoner DC, Aubry LM. Evaluation of harvest indices for monitoring cougar survival and abundance. J Wildl Manag. 2016;80: 27–36. doi: 10.1002/jwmg.985

82. Clark TW, Curlee AP, Reading RP. Crafting effective solutions to the large carnivore conservation problem. Conserv Biol. 1996;10: 940–948. doi: 10.1046/j.1523-1739.1996.10040940.x

83. Bruskotter JT. The predator pendulum revisited: Social conflict over wolves and their management in the western United States. Wildl Soc Bull. 2013;37: 674–679. doi: 10.1002/wsb.293

84. Young JK, Ma Z, Laudati A, Berger J. Human–carnivore interactions: lessons learned from communities in the American west. Hum Dimens Wildl. 2015;20: 349–366. doi: 10.1080/10871209.2015.1016388

85. Fuller T, Sievert PR. Carnivore demography and the consequences of changes in prey availability. 2001; 163–178.

86. Garrott RA, White PJ, Rotella JJ. The Madison Headwaters elk herd: transitioning from bottom–up regulation to top–down limitation. In: Garrott RA, White PJ, Watson FGR, editors. Terrestrial Ecology. Elsevier; 2008. pp. 489–517. doi: 10.1016/S1936-7961(08)00223-6


Článek vyšel v časopise

PLOS One


2019 Číslo 12