Likelihood based population viability analysis in the presence of observation error

Oikos

Volumn 121, Issue 10, 2012, Pages 1656-1664

  Nadeem, Khurram ^a   Lele, Subhash R ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

AKAIKE INFORMATION CRITERION; DISPERSAL; EXTINCTION RISK; GROWTH MODELING; POPULATION SIZE; POPULATION VIABILITY ANALYSIS; SONGBIRD; SPECIES CONSERVATION; STOCHASTICITY; TIME SERIES;

BRITISH COLUMBIA; CANADA; MANDARTE ISLAND;

MELOSPIZA; MELOSPIZA MELODIA;

EID: 84866144669     PISSN: 00301299     EISSN: 16000706     Source Type: Journal    
DOI: 10.1111/j.1600-0706.2011.20010.x     Document Type: Article

References (65)

1. Akçakaya H. R. 2000. Population viability analyses with demographically and spatially structured models. Ecol. Bull. 48: 23-38.
2. Akçakaya H. R. and Raphael M. G. 1998. Assessing human impact despite uncertainty: viability of the northern spotted owl metapopulation in the northwestern USA. Biodivers. Conserv. 7: 875-894.
3. Akçakaya H. R. et al. (eds) 2007. The role of metapopulations in conservation. - Blackwell.
4. Arcese P. and Marr A. B. 2006. Population viability in the presence and absence of cowbirds, catastrophic mortality, and immigration. - In: Smith J. N. M. et al. (eds), Conservation and biology of small populations: the song sparrows of Mandarte Island. Oxford Univ. Press, pp. 175-192.
5. Arcese P. et al. 1992. Stability, regulation, and the determination of abundance in an insular song sparrow population. Ecology 73: 805-822.
6. Barker D. and Sibly R. M. 2008. The effects of environmental perturbation and measurement error on estimates of the shape parameter in the theta-logistic model of population regulation. Ecol. Modell. 219: 170-177.
7. Beissinger S. R. 2002. Population viability analysis: past, present, future. - In: Beissinger S. R. and Maccullough D. R. (eds), Population viability analysis. Univ. of Chicago Press, pp. 5-17.
8. Burnham K. P. and Anderson D. R. 2004. Multimodel inference: understanding AIC and BIC in model selection. Soc. Meth. Res. 33: 261-304.
9. Clark F. L. et al. 2010. Theta-logistic is unreliable for modeling most census data. Meth. Ecol. Evol. 1: 253-262.
10. Clark J. S. and Bjørnstad O. N. 2004. Population time series: process variability, observation errors, missing values, lags and hidden states. Ecology 85: 3140-3150.
11. de Valpine P. 2002. Review of methods for fitting time-series models with process and observation error and likelihood calculations for nonlinear, non-Gaussian state-space models. Bull. Mar. Sci. 70: 455-471.
12. de Valpine P. and Hastings A. 2002. Fitting population models incorporating process noise and observation error. Ecol. Monogr. 72: 57-76.
13. Dennis B. and Taper M. L. 1994. Density dependence in time series observations of natural populations: estimation and testing. Ecol. Monogr. 64: 205-224.
14. Dennis B. and Otten M. R. M. 2000. Joint effects of density dependence and rainfall on abundance of San Joaquin kit fox. J. Wildlife Manage. 64: 388-400.
15. Dennis B. et al. 1991. Estimation of growth and extinction para meters for endangered species. Ecol. Monogr. 61: 115-143.
16. Dennis B. et al. 1998. Joint density dependence. Ecology 79: 426-441.
17. Dennis B. et al. 2006. Estimating density dependence, process noise, and observation error. Ecol. Monogr. 76: 323-341.
18. Dunning J. B. et al. 1995. Spatially explicit population models: current forms and future uses. Ecol. Appl. 5: 3-11.
19. Engen S. et al. 1998. Demographic and environmental stochasticity-concepts and definitions. Biometrics. 54: 840-846.
20. Freckleton R. P. et al. 2006. Census error and the detection of density dependence. J. Anim. Ecol. 75: 837-851.
21. Gilpin M. E. and Ayala F. J. 1973. Global models of growth and competition. Proc. Natl Acad. Sci. USA 70: 3590-3593.
22. Gompertz B. 1825. On the nature of the function expressive of the law of human mortality, and on a new mode of determining life contingencies. Phil. Trans. R. Soc. B 115: 513-583.
23. Grimm V. and Wissel C. 2004. The intrinsic mean time to extinction: a unifying approach to analysing persistence and viability of populations. Oikos 105: 501-511.
24. Groom M. J. and Pascual M. A. 1998. The analysis of population persistence: an outlook on the practice of viability ana lysis. - In: Fiedler P. L. and Kareiva P. M. (eds), Conservation biology: for the coming decade. Chapman and Hall, pp. 4-26.
25. Hamilton J. D. 1986. A standard error for the estimated state vector of a state-space model. J. Econ. 33: 387-397.
26. Harris I. R. 1989. Predictive fit for natural exponential families. Biometrika 76: 675-684.
27. Harvey A. C. 1993. Time series models, 2nd edn. - MIT Press.
28. Hassell M. P. et al. 1976. Patterns of dynamical behaviour in singlespecies populations. J. Anim. Ecol. 45: 471-486.
29. Henle K. et al. 2004. The role of density regulation and extinction processes in population viability analysis. Biodivers. Conserv. 13: 9-52.
30. Hochachka W. M. et al. (eds) 1989. Song sparrow. - Academic Press.
31. Jonzén N. et al. 2005. Of sheep and rain: large-scale population dynamics of the red kangaroo. J. Anim. Ecol. 74: 22-30.
32. Kitagawa G. 1987. Non-Gaussian state-space modeling of nonstationary time series (with discussion). J. Am. Stat. Ass. 82: 1032-1063.
33. Lele S. R. 2006. Sampling variability and estimates of density dependence: a composite-likelihood approach. Ecology 87: 189-202.
34. Lele S. R. et al. 1998. Statistical analysis of population dynamics in space and time using estimating functions. Ecology 79: 1489-1502.
35. Lele S. R. et al. 2007. Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecol. Lett. 10: 551-563.
36. Lele S. R. et al. 2010. Estimability and likelihood inference for generalized linear mixed models using data cloning. J. Am. Stat. Ass. 105: 1617-1625.
37. Ludwig D. 1999. Is it meaningful to estimate a probability of extinction? Ecology 80: 298-310.
38. Maynard-Smith J. and Slatkin M. 1973. The stability of predator-prey systems. Ecology 54: 384-391.
39. McGowan C. P. et al. 2011. Incorporating parametric uncertainty into population viability analysis models. Biol. Conserv. 144: 1400-1408.
40. Mills L. S. 2008. Conservation of wildlife populations: demography, genetics and management. - Blackwell.
41. Morris W. F. and Doak D. F. 2002. Quantitative conservation biology: theory and practice of population viability analysis. - Sinauer.
42. Newman K. B. et al. 2006. Hidden process models for animal population dynamics. Ecol. Appl. 16: 74-86.
43. Pascual M. A. et al. 1997. The influence of model structure on conclusions about the viability and harvesting of serengeti wilderbeest. Conserv. Biol. 611: 966-976.
44. Pederson M. W. et al. 2011. Estimation methods for non-linear stat-space modeling in ecology. Ecol. Modell. 22: 1394-1400.
45. Polansky L. et al. 2009. Likelihood ridges and multimodality in population growth rate models. Ecology 90: 2313-2320.
46. Ponciano J. M. et al. 2009. Hierarchical models in ecology: confidence intervals, hypothesis testing, and model selection using data cloning. Ecology 90: 356-362.
47. Ralls K. et al. 2002. Guidelines for using population viability analysis in endangered-species management. - In: Beissinger S. R. and Maccullough D. R. (eds), Population viability ana lysis. Univ. of Chicago Press, pp. 521-550.
48. Ricker W. E. 1954. Stock and recruitment. J. Fish. Res. Can. II(5): 559-623.
49. Robert C. P. and Casella G. 2005. Monte Carlo statistical methods. - Springer.
50. Sæther B. et al. 1998. Environmental stochasticity and extinction risk in a population of a small songbird, the great tit. Am. Nat. 151: 441-450.
51. Sæther B. et al. 2000. Estimating the time to extinction in an island population of song sparrows. Proc. R. Soc. B 267: 621-626.
52. Sæther B. et al. 2007. Predicting fluctuations of reintroduced ibex populations: the importance of density dependence, environmental stochasticity and uncertain population estimates. J. Anim. Ecol. 76: 326-336.
53. Schnute J. 1994. A general framework for developing sequential fisheries models. Can. J. Fish. Aquat. Sci. 51: 1676-1688.
54. Schtickzelle N. and Baguette M. 2004. Metapopulation viability analysis of the bog fritillary butterfly using RAMAS/GIS. Oikos 104: 277-290.
55. Shaffer M. L. 1981. Minimum population sizes for species conservation. Bioscience 31: 131-134.
56. Smith J. N. M. (ed.) 1988. Determinants of lifetime reproductive success in the song sparrow. - Univ. of Chicago Press.
57. Smith J. N. M. and Arcese P. 1989. How fit are floaters? Consequences of alternative territorial behaviors in a nonmigratory sparrow. Am. Nat. 133: 830-845.
58. Smith J. N. M. et al. 1996. A metapopulation approach to the population biology of the song sparrow melospiza melodia. Ibis 138: 120-128.
59. Smith J. N. M. et al. 2006. Fluctuations in numbers: population regulation and catastrophic mortality. - In: Smith J. N. M. et al. (eds), Conservation and biology of small populations: the song sparrows of Mandarte Island. Oxford Univ. Press, pp. 43-64.
60. Solymos P. 2010. dclone: data cloning in R. The R Journal 2: 29-37.
61. Stacey P. B. and Taper M. L. 1992. Environmental variation and the persistence of small populations. Ecol. Appl. 2: 18-29.
62. Staples D. F. et al. 2004. Estimating population trend and process variation for PVA in the presence of sampling error. Ecology 85: 923-929.
63. Taylor B. L. 1995. The reliability of using population viability analysis for risk classification of species. Conserv. Biol. 9: 551-558.
64. Torabi M. et al. 2011. Likelihood inference for small area estimation using data cloning. - Biometrics (submitted manuscript).
65. Walters C. J. 1986. Adaptive management of renewable resources. - McMillan.