The effect of stochastic technique on estimates of population viability from transition matrix models

Ecology

Volumn 84, Issue 6, 2003, Pages 1464-1476

  Kaye, Thomas N ^a,c   Pyke, David A ^b  

^a OREGON STATE UNIVERSITY   (United States)

^b FOREST AND RANGELAND ECOSYSTEM SCIENCE CENTER   (United States)

^c Institute for Applied Ecology   (United States)

Author keywords

Element selection; Matrix selection; Population viability; Stochastic growth rate; Survival constraint; Transition matrix model

Indexed keywords

CONSERVATION; GROWTH RATE; POPULATION GROWTH; POPULATION MODELING; STOCHASTICITY; VIABILITY;

EID: 0345015936     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/0012-9658(2003)084[1464:TEOSTO]2.0.CO;2     Document Type: Article

References (63)

1. Akçakaya, H. R., and M. G. Raphael. 1998. Assessing human impact despite uncertainty: viability of the northern spotted owl metapopulation in the northwestern USA. Biodiversity and Conservation 7:875-894.
2. Beissinger, S. R., and M. I. Westphal. 1998. On the use of demographic models of population viability in endangered species management. Journal of Wildlife Management 62: 821-841.
3. Bierzychudek, P. 1982. The demography of jack-in-the-pulpit, a forest perennial that changes sex. Ecological Monographs 52:335-351.
4. Brook, B. W., M. A. Burgman, and R. Frankham. 2000a. Differences and congruencies between PVA packages: the importance of sex ratio for predictions of extinction risk. Conservation Ecology 4(4). [Online: 〈http://www. consecol.org/ vol4/iss1/art6〉.]
5. Brook, B. W., J. R. Cannon, R. C. Lacey, C. Mirande, and R. Frankham. 1999. A comparison of the population viability analysis packages GAPSS, INMAT, RAMAS, and VORTEX for the Whooping Crane (Grus americana). Animal Conservation 2:23-31.
6. Brook, B. W., J. J. O'Grady, A. P. Chapman, M. A. Burgman, H. R. Akçakaya, and R. Frankham. 2000b. Predictive accuracy of population viability analysis in conservation biology. Nature 404:385-387.
7. Bukowski, J., L. Korn, and D. Wartenberg. 1995. Correlated inputs in quantitative risk assessment: the effects of distributional shape. Risk Analysis 15:215-219.
8. Burgman, M. A., S. Ferson, and H. R. Akçakaya. 1993. Risk assessment in conservation biology. Chapman and Hall, London, UK.
9. Burgman, M. A., and V. A. Gerard. 1990. A stage-structured, stochastic population model for the giant kelp Macrocystis pyrifera. Marine Biology 105:15-23.
10. Caswell, H. 1989. The analysis of life-table response experiments. I. Decomposition of effects on population growth rate. Ecological Modelling 46:221-237.
11. Caswell, H. 2000. Prospective and retrospective perturbation analyses: their roles in conservation biology. Ecology 81: 619-627.
12. Caswell, H. 2001. Matrix population models. Second edition. Sinauer, Sunderland, Massachusetts, USA.
13. Caswell, H., and T. N. Kaye. 2001. Stochastic demography and conservation of an endangered perennial plant (Lomatium bradshawii) in a dynamic fire regime. Advances in Ecological Research 32:1-51.
14. Cisneros-Mata, M. A., L. W. Botsford, and J. F. Quinn. 1997. Projecting viability of Totoaba macdonaldi, a population with unknown age-dependent variability. Ecological Applications 7:968-980.
15. Doak, D., P. Kareiva, and B. Klepetka. 1994. Modeling population viability for the desert tortoise in the western Mojave Desert. Ecological Applications 4:446-460.
16. Evans, M., N. Hastings, and B. Peacock. 2000. Statistical distributions. Third edition. John Wiley and Sons, New York, New York, USA.
17. Ferson, S., and M. A. Burgman. 1995. Correlations, dependency bounds and extinction risks. Biological Conservation 73:101-105.
18. Fieberg, J., and S. P. Ellner. 2000. When is it meaningful to estimate an extinction probability? Ecology 81:2040-2047.
19. Fieberg, J., and S. P. Ellner. 2001. Stochastic models for conservation and management: a comparative review of methods. Ecology Letters 4:244-266.
20. Fisher, D. O., S. D. Hoyle, and S. P. Bloomberg. 2000. Population dynamics and survival of an endangered wallaby: a comparison of four methods. Ecological Applications 10: 901-910.
21. Frey, H. C., and D. E. Burmaster. 1999. Methods of characterizing variability and uncertainty: comparison of bootstrap simulations and likelihood-based approaches. Risk Analysis 19:109-130.
22. Frye, R. J. 1998. Population viability analysis of Pediocactus paradinei. Pages 39-46 in J. Maschinski, H. D. Hammond, and L. Holter, editors. Southwestern rare and endangered plants: proceedings of the second conference. General Technical Report RM-GTR-283. U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, Fort Collins, Colorado, USA.
23. Greenlee, J., and T. N. Kaye. 1997. Stochastic matrix projections: a comparison of the effect of element and matrix selection methods on quasi-extinction risk for Haplopappus radiatus (Asteraceae). Pages 66-71 in T. N. Kaye, A. Liston, R. M. Love, D. Luoma, R. J. Meinke, and M. V. Wilson, editors. Conservation and management of native plants and fungi. Native Plant Society of Oregon, Corvallis, Oregon, USA.
24. Griffiths, D. A. 1973. Maximum likelihood estimation for the beta-binomial distribution and an application to the household distribution of the total number of cases of disease. Biometrics 29:637-648.
25. Gross, K., J. R. Lockwood III, C. C. Frost, and W. F. Morris. 1998. Modeling controlled burning and trampling reduction for conservation of Hudsonia montana. Conservation Biology 12:1291-1301.
26. Guerrant, E. O. 1996. Designing populations: demographic, genetic, and horticultural dimensions. Pages 171-207 in D. A. Falk, C. I. Millar, and M. Olwell, editors. Restoring diversity. Island, Washington, D.C., USA.
27. Haas, C. N. 1997. Importance of distributional form in characterizing inputs to Monte Carlo risk assessments. Risk Analysis 17:107-113.
28. Haas, C. N. 1999. On modeling correlated random variables in risk assessment. Risk Analysis 19:1205-1214.
29. Hamed, M. M., and P. B. Bedient. 1997. On the effect of probability distributions of input variables in public health risk assessment. Risk Analysis 17:97-105.
30. Horvitz, C. C., and D. W. Schemske. 1995. Spatiotemporal variation in demographic transitions of a tropical under-story herb: projection matrix analysis. Ecological Monographs 65:155-192.
31. Horvitz, C. C., D. W. Schemske, and H. Caswell. 1997. The relative "importance" of life-history stages to population growth: prospective and retrospective studies. Pages 248-271 in S. Tuljapurkar and H. Caswell, editors. Structured-population models in marine, terrestrial and freshwater systems. Chapman and Hall, New York, New York, USA.
32. Kahn, M. J., and A. E. Raftery. 1996. Discharge rates of Medicare stroke patients to skilled nursing facilities: Bayesian logistic regression with observed heterogeneity. Journal of the American Statistical Association - Applications and Case Studies 91:29-41.
33. Karian, Z. A., and E. J. Dudewicz. 2000. Fitting statistical distributions. CRC, Boca Raton, Florida, USA.
34. Kaye, T. N. 2001. Population viability analysis of endangered plant species: an evaluation of stochastic methods and an application to a rare prairie plant. Dissertation. Oregon State University, Corvallis, Oregon, USA.
35. Kaye, T. N., K. L. Pendergrass, K. Finley, and J. B. Kauffman. 2001. The effect of fire on the population viability of an endangered prairie plant. Ecological Applications 11:1366-1380.
36. Kendall, B. E. 1998. Estimating the magnitude of environmental stochasticity in survivorship data. Ecological Applications 8:184-193.
37. Lennartsson, T 2000. Management and population viability of the pasture plant Gentiana campestris: the role of interactions between habitat factors. Ecological Bulletin 48: 111-121.
38. Lindenmeyer, D. B., M. A. Burgman, H. R. Akçakaya, R. C. Lacy, and H. P. Possingham. 1995. A review of the generic computer programs ALEX, RAMAS/space and VORTEX for modelling the viability of wildlife populations. Ecological Modelling 82:161-174.
39. Ludwig, D. 1999. Is it meaningful to estimate a probability of extinction? Ecology 80:298-310.
40. Maguire, L. A., G. F. Wilhere, and Q. Dong. 1995. Population viability analysis for Red-cockaded Woodpeckers in the Georgia Piedmont. Journal of Wildlife Management 59: 533-542.
41. Maschinski, J., R. Frye, and S. Rutman. 1997. Demography and population viability of an endangered plant species before and after protection from trampling. Conservation Biology 11:990-999.
42. Mathworks. 1998. Matlab. Version 5. The Mathworks, Natick, Massachusetts, USA.
43. McCrimmon, K. R., and C. A. Ryavec. 1964. An analytical study of PERT assumptions. Operations Research 12:113-124.
44. Menges, E. S. 1992. Stochastic modeling of extinction in plant populations. Pages 253-275 in P. L. Fiedler and S. K. Jain, editors. Conservation biology: the theory and practice of nature conservation, preservation, and management. Chapman and Hall, New York, New York, USA.
45. Menges, E. S. 2000. Population viability analyses in plants: challenges and opportunities. Trends in Ecology and Evolution 15:51-56.
46. Mills, L. S., S. G. Hayes, C. Baldwin, M. J. Wisdom, J. Citta, D. J. Mattson, and K. Murphy. 1996. Factors leading to different viability predictions for a grizzly bear data set. Conservation Biology 10:863-873.
47. Nakoaka, M. 1996. Dynamics of age-and size-structured populations in fluctuating environments: applications of stochastic matrix models to natural populations. Researches in Population Ecology 38:141-152.
48. Nakoaka, M. 1997. Demography of the marine bivalve Yoldia notabilis in fluctuating environments: an analysis using a stochastic matrix model. Oikos 79:59-68.
49. Noon, B. R., and K. S. McKelvey. 1996. Management for the Spotted Owl: a case history in conservation biology. Annual Review of Ecology and Systematics 27:135-162.
50. Oostermeijer, G., M. L. Brugman, E. R. de Boer, and H. C. M. den Nijs. 1996. Temporal and spatial variation in the demography of Gentiana pneumonanthe, a rare perennial herb. Journal of Ecology 84:153-166.
51. Oregon Natural Heritage Program. 2001. Rare, threatened and endangered plants and animals of Oregon. Oregon Natural Heritage Program, Portland, Oregon, USA.
52. Pascual, M. A., P. Kareiva, and R. Hilborn. 1997. The influence of model structure on conclusions about the viability of harvesting of Serengeti wildebeest. Conservation Biology 11:966-976.
53. Pfab, M. F., and E. T. F. Witkowski. 2000. A simple population viability analysis of the critically endangered Euphorbia clivicola R. A. Dyer under four management scenarios. Biological Conservation 96:263-270.
54. Ratner, S., R. Lande, and B. B. Roper. 1997. Population viability analysis of spring Chinook salmon in the South Umpqua River, Oregon. Conservation Biology 11:879-889.
55. SAS Institute. 1990. SAS/STAT user's guide. Version 6. Fourth edition. Volume 2. SAS Institute, Cary, North Carolina, USA.
56. Schemske, D. W., B. C. Husband, M. H. Ruckleshaus, C. Goodwillie, I. M. Parker, and J. H. Bishop. 1994. Evaluating approaches to the conservation of rare and endangered plants. Ecology 75:584-606.
57. Slade, N. A., and H. Levinson. 1984. The effects of skewed distributions of vital statistics on growth of age-structured populations. Theoretical Population Biology 26:361-366.
58. Sorribas, A., J. March, and E. O. Voit. 2000. Estimating age-related trends in cross-sectional studies using S-distributions. Statistics in Medicine 19:697-713.
59. Tallie, C., G. P. Patil, and R. C. Hennemuth. 1995. Modelling and analysis of recruitment distributions. Environmental and Ecological Statistics 2:315-329.
60. Tamura, R. N., and S. S. Young. 1987. A stabilized moment estimator for the beta-binomial distribution. Biometrics 43: 813-824.
61. Thompson, J. N. 1985. Postdispersal seed predation in Lomatium spp. (Umbelliferae): variation among individuals and species. Ecology 66:1608-1616.
62. Tuljapurkar, S. D. 1990. Population dynamics in variable environments. Springer-Verlag, New York, New York, USA.
63. Voit, E. O., and L. H. Schwake. 2000. Random number generation from right-skewed, symmetric, and left-skewed distributions. Risk Analysis 20:59-71.