Replicated sampling increases efficiency in monitoring biological populations

Ecology

Volumn 91, Issue 2, 2010, Pages 610-620

  Brian, Dennis ^a   Ponciano, José Miguel ^b   Taper, Mark L ^c  

^a UNIVERSITY OF IDAHO   (United States)

^b CENTRO DE INVESTIGACIÓN EN MATEMÁTICAS   (Mexico)

^c MONTANA STATE UNIVERSITY   (United States)

Author keywords

Breeding bird survey; Density dependence; Environmental noise; Gompertz growth model; Kaiman filter; Measurement error; Observation error; Process noise; Profile likelihood; Sampling error; Statespace model; Stochastic population model

Indexed keywords

ABUNDANCE; BIRD; BREEDING POPULATION; DENSITY DEPENDENCE; ERROR ANALYSIS; GROWTH MODELING; KALMAN FILTER; SAMPLING; STOCHASTICITY;

ANIMAL; ARTICLE; BIOLOGICAL MODEL; BIRD; COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; METHODOLOGY; PHYSIOLOGY; POPULATION DYNAMICS; RESEARCH; TIME;

ANIMALS; BIRDS; COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; MODELS, BIOLOGICAL; POPULATION DYNAMICS; RESEARCH; TIME FACTORS;

AVES;

EID: 77949300708     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/08-1095.1     Document Type: Article

References (44)

1. Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. Pages 267-281 in B. N. Petrov and F. Csaki, editors. Second International Symposium on Information Theory. Akademiai Kiado, Budapest, Hungary.
2. Bellows, T. S. 1981. The descriptive properties of some models for density dependence. Journal of Animal Ecology 50:139-156.
3. Besbeas, P., S. N. Freeman, B. J. T. Morgan, and E. A. Catchpole. 2002. Integrating mark-recapture-recovery and census data to estimate animal abundance and demographic parameters. Biometrics 58:540-547.
4. Brook, B. W., and C. J. A. Bradshaw. 2006. Strength of evidence for density dependence in abundance time series of 1198 species. Ecology 87:1445-1451.
5. Clark, J. S., and O. N. BJømstad. 2004. Population time series: process variability, observation errors, missing values, lags, and hidden states. Ecology 85:3140-3150.
6. Cunningham, R. B., D. B. Lindenmayer, H. A. Nix, and B. D. Lindenmayer. 1999. Quantifying observer heterogeneity in bird counts. Australian Journal of Ecology 24:270-277.
7. Dennis, B. 1996. Discussion: should ecologists become Bayesians? Ecological Applications 6:1095-1103.
8. Dennis, B. 2004. Statistics and the scientific method in ecology (with commentary). Pages 327-378 in M. L. Taper and S. R. Lele, editors. The nature of scientific evidence: statistical, philosophical, and empirical considerations. University of Chicago Press, Chicago, Illinois, USA.
9. Dennis, B., J. M. Ponciano, S. R. Lele, M. L. Taper, and D. F. Staples. 2006. Estimating density dependence, process noise, and observation error. Ecological Monographs 76:323-341.
10. Dennis, B., and M. L. Taper. 1994. Density dependence in time series observations of natural populations: estimation and testing. Ecological Monographs 64:205-224.
11. de Valpine, P. 2003. Better inferences from populationdynamics experiments using Monte Carlo state-space likelihood methods. Ecology 84:3064-3077.
12. de Valpine, P. 2004. Monte Carlo state-space likelihoods by weighted posterior kernel density estimation. Journal of the American Statistical Association 99:523-536.
13. de Valpine, P., and A. Hastings. 2002. Fitting population models incorporating process noise and observation error. Ecological Monographs 72:57-76.
14. Freckleton, R. P., A. R. Watkinson, R. E. Green, and W. J. Sutherland. 2006. Census error and the detection of density dependence. Journal of Animal Ecology 75:837-851.
15. Harvey, A. C. 1993. Time series models. Second edition. MIT Press, Cambridge, Massachusetts, USA.
16. Holmes, E. E. 2001. Estimating risks in declining populations with poor data. Proceedings of the National Academy of Sciences (USA) 98:5072-5077.
17. Holmes, E. E., and W. F. Fagan. 2002. Validating population viability analysis for corrupted data sets. Ecology 83:2379-2386.
18. Ionides, E. L., C. Bretó, and A. A. King. 2006. Inference for nonlinear dynamical systems. Proceedings of the National Academy of Sciences (USA) 103:18438-18443.
19. Ives, A. R., B. Dennis, K. L. Cottingham, and S. R. Carpenter. 2003. Estimating community stability and ecological interactions from time-series data. Ecological Monographs 73: 301-330.
20. Knape, J. 2008. Estimability of density dependence in models of time series data. Ecology 89:2994-3000.
21. Langton, S. D., N. J. Aebischer, and P. A. Robertson. 2002. The estimation of density dependence using census data from several sites. Oecologia 133:466-473.
22. Lele, S. R., and B. Dennis. 2009. Bayesian methods for hierarchical models: Are ecologists making a Faustian bargain? Ecological Applications 19:581-584.
23. Lele, S. R., B. Dennis, and F. Lutscher. 2007. Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods. Ecology Letters 10:551-563.
24. Link, W. A., R. J. Barker, J. R. Sauer, and S. Droege. 1994. Within-site variability in surveys of wildlife populations. Ecology 75:1097-1108.
25. Link, W. A., and J. R. Sauer. 1998. Estimating population change from count data: application to the North American Breeding Bird Survey. Ecological Applications 8:258-268.
26. Muhlfeld, C. C., M. L. Taper, D. F. Staples, and B. B. Shepard. 2006. Observer error structure in bull trout redd counts in Montana streams: implications for inference on true redd numbers. Transactions of the American Fisheries Society 135:643-654.
27. Newman, K. 1998. State-space modeling of animal movement and mortality with application to salmon. Biometrics 54:274-297.
28. Ospina, R., F. Cribari-Neto, and K. L. P. Vasconcellos. 2006. Improved point and interval estimation for a beta regression model. Computational Statistics and Data Analysis 51:960-981.
29. Pawitan, Y. 2001. In all likelihood: statistical modeling and inference using likelihood. Oxford University Press, Oxford, UK.
30. Peterjohn, B. G. 1994. The North American breeding bird survey. Birding 26:386-398.
31. Pollard, E., K. H. Lakhani, and P. Rothery. 1987. The detection of density dependence from a series of annual censuses. Ecology 68:2046-2055.
32. Rehfeldt, G. E., N. L. Crookston, M. V. Warwell, and J. S. Evans. 2006. Empirical analyses of plant-climate relationships for the western United States. International Journal of Plant Sciences 167:1123-1150.
33. Sakamoto, Y., M. Ishiguro, and G. Kitagawa. 1986. Akaike information criterion statistics. KTK Scientific, Tokyo, Japan.
34. Searle, S. R., G. Casella, and C. E. McCulloch. 1992. Variance components. John Wiley and Sons, New York, New York, USA.
35. Shenk, T. M., G. C. White, and K. P. Burnham. 1998. Sampling-variance effects on detecting density dependence from temporal trends in natural populations. Ecological Monographs 68:445-463.
36. Sibly, R. M., D. Barker, M. C. Denham, J. Hone, and M. Pagel. 2005. On the regulation of populations of mammals, birds, fish, and insects. Science 309:607-610.
37. Solow, A. R. 1990. Testing for density dependence: a cautionary note. Oecologia 83:47-49.
38. Solow, A. R. 2001. Observation error and the detection of delayed density dependence. Ecology 82:3263-3264.
39. Spearpoint, J. A., B. Every, and L. G. Underhill. 1988. Waders (Charadrii) and other shorebirds at Cape Recife, Algoa Bay, South Africa: seasonality, trends, conservation, and reliability of surveys. Ostrich 59:166-177.
40. Staples, D. F., M. L. Taper, and B. Dennis. 2004. Estimating population trend and process variation for PVA in the presence of sampling error. Ecology 85:923-929.
41. Sullivan, P. J. 1992. A Kaiman filter approach to catch-atlength analysis. Biometrics 48:237-257.
42. Ver Hoef, J. M., and K. J. Frost. 2003. A Bayesian hierarchical model for monitoring harbor seal changes in Prince William Sound, Alaska. Environmental and Ecological Statistics 10: 201-219.
43. Woiwod, I. P., and I. Hanski. 1992. Patterns of density dependence in moths and aphids. Journal of Animal Ecology 61:619-629.
44. Zeng, Z., R. M. Nowierski, M. L. Taper, B. Dennis, and W. P. Kemp. 1998. Complex population dynamics in the real world: modeling the influence of time-varying parameters and time lags. Ecology 79:2193-2209.