Conditional likelihood approach for analyzing single visit abundance survey data in the presence of zero inflation and detection error

Environmetrics

Volumn 23, Issue 2, 2012, Pages 197-205

  Sólymos, Péter ^a   Lele, Subhash ^a   Bayne, Erin ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Closed populations; Conditional likelihood; Ecological monitoring; Mixture models; Open populations; Pseudo likelihood

Indexed keywords

ABUNDANCE; DATA SET; DETECTION METHOD; ENVIRONMENTAL MONITORING; ERROR ANALYSIS; GEOGRAPHICAL REGION; POISSON RATIO; POPULATION STRUCTURE; REGRESSION ANALYSIS; RESEARCH PROGRAM; SONGBIRD; SURVEY;

SEIURUS; SEIURUS AUROCAPILLUS;

EID: 84857794462     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.1149     Document Type: Article

References (35)

1. Anderson EB. 1970. Asymptotic properties of conditional maximum likelihood estimators. Journal of the Royal Statistical Society: Series B 32: 283-301.
2. Bayne EM. 2000. Effects of forest fragmentation on the demography of Ovenbirds (Seiurus aurocapillus) in the boreal forest. PhD Thesis, University of Saskatchewan, Saskatoon, Canada.
3. Buckland ST, Anderson DR, Burnham KP, Laake JL. 1993. Distance Sampling. Chapman and Hall: London.
4. Casella G, Berger RL. 2002. Statistical Inference, 2nd ed. Thomson Learning: Australia, Pacific Grove, CA. p. 660.
5. Chandler RB, Royle A, King DI. 2011. Inference about density and temporary emigration in unmarked populations. Ecology 92: 1429-1435.
6. Dail D, Madsen L. 2011. Models for estimating abundance from repeated counts of an open metapopulation. Biometrics 67: 577-587.
7. Downes CM, Collins BT. 2003. The Canadian Breeding Bird Survey, 1967-2000. Canadian Wildlife Service, Progress Notes No. 219. National Wildlife Research Centre: Ottawa, ON.
8. Efron B, Tibshirani R. 1994. An Introduction to the Bootstrap. Chapman & Hall/CRC: New York. p. 436.
9. Farnsworth GL, Pollock KH, Nichols JD, Simons TR, Hines JE, Sauer JR. 2002. A removal model for estimating detection probabilities from point count surveys. Auk 119: 414-425.
10. Gong G, Samaniego FJ. 1981. Pseudo-likelihood estimation: theory and applications. Annals of Statistics 9: 861-869.
11. Gu W, Swihart RK. 2004. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation 116: 195-203.
12. Hobson KA, Bayne EM. 2002. Breeding bird communities in boreal forest of Western Canada: consequences of "unmixing" the mixed woods. Condor 102: 759-769.
13. Joseph LN, Elkin C, Martin TG, Possingham HP. 2009. Modeling abundance using N-mixture models: the importance of considering ecological mechanisms. Ecological Applications 19: 631-642.
14. Kalbfleish JD, Sprott DA. 1973. Marginal and conditional likelihoods. Sankhya 35: 311-328.
15. Kendall WL, White GC. 2009. A cautionary note on substituting spatial subunits for repeated temporal sampling in studies of site occupancy. Journal of Applied Ecology 46: 1182-1188.
16. Krebs CJ. 1985. Ecology: the Experimental Analysis of Distribution and Abundance, 3rd ed. Harper and Row: New York.
17. Lele SR. 2010. Model complexity and information in the data: could it be a house built on sand? Ecology 91: 3503-3514.
18. Lele SR, Moreno M, Bayne E. Dealing with detection error in site occupancy surveys: what can we do with a single survey? Journal of Plant Ecology in press.
19. Lele SR, Nadeem K, Schmuland B. 2010. Estimability and likelihood inference for generalized linear mixed models using data cloning. Journal of the American Statistical Association 105: 1617-1625.
20. MacKenzie DI, Nichols JD, Lachman GB, Droege S, Royle JA, Langtimm CA. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248-2255.
21. MacKenzie DI, Nichols JD, Royle AJ, Pollock KH, Bailey LL, Hines JE. 2006. In Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence. Elsevier: Amsterdam, Netherlands. p. 324.
22. MacTavish P. 1995. Saskatchewan digital landcover mapping project. Technical Report 4900-15-B-95, Saskatchewan Research Council, Saskatoon, SK.
23. Martin TG, Wintle BA, Rhodes JR, Kuhnert PM, Field SA, Low-Choy SJ, Tyre AJ, Possingham HP. 2005. Zero tolerance ecology: improving ecological inference by modeling the source of zero observations. Ecology Letters 8: 1235-1246.
24. Moreno M. 2011. Site occupancy models. PhD thesis, University of Alberta, Edmonton, AB. p. 206.
25. R Development Core Team. 2011. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria. ISBN 3-900051-07-0, [accessed 1 January 2011].
26. Rosenberg KV, Blancher PJ. 2005. Setting numerical population objectives for priority landbird species. In Bird Conservation and Implementation in the Americas: Proceedings of the Third International Partners in Flight Conference, Vol. 1, Ralph CJ, Rich TD (eds). Department of Agriculture, Forest Service, General Technical Report PSW-GTR-191: U.S; 57-67.
27. Rota CT, Fletcher RJ, Dorazio RM, Betts MG. 2009. Occupancy estimation and the closure assumption. Journal of Applied Ecology 46: 1173-1181.
28. Royle JA. 2004. N-mixture models for estimating population size from spatially replicated counts. Biometrics 60: 108-115.
29. Royle JA, Dorazio RM. 2008. Hierarchical Modeling and Inference in Ecology: the Analysis of Data from Populations, Metapopulations and Communities. Academic Press: San Diego, CA. xviii. p. 444.
30. Royle JA, Nichols JD, Kéry M. 2005. Modelling occurrence and abundance of species when detection is imperfect. Oikos 110: 353-359.
31. Schieck J. 1997. Biased detection of bird vocalizations affects comparisons of bird abundance among forested habitats. The Condor 99: 179-190.
32. Sólymos P, Moreno M, Lele SR. 2011. 'detect': Analyzing wildlife data with detection error. R package version 0.1-0. [accessed 19 October 2011].
33. Tyre AJ, Tenhumberg B, Field SA, Niejalke D, Parris K, Possingham HP. 2003. Improving precision and reducing bias in biological surveys: estimating false negative error rates. Ecological Applications 13: 1790-1801.
34. Wenger SJ, Freeman MC. 2008. Estimating species occurrence, abundance, and detection probability using zero-inflated distributions. Ecology 89: 2953-2959.
35. Yoccoz NG, Nichols JD, Boulinier T. 2001. Monitoring of biological diversity in space and time. Ecology and Evolution 16: 446-453.