Does accounting for imperfect detection improve species distribution models?

Ecography

Volumn 34, Issue 4, 2011, Pages 659-670

  Rota, Christopher T ^a   Fletcher, Robert J ^a   Evans, Jason M ^a   Hutto, Richard L ^b  

^a UNIVERSITY OF FLORIDA   (United States)

^b UNIVERSITY OF MONTANA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIRD; DATABASE; DETECTION METHOD; ECOLOGICAL MODELING; ENTROPY; ERROR ANALYSIS; HIERARCHICAL SYSTEM; MAXIMUM ENTROPY ANALYSIS; POPULATION DISTRIBUTION; REGRESSION ANALYSIS; SPECIES OCCURRENCE;

IDAHO; MONTANA; UNITED STATES;

EID: 79958091691     PISSN: 09067590     EISSN: 16000587     Source Type: Journal    
DOI: 10.1111/j.1600-0587.2010.06433.x     Document Type: Article

References (59)

1. Austin, M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecol. Model. 200: 1-19.
2. Austin, M. P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol. Model. 157: 101-118.
3. Bart, J. et al. 2004. Goals and strategies for estimating trends in landbird abundance. J. Wildl. Manage. 68: 611-626.
4. Bowen, R. V. 1997. Townsend's solitaire (Myadestes townsendi). - In: Poole, A. (ed.), The birds of North America online Cornell Lab of Ornithology. Ithaca .
5. Brewer, C. K. et al. 2004. Northern Region Vegetation Mapping Project Summary Report and Spatial Datasets. Northern Region USFS.
6. Brotons, L. et al. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27: 437-448.
7. Bull, E. L. and Jackson, J. A. 1995. Pileated woodpecker (Dryocopus pileatus). - In: Poole, A. (ed.), The birds of North America online Cornell Lab of Ornithology. Ithaca .
8. Carroll, C. and Johnson, D. S. 2008. The importance of being spatial (and reserved): assessing northern spotted owl habitat relationships with hierarchical Bayesian models. Conserv. Biol. 22: 1026-1036.
9. Dahlsten, D. L. et al. 2002. Chestnut-backed chickadee (Poecile rufescens). - In: Poole, A. (ed.), The birds of North America online Cornell Lab of Ornithology. Ithaca .
10. Dormann, C. F. et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30: 609-628.
11. Elith, J. et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151.
12. Farnsworth, G. L. et al. 2002. A removal model for estimating detection probabilities from point-count surveys. Auk 119: 414-425.
13. Fernandez, N. et al. 2006. Landscape evaluation in conservation: molecular sampling and habitat modeling for the Iberian lynx. Ecol. Appl. 16: 1037-1049.
14. Fielding, A. H. and Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24: 38-49.
15. Fletcher, R. J.Jr. 2009. Does attraction to conspecifics explain the patch-size effect? An experimental test. Oikos 118: 1139-1147.
16. Fletcher, R. J. et al. 2010. JrInsights from ecological theory on temporal dynamics and species distribution modeling. - In: Drew, A. et al (eds), Predictive modeling in landscape ecology. Springer in press..
17. Gelman, A. 2006. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1: 515-533.
18. George, T. L. 2000. Varied thrush (Ixoreus naevius). - In: Poole, A. (ed.), The birds of North America online Cornell Lab of Ornithology Ithaca .
19. Gu, W. D. and Swihart, R. K. 2004. Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biol. Conserv. 116: 195-203.
20. Guisan, A. and Zimmermann, N. E.. 2000. Predictive habitat distribution models in ecology. Ecol. Model. 135: 147-186.
21. Guisan, A. and Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8: 993-1009.
22. Hutto, R. L. and Young, J. S. 1999. Habitat relationships of landbirds in the northern region. USDA Forest Service, general technical report RMRS-GTR-32, USDA Forest Service.
23. Hutto, R. L. and Young, J. S. 2002. Regional landbird monitoring: perspectives from the Northern Rocky Mountains. Wildl. Soc. Bull. 30: 738-750.
24. Ingold, J. L. and Galati, R. 1997. Golden-crowned kinglet (Regulus satrapa). - In: Poole, A (ed.), The birds of North America online. Cornell Lab of Ornithology, Ithaca .
25. Keitt, T. H. et al. 2002. Accounting for spatial pattern when modeling organism-environment interactions. Ecography 25: 616-625.
26. Kuo, L. and Mallick, B. 1998. Variable selection for regression models. Indian J. Stat. 60B: 65-81.
27. Lassalle, G. et al. 2008. Diadromous fish conservation plans need to consider global warming issues: an approach using biogeographical models. Biol. Conserv. 141: 1105-1118.
28. Liu, C. et al. 2005. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28: 385-393.
29. Lobo, J. M. et al. 2008. AUC: a misleading measure of the performance of predictive distribution models. Global Ecol. Biogeogr. 17: 145-151.
30. Lowther, P. E. 1993. Brown-headed cowbird (Molothrus ater). - In: Poole, A. (ed.), The birds of North America online Ithaca .
31. Mack, D. E. and Yong, W. 2000. Swainson's thrush (Catharus ustulatus). - In: Poole, A. (ed.), The birds of North America online Cornell Lab of Ornithology, Ithaca .
32. MacKenzie, D. I. and Royle, J. A. 2005. Designing occupancy studies: general advice and allocating survey effort. J. Appl. Ecol. 42: 1105-1114.
33. MacKenzie, D. I. et al. 2002. Estimating site occupancy rates when detection probabilities are less than one. Ecology 83: 2248-2255.
34. MacKenzie, D. I. et al. 2006. Occupancy estimation and modeling: inferring patterns and dynamics of species occurrence. Elsevier.
35. Manly, B. F. J. et al. 2002. Resource selection by animals: statistical design and analysis for field studies, 2nd ed. Kluwer.
36. Marsh, D. M. and Trenham, P. C. 2008. Current trends in plant and animal population monitoring. Conserv. Biol. 22: 647-655.
37. Martin, T. G. et al. 2005. Zero tolerance ecology: improving ecological inference by modelling the source of zero observations. Ecol. Lett. 8: 1235-1246.
38. Montana Dept of Commerce. 2001. TIGER redistricting 2000 data, with 2000 census block population counts. Montana census and economic information center .
39. Phillips, S. J. and Dudik, M. 2008. Modeling of species distribution with Maxent: new extensions and a comprehensive evaluation. Ecography 31: 161-175.
40. Phillips, S. J. et al. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190: 231-259.
41. Phillips, S. J. et al. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19: 181-197.
42. Pitocchelli, J. 1995. MacGillivray's warbler (Oporornis tolmiei). - In: Poole, A. (ed.), The birds of North America online Cornell Lab of Ornithology. Ithaca .
43. PRISM. 2006. Precipitation: annual climatology (1971-2000). PRISM Climate Group, Oregon State Univ , accessed 30 June, 2009..
44. R Development Core Team. 2009. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria .
45. Ralph, C. J. et al. 1993. Handbook of field methods for monitoring landbirds. General technical report PSW-GTR-144, USFS Pacific Southwest Research Station.
46. Rota, C. T. 2009. Confronting assumptions of occupancy estimation and species distribution models: the problem of detectability in wildlife populations MS thesis, The University of Florida, Gainesville, FL..
47. Rota, C. T. et al. 2009. Occupancy estimation and the closure assumption. J. Appl. Ecol. 46: 1173-1181.
48. Royle, J. A. and Nichols, J. D. 2003. Estimating abundance from repeated presence-absence data or point counts. Ecology 84: 777-790.
49. Royle, J. A. and Kery, M. 2007. A Bayesian state-space formulation of dynamic occupancy models. Ecology 88: 1813-1823.
50. Royle, J. A. and Dorazio, R. M. 2008. Hierarchical modeling and inference in ecology. Academic Press.
51. Sauer, J. R. et al. 1994. Observer differences in the North-American breeding bird survey. Auk 111: 50-62.
52. Sing, T. et al. 2005. ROCR: visualizing classifier performance in R. Bioinformatics 21: 3940-3941.
53. Spiegelhalter, D. J. et al. 2003. WinBUGS user manual, version 1.4. MRC Biostatistics Unit, Cambridge .
54. Sturtz, U. et al. 2005. R2WinBUGS: a package for running WinBUGS from R. J. Stat. Software 12: 1-16.
55. Thogmartin, W. E. et al. 2004. A hierarchical spatial model of avian abundance with application to cerulean warblers. Ecol. Appl. 14: 1766-1779.
56. Tyre, A. J. et al. 2003. Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecol. Appl. 13: 1790-1801.
57. USDA Forest Service. 2008. Travel routes for region 1.-USDA Forest Service, R1, regional office engineering .
58. Ward, G. et al. 2008. Presence-only data and the EM algorithm. Biometrics 65: 554-563.
59. Wright, A. L. et al. 1998. Townsend's warbler (Dendroica townsendi). - In: Poole, A. (ed.), The birds of North America online. Cornell Lab of Ornithology, Ithaca .