Predicting the Geographic Distribution of a Species from Presence-Only Data Subject to Detection Errors

Biometrics

Volumn 68, Issue 4, 2012, Pages 1303-1312

  Dorazio, Robert M ^a  

^a U S GEOLOGICAL SURVEY   (United States)

Author keywords

Case augmented design; Case control design; Site occupancy model; Spatial point process; Species distribution model; Use availability design

Indexed keywords

ERRORS; GEOGRAPHICAL DISTRIBUTION; POPULATION DISTRIBUTION; REGRESSION ANALYSIS;

AVAILABILITY DESIGN; CASE-AUGMENTED DESIGN; CASE-CONTROL; CASE-CONTROL DESIGN; CONTROL DESIGN; SITE-OCCUPANCY; SITE-OCCUPANCY MODEL; SPATIAL POINT PROCESS; SPECIES DISTRIBUTION MODELING; USE-AVAILABILITY DESIGN;

LOCATION;

ABUNDANCE; AVIFAUNA; BIRD; DATA SET; DESIGN; ECOLOGICAL MODELING; ERROR ANALYSIS; GEOGRAPHICAL DISTRIBUTION; MEASUREMENT METHOD; NUMERICAL MODEL; PREDICTION; SPECIES OCCURRENCE;

IDAHO; MONTANA; UNITED STATES;

AVES;

EID: 84871659770     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2012.01779.x     Document Type: Article

References (40)

1. Aarts, G., Fieberg, J., and Matthiopoulos, J. (2012). Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods in Ecology and Evolution 3, 177-187.
2. Berman, M. and Turner, T. R. (1992). Approximating point process likelihoods with GLIM. Applied Statistics 41, 31-38.
3. Cabeza, M., Araújo, M. B., Wilson, R. J., Thomas, C. D., Cowley, M. J. R., and Moilanen, A. (2004). Combining probabilities of occurrence with spatial reserve design. Journal of Applied Ecology 41, 252-262.
4. Chakraborty, A., Gelfand, A. E., Wilson, A. M., Latimer, A. M., and Silander, J. A. (2011). Point pattern modelling for degraded presence-only data over large regions. Applied Statistics 60, 757-776.
5. Coslett, S. (1981). Maximum likelihood estimator for choice-based samples. Econometrica 49, 1289-1316.
6. Cressie, N. and Wikle, C. K. (2011). Statistics for Spatio-Temporal Data New Jersey: John Wiley & Sons, Hoboken.
7. Cressie, N. A. C. (1993). Statistics for Spatial Data New York: John Wiley & Sons.
8. Di Lorenzo, B., Farcomeni, A., and Golini, N. (2011). A Bayesian model for presence-only semicontinuous data, with application to prediction of abundance of Taxus baccata in two italian regions. Journal of Agricultural, Biological, and Environmental Statistics 16, 339-356.
9. Elith, J., Graham, C. H., Anderson, R. P., Dudik, M., Ferrier, S., Guisan, A., Hijmans, R. J., Huettmann, F., Leathwick, J. R., Lehmann, A., Li, J., Lohmann, L. G., Loiselle, B. A., Manion, G., Moritz, C., Nakamura, M., Nakazawa, Y., Overton, J. M., Peterson, A. T., Phillips, S. J., Richardson, K., Scachetti-Pereira, R., Schapire, R. E., Soberon, J., Williams, S., Wisz, M. S., and Zimmerman, N. E. (2006). Novel methods improve prediction of species' distributions from occurrence data. Ecography 29, 129-151.
10. Elith, J. and Leathwick, J. R. (2009). Species distribution models: Ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40, 677-697.
11. Elith, J., Phillips, S. J., Hastie, T., Dudik, M., Chee, Y. E., and Yates, C. J. (2010). A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17, 43-57.
12. Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion). Annals of Statistics 19, 1-141.
13. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics 29, 1189-1232.
14. Gu, W. and Swihart, R. K. (2004). Absent or undetected? Effects of non-detection of species occurrence on wildlife-habitat models. Biological Conservation 116, 195-203.
15. Guisan, A., Graham, C. H., Elith, J., Huettmann, and the NCEAS Species Distribution Modelling Group. (2007). Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions 13, 332-340.
16. Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models London: Chapman and Hall.
17. Hutto, R. L. and Young, J. S. (2002). Regional landbird monitoring: Perspectives, from the Northern Rocky Mountains. Wildlife Society Bulletin 30, 738-750.
18. Keating, K. A. and Cherry, S. (2004). Use and interpretation of logistic regression in habitat selection studies. Journal of Wildlife Management 68, 774-789.
19. Kéry, M., Royle, J. A., and Schmid, H. (2005). Modeling avian abundance from replicated counts using binomial mixture models. Ecological Applications 15, 1450-1461.
20. Kéry, M., Gardner, B., and Monnerat, (2010). Predicting species distributions from checklist data using site-occupancy models. Journal of Biogeography 37, 1851-1862.
21. Lancaster, T. and Imbens, G. (1996). Case-control studies with contaminated controls. Journal of Econometrics 71, 145-160.
22. Lee, A. J., Scott, A. J., and Wild, C. J. (2006). Fitting binary regression models with case-augmented samples. Biometrika 93, 385-397.
23. Lele, S. R. and Keim, J. L. (2006). Weighted distributions and estimation of resource selection probability functions. Ecology 87, 3021-3028.
24. MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Royle, J. A., and Langtimm, C. A. (2002). Estimating site occupancy rates when detection probabilities are less than one. Ecology 83, 2248-2255.
25. MacKenzie, D. I., Nichols, J. D., Royle, J. A., Pollock, K. H., Bailey, L. L., and Hines, J. E. (2006). Occupancy estimation and modeling Amsterdam: Elsevier.
26. MacKenzie, D. I. and Royle, J. A. (2005). Designing occupancy studies: General advice and allocating survey effort. Journal of Applied Ecology 42, 1105-1114.
27. McClintock, B. T., Bailey, L. L., Pollock, K. H., and Simon, T. R. (2010a). Experimental investigation of observation error in anuran call surveys. Journal of Wildlife Management 74, 1882-1893.
28. McClintock, B. T., Bailey, L. L., Pollock, K. H., and Simon, T. R. (2010b). Unmodeled observation error induces bias when inferring patterns and dynamics of species occurrence via aural detections. Ecology 91, 2446-2454.
29. Pearce, J. L. and Boyce, M. S. (2006). Modelling distribution and abundance with presence-only data. Journal of Applied Ecology 43, 405-412.
30. Phillips, S. J., Anderson, R. P., and Schapire, R. E. (2006). Maximum entropy modeling of species geographic distributions. Ecological Modelling 190, 231-259.
31. Phillips, S. J., Dudik, M., Elith, J., Graham, C. H., Lehmann, A., Leathwick, J., and Ferrier, S. (2009). Sample selection bias and presence-only distribution models: implications for background and pseudo absence data. Ecological Applications 19, 181-197.
32. Rota, C. T., Fletcher, Jr., R. J., Evans, J. M., and Hutto, R. L. (2011). Does accounting for imperfect detection improve species distribution models? Ecography 34, 659-670.
33. Royle, J. A., Chandler, R. B., Yackulic, C., and Nichols, J. D. (2012). Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods in Ecology and Evolution, doi: 10.1111/j.2041-210X.2011.00182.x.
34. Royle, J. A. and Link, W. A. (2006). Generalized site occupancy models allowing for false positive and false negative errors. Ecology 87, 835-841.
35. Sauer, J. R., Peterjohn, B. G., and Link, W. A. (1994). Observer differences in the North American Breeding Bird Survey. Auk 111, 50-62.
36. Scott, J. M., Heglund, P. J., Morrison, M. L., Haufler, J. B., Raphael, M. G., Wall, W. A., and Samson, F. B. (2002). Predicting Species Occurrences: Issues of Accuracy and Scale Washington: Island Press.
37. Simons, T. R., Alldredge, M. W., Pollock, K. H., and Wettroth, J. M. (2007). Experimental analysis of the auditory detection process on avian point counts. Auk 124, 986-999.
38. Tyre, A. J., Tenhumberg, B., Field, S. A., Niejalke, D., Parris, K., and Possingham, H. P. (2003). Improving precision and reducing bias in biological surveys: estimating false-negative error rates. Ecological Applications 13, 1790-1801.
39. Ward, G., Hastie, T., Barry, S., Elith, J., and Leathwick, J. R. (2009). Presence-only data and the EM algorithm. Biometrics 65, 554-563.
40. Warton, D. I. and Shepherd, L. C. (2010). Poisson point process models solve the "pseudo absence problem" for presence-only data in ecology. Annals of Applied Statistics 4, 1383-1402.