On estimating probability of presence from use-availability or presence-background data

Ecology

Volumn 94, Issue 6, 2013, Pages 1409-1419

  Phillips, Steven J ^a   Elith, Jane ^b  

^a AT AND T LABS RESEARCH   (United States)

^b UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

Availability; Background; Identifiability; Logistic; Measuring use vs. non use; Presence background; Prevalence; Resource selection; Species distribution model

Indexed keywords

DATA SET; ECOLOGICAL MODELING; ENVIRONMENTAL CONDITIONS; ERROR ANALYSIS; IDENTIFICATION METHOD; LOGISTICS; NUMERICAL MODEL; POPULATION DISTRIBUTION; RESOURCE SELECTION;

ANIMAL; ARTICLE; BIOLOGICAL MODEL; ECOSYSTEM; PROBABILITY; STATISTICAL MODEL;

ANIMALS; ECOSYSTEM; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PROBABILITY;

EID: 84879580140     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/12-1520.1     Document Type: Article

References (32)

1. Aarts, G., J. Fieberg, and J. Matthiopoulos. 2012. Comparative interpretation of count, presence-absence and point methods for species distribution models. Methods in Ecology and Evolution 3:177-187.
2. Aarts, G., M. MacKenzie, B. McConnell, M. Fedak, and J. Matthiopoulos. 2008. Estimating space-use and habitat preference from wildlife telemetry data. Ecography 31:140-160. (Pubitemid 351280981)
3. Beyer, H. L., D. T. Haydon, J. M. Morales, J. L. Frair, M. Hebblewhite, M. Mitchell, and J. Matthiopoulos. 2010. The interpretation of habitat preference metrics under use-availability designs. Philosophical Transactions of the Royal Society B 365:2245-2254.
4. Bio, A., R. Alkemande, and A. Barendregt. 1998. Determining alternative models for vegetation response analysis: a nonparametric approach. Journal of Vegetation Science 9:5-16. (Pubitemid 28165413)
5. Chakraborty, A., A. E. Gelfand, A. M. Wilson, A. M. Latimer, and J. A. Silander. 2011. Point pattern modelling for degraded presence-only data over large regions. Applied Statistics 60:757-776.
6. Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B 39:1-38.
7. Desrochers, A., E. J. McIntire, S. G. Cumming, J. Nowak, and S. Sharma. 2010. False negatives -a false problem in studies of habitat selectionIdeas in Ecology and Evolution 3:20-25.
8. Dorazio, R. 2012. Predicting the geographic distribution of a species from presence-only data subject to detection errors. Biometrics 68:1303-1312.
9. Elith, J., et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151.
10. Elith, J., S. Phillips, T. Hastie, M. Dudík, Y. E. Chee, and C. Yates. 2011. A statistical explanation of MaxEnt for ecologists. Diversity and Distributions 17:43-57.
11. Ferrier, S., G. Watson, J. Pearce, and M. Drielsma. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. 1. Species-level modelling. Biodiversity and Conservation 11:2275-2307. (Pubitemid 36072839)
12. Franklin, J. 2010. Mapping species distributions: spatial inference and prediction. Cambridge University Press, Cambridge, UK.
13. Jiménez-Valverde, A., J. M. Lobo, and J. Hortal. 2008. Not as good as they seem: the importance of concepts in species distribution modelling. Diversity and Distributions 14:885-890.
14. Johnson, C. J., S. E. Nielsen, E. H. Merrill, T. L. McDonald, and M. S. Boyce. 2006. Resource selection functions based on use-availability data: Theoretical motivation and evaluation methods. Journal of Wildlife Management 70:347-357. (Pubitemid 43736900)
15. Keating, K. A., and S. Cherry. 2004. Use and interpretation of logistic regression in habitat-selection studies. Journal of Wildlife Management 68:774-789. (Pubitemid 40107285)
16. Lancaster, T., and G. Imbens. 1996. Case-control studies with contaminated controls. Journal of Econometrics 71:145-160. (Pubitemid 126348854)
17. Lee, A., A. Scott, and C. Wild. 2006. Fitting binary regression models with case-augmented samples. Biometrika 93:385-397. (Pubitemid 43992199)
18. Lele, S. R. 2009. A new method for estimation of resource selection probability function. Journal of Wildlife Management 73:122-127.
19. Lele, S. R., and J. L. Keim. 2006. Weighted distributions and estimation of resource selection probability functions. Ecology 87:3021-3028. (Pubitemid 44969253)
20. Li, W., Q. Guo, and C. Elkan. 2011. Can we model the probability of presence of species without absence dataEcography 34:1096-1105.
21. Manly, B., L. McDonald, D. Thomas, T. McDonald, and W. Erickson. 2002. Resource selection by animals: statistical design and analysis for field studies. Second edition. Kluwer, New York, New York, USA.
22. Phillips, S. 2012. Inferring prevalence from presence-only data: a response to 'Can we model the probability of presence of species without absence data?' Ecography 35:385-387.
23. Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231-259. (Pubitemid 41680650)
24. Phillips, S. J., and M. Dudík. 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161-175. (Pubitemid 351457533)
25. Phillips, S., M. Dudík, J. Elith, C. Graham, A. Lehmann, J. Leathwick, and S. Ferrier. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications 19:181-197.
26. Phillips, S., and J. Elith. 2011. Logistic methods for resource selection functions and presence-only species distribution models. Pages 1384-1389 in Proceedings of the 25th AAAI Conference on Artificial Intelligence, San Francisco, California, USA.
27. Pulliam, H. R. 2000. On the relationship between niche and distribution. Ecology Letters 3:349-361. (Pubitemid 30597802)
28. Reddy, S., and L. M. Dá valos. 2003. Geographical sampling bias and its implications for conservation priorities in Africa. Journal of Biogeography 30:1719-1727. (Pubitemid 37399756)
29. Royle, J. A., R. B. Chandler, C. Yackulic, and J. D. Nichols. 2012. Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. Methods in Ecology and Evolution 3(3):545-554.
30. Steinberg, D., and N. S. Cardell. 1992. Estimating logistic regression models when the dependent variable has no variance. Communications in Statistics -Theory and Methods 21(2):423-450.
31. Ward, G., T. Hastie, S. Barry, J. Elith, and J. Leathwick. 2009. Presence-only data and the EM algorithm. Biometrics 65: 554-563.
32. Warton, D., and L. Shepherd. 2010. Poisson point process models solve the ''pseudo-absence problem'' for presenceonly data in ecology. Annals of Applied Statistics 4:1383-1402.