Inferring prevalence from presence-only data: A response to 'Can we model the probability of presence of species without absence data?'

Ecography

Volumn 35, Issue 5, 2012, Pages 385-387

  Phillips, Steven ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA INTERPRETATION; ECOLOGICAL MODELING; ENVIRONMENTAL CONDITIONS; PROBABILITY; SPECIES OCCURRENCE;

EID: 84860612628     PISSN: 09067590     EISSN: 16000587     Source Type: Journal    
DOI: 10.1111/j.1600-0587.2011.07285.x     Document Type: Article

References (15)

1. Colwell R. K. and Rangel T. F. 2009. Hutchinson's duality: the once and future niche. Proc. Natl Acad. Sci. USA 106: 19651-19658.
2. Elith J. et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151.
3. Elith J. et al. 2011. A statistical explanation of maxent for eco logists. Divers. Distrib. 17: 43-57.
4. Hirzel A. H. et al. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? Ecology 87: 2027-2036.
5. Keating K. A. and Cherry S. 2004. Use and interpretation of logistic regression in habitat-selection studies. J. Wildl. Manage. 68: 774-789.
6. Lancaster T. and Imbens G. 1996. Case-control studies with contaminated controls. J. Econometrics 71: 145-160.
7. Lele S. R. and Keim J. L. 2006. Weighted distributions and estimation of resource selection probability functions. Ecology 87: 3021-3028.
8. Li W. et al. 2011. Can we model the probability of presence of species without absence data? Ecography 34: 1096-1105.
9. Manly B. et al. 2002. Resource selection by animals: statistical design and analysis for field studies, 2nd ed. - Kluwer.
10. Phillips S. and Elith J. 2011. Logistic methods for resource selection functions and presence-only species distribution models. AAAI, pp. 1384-1389.
11. Phillips S. et al. 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19: 181-197.
12. Phillips S. J. et al. 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190: 231-259.
13. Steinberg D. and Cardell N. S. 1992. Estimating logistic regression models when the dependent variable has no variance. Commun. Stat. Theory Methods 21: 423-450.
14. Stockwell D. and Peters D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. Int. J. Geogr. Inf. Sci. 13: 143-158.
15. Ward G. et al. 2009. Presence-only data and the EM algorithm. Biometrics 65: 554-563.