Maxent is not a presence-absence method: A comment on Thibaud et al.

Methods in Ecology and Evolution

Volumn 5, Issue 11, 2014, Pages 1192-1197

  Guillera Arroita, Gurutzeta ^a   Lahoz Monfort, José J ^a   Elith, Jane ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

Author keywords

glmnet; Maxent; Overfitting; Presence absence; Presence background; Presence only; Prevalence; Probabilities; Relative suitability; Species distribution model

Indexed keywords

EID: 84913601178     PISSN: None     EISSN: 2041210X     Source Type: Journal    
DOI: 10.1111/2041-210X.12252     Document Type: Article

References (20)

1. Aarts, G., Fieberg, J. & 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. Baddeley, A., Berman, M., Fisher, N.I., Hardegen, A., Milne, R.K., Schuhmacher, D., Shah, R. & Turner, R. (2010) Spatial logistic regression and change-of-support in Poisson point processes. Electronic Journal of Statistics, 4, 1151-1201.
3. Elith, J., Phillips, S.J., Hastie, T., Dudik, M., Chee, Y.E. & Yates, C.J. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57.
4. Fithian, W. & Hastie, T. (2013) Finite-sample equivalence in statistical models for presence-only data. Annals of Applied Statistics, 7, 1917-1939.
5. Harrell, F.E. (2001) Regression Modeling Strategies: With Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer, New York.
6. Harrell, F.E., Lee, K.L. & Mark, D.B. (1996) Multivariate prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Statistics in Medicine, 15, 361-387.
7. Hastie, T. & Fithian, W. (2013) Inference from presence-only data; the ongoing controversy. Ecography, 36, 864-867.
8. Hastie, T., Tibshirani, R. & Friedman, J.H. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer-Verlag, New York.
9. Jaynes, E.T. (1957) Information theory and statistical mechanics. Physical Review, 106, 620-630.
10. Lawson, C.R., Hodgson, J.A., Wilson, R.J. & Richards, S.A. (2014) Prevalence, thresholds and the performance of presence-absence models. Methods in Ecology and Evolution, 5, 54-64.
11. Merow, C., Smith, M.J. & Silander, J.A. (2013) A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography, 36, 1058-1069.
12. Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modelling of species geographic distributions. Ecological Modelling, 190, 231-259.
13. Phillips, S.J. & Dudík, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161-175.
14. Phillips, S.J. & Elith, J. (2013) On estimating probability of presence from use-availability or presence-background data. Ecology, 94, 1409-1419.
15. Phillips, S.J., Dudik, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J. & Ferrier, S. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19, 181-197.
16. Renner, I.W. & Warton, D.I. (2013) Equivalence of MAXENT and Poisson point process models for species distribution modeling in ecology. Biometrics, 69, 274-281.
17. Steyerberg, E.W., Eijkemans, M.J.C., Harrell, F.E. & Habbema, J.D.F. (2000) Prognostic modelling with logistic regression analysis: a comparison of selection and estimation methods in small data sets. Statistics in Medicine, 19, 1059-1079.
18. Thibaud, E., Petitpierre, B., Broennimann, O., Davison, A.C. & Guisan, A. (2014) Measuring the relative effect of factors affecting species distribution model predictions. Methods in Ecology and Evolution 5, 947-955.
19. Warton, D.I. & 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.
20. Zou, H. & Hastie, T. (2005) Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society B, 67, 301-320.