ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models

Methods in Ecology and Evolution

Volumn 5, Issue 11, 2014, Pages 1198-1205

  Muscarella, Robert ^a   Galante, Peter J ^b   Soley Guardia, Mariano ^b,c   Boria, Robert A ^b   Kass, Jamie M ^b,c   Uriarte, María ^a   Anderson, Robert P ^b,c,d  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b CITY COLLEGE OF NEW YORK   (United States)

^c CITY UNIVERSITY OF NEW YORK   (United States)

^d DIVISION OF INVERTEBRATE ZOOLOGY   (United States)

Author keywords

AIC; Bioinformatics; Ecological niche model; Model complexity; Overfitting; Software; Species distribution model

Indexed keywords

EID: 85179648316     PISSN: None     EISSN: 2041210X     Source Type: Journal    
DOI: 10.1111/2041-210X.12261     Document Type: Article

References (40)

1. Anderson, R.P. (2003) Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela. Journal of Biogeography, 30, 591-605.
2. Anderson, R.P. (2013) A framework for using niche models to estimate impacts of climate change on species distributions. Annals of the New York Academy of Sciences, 1297, 8-28.
3. Anderson, R.P. & Gonzalez, J.I. (2011) Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecological Modelling, 222, 2796-2811.
4. Araújo, M.B. & Guisan, A. (2006) Five (or so) challenges for species distribution modelling. Journal of Biogeography, 33, 1677-1688.
5. Araújo, M. & Peterson, A.T. (2012) Uses and misuses of bioclimatic envelope modelling. Ecology, 93, 1527-1539.
6. Bawiec, W.J., ed. (1999) Geology, geochemistry, geophysics, mineral occurrences and mineral resource assessment for the Commonwealth of Puerto Rico: U.S. Geological Survey Open-File Report 98-038, available online only.
7. Burnham, K.P. & Anderson, D.R. (2004) Multimodel inference: understanding AIC and BIC in Model Selection. Sociological Methods and Research, 33, 261-304.
8. Cao, Y., DeWalt, R.E., Robinson, J.L., Tweddale, T., Hinz, L. & Pessino, M. (2013) Using Maxent to model the historic distributions of stonefly species in Illinois streams: the effects of regularization and threshold selections. Ecological Modelling, 259, 30-39.
9. Daly, C., Helmer, E.H. & Quiñones, M. (2003) Mapping the climate of Puerto Rico, Vieques and Culebra. International Journal of Climatology, 23, 1359-1381.
10. Dormann, C.F., Schymanski, S.J., Cabral, J., Chuine, I., Graham, C., Hartig, F. et al. (2012) Correlation and process in species distribution models: bridging a dichotomy. Journal of Biogeography, 39, 2119-2131.
11. Elith, J., Kearney, M. & Phillips, S. (2010) The art of modelling range-shifting species. Methods in Ecology and Evolution, 1, 330-342.
12. Elith, J., Graham, C.H., Anderson, R.P., Dudík, M., Ferrier, S., Guisan, A. et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129-151.
13. Elith, J., Phillips, S.J., Hastie, T., Dudík, M., Chee, Y.E. & Yates, C.J. (2011) A statistical explanation of MaxEnt for ecologists. Diversity and Distributions, 17, 43-57.
14. Fielding, A.H. & Bell, J.F. (1997) A review of methods for the assessment of prediction errors in conservation presence-absence models. Environmental Conservation, 24, 38-49.
15. Fithian, W. & Hastie, T. (2013) Finite-sample equivalence in statistical models for presence-only data. Annals of Applied Statistics, 7, 1917-1939.
16. Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993-1009.
17. Hanley, J.A. & McNeil, B.J. (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 29-36.
18. Hastie, T., Tibshirani, R. & Friedman, J.H. (2009) The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer-Verlag, New York, New York, USA.
19. Hijmans, R.J. (2012) Cross-validation of species distribution models: removing spatial sorting bias and calibration with a null model. Ecology, 93, 679-688.
20. Hijmans, R.J., Phillips, S., Leathwick, J. & Elith, J. (2011) Package 'dismo'. Available online at: http://cran.r-project.org/web/packages/dismo/index.html.
21. Kozak, K.H., Graham, C.H. & Wiens, J.J. (2008) Integrating GIS-based environmental data into evolutionary biology. Trends in Ecology and Evolution, 23, 141-148.
22. Lobo, J.M., Jiménez-Valverde, A. & Real, R. (2008) AUC: a misleading measure of the performance of predictive distribution models. Global Ecology & Biogeography, 17, 145-151.
23. Merow, C., Smith, M. & Silander, J.A. (2013) A practical guide to Maxent: what it does, and why inputs and settings matter. Ecography, 36, 1-12.
24. Pearson, R.G., Raxworthy, C.J., Nakamura, M. & Peterson, A.T. (2007) Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. Journal of Biogeography, 34, 102-117.
25. Peterson, A.T., Soberón, J., Pearson, R.G., Anderson, R.P., Martínez-Meyer, E., Nakamura, M. et al. (2011) Ecological Niches and Geographic Distributions. Monographs in Population Biology, 49. Princeton University Press, Princeton, New Jersey, USA.
26. Phillips, S. (2008) Transferability, sample selection bias and background data in presence-only modelling: a response to Peterson et al. (2007). Ecography, 31, 272-278.
27. Phillips, S.J., Anderson, R.P. & Schapire, R.E. (2006) Maximum entropy modeling of species geographic distributions. Ecological Modelling, 190, 231-259.
28. Phillips, S.J. & Dudík, M. (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography, 31, 161-175.
29. Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J. et al. (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecological Applications, 19, 181-197.
30. Radosavljevic, A. & Anderson, R.P. (2014) Making better Maxent models of species distributions: complexity, overfitting and evaluation. Journal of Biogeography, 41, 629-643.
31. 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.
32. Schoener, T.W. (1968) The Anolis lizards of Bimini: resource partitioning in a complex fauna. Ecology, 49, 704-726.
33. Shcheglovitova, M. & Anderson, R.P. (2013) Estimating optimal complexity for ecological niche models: a jackknife approach for species with small sample sizes. Ecological Modelling, 269, 9-17.
34. Syfert, M.M., Smith, M.J. & Coomes, D.A. (2013) The effects of sampling bias and model complexity on the predictive performance of MaxEnt species distribution models. PLoS ONE, 8, e55158.
35. Veloz, S.D. (2009) Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. Journal of Biogeography, 36, 2290-2299.
36. Warren, D.L. & Seifert, S.N. (2011) Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21, 335-342.
37. Warren, D.L., Glor, R.E., Turelli, M. & Funk, D. (2009) Environmental niche equivalency versus conservatism: quantitative approaches to niche evolution. Evolution, 62, 2868-2883; Erratum: Evolution 2865: 1215.
38. Warren, D.L., Wright, A.N., Seifert, S.N. & Shaffer, H.B. (2014) Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 California vertebrate species of concern. Diversity and Distributions, 20, 334-343.
39. Wenger, S.J. & Olden, J.D. (2012) Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods in Ecology and Evolution, 3, 260-267.
40. Yackulic, C.B., Chandler, R., Zipkin, E.F., Royle, J.A., Nichols, J.D., Campbell Grant, E.H. et al. (2013) Presence-only modelling using MAXENT: when can we trust the inferences? Methods in Ecology and Evolution, 4, 236-243.