Sdm: A reproducible and extensible R platform for species distribution modelling

Ecography

Volumn 39, Issue 4, 2016, Pages 368-375

  Naimi, Babak ^a,b   Araújo, Miguel B ^a,b,c,d  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF COPENHAGEN   (Denmark)

^c MUSEO NACIONAL DE CIENCIAS NATURALES   (Spain)

^d UNIVERSITY OF ÉVORA   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CELLULAR AUTOMATON; DATA ACQUISITION; DATA SET; ECOLOGICAL MODELING; SIMULATION; SOFTWARE; SPATIOTEMPORAL ANALYSIS;

EID: 84959475541     PISSN: 09067590     EISSN: 16000587     Source Type: Journal    
DOI: 10.1111/ecog.01881     Document Type: Article

References (60)

1. Alfons, A. et al. 2010. An object-oriented framework for statistical simulation: the R package simFrame. - J. Stat. Softw. 37: 1-36.
2. Araújo, M. B. and New, M. 2007. Ensemble forecasting of species distributions. - Trends Ecol. Evol. 22: 42-47.
3. Araújo, M. B. and Peterson, A. T. 2012. Uses and misuses of bioclimatic envelope modeling. - Ecology 93: 1527-1539.
4. Araújo, M. B. et al. 2005a. Validation of species-climate impact models under climate change. - Global Change Biol. 11: 1504-1513.
5. Araújo, M. B. et al. 2005b. Reducing uncertainty in projections of extinction risk from climate change. - Global Ecol. Biogeogr. 14: 529-538.
6. Austin, M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. - Ecol. Model. 200: 1-19.
7. Barbet-Massin, M. et al. 2012. Selecting pseudo-absences for species distribution models: how, where and how many? - Methods Ecol. Evol. 3: 327-338.
8. Baselga, A. and Araújo, M. B. 2009. Individualistic vs community modelling of species distributions under climate change. - Ecography 32: 55-65.
9. Bivand, R. S. et al. 2008. Applied spatial data analysis with R. - Springer.
10. Breiman, L. 2001. Random forests. - Mach. Learn. 45: 5-32.
11. Breiman, L. et al. 1984. Classification and regression trees. - Wadsworth International Group, Belmont, CA, USA.
12. Busby, J. R. 1991. BIOCLIM - a bioclimate analysis and prediction system. - Plant Protection Quarterly, Australia.
13. Carpenter, G. et al. 1993. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. - Biodivers. Conserv. 2: 667-680.
14. Chambers, J. M. 2014. Object-oriented programming, functional programming and R. - Stat. Sci. 29: 167-180.
15. Chatterjee, S. and Hadi, A. S. 2006. Regression analysis by example. - John Wiley and Sons.
16. de Souza Muñoz, M. et al. 2009. openModeller: a generic approach to species' potential distribution modelling. - GeoInformatica 15: 111-135.
17. Diniz-Filho, J. A. F. et al. 2009. Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. - Ecography 32: 897-906.
18. Dormann, C. F. 2011. Modelling species' distributions. - In: Jopp, F. et al. (eds), Modelling complex ecological dynamics. Springer, pp. 179-196.
19. Dormann, C. F. et al. 2013. Collinearity: a review of methods to deal with it and a simulation study evaluating their performance. - Ecography 36: 27-46.
20. Elith, J. et al. 2005. The evaluation strip: a new and robust method for plotting predicted responses from species distribution models. - Ecol. Model. 186: 280-289.
21. Elith, J. et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. - Ecography 29: 129-151.
22. Farber, O. and Kadmon, R. 2003. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. - Ecol. Model. 160: 115-130.
23. Fielding, A. H. and Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. - Environ. Conserv. 24: 38-49.
24. Friedman, J. H. 1991. Multivariate adaptive regression splines. - Ann. Stat. 19: 1-67.
25. Friedman, J. H. 2001. Greedy function approximation: a gradient boosting machine. - Ann. Stat. 29: 1189-1232.
26. Garcia, R. A. et al. 2012. Exploring consensus in 21st century projections of climatically suitable areas for African vertebrates. - Global Change Biol. 18: 1253-1269.
27. Graham, C. H. et al. 2004. New developments in museum-based informatics and applications in biodiversity analysis. - Trends Ecol. Evol. 19: 497-503.
28. Guisan, A. and Zimmermann, N. E. 2000. Predictive habitat distribution models in ecology. - Ecol. Model. 135: 147-186.
29. Guo, Q. and Liu, Y. 2010. ModEco: an integrated software package for ecological niche modeling. - Ecography 33: 637-642.
30. Hastie, T. and Tibshirani, R. 1990. Generalised additive models. - Chapman and Hall.
31. Hastie, T. et al. 1994. Flexible discriminant analysis by optimal scoring. - J. Am. Stat. Assoc. 89: 1255-1270.
32. Hastie, T. et al. 2009. The elements of statistical learning. - Springer.
33. Heikkinen, R. K. et al. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. - Prog. Phys. Geogr. 30: 751-777.
34. Hijmans, R. J. and Elith, J. 2013. dismo: species distribution modeling with R. - R project, species distribution modeling with R.
35. Hirzel, A. H. et al. 2002. Ecological-niche factor analysis: how to compute habitat-suitability maps without absence data? - Ecology 83: 2027-2036.
36. Joppa, L. N. et al. 2013. Troubling trends in scientific software use. - Science 340: 814-815.
37. Kuhn, M. 2008. Building predictive models in R using the caret package. - J. Stat. Softw. 28: 1-26.
38. Madon, B. et al. 2013. Community-level vs species-specific approaches to model selection. - Ecography 36: 1291-1298.
39. Marquardt, D. W. 1970. Generalized inverses, ridge regression, biased linear estimation, and nonlinear estimation. - Technometrics 12: 591-612.
40. McCullagh, P. and Nelder, J. A. 1989. Generalized linear models. - Chapman and Hall.
41. Murray, K. and Conner, M. M. 2009. Methods to quantify variable importance: implications for the analysis of noisy ecological data. - Ecology 90: 348-355.
42. Naimi, B. and Voinov, A. 2012. StellaR: a software to translate Stella models into R open-source environment. - Environ. Model. Softw. 38: 117-118.
43. Naimi, B. et al. 2011. Spatial autocorrelation in predictors reduces the impact of positional uncertainty in occurrence data on species distribution modelling. - J. Biogeogr. 38: 1497-1509.
44. Naimi, B. et al. 2014. Where is positional uncertainty a problem for species distribution modelling? - Ecography 37: 191-203.
45. Pearson, R. G. et al. 2006. Model-based uncertainty in species range prediction. - J. Biogeogr. 33: 1704-1711.
46. Pearson, R. G. et al. 2007. Predicting species distributions from small numbers of occurrence records: a test case using cryptic geckos in Madagascar. - J. Biogeogr. 34: 102-117.
47. Peterson, A. T. et al. 2011. Ecological niches and geographic distributions: e-book. - Princeton Univ. Press.
48. Phillips, S. J. et al. 2006. Maximum entropy modeling of species geographic distributions. - Ecol. Model. 190: 231-259.
49. Randin, C. F. et al. 2006. Are niche-based species distribution models transferable in space? - J. Biogeogr. 33: 1689-1703.
50. Rosenblatt, F. 1958. The perceptron: a probabilistic model for information storage and organization in the brain. - Psychol. Rev. 65: 386.
51. Rowe, R. J. 2005. Elevational gradient analyses and the use of historical museum specimens: a cautionary tale. - J. Biogeogr. 32: 1883-1897.
52. Royle, J. A. et al. 2012. Likelihood analysis of species occurrence probability from presence-only data for modelling species distributions. - Methods Ecol. Evol. 3: 545-554.
53. Segurado, P. and Araújo, M. B. 2004. An evaluation of methods for modelling species distributions. - J. Biogeogr. 31: 1555-1568.
54. Soetaert, K. et al. 2010. Solving differential equations in R. - In: Psihoyios, G. and Tsitouras, C. (eds), Numerical analysis and applied mathematics, vol. I-III. Am. Inst. Physics, pp. 31-34.
55. Stockwell, D. and Peters, D. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. - Int. J. Geogr. Inform. Sci. 13: 143-158.
56. Thuiller, W. et al. 2004. Do we need land-cover data to model species distributions in Europe? - J. Biogeogr. 31: 353-361.
57. Thuiller, W. et al. 2009. BIOMOD - a platform for ensemble forecasting of species distributions. - Ecography 32: 369-373.
58. VanDerWal, J. et al. 2011. SDMTools: species distribution modelling tools: tools for processing data associated with species distribution modelling exercises. - R package.
59. Vapnik, V. 1995. The nature of statistical learning theory. - Springer.
60. Wood, C. et al. 2011. eBird: engaging birders in science and conservation. - PLoS Biol. 9: e1001220.