Components of uncertainty in species distribution analysis: A case study of the great grey shrike

Ecology

Volumn 89, Issue 12, 2008, Pages 3371-3386

  Dormann, Carsten F ^a   Purschke, Oliver ^a,b   Márquez, Jaime R García ^a,c   Lautenbach, Sven ^a   Schröder, Boris ^d  

^a HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^b LUND UNIVERSITY   (Sweden)

^c UNIVERSITY OF BONN   (Germany)

^d UNIVERSITY OF POTSDAM   (Germany)

Author keywords

Artificial neural network; Best subset regression; Climate change; Collinearity; Data uncertainty; GAM; Generalized additive models; Generalized linear models; GLM; Prediction; Saxony, Germany; Sequential regression

Indexed keywords

ARTIFICIAL NEURAL NETWORK; CLIMATE CHANGE; ECOLOGICAL MODELING; PREDICTION; REGRESSION ANALYSIS; SONGBIRD; SPATIAL ANALYSIS; SPATIAL DISTRIBUTION; STATISTICAL ANALYSIS; UNCERTAINTY ANALYSIS;

CENTRAL EUROPE; EURASIA; EUROPE; GERMANY; SAXONY;

RAIN;

ANIMAL; AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; BIOLOGICAL MODEL; CLIMATE; COMPUTER SIMULATION; ENVIRONMENTAL PROTECTION; FACTORIAL ANALYSIS; GERMANY; METHODOLOGY; PHYSIOLOGY; POPULATION DENSITY; POPULATION GROWTH; PREDICTION AND FORECASTING; SONGBIRD; SPECIES DIFFERENCE; STATISTICAL MODEL;

ANIMALS; AREA UNDER CURVE; CLIMATE; COMPUTER SIMULATION; CONSERVATION OF NATURAL RESOURCES; FACTOR ANALYSIS, STATISTICAL; GERMANY; LINEAR MODELS; MODELS, BIOLOGICAL; NEURAL NETWORKS (COMPUTER); PASSERIFORMES; POPULATION DENSITY; POPULATION GROWTH; PREDICTIVE VALUE OF TESTS; RAIN; SPECIES SPECIFICITY;

EID: 60249084749     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/07-1772.1     Document Type: Article

References (86)

1. Applequist, W. L., D. J. McGlinn, M. Miller, Q. G. Long, and J. S. Miller. 2007. How well do herbarium data predict the location of present populations? A test using Echinacea species in Missouri. Biodiversity and Conservation 16:1397-1407.
2. Araújo, M. B., and A. Guisan. 2006. Five (or so) challenges for species distribution modelling. Journal of Biogeography 33: 1677-1688.
3. Araújo, M. B., R. G. Pearson, W. Thuiller, and M. Erhard. 2005. Validation of species-climate impact models under climate change. Global Change Biology 11:1504-1513.
4. Araújo, M. B., and C. Rahbek. 2006. How does climate change affect biodiversity? Science 313:1396-1397.
5. Austin, M. P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157:101-118.
6. Austin, M. 2007. Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling 200:1-19.
7. Austin, M. P., L. Belbin, J. A. Meyers, M. D. Doherty, and M. Luoto. 2006. Evaluation of statistical models used for predicting plant species distributions: role of artificial data and theory. Ecological Modelling 199:197-216.
8. Austin, M. P., A. O. Nicholls, and C. R. Margules. 1990. Measurement of the realized qualitative niche: environmental niche of five Eucalyptus species, Ecological Monographs 60: 161-178.
9. Barry, S., and J. Elith. 2006, Error and uncertainty in habitat models. Journal of Applied Ecology 43:413-423.
10. Belsley, D. A. 1991. Conditioning diagnostics: collinearity and weak data regression. Wiley, New York, New York, USA.
11. Beven, K., and J. Freer. 2001. Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology. Journal of Hydrology: 11-29.
12. Bolliger, J., F. Kienast, R. Soliva, and G. Rutherford. 2007. Spatial sensitivity of species habitat patterns to scenarios of land-use change (Switzerland). Landscape Ecology 22:773-789.
13. Booth, G. D., M. J. Niccolucci, and E, G. Schuster, 1994, Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. Research Paper INT-470, USDA Forest Service, Forest Service, Ogden, Uah, USA.
14. Brotons, L., S. Herrando, and M. Pla. 2007. Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long-term monitoring programs. Diversity and Distributions 13:276-288.
15. Brotons, L., W. Thuiller, M. B. Araújo, and A. H. Hirzel. 2004. Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27:437-448.
16. Burnham, K. P., and D. R. Anderson. 2002. Model selection and multi-model inference: a practical information-theoretical approach. Springer, Berlin, Germany.
17. Chatfield, C. 1995. Model uncertainty, data mining and statistical inference (with discussion). Journal of the Royal Statistical Society A 158:419-466.
18. Chevan, A., and M. Sutherland. 1991. Hierarchical partitioning, American Statistician 45:90-96.
19. Coudun, C., J.-C. Gegout, C. Piedallu, and J.-C. Rameau. 2006. Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. Journal of Biogeography 33:1750-1763.
20. Crosetto, M., S. Tarantola, and A. Saltelli. 2000. Sensitivity and uncertainty analysis in spatial modelling based on GIS. Agriculture, Ecosystems and Environment 81:71-79.
21. Dijksta, T, K. 1988. On model uncertainty and its statistical implications. Springer, Berlin, Germany.
22. Dormann, C. F. 2007, Promising the future? Global change predictions of species distributions. Basic and Applied Ecology 8:387-397.
23. Dormann, C. F., et al. 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609-628.
24. Elder, J. F. 2003. The generalization paradox of ensembles. Journal of Computational and Graphical Statistics 12:853-864.
25. Elith, J., M. A. Burgman, and H. M. Regan. 2002. Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecological Modelling 157:313-329.
26. Elith, J., et al. 2006. Novel methods improve prediction of species' distributions from occurrence data. Ecography 29: 129-151.
27. Faraway. J. J. 1992. On the cost of data analysis. Journal of Computational and Graphical Statistics 1:215-231.
28. Fielding, A. H., and J. F. Bell. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environmental Conservation 24:38-49.
29. Giralt, D., and F. Valera. 2007. Population trends and spatial synchrony in peripheral populations of the endangered Lesser grey shrike in response to environmental change. Biodiversity and Conservation 16:841-856.
30. Graham, C. H., S. Ferrier, F. Huettman. C. Moritz, and A. T. Peterson. 2004. New developments in museum-based informatics and application in biodiversity analysis. Trends in Ecology and Evolution 19:497-503.
31. Graham, M. H. 2003. Confronting multicollinearity in ecological multiple regression. Ecology 84:2809-2815.
32. Guisan, A., C. H. Graham, J. Elith, and F. Huettmann, and the NCEAS Species Distribution Modelling Group. 2007. Sensitivity of predictive species distribution models to change in grain size. Diversity and Distributions 13:332-340.
33. Guisan, A., A, Lehmann, S. Ferrier, M. Austin, J. M. C. Overton, R. Aspinall, and T. Hastie. 2006. Making better biogeographical predictions of species' distributions. Journal of Applied Ecology 43:386-392.
34. Guisan, A., and W. Thuiller. 2005. Predicting species distributions: offering more than simple habitat models. Ecology Letters 8:993-1009.
35. Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135: 147-186.
36. Guralnick, R. P., A, W. Hill, and M. Lane, 2007, Towards a collaborative, global infrastructure for biodiversity assessment. Ecology Letters 10:663-672.
37. Harreil, F. E., Jr. 2001 Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Springer, New York, New York, USA.
38. Hastie, T., R. Tibshirani, and J, H, Friedman. 2001. Tbe elements of statistical learning: data mining, inference, and prediction. Springer, Berlin, Germany.
39. Heikkinen, R. K., M. Luoto, M. B. Araujo, R, Virkkala, W. Thuiller, and M. T. Sykes. 2006. Methods and uncertainties in bioclimatic envelope modelling under climate change. Progress in Physical Geography 30:751-777.
40. Heikkinen, R. K., M. Luoto, M. Kuussaari, and J. Pöyry. 2005. New insights into butterfly-environment relationships using partitioning methods. Proceedings of the Royal Society 272: 2203-2210.
41. Heuvelink, G, B, M. 1998, Error propagation in environmental modelling with GIS. Taylor and Francis, London, UK.
42. Hilbert, D. W., and B. Ostendorf. 2001. The utility of artificial neural networks for modelling the distribution of vegetation in past, present and future climates. Ecological Modelling 146:311-327.
43. Hirzel, A. H., V. Helfer, and F. Metral. 2001. Assessing habitatsuitability models with a virtual species. Ecological Modelling 145:111-121.
44. IPCC. 2001. Climate Change 2001: The scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. J. T. Houghton, Y. Ding, D. J. Griggs, M. Noguer, P. J. van der Linden, X. Dai, K. Maskell, and C. A. Johnson, editors. Cambridge University Press, Cambridge., UK.
45. Legendre, P., and L. Legendre. 1998. Numerical ecology. Second edition. Elsevier Science, Oxford, UK,
46. Lexer, M. J., and K. Hönninger. 2004. Effects of error in model input: experiments with a forest patch model. Ecological Modelling 173:159-176.
47. Luoto, M., R. K. Heikkinen, J. Poyry, and K. Saarinen. 2006. Determinants of the biogeographical distribution of butterflics in boreal regions. Journal of Biogeography 33:1764-1778.
48. Mac Nally, R. 2000. Regression and model-building in conservation biology, biogeography and ecology: the distinction between - and reconciliation of - 'predictive' and 'explanatory' models. Biodiversity and Conservation 9:655-671.
49. Mac Nally, R. 2002. Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables. Biodiversity and Conservation 11:1397-1401.
50. Maggini, R., A. Lehmann, N. E. Zimmermann, and A. Guisan. 2006. Improving generalized regression analysis for the spatial prediction of forest communities. Journal of Biogeography 33:1729-1749.
51. McCullough, P., and J. A. Neider. 1989. Generalized linear models. Second edition. Chapman and Hall, London, UK.
52. Mela, C. F., and P. K. Kopalle. 2002. The impact of collincarity on regression analysis: the asymmetric effect of negative and positive correlations. Applied Economics 34:667-677.
53. Meynard, C. N., and J. F. Quinn. 2007. Predicting species distributions: a critical comparison of the most common statistical models using artificial species. Journal of Biogeography 34:1455-1469.
54. Midgley, G. F., G. O. Hughes, W. Thuiller, and A. G. Rebelo. 2006. Migration rate limitations on climate change-induced range shifts in Cape Proteaceae. Diversity and Distributions 12:555-562,
55. Mikusinski, G., and L. Edenius. 2006. Assessment of spatial functionality of old forest in Sweden as habitat for virtual species. Scandinavian Journal of Forest Research 21:73-83.
56. Olden, J. D., and D. A. Jackson. 2002. A comparison of statistical approaches for modelling fish species distributions. Freshwater Biology 47:1976-1995.
57. O'Neill, R. V., and R. H. Gardner. 1979. Sources of uncertainty in ecological models, Pages 447-463 in B, P. Zeigler, M. S. Elizas, G. J. Klir, and H. I. Oren, editors. Methodology in systems modeling and simulation. North-Holland Publishing, Amsterdam, The Netherlands.
58. Pearson, R. G., W. Thuiller, M. B. Araujo, E. Martinez-Meyer, L. Brotons, C., McClean, L. Miles, P. Segurado, T. P. Dawson, and D. C. Lees. 2006. Model-based uncertainty in species range prediction. Journal of Biogeography 33:1704-1711.
59. R Development Core Team. 2008. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (http://www.R-project.org)
60. Raat, K. J., J. A. Vrugt, W. Bouten, and A. Tietema. 2004. Towards reduced uncertainty in catchment nitrogen modelling: quantifying the effect of field observation uncertainty on model calibration. Hydrology and Earth System Sciences 8: 751-763.
61. Randin, C. F., T. Dirnbock, S. Dullinger, N. E. Zimmermann, M. Zappa, and A. Guisan. 2006. Are niche-based species distribution models transferable in space? Journal of Biogeography 33:1689-1703.
62. Rau, S., R. Steffens, and U. Zöphel. 1999. Rote Liste Wirbeltiere. Sächsisches Landesamt für Umwelt und Geologie, Dresden, Germany.
63. Recknagel, F. 2001, Applications of machine learning to ecological modelling. Ecological Modelling 146:303-310.
64. Reineking, B., and B. Schröder. 2006. Constrain to perform: regularization of habitat models. Ecological Modelling 193: 675-690.
65. Rondinini, C., K. A. Wilson, L. Boitani, H. Grantham, and H. P. Possingham. 2006. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecology Letters 9:1136-1145.
66. Rounsevell, M. D. A., et al. 2006. A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems and Environment 114:57-68.
67. Schröder, B., and O. Richter. 1999. Are habitat models transferable in space and time? Zeitschrift für Ökologie und Naturschutz 8:195-205.
68. Schröter, D., et al. 2005. Ecosystem service supply and vulnerability to global change in Europe. Science 310:1333-1337.
69. Scott, J. M., P. J. Heglund, M. Morrison, J. B. Haufler, and W. A. Wall, editors. 2002. Predicting species occurrences: issues of accuracy and scale. Island Press, Covello, California, USA.
70. Segurado, P., and M. B. Araújo. 2004. An evaluation of methods for modelling species distributions. Journal of Biogeography 31:1555-1568.
71. Stainforth, D. A., et al. 2005. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature 433:403-406.
72. Swanson, F. J., T. K. Kratz, N. Caine, and R. G. Woodmansee. 1988. Landform effects on ecosystem patterns and processes. BioScience 38:92-98.
73. Thuiller, W. 2003. BIOMOD: optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biology 9:1353-1362.
74. Thuiller, W. 2004. Patterns and uncertainties of species' range shifts under climate change. Global Change Biology 10:2020-2027.
75. Thuiller, W, M. B. Araújo, R, G. Pearson, R. J, Whittaker, L. Brotons, and S. Lavorel. 2004. Uncertainty in predictions of extinction risk. Nature 430:145-148.
76. Tibshirani, R. 1995. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society B 58:267-288.
77. Tsoar, A., O. Allouche, O. Steinitz, D. Rotem, and R. Kadmon. 2007. A comparative evaluation of presence-only methods for modelling species distribution. Diversity and Distributions 13:397-405. Van Hörne, B. 2002. Approaches to habitat modeling: the tensions between pattern and process and between specificity and generality. Pages 63-72 in J. M. Scott, P. J. Heglund, M. L. Morrison, J. B. Haufler, M. G. Raphael, W. B. Wall, and F. Samson, editors. Predicting species occurrences: issues of accuracy and scale. Island Press, Washington, D.C., USA.
78. Van Vuuren, D. P., O. E. Sala, and H. M. Pereira. 2006. The future of vascular plant diversity under four global scenarios. Ecology and Society 11:25. (http://www.eeologyandsociety. org/vol11/iss22/art25/)
79. Vaughan, I. P., and S. J. Ormerod. 2005. The continuing challenges of testing species distribution models. Journal of Applied Ecology 42:720-730.
80. Venables, W. N., and B. D. Ripley. 2002. Modern applied statistics with S. Fourth edition. Springer, New York, New York, USA.
81. Wentz, F. J., L. Ricciardulli, K. Hilburn, and C. Mears. 2007. How much more rain will global warming bring? Science 317: 233-235.
82. Whittingham, M. J., J. R. Krebs, R. D. Swetnam, J. A. Vickery, J. D. Wilson, and R. P. Freckleton. 2007. Should conservation strategies consider spatial generality? Farmland birds show regional not national patterns of habitat association. Ecology Letters 10:25-35.
83. Whittingham, M. J., P. A. Stephens, R. B. Bradbury, and R. P. Freckleton. 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology 75:1182-1189.
84. Wintle, B. A., M. A. McCarthy, C. T. Volinsky, and R. P. Kavanagh. 2003. The use of Bayesian Model Averaging to better represent uncertainty in ecological models. Conservation Biology 17:1579-1590.
85. Wood, S. N. 2006. Generalized additive models. Chapman and Hall/CRC., New York, New York, USA.
86. Ye, J. 1998. On measuring and correcting the effects of data mining and model selection. Journal of the American Statistical Association 93:120-131.