Static species distribution models in dynamically changing systems: How good can predictions really be?

Ecography

Volumn 32, Issue 5, 2009, Pages 733-744

  Zurell, Damaris ^a   Jeltsch, Florian ^a   Dormann, Carsten F ^b   Schröder, Boris ^a,c  

^a UNIVERSITY OF POTSDAM   (Germany)

^b HELMHOLTZ CENTRE FOR ENVIRONMENTAL RESEARCH UFZ   (Germany)

^c LEIBNIZ CENTRE FOR AGRICULTURAL LANDSCAPE RESEARCH ZALF   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLIMATE CHANGE; COMMUNITY RESPONSE; CONSERVATION PLANNING; ENVIRONMENTAL CHANGE; GEOGRAPHICAL DISTRIBUTION; HOME RANGE; POLICY MAKING; POPULATION MODELING; TEMPORAL VARIATION;

EID: 70349694123     PISSN: 09067590     EISSN: 16000587     Source Type: Journal    
DOI: 10.1111/j.1600-0587.2009.05810.x     Document Type: Article

References (70)

1. Akaike, H. 1974. A new look at the statistical model identification IEEE T. Autom. Contr. 19 : 716 723.
2. Albert, C. H. Thuiller, W. 2008. Favourability functions versus probability of presence: advantages and misuses Ecography 31 : 417 422.
3. Albert, C. H. et al. 2008. Land-use change and subalpine tree dynamics: colonization of Larix decidua in French subalpine grasslands J. Appl. Ecol. 45 : 659 669.
4. Araújo, M. B. Rahbek, C. 2006. How does climate change affect biodiversity? Science 313 : 1396 1397.
5. Araújo, M. B. et al. 2005. Validation of species-climate impact models under climate change Global Change Biol. 11 : 1504 1513.
6. Araújo, M. B. et al. 2008. Quaternary climate changes explain diversity among reptiles and amphibians Ecography 31 : 8 15.
7. Austin, M. P. et al. 2006. Evaluation of statistical models used for predicting plant species distributions: role of artificial data and theory Ecol. Model. 199 : 197 216.
8. Bakkenes, M. et al. 2002. Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050 Global Change Biol. 8 : 390 407.
9. Berger, U. et al. 1999. Virtual biologists observe virtual grasshoppers: an assessment of different mobility parameters for the analysis of movement patterns Ecol. Model. 115 : 119 127.
10. Bonsall, M. B. Hassel, M. P. 2000. The effects of metapopulation structure on indirect interactions in host-parasitoid assemblages Proc. R. Soc. B 267 : 2207 2212.
11. Brotons, L. et al. 2007. Updating bird species distribution at large spatial scales: applications of habitat modelling to data from long-term monitoring programs Divers. Distrib. 13 : 276 288.
12. Comins, H. N. et al. 1992. The spatial dynamics of host-parasitoid systems J. Anim. Ecol. 61 : 735 748.
13. Davis, A. J. et al. 1998. Making mistakes when predicting shifts in species range in response to global warming Nature 391 : 783 786.
14. del Barrio, G. et al. 2006. Integrating multiple modelling approaches to predict the potential impacts of climate change on species' distributions in contrasting regions: comparison and implications for policy Environ. Sci. Policy 9 : 129 147.
15. Diez, J. M. Pulliam, H. R. 2007. Hierarchical analysis of species distributions and abundance across environmental gradients Ecology 88 : 3144 3152.
16. Dormann, C. F. 2007. Promising the future? Global change projections of species distributions Basic Appl. Ecol. 8 : 387 397.
17. Dormann, C. F. et al. 2008. Components of uncertainty in species distribution analysis: a case study of the great grey shrike Lanius excubitor L Ecology 89 : 3371 3386.
18. Elith, J. et al. 2008. A working guide to boosted regression trees J. Anim. Ecol. 77 : 802 813.
19. Fielding, A. H. Haworth, P. F. 1995. Testing the generality of bird-habitat models Conserv. Biol. 9 : 1466 1481.
20. Fielding, A. H. Bell, J. F. 1997. A review of methods for the assessment of prediction errors in conservation presence/absence models Environ. Conserv. 24 : 38 49.
21. Garrison, B. A. Lupo, T. 2002. Accuracy of bird range maps based on habitat maps and habitat relationship models Scott, J. M. Predicting species occurrences - issues of accuracy and scale. Island Press 367 375.
22. Gelman, A. Hill, J. 2007. Data analysis using regression and multilevel/hierarchical models. Cambridge Univ. Press.
23. Graf, R. F. et al. 2005. The importance of spatial scale in habitat models: capercaillie in the Swiss Alps Landscape Ecol. 20 : 703 717.
24. Guisan, A. Thuiller, W. 2005. Predicting species distribution: offering more than simple habitat models Ecol. Lett. 8 : 993 1009.
25. Harrell, J. F. E. 2001. Regression modeling strategies. Springer.
26. Hassell, M. P. 2000. Host-parasitoid population dynamics J. Anim. Ecol. 69 : 543 566.
27. Hassell, M. P. et al. 1991. Spatial structure and chaos in insect population dynamics Nature 353 : 255 258.
28. Hawkins, B. A. Porter, E. E. 2003. Does herbivore diversity depend on plant diversity? The case of California butterflies Am. Nat. 161 : 40 49.
29. Hernandez, P. A. et al. 2006. The effect of sample size and species characteristics on performance of different species distribution modeling methods Ecography 29 : 773 785.
30. Hirzel, A. Guisan, A. 2002. Which is the optimal sampling strategy for habitat suitability modelling Ecol. Model. 157 : 331 341.
31. Hirzel, A. H. et al. 2001. Assessing habitat-suitability models with a virtual species Ecol. Model. 145 : 111 121.
32. Hosmer, D. W. Lemeshow, S. 2000. Applied logistic regression. Wiley.
33. Houghton, J. T. et al. 2001. Climate change 2001: the scientific basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge Univ. Press.
34. Jiménez-Valverde, A. Lobo, J. M. 2007. Threshold criteria for conversion of probability of species presence to either-or presence-absence Acta Oecol. 31 : 361 369.
35. Keith, D. A. et al. 2008. Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models Biol. Lett. 4 : 560 563.
36. King, A. A. Hastings, A. 2003. Spatial mechanisms for coexistence of species sharing a common natural enemy Theor. Popul. Biol. 64 : 431 438.
37. Leathwick, J. R. et al. 2006. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees Mar. Ecol. Prog. Ser. 312 : 267 281.
38. Mackey, B. G. Lindenmayer, D. B. 2001. Towards a hierarchical framework for modelling the spatial distribution of animals J. Biogeogr. 28 : 1147 1166.
39. McMahon, S. M. Diez, J. M. 2007. Scales of association: hierarchical linear models and the measurement of ecological systems Ecol. Lett. 10 : 437 452.
40. McPherson, J. M. Jetz, W. 2007. Effects of species' ecology on the accuracy of distribution models Ecography 30 : 135 151.
41. Menard, S. 2000. Coefficients of determination for multiple logistic regression analysis Am. Stat. 54 : 17 24.
42. Meynard, C. N. Quinn, J. F. 2007. Predicting species distributions: a critical comparison of the most common statistical models using artificial species J. Biogeogr. 34 : 1455 1469.
43. Midgley, G. F. et al. 2002. Assessing the vulnerability of species richness to anthropogenic climate change in a biodiversity hotspot Global Ecol. Biogeogr. 11 : 445 451.
44. Midgley, G. F. et al. 2006. Migration rate limitations on climate change-induced range shifts in Cape Proteaceae Divers. Distrib. 12 : 555 562.
45. Mills, N. J. Getz, W. M. 1996. Modelling the biological control of insect pests: a review of host-parasitoid models Ecol. Model. 92 : 121 143.
46. Morin, X. Thuiller, W. 2009. Species' range shift under climate change - reducing predictions' uncertainties through the comparison of niche and process-based approaches Ecology in press.
47. Nicholson, A. J. 1933. The balance of animal populations J. Anim. Ecol. 2 : 131 178.
48. Osborne, P. E. Suarez-Seoane, S. 2007. Identifying core areas in a species' range using temporal suitability analysis: an example using little bustards Tetrax tetrax L. in Spain Biodivers. Conserv. 16 : 3505 3518.
49. Pearce, J. Ferrier, S. 2000. An evaluation of alternative algorithms for fitting species distribution models using logistic regression Ecol. Model. 128 : 127 147.
50. Pearce, J. et al. 2001. An evaluation of the predictive performance of distributional models for flora and fauna in north-east New South Wales J. Environ. Manage. 62 : 171 184.
51. Pearson, R. G. Dawson, T. P. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecol. Biogeogr. 12 : 361 371.
52. Pearson, R. G. et al. 2002. SPECIES: a spatial evaluation of climate impact on the envelope of species Ecol. Model. 154 : 289 300.
53. Reese, G. C. et al. 2005. Factors affecting species distribution predictions: a simulation modeling experiment Ecol. Appl. 15 : 554 564.
54. Reineking, B. Schröder, B. 2006. Constrain to perform: regularization of habitat models Ecol. Model. 193 : 675 690.
55. Ridgeway, G. 1999. The state of boosting Comput. Sci. Stat. 31 : 172 181.
56. Schröder, B. Seppelt, R. 2006. Analysis of pattern-process- interactions based on landscape models - overview, general concepts, methodological issues Ecol. Model. 199 : 505 516.
57. Schröder, B. et al. 2008. A landscape model for quantifying the trade-off between conservation needs and economic constraints in the management of a semi-natural grassland community Biol. Conserv. 141 : 719 732.
58. Schweiger, O. et al. 2008. Climate change can cause spatial mismatch of trophically interacting species Ecology 89 : 3472 3479.
59. Scott, J. M. et al. 2002. Predicting species occurences - issues of accuracy and scale. Island Press.
60. Segurado, P. Araújo, M. B. 2004. An evaluation of methods for modelling species distributions J. Biogeogr. 31 : 1555 1568.
61. Seoane, J. et al. 2005. Species-specific traits associated to prediction errors in bird habitat suitability modelling Ecol. Model. 185 : 299 308.
62. Stockwell, D. R. B. Peterson, A. T. 2002. Effects of sample size on accuracy of species distribution models Ecol. Model. 148 : 1 13.
63. Thomas, C. D. et al. 2004. Extinction risk from climate change Nature 427 : 145 148.
64. Thuiller, W. 2004. Patterns and uncertainties of species' range shifts under climate change Global Change Biol. 10 : 2020 2027.
65. Thuiller, W. et al. 2004. Effects of restricting environmental range of data to project current and future species distributions Ecography 27 : 165 172.
66. Thuiller, W. et al. 2005. Climate change threats to plant diversity in Europe Proc. Nat. Acad. Sci. USA 102 : 8245 8250.
67. Thuiller, W. et al. 2006. Predicting patterns of plant species richness in megadiverse South Africa Ecography 29 : 733 744.
68. Thuiller, W. et al. 2008. Predicting global change impacts on plant species' distributions: future challenges Perspect. Plant Ecol. Evol. Syst. 9 : 137 152.
69. Tyre, A. J. et al. 2001. Inferring process from pattern: can territory occupancy provide information about life history parameters? Ecol. Appl. 11 : 1722 1737.
70. Vaughan, I. P. Ormerod, S. J. 2005. The continuing challenges of testing species distribution models J. Appl. Ecol. 42 : 720 730.