Weak climatic associations among British plant distributions

Global Ecology and Biogeography

Volumn 19, Issue 6, 2010, Pages 831-841

  Chapman, Daniel S ^a  

^a CENTRE FOR ECOLOGY AND HYDROLOGY   (United Kingdom)

Author keywords

AUC; Bioclimate envelope; Circulant embedding; Gaussian random field; Macroecology; Niche model; Species distribution model; United Kingdom

Indexed keywords

ALGORITHM; CLIMATE CHANGE; COMMUNITY STRUCTURE; GAUSSIAN METHOD; GEOGRAPHICAL DISTRIBUTION; MACROECOLOGY; MODELING; NICHE; RANGE SIZE;

UNITED KINGDOM;

EID: 77958460187     PISSN: 1466822X     EISSN: 14668238     Source Type: Journal    
DOI: 10.1111/j.1466-8238.2010.00561.x     Document Type: Article

References (53)

1. Araújo, M.B. & Rahbek, C. (2006) How does climate change affect biodiversity? Science, 313, 1396-1397.
2. Araújo, M.B., Thuiller, W. & Yoccoz, N.G. (2009) Reopening the climate envelope reveals macroscale associations with climate in European birds. Proceedings of the National Academy of Sciences USA, 106, E45-E46.
3. Aspinall, R.J., Miller, J.A. & Franklin, J. (2009) Calculations on the back of a climate envelope: addressing the geography of species distributions. Proceedings of the National Academy of Sciences USA, 106, E44.
4. Augustin, N.H., Mugglestone, M.A. & Buckland, S.T. (1996) An autologistic model for the spatial distribution of wildlife. Journal of Applied Ecology, 33, 339-347.
5. Austin, M.P. (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling, 157, 101-118.
6. Bahn, V. & McGill, B.J. (2007) Can niche-based distribution models outperform spatial interpolation? Global Ecology and Biogeography, 16, 733-742.
7. Bahn, V., O'Connor, R.J. & Krohn, W.B. (2006) Importance of spatial autocorrelation in modeling bird distributions at a continental scale. Ecography, 29, 835-844.
8. Beale, C.M., Lennon, J.J. & Gimona, A. (2008) Opening the climate envelope reveals no macroscale associations with climate in European birds. Proceedings of the National Academy of Sciences USA, 105, 14908-14912.
9. Beale, C.M., Lennon, J.J. & Gimona, A. (2009) European bird distributions still show few climate associations. Proceedings of the National Academy of Sciences USA, 106, E41-E43.
10. Besag, J. (1974) Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society, 2, 192-236.
11. Bini, L.M., Diniz-Filho, J.A.F., Rangel, T.F.L.V.B. et al. (2009) Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression. Ecography, 32, 193-204.
12. Breiman, L. (2001) Random forests. Machine Learning, 45, 5-32.
13. Breiman, L., Friedman, J., Olshen, R.A. & Stone, C.J. (1984) Classification and regression trees. Wadsworth International Group, Belmont, CA.
14. Burnham, K.P. & Anderson, D.R. (2002) Model selection and multi-model inference, 2nd edn. Springer, New York.
15. Chan, G. & Wood, A.T.A. (1997) Algorithm AS 312: an algorithm for simulating stationary Gaussian random fields. Journal of the Royal Statistical Society: Series C (Applied Statistics), 46, 171-181.
16. Chapman, D.S., Oxford, G.S. & Dytham, C. (2009) Process from pattern in the distribution of an endangered leaf beetle. Ecography, 32, 259-268.
17. Chapman, D.S., Bonn, A., Kunin, W.E. & Cornell, S.J. (2010) Random Forest characterization of upland vegetation and management burning from aerial imagery. Journal of Biogeography, 37, 37-46.
18. Cressie, N. (1985) Fitting variogram models by weighted least squares. Mathematical Geology, 17, 563-586.
19. Currie, D.J. (2007) Disentangling the roles of environment and space in ecology. Journal of Biogeography, 34, 2009-2011.
20. Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A., Hess, K.T., Gibson, J. & Lawler, J. (2007) Random forests for classification in ecology. Ecology, 88, 2783-2792.
21. Dietrich, C.R. & Newsam, G.N. (1993) A fast and exact method for multidimensional Gaussian stochastic simulations. Water Resources Research, 29, 2861-2869.
22. Diggle, P.J. & Ribeiro, P.J., Jr (2007) Model-based geostatistics, 1st edn. Springer, New York.
23. Diniz-Filho, J.A., Rangel, T. & Bini, L.M. (2008) Model selection and information theory in geographical ecology. Global Ecology and Biogeography, 17, 479-488.
24. Dormann, C.F. (2007a) Assessing the validity of autologistic regression. Ecological Modelling, 207, 234-242.
25. Dormann, C.F. (2007b) Effects of incorporating spatial autocorrelation into the analysis of species distribution data. Global Ecology and Biogeography, 16, 129-138.
26. Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, D.W., Kühn, I., Ohlemüller, R., Peres-Neto, P.R., Reineking, B., Schröder, B., Schurr, F.M. & Wilson, R. (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography, 30, 609-628.
27. Elith, J., Graham, C.H., Anderson, R.P. et al. (2006) Novel methods improve prediction of species' distributions from occurrence data. Ecography, 29, 129-151.
28. Field, R., Hawkins, B.A., Cornell, H.V., Currie, D.J., Diniz-Filho, J.A.F., Guégan, J.-F., Kaufman, D.M., Kerr, J.T., Mittelbach, G.G., Oberdorff, T., O'Brien, E.M. & Turner, J.R.G. (2009) Spatial species-richness gradients across scales: a meta-analysis. Journal of Biogeography, 36, 132-147.
29. 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.
30. Garzón, M.B., Blazek, R., Neteler, M., Dios, R.S.D., Ollero, H.S. & Furlanello, C. (2006) Predicting habitat suitability with machine learning models: the potential area of Pinus sylvestris L. in the Iberian Peninsula. Ecological Modelling, 197, 383-393.
31. Guisan, A. & Thuiller, W. (2005) Predicting species distribution: offering more than simple habitat models. Ecology Letters, 8, 993-1009.
32. Guisan, A., Lehmann, A., Ferrier, S., Austin, M., Overton, J.M.C., Aspinall, R. & Hastie, T. (2006) Making better biogeographical predictions of species distributions. Journal of Applied Ecology, 43, 386-392.
33. Hickling, R., Roy, D.B., Hill, J.K., Fox, R. & Thomas, C.D. (2006) The distributions of a wide range of taxonomic groups are expanding polewards. Global Change Biology, 12, 450-455.
34. Iverson, L.R. & Prasad, A.M. (1998) Predicting abundance of 80 tree species following climate change in the eastern United States. Ecological Monographs, 68, 465-485.
35. Keitt, T.H., Bjørnstad, O.N., Dixon, P.M. & Citron-Pousty, S. (2002) Accounting for spatial pattern when modeling organism-environment interactions. Ecography, 25, 616-625.
36. Kremen, C., Cameron, A., Moilanen, A. et al. (2008) Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science, 320, 222-226.
37. Kühn, I. (2007) Incorporating spatial autocorrelation may invert observed patterns. Diversity and Distributions, 13, 66-69.
38. Lantuéjoul, C. (2002) Geostatistical simulation: models and algorithms, 1st edn Springer, New York.
39. Legendre, P. & Legendre, L. (1998) Numerical ecology, 2nd edn. Elsevier, Amsterdam.
40. Lennon, J.J. (2000) Red-shifts and red herrings in geographical ecology. Ecography, 23, 101-113.
41. Liaw, A. & Wiener, M. (2002) Classification and regression by randomForest. R News, 2, 18-22.
42. Pearson, R.G. & Dawson, T.P. (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Global Ecology and Biogeography, 12, 361-371.
43. Perry, M. & Hollis, D. (2005) The generation of monthly gridded datasets for a range of climatic variables over the UK. International Journal of Climatology, 25, 1041-1054.
44. Peterson, A.T., Barve, N., Bini, L.M., Diniz-Filho, J.A., Jiménez-Valverde, A., Lira-Noriegaa, A., Lobo, J., Maher, S., De Marco, P., Martínez-Meyer, E., Nakazawa, Y. & Soberón, J. (2009) The climate envelope may not be empty. Proceedings of the National Academy of Sciences USA, 106, E47.
45. Prasad, A.M., Iverson, L.R. & Liaw, A. (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, 181-199.
46. R Development Core Team (2008) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna.
47. Ribeiro, P.J., Jr & Diggle, P.J. (2001) geoR: a package for geostatistical analysis. R News, 1, 15-18.
48. Schlather, M. (2001) Simulation and analysis of random fields. R News, 1, 18-20.
49. Segurado, P. & Araújo, M.B. (2004) An evaluation of methods for modelling species distributions. Journal of Biogeography, 31, 1555-1568.
50. Segurado, P., Araújo, M.B. & Kunin, W.E. (2006) Consequences of spatial autocorrelation for niche-based models. Journal of Applied. Ecology, 43, 433-444.
51. Thuiller, W., Lavorel, S., Araújo, M.B., Sykes, M.T. & Prentice, I.C. (2005) Climate change threats to plant diversity in Europe. Proceedings of the National Academy of Sciences USA, 102, 8245-8250.
52. Whittingham, M.J., Stephens, P.A., Bradbury, R.B. & Freckleton, R.P. (2006) Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75, 1182-1189.
53. Wintle, B.A. & Bardos, D.C. (2006) Modelling species-habitat relationships with spatially autocorrelated observation data. Ecological Applications, 16, 1945-1958.