Transferability of species distribution models: The case of Phytophthora cinnamomi in Southwest Spain and Southwest Australia

Ecological Modelling

Volumn 320, Issue , 2016, Pages 62-70

  Duque Lazo, J ^a,b,c   van Gils, H ^b   Groen, T A ^b   Navarro Cerrillo, R M ^a  

^a UNIVERSITY OF CÓRDOBA   (Spain)

^b UNIVERSITY OF TWENTE   (Netherlands)

^c UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

General Linear Models (GLM); Mahalanobis distance; Maximum entropy models (MaxEnt); Model transferability; Phytophthora cinnamomi; Species distribution models

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CALIBRATION; LEARNING SYSTEMS; PLANTS (BOTANY); PROBABILITY DISTRIBUTIONS; REGRESSION ANALYSIS;

GENERAL LINEAR MODELS; MAHALANOBIS DISTANCES; MAXIMUM ENTROPY MODELS; MODEL TRANSFERABILITIES; PHYTOPHTHORA; SPECIES DISTRIBUTION MODELS;

POPULATION DISTRIBUTION;

ACCURACY ASSESSMENT; ALGORITHM; CALIBRATION; EUKARYOTE; IN SITU MEASUREMENT; LINEAR PROGRAMING; MACHINE LEARNING; MAXIMUM ENTROPY ANALYSIS; PERFORMANCE ASSESSMENT; POPULATION DISTRIBUTION; POPULATION MODELING; REGRESSION ANALYSIS; SPATIAL DISTRIBUTION; SPECIES OCCURRENCE;

AUSTRALIA; SPAIN;

EUCALYPTUS MARGINATA; PHYTOPHTHORA CINNAMOMI;

EID: 84943642897     PISSN: 03043800     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ecolmodel.2015.09.019     Document Type: Article

References (62)

1. ABARES The 2011 Australian national map layers, Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), Geoscience Australia, CSIRO Land and Water, the Department of Sustainability, Environment, Water, Population and Communities (Environmental Resources Information Network), the Australian Bureau of Statistics, and the National Centre for Social Applications of Geographic Information Systems ed. Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) 2011, Retrieved 15 October, 2012, from 〈www.daff.gov.au/abares/mcass〉.
2. Allouche O., Tsoar A., Kadmon R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 2006, 43:1223-1232.
3. Anderson R.P., Gonzalez I. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol. Modell. 2011, 222:2796-2811.
4. Brasier C.M. Phytophthora cinnamomi and oak decline in southern Europe. Environmental constraints including climate change. Ann. Sci. Forest. 1996, 53:347-358.
5. Breiman L. Random forests. Mach. Learn. 2001, 45:5-32.
6. Busby J.R. BIOCLIM-a bioclimatic analysis and prediction system. Nature Conservation: Cost Effective Biological Surveys and Data Analysis (1991) 1991, vol. 1:64-68. CSIRO Publishing, Australia.
7. Casas A.T., Díaz R.M.J. Etiología, importancia y distribución de la seca del garbanzo en el Valle del Guadalquivir. Boletín de Sanidad Vegetal-Plagas 1985, 11:69-145.
8. Cohen J. A Coefficient of agreement for nominal scales. Educ. Psychol. Meas. 1960, 20:37-46.
9. Consejería de Medio Ambiente Estudio de procesos de mortalidad de pinares (sierra de Filabres y Baza - Almería) y especies del genero Quercus en Andalucía 2010, Consejería de Medio Ambiente, Córdoba.
10. Edwards T.C., Cutler D.R., Zimmermann N.E., Geiser L., Moisen G.G. Effects of sample survey design on the accuracy of classification tree models in species distribution models. Ecol. Modell. 2006, 199:132-141.
11. Elith J.H., Graham C.P., Anderson R., Dudík M., Ferrier S., Guisan A.J., Hijmans R., Huettmann F.R., Leathwick J., Lehmann A., Li J.G., Lohmann L.A., Loiselle B., Manion G., Moritz C., Nakamura M., Nakazawa Y.McC.M., Overton J., Townsend Peterson A.J., Phillips S., Richardson K., Scachetti-Pereira R.E., Schapire R., Soberón J., Williams S.S., Wisz M.E., Zimmermann N. Novel methods improve prediction of species' distributions from occurrence data. Ecography 2006, 29:129-151.
12. Elith J., Leathwick J.R. Species distribution models: ecological explanation and prediction across space and time. Annu. Rev. Ecol. Syst. Evol. Syst. 2009, 40:677-697.
13. Elith J., Leathwick J.R., Hastie T. A working guide to boosted regression trees. J. Anim. Ecol. 2008, 77:802-813.
14. Farber O., Kadmon R. Assessment of alternative approaches for bioclimatic modeling with special emphasis on the Mahalanobis distance. Ecol. Modell. 2003, 160:115-130.
15. Filzmoser P. A multivariate outlier detection method. Proceedings of the Seventh International Conference on Computer Data Analysis and Modeling 2004, 18-22. Belarusian State University, Minsk.
16. Franklin J. Mapping Species Distributions: Spatial Inference and Prediction 2010, 340. Cambridge University Press, NY, U.S.A.
17. Friedman J.H. Multivariate adaptive regression splines. Ann. Stat. 1991, 19:1-67.
18. Gomez-Aparicio L., Ibanez B., Serrano M.S., De Vita P., Avila J.M., Perez-Ramos I.M., Garcia L.V., Sanchez M.E., Maranon T. Spatial patterns of soil pathogens in declining Mediterranean forests: implications for tree species regeneration. New Phytol. 2012, 194:1014-1024.
19. Guisan A., Edwards T.C., Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol. Modell. 2002, 157:89-100.
20. Guisan A., Thuiller W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 2005, 8:993-1009.
21. Hanberry B.B., He H.S., Dey D.C. Sample sizes and model comparison metrics for species distribution models. Ecol. Modell. 2012, 227:29-33.
22. Hardham A.R. Phytophthora cinnamomi. Mol. Plant Pathol. 2005, 6:589-604.
23. Heikkinen R.K., Marmion M., Luoto M. Does the interpolation accuracy of species distribution models come at the expense of transferability?. Ecography 2012, 35:276-288.
24. Legendre P., Fortin M.J. Spatial pattern and ecological analysis. Vegetatio 1989, 80:107-138.
25. Lek S., Guegan J.F. Artificial neural networks as a tool in ecological modelling, an introduction. Ecol. Modell. 1999, 120:65-73.
26. Lobo J.M., Jiménez-Valverde A., Real R. AUC: a misleading measure of the performance of predictive distribution models. Global Ecol. Biogeogr. 2008, 17:145-151.
27. Lobo J.M., Jiménez-Valverde A., Hortal J. The uncertain nature of absences and their importance in species distribution modelling. Ecography 2010, 33:103-114.
28. Lowe S., Browne M., Boudjelas S., De Poorter M. 100 of the World's Worst Invasive Alien Species: A Selection from the Global Invasive Species Database 2000, Invasive Species Specialist Group Auckland, New Zealand.
29. MAGRAMA Tercer Inventario Forestal Nacional (IFN3) 2007, Ministerio de Medio Ambiente, Gobierno de España, Madrid.
30. Merow C., Smith M.J., Edwards T.C., Guisan A., McMahon S.M., Normand S., Thuiller W., Wüest R.O., Zimmermann N.E., Elith J. What do we gain from simplicity versus complexity in species distribution models?. Ecography 2014, 37:1267-1281.
31. Moreno-Amat E., Mateo R.G., Nieto-Lugilde D., Morueta-Holme N., Svenning J.-C., García-Amorena I. Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data. Ecol. Modell. 2015, 312:308-317.
32. Myers N., Mittermeier R.A., Mittermeier C.G., da Fonseca G.A., Kent J. Biodiversity hotspots for conservation priorities. Nature 2000, 403:853-858.
33. Navarro-Cerrillo R.M., Hernandez-Bermejo J.E., Hernandez-Clemente R. Evaluating models to assess the distribution of Buxus balearica in southern Spain. Appl. Veg. Sci. 2011, 14:256-267.
34. Peel M.C., Finlayson B.L., McMahon T.A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. Discuss. 2007, 4:439-473.
35. Phillips S.J., Anderson R.P., Schapire R.E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 2006, 190:231-259.
36. Prasad A., Iverson L., Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 2006, 9:181-199.
37. Quinn G.G.P., Keough M.J. Experimental Design and Data Analysis for Biologists 2002, 557. Cambridge University Press, NY, U.S.A.
38. R Core Development Team R: A Language and Environment for Statistical Computing 2014, R Foundation for Statistical Computing, Vienna, Austria.
39. Sánchez M.E., Sánchez J.E., Navarro Cerrillo R.M., Fernández P., Trapero A. Incidencia de la podredumbre radical causada por Phytophthora cinnamomi en masas de Quercus en Andalucía. Boletín de Sanidad Vegetal-Plagas 2003, 29:87-108.
40. Shearer B.L., Crane C.E., Barrett S., Cochrane A. Phytophthora cinnamomi invasion, a major threatening process to conservation of flora diversity in the South-west Botanical Province of Western Australia. Aust. J. Bot. 2007, 55:225-238.
41. Stukely M., Webster J., Ciampini J. Results of Phytophthora sample testing. Vegetation Health Service: Annual Report 2011-2012 Phytophthora Detection 2012.
42. Syphard A.D., Franklin J. Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors. Ecography 2009, 32:907-918.
43. Thuiller W. BIOMOD-optimizing predictions of species distributions and projecting potential future shifts under global change. Global Change Biol. 2003, 9:1353-1362.
44. Thuiller W., Albert C., Araujo M.B., Berry P.M., Cabeza M., Guisan A., Hickler T., Midgely G.F., Paterson J., Schurr F.M., Sykes M.T., Zimmermann N.E. Predicting global change impacts on plant species' distributions: future challenges. Perspect. Plant Ecol. Evol. Syst. 2008, 9:137-152.
45. Thuiller W., Lafourcade B., Engler R., Araujo M.B. BIOMOD-a platform for ensemble forecasting of species distributions. Ecography 2009, 32:369-373.
46. Thuiller W., Lavergne S., Roquet C., Boulangeat I., Lafourcade B., Araujo M.B. Consequences of climate change on the tree of life in Europe. Nature 2011, 470:531-534.
47. Tobler W.R. A computer movie simulating urban growth in the Detroit region. Econ. Geogr. 1970, 46:234-240.
48. Trevor H., Robert T., Andreas B. Flexible discriminant analysis by optimal scoring. J. Am. Stat. Assoc. 1994, 89:1255-1270.
49. Václavík T., Kanaskie A., Hansen E.M., Ohmann J.L., Meentemeyer R.K. Predicting potential and actual distribution of sudden oak death in Oregon: prioritizing landscape contexts for early detection and eradication of disease outbreaks. For. Ecol. Manage. 2010, 260:1026-1035.
50. Václavík T., Meentemeyer R.K. Invasive species distribution modeling (iSDM): are absence data and dispersal constraints needed to predict actual distributions?. Ecol. Modell. 2009, 220:3248-3258.
51. van Gils H., Batsukh O., Rossiter D., Munthali W., Liberatoscioli E. Forecasting the pattern and pace of Fagus forest expansion in Majella National Park, Italy. Appl. Veg. Sci. 2008, 11:539-546.
52. van Gils H., Conti F., Ciaschetti G., Westinga E. Fine resolution distribution modelling of endemics in Majella National Park, Central Italy. Plant Biosyst. 2012, 146:276-287.
53. van Gils H., Westinga E., Carafa M., Antonucci A., Ciaschetti G. Where the bears roam in Majella National Park, Italy. J. Nat. Conserv. 2014, 22:276-287.
54. Vayssieres M.P., Plant R.E., Allen-Diaz B.H. Classification trees: an alternative non-parametric approach for predicting species distributions. J. Veg. Sci. 2000, 11:679-694.
55. Veloz S.D. Spatially autocorrelated sampling falsely inflates measures of accuracy for presence-only niche models. J. Biogeogr. 2009, 36:2290-2299.
56. Verbruggen H., Tyberghein L., Belton G.S., Mineur F., Jueterbock A., Hoarau G., Gurgel C.F.D., De Clerck O. Improving transferability of introduced species' distribution models: new tools to forecast the spread of a highly invasive seaweed. PLoS ONE 2013, 8:e68337.
57. Wenger S.J., Olden J.D. Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods Ecol. Evol. 2012, 3:260-267.
58. Wenger S.J., Som N.A., Dauwalter D.C., Isaak D.J., Neville H.M., Luce C.H., Dunham J.B., Young M.K., Fausch K.D., Rieman B.E. Probabilistic accounting of uncertainty in forecasts of species distributions under climate change. Global Change Biol. 2013, 19:3343-3354.
59. Weste G., Marks G.C. The biology of Phytophthora cinnamomi in Australasian forests. Annu. Rev. Phytopathol. 1987, 25:207-229.
60. Wiens J.A., Stralberg D., Jongsomjit D., Howell C.A., Snyder M.A. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc. Natl. Acad. Sci. U.S.A. 2009, 106:19729-19736.
61. Yu F., Wang T., Groen T.A., Skidmore A.K., Yang X., Geng Y., Ma K. Multi-scale comparison of topographic complexity indices in relation to plant species richness. Ecol. Complexity 2015, 22:93-101.
62. Zentmyer G.A. Origin and distribution of four species of Phytophthora. Trans. Br. Mycol. Soc. 1988, 91:367-378.