Spline models of contemporary, 2030, 2060 and 2090 climates for Mexico and their use in understanding climate-change impacts on the vegetation


Spatial climate models were developed for México and its periphery (southern USA, Cuba, Belize and Guatemala) for monthly normals (1961–1990) of average, maximum and minimum temperature and precipitation using thin plate smoothing splines of ANUSPLIN software on ca. 3,800 observations. The fit of the model was generally good: the signal was considerably less than one-half of the number of observations, and reasonable standard errors for the surfaces would be less than 1°C for temperature and 10–15% for precipitation. Monthly normals were updated for three time periods according to three General Circulation Models and three emission scenarios. On average, mean annual temperature would increase 1.5°C by year 2030, 2.3°C by year 2060 and 3.7°C by year 2090; annual precipitation would decrease −6.7% by year 2030, −9.0% by year 2060 and −18.2% by year 2090. By converting monthly means into a series of variables relevant to biology (e. g., degree-days > 5°C, aridity index), the models are directly suited for inferring plant–climate relationships and, therefore, in assessing impact of and developing programs for accommodating global warming. Programs are outlined for (a) assisting migration of four commercially important species of pine distributed in altitudinal sequence in Michoacán State (b) developing conservation programs in the floristically diverse Tehuacán Valley, and (c) perpetuating Pinus chiapensis, a threatened endemic. Climate surfaces, point or gridded climatic estimates and maps are available at

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  1. Aitken SN, Yeaman S, Holliday JA, Wang T, Curtis-McLane S (2008) Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol Appl 1:95–111

  2. Bates D, Wahba G (1982) Computational methods for generalized cross validation with large data sets. In: Baker CTH, Miller GF (eds) Treatment of integral equations by numerical methods. Academic, New York, pp 283–296

  3. Beaulieu J, Perron M, Bousquet J (2004) Multivariate patterns of adaptive genetic variation and seed source transfer in black spruce. Can J For Res 34:531–545

  4. Beg N, Morlot JC, Davidson O, Afrane-Okesse Y, Tyani L, Denton F, Sokona Y, Thomas JP, LaRobere EL, Parikh JK, Parik K, Rahman AA (2002) Linkages between climate change and sustainable development. Clim Policy 2:129–144

  5. Boer EJ, De Beurs K, Hartkamp AD (2001) Krigging and thin plate splines for mapping climate variables. Int J Appl Earth Observ Geoinform 3(2):146–154

  6. Breiman L (2001) Random forests. Mach Learn 45:5–32

  7. Brown JH, Gibson AC (1983) Biogeography. Mosby, St. Louis

  8. Brown DE, Reichenbacher F, Franson SE (1998) A classification of North American biotic communities. University of Utah, Salt Lake City

  9. Christensen JH, Hewiston B, Busuioc A, Chen A, Gao X, Held I, Jones R, Kolli RK, Kwon WT, Laprise R, Magaña Rueda V, Means L, Menémdez CG, Räisänen J, Rinke A, Sarr A, Whetton P (2007) Regional climate projections. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Climate change 2007: the physical science basis. Contribution of working group I to the forth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 847–940

  10. Cué-Bär EM, Villaseñor JL, Arredondo-Amezcua L, Cornejo-Tenorio G, Ibarra-Manríquez G (2006) La flora arbórea de Michoacán, México. Bol Soc Bot Méx 78:47–81

  11. Davis MB (1989) Lags in vegetation response to greenhouse warming. Clim Change 15:75–82

  12. Davis MB, Shaw RG, Etterson JR (2005) Evolutionary responses to changing climate. Ecology 86:1704–1714

  13. del Castillo RF, Trujillo A (2008) The effect of inbreeding depression on outcrossing estimates in populations of a tropical pine. New Phytol 177:517–524

  14. del Castillo RF, Trujillo-Argueta S, Sáenz-Romero C (2009) Pinus chiapensis, a keystone species: genetics ecology, and conservation. For Ecol Manag 257:2201–2208

  15. Donahue JK, Lopez-Upton J (1996) Geographic variation in leaf, cone and seed morphology of Pinus greggii in native forests. For Ecol Manag 82:145–157

  16. Dvorak WS, Donahue JK, Vasquez JA (1996a) Provenance and progeny results for the tropical white pine, Pinus chiapensis, at five and eight years of age. New For 12:125–140

  17. Dvorak WS, Kietzka JE, Donahue JK (1996b) Three-year survival and growth of provenances of Pinus greggii in the tropics and subtropics. For Ecol Manag 83:123–131

  18. EarthInfo Inc (1994) Database guide. EarthInfo, Boulder

  19. GLOBE Task Team (1999) The Global Land One-kilometer Base elevation (GLOBE) digital elevation model, version 1.0. National Oceanic and Atmospheric Administration, National Geophysical Data Center, Boulder

  20. Gomez-Mendoza L, Arriaga L (2007) Modeling the effect of climate change on the distribution of oak and pine species of México. Conserv Biol 21(6):1545–1555

  21. Hamann A, Wang T (2006) Potential effects of climate change on ecosystem and tree species distribution in British Columbia. Ecology 87(11):2773–2786

  22. Hartkamp AD, De Beurs K, Stein A, White JW (1999) Techniques for climate variables. NRG-GIS Series 99-01. CIMMYT, México

  23. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very height resolution interpolated surfaces for global land areas. Int J Climatol 25:1965–1978

  24. Hughes L (2000) Biological consequences of global warming: is the signal already. Trends Ecol Evol 15(2):56–61

  25. Hutchinson MF (1993) On thin plate splines and kriging. In: Tarter ME, Lock MD (eds) Computing and science in statistics 25. University of California-Berkeley, Interface Foundation of North America, Berkeley, pp 55–62

  26. Hutchinson MF (1995) Interpolating mean rainfall using thin plate smoothing splines. Int J Geogr Inf Sci 9:305–403

  27. Hutchinson MF (1998a) Interpolation of rainfall data with thin plate smoothing splines. I. Two dimensional smoothing of data with short range correlation. J Geogr Inform Decis Anal 2(2):152–167

  28. Hutchinson MF (1998b) Interpolation of rainfall data with thin plate smoothing splines. II. Analysis of topographic dependence. J Geogr Inform Decis Anal 2(2):168–185

  29. Hutchinson MF (2004) ANUSPLIN version 4.3 user guide. Centre for Resource and Environmental Studies, The Australian National University, Canberra, 54 pp

  30. Hutchinson MF, Gessler PE (1994) Splines—more than just a smooth interpolator. Geoderma 62:45–67

  31. Intergovernmental Panel on Climate Change (IPCC) (2000) Emissions scenarios; summary for policymakers. Special report of IPCCC working group III. USA, IPCC, 21 pp. (

  32. Iverson LR, Prasad AM, Matthews S (2008) Modeling potential climate change impacts on the trees of the northeastern United States. Mitig Adapt Strategies Glob Chang 13:487–516

  33. Kohn R, Ansley CF, Tharm D (1991) The performance of cross-validation and maximum likelihood estimators of spline smoothing parameters. J Am Stat Assoc 86:1042–1049

  34. Ledig FT, Kitzmiller JH (1992) Genetic strategies for reforestation in the face of global climate change. For Ecol Manag 50(1–2):153–169

  35. McKenney DW, Hutchinson MF, Kesteven JL, Venier LA (2001) Canada’s plant hardiness zones revisited using modern climate interpolation techniques. Can J Plant Sci 81:129–143

  36. McLachlan J, Hellmann JJ, Schwartz MW (2007) A framework for debate of assisted migration in an era of climate change. Conserv Biol 21(2):297–302

  37. Minami M (2000). Using ArcMap; GIS by ESRI. Environmental Systems Research Institute (ESRI), Redlands

  38. Mittermeier RA (1988) Primate diversity and the tropical forest: case studies from Brazil and Madagascar and the importance of the megadiversity countries. In: Wilson E (ed) Biodiversity. National Academic, Washington, DC

  39. Newton AC, Allnutt TR, Dvorak WS, del Castillo RF, Ennos RA (2002) Patterns of genetic variation in Pinus chiapensis, a threatened Mexican pine, detected by RAPD and mitochondrial DNA RFLP markers. Heredity 89:191–198

  40. Nixon KC (1993) The genus Quercus in México. In: Ramamoorthy TP, Bye R, Lot A, Fa J (eds) Biological diversity of México: origins and distribution. Oxford University Press, New York, pp 447–458

  41. Pachauri RK (2004) Climate and humanity. Glob Environ Change 14:101–103

  42. Parry ML, Rosenzweig C, Iglesis A, Livermore M, Fisher G (2004) Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Glob Environ Change 14:53–67

  43. Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–371

  44. Peterson AT, Ortega-Huerta MA, Bartley J, Sánchez-Cordero V, Soberón J, Buddemeier RH, Stockwell DRB (2002) Future projections for Mexican faunas under global climate change scenarios. Nature 416:626–629

  45. Price DT, McKenney DW, Nalder IA, Hutchinson MF, Kesteven JL (2000) A comparison of two statistical methods for spatial interpolation of Canadian monthly mean climate date. Agric For Meteorol 101:81–94

  46. Ramamoorthy TP, Bye R, Lot A, Fa J (eds) (1993) Biological diversity of Mexico: origins and distribution. Oxford University Press, New York

  47. Rehfeldt GE (2004) Interspecific and intraspecific variation in Picea engelmannii and its congeneric cohorts: biosystematics, genecology, and climate change. General technical report RMRS-GTR-134. US Department of Agriculture, Forest Service, Rocky Mountain Research, Fort Collins

  48. Rehfeldt GE (2006) A spline model of climate for the western United States. Gen. Tech. Rep. RMRS-GTR-165. USDA Forest Service, Fort Collins, 21 p

  49. Rehfeldt GE, Ferguson DE, Crookston NL (2008) Quantifying the abundance of co-occurring conifers along inland northwest (USA) climate gradients. Ecology 89(8):2127–2139

  50. Rehfeldt GE, Ferguson DE, Crookston NL (2009) Aspen, climate, and sudden decline in western USA. For Ecol Manag 258:2353–2364

  51. Rehfeldt GE, Wykoff RA, Ying CC (2001) Physiologic plasticity, evolution, and impacts of a changing climate in Pinus contorta. Clim Change 50:355–376

  52. Rehfeldt GE, Crookston NL, Warwell MV, Evans JS (2006) Empirical analyses of plant–climate relationship for the western United States. Int J Plant Sci 167(6):1123–1150

  53. Rehfeldt GE, Ying CC, Spittlehouse DL, Hamilton DA Jr (1999) Genetic responses to climate in Pinus contorta: niche breadth, climate change, and reforestation. Ecol Monogr 69(3):375–407

  54. Rehfeldt GE, Tchebakova NM, Parfenova YI, Wykoff RA, Kuzmina NA, Milyutin LI (2002) Intraspecific responses to climate in Pinus sylvestris. Glob Chang Biol 8:912–929

  55. Ricker M, Ramírez-Krauss I, Ibarra-Manríquez G, Martínez E, Ramos CH, González-Medellin G, Gomez-Rodríguez G, Palacio-Prieto JL, Hernandez HM (2007) Optimizing conservation of forest diversity: a country-wide approach in México. Biodivers Conserv 16(6):1927–1957

  56. Rosenberg NJ (1974) Microclimate: the biological environment. Wiley, New York

  57. Rzedowski J (1978) Vegetación de México. Limusa, México

  58. Rzedowski J (1993) Diversity and origins of the phanerogamic flora of México. In: Ramamoorthy TP, Bye R, Lot A, Fa J (eds) Biological diversity of México: origins and distribution. Oxford University Press, New York, pp 129–144

  59. Sáenz-Romero C, Tapia-Olivares BL (2008) Genetic variation in frost damage and seed zone delineation within an altitudinal transect of Pinus devoniana (P. michoacana) in México. Silvae Genet 57(3):165–170

  60. Sáenz-Romero C, Snively A, Lindig-Cisneros R (2003) Conservation and restoration of pine forest genetic resources in México. Silvae Genet 52(5–6):233–237

  61. Sáenz-Romero C, Guzmán-Reyna R, Rehfeldt GE (2006) Altitudinal genetic variation among Pinus oocarpa populations in Michoacán, México; implications for seed zoning, conservation of forest genetic resources, tree breeding and global warming. For Ecol Manag 229:340–350

  62. SAS Institute Inc (1998) SAS/STAT Guide for personal computers, version 8.0. SAS Institute, Cary

  63. St Clair JD, Howe GT (2007) Genetic maladaptation of coastal Douglas-fir seedlings to future climates. Glob Chang Biol 13:1441–1454

  64. Steffen W (2008) Working group 1 report of the IPCC fourth assessment—an editorial. Glob Environ Change 18:1–3

  65. Styles BT (1993) The genus Pinus: a México purview. In: Ramamoorthy TP, Bye R, Lot A, Fa J (eds) Biological diversity of México: origins and distribution. Oxford University Press, New York, pp 397–420

  66. Tchebakova NM, Rehfeldt GE, Parfenova EI (2005) Impacts of climate change on the distribution of Larix spp. and Pinus sylvestris and their climatypes in Siberia. Mitig Adapt Strategies Glob Chang 11:861–882

  67. Téllez-Valdés O, Dávila-Aranda P (2003) Protected areas and climate change: a case study of the cacti in the Tehuacán-Cuicatlán biosphere reserve, México. Conserv Biol 17(3):846–853

  68. Téllez-Valdés O, Dávila-Aranda P, Lira-Saade R (2006) The effects of climate change on the long-term conservation of Fagus grandiflora var. mexicana, an important species of the cloud forest in eastern México. Biodivers Conserv 15(4):1095–1107

  69. Tukanen S (1980) Climatic parameters and indices in plant geography. Acta Phytogreogr Suecica 67:1–105

  70. United States Department of Commerce (1994) US divisional and station climatic data and normals, vol 1. National Oceanic and Atmospheric Administration, National Climatic Data Center, Ashville

  71. US Department of Commerce (2008) National Climate Data Center.

  72. van Zonneveld M, Jarvis A, Dvorak W, Lema G, Leibing C (2009) Climate change impact predictions on Pinus patula and Pinus tecunumanii populations in Mexico and Central America. For Ecol Manag 257:1566–1576

  73. Viveros-Viveros H, Sáenz-Romero C, López-Upton J, Vargas-Hernández JJ (2005) Altitudinal genetic variation in plant growth of Pinus pseudostrobus Lindl. in field testing. Agrociencia 39(5):575–587

  74. Viveros-Viveros H, Sáenz-Romero C, Vargas-Hernández JJ, López-Upton J, Ramírez-Valverde G, Santacruz-Varela A (2009) Altitudinal genetic variation in Pinus hartwegii Lindl. I.: height growth, shoot phenology, and frost damage in seedlings. For Ecol Manag 257:836–842

  75. Wahba G (1985) A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem. Ann Stat 13:1378–1402

  76. Wang T, Hamann A, Yanchuk A, O’Neill GA, Aitken SN (2006) Use of response functions in selecting lodgepole pine populations for future climates. Glob Chang Biol 12(2):2404–2416

  77. Woodward FI (1987) Climate and plant distribution. Cambridge University Press, London

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Sáenz-Romero, C., Rehfeldt, G.E., Crookston, N.L. et al. Spline models of contemporary, 2030, 2060 and 2090 climates for Mexico and their use in understanding climate-change impacts on the vegetation. Climatic Change 102, 595–623 (2010).

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  • Aridity Index
  • Glob Chang Biol
  • Climatic Niche
  • Spline Surface
  • Climate Relationship