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The Science of Nature

, 104:32 | Cite as

Combining climatic and soil properties better predicts covers of Brazilian biomes

  • Daniel M. Arruda
  • Elpídio I. Fernandes-Filho
  • Ricardo R. C. Solar
  • Carlos E. G. R. Schaefer
Original Paper

Abstract

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Keywords

Biogeography Biome model Ecological niche model Map comparison Plant functional types Random forest 

Notes

Acknowledgements

This paper was submitted in partial fulfilment of the requirements for the PhD degree of DMA at Universidade Federal de Viçosa. DMA and RRCS were supported by grants from the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES). The authors are grateful for Drs Rúbia Fonseca, Markus Gastauer, Marcelo Bueno, and some anonymous reviewers for their valuable suggestions to the manuscript.

Supplementary material

114_2017_1456_MOESM1_ESM.docx (283 kb)
ESM 1 (DOCX 283 kb)
114_2017_1456_MOESM2_ESM.xls (30.6 mb)
ESM 2 (DOCX 30.5 mb)

References

  1. Ab’Saber AN (2000) The natural organization of Brazilian inter- an subtropical landscapes. R Inst Geo 21:57–70Google Scholar
  2. Anderson MJ (2001) A new method for non-parametric multivariate analysis of variance. Austral Ecol 26:32–46Google Scholar
  3. Anderson MJ (2006) Distance-based tests for homogeneity of multivariate dispersions. Biometrics 62:245–253CrossRefPubMedGoogle Scholar
  4. Anderson MJ, Walsh DC (2013) PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing? Ecol Monogr 83:557–574CrossRefGoogle Scholar
  5. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47CrossRefPubMedGoogle Scholar
  6. Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539CrossRefPubMedGoogle Scholar
  7. Arruda DM, Schaefer CE, Corrêa GR, Rodrigues PM, Duque-Brasil R, Ferreira-JR WG, Oliveira-Filho AT (2015) Landforms and soil attributes determine the vegetation structure in the Brazilian semiarid. Folia Geobot 50:175–184CrossRefGoogle Scholar
  8. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  9. Bueno M, Pennington RT, Dexter KG, Kamino LHY, Pontara L, Neves DRM, Ratter JA, Oliveira-Filho AT (2016) Effects of quaternary climatic fluctuations on the distribution of neotropical savanna tree species. Ecography. doi: 10.1111/ecog.01860 Google Scholar
  10. Carnaval AC, Moritz C (2008) Historical climate modelling predicts patterns of current biodiversity in the Brazilian Atlantic forest. J Biogeogr 35:1187–1201CrossRefGoogle Scholar
  11. Cawsey EM, Austin MP, Baker BL (2002) Regional vegetation mapping in Australia: a case study in the practical use of statistical modelling. Biodivers Conserv 11:2239–2274CrossRefGoogle Scholar
  12. Clark DB, Clark DA, Read JM (1998) Edaphic variation and the mesoscale distribution of tree species in a neotropical rain forest. J Ecol 86:101–112CrossRefGoogle Scholar
  13. Collevatti RG, Terribile LC, Oliveira G, Lima-Ribeiro MS, Nabout JC, Rangel TF, Diniz-Filho JAF (2013) Drawbacks to palaeodistribution modelling: the case of south American seasonally dry forests. J Biogeogr 40:345–358CrossRefGoogle Scholar
  14. Colwell RK, Rangel TF (2009) Hutchinson’s duality: the once and future niche. Proc Natl Acad Sci U S A 106(Suppl 2):19651–19658CrossRefPubMedPubMedCentralGoogle Scholar
  15. Condit R, Engelbrecht BMJ, Pino P, Pérez R, Turner B (2013) Species distributions in response to individual soil nutrients and seasonal drought across a community of tropical trees. Proc Nat Acad Sci USA 110:5064–5068CrossRefPubMedPubMedCentralGoogle Scholar
  16. Coudun C, Gégout JC (2007) Quantitative prediction of the distribution and abundance of Vaccinium myrtillus with climatic and edaphic factors. J Veg Sci 18:517–524CrossRefGoogle Scholar
  17. Coudun C, Gégout JC, Piedallu C, Rameau JC (2006) Soil nutritional factors improve models of plant species distribution: an illustration with Acer campestre (L.) in France. J Biogeogr 33:1750–1763CrossRefGoogle Scholar
  18. Coutinho LM (2006) O Conceito de bioma. Acta Bot Bras 20:13–23CrossRefGoogle Scholar
  19. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88:2783–2792CrossRefPubMedGoogle Scholar
  20. Elith J, Burgman MA, Regan HM (2002) Mapping epistemic uncertainties and vague concepts in predictions of species distribution. Ecol Model 157:313–329CrossRefGoogle Scholar
  21. Ellenberg H, Mueller-Dombois D (1967) Tentative physiognomic-ecological classification of plant formations of the earth. Ber Geobot Inst 37:21–55Google Scholar
  22. ESRI (2012) ArcGIS desktop: release 10.1. Environmental Systems Research Institute, RedlandsGoogle Scholar
  23. Field R, O’Brien EM, Whittaker RJ (2005) Global models for predicting woody plant richness from climate: development and evaluation. Ecology 86:2263–2277CrossRefGoogle Scholar
  24. FontQuer P (2001) Diccionario de Botánica. Ediciones Península, BarcelonaGoogle Scholar
  25. Furley PA, Ratter JA (1988) Soil resources and plant communities of central Brazilian cerrado and their development. J Biogeogr 15:97–108CrossRefGoogle Scholar
  26. Gotelli NJ, Ellison AM (2011) Princípios de estatística em ecologia. Artmed, Porto AlegreGoogle Scholar
  27. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  28. Hargrove WW, Hoffman FM (2004) Potential of multivariate quantitative methods for delineation and visualization of ecoregions. Environ Manag 34(Suppl 1):39–60CrossRefGoogle Scholar
  29. Harrison SP, Prentice IC, Barboni D (2010) Ecophysiological and bioclimatic foundations for a global plant functional classification. J Veg Sci 21:300–317CrossRefGoogle Scholar
  30. Hawkins BA, Field R, Cornell HV, Currie DJ, Guégan JF, Kaufman DM, Kerr JT, Mittelbach GG, Oberdorff T, O’Brien EM, Porter EE, Turner JR (2003) Energy, water, and broad-scale geographic patterns of species richness. Ecology 84:3105–3117Google Scholar
  31. Hawkins BA, Montoya D, Rodríguez MÁ, Olalla-Tárraga MÁ, Zavala MÁ (2007) Global models for predicting woody plant richness from climate: comment. Ecology 88:255–259CrossRefPubMedGoogle Scholar
  32. Heubes J, Kühn I, König K (2011) Modelling biome shifts and tree cover change for 2050 in West Africa. J Biogeogr 38:2248–2258CrossRefGoogle Scholar
  33. Hijmans RJ, Cameron SE, Parra JL (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  34. Holdridge LR (1967) Life zone ecology. Tropical Science Center, San JoseGoogle Scholar
  35. Holt RD (2009) Bringing the Hutchinsonian niche into the 21st century: ecological and evolutionary perspectives. Proc Nat Acad Sci USA 106:19659–19665CrossRefPubMedPubMedCentralGoogle Scholar
  36. IBGE - Instituto Brasileiro de Geografia e Estatística (2001) Mapa de Solos do Brasil. Escala 1:5.000.000. IBGE, BrasíliaGoogle Scholar
  37. IBGE - Instituto Brasileiro de Geografia e Estatística (2004) Mapa de Vegetação do Brasil. Escala 1:5.000.000. IBGE, Brasília. Available in < ftp://geoftp.ibge.gov.br/informacoes_ambientais/vegetacao/mapas/brasil/vegetacao.pdf>. Accessed in 01/10/2015.
  38. IBGE - Instituto Brasileiro de Geografia e Estatística (2012) Manual técnico da vegetação brasileira. IBGE, Rio de JaneiroGoogle Scholar
  39. Jaramillo VJ, Sanford R (1995) Nutrient cycling in tropical deciduous forests. In: Bullock SH, Mooney HA, Medina E (eds) Seasonally dry tropical forests. Cambridge University Press, Cambridge, pp 346–361CrossRefGoogle Scholar
  40. Kucharik CJ, Foley JA, Delire C, Fisher VA, Coe MT, Lenters JD, Young-Molling C, Ramankutty N, Norman JM, Gower ST (2000) Testing the performance of a dynamic global ecosystem model: water balance, carbon balance, and vegetation structure. Glob Biogeochem Cycles 14:795–825CrossRefGoogle Scholar
  41. Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw. doi: 10.18637/jss.v028.i05 Google Scholar
  42. Lawler JJ, White D, Neilson RP, Blaustein AR (2006) Predicting climate-induced range shifts: model differences and model reliability. Glob Chang Biol 12:1568–1584CrossRefGoogle Scholar
  43. Lima JR, Santos ND, Tozzi AM, Mansano VF (2016) Using legumes as indicators in the seasonally dry vegetation types in South America. Ecol Indic. doi: 10.1016/j.ecolind.2016.10.030 Google Scholar
  44. Maiorano L, Cheddadi R, Zimmermann NE, Pellissier L, Petitpierre B, Pottier J, Laborde H, Hurdu BI, Pearman PB, Psomas A, Singarayer JS (2013) Building the niche through time: using 13,000 years of data to predict the effects of climate change on three tree species in Europe. Glob Ecol Biogeogr 22:302–317CrossRefGoogle Scholar
  45. Mendonça BAF, Simas FNB, Schaefer CEGR, Fernandes-Filho EI, Vale-Júnior JF, Mendonça JGF (2014) Podzolized soils and paleoenvironmental implications of white-sand vegetation (Campinarana) in the Viruá National Park, Brazil. Geoderma Reg 2-3:9–20CrossRefGoogle Scholar
  46. Miatto RC, Wright IJ, Batalha MA (2016) Relationships between soil nutrient status and nutrient-related leaf traits in Brazilian cerrado and seasonal forest communities. Plant Soil 404:13–33CrossRefGoogle Scholar
  47. Monserud RA, Leemans R (1992) Comparing global vegetation maps with the kappa statistic. Ecol Model 62:275–293CrossRefGoogle Scholar
  48. Mueller-Dombois D, Ellenberg H (2003) Aims and methods of vegetation ecology. Blackburn Press, CaldwellGoogle Scholar
  49. Oliveira-Filho AT, Shepherd GJ, Martins FR, Stubblebine WH (1989) Environmental factors affecting physiognomic and floristic variation in an area of cerrado in Central Brazil. J Trop Ecol 5:413–431CrossRefGoogle Scholar
  50. Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D'Amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions of the world: a new map of life on earth. Bioscience 51:933–938Google Scholar
  51. Pappas C, Fatichi S, Rimkus S (2015) The role of local-scale heterogeneities in terrestrial ecosystem modeling. J Geophys Res Biogeosci 120:341–360CrossRefGoogle Scholar
  52. Pennington TR, Prado DE, Pendry CA (2000) Neotropical seasonally dry forests and quaternary vegetation changes. J Biogeogr 27:261–273CrossRefGoogle Scholar
  53. Peres-Neto PR, Jackson DA, Somers KM (2005) How many principal components? Stopping rules for determining the number of non-trivial axes revisited. Comput Stat Data Anal 49:974–997CrossRefGoogle Scholar
  54. Peterson AT, Soberón J, Pearson RG, Anderson RP, Martínez-Meyer E, Nakamura M, Araújo MB (2011) Ecological niche and geographical distribution. Princeton University Press, PrincetonGoogle Scholar
  55. Prado DE (2000) Seasonally dry forests of tropical South America: from forgotten ecosystems to a new phytogeographic unit. Edinb J Bot 57:437–461CrossRefGoogle Scholar
  56. Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199CrossRefGoogle Scholar
  57. Prentice IC, Cramer W, Harrison SP, Leemans R, Monserud RA, Solomon AM (1992) Special paper: a global biome model based on plant physiology and dominance, soil properties and climate. J Biogeogr 19:117–134CrossRefGoogle Scholar
  58. Prentice IC, Harrison SP, Bartlein PJ (2011) Global vegetation and terrestrial carbon cycle changes after the last ice age. New Phytol 189:988–998CrossRefPubMedGoogle Scholar
  59. Queiroz LP (2006) The Brazilian caatinga: Phytogeographical pattern inferred from distribution data of the Leguminosae. In: Pennington RT, Lewis GP, Ratter JA (eds) Neotropical savannas and dry forests: plant diversity, biogeography, and conservation. Taylor and Francis CRC Press, Oxford, pp 113–149Google Scholar
  60. R Development Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  61. Ratter JA, Richards PW, Argent G, Gifford DR (1973) Observations on the vegetation of northeastern Mato Grosso: I. The woody vegetation types of the Xavantina-Cachimbo expedition area. Philos Trans R Soc B 880:449–492CrossRefGoogle Scholar
  62. Roberts DR, Hamann A (2012) Predicting potential climate change impacts with bioclimate envelope models: a palaeoecological perspective. Glob Ecol Biogeogr 21:121–133CrossRefGoogle Scholar
  63. Rodrigues PMS, Silva JO, Eisenlohr PV, Schaefer CEGR (2015) Climate change effects on the geographic distribution of specialist tree species of the Brazilian tropical dry forests. Braz J Biol 75:679–684CrossRefPubMedGoogle Scholar
  64. Rossatto DR, Carvalho FA, Haridasan M (2015) Soil and leaf nutrient content of tree species support deciduous forests on limestone outcrops as a eutrophic ecosystem. Acta Bot Bras 29:231–238CrossRefGoogle Scholar
  65. Salazar LF, Nobre CA, Oyama MD (2007) Climate change consequences on the biome distribution in tropical South America. Geophys Res Lett 34. doi: 10.1029/2007GL029695
  66. Santos RM, Oliveira-Filho AT, Eisenlohr PV, Queiroz LP, Cardoso DBOS, Rodal MJN (2012) Identity and relationships of the arboreal caatinga among other floristic units of seasonally dry tropical forests (SDTFs) of north-eastern and Central Brazil. Ecol Evol 2:409–428CrossRefPubMedPubMedCentralGoogle Scholar
  67. Särkinen T, Iganci JRV, Linares-Palomino R, Simon MF, Prado D (2011) Forgotten forests – issues and prospects in biome mapping using seasonally dry tropical forests as a case study. BMC Ecol 11Google Scholar
  68. Saxon E, Baker B, Hargrove W, Hoffman F, Zganjar C (2005) Mapping environments at risk under different global climate change scenarios. Ecol Lett 8:53–60CrossRefGoogle Scholar
  69. Schaefer CEGR (2013) Bases físicas da paisagem brasileira: estrutura geológica, relevo e solos. In: Araújo AP, BJR A (eds) Tópicos em ciência do solo. Sociedade Brasileira de Ciência do Solo, Viçosa, pp 1–69Google Scholar
  70. Schaefer CEGR, Amaral EF, Mendonca BAF, Oliveira H, Lani JL, Costa LM, Fernandes-Filho EI (2008) Soil and vegetation carbon stocks in Brazilian western Amazonia: relationships and ecological implications for natural landscapes. Environ Monit Assess 140:279–289CrossRefPubMedGoogle Scholar
  71. Schaefer CEGR, Nunes JA, Neri AV, Mendonca BAF, Ferreira-Junior WG, Arruda DM, Duque-Brasil R (2015) Relação solo-vegetação em formações vegetacionais brasileiras: metodologia e estudos de caso. In: Eisenlohr PV, Fagg CW, MMRF M, Andrade LA, JAA M-N (eds) Fitossociologia no Brasil: Métodos e Estudos de Casos, vol II. Editora UFV, Viçosa, pp 1–15Google Scholar
  72. Sitch S, Smith B, Prentice IC, Arneth A, Bondeau A, Cramer W, Kaplan JO, Levis S, Lucht W, Sykes MT, Thonicke K, Venevsky S (2003) Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob Chang Biol 9:161–185CrossRefGoogle Scholar
  73. Svenning JC, Fløjgaard C, Marske KA, Nógues-Bravo D, Normand S (2011) Applications of species distribution modeling to paleobiology. Quat Sci Rev 30:2930–2947CrossRefGoogle Scholar
  74. Swaine MD (1996) Rainfall and soil fertility as factors limiting forest species distributions in Ghana. J Ecol 84:419–428CrossRefGoogle Scholar
  75. Warton DI, Wright ST, Wang Y (2012) Distance-based multivariate analyses confound location and dispersion effects. Methods Ecol Evol 3:89–101CrossRefGoogle Scholar
  76. Webster R, Oliver MA (1990) Statistical methods in soil and land resource survey. Oxford University Press, OxfordGoogle Scholar
  77. Werneck FP, Costa GC, Colli GR (2011) Revisiting the historical distribution of seasonally dry tropical forests: new insights based on palaeodistribution modelling and palynological evidence. Glob Ecol Biogeogr 20:272–288CrossRefGoogle Scholar
  78. Whittaker RH (1971) Communities and ecosystems, 4th edn. The Macmillan co, New YorkGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  • Daniel M. Arruda
    • 1
    • 2
  • Elpídio I. Fernandes-Filho
    • 3
  • Ricardo R. C. Solar
    • 4
  • Carlos E. G. R. Schaefer
    • 3
  1. 1.Departamento de Biologia VegetalUniversidade Federal de ViçosaViçosaBrazil
  2. 2.Instituto de Ciências AgráriasUniversidade Federal de Minas GeraisMontes ClarosBrazil
  3. 3.Departamento de SolosUniversidade Federal de ViçosaViçosaBrazil
  4. 4.Departamento de Biologia Geral, Instituto de Ciências BiológicasUniversidade Federal de Minas GeraisBelo HorizonteBrazil

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