Regional Environmental Change

, Volume 13, Issue 6, pp 1245–1257 | Cite as

Impacts of climate change on the distribution of species and communities in the Chilean Mediterranean ecosystem

  • Nicolas Bambach
  • Francisco J. Meza
  • Horacio Gilabert
  • Marcelo Miranda
Original Article

Abstract

The Mediterranean region of Chile is considered a biodiversity hot spot. An increase in temperature and decrease in precipitation, as projected for the end of this century by global circulation models, would likely change the distribution of the sclerophyllous thorny shrubland and woodland. In order to assess those potential impacts, the MAXENT algorithm was used to project potential changes in the distribution of the Mediterranean ecosystem. Ecological niche models were fitted and used to project the potential distribution of these forest ecosystems by the end of the century. Projections were made using data from the PRECIS model for the A2 and B2 climate change scenarios and two strategies of occupancy: free migration and non-migration. Distribution models of sclerophyllous, woodland and shrubland performed accurately representing current species’ distribution. When we assume non-migration responses under climate change scenarios, results reveal a decrease in the distribution area for all the species. The areas where the highest reduction in a suitable environment was found are located along the coastline, where higher temperature increases have been projected. For native ecosystems from the Andean Range region, such as communities dominated by thorny species, a stable habitat was found, associated with a higher adaptation capability to future climatic projections. Hence, in the future, buffer zones originated by “topo-climatic” conditions might play a key role in protecting Central Chile biodiversity.

Keywords

Bioclimatic models Ecological niche Climate change Mediterranean ecosystems MAXENT 

References

  1. Algar AC, Kharouba HM, Young ER, Kerr JT (2009) Predicting the future of species diversity: macroecological theory, climate change, and direct tests of alternative forecasting methods. Ecography 32:22–33CrossRefGoogle Scholar
  2. Arroyo MTK, Armesto J, Squeo F, Gutiérrez J (1993) Global change: the flora and vegetation of Chile. In: Mooney H, Fuentes E, Kronberg BI (eds) Earth system response to global change: contrast between North and South America. Academic Press, San Diego, pp 239–263Google Scholar
  3. Bakkenes M, Alkemade JRM, Ihle F, Leemans R, Latour JB (2002) Assessing effects of forecasted climate change on the diversity and distribution of European higher plants for 2050. Glob Change Biol 8:390–407CrossRefGoogle Scholar
  4. Benito BM, Martínez-Ortega MM, Muñoz LM, Lorite J, Peñas J (2009) Assessing extinction-risk of endangered plants using species distribution models: a case study of habitat depletion caused by the spread of greenhouses. Biodivers Conserv 18:2509–2520CrossRefGoogle Scholar
  5. Blondel J, Aronson J (1995) Biodiversity and ecosystem function in the mediterranean basin human and non-human determinants. In: Richardson DM, Davis GW (eds) Mediterranean-type ecosystems the function of biodiversity. Springer, Berlin, pp 43–119CrossRefGoogle Scholar
  6. Boubli JP, de Lima MG (2009) Modeling the geographical distribution and fundamental niches of Cacajao spp and Chiropotes israelita in northwestern Amazonia via a maximum entropy algorithm. Int J Primatol 30:217–228CrossRefGoogle Scholar
  7. Buermann W, Saatchi S, Smith TB, Zutta BR, Chaves JA, Milá B, Graham CH (2008) Predicting species distributions across the Amazonian and Andean regions using remote sensing data. J Biogeogr 35:1160–1176CrossRefGoogle Scholar
  8. Busby JR (1991) BIOCLIM—A bioclimate analysis and prediction system. In: Margules CR, Austin MP (eds) Nature conservation: cost effective biological surveys and data analysis, Chapter 10. CSIRO, MelbourneGoogle Scholar
  9. CONAF (1999) Catastro y Evaluación de los Recursos Vegetacionales Nativos de Chile Santiago, ChileGoogle Scholar
  10. Cowling RM, Rundel PW, Lamont BB, Arroyo MK, Arianoutsou M (1996) Plan diversity in mediterranean-climate region. Tree 11:362–366Google Scholar
  11. del Barrio G, Harrison PA, Berry PM, Butt N, Sanjuan ME, Pearson RG, Dawson T (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 9:129–147Google Scholar
  12. del Fierro P (1998) Experiencia silvicultural del bosque nativo de Chile Recopilación de antecedentes para 57 especies arbóreas y evaluación de prácticas silviculturales Chile, 420 pp.Google Scholar
  13. DGF-CONAMA (2007) Estudio de la variabilidad climática en Chile para el siglo XXI Santiago, ChileGoogle Scholar
  14. Di Castri F (1973) Climatographical comparisons between Chile and the western coast of North America. In: Di Castri F, Mooney HA (eds) Mediterranean-type ecosystems. Springer, Berlin, pp 21–36CrossRefGoogle Scholar
  15. Di Castri F, Hajek E (1976) Bioclimatología de Chile. Universidad Católica de Chile, Santiago 128 ppGoogle Scholar
  16. Donoso C (2005) Árboles nativos de Chile Guía de reconocimiento Edición 4 Marisa Cuneo Ediciones. Valdivia, Chile 136 ppGoogle Scholar
  17. Dudík M, Phillips SJ, Schapire RE (2007) Maximum entropy density estimation with generalized regularization and an application to species distribution modeling. J Mach Learn Res 8:1217–1260Google Scholar
  18. Elith J, Graham CH (2009) Do they? How do they? Why do they differ? On finding reasons for differing performances of species distribution models. Ecography 32:66–77CrossRefGoogle Scholar
  19. Elith J, Graham CH, Anderson RP et al (2006) Novel methods improve predictions of species’ distributions from occurrence data. Ecography 29:129–151CrossRefGoogle Scholar
  20. Elith J, Phillips SJ, Hastie T, Dudik M, En Chee Y, Colin Y (2011) A statistical explanation of MaxEnt for Ecologist. Divers Distrib 17:43–57CrossRefGoogle Scholar
  21. Evangelista P, Kumar S, Stohlgren TJ, Jarnevich CS, Crall AW, Norman JB III, Barnett D (2008) Modelling invasion for an habitat generalist and a specialist plant species. Divers Distrib 14:808–817CrossRefGoogle Scholar
  22. Falvey M, Garreaud R (2009) Regional cooling in a warming world: recent temperature trends in the southeast pacific and along the west coast of subtropical South America (1979–2006). J Geophys Res 114:1–16CrossRefGoogle Scholar
  23. Fischlin A, Midgley GF, Price JT et al (2007) Ecosystems, their properties, goods, and services. In: Parry ML, Canziani OF, Palutikof JP et al (eds) Climate change 2007: impacts, adaptation and vulnerability. Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 211–272Google Scholar
  24. Fitzpatrick MC, Gove AD, Sanders NJ, Dunn RR (2008) Climate change, plant migration, and range collapse in a global biodiversity hotspot: the Banksia (Proteaceae) of Western Australia. Glob Change Biol 14:1337–1352CrossRefGoogle Scholar
  25. Franklin J (1998) Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. J Veg Sci 9:733–748CrossRefGoogle Scholar
  26. Fuentes ER, Muñoz MR (1995) The human role in changing landscapes in central Chile: implications for intercontinental comparisons. In: Arroyo MTK, Zedler PH, Fox MD (eds) Ecology and biogeography of Mediterranean ecosystems in Chile, California, and Australia. Springer-Verlag, New York, pp 401–417Google Scholar
  27. Gajardo R (1994) La Vegetación Natural de Chile: clasificación y distribución geográfica. Editorial Universitaria, Santiago 165 ppGoogle Scholar
  28. Goodchild MF, Parks BO, Steyaert LT (1993) Environmental modeling with GIS. Oxford University Press, Oxford, pp 488Google Scholar
  29. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  30. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  31. Guisan A, Zimmermann NE, Elith J, Graham CH, Phillips S, Peterson AT (2007) What matters for predicting the occurrences of trees: techniques, data, or species’ characteristics? Ecol Monogr 77:615–630CrossRefGoogle Scholar
  32. Hayhoe K, Cayan D, Field CB, Frumhoff PC, Maurer EP, Miller NL, Moser SC, Schneider SH, Cahill K, Cleland EE, Dale L, Drapek R, Hanemann RM, Kalkstein LS, Lenihan J, Lunch CK, Neilson RP, Sheridan SC, Verville JH (2004) Emissions pathways, climate change, and impacts on California. Proc Natl Acad Sci USA 101(34):12422–12427Google Scholar
  33. Hijmans RJ, Graham CH (2006) The ability of climate envelope models to predict the effect of climate change on species distributions. Glob Change Biol 12:1–10CrossRefGoogle Scholar
  34. Hoffmann A (1998) Flora Silvestre de Chile, Zona Central Edición 4 Fundación Claudio Gay, Santiago 254 ppGoogle Scholar
  35. IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, p 996Google Scholar
  36. Ishida T, Kawashima S (1993) Use of co-kriging to estimate surface air temperature from elevation. Theoret Appl Climatol 47:147–157CrossRefGoogle Scholar
  37. Kalnay E, Kanamitsu M, Kistler R et al (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–472CrossRefGoogle Scholar
  38. Köeppen W (1931) Die Klimate der Erder, Grundriss der Klimakunde, 2nd ed. BerlinGoogle Scholar
  39. Kottek M, Grieser J, Beck C, Rudolf B, Rubel F (2006) World map of Köppen-Geiger climate classification updated. Meteorol Z 15:259–263CrossRefGoogle Scholar
  40. Kumar S, Stohlgren TJ (2009) Maxent modeling for predicting suitable habitat for threatened and endangered tree Canacomyrica monticola in New Caledonia. J Ecol Nat Environ 1:94–98Google Scholar
  41. Lavorel S, Canadell J, Rambal S, Terradas J (1998) Mediterranean terrestrial ecosystems: research priorities on global change effects. Glob Ecol Biogeogr 7:157–166CrossRefGoogle Scholar
  42. Lobo JM, Jiménez-Valverde A, Real R (2008) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151CrossRefGoogle Scholar
  43. Loiselle BA, Jřrgensen PM, Consiglio T, Jiménez I, Blake JG, Lohmann LG, Montiel OM (2008) Predicting species distributions from herbarium collections: does climate bias in collection sampling influence model outcomes? J Biogeogr 35:105–116Google Scholar
  44. Luebert F, Pliscoff P (2006) Sinopsis Bioclimática y Vegetacional de Chile First edition. Editorial Universitaria, Santiago 316 ppGoogle Scholar
  45. Marino H (2002) Respuestas ecofisiológicas de plantas de ecosistemas de zonas con clima mediterráneo y ambientes de altamontaña. Revista Chilena de Historia Natural 75:625–637Google Scholar
  46. McLachlan JS, Clark J, Manos P (2005) Molecular indicators of tree migration capacity under rapid climate change. Ecology 86:2088–2098CrossRefGoogle Scholar
  47. Médail F, Quézel P (1997) Hot-spots analysis for conservation of plant biodiversity in the Mediterranean basin. Ann Mo Bot Gard 84:112–127CrossRefGoogle Scholar
  48. Montenegro G (2000) Chile, Nuestra Flora Útil Guía de Uso Apícola, Alimentario, Medicinal Folclórico, Artesanal y Ornamental Colección en Agricultura Ediciones Universidad Católica de Chile Santiago, Chile 267 ppGoogle Scholar
  49. Mooney HA, Dunn EL (1970) Photosynthetic systems of mediterranean-climate shrubs and trees of California and Chile. Am Nat 104:447-453Google Scholar
  50. Mueller-Dombois D, Ellenberg H (1974) Aims and methods of vegetation ecology. Wiley, New York 547 ppGoogle Scholar
  51. Myers N, Mittermeier RA, Mittermeier CG, da Fonseca GAB, Kent J (2000) Biodiversity hotspots for conservation priorities. Nature 403:853–858CrossRefGoogle Scholar
  52. Nakićenović N, Alcamo J, Davis G, De Vries B, Fenhann J, Gaffin S, Kram T (2000) IPCC special report on emissions scenarios (SRES)Google Scholar
  53. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42CrossRefGoogle Scholar
  54. Pearson RG (2006) Climate change and the migration capacity of species. Trends Ecol Evol 21:111–113CrossRefGoogle Scholar
  55. Pearson RG, Dawson TE (2003) Predicting the impacts of climate change on the distribution of species: Are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–372CrossRefGoogle Scholar
  56. Phillips SJ, Dudik M (2008) Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31:161–175CrossRefGoogle Scholar
  57. Phillips SJ, Anderson R, Shapire R (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  58. Pliscoff P (2002) Priorización de áreas para fortalecer la conservación de la flora arbórea nativa en la zona mediterránea de Chile M Sc Thesis, Universidad de Chile, Santiago de ChileGoogle Scholar
  59. Rodriguez E, Morris CS, Belz JE, Chapin EC, Martin JM, Daffer W, Hensley S (2005) An assessment of the SRTM topographic products, Technical Report JPL D-31639. Jet Propulsion Laboratory, Pasadena 143 ppGoogle Scholar
  60. Rundel P (1998) Landscape disturbance in mediterranean-type ecosystem: an overview. In: Rundel P, Montenegro G, Jacsic F (eds) Landscape disturbance and biodiversity in mediterranean-type ecosystems. Springer, Berlin, pp 3–22CrossRefGoogle Scholar
  61. Sala OE, Chapin FS, Armesto JJ, Berlow E, Bloomfield J, Dirzo R, Huber-Sanwald E, Huenneke LF, Jackson RB, Kinzing A, Leemans R, Lodge DM, Mooney HA, Oesterheld M, LeRoy Poff N, Sykes MT, Walker BH, Walker M, Wall DH (2000) Global biodiversity scenarios for the year 2100. Science 287(5459):1770–1774Google Scholar
  62. Shelford VE (1931) Some concepts of bioecology. Ecology 12:455–467CrossRefGoogle Scholar
  63. Stockwell D, Peters D (1999) The GARP modelling system: problems and solutions to automated spatial prediction. Int J Geogr Inf Sc 13:143–158CrossRefGoogle Scholar
  64. Thomas CD, Cameron A, Green RE et al (2004) Extinction risk from climate change. Nature 427:145–147CrossRefGoogle Scholar
  65. Thuiller W, Araújo MB, Pearson RG, Whittaker RJ, Brotons L, Lavorel S (2004) Biodiversity conservation: uncertainty in predictions of extinction risk. Nature 430 (6995)Google Scholar
  66. Thuiller W, Richardson DM, Pysek P, Midgley GF, Hughes GO, Rouget M (2005) Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob Change Biol 11:2234–2250CrossRefGoogle Scholar
  67. Willis KJ, Bhagwat SA (2009) Biodiversity and climate change. Science 326:806–807CrossRefGoogle Scholar
  68. Woodward SL (2003) Biomes of Earth: terrestrial, aquatic, and human-dominated. Greenwood Press, California, pp 435Google Scholar
  69. Yost AC, Petersen SL, Gregg M, Miller R (2008) Predictive modeling and mapping sage grouse (Centrocercus urophasianus) nesting habitat using maximum entropy and a long-term dataset from southern Oregon. Ecol Inform 3:375–386CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nicolas Bambach
    • 1
    • 2
    • 3
  • Francisco J. Meza
    • 2
    • 3
  • Horacio Gilabert
    • 2
  • Marcelo Miranda
    • 2
  1. 1.Department of Land, Air and Water ResourcesUniversity of CaliforniaDavisUSA
  2. 2.Departamento de Ecosistemas y Medio AmbientePontificia Universidad Católica de ChileSantiagoChile
  3. 3.Centro Interdisciplinario de Cambio GlobalPontificia Universidad Católica de ChileSantiagoChile

Personalised recommendations