Thông tin tài nguyên

One hundred years of climate change in Mexico

pmc-PMC7365465.pdf · Phiên bản 1 · 19 trang

18
Bản gốc
Trang 16 · Đoạn 41Vị trí nguồn

climate change and human disturbance through the analysis of life-history plasticity. Perspectives in Plant Ecology, Evolution and Systematics 14: 393–401. 7. Saunders DA, Hobbs RJ, Margules CR (1991) Biological consequences of ecosystem fragmentation: A review. 5: 18–32. 8. Guisan A, Petitpierre B, Broennimann O, Daehler C, Kueffer CJ (2014) Unifying niche shift studies: Insights from biological invasions. 29: 260–269. 9. IPCC (2013) Climate change 2013: The physical science basis. Contribution of working group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Intergovernmental Panel on Climate Change. Stocker TF, Qin D, Plattner G, Tignor M, Allen S, Boschung J, Nauels A, Xia Y, Bex V and Midgley M (Editors). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp 10. Loarie SR, Duffy PB, Hamilton H, Asner GP, Field CB, et al. (2009) The velocity of climate change. Nature 462: 1052–1055. https://doi.org/10.1038/nature08649 PMID: 20033047 11. Burrows MT, Schoeman DS, Buckley LB, Moore P, Poloczanska ES, et al. (2011) The pace of shifting climate in marine and terrestrial ecosystems. Science 334: 652–655. https://doi.org/10.1126/science. 1210288 PMID: 22053045 12. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very-high resolution interpolated cli- mate surfaces for global land areas. International Journal of Climatology 25: 1965–1978. 13. Pecl GT, Arau´ jo MB, Bell JD, Blanchard J, Bonebrake TC, et al. (2017) Biodiversity redistribution under

Trang 16 · Đoạn 42Vị trí nguồn

climate change: Impacts on ecosystems and human well-being. Science 355: eaai9214. https://doi.org/ 10.1126/science.aai9214 PMID: 28360268 14. Kriticos DJ, Webber BL, Leriche A, Ota N, Macadam I, et al. (2012) CliMond: Global high-resolution his- torical and future scenario climate surfaces for bioclimatic modelling. Methods in Ecology and Evolution 3: 53–64. 15. Sa´ enz-Romero C, Rehfeldt GE, Crookston NL, Duval P, St-Amant R, et al. (2010) Spline models of con- temporary, 2030, 2060 and 2090 climates for Mexico and their use in understanding climate-change impacts on the vegetation. Climatic Change 102: 595–623. PLOS ONE Historical climate change in Mexico PLOS ONE | https://doi.org/10.1371/journal.pone.0209808 July 16, 2020 16 / 19

Trang 17 · Đoạn 43Vị trí nguồn

16. Te´ llez O, Hutchinson MA, Nix HA (2011) Desarrollo de coberturas digitales clima´ ticas para Me´xico.; Sa´ nchez Rojas G BBC, Pavo´ n NP, editor. Pachuca, Me´xico: Universidad Auto´noma del Estado de Hidalgo. 17. Cuervo-Robayo AP, Te´ llez-Valde´ s O, Go´ mez-Albores MA, Venegas-Barrera CS, Manjarrez J, et al. (2014) An update of high-resolution monthly climate surfaces for Mexico. International Journal of Clima- tology 34: 2427–2437. 18. Englehart PJ, Douglas AV (2004) Characterizing regional-scale variations in monthly and seasonal sur- face air temperature over Mexico. International Journal of Climatology: A Journal of the Royal Meteoro- logical Society 24: 1897–1909. 19. Pavia EG, Graef F, Reyes J (2009) Annual and seasonal surface air temperature trends in Mexico. International Journal of Climatology 29: 1324–1329. 20. Arau´jo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species–climate impact models under climate change. Global Change Biology 11: 1504–1513. 21. Parra JL, Monahan WB (2008) Variability in 20th century climate change reconstructions and its conse- quences for predicting geographic responses of California mammals. Global change biology 14: 2215– 2231. 22. Kharouba HM, Algar AC, Kerr JT (2009) Historically calibrated predictions of butterfly species’ range shift using global change as a pseudo-experiment. Ecology 90: 2213–2222. https://doi.org/10.1890/08- 1304.1 PMID: 19739383 23. Rapacciuolo G, Roy DB, Gillings S, Fox R, Walker K, et al. (2012) Climatic associations of British spe-

Trang 17 · Đoạn 44Vị trí nguồn

cies distributions show good transferability in time but low predictive accuracy for range change. PLoS One 7: e40212. https://doi.org/10.1371/journal.pone.0040212 PMID: 22792243 24. Rapacciuolo G, Maher SP, Schneider AC, Hammond TT, Jabis MD, et al. (2014) Beyond a warming fin- gerprint: Individualistic biogeographic responses to heterogeneous climate change in California. Global change biology 20: 2841–2855. https://doi.org/10.1111/gcb.12638 PMID: 24934878 25. Ameca y Jua´rez EI, Mace GM, Cowlishaw G, Cornforth WA, Pettorelli N (2013) Assessing exposure to extreme climatic events for terrestrial mammals. Conservation Letters 6: 145–153. 26. Eskildsen A, le Roux PC, Heikkinen RK, Høye TT, Kissling WD, et al. (2013) Testing species distribution models across space and time: high latitude butterflies and recent warming. Global Ecology and Bioge- ography 22: 1293–1303. 27. Smith AB (2013) On evaluating species distribution models with random background sites in place of absences when test presences disproportionately sample suitable habitat. Diversity and Distributions 19: 867–872. 28. Peterson AT, Navarro-Sigu¨ enza AG, Martı´nez-Meyer E, Cuervo-Robayo AP, Berlanga H, et al. (2015) Twentieth century turnover of Mexican endemic avifaunas: Landscape change versus climate drivers. Science Advances 1: e1400071. https://doi.org/10.1126/sciadv.1400071 PMID: 26601171 29. Audusseau H, Le Vaillant M, Janz N, Nylin S, Karlsson B, et al. (2017) Species range expansion con- strains the ecological niches of resident butterflies. Journal of Biogeography 44: 28–38.

Trang 17 · Đoạn 45Vị trí nguồn

30. Grenouillet G, Comte L (2014) Illuminating geographical patterns in species’ range shifts. Global change biology 20: 3080–3091. https://doi.org/10.1111/gcb.12570 PMID: 24616088 31. Holling C (1973) Resilience and stability of ecological systems. 4: 1–23. 32. Ficetola GF, Maiorano L, Falcucci A, Dendoncker N, Boitani L, et al. (2010) Knowing the past to predict the future: Land-use change and the distribution of invasive bullfrogs. Global change Biology 16: 528– 537. 33. Dawson TP, Jackson ST, House JI, Prentice IC, Mace GM (2011) Beyond predictions: biodiversity con- servation in a changing climate. Science 332: 53–58. https://doi.org/10.1126/science.1200303 PMID: 21454781 34. Macias-Fauria M, Willis KJ (2013) Landscape planning for the future: Using fossil records to indepen- dently validate bioclimatic envelope models for economically valuable tree species in Europe. Global Ecology and Biogeography 22: 318–333. 35. VanDerWal J, Murphy HT, Kutt AS, Perkins GC, Bateman BL, et al. (2013) Focus on poleward shifts in species’ distribution underestimates the fingerprint of climate change. Nature Climate Change 3: 239. 36. Iwamura T, Guisan A, Wilson KA, Possingham HP (2013) How robust are global conservation priorities to climate change? Global Environmental Change 23: 1277–1284. 37. Margules CR, Pressey RL (2000) Systematic conservation planning. Nature 405: 243. https://doi.org/ 10.1038/35012251 PMID: 10821285 38. Serreze M, Walsh J, Chapin FS, Osterkamp T, Dyurgerov M, et al. (2000) Observational evidence of

Trang 17 · Đoạn 46Vị trí nguồn

recent change in the northern high-latitude environment. Climatic Change 46: 159–207. PLOS ONE Historical climate change in Mexico PLOS ONE | https://doi.org/10.1371/journal.pone.0209808 July 16, 2020 17 / 19

Trang 18 · Đoạn 47Vị trí nguồn

39. Rahmstorf S, Cazenave A, Church JA, Hansen JE, Keeling RF, et al. (2007) Recent climate observa- tions compared to projections. Science 316: 709–709. https://doi.org/10.1126/science.1136843 PMID: 17272686 40. Jauregui E (2004) Variabilidad clima´ tica en los registros instrumentales en Me´xico. In: Martı´nez J, Fer- na´ ndez A, editors. Cambio clima´ tico: una visio´ n desde Me´ xico. Me´xico, D.F.: Secretaria de Medio Ambiente y Recursos Naturales. pp. p 279–290. 41. Quentin E, Dı´az-Delgado C, Go´ mez-Albores MA (2007) Desarrollo Geoma´ tico para la Gestio´ n Inte- grada del Agua. XI Conferencia Iberoamericana de Sistemas de Informacio´n Geogra´ fica (XI CONFIB- SIG), Buenos Aires, Argentina. 42. Hutchinson MF (2007) ANUSPLIN 4.37 Version. User Guide. 43. Guerreiro SB, Kilsby C, Fowler HJJIJoC (2017) Assessing the threat of future megadrought in Iberia. International Journal of Climatology 37: 5024–5034. 44. Price DT, McKenney DW, Nalder IA, Hutchinson MF, Kesteven JL (2000) A comparison of two statisti- cal methods for spatial interpolation of Canadian monthly mean climate data. Agricultural and Forest Meteorology 101: 81–94. 45. Daly C, Neilson RP, Phillips DLJJoam (1994) A statistical-topographic model for mapping climatological precipitation over mountainous terrain. Journal of Applied Meterology 33: 140–158. 46. Hutchinson M, Gessler P (1994) Splines—more than just a smooth interpolator. Geoderma 62: 45–67. 47. Hutchinson MF, McKenney DW, Lawrence K, Pedlar JH, Hopkinson RF, et al. (2009) Development and

Trang 18 · Đoạn 48Vị trí nguồn

testing of Canada-wide interpolated spatial models of daily minimum–maximum temperature and pre- cipitation for 1961–2003. Journal of Applied Meteorology and Climatology 48: 725–741. 48. McKenney DW, Hutchinson MF, Papadopol P, Lawrence K, Pedlar J, et al. (2011) Customized spatial climate models for North America. Bulletin of the American Meteorological Society 92: 1611–1622. 49. McKenney DW, Pedlar JH, Papadopol P, Hutchinson MF (2006) The development of 1901–2000 histor- ical monthly climate models for Canada and the United States. Agricultural and Forest Meteorology 138: 69–81. 50. Hijmans RJ, Elith J (2015) dismo: Species Distribution Modeling. R package version 1.1–4. https:// CRAN.R-project.org/package=dismo 51. R Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statis- tical Computing, Vienna, Austria. URL http://www.R-project.org/. 52. StatSoft (2011) STATISTICA (data analysis software system), version 10. 53. Eastman J (2012) IDRISI Selva Manual. Versio´n 17.1. Clark Labs 1–324. https://doi.org/10.1109/ TGRS.2002.802519 54. Hopkinson RF, Hutchinson MF, McKenney DW, Milewska EJ, Papadopol P (2012) Optimizing input data for gridding climate normals for Canada. Journal of Applied Meteorology and Climatology 51: 1508–1518. 55. Llorente-Bousquets J, Ocegueda S (2008) Estado del conocimiento de la biota. In: CONABIO, editor. Capital natural de Me´ xico, vol I: Conocimiento actual de la biodiversidad. Me´ xico. pp. 283–322.

Trang 18 · Đoạn 49Vị trí nguồn

56. Espinosa D, Ocegueda S, Aguilar C, Flores O, Llorente-Bousquets J, et al. (2008) El conocimiento bio- geogra´ fico de las especies y su regionalizacio´ n natural. In: CONABIO, editor. Capital Natural de Me´xico. Me´xico, D.F. pp. 33–65. 57. Comisio´ n Nacional para el Conocimiento y Uso de la Biodiversidad (1997) ’Provincias biogeogra´ ficas de Me´xico’. Escala 1:4 000 000. Comisio´n Nacional para el Conocimiento y Uso de la Biodiversidad, Me´xico, D. F. http://www.conabio.gob.mx/informacion/gis/?vns=gis_root/region/biotic/rbiog4mgw 58. Pettitt A (1979). A non-parametric approach to the change-point problem. Journal of the Royal Statisti- cal Society: Series C Applied Statistics 28: 126–135. 59. Wijngaard J, Klein Tank A, Ko¨ nnen G (2003) Homogeneity of 20th century European daily temperature and precipitation series. International Journal of Climatology 23: 679–692. 60. Kuglitsch F, Toreti A, Xoplaki E, Della-Marta P, Luterbacher J, Wanner H. (2009) Homogenization of daily maximum temperature series in the Mediterranean. Journal of Geophysical Research: Atmo- spheres 114: D15. 61. Pohlert T (2020). trend: Non-Parametric Trend Tests and Change-Point Detection. R package version 1.1.2. https://CRAN.R-project.org/package=trend) 62. Beaumont LJ, Pitman A, Perkins S, Zimmermann NE, Yoccoz NG, et al. (2011) Impacts of climate change on the world’s most exceptional ecoregions. Proceedings of the National Academy of Sciences: 201007217. 63. Magaña VO, Conde C (2000) Climate and freshwater resources in northern Mexico: Sonora, a case

Trang 18 · Đoạn 50Vị trí nguồn

study. Environmental Monitoring and Assessment 61: 167–185. PLOS ONE Historical climate change in Mexico PLOS ONE | https://doi.org/10.1371/journal.pone.0209808 July 16, 2020 18 / 19

Trang 19 · Đoạn 51Vị trí nguồn

64. Liverman DM (1990) Drought impacts in Mexico: climate, agriculture, technology, and land tenure in Sonora and Puebla. Annals of the Association of American Geographers 80: 49–72. 65. Magaña V, Conde C, Sa´ nchez O, Gay C (2000) Evaluacio´ n de escenarios regionales de clima actual y de cambio clima´tico futuro para Me´xico. In: Me´ xico: Una visio´n hacia el siglo XXI. El cambio clima´tico en Me´xico. Me´xico D.F.: Instituto Nacional de Ecologı´a. pp. 15–21. 66. Weltzin JF, Loik ME, Schwinning S, Williams DG, Fay PA, et al. (2003) Assessing the response of ter- restrial ecosystems to potential changes in precipitation. AIBS Bulletin 53: 941–952. 67. Challenger A, Dirzo R, Lo´ pez JC, Mendoza E, Lira-Noriega A, et al. (2009) Factores de cambio y estado de la biodiversidad. Capital natural de Me´ xico 2: 37–73. 68. Go´mez Mendoza L, Arriaga L (2007) Modeling the effect of climate change on the distribution of oak and pine species of Mexico. Conservation Biology 21: 1545–1555. https://doi.org/10.1111/j.1523-1739. 2007.00814.x PMID: 18173478 69. Ponce-Reyes R, Reynoso-Rosales V-H, Watson JE, VanDerWal J, Fuller RA, et al. (2012) Vulnerability of cloud forest reserves in Mexico to climate change. Nature Climate Change 2: 448–452. 70. Olson DM, Dinerstein E (1998) The Global 200: A representation approach to conserving the Earth’s most biologically valuable ecoregions. Conservation Biology 12: 502–515. 71. Te´ llez-Valde´ s O, Da´ vila-Aranda P, Lira-Saade R (2006) The effects of climate change on the long-term

Trang 19 · Đoạn 52Vị trí nguồn

conservation of Fagus grandifolia var. mexicana, an important species of the cloud forest in eastern Mexico. Biodiversity & Conservation 15: 1095–1107. 72. Shaver GR, Canadell J, Chapin FS, Gurevitch J, Harte J, et al. (2000) Global warming and terrestrial ecosystems: A conceptual framework for analysis: Ecosystem responses to global warming will be complex and varied. Ecosystem warming experiments hold great potential for providing insights on ways terrestrial ecosystems will respond to upcoming decades of climate change. Documentation of ini- tial conditions provides the context for understanding and predicting ecosystem responses. AIBS Bulle- tin 50: 871–882. 73. Comisio´ n Nacional del Agua-CONAGUA (2012). Servicio Meteorolo´ gico Nacional: 135 años de historia en Me´xico. http://www.senado.gob.mx/comisiones/recursos_hidraulicos/docs/doc22.pdf 74. Daly C. (2013). Descriptions of PRISM spatial climate datasets for the conterminous United States. PRISM Doc. http://prism.oregonstate.edu/documents/PRISM_datasets.pdf 75. Baker DJ, Hartley AJ, Butchart SH, Willis SG. (2016). Choice of baseline climate data impacts projected species’ responses to climate change. Global Change Biology 22: 2392–404. https://doi.org/10.1111/ gcb.13273 PMID: 26950769 76. Deblauwe V, Droissart V, Bose R, Sonke´ B, Blach-Overgaard A, et al. (2016) Remotely sensed temper- ature and precipitation data improve species distribution modeling in the tropics. Global Ecol. Biogeogr. 25: 443–454. 77. Marchi M, Sinjur I, Bozzano M, Westergren M (2019) Evaluating WorldClim Version 1 (1961–1990) as

Trang 19 · Đoạn 53Vị trí nguồn

the baseline for sustainable use of forest and environmental responses in a changing climate. Sustain- ability 11: 3043. 78. Tang Y, Winkler JA, Viña A, Liu J, Zhang Y, et al. (2018) Uncertainty of future projections of species dis- tributions in mountainous regions. PloS One 13: 1. PLOS ONE Historical climate change in Mexico PLOS ONE | https://doi.org/10.1371/journal.pone.0209808 July 16, 2020 19 / 19