Climatic Change

, Volume 134, Issue 4, pp 697–711 | Cite as

Mapping current and future potential snakebite risk in the new world

  • Carlos Yañez-ArenasEmail author
  • A. Townsend Peterson
  • Karla Rodríguez-Medina
  • Narayani Barve


Snakebite envenoming is an important public health concern worldwide. In the Americas, ~300,000 bites occur annually, leaving 84,110–140,981 envenomings and 652–3466 deaths. Here, we modeled current and future snakebite risk using ecological niche models (ENMs) of 90 venomous snake taxa. Current snakebite risk predictions were corroborated by incidence data from eight regions/periods with different characteristics. Detailed projections of potential future range shifts on distributions of the medically most relevant species indicated that North American species’ ranges are likely to increase in the future, but mixed results were obtained for Latin American snakes. A likely expansion of overall risk area and an increase of rural population at risk were observed from a consensus model among future scenarios. Our study highlights the capacity of ENMs to provide detailed information on current and future potential distributions of venomous snakes, as well as useful perspectives on snakebite risk, at least broad scales.


Geographic Information System General Circulation Model Couple Model Intercomparison Project Phase Venomous Snake Representative Concentration Pathway 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank CONACYT for support the postdoctoral studies of Carlos Yañez-Arenas at the University of Kansas. Andrés Lira, Rafe Brown, Lindsay Campbell, Jorge Soberón, Enrique Martínez-Meyer, and Abdallah Samy provided valuable comments and discussion.

Supplementary material

10584_2015_1544_MOESM1_ESM.pdf (228 kb)
ESM 1 (PDF 228 kb)
10584_2015_1544_MOESM2_ESM.pdf (952 kb)
ESM 2 (PDF 951 kb)
10584_2015_1544_MOESM3_ESM.pdf (548 kb)
ESM 3 (PDF 548 kb)
10584_2015_1544_MOESM4_ESM.pdf (691 kb)
ESM 4 (PDF 690 kb)
10584_2015_1544_MOESM5_ESM.pdf (13.5 mb)
ESM 5 (PDF 13803 kb)
10584_2015_1544_MOESM6_ESM.pdf (518 kb)
ESM 6 (PDF 518 kb)
10584_2015_1544_MOESM7_ESM.pdf (559 kb)
ESM 7 (PDF 558 kb)
10584_2015_1544_MOESM8_ESM.pdf (213 kb)
ESM 8 (PDF 212 kb)
10584_2015_1544_MOESM9_ESM.pdf (444 kb)
ESM 9 (PDF 443 kb)
10584_2015_1544_MOESM10_ESM.pdf (385 kb)
ESM 10 (PDF 384 kb)


  1. Araújo MB, New M (2007) Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–47CrossRefGoogle Scholar
  2. Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539CrossRefGoogle Scholar
  3. Araújo MB, Thuiller W, Pearson RG (2006) Climate warming and the decline of amphibians and reptiles in Europe. J Biogeogr 33:1712–1728CrossRefGoogle Scholar
  4. Barbosa AM, Brown JA, Real R (2014) modEvA – an R package for model evaluation and analysis. R package, version 0.1.
  5. Barve N (2008) Tool for Partial-ROC, ver 1.0. Biodiversity Institute, Lawrence, KSGoogle Scholar
  6. Barve N, Barve V (2013) ENMGadgets: tools for pre and post processing in ENM workflows;
  7. Barve N et al. (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model 222:1810–1819CrossRefGoogle Scholar
  8. Boria RA, Olson LE, Goodman SM, Anderson RP (2014) Spatial filtering to reduce sampling bias can improve the performance of ecological niche models. Ecol Model 275:73–77CrossRefGoogle Scholar
  9. Campbell JA, Lamar WW (2004) The venomous reptiles of the western hemisphere. Cornell University Press, IthacaGoogle Scholar
  10. Campbell LP, Luther C, Moo-Llanes D, Ramsey JM, Danis-Lozano R, Peterson AT (2015) Climate change influences on global distributions of dengue and chikungunya virus vectors. Philos Trans R Soc B 370:20140135CrossRefGoogle Scholar
  11. Center for International Earth Science Information Network (CIESIN), Columbia University; United Nations Food and Agriculture Programme (FAO); and Centro Internacional de Agricultura Tropical (CIAT) (2005) Gridded Population of the World: Future Estimates (GPWFE). Palisades, NY: Socioeconomic Data and Applications Center (SEDAC), Columbia University.
  12. Chippaux JP (2008) Estimating the global burden of snakebite can help to improve management. PLoS Med 5:e221CrossRefGoogle Scholar
  13. Chippaux JP (2012) Epidemiology of snakebites in Europe: a systematic review of the literature. Toxicon 59:86–99CrossRefGoogle Scholar
  14. Cruz LS, Vargas R, Lopes AA (2009) Snakebite envenomation and death in the developing world. Ethn Dis 19:42Google Scholar
  15. da Fonseca GA et al. (2000) Following Africa’s lead in setting priorities. Nature 405:393–394CrossRefGoogle Scholar
  16. Diniz-Filho JAF, Mauricio Bini L, Fernando Rangel T, Loyola RD, Hof C, Nogués-Bravo D, Araújo MB (2009) Partitioning and mapping uncertainties in ensembles of forecasts of species turnover under climate change. Ecography 32:897–906CrossRefGoogle Scholar
  17. Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of MaxEnt for ecologists. Divers Distrib 17:43–57CrossRefGoogle Scholar
  18. Ellis, E.C., K.K. Goldewijk, S. Siebert, D. Lightman, and N. Ramankutty (2013) Anthropogenic Biomes of the World, Version 2, 2000. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).
  19. Feeley KJ, Silman MR (2011) The data void in modeling current and future distributions of tropical species. Glob Chang Biol 17:626–630CrossRefGoogle Scholar
  20. Fielding AH, Bell JF (1997) A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ Conserv 24:38–49CrossRefGoogle Scholar
  21. Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186CrossRefGoogle Scholar
  22. Gutiérrez JM, Williams D, Fan HW, Warrell DA (2010) Snakebite envenoming from a global perspective: towards an integrated approach. Toxicon 56:1223–1235CrossRefGoogle Scholar
  23. Hansson E, Cuadra S, Oudin A, de Jong K, Stroh E, Torén K, Albin M (2010) Mapping snakebite epidemiology in Nicaragua-pitfalls and possible solutions. PLoS Negl Trop Dis 4:e896CrossRefGoogle Scholar
  24. Hansson E, Sasa M, Mattisson K, Robles A, Gutiérrez JM (2013) Using geographical information systems to identify populations in need of improved accessibility to antivenom treatment for snakebite envenoming in Costa Rica. PLoS Negl Trop Dis 7:e2009CrossRefGoogle Scholar
  25. Hickling R, Roy DB, Hill JK, Fox R, Thomas CD (2006) The distributions of a wide range of taxonomic groups are expanding polewards. Glob Chang Biol 12:450–455CrossRefGoogle Scholar
  26. Hijmans RJ, Van Etten J (2010) raster: geographic analysis and modeling with raster data - R package version 1.3–11.
  27. Hijmans RJ, Guarino L, Bussink C, Mathur P, Cruz M, Barrantes I, Rojas E (2004) DIVA-GIS, version 4: geographic information system for the analysis of biodiversity data. Manual.
  28. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  29. Hijmans RJ, Phillips SJ, Leathwick J, Elith J (2011) dismo: species distribution modeling - R package version 0.7–17.
  30. Huey RB, Deutsch CA, Tewksbury JJ, Vitt LJ, Hertz PE, Álvarez-Pérez HJ, Garland T (2009) Why tropical forest lizards are vulnerable to climate warming. Proc R Soc B 276:1939–1948CrossRefGoogle Scholar
  31. Kasturiratne A et al. (2008) The global burden of snakebite: a literature analysis and modelling based on regional estimates of envenoming and deaths. PLoS Med 5:1591–1604CrossRefGoogle Scholar
  32. Küper W, Sommer J, Lovett J, Barthlott W (2006) Deficiency in African plant distribution data–missing pieces of the puzzle. Bot J Linn Soc 150:355–368CrossRefGoogle Scholar
  33. Leynaud GC, Reati GJ (2009) Identifying areas of high risk for ophidism in Cordoba, Argentina, using SIGEpi software. Rev Panam Salud Publica 26:64–69CrossRefGoogle Scholar
  34. Lobo JM, Jiménez-Valverde A, Real R (2007) AUC: a misleading measure of the performance of predictive distribution models. Glob Ecol Biogeogr 17:145–151CrossRefGoogle Scholar
  35. Martínez-Meyer E, Díaz-Porras DF, Peterson AT, Yañez-Arenas C (2013) Ecological niche structure and rangewide abundance patterns of species. Biol Lett 9:20120637CrossRefGoogle Scholar
  36. Moreno-Rueda G, Pleguezuelos JM, Pizarro M, Montori A (2012) Northward shifts of the distributions of Spanish reptiles in association with climate change. Conserv Biol 26:278–283CrossRefGoogle Scholar
  37. Nori J, Carrasco PA, Leynaud GC (2014) Venomous snakes and climate change: ophidism as a dynamic problem. Clim Chang 122:67–80CrossRefGoogle Scholar
  38. O’Neil ME, Mack KA, Gilchrist J, Wozniak EJ (2007) Snakebite injuries treated in United States emergency departments, 2001–2004. Wilderness Environ Med 18:281–287CrossRefGoogle Scholar
  39. Owens HL, Campbell LP, Dornak LL, Saupe EE, Barve N, Soberón J, Peterson AT (2013) Constraints on interpretation of ecological niche models by limited environmental ranges on calibration areas. Ecol Model 263:10–18CrossRefGoogle Scholar
  40. Parmesan C, Yohe G (2003) A globally coherent fingerprint of climate change impacts across natural systems. Nature 421:37–42CrossRefGoogle Scholar
  41. Parrish HM (1966) Incidence of treated snakebites in the United States. Public Health Rep 81:269–276CrossRefGoogle Scholar
  42. Pearson RG, Dawson TP (2003) Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–371CrossRefGoogle Scholar
  43. Peterson AT, Ortega-Huerta MA, Bartley J, Sánchez-Cordero V, Soberón J, Buddemeier RH, Stockwell D (2002) Future projections for Mexican faunas under global climate change scenarios. Nature 416:626–629CrossRefGoogle Scholar
  44. Peterson AT, Papeş M, Soberón J (2008) Rethinking receiver operating characteristic analysis applications in ecological niche modelling. Ecol Model 213:63–72CrossRefGoogle Scholar
  45. Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species geographic distributions. Ecol Model 190:231–259CrossRefGoogle Scholar
  46. Platts PJ, Garcia RA, Hof C, Foden W, Hansen LA, Rahbek C, Burgess ND (2014) Conservation implications of omitting narrow-ranging taxa from species distribution models, now and in the future. Divers Distrib 20:1307–1320CrossRefGoogle Scholar
  47. R Development Core Team (2012) R: a language and environment for statistical computing. Version 2.15.1. R Foundation for Statistical Computing, Vienna
  48. Sokal RR, Rohlf FJ (1995) Biometry. W. H. FreemanGoogle Scholar
  49. Stock RP, Massougbodji A, Alagón A, Chippaux JP (2007) Bringing antivenoms to sub-Saharan Africa. Nat Biotechnol 25:173–177CrossRefGoogle Scholar
  50. Stockwell D, Peterson AT (2002) Effects of sample size on accuracy of species distribution models. Ecol Model 148:1–13CrossRefGoogle Scholar
  51. Taylor KE, Stouffer RJ, Meehl GA (2012) An overview of CMIP5 and the experiment design. B Am Meteorol Soc 93:485–498CrossRefGoogle Scholar
  52. Thomas CD et al. (2004) Extinction risk from climate change. Nature 427:145–148CrossRefGoogle Scholar
  53. Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC (2005) Climate change threats to plant diversity in Europe. Proc Natl Acad Sci U S A 102:8245–8250CrossRefGoogle Scholar
  54. Tingley MW, Koo MS, Moritz C, Rush AC, Beissinger SR (2012) The push and pull of climate change causes heterogeneous shifts in avian elevational ranges. Glob Chang Biol 18:3279–3290CrossRefGoogle Scholar
  55. Warrell D (2010) Snake bite. Lancet 375:77–88CrossRefGoogle Scholar
  56. Warren DL (2012) In defense of ‘niche modeling’. Trends Ecol Evol 27:497–500CrossRefGoogle Scholar
  57. WHO (2009) Neglected tropical diseases: snakebite. Accessed 15 April 2014
  58. Williams D, Gutiérrez JM, Harrison R, Warrell DA, White J, Winkel KD, Gopalakrishnakone P (2010) The global snake bite initiative: an antidote for snake bite. Lancet 375:89–91CrossRefGoogle Scholar
  59. WWF (2006) Conservation Science Ecoregions. Accessed 21 July 2014
  60. Yañez-Arenas C, Peterson AT, Mokondoko P, Rojas-Soto O, Martínez-Meyer E (2014) The use of ecological niche modeling to infer potential risk areas of snakebite in the Mexican state of Veracruz. PLoS One 9:e100957CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Biodiversity InstituteUniversity of KansasLawrenceUSA

Personalised recommendations