Regional Environmental Change

, Volume 13, Supplement 1, pp 57–68 | Cite as

Climate downscaling effects on predictive ecological models: a case study for threatened and endangered vertebrates in the southeastern United States

  • David N. Bucklin
  • James I. Watling
  • Carolina Speroterra
  • Laura A. Brandt
  • Frank J. Mazzotti
  • Stephanie S. Romañach
Original Article

Abstract

High-resolution (downscaled) projections of future climate conditions are critical inputs to a wide variety of ecological and socioeconomic models and are created using numerous different approaches. Here, we conduct a sensitivity analysis of spatial predictions from climate envelope models for threatened and endangered vertebrates in the southeastern United States to determine whether two different downscaling approaches (with and without the use of a regional climate model) affect climate envelope model predictions when all other sources of variation are held constant. We found that prediction maps differed spatially between downscaling approaches and that the variation attributable to downscaling technique was comparable to variation between maps generated using different general circulation models (GCMs). Precipitation variables tended to show greater discrepancies between downscaling techniques than temperature variables, and for one GCM, there was evidence that more poorly resolved precipitation variables contributed relatively more to model uncertainty than more well-resolved variables. Our work suggests that ecological modelers requiring high-resolution climate projections should carefully consider the type of downscaling applied to the climate projections prior to their use in predictive ecological modeling. The uncertainty associated with alternative downscaling methods may rival that of other, more widely appreciated sources of variation, such as the general circulation model or emissions scenario with which future climate projections are created.

Keywords

Climate change Climate envelope model Downscaling Species distribution model Florida Endangered species 

References

  1. Acevedo P, Jiménez-Valverde A, Lobo JM, Real R (2012) Delimiting the geographical background in species distribution modelling. J Biogeogr 39:1383–1390CrossRefGoogle Scholar
  2. Allouche O, Tsoar A, Kadmon R (2006) Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 43:1223–1232CrossRefGoogle Scholar
  3. Araújo MB, Guisan A (2006) Five (or so) challenges for species distribution modeling. J Biogeogr 33:1677–1688CrossRefGoogle Scholar
  4. Araújo MB, Luoto M (2007) The importance of biotic interactions for modelling species distributions under climate change. Global Ecol Biogeogr 16:743–753CrossRefGoogle Scholar
  5. Araújo MB, Peterson AT (2012) Uses and misuses of bioclimatic envelope modeling. Ecology 93:1527–1539CrossRefGoogle Scholar
  6. Araújo MB, Pearson RG, Thuiller W, Erhard M (2005) Validation of species-climate impact models under climate change. Glob Change Biol 11:1504–1513CrossRefGoogle Scholar
  7. Austin MP (2002) Spatial prediction of species distribution: an interface between ecological theory and statistical modeling. Ecol Model 157:101–118CrossRefGoogle Scholar
  8. Barve N, Barve V, Jiménez-Valverde A, Lira-Noriega A, Maher SP, Peterson AT, Soberón J, Villalobos F (2011) The crucial role of the accessible area in ecological niche modeling and species distribution modeling. Ecol Model 222:1810–1819CrossRefGoogle Scholar
  9. Cahill AE, Aiello-Lammens ME, Fisher-Reid MC, Hua X, Karanewsky CJ, Ryu HY, Sbeglia GC, Spagnolo F, Waldron JB, Warsi O, Wiens JJ (2012) How does climate change cause extinction? Proc R Soc B, published online 17 Oct 2012. doi:10.1098/rspb.2012.1890
  10. Chapman DS (2010) Weak climatic associations among British plant distributions. Glob Ecol Biogeogr 19:831–841CrossRefGoogle Scholar
  11. Cutler DR, Edwards TC Jr, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ (2007) Random forests for classification in ecology. Ecology 88:2783–2792CrossRefGoogle Scholar
  12. 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
  13. Elith J, Kearney M, Phillips S (2010) The art of modelling range-shifting species. Methods Ecol Evol 1:330–340CrossRefGoogle Scholar
  14. 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
  15. Fowler HJ, Blenkinsop S, Tebaldi C (2007) Linking climate change modelling to impacts studies: recent advances in downscaling techniques for hydrological modelling. Int J Climatol 27:1547–1578CrossRefGoogle Scholar
  16. Franklin J (2009) Mapping species distributions: spatial inference and prediction. Cambridge University Press, New YorkGoogle Scholar
  17. Freeman EA, Moisen GG (2008) A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol Model 217:48–58CrossRefGoogle Scholar
  18. Graham CH, Loiselle BA, Velásquez-Tibatá J, Cuesta FC (2011) Species distribution modeling and the challenge of predicting future distributions. In: Herzog SK, Martínez R, Jørgensen PM, Tiessen H (ed) Climate change and biodiversity in the Tropical Andes, Inter-American Institute for Global Change Research (IAI) and Scientific Committee on Problems of the Environment. São José dos Campos, Brazil, pp 295–310. http://www.iai.int
  19. Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009CrossRefGoogle Scholar
  20. Hellström C, Chen D, Achberger C, Räisänen J (2001) Comparison of climate change scenarios for Sweden based on statistical and dynamical downscaling of monthly precipitation. Clim Res 19:45–55CrossRefGoogle Scholar
  21. Hijmans RS, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution climate surfaces for global land areas. Int J Climatol 25:1965–1978CrossRefGoogle Scholar
  22. Hirzel AH, Hausser J, Chessel JD, Perrin N (2002) Ecological niche-factor analysis: how to compute habitat-suitability maps without absence data? Ecology 83:2027–2036CrossRefGoogle Scholar
  23. Intergovernmental Panel on Climate Change (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 1 to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, pp 433–498Google Scholar
  24. Kanamitsu M, Yoshimura K, Yhang Y-B, Hong S-Y (2010) Errors of interannual variability and trend in dynamical downscaling of reanalysis. J Geoph Res 115:D17115CrossRefGoogle Scholar
  25. Kremen C et al (2008) Aligning conservation priorities across taxa in Madagascar with high-resolution planning tools. Science 320:222–226CrossRefGoogle Scholar
  26. Liaw A, Wiener M (2002) Classification and regression by randomForest. R News 2(3):18–22Google Scholar
  27. Lomba A, Pellissier L, Randin C, Vicente J, Moreira F, Honrado J, Guisan A (2010) Overcoming the rare species modelling paradox: a novel hierarchical framework applied to an Iberian endemic plant. Biol Conserv 143:2647–2657CrossRefGoogle Scholar
  28. Manel S, Williams HC, Ormerod SJ (2001) Evaluating presence–absence models in ecology: the need to account for prevalence. J Appl Ecol 38:921–931CrossRefGoogle Scholar
  29. Maraun D, Wetterhall H, Ireson AM, Chandler RE, Kendon EJ, Widmann M, Brienen S, Rust HW, Sauter T, Themeßl M, Venema VKC, Chun KP, Goodess CM, Jones RG, Onof C, Vrac M, Thiele-Eich I (2010) Precipitation downscaling under climate change: recent developments to bridge the gap between dynamical models and the end user. Rev Geophys 48:RG3003CrossRefGoogle Scholar
  30. Mitikka V, Heikkinen RK, Luoto M, Araújo MB, Saarinen K, Poyry J, Fronzek S (2008) Predicting range expansion of the map butterfly in northern Europe using bioclimatic models. Biodivers Conserv 17:623–641CrossRefGoogle Scholar
  31. New M, Lister D, Hulme M, Makin I (2002) A high-resolution data set of surface climate over global land areas. Clim Res 21:1–25CrossRefGoogle Scholar
  32. Phillips SJ, Dudík M, Elith J, Graham CM, Lehmann A, Leathwick J, Ferrier S (2009) Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol Appl 19:181–197CrossRefGoogle Scholar
  33. Povilitis A, Suckling K (2010) Addressing climate change threats to endangered species in U.S. recovery plans. Conserv Biol 24:372–376CrossRefGoogle Scholar
  34. Ramirez J, Jarvis A (2008) High resolution statistically downscaled future climate surfaces, International Center for Tropical Agriculture (CIAT); CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Cali, Colombia. http://www.ccafs-climate.org/statistical_downscaling_delta
  35. Rapacciuolo G, Roy DB, Gillings S, Fox R, Walker K, Purvis A (2012) Climatic Associations of British species distributions show good transferability in time but low predictive accuracy for range change. PLoS ONE 7:1–7CrossRefGoogle Scholar
  36. Real R, Márquez AL, Olivero J, Estrada A (2010) Species distribution models in climate change scenarios are still not useful for informing policy planning: an uncertainty assessment using fuzzy logic. Ecography 33:304–314Google Scholar
  37. Rowland EL, Davison JE, Graumlich LJ (2011) Approaches to evaluating climate change impacts on species: a guide to initiating the adaptation planning process. Environ Manage 47:322–337CrossRefGoogle Scholar
  38. R Development Core Team (2012) R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna. www.R-project.org
  39. Stefanova L, Misra V, Chan S, Griffin M, O’Brien JJ, Smith TJ III (2012) A proxy for high-resolution regional reanalysis for the Southeast United States: assessment of precipitation variability in dynamically downscaled reanalyses. Clim Dynam 38:2449–2466CrossRefGoogle Scholar
  40. Syphard AD, Franklin J (2009) Differences in spatial predictions among species distribution modeling methods vary with species traits and environmental predictors. Ecography 32:907–918CrossRefGoogle Scholar
  41. Tabor K, Williams JW (2010) Globally downscaled climate projections for assessing the conservation impacts of climate change. Ecol Appl 20:554–565CrossRefGoogle Scholar
  42. VanDerWal J, Shoo LP, Graham C, Williams SE (2009) Selecting pseudo-absence data for presence-only distribution modeling: how far should you stray from what you know? Ecol Model 220:589–594CrossRefGoogle Scholar
  43. Watling JI, Romañach SS, Bucklin DN, Speroterra C, Brandt LA, Pearlstine LG, Mazzotti FJ (2012) Do bioclimate variables improve performance of climate envelope models? Ecol Model 246:79–85CrossRefGoogle Scholar
  44. Wilby RL, Fowler HJ (2010) Regional climate downscaling. In: Fung F, Lopez A, New M (eds) Modelling the impact of climate change on water resources. Wiley, Chichester, pp 34–85Google Scholar
  45. Wood AW, Maurer EP, Kumar A, Letternmaier DP (2002) Long-range experimental hydrologic forecasting for the eastern United States. J Geophys Res 107:4429CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David N. Bucklin
    • 1
  • James I. Watling
    • 1
  • Carolina Speroterra
    • 1
  • Laura A. Brandt
    • 2
  • Frank J. Mazzotti
    • 1
  • Stephanie S. Romañach
    • 3
  1. 1.Fort Lauderdale Research and Education CenterUniversity of FloridaFort LauderdaleUSA
  2. 2.U.S. Fish and Wildlife ServiceFort LauderdaleUSA
  3. 3.U.S. Geological SurveySoutheast Ecological Science CenterFort LauderdaleUSA

Personalised recommendations