, Volume 14, Issue 6, pp 1005–1020 | Cite as

Lessons Learned While Integrating Habitat, Dispersal, Disturbance, and Life-History Traits into Species Habitat Models Under Climate Change

  • Louis R. IversonEmail author
  • Anantha M. Prasad
  • Stephen N. Matthews
  • Matthew P. Peters


We present an approach to modeling potential climate-driven changes in habitat for tree and bird species in the eastern United States. First, we took an empirical-statistical modeling approach, using randomForest, with species abundance data from national inventories combined with soil, climate, and landscape variables, to build abundance-based habitat models for 134 tree and 147 bird species. We produced lists of species for which suitable habitat tends to increase, decrease, or stay the same for any region. Independent assessments of trends of large trees versus seedlings across the eastern U.S. show that 37 of 40 species in common under both studies are currently trending as modeled. We developed a framework, ModFacs, in which we used the literature to assign default modification factor scores for species characteristics that cannot be readily assessed in such models, including 12 disturbance factors (for example, drought, fire, insect pests), nine biological factors (for example, dispersal, shade tolerance), and assessment scores of novel climates, long-distance extrapolations, and output variability by climate model and emission scenario. We also used a spatially explicit cellular model, SHIFT, to calculate colonization potentials for some species, based on their abundance, historic dispersal distances, and the fragmented nature of the landscape. By combining results from the three efforts, we can create projections of potential climate change impacts over the next 100 years or so. Here we emphasize some of the lessons we have learned over 16 years in hopes that they may help guide future experiments, modeling efforts, and management.


climate change eastern United States randomForest statistical modeling migration trees birds DISTRIB SHIFT ModFacs 



The authors are grateful to a great number of associates, users, critics, supporters, and reviewers over the years for their help in making this work possible. Funding support has primarily been through the U.S. Forest Service’s Northern Global Change Program. Special thanks to Janet Franklin, Matthew Fitzpatrick, Susan Wright, Susan Stout, and two anonymous reviewers for their reviews.


  1. Ackerly DD. 2003. Community assembly, niche conservatism, and adaptive evolution in changing environments. Int J Plant Sci 164:S165–84.CrossRefGoogle Scholar
  2. Allen C, Macaladyb A, Chenchounic H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshear D, Hoggi E, Gonzalezk P, Fensham R, Zhangm Z, Castron J, Demidavao N, Lim J-H, Allard G, Running S, Semerci A, Cobb N. 2010. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For Ecol Manag 259:660–84.CrossRefGoogle Scholar
  3. Anderson BJ, Akcakaya HR, Araujo MB, Fordham DA, Martinez-Meyer E, Thuiller W, Brook BW. 2009. Dynamics of range margins for metapopulations under climate change. Proc R Soc B Biol Sci 276:1415–20.CrossRefGoogle Scholar
  4. Araujo M, New M. 2007. Ensemble forecasting of species distributions. Trends Ecol Evol 22:42–7.PubMedCrossRefGoogle Scholar
  5. Austin MP. 1980. Searching for a model for use in vegetation analysis. Plant Ecol 42:11–21.CrossRefGoogle Scholar
  6. Austin MP. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecol Modell 157:101–18.CrossRefGoogle Scholar
  7. Bachelet D, Neilson RP, Lenihan JM, Drapek RJ. 2001. Climate change effects on vegetation distribution and carbon budget in the United States. Ecosystems 4:164–85.CrossRefGoogle Scholar
  8. Bachelet D, Neilson RP, Hickler T, Drapek RJ, Lenihan JM, Sykes MT, Smith B, Sitch S, Thonicke K. 2003. Simulating past and future dynamics of natural ecosystems in the United States. Global Biogeochem Cycles 17:14–21.CrossRefGoogle Scholar
  9. Beale CM, Lennon JJ, Gimona A. 2008. Opening the climate envelope reveals no macroscale associations with climate in European birds. Proc Natl Acad Sci USA 105:14908–12.PubMedCrossRefGoogle Scholar
  10. Botkin DB, Saxe H, Araujo MB, Betts R, Bradshaw RHW, Cedhagen T, Chesson P, Dawson TP, Etterson JR, Faith DP, Ferrier S, Guisan A, Hansen AS, Hilbert DW, Loehle C, Margules C, New M, Sobel MJ, Stockwell DRB. 2007. Forecasting the effects of global warming on biodiversity. Bioscience 57:227–36.CrossRefGoogle Scholar
  11. Box G, Draper NR. 1987. Empirical model-building and response surfaces. New York: Wiley.Google Scholar
  12. Box EO, Crumpacker DW, Hardin ED. 1999. Predicted effects of climatic change on distribution of ecologically important native tree and shrub species in Florida. Climatic Change 41:213–48.CrossRefGoogle Scholar
  13. Breiman L. 2001. Random forests. Machine Learning 45:5–32.CrossRefGoogle Scholar
  14. Breshears DD, Cobb NS, Rich PM, Price KP, Allen CD, Balice RG, Romme WH, Kastens JH, Floyd ML, Belnap J, Anderson JJ, Myers OB, Meyer CW. 2005. Regional vegetation die-off in response to global-change-type drought. Proc Natl Acad Sci USA 102:15144–8.PubMedCrossRefGoogle Scholar
  15. Bu R, He HS, Hu Y, Chang Y, Larsen DR. 2008. Using the LANDIS model to evaluate forest harvesting and planting strategies under possible warming climates in northeastern China. For Ecol Manag 254:407–19.CrossRefGoogle Scholar
  16. Burns RM, Honkala BH. 1990a. Silvics of North America: 1. Conifers. Washington, DC: U.S. Department of Agriculture Forest Service.Google Scholar
  17. Burns RM, Honkala BH. 1990b. Silvics of North America: 2. Hardwoods. Washington, DC: U.S. Department of Agriculture Forest Service.Google Scholar
  18. Canadell JG, Le Quere C, Raupach MR, Field CB, Buitenhuis ET, Ciais P, Conway TJ, Gillett NP, Houghton RA, Marland G. 2007. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc Natl Acad Sci USA 104:18866–70.PubMedCrossRefGoogle Scholar
  19. Clark JS, Bell DM, Hersh MH, Nichols L. 2011. Climate change vulnerability of forest biodiversity: climate and resource tracking of demographic rates. Global Change Biol. doi: 10.1111/j.1365-2486.2010.02380.x.
  20. Cramer W, Kicklighter DW, Bondeau A, Iii BM, Churkina G, Nemry B, Ruimy A, Schloss AL, The Participants of the Potsdam NPP Model Intercomparison. 1999. Comparing global models of terrestrial net primary productivity (NPP): Overview and key results. Global Change Biol 5(Suppl 1):1–15.CrossRefGoogle Scholar
  21. Crookston NL, Rehfeldt GE, Dixon GE, Weiskittel AR. 2010. Addressing climate change in the forest vegetation simulator to assess impacts on landscape forest dynamics. For Ecol Manag 260:1198–211.CrossRefGoogle Scholar
  22. Cutler DR, Edwards TC, Beard KH, Cutler A, Hess KT, Gibson J, Lawler JJ. 2007. Random forests for classification in ecology. Ecology 88:2783–92.PubMedCrossRefGoogle Scholar
  23. Davis MB. 1981. Quaternary history and the stability of forest communities. In: West DC, Shugart HH, Eds. Forest succession: concepts and application. New York: Springer-Verlag. p 132–53.Google Scholar
  24. DeHayes DH, Jacobson GL, Schaber PG, Bongarten B, Iverson LR, Dieffenbacker-Krall A. 2000. Forest responses to changing climate: lessons from the past and uncertainty for the future. In: Mickler RA, Birdsey RA, Hom JL, Eds. Responses of northern forests to environmental change. Ecological Studies Series. New York: Springer-Verlag. p 495–540.CrossRefGoogle Scholar
  25. Dormann CF. 2007. Promising the future? Global change projections of species distributions. Basic Appl Ecol 8:387–97.CrossRefGoogle Scholar
  26. 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 32:66–77.CrossRefGoogle Scholar
  27. Elith J, Graham CH, Anderson RP, Dudyk M, Ferrier S, Guisan A, Hijmans RJ, Huettmann F, Leathwick JR, Lehmann A, Li J, Lohmann LG, Loiselle BA, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton JM, Peterson AT, Phillips SJ, Richardson K, Scahetti-Pereira R, Schapire RE, Sobero’n J, Williams S, Wisz MS, Zimmermann NE. 2006. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29:129–51.CrossRefGoogle Scholar
  28. Elith J, Kearney M, Phillips S. 2010. The art of modelling range-shifting species. Methods Ecol Evol 1:330–42.CrossRefGoogle Scholar
  29. Engler R, Guisan A. 2009. Migclim: Predicting plant distribution and dispersal in a changing climate. Divers Distrib 15:590–601.CrossRefGoogle Scholar
  30. Engler R, Randin CF, Vittoz P, Czaka T, Beniston M, Zimmermann NE, Guisan A. 2009. Predicting future distributions of mountain plants under climate change: Does dispersal capacity matter? Ecography 32:34–45.CrossRefGoogle Scholar
  31. Ferrier S, Guisan A. 2006. Spatial modelling of biodiversity at the community level. J Appl Ecol 43:393–404.CrossRefGoogle Scholar
  32. Ferrier S, Manion G, Elith J, Richardson K. 2007. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Divers Distrib 13:252–64.CrossRefGoogle Scholar
  33. 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. Global Change Biol 14:1337–52.CrossRefGoogle Scholar
  34. Franklin J. 2009. Mapping species distributions: Spatial inference and prediction. Cambridge, UK: Cambridge University Press. 320p.Google Scholar
  35. Franklin J. 2010. Moving beyond static species distribution models in support of conservation biogeography. Divers Distrib 16:321–30.CrossRefGoogle Scholar
  36. Frumhoff PC, McCarthy JJ, Mellilo JM, Moser SC, Wuebbles DJ. 2007. Confronting climate change in the U.S. Northeast: Science, impacts, and solutions. Synthesis report of the Northeast Climate Impacts Assessment (NECIA). Cambridge (MA): Union of Concerned Scientists.Google Scholar
  37. Gonzalez P, Neilson RP, Lenihan JM, Drapek RJ. 2010. Global patterns in the vulnerability of ecosystems to vegetation shifts due to climate change. Global Ecol Biogeogr 19:755–68.CrossRefGoogle Scholar
  38. Guisan A, Thuiller W. 2005. Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009.CrossRefGoogle Scholar
  39. Guisan A, Zimmermann NE. 2000. Predictive habitat distribution models in ecology. Ecol Modell 135:147–86.CrossRefGoogle Scholar
  40. Hargrove WW, Potter KM, Koch FH. 2010. The ForeCASTS project: Forecasts of climate-associated shifts in tree species. Eastern Forest Environmental Threat Assessment Center. USDA Forest Service.
  41. Hayhoe K, Wuebbles D, Climate Science Team. 2008. Climate change and Chicago: Projections and potential impacts. Chicago (IL): Chicago Climate Action Plan. 33p.Google Scholar
  42. He HS, Mladenoff DJ, Crow TR. 1999. Linking an ecosystem model and a landscape model to study forest species response to climate warming. Ecol Modell 114:213–33.CrossRefGoogle Scholar
  43. He H, Keane RK, Iverson LR. 2008. Forest landscape models, a tool for understanding the effect of the large-scale and long-term landscape processes. For Ecol Manag 274:371–4.CrossRefGoogle Scholar
  44. Huntley B, Barnard P, Altwegg R, Chambers L, Coetzee BWT, Gibson L, Hockey PAR, Hole DG, Midgley GF, Underhill LG, Willis SG. 2010. Beyond bioclimatic envelopes: dynamic species’ range and abundance modelling in the context of climatic change. Ecography 33:621–6.Google Scholar
  45. Ibanez I, Clark JS, Dietze MC, Felley K, Hersh M, LaDeau S, McBride A, Welch NE, Wolosin MS. 2006. Predicting biodiversity change: outside the climate envelope, beyond the species-area curve. Ecology 87:1896–906.PubMedCrossRefGoogle Scholar
  46. Iverson LR, Prasad AM. 1998. Predicting abundance of 80 tree species following climate change in the eastern United States. Ecol Monogr 68:465–85.CrossRefGoogle Scholar
  47. Iverson LR, Prasad AM. 2001. Potential changes in tree species richness and forest community types following climate change. Ecosystems 4:186–99.CrossRefGoogle Scholar
  48. Iverson LR, Prasad AM. 2002. Potential redistribution of tree species habitat under five climate change scenarios in the eastern US. For Ecol Manag 155:205–22.CrossRefGoogle Scholar
  49. Iverson LR, Prasad A, Scott CT. 1996. Preparation of forest inventory and analysis (FIA) and state soil geographic data base (STATSGO) data for global change research in the eastern United States. In: Hom J, Birdsey R, O’Brian K, Eds. Proceedings, 1995 meeting of the northern global change program. General Technical Report NE-214, Forest Service, Northeastern Forest Experiment Station, U.S. Department of Agriculture, Radnor, PA, pp. 209–14.Google Scholar
  50. Iverson LR, Prasad AM, Hale BJ, Sutherland EK. 1999a. An atlas of current and potential future distributions of common trees of the eastern United States. General Technical Report NE-265. Radnor (PA): Northeastern Research Station, USDA Forest Service. 245 p.Google Scholar
  51. Iverson LR, Prasad AM, Schwartz MW. 1999b. Modeling potential future individual tree-species distributions in the eastern United States under a climate change scenario: a case study with Pinus virginiana. Ecol Modell 115:77–93.CrossRefGoogle Scholar
  52. Iverson LR, Schwartz MW, Prasad A. 2004a. How fast and far might tree species migrate under climate change in the eastern United States? Glob Ecol Biogeogr 13:209–19.CrossRefGoogle Scholar
  53. Iverson LR, Schwartz MW, Prasad AM. 2004b. Potential colonization of new available tree species habitat under climate change: an analysis for five eastern US species. Landscape Ecol 19:787–99.CrossRefGoogle Scholar
  54. Iverson LR, Prasad AM, Matthews SN. 2008a. Modeling potential climate change impacts on the trees of the northeastern United States. Mitig Adapt Strat Glob Change 13:487–516.CrossRefGoogle Scholar
  55. Iverson LR, Prasad AM, Matthews SN, Peters M. 2008b. Estimating potential habitat for 134 eastern US tree species under six climate scenarios. For Ecol Manag 254:390–406.CrossRefGoogle Scholar
  56. Keane RE, Holsinger LM, Parsons RA, Gray K. 2008. Climate change effects on historical range and variability of two large landscapes in western Montana, USA. For Ecol Manag 254:375–89.CrossRefGoogle Scholar
  57. Keddy PA. 1992. Assembly and response rules: two goals for predictive community ecology. J Veg Sci 3:157–64.CrossRefGoogle Scholar
  58. Keith DA, Akçakaya HR, Thuiller W, Midgley GF, Pearson RG, Phillips SJ, Regan HM, Araújo MB, Rebelo TG. 2008. Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biol Lett 4:560–3.PubMedCrossRefGoogle Scholar
  59. Kirilenko AP, Belotelov NV, Bogatyrev BG. 2000. Global model of vegetation migration: incorporation of climatic variability. Ecol Modell 132:125–33.CrossRefGoogle Scholar
  60. Lawler JJ, O’Connor RJ. 2004. How well do consistently monitored breeding bird survey routes represent the environments of the conterminous United States? Condor 106:801–14.CrossRefGoogle Scholar
  61. Lawler JJ, White D, Neilson RP, Blaustein AR. 2006. Predicting climate-induced range shifts: model differences and model reliability. Global Change Biol 12:1568–84.CrossRefGoogle Scholar
  62. Leng W, He HS, Bu R, Dai L, Hu Y, Wang X. 2008. Predicting the distributions of suitable habitat for three larch species under climate warming in northeastern China. For Ecol Manag 254:420–8.CrossRefGoogle Scholar
  63. Little EL. 1971. Atlas of United States trees. Volume 1. Conifers and important hardwoods. Miscellaneous publication 1146. Washington, DC: U.S. Department of Agriculture, Forest Service.Google Scholar
  64. Lo Y-H, Blanco JA, Kimmins J. 2010. A word of caution when planning forest management using projections of tree species range shifts. Forestry Chronicle 86:312–16.Google Scholar
  65. Matthews SN, Iverson LR, Prasad AM, Peters MP. 2011. Potential habitat changes of 147 North American bird species to redistribution of vegetation and climate following predicted climate change. Ecography. doi: 10.1111/j.1600-0587.2011.06803.x.
  66. Matthews SN, Iverson LR, Prasad AM, Peters MP. 2007. A climate change atlas for bird species of the eastern United States [database]. Northern Research Station, USDA Forest Service, Delaware, OH.
  67. Matthews SN, Iverson LR, Prasad AM, Peters MP, Rodewald PG. Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history factors. For Ecol Manag (in press).Google Scholar
  68. McKenney DW, Pedlar JH, Hutchinson MF, Lawrence K, Campbell K. 2007. Potential impacts of climate change on the distribution of North American trees. Bioscience 57:939–48.CrossRefGoogle Scholar
  69. McLachlan JS, Clark JS, Manos PS. 2005. Molecular indicators of tree migration capacity under rapid climate change. Ecology 86:2007–17.CrossRefGoogle Scholar
  70. Meentemeyer RK, Anacker B, Mark W, Rizzo D. 2008. Early detection of emerging forest disease using dispersal estimation and ecological niche modeling. Ecol Appl 18:377–90.PubMedCrossRefGoogle Scholar
  71. Midgley GF, Davies ID, Albert CH, Altwegg R, Hannah L, Hughes GO, O’Halloran LR, Seo C, Thorne JH, Thuiller W. 2010. BioMove—an integrated platform simulating the dynamic response of species to environmental change. Ecography 33:612–16.Google Scholar
  72. Miles PD, Brand GJ, Alerich CL, Bednar LR, Woudenberg SW, Glover JF, Ezzell EN. 2001. The forest inventory and analysis database: database description and users manual version 1.0. General Technical Report NC-218. St. Paul (MN): North Central Research Station, USDA Forest Service. 130 p.Google Scholar
  73. Moore DE, Lees BG, Davey SM. 1991. A new method for predicting vegetation distributions using decision tree analysis in a geographic information system. J Environ Manag 15:59–71.CrossRefGoogle Scholar
  74. Neilson RP, Pitelka LF, Solomon AM, Nathan RAN, Midgley GF, Fragoso JMV, Lischke H, Thompson KEN. 2005. Forecasting regional to global plant migration in response to climate change. Bioscience 55:749–59.CrossRefGoogle Scholar
  75. Pearson RG, Dawson TP. 2003. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob Ecol Biogeogr 12:361–71.CrossRefGoogle Scholar
  76. Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L, McClean C, Miles L, Segurado P, Dawson TP, Lees DC. 2006. Model-based uncertainty in species range prediction. J Biogeogr 33:1704–11.CrossRefGoogle Scholar
  77. Peterson AT, Stewart A, Mohamed KI, Araujo MB. 2008. Shifting global invasive potential of European plants with climate change. Plos One 3(6):e2441. doi: 10.1371/journal.pone.0002441.PubMedCrossRefGoogle Scholar
  78. Prasad AM, Iverson LR. 1999. A climate change atlas for 80 forest tree species of the eastern United States [database]. Northeastern Research Station, USDA Forest Service, Delaware, OH.
  79. Prasad AM, Iverson LR, Liaw A. 2006. Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–99.CrossRefGoogle Scholar
  80. Prasad AM, Iverson LR, Matthews S, Peters M. 2007. A climate change atlas for 134 forest tree species of the eastern United States [database]. Northern Research Station, USDA Forest Service, Delaware, OH.
  81. Prasad A, Iverson L, Matthews S, Peters M. 2009. Atlases of tree and bird species habitats for current and future climates. Ecol Restor 27:260–3.CrossRefGoogle Scholar
  82. Prasad A, Iverson L, Peters M, Bossenbroek J, Matthews SN, Sydnor D, Schwartz M. 2010. Modeling the invasive emerald ash borer risk of spread using a spatially explicit cellular model. Landscape Ecol 25:353–69.CrossRefGoogle Scholar
  83. 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 33:304–14.Google Scholar
  84. Sauer JR, Hines JE, Fallon J. 2001. The North American Breeding Bird Survey, results and analysis, 1966–2000. Laurel (MD): USGS Patuxent Wildlife Research Center.Google Scholar
  85. Scheller RM, Mladenoff DJ. 2008. Simulated effects of climate change, fragmentation, and inter-specific competition on tree species migration in northern Wisconsin, USA. Clim Res 36:191–202.CrossRefGoogle Scholar
  86. Schwartz MW, Iverson LR, Prasad AM. 2001. Predicting the potential future distribution of four tree species in Ohio, USA, using current habitat availability and climatic forcing. Ecosystems 4:568–81.CrossRefGoogle Scholar
  87. Schwartz MW, Iverson LR, Prasad AM, Matthews SN, O’Connor RJ. 2006. Predicting extinctions as a result of climate change. Ecology 87:1611–15.PubMedCrossRefGoogle Scholar
  88. Shifley S, Thompson F, Dijak W, Larson M, Millspaugh J. 2006. Simulated effects of forest management alternatives on landscape structure and habitat suitability in the midwestern United States. For Ecol Manag 229:361–77.CrossRefGoogle Scholar
  89. Sitch S, Smith B, Prentice I, Arneth A, Bondeau A, Cramer W, Kaplan J, Levis S, Lucht W, Sykes M, Thonicke K, Venevsky S. 2003. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic vegetation model. Global Change Biol 9:161–85.CrossRefGoogle Scholar
  90. Swanston C, Janowiak M, Iverson L, Parker L, Mladenoff D, Brandt L, Butler P, St. Pierre M, Prasad AM, Matthews S, Peters M, Higgins D. 2011. Ecosystem vulnerability assessment and synthesis: a report from the climate change response framework at Chequamegon-Nicolet National Forest project, version 1. Hougton, MI: Northern Research Station, USDA Forest Service.Google Scholar
  91. Tchebakova NM, Rehfeldt GE, Parfenova EI. 2006. Impacts of climate change on the distribution of Larix spp. and Pinus sylvestris and their climatypes in Siberia. Mitig Adapt Strat Glob Change 11:861–82.CrossRefGoogle Scholar
  92. Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BF, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, Van Jaarsveld A, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE. 2004. Extinction risk from climate change. Nature 427:145–8.PubMedCrossRefGoogle Scholar
  93. Thuiller W, Lavorel S, Sykes MT, Araujo MB. 2006a. Using niche-based modelling to assess the impact of climate change on tree functional diversity in Europe. Divers Distrib 12:49–60.CrossRefGoogle Scholar
  94. Thuiller W, Midgley GF, Hughes GO, Bomhard B, Drew G, Rutherford MC, Woodward FI. 2006b. Endemic species and ecosystem sensitivity to climate change in Namibia. Global Change Biol 12:759–76.CrossRefGoogle Scholar
  95. Thuiller W, Albert C, Araujo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgely GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE. 2008. Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol Evol Syst 9:137–52.CrossRefGoogle Scholar
  96. Union of Concerned Scientists. 2008. Climate change in Pennsylvania: impacts and solutions for the keystone state. Cambridge (MA): Union of Concerned Scientists.Google Scholar
  97. U.S. National Assessment Synthesis Team. 2000. Climate change impacts on the United States: the potential consequences of climate variability and change Foundation report. Washington, DC: U.S. Global Change Research Program.Google Scholar
  98. Webb T, Bartlein PJ. 1992. Global changes during the last 3 million years: climatic controls and biotic responses. Annu Rev Ecol Syst 23:141–73.CrossRefGoogle Scholar
  99. Westerling AL. 2006. Warming and earlier spring increase western U.S. forest wildfire activity. Science 313:940–3.PubMedCrossRefGoogle Scholar
  100. Wiens JA. 1989. Spatial scaling in ecology. Funct Ecol 3:385–97.CrossRefGoogle Scholar
  101. Wiens JA, Stralberg D, Jongsomjit D, Howell CA, Snyder MA. 2009. Niches, models, and climate change: assessing the assumptions and uncertainties. Proc Natl Acad Sci USA 106:19729–36.PubMedCrossRefGoogle Scholar
  102. Williams JW, Jackson ST, Kutzbach JE. 2007. Projected distributions of novel and disappearing climates by 2100 AD. Proc Natl Acad Sci USA 104:5738–42.PubMedCrossRefGoogle Scholar
  103. Williams NSG, Hahs AK, Morgan JW. 2008. A dispersal-constrained habitat suitability model for predicting invasion of alpine vegetation. Ecol Appl 18:347–59.PubMedCrossRefGoogle Scholar
  104. Woodall C, Oswalt CM, Westfall JA, Perry CH, Nelson MD, Finley AO. 2009. An indicator of tree migration in forests of the eastern United States. For Ecol Manag 257:1434–44.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC (outside the USA) 2011

Authors and Affiliations

  • Louis R. Iverson
    • 1
    Email author
  • Anantha M. Prasad
    • 1
  • Stephen N. Matthews
    • 1
    • 2
  • Matthew P. Peters
    • 1
  1. 1.Northern Research StationUS Forest ServiceDelawareUSA
  2. 2.School of Environment and Natural ResourcesOhio State UniversityColumbusUSA

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