Landscape Ecology

, Volume 32, Issue 7, pp 1327–1346 | Cite as

Multi-model comparison on the effects of climate change on tree species in the eastern U.S.: results from an enhanced niche model and process-based ecosystem and landscape models

  • Louis R. IversonEmail author
  • Frank R. ThompsonIII
  • Stephen Matthews
  • Matthew Peters
  • Anantha Prasad
  • William D. Dijak
  • Jacob Fraser
  • Wen J. Wang
  • Brice Hanberry
  • Hong He
  • Maria Janowiak
  • Patricia Butler
  • Leslie Brandt
  • Christopher Swanston
Research Article



Species distribution models (SDM) establish statistical relationships between the current distribution of species and key attributes whereas process-based models simulate ecosystem and tree species dynamics based on representations of physical and biological processes. TreeAtlas, which uses DISTRIB SDM, and Linkages and LANDIS PRO, process-based ecosystem and landscape models, respectively, were used concurrently on four regional climate change assessments in the eastern Unites States.


We compared predictions for 30 species from TreeAtlas, Linkages, and LANDIS PRO, using two climate change scenarios on four regions, to derive a more robust assessment of species change in response to climate change.


We calculated the ratio of future importance or biomass to current for each species, then compared agreement among models by species, region, and climate scenario using change classes, an ordinal agreement score, spearman rank correlations, and model averaged change ratios.


Comparisons indicated high agreement for many species, especially northern species modeled to lose habitat. TreeAtlas and Linkages agreed the most but each also agreed with many species outputs from LANDIS PRO, particularly when succession within LANDIS PRO was simulated to 2300. A geographic analysis showed that a simple difference (in latitude degrees) of the weighted mean center of a species distribution versus the geographic center of the region of interest provides an initial estimate for the species’ potential to gain, lose, or remain stable under climate change.


This analysis of multiple models provides a useful approach to compare among disparate models and a more consistent interpretation of the future for use in vulnerability assessments and adaptation planning.


Climate change Eastern United States Multi-model comparison TreeAtlas DISTRIB LANDIS PRO Linkages Forests 



Many people are to be thanked, for we authors are dependent on data collected by others! Forest inventory data are paramount, as are historic and potential future climate data along with environmental data, all geographically referenced. We thank Eric Gustafson and journal reviewers for reviewing the manuscript. This project was funded by the USDA Forest Service Northern Research Station and Eastern Region, the United States Geological Survey Northeast Climate Science Center, and the University of Missouri-Columbia. Its contents are solely the responsibility of the authors and do not necessarily represent views of the funding agencies. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for governmental purposes.

Supplementary material

10980_2016_404_MOESM1_ESM.docx (725 kb)
Supplementary material 1 (DOCX 725 kb)


  1. Abrams MD (1998) The red maple paradox. BioScience 48:355–364CrossRefGoogle Scholar
  2. Allen CD, Breshears DD, McDowell NG (2015) On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere 6(8):1–55CrossRefGoogle Scholar
  3. Beckage B, Osborne B, Gavin DG, Pucko C, Siccama T, Perkins T (2008) A rapid upward shift of a forest ecotone during 40 years of warming in the Green Mountains of Vermont. Proc Natl Acad Sci USA 105(11):4197–4202CrossRefPubMedPubMedCentralGoogle Scholar
  4. Blum B (1990) Picea rubens Sarg. Red Spruce. In: Burns RM, Honkala BH (eds) Silvics of North America, Conifers, vol 1. USDA Forest Service, Agriculture Handbook 654, Washington, DC, pp 250–259Google Scholar
  5. Boisvert-Marsh L, Périé C, de Blois S (2014) Shifting with climate? Evidence for recent changes in tree species distribution at high latitudes. Ecosphere 5(7):1–33CrossRefGoogle Scholar
  6. Box G, Draper NR (1987) Empirical model-building and response surfaces. Wiley, New YorkGoogle Scholar
  7. Brandt L, He H, Iverson L, Thompson FR, Butler P, Handler S, Blume-Weaver R et al (2014) Central Hardwoods ecosystem vulnerability assessment and synthesis: a report from the Central Hardwoods climate change response framework project. U.S. Department of Agriculture, Forest Service, Northern Research Station, General technical report NRS-124, Newtown Square, PAGoogle Scholar
  8. Brose PH, Dey DC, Phillips RJ, Waldrop TA (2013) A meta-analysis of the fire-oak hypothesis: does prescribed burning promote oak reproduction in Eastern North America? For Sci 59(3):322–334Google Scholar
  9. Brose PH, Dey DC, Waldrop TA (2014) The fire-oak literature of Eastern North America: synthesis and guidelines. General technical report NRS-135. U.S. Department of Agriculture, Forest Service, Northern Research Station, Newtown Square, PAGoogle Scholar
  10. Butler PR, Iverson L, Thompson FR, Brandt L, Handler S, Janowiak M, Connolly S et al (2015) Central Appalachians forest ecosystem vulnerability assessment and synthesis: a report from the Central Appalachians climate change response framework project. U.S. Department of Agriculture, Forest Service, Northern Research Station, General technical report NRS-146, Newtown Square, PAGoogle Scholar
  11. Coumou D, Rahmstorf S (2012) A decade of weather extremes. Nat Clim Change 2:491–496Google Scholar
  12. Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Simberloff D (2001) Climate change and forest disturbances. BioScience 51(9):723–734CrossRefGoogle Scholar
  13. DeHayes DH, Jacobson GL, Schaberg PG, Bongarten B, Iverson L, Dieffenbacher-Krall AC (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. Springer, New York, pp 495–540CrossRefGoogle Scholar
  14. Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA, Durachta JW (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19(5):643–674CrossRefGoogle Scholar
  15. Dijak W, Hanberry B, Fraser JS, He HS, Thompson III FR, Wang WJ (in press) Revision and application of the LINKAGES model to simulate forest growth in Central Hardwood landscapes in response to climate change. Landscape EcolGoogle Scholar
  16. Dobrowski SZ, Thorne JH, Greenberg JA, Safford HD, Mynsberge AR, Crimmins SM, Swanson AK (2011) Modeling plant ranges over 75 years of climate change in California, USA: temporal transferability and species traits. Ecol Monogr 81(2):241–257CrossRefGoogle Scholar
  17. Foster JR, D’Amato AW (2015) Montane forest ecotones moved downslope in Northeastern US in spite of warming between 1984 and 2011. Glob Change Biol 21:4497–4507CrossRefGoogle Scholar
  18. Franklin J (2010) Moving beyond static species distribution models in support of conservation biogeography. Divers Distrib 16(3):321–330CrossRefGoogle Scholar
  19. He HS, Hao Z, Mladenoff DJ, Shao G, Hu Y, Chang Y (2005) Simulating forest ecosystem response to climate warming incorporating spatial effects in northeastern China. J Biogeogr 32:2043–2056CrossRefGoogle Scholar
  20. Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70Google Scholar
  21. Holzinger B, Hulber K, Camenisch M, Grabherr G (2008) Changes in plant species richness over the last century in the eastern Swiss Alps: elevational gradient, bedrock effects and migration rates. Plant Ecol 195(2):179–196CrossRefGoogle Scholar
  22. Hutchinson TF, Sutherland EK, Yaussy DA (2005) Effects of repeated fires on the structure, composition, and regeneration of mixed-oak forests in Ohio. For Ecol Manag 218:210–228CrossRefGoogle Scholar
  23. IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Working group II contribution to the IPCC 5th assessment report. Intergovernmental Panel on Climate Change, StanfordGoogle Scholar
  24. Iverson L, McKenzie D (2013) Tree-species range shifts in a changing climate—detecting, modeling, assisting. Landscape Ecol 28:879–889CrossRefGoogle Scholar
  25. Iverson LR, Hutchinson TF, Prasad AM, Peters MP (2008a) Thinning, fire, and oak regeneration across a heterogeneous landscape in the eastern U.S.: 7-year results. For Ecol Manag 255(7):3035–3050CrossRefGoogle Scholar
  26. 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 254:390–406CrossRefGoogle Scholar
  27. Iverson LR , Prasad AM, Matthews SN , Peters M (2011) Lessons learned while integrating habitat, dispersal, disturbance, and life-history traits into species habitat models under climate change. Ecosystems 14:1005–1020CrossRefGoogle Scholar
  28. Iverson LR, Schwartz MW, Prasad A (2004) How fast and far might tree species migrate under climate change in the eastern United States? Glob Ecol Biogeogr 13:209–219CrossRefGoogle Scholar
  29. Janowiak MK, Swanston CW, Nagel LM, Brandt LA, Butler PR, Handler SD, Shannon PD, Iverson LR, Matthews SN, Prasad A, Peters MP (2014) A practical approach for translating climate change adaptation principles into forest management actions. J For 112:423–433Google Scholar
  30. Johnson P, Shifley S, Rogers R (2009) The ecology and silviculture of oaks. CABI, New YorkCrossRefGoogle Scholar
  31. Keith DA, Akçakaya HR, Thuiller W, Midgley GF, Pearson RG, Phillips SJ, Rebelo TG (2008) Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models. Biol Lett 4(5):560–563CrossRefPubMedPubMedCentralGoogle Scholar
  32. Kennedy M, Ford E (2011) Using multi-criteria analysis of simulation models to understand complex biological systems. BioScience 61:994–1004CrossRefGoogle Scholar
  33. Landscape Change Research Group (2014) Climate change atlas. Northern Research Station, US Forest Service, Delaware.
  34. Lawler JJ, White D, Neilson RP, Blaustein AR (2006) Predicting climate-induced range shifts: model differences and model reliability. Glob Change Biol 12:1568–1584CrossRefGoogle Scholar
  35. Lenderink G, van Meijgaard E (2008) Increase in hourly precipitation extremes beyond expectations from temperature changes. Nat Geosci 1(8):511–514CrossRefGoogle Scholar
  36. Lenoir J, Gégout JC, Marquet PA, de Ruffray P, Brisse H (2008) A significant upward shift in plant species optimum elevation during the 20th century. Science 320(5884):1768–1771CrossRefPubMedGoogle Scholar
  37. Matthews SN, Iverson L, Peters M, Prasad AM (in press) Assessing potential climate change pressures throughout this century across the Conterminous United States: mapping plant hardiness zones, heat zones, and growing degree days. Northern Research Station Research map.Google Scholar
  38. Matthews SN, Iverson LR, Prasad AM, Peters MP, Rodewald PG (2011) Modifying climate change habitat models using tree species-specific assessments of model uncertainty and life history factors. For Ecol Manag 262:1460–1472CrossRefGoogle Scholar
  39. McKenney DW, Pedlar JH, Rood RB, Price D (2011) Revisiting projected shifts in the climate envelopes of North American trees using updated general circulation models. Glob Change Biol 17(8):2720–2730CrossRefGoogle Scholar
  40. Melillo JM, Melillo TC, Richmond T, Yohe GW (2014) Climate change impacts in the United States: the third national climate assessment. U.S. Global Change Research Program, Washington, DCGoogle Scholar
  41. Morin X, Thuiller W (2009) Comparing niche- and process-based models to reduce prediction uncertainty in species range shifts under climate change. Ecology 90:1301–1313CrossRefPubMedGoogle Scholar
  42. Murphy HT, VanDerWal J, Lovett-Doust J (2010) Signatures of range expansion and erosion in eastern North American trees. Ecol Lett 13(10):1233–1244CrossRefPubMedGoogle Scholar
  43. Nakicenovic N, Alcamo J, Davis G, Cambridgede Vries B, Fenhann J, Gaffin S (2000) IPCC special report on emissions scenarios. Cambridge University Press, CambridgeGoogle Scholar
  44. Nowacki GJ, Abrams MD (2008) The demise of fire and “mesophication” of forests in the eastern United States. BioScience 58(2):123–138CrossRefGoogle Scholar
  45. Pearson RG, Thuiller W, Araújo MB, Martinez-Meyer E, Brotons L, McClean C, Lees DC (2006) Model-based uncertainty in species range prediction. J Biogeogr 33(10):1704–1711CrossRefGoogle Scholar
  46. Pederson N, D’Amato AW, Dyer JM, Foster DR, Goldblum D, Hart JL et al (2014) Climate remains an important driver of post-European vegetation change in the eastern United States. Glob Change Biol 2:2105–2110Google Scholar
  47. Prasad AM, Gardiner J, Iverson L, Matthews S, Peters M (2013) Exploring tree species colonization potentials using a spatially explicit simulation model: implications for four oaks under climate change. Glob Change Biol 19(7):2196–2208CrossRefGoogle Scholar
  48. Prasad AM, Iverson LR, Liaw A (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems 9:181–199CrossRefGoogle Scholar
  49. Prasad AM, Iverson LR, Matthews SN, Peters MP (2016) A multistage decision support framework to guide tree species management under climate change via habitat suitability and colonization models, and a knowledge-based scoring system. Landscape Ecol. doi: 10.1007/s10980-016-0369-7
  50. Reich PB, Sendall KM, Rice K, Rich RL, Stefanski A, Hobbie SE, Montgomery RA (2015) Geographic range predicts photosynthetic and growth response to warming in co-occurring tree species. Nat Clim Change 5(2):148–152CrossRefGoogle Scholar
  51. Rohde R, Muller RA, Jacobsen R, Muller E, Perlmutter S, Rosenfeld A, Wurtele J, Groom D, Wickham C (2012) A new estimate of the average earth surface land temperature spanning 1753–2011. Geoinfor Geostat 1:1Google Scholar
  52. Seminov V (2012) Arctic warming favours extremes. Nat Clim Change 2:315–316CrossRefGoogle Scholar
  53. Serra-Diaz JM, Franklin J, Ninyerola M, Davis FW, Syphard AD, Regan HM, Ikegami M (2014) Bioclimatic velocity: the pace of species exposure to climate change. Divers Distrib 20(2):169–180CrossRefGoogle Scholar
  54. Stoner AMK, Hayhoe K, Yang X, Wuebbles DJ (2013) An asynchronous regional regression model for statistical downscaling of daily climate variables. Int J Climatol 33(11):2473–2494CrossRefGoogle Scholar
  55. Swanston CW, Janowiak MK (2012) Forest adaptation resources: climate change tools and approaches for land managers. General technical report NRS-87. U.S. Department of Agriculture, Forest Service, Northern Research Station, Newtown Square, PAGoogle Scholar
  56. Schneiderman J, He H, Thompson F III, Dijak W, Fraser J (2015) Comparison of a species distribution model and a process model from a hierarchical perspective to quantify effects of projected climate change on tree species. Landscape Ecol 30:1879–1892CrossRefGoogle Scholar
  57. Thuiller W, Albert C, Araújo MB, Berry PM, Cabeza M, Guisan A, Sykes MT (2008) Predicting global change impacts on plant species’ distributions: Future challenges. Perspect Plant Ecol 9(3–4):137–152CrossRefGoogle Scholar
  58. Tokarska KB, Gillett NP, Weaver AJ, Arora VK, Eby M (2016) The climate response to five trillion tonnes of carbon. Nat Clim Change. doi: 10.1038/nclimate3036 Google Scholar
  59. Wang WJ, He HS, Fraser JS, Thompson FR III, Shifley SR, Spetich MA (2014a) LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales. Ecography 37(3):225–229CrossRefGoogle Scholar
  60. Wang WJ, He HS, Spetich MA, Shifley SR, Thompson FR III (2014b) Evaluating forest landscape model predictions using empirical data and knowledge. Environ Model Softw 62:230–239CrossRefGoogle Scholar
  61. Wang WJ, He HS, Spetich MA, Shifley SR, Thompson FR, Larsen DR, Yang J (2013) A large-scale forest landscape model incorporating multi-scale processes and utilizing forest inventory data. Ecosphere 4(9):1–22CrossRefGoogle Scholar
  62. Wang W, He H, Thompson FR III, Fraser J, Hanberry B, Dijak W (2015) Importance of succession, harvest, and climate change in determining future composition in U.S. Central Hardwood Forests. Ecosphere 6(12):art277CrossRefGoogle Scholar
  63. Wang W, He HS, Thompson III FR, Fraser J, Dijak W (in press) Forest biomass and species distributions under climate change in the Northeastern U.S.: accounting for effects of succession and harvest. Landscape EcolGoogle Scholar
  64. Washington WM, Weatherly JW, Meehl GA, Semtner AJ Jr, Bettge TW, Craig AP, Zhang Y (2000) Parallel climate model (PCM) control and transient simulations. Clim Dyn 16:755–774CrossRefGoogle Scholar
  65. Webb T III, Bartlein PJ (1992) Global changes during the last 3 million years: climatic controls and biotic responses. Annu Rev Ecol Syst 23:141–173CrossRefGoogle Scholar
  66. 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(Supplement 2):19729–19736CrossRefPubMedPubMedCentralGoogle Scholar
  67. Woodall C, Conkling B, Amacher M, Coulston J, Jovan S, Perry C, Schulz B, Smith G, Wolf SW (2010) The forest inventory and analysis database. Version 4.0: database description and users manual for Phase 3. USDA Forest Service, Northern Research Station, Newtown SquareGoogle Scholar
  68. 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 257:1434–1444CrossRefGoogle Scholar
  69. Woodward FI, Williams BG (1987) Climate and plant distribution at global and local scales. Vegetatio 69:189–197CrossRefGoogle Scholar
  70. Wullschleger S, Gunderson C, Tharp ML, West D, Post W (2003) Simulated patterns of forest succession and productivity as a consequence of altered precipitation. In: Hanson P, Wullschleger S (eds) North American temperate deciduous forest responses to changing precipitation regimes. Ecological studies. Springer, New York, pp 433–446CrossRefGoogle Scholar
  71. Xu C, Gertner GZ, Scheller RM (2009) Uncertainties in the response of a forest landscape to global climatic change. Glob Change Biol 15(1):116–131CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht(outside the USA) 2016

Authors and Affiliations

  • Louis R. Iverson
    • 1
    Email author
  • Frank R. ThompsonIII
    • 2
  • Stephen Matthews
    • 3
  • Matthew Peters
    • 1
  • Anantha Prasad
    • 1
  • William D. Dijak
    • 2
  • Jacob Fraser
    • 4
  • Wen J. Wang
    • 4
  • Brice Hanberry
    • 4
  • Hong He
    • 4
  • Maria Janowiak
    • 5
  • Patricia Butler
    • 5
    • 6
  • Leslie Brandt
    • 7
  • Christopher Swanston
    • 5
  1. 1.USDA Forest Service Northern Research StationDelawareUSA
  2. 2.USDA Forest Service Northern Research StationColumbiaUSA
  3. 3.Ohio State University and USDA Forest Service Northern Research StationColumbusUSA
  4. 4.School of Natural ResourcesUniversity of MissouriColumbiaUSA
  5. 5.USDA Forest Service Northern Research StationHoughtonUSA
  6. 6.School of Forest Resources and Environmental ScienceMichigan Technical UniversityHoughtonUSA
  7. 7.USDA Forest Service Northern Research StationSt. PaulUSA

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