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Landscape Ecology

, Volume 32, Issue 7, pp 1365–1384 | Cite as

Revision and application of the LINKAGES model to simulate forest growth in central hardwood landscapes in response to climate change

  • William D. DijakEmail author
  • Brice B. Hanberry
  • Jacob S. Fraser
  • Hong S. He
  • Wen J. Wang
  • Frank R. ThompsonIII
Research Article

Abstract

Context

Global climate change impacts forest growth and methods of modeling those impacts at the landscape scale are needed to forecast future forest species composition change and abundance. Changes in forest landscapes will affect ecosystem processes and services such as succession and disturbance, wildlife habitat, and production of forest products at regional, landscape and global scales.

Objectives

LINKAGES 2.2 was revised to create LINKAGES 3.0 and used it to evaluate tree species growth potential and total biomass production under alternative climate scenarios. This information is needed to understand species potential under future climate and to parameterize forest landscape models (FLMs) used to evaluate forest succession under climate change.

Methods

We simulated total tree biomass and responses of individual tree species in each of the 74 ecological subsections across the central hardwood region of the United States under current climate and projected climate at the end of the century from two general circulation models and two representative greenhouse gas concentration pathways.

Results

Forest composition and abundance varied by ecological subsection with more dramatic changes occurring with greater changes in temperature and precipitation and on soils with lower water holding capacity. Biomass production across the region followed patterns of soil quality.

Conclusions

Linkages 3.0 predicted realistic responses to soil and climate gradients and its application was a useful approach for considering growth potential and maximum growing space under future climates. We suggest Linkages 3.0 can also can used to inform parameter estimates in FLMs such as species establishment and maximum growing space.

Keywords

LINKAGES Climate change Central hardwood region Ecosystem modeling 

Notes

Acknowledgements

We thank Stanley Wullschleger and Wilfred Post for help in initializing LINKAGES v2.2 and Stephen Shifley, John Kabrick and Dan Dey for support and knowledge provided about forest ecology and forest soils. We thank Steve Pallardy and Oak Ridge National Laboratory for access to Ameriflux data. This project was funded by the U.S.D.A. Forest Service Northern Research Station, a cooperative agreement with the United States Geological Survey Northeast Climate Science Center, Department of Interior USGS Northeast Climate Science Center graduate and post-graduate fellowships, and the University of Missouri-Columbia. Its contents are solely the responsibility of the authors and do not necessarily represent views of the Northeast Climate Science Center or the USGS. 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_473_MOESM1_ESM.docx (18 kb)
Supplementary material 1 (DOCX 17 kb)

References

  1. Allen CD, Macalady AK, Chenchouni H, Bachelet D, McDowell N, Vennetier M, Kitzberger T, Rigling A, Breshears DD, Hogg EH, Gonzalez P, Fensham R, Zhang Z, Castro J, Demidova N, Lim J-H, Allard G, Running SG, 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 259:660–684CrossRefGoogle Scholar
  2. Arner SL, Woudenber S, Waters S, Vissage J, MacLean C, Thompson M, Hansen M (2001) National algorithms for determining stocking class, stand size class, and forest type for forest inventory and analysis plots. http://www.fs.fed.us/fmsc/ftp/fvs/docs/gtr/Arner2001.pdf
  3. Botkin DB (1993) Forest dynamics an ecological model, 1st edn. Oxford University Press, New YorkGoogle Scholar
  4. Botkin DB, Janak JF, Wallis JR (1972) Some ecological consequences of a computer model of forest growth. J Ecol 60(3):849CrossRefGoogle Scholar
  5. Braun EL (1950) Deciduous forests of eastern North America. Blakiston CO., Philadelphia, Toronto, pp 596Google Scholar
  6. Brown SL, Schroeder P, Birdsey R (1997) Aboveground biomass distribution of US eastern hardwood forests and the use of large trees as an indicator of forest development. For Ecol Manag 96:37–47CrossRefGoogle Scholar
  7. Brown SL, Schroeder P, Kern JS (1999) Spatial distribution of biomass in forests of the eastern USA. For Ecol Manag 123:81–90CrossRefGoogle Scholar
  8. Burns RM, Honkala BH (1990) Silvics of North America: 1. Conifers; 2. Hardwood. Agriculture Handbook 654, USDA Forest Service, Washington, DCGoogle Scholar
  9. Cleland DT, Freeouf JA, Keys JE, Nowacki GJ, Carpenter C, McNab WH (2007) Ecological subregions: sections of the coterminous United States. USDA forest service, Gen Tech Rep WO-76, Washington DCGoogle Scholar
  10. Delworth TL, Broccoli AJ, Rosati A, Stouffer RJ, Balaji V, Beesley JA (2006) GFDL’s CM2 global coupled climate models. Part I: formulation and simulation characteristics. J Clim 19:643–674CrossRefGoogle Scholar
  11. Federer CA (1995) BROOK90: a simulation model for evaporation, soil water, and streamflow, Version 3.1. USDA Forest ServiceGoogle Scholar
  12. Federer CA (2015) The BROOK90 hydrologic model for evapotranspiration, soil water and streamflow. http://www.ecoshift.net/brook/brook90.html
  13. Fralish JS (2003) The central hardwood forest: its boundaries and physiographic provinces, Proceedings 13th central hardwoods conference. In: Van Sambeek JW, Dawson JO, Ponder F, Lowenstein EF, Fralish JS (eds) USDA Forest Service, North Central Research Station, Gen Tech Rep NC-234, St. PaulGoogle Scholar
  14. Gu L, Pallardy SG, Hosman KP, Sun Y (2015) Drought influenced mortality of tree species with different predawn leaf water dynamics in a decade-long study of a central US forest. Biogeosciences 12:2831–2845CrossRefGoogle Scholar
  15. Gustafson EJ, Keene R (2014). Predicting changes in forest composition and dynamics—landscape-scale process models. U.S. Department of Agriculture, Forest Service, Climate Change Resource CenterGoogle Scholar
  16. Gustafson EJ, Shinneman DJ (2015) Approaches to modeling landscape-scale drought-induced forest mortality. In: Perera AH, Sturtevant BR, Buse LJ (eds) Simulation modeling of forest landscape disturbances, Chap 3. Springer, New York, pp 45–71CrossRefGoogle Scholar
  17. Gustafson EJ, Shvidenko AZ, Sturtevant BR, Scheller RM (2010) Predicting global change effects on forest biomass and composition in south-central Siberia. Ecol Appl 20:700–715CrossRefPubMedGoogle Scholar
  18. Guyette R, Kabrick JM (2000) The legacy and continuity of forest disturbance, succession, and species at the MOFEP sites. In: Shifley SR, Kabrick JM (eds) Proceedings of the second Missouri Ozark forest ecosystem project symposium: post treatment results of the landscape experiment. U.S. Department of Agriculture, Forest Service, GTR NC-227 26-44Google Scholar
  19. He HS (2008) Forest landscape models, definition, characterization, and classification. For Ecol Manag 254:484–498CrossRefGoogle Scholar
  20. He HS, Mladenoff DJ (1999) Spatially explicit and stochastic simulation of forest landscape fire disturbance and succession. Ecology 80:81–99CrossRefGoogle Scholar
  21. He HS, Mladenoff DJ, Crow TR (1999) Linking an ecosystem model and a landscape model to study forest species response to climate warming. Ecol Model 114:213–233CrossRefGoogle Scholar
  22. IPCC (2013) Annex III: glossary [Planton S. (ed.)]. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, CambridgeGoogle Scholar
  23. Iverson LR, Prasad AM, Matthews SN, Peters M (2008) Estimating potential habitat for 134 eastern US tree species under six climate scenarios. For Ecol Manag 254:390–406CrossRefGoogle Scholar
  24. Iverson LR, Thompson FR III, Mathews S, Peters M, Prasad A, Dijak WD, Fraser JS, Wang WJ, Hanberry B, He HS, Janowiak M, Butler P, Brandt L, Swanston C (2016) 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. Landscape. doi: 10.1007/s10980-016-0404-8 Google Scholar
  25. Jenkins JC, Birdsey RA, Pan Y (2001) Biomass and NPP estimation for the mid-Atlantic region (USA) using plot-level forest inventory data. Ecol Appl 11:1174–1193CrossRefGoogle Scholar
  26. Johnson PS, Shifley SR, Rogers R (2009) The ecology and silviculture of oaks, 2nd edn. CAB International, OxfordshireCrossRefGoogle Scholar
  27. Kabrick JM, Jensen RG, Shifley SR, Larsen (2002) Woody vegetation following even-aged, uneven-aged and no-harvest treatments on the Missouri Ozarks forest ecosystem project sites. USDA General Technical Report NC-227Google Scholar
  28. Li X, Niu J, Xie B, Johnson SJ (2013) Study on hydrological functions of litter layers in North China. PLoS ONE 8(7):e70328CrossRefPubMedPubMedCentralGoogle Scholar
  29. Lischke H, Loffler TJ, Fischlin A (1998) Forest succession models: capturing variability with height structured, random, spatial distributions. Theor Popul Biol 54:213–226CrossRefPubMedGoogle Scholar
  30. Lischke H, Zimmermann NE, Bolliger J, Rickebusch S, Loffler TJ (2006) TreeMig: a forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol Model 199:409–420CrossRefGoogle Scholar
  31. Little EL Jr (1971) Atlas of United States trees, volume 1, conifers and important hardwoods: U.S. Department of Agriculture Miscellaneous Publication 1146, 200 mapsGoogle Scholar
  32. Little EL Jr (1977) Atlas of United States trees, volume 4, minor Eastern hardwoods: U.S. Department of Agriculture Miscellaneous Publication 1342, 230 mapsGoogle Scholar
  33. Livneh B, Rosenberg EA, Lin C, Nijssen B, Mishra V, Andreadis KM, Maurer EP, Lettenmaier DP (2013) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States: update and extensions. J Clim 26:9384–9392CrossRefGoogle Scholar
  34. MacCleery DW. (1992) American forests: a history of resilience and recovery. USDA forest Service FS-540Google Scholar
  35. McGarvey JC, Thompson JR, Epstein HE, Shugart HH Jr (2015) Carbon storage in old-growth forests of the Mid-Atlantic: toward better understanding the eastern forest carbon sink. Ecology 96:311–317CrossRefPubMedGoogle Scholar
  36. 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
  37. Morin X, Viner D, Chuine I (2008) Tree species range shifts at a continental scale: new predictive insights from a process-based model. J Ecol 96(4):784–794CrossRefGoogle Scholar
  38. Niinemets U, Valladares F (2006) Tolerance to shade, drought and waterlogging of temperate northern hemisphere trees and shrubs. Ecol Monogr 76:521–547CrossRefGoogle Scholar
  39. O’Connell, BM, LaPoint EB, Turner JA, Ridley T, Boyer D, Wilson AM, Waddell KL, Conkling BL (2013) The forest inventory and analysis database: database description and user guide for Phase 2 (version 6.0.2). http://www.fia.fs.fed.us
  40. Pastor J, Post WM (1985) Development of a linked forest productivity-soil process model. In: ORNL/TM-9519. Oak Ridge National Laboratory, Oak RidgeGoogle Scholar
  41. Purves D, Pacala S (2008) Predictive models of forest dynamics. Science 320(5882):1452–1453CrossRefPubMedGoogle Scholar
  42. Reich PB, Frelich LE (2001) Temperate deciduous forests. Encyclopedia of global change. Macmillan Reference USA, Biology for studentsGoogle Scholar
  43. Saxton KE, Rawls WJ, Romberger JS, Papendick RI (1986) Estimating generalized soil-water characteristics from texture. Soil Sci Soc Am J 50:1031–1036CrossRefGoogle Scholar
  44. Scheller RM, Mladenoff DJ (2005) A spatially dynamic simulation of the effects of climate change, harvesting, wind, and tree species migration on the forest composition, and biomass in northern Wisconsin, USA. Glob Change Biol 11:307–321CrossRefGoogle Scholar
  45. Scheller RM, Van Tuyl S, Clark KL, Hayden NG, Hom J, Mladenoff DJ (2007) Simulation of forest change in the New Jersey Pine Barrens under current and pre-colonial conditions. For Ecol Manage 255:1489–1500CrossRefGoogle Scholar
  46. Schneiderman JE, He HS, Thompson FR, Dijak WD, Fraser JS (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(10):1879–1892CrossRefGoogle Scholar
  47. Schroeder P, Brown S, Mo JM, Birdsey R, Cieszewski C (1997) Biomass estimation for temperate broadleaf forests of the United States using inventory data. For Sci 42:424–434Google Scholar
  48. Schumacher S, Bugmann H, Mladenoff DJ (2004) Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol Model 180:175–194CrossRefGoogle Scholar
  49. Seidl R, Rammer W, Scheller RM, Spies T (2012) An individual-based process model to simulate landscape-scale forest ecosystem dynamics. Ecol Model 231:87–100CrossRefGoogle Scholar
  50. Shifley SR, He HS, Lischke H, Wang W, Jin W, Gustafson EJ, Thompson JR, Thompson FR III, Dijak WD, Wang J (in press) The past and future of modeling forest dynamics: from growth and yield curves to forest landscape models. Landscape EcologyGoogle Scholar
  51. Shugart HH, West DC (1980) Forest succession models. BioScience 30(5):308–313CrossRefGoogle Scholar
  52. Solomon AM (1986) Transient response of forest to CO2-induced climate change: simulation modeling experiments in eastern North America. Oecologia 68:567–579CrossRefPubMedGoogle Scholar
  53. Taylor JA (1967) Growing season as affected by land aspect and soil texture. In: Taylor JA (ed) Weather and agriculture. Pergamon Press, Oxford, pp 15–36CrossRefGoogle Scholar
  54. Thompson JR, Foster DR, Scheller RM, Kittredge D (2011) The influence of land use and climate change on forest biomass and composition in Massachusetts, USA. Ecol Appl 21(7):2425–2444CrossRefPubMedGoogle Scholar
  55. Thornton PE, Thornton MM, Mayer BW, Wilhelmi N, Wei Y, Deveraconda R, Cook RB (2016) Daymet: daily surface weather data on a 1 km grid for North America, 1980–2012. (http://daymet.ornl.gov/) Accessed 13 Jul 2016, Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge
  56. United States Department of Agriculture, Natural Resources Conservation Service. (2006) Land Resource Regions and Major Land Resource Areas of the United States, the Caribbean, and the Pacific Basin. U.S. Department of Agriculture Handbook 296Google Scholar
  57. Vanclay JK (1991) Aggregating tree species to develop diameter increment equations for tropical rainforests. For Ecol Manag 42:143–168CrossRefGoogle Scholar
  58. Vanderwel MC, Purves DW (2014) How do disturbances and environmental heterogeneity affect the pace of forest distribution shifts under climate change? Ecography 37:10–20CrossRefGoogle Scholar
  59. Wang WJ, He HS, Spetich MA, Shifley SR, Thompson FR III (2014) LANDIS PRO: a landscape model that predicts forest composition and structure changes at regional scales. Ecography 37:225–229CrossRefGoogle Scholar
  60. Wang WJ, He HS, Thompson FR III, Fraser JS, Hanberry BB, Dijak WD (2015) Importance of succession, harvest, and climate change in determining future composition in U.S. central hardwood forests. Ecosphere 6(12):1–18CrossRefGoogle Scholar
  61. Wang WJ, He HS, Thompson FR III, Fraser JS, Dijak WD (2016) Changes in forest biomass and tree species distribution under climate change in the northeastern United States. Landscape Ecol. doi: 10.1007/s10980-016-0429-z Google Scholar
  62. Way D, Montgomery R (2015) Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ 38:1725–1736CrossRefPubMedGoogle Scholar
  63. Wullschleger SD, Gunderson CA, Tharp ML, West DC, Post WM (2003) Simulated patterns of forest succession and productivity as a consequence of altered precipitation. In: Hanson PJ, Wullschleger SD (eds) North American temperate deciduous forest responses to changing precipitation regimes. Springer, New York, pp 433–446CrossRefGoogle Scholar
  64. Yukimoto S, Adachi Y, Hosaka M, Sakami T, Yoshimura H, Hirabara M, Tanaka TY, Shindo E, Tsujino H, Deushi M, Mizuta R, Yabu S, Obata A, Nakano H, Koshiro T, Ose T, Kitoh A (2012) A new global climate model of the meteorological research institute: MRI-CGCM3—model description and basic performance. J Meterol Soc Japn 90A:23–64CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.USDA Forest Service, Northern Research StationColumbiaUSA
  2. 2.School of Natural ResourcesUniversity of MissouriColumbiaUSA

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