Advertisement

Landscape Ecology

, Volume 32, Issue 7, pp 1307–1325 | Cite as

The past and future of modeling forest dynamics: from growth and yield curves to forest landscape models

  • Stephen R. ShifleyEmail author
  • Hong S. He
  • Heike Lischke
  • Wen J. Wang
  • Wenchi Jin
  • Eric J. Gustafson
  • Jonathan R. Thompson
  • Frank R. ThompsonIII
  • William D. Dijak
  • Jian Yang
Review Article

Abstract

Context

Quantitative models of forest dynamics have followed a progression toward methods with increased detail, complexity, and spatial extent.

Objectives

We highlight milestones in the development of forest dynamics models and identify future research and application opportunities.

Methods

We reviewed milestones in the evolution of forest dynamics models from the 1930s to the present with emphasis on forest growth and yield models and forest landscape models We combined past trends with emerging issues to identify future needs.

Results

Historically, capacity to model forest dynamics at tree, stand, and landscape scales was constrained by available data for model calibration and validation; computing capacity; model applicability to real-world problems; and ability to integrate biological, social, and economic drivers of change. As computing and data resources improved, a new class of spatially explicit forest landscape models emerged.

Conclusions

We are at a point of great opportunity in development and application of forest dynamics models. Past limitations in computing capacity and in data suitable for model calibration or evaluation are becoming less restrictive. Forest landscape models, in particular, are ready to transition to a central role supporting forest management, planning, and policy decisions.

Recommendations

Transitioning forest landscape models to a central role in applied decision making will require greater attention to evaluating performance; building application support staffs; expanding the included drivers of change, and incorporating metrics for social and economic inputs and outputs.

Keywords

Process model Individual-tree model Gap model Model validation Ecosystem services LANDIS TreeMig Forest Vegetation Simulator 

Notes

Acknowledgements

We thank David Mladenoff and two anonymous reviewers for insightful comments that were a great help in improving earlier versions of this manuscript. We, along with other scientists and forest managers, are deeply indebted to pioneers in forest tree, stand, and landscape modelling over the past eight decades. There are many, but John Moser, Alan Ek, Al Stage, Rolfe Leary, Bob Monserud, Nick Crookston, David Mladenoff, John Pastor, and W.M. Post have been particularly influential. We thank them for their leadership, insight, and encouragement. This research was partially supported by the U.S.D.A. Forest Service Northern Research Station, the Department of Interior USGS Northeast Climate Science Center graduate and post-doctoral fellowships, and the University of Missouri-Columbia. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the views of the United States Government. This manuscript is submitted for publication with the understanding that the United States Government is authorized to reproduce and distribute reprints for Governmental purposes.

References

  1. Aber JD, Federer CA (1992) A generalized, lumped-parameter model of photosynthesis, evapotranspiration and net primary production in temperate and boreal forest ecosystems. Oecologia 92(4):463–474PubMedCrossRefGoogle Scholar
  2. Aber JD, Ollinger SV, Federer CA, Reich PB, Goulden ML, Kicklighter DW, Melillo JM, Lathrop RG (1995) Predicting the effects of climate change on water yield and forest production in the northeastern United States. Clim Res 5(3):207–222CrossRefGoogle Scholar
  3. Adams MB, Loughry L, Plaugher L (comps) (2008) Experimental forests and ranges of the USDA Forest Service. U.S. Forest Service, Northeastern Research Station, General Technical Report NE-321, Newtown Square, PA, USAGoogle Scholar
  4. Akçakaya HR (2006) RAMAS GIS: linking spatial data with population viability analysis. Version 5.0Google Scholar
  5. Akçakaya HR, Brook BW (2009) Methods for determining viability of wildlife populations in large landscapes. In: Millspaugh JJ, Thompson FR III (eds) Models for planning wildlife conservation in large landscapes. Academic Press, Burlington, pp 449–472CrossRefGoogle Scholar
  6. Akçakaya HR, Radeloff VC, Mladenoff DJ, He HS (2004) Integrating landscape and metapopulation modeling approaches: viability of the sharp-tailed grouse in a dynamic landscape. Conserv Bio 18:526–537CrossRefGoogle Scholar
  7. Arney JD (1972) Computer simulation of Douglas-fir tree and stand growth. Environment Canada, Canadian Forestry Service, Pacific Forest Research Centre, Internal Report BC-27. Victoria, BC, CanadaGoogle Scholar
  8. Bailey RL, Dell TR (1973) Quantifying diameter distributions with the Weibull function. For Sci 19:97–104Google Scholar
  9. Baker WL, Egbert SL, Frazier GF (1991) A spatial model for studying the effects of climatic change on the structure of landscapes subject to large disturbances. Ecol Model 56:109–125CrossRefGoogle Scholar
  10. Baker WL, Mladenoff DJ (1999) Progress and future directions in spatial modeling of forest landscapes, Chapter 13. In: Mladenoff DJ, Baker WL (eds) Spatial modeling of forest landscape change: approaches and applications. Cambridge University Press, CambridgeGoogle Scholar
  11. Battaglia M, Sands PJ (1998) Process-based forest productivity models and their application in forest management. For Ecol Manag 102(1):13–32CrossRefGoogle Scholar
  12. Beers TW (1962) Components of forest growth. J For 60:245–248Google Scholar
  13. Bekessy S, Wintle B, Gordon A, Chisholm R, Venier L, Pearce J (2009) Dynamic landscape metapopulation models and sustainable forest management. In: Millspaugh JJ, Thompson FR III (eds) Models for planning wildlife conservation in large landscapes. Academic Press, Burlington, pp 473–500CrossRefGoogle Scholar
  14. Botkin DB, Janak JF, Wallis JR (1972a) Rationale, limitations, and assumptions of a northeastern forest growth simulator. IBM J Res Dev 16:101–116CrossRefGoogle Scholar
  15. Botkin DB, Janak JF, Wallis JR (1972b) Some ecological consequences of a computer model of forest growth. J Ecol 60:849–872CrossRefGoogle Scholar
  16. Brandt L, He H, Iverson L, Thompson FR III, Butler P, Handler S, Janowiak M, Shannon PD, Swanston C, Albrecht M, Blume-Weaver R, Deizman P, DePuy J, Dijak WD, Dinkel G, Fei S, Jones-Farrand DT, Leahy M, Matthews S, Nelson P, Oberle B, Perez J, Peters M, Prasad A, Schneiderman JE, Shuey J, Smith AB, Studyvin C, Tirpak JM, Walk JW, Wang WJ, Watts L, Weigel D, Westin S (2014) Central Hardwoods ecosystem vulnerability assessment and synthesis: a report from the Central Hardwoods Climate Change Response Framework Project. U.S. Forest Service, Northern Research Station, General Technical Report NRS-124. Newtown Square, PA, USAGoogle Scholar
  17. Brown AE, Zhang L, McMahon TA, Western AW, Vertesy RA (2005) A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J Hydrol 310:28–61CrossRefGoogle Scholar
  18. Buchman RG, Shifley SR (1983) Guide to evaluating forest growth projection systems. J For 81(232–234):254Google Scholar
  19. Bugmann H (2001) A review of forest gap models. Clim Change 51:259–305CrossRefGoogle Scholar
  20. Bugmann HKM, Brang P, Elkin C, Henne P, Jakoby O, Lévesque M, Lischke H, Psomas A, Rigling A, Wermelinger B, Zimmermann NE (2014) Climate change impacts on tree species, forest properties, and ecosystem services. In: OCCR, FOEN, MeteoSwiss, C2SM, Agroscope, ProClim (eds) CH2014-impacts (2014): toward quantitative scenarios of climate change impacts in Switzerland, Bern, Switzerland, pp. 79–89Google Scholar
  21. Burkhart HE (1971) Slash pine plantation yield estimates based on diameter distributions: an evaluation. For Sci 17:452–453Google Scholar
  22. Burkhart HE, Tomé M (2012) Modeling forest trees and stands. Springer, DordrechtCrossRefGoogle Scholar
  23. Butler PR, Iverson L, Thompson FR III, Brandt L, Handler S, Janowiak M, Shannon PD, Swanston C, Karriker K, Bartig J, Connolly S, Dijak WD, Bearer S, Blatt S, Brandon A, Byers E, Coon C, Culbreth T, Daly J, Dorsey W, Ede D, Euler C, Gillies N, Hix DM, Johnson C, Lyte L, Matthews S, McCarthy D, Minney D, Murphy D, O’Dea C, Orwan R, Peters M, Prasad A, Randall C, Reed J, Sandeno C, Schuler T, Sneddon L, Stanley B, Steele A, Stout S, Swaty R, Teets J, Tomon T, Vanderhorst J, Whatley J, Zegre N (2015) Central Appalachians forest ecosystem vulnerability assessment and synthesis: a report from the Central Appalachians Climate Change Response Framework Project. U.S. Forest Service, Northern Research Station, General Technical Report NRS-146. Newtown Square, PA, USAGoogle Scholar
  24. Cary GJ (1998) Predicting fire regimes and their ecological effects in spatially complex landscapes. Dissertation, The Australian National UniversityGoogle Scholar
  25. Clutter JL (1963) Compatible growth and yield models for loblolly pine. For Sci 9:354–371Google Scholar
  26. Clutter JL, Bennett FA (1965) Diameter distributions in old-field slash pine plantations. Georgia Forest Research Council Report 13, Macon, GA, USAGoogle Scholar
  27. Community Earth System Model (2016) Community earth system model (CESM). University Corporation for Atmospheric Research. Accessed December 2016Google Scholar
  28. Council on Environmental Quality (1997) Considering cumulative effects under the National Environmental Policy Act. Council on Environmental Quality, Washington, DC. https://ceq.doe.gov/nepa/ccenepa/exec.pdf. Accessed Dec 2015
  29. 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 260:1198–1211CrossRefGoogle Scholar
  30. Crookston NL, Stage AR (1991) User’s guide to the parallel processing extension of the prognosis model. U.S. Forest Service, Intermountain Research Station, General Technical Report INT-281. Ogden, UT, USAGoogle Scholar
  31. De Bruijn A, Gustafson EJ, Sturtevant BR, Foster JR, Miranda BR, Lichti NI, Jacobs DF (2014) Toward more robust projections of forest landscape dynamics under novel environmental conditions: embedding PnET within LANDIS-II. Ecol Model 287:44–57CrossRefGoogle Scholar
  32. De Jager NR, Drohan PJ, Miranda BM, Sturtevant BR, Stout SL, Royo AA, Gustafson EJ, Romanski MC (2016) Simulating ungulate herbivory across forest landscapes: an ungulate browsing extension for LANDIS-II. Ecol Model 350:11–29CrossRefGoogle Scholar
  33. Dijak W (2013) Landscape builder: software for the creation of initial landscapes for LANDIS from FIA data. Comput Ecol Software 3(2):17–25Google Scholar
  34. Dijak WD, Hanberry BB, Fraser JS, He HS, Wang WJ, Thompson FR (2017) Revision and application of the LINKAGES model to simulate forest growth in Central Hardwood landscapes in response to climate change. Landscape Ecol. doi: 10.1007/s10980-016-0473-8 Google Scholar
  35. Dijak WD, Rittenhouse CD (2009) Development and application of habitat suitability models to large landscapes. In: Millspaugh JJ, Thompson FR III (eds) Models for planning wildlife conservation in large landscapes. Academic Press, Burlington, pp 367–390CrossRefGoogle Scholar
  36. Dixon GE (comp) (2002) Essential FVS: a user’s guide to the Forest Vegetation Simulator. U.S. Forest Service, Forest Management Service Center, Ft Collins, CO USA. (Revised 2 Nov 2 2015)Google Scholar
  37. Donovan ML, Rabe DL, Olson CE Jr (1987) Use of geographic information systems to develop habitat suitability models. Wildl Soc Bull 15:574–579Google Scholar
  38. Duveneusck MJ, Thompson JR, Wilson BT (2015) An imputed forest composition map for New England screened by species range boundaries. For Ecol Manag 347:107–115CrossRefGoogle Scholar
  39. Ek AR, Monserud RA (1974) FOREST: a computer model for simulating the growth and reproduction of mixed species forest stands. University of Wisconsin-Madison, College of Agriculure and Life Science, Research Report R2635, Madison, WI, USAGoogle Scholar
  40. Ek AR, Shifley SR, Burk TE (1988) Forest growth modeling and prediction (volumes 1 & 2). General Technical Report NC-120. St. Paul, MN: U.S. Dept. of Agriculture, Forest Service, North Central Forest Experiment Station U.S. Forest Service, Northern Central Forest Experiment Station, General Technical Report NC-120. St. Paul, MN, USAGoogle Scholar
  41. Elkin C, Gutiérrez AG, Leuzinger S, Manusch C, Temperli C, Rasche L, Bugmann H (2013) A 2 °C warmer world is not safe for ecosystem services in the European Alps. Global Change Biol 19:1827–1840CrossRefGoogle Scholar
  42. Elliot WJ, Miller IS, Audin L (eds) (2010) Cumulative watershed effects of fuel management in the western United States. U.S. Forest Service, Rocky Mountain Research Station, General Technical Report RMRS-231, Fort Collins, CO, USAGoogle Scholar
  43. Gates WL, Mitchell JFB, Boer GJ, Cubasch U, Meleshko VP (1992) Climate modelling, climate prediction and model validation. In: Houghton JT, Callander BA, Varney SK (eds) Climate change 1992: the supplementary report to the IPCC scientific assessment. Intergovernmental Panel on Climate Change. University Press, Cambridge, pp 97–134Google Scholar
  44. Gingrich SF (1967) Measuring and evaluating stocking and stand density in upland hardwood forests in the central states. For Sci 13:38–53Google Scholar
  45. Gustafson EJ, De Bruijn AMG, Kubiske ME, Pangle RE, Limousin J, McDowell N, Sturtevant BR, Muss J, Pockman WT (2015) Integrating ecophysiology and forest landscape models to better project drought effects under climate change. Glob Chang Biol 21:843–856PubMedCrossRefGoogle Scholar
  46. Gustafson EJ, Lucash M, Liem J, Jenny H, Scheller RM, Barrett K (2016) Seeing the future impacts of climate change and forest management: a landscape visualization system for forest managers. U.S. Forest Service, Northern Research Station, General Technical Report NRS-164. Newtown Square, PA, USAGoogle Scholar
  47. Gustafson EJ, Zollner PA, Sturtevant BR, He HS, Mladenoff DJ (2004) Human influence on the abundance and connectivity of high-risk fuels in mixed forests of northern Wisconsin, USA. Landscape Ecol 19:327–341CrossRefGoogle Scholar
  48. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz SJ, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853PubMedCrossRefGoogle Scholar
  49. Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Thau D, Stehman SV, Goetz J, Loveland TR, Kommareddy A, Egorov A, Chini L, Justice CO, Townshend JRG (2017) Global forest change. https://earthenginepartners.appspot.com/science-2013-global-forest. Accessed Dec 2016
  50. He HS (2008) Forest landscape models, definition, characterization, and classification. For Ecol Manag 254:484–498CrossRefGoogle Scholar
  51. He HS, Li W, Sturtevant BR, Yang J, Shang BZ, Gustafson EJ, Mladenoff DJ (2005) LANDIS, a spatially explicit model of forest landscape disturbance, management, and succession—LANDIS 4.0 User’s Guide. U.S. Forest Service, North Central Research Station, General Technical Report NC-263, St. Paul, Minnesota, USAGoogle Scholar
  52. He HS, Shang ZB, Crow TR, Gustafson EJ, Shifley SR (2004) Simulating forest fuel and fire risk dynamics across landscapes—LANDIS fuel module design. Ecol Model 180:135–151CrossRefGoogle Scholar
  53. Hibbert AR (1967) Forest treatment effects on water yield. In: Sopper WE, Lull HW (eds) International symposium on forest hydrology. Pergamon Press, Oxford, UK, pp 527–543Google Scholar
  54. Hornbeck JW, Adams MB, Corbett ES, Verry ES, Lynch JA (1995) A summary of water yield experiments on hardwood forested watersheds in northeastern United States. In: Gottschalk KW, Fosbroke SLC (eds) Proceedings, 10th central hardwood forest conference. US Forest Service, Northeastern Forest Experiment Station, General Technical Report NE-197, Radnor, PA, pp 282–295Google Scholar
  55. Hostler JA, Chandler RB (2015) Improved stat-space models for inference about spatial and temporal variation in abundance from count data. Ecology 96:1713–1723CrossRefGoogle Scholar
  56. Iverson LR, Prasad AM, Hale BJ, Sutherland EK (1999) An atlas of current and potential future distributions of common trees of the eastern United States. U.S. Forest Service, Northeastern Research Station, General Technical Report NE-265. Radnor, PA, USAGoogle Scholar
  57. Iverson LR, Thompson FR, Matthews S, Peters M, Prasad A, Dijak WD, Fraser J, Wang WJ, Hanberry B, He H, 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 Ecol. doi: 10.1007/s10980-016-0404-8 Google Scholar
  58. Jin W, He HS, Thompson FR III (2016) Are more complex physiological models of forest ecosystems better choices for plot and regional predictions? Environ model softw 75:1–14CrossRefGoogle Scholar
  59. Johnsen K, Samuelson L, Teskey R, McNulty S, Fox T (2001) Process models as tools in forestry research and management. For Sci 47(1):2–8Google Scholar
  60. Kennedy R, Yang Z, Braaten J (2017) LandTrendr. http://landtrendr.forestry.oregonstate.edu/. Accessed Dec 2016
  61. Landsberg J (2003) Modelling forest ecosystems: state of the art, challenges, and future directions. Can J For Res 33(3):385–397CrossRefGoogle Scholar
  62. Landsberg JJ, Kaufmann MR, Binkley D, Isebrands J, Jarvis PG (1991) Evaluating progress toward closed forest models based on fluxes of carbon, water and nutrients. Tree Physiol 9(1–2):1–15PubMedCrossRefGoogle Scholar
  63. Larson MA, Millspaugh JJ, Thompson FR III (2009) A review of methods for quantifying wildlife habitat in large landscapes. In: Millspaugh JJ, Thompson FR III (eds) Models for planning wildlife conservation in large landscapes. Academic Press, Burlington, pp 225–250CrossRefGoogle Scholar
  64. Larson MA, Thompson FR III, Millspaugh JJ, Dijak WD, Shifley SR (2004) Linking population viability, habitat suitability, and landscape simulation models for conservation planning. Ecol Model 180:103–118CrossRefGoogle Scholar
  65. Leary RA (1979) Design. U.S. Forest Service, North Central Research Station, General Technical Report NC-49, St. Paul, Minnesota, USA, pp 5–15Google Scholar
  66. Leary RA (1988) Some factors that will affect the next generation of forest growth models. U.S. Forest Service, North Central Forest Experiment Station, General Technical Report NC-120, St. Paul, Minnesota, USA, pp 22–32Google Scholar
  67. Leary RA (1997) Testing models of unthinned red pine plantation dynamics using a modified Bakuzis matrix of stand properties. Ecol Model 98:35–46CrossRefGoogle Scholar
  68. LeBrun JJ, Schneiderman JE, Thompson FR III, Dijak WD, Fraser JS, He HS, Millspaugh JJ (2017) Bird response to future climate and forest management focused on mitigating climate change. Landscape Ecol. doi: 10.1007/s10980-016-0463-x Google Scholar
  69. Leefers LA, Gustafson EJ, Freeman P (2003) Linking temporal-optimization and spatial-simulation models for forest planning. In: Arthaud GJ, Barrett TM (eds) Systems analysis in forest resources: proceedings of the 8th symposium; Snowmass Village, CO. Kluwer Academic Publishers, Dordrecht, pp 165–173CrossRefGoogle Scholar
  70. Li C, Ter-Mikaelian M, Perer A (1997) Temporal fire disturbance patterns on a forest landscape. Ecol Model 99:137–150CrossRefGoogle Scholar
  71. Li HB, Gartner DI, Mou P, Trettin CC (2000) A landscape model (LEEMATH) to evaluate effects of management impacts on timber and wildlife habitat. Comput Electron Agric 27:263–292CrossRefGoogle Scholar
  72. Lischke H, Löffler TJ, Fischlin A (1998) Aggregation of individual trees and patches in forest succession models: capturing variability with height structured, random, spatial distributions. Theor Popul Biol 54:213–226PubMedCrossRefGoogle Scholar
  73. Lischke H, Löffler TJ, Thornton PE, Zimmermann NE (2007) Model Up-scaling in Landscape Research. In: Kienast F, Ghosh S, Wildi O (eds) A changing world: challenges for landscape research. Kluwer, Dordrecht, pp 259–282Google Scholar
  74. Lischke H, Zierl B (2002) Feedback between structured vegetation and soil water in a changing climate: a simulation study. In: Beniston M (ed) Climatic change: implications for the hydrological cycle and for water management. Kluwer Academic Publishers, Dordrecht, pp 349–377CrossRefGoogle Scholar
  75. Lischke H, Zimmermann NE, Bolliger J, Rickebusch S, Löffler TJ (2006) TreeMig: a forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol Model 199(4):409–420CrossRefGoogle Scholar
  76. Liu J (1993) ECOLECON: an ECOLogical-ECONomic model for species conservation in complex forest landscapes. Ecol Model 70:63–87CrossRefGoogle Scholar
  77. Liu J, Dunning JB, Pulliam HR (1995) Potential effects of a forest management plan on Bachman’s sparrows (Aimophila aestivalis): linking a spatially explicit models with GIS. Conserv Bio 9:62–75CrossRefGoogle Scholar
  78. MacKinney AL, Schumacher FX, Chaiken LE (1937) Construction of yield tables for non-normal loblolly pine stands. J Agric Res 54:531–545Google Scholar
  79. Marzluff JM, Millspaugh JJ, Ceder KR, Oliver CD, Withey J, McCarter JB, Mason CL, Comnick J (2002) Modeling changes in wildlife habitat and timber revenues in response to forest management. For Sci 48:191–202Google Scholar
  80. Medlyn BE, Duursma RA, Zeppel MJB (2011) Forest productivity under climate change: a checklist for evaluating model studies. Wiley Interdisciplinary Reviews. Clim Change 2(3):332–355Google Scholar
  81. Medvigy D, Wofsy SC, Munger JW, Hollinger DY, Moorcroft PR (2009) Mechanistic scaling of ecosystem function and dynamics in space and time: ecosystem demography model version 2. J Geophys Res 114(G1):G01002CrossRefGoogle Scholar
  82. Millspaugh JJ, Thompson FR III (eds) (2009) Models for planning wildlife conservation in large landscapes. Academic Press, Burlington, p 688Google Scholar
  83. Miner CL, Walters NR, Belli ML (1988) A guide to the TWIGS program for the North Central United States. U.S. Forest Service, North Central Forest Experiment Station, General Technical Report NC-125, St. Paul, Minnesota, USAGoogle Scholar
  84. Mladenoff DJ (2004) LANDIS and forest landscape models. Ecol Model 180:7–19CrossRefGoogle Scholar
  85. Mladenoff DJ (2005) The promise of landscape modeling: successes, failures, and evolution. In: Weins JA, Moss MR (eds) Issues and prespectives in landscape ecology. Cambridge University Press, Cambridge, pp 90–100CrossRefGoogle Scholar
  86. Mladenoff DJ, Baker WL (1999) Development of forest and landscape modeling approaches, Chapter 1. In: Mladenoff DJ, Baker WL (eds) Spatial modeling of forest landscape change: approaches and applications. Cambridge University Press, CambridgeGoogle Scholar
  87. Mladenoff DJ, He HS (1999) Design, behavior and applications of LANDIS, an object-oriented model of forest landscape disturbance and succession, Chapter 6. In: Mladenoff DJ, Baker WL (eds) Spatial modeling of forest landscape change: approaches and applications. Cambridge University Press, CambridgeGoogle Scholar
  88. Mladenoff DJ, Host GE, Boeder J, Crow TR (1996) LANDIS: a spatial model of forest landscape disturbance, succession, and management. In: Goodchild MF, Steyaert LT, Parks BO, Johnston C, Maidment D, Crane M, Glendining S (eds) GIS and environmental modeling. GIS World Books, Fort CollinsGoogle Scholar
  89. Moorcroft PR, Hurtt GC, Pacala SW (2001) A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol Monogr 71(4):557–585CrossRefGoogle Scholar
  90. Moore GE (1965) Cramming more components onto integrated circuits. Electronics 38:114–117Google Scholar
  91. Moser JW Jr (1974) A system of equations for the components of forest growth. In: Fries J (ed) Growth models for tree and stand simulation. Royal College of Forestry, Stockholm, pp 260–288Google Scholar
  92. Moser JW Jr (1980) Historical chapters in the development of modern forest growth and yield theory. In: Brown KM, Clarke FR (eds) Forecasting forest and stand dynamics: proceedings of the Workshop held at the School of Forestry, Lakehead University. Thunderbay, Ontario, pp 42–61Google Scholar
  93. Moser JW Jr, Hall OF (1969) Deriving growth and yield functions for uneven-aged forest stands. For Sci 15:183–188Google Scholar
  94. Nabel JEMS (2015) Upscaling with the dynamic two-layer classification concept (D2C): TreeMig-2L, an efficient implementation of the forest-landscape model TreeMig. Geosci Mol Dev 8:3563–3577CrossRefGoogle Scholar
  95. National Science Foundation (2017) NEON national ecological observatory network. http://www.neonscience.org/. Accessed Dec 2017
  96. Oak Ridge National Lab (2017) FLUXNET. https://fluxnet.ornl.gov/. Accessed Dec 2017
  97. Oliver CD, Larson BC (1996) Forest stand dynamics. Wiley, New YorkGoogle Scholar
  98. Pastor J, Post WM (1986) Influence of climate, soil moisture, and succession on forest carbon and nitrogen cycles. Biogeochemistry 2:3–27CrossRefGoogle Scholar
  99. Powell JR (2008) The quantum limit to Moore’s law. Proc IEEE 96(8):1247–1248CrossRefGoogle Scholar
  100. Purdue University (2016) Purdue hardwood ecosystem experiment. http://www.heeforeststudy.org/. Accessed Feb 2016
  101. Rebain SA (comp) 2010 The fire and fuels extension to the Forest Vegetation Simulator: updated model documentation. U.S. Forest Service, Forest Management Service Center, Ft Collins, CO, USAGoogle Scholar
  102. Reineke LH (1933) Perfecting a stand density index for even-aged forests. J Agric Res 46:627–638Google Scholar
  103. Riitters KH, O’Neill RV, Jones KB (1997) Assessing habitat suitability at multiple scales: a landscape-level approach. Biol Conserv 81:191–202CrossRefGoogle Scholar
  104. Risser PG, Iverson LR (2013) 30 years later—landscape ecology: directions and approaches. Landscape Ecol 28:367–369CrossRefGoogle Scholar
  105. Rittenhouse CD, Shifley SR, Dijak WD, Fan Z, Thompson FR, Millspaugh JJ, Perez JA, Sandeno CM (2011) Chapter 13: application of landscape and habitat suitability models to conservation: the Hoosier National Forest land-management plan. In: Li C, Lafortezza R, Chen J (eds) Landscape ecology in forest management and conservation. Challenges and solutions for global change. Higher Education Press, Berlin, pp 299–328CrossRefGoogle Scholar
  106. Rykiel EJ Jr (1996) Testing ecological models: the meaning of validation. Ecol Model 90:229–244CrossRefGoogle Scholar
  107. Schattan P, Zappa M, Lischke H, Bernhard L, Thürig E, Diekkrüger B (2013) An approach for transient consideration of forest change in hydrological impact studies. In: Climate and land surface changes in hydrology. H01, IAHS-IAPSO-IASPEI Assembly, Gothenburg, Sweden, pp 311–319Google Scholar
  108. Schaub M, Abadi F (2011) Integrated population models: a novel analysis framework for deeper insights into population dynamics. J Ornithol 152:S227–S237CrossRefGoogle Scholar
  109. Scheller RM, Domingo JB, Sturtevant BR, Williams JS, Rudy A, Gustafson EJ, Mladenoff DJ (2007) Design, development, and application of LANDIS-II, a spatial landscape simulation model with flexible temporal and spatial resolution. Ecol Model 201:409–419CrossRefGoogle Scholar
  110. Scheller RM, Mladenoff DM (2007) An ecological classification of forest landscape simulation models: tools and strategies for understanding broad-scale forested ecosystems. Landscape Ecol 22:491–505CrossRefGoogle Scholar
  111. Scheller RM, Sturtevant BR, Gustafson EJ, Ward BC, Mladenoff DM (2010) Increasing the reliability of ecological models using modern software engineering techniques. Front Ecol Environ 8(5):253–260CrossRefGoogle Scholar
  112. Scherstjanoi M, Kaplan JO, Thürig E, Lischke H (2013) GAPPARD: a computationally efficient method of approximating gap-scale disturbance in vegetation models. Geosci Mol Dev 6:1517–1542CrossRefGoogle Scholar
  113. Schumacher S, Bugmann H, Mladenoff DJ (2004) Improving the formulation of tree growth and succession in a spatially explicit landscape model. Ecol Model 180(1):175–194CrossRefGoogle Scholar
  114. Seidl R, Rammer W, Scheller RM, Spies TA (2012) An individual-based process model to simulate landscape-scale forest ecosystem dynamics. Ecol Model 231:87–100CrossRefGoogle Scholar
  115. Shifley SR, Brookshire BL (eds) (2000) Missouri Ozark Forest Ecosystem Project: site history, soils, landforms, woody and herbaceous vegetation, down wood, and inventory methods for the landscape experiment. U.S. Forest Service, North Central Forest Experiment Station, General Technical Report NC-208, St. Paul, MN, USAGoogle Scholar
  116. Shifley SR, Thompson FR III, Dijak WD, Larson MA, Millspaugh JJ (2006) Simulated effects of forest management alternatives on landscape structure and habitat suitability in the Midwestern United States. For Ecol Manag 229:361–377CrossRefGoogle Scholar
  117. Shugart HH (1984) A theory of forest dynamics. Springer, New YorkCrossRefGoogle Scholar
  118. Smith JE, Heath LS, Skog KE, Birdsey RA (2006) Methods for calculating forest ecosystem and harvested carbon with standard estimates for forest types of the United States. U.S. Forest Service, Northeastern Research Station, General Technical Report NE-343, Newtown Square, PA, USAGoogle Scholar
  119. Sohl TL, Sayler KL, Bouchard MA, Reker RA, Friesz AM, Bennett SL, Sleeter BM, Sleeter RR, Wilson T, Soulard C, Knuppe M, Van Hofwegen T (2014) Spatially explicit modeling of 1992–2100 land cover and forest stand age for the conterminous United States. Ecol App 24:1015–1036CrossRefGoogle Scholar
  120. Stage AR (1973) Prognosis model for stand development. U.S. Forest Service, Intermountain Forest and Range Experiment Station, Research Paper INT-321, Ogden, Utah, USAGoogle Scholar
  121. Stednick JD (1996) Monitoring the effects of timber harvest on annual water yield. J Hydrol 176:79–95CrossRefGoogle Scholar
  122. Sun G, McNulty SG, Moore Myers JA, Cohen EC (2008) Impacts of multiple stresses on water demand and supply across the Southeastern United States. J Am Water Resour Assoc 44:1441–1457CrossRefGoogle Scholar
  123. Tavernia BG, Nelson MD, Caldwell P, Sun G (2013) Water stress projections for the Northeastern and Midwestern United States in 2060: anthropogenic and ecological consequences. J Am Water Resour Assoc 49:938–952CrossRefGoogle Scholar
  124. Tavernia BG, Nelson MD, Seilheimer TS, Gormanson DD, Perry CH, Caldwell PV, Sun, G (2016) Chapter 6: conservation and maintenance of soil and water resources. In: Shifley SR, Moser, WK (eds) Future forests of the northern United States. U.S. Forest Service, General Technical Report NRS-151, Newtown Square, PA, pp 145–175Google Scholar
  125. Thompson JR, Fallon-Lambert K, Foster DR, Blumstein M, Broadbent EN, Almeyda Zambrano AM (2014) Changes to the land: four scenarios for the future of the Massachusetts landscape. Harvard Forest, Harvard University, Petersham. ISBN: 9780615985268Google Scholar
  126. Thompson JR, Simons-Legaard E, Legaard KR, Domingo JB (2016) A LANDIS-II extension for incorporating land use and other disturbances. Environ Model Softw (in press)Google Scholar
  127. Tiktak A, Van Grinsven HJ (1995) Review of sixteen forest-soil-atmosphere models. Ecol Model 83(1):35–53CrossRefGoogle Scholar
  128. Tilghman NG (1989) Impacts of white-tailed deer on forest regeneration in northwestern Pennsylvania. J Wild Manag 53:524–532CrossRefGoogle Scholar
  129. Troendle CA, Leaf CF (1980) Chapter III, Hydrology. In: U.S. Environmental Protection Agency. An approach to water resources evaluation of non-point silvicultural sources. U.S. Environmental Protection Agency, EPA-600/8-80-012, Athens, GA, pp III.1–III.173Google Scholar
  130. US Fish and Wildlife Service (1973) Endangered Species Act of 1973 as amended through the 108th Congress. Department of the Interior, Washington, DCGoogle Scholar
  131. US Fish and Wildlife Service (1981) Standards for the development of habitat suitability index models for use in the habitat evaluation procedure. Division of Ecological Services Manual, Washington, DCGoogle Scholar
  132. US Forest Service (2016a) Forest Inventory and analysis national program: data and tools. http://www.fia.fs.fed.us/tools-data/. Accessed Feb 2016
  133. US Forest Service (2016b) Forest vegetation simulator: FVS technical support. http://www.fs.fed.us/fmsc/fvs/support/index.shtml. Accessed Feb 2016
  134. Wang WJ, He HS, Fraser JS, Thompson FR, 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
  135. Wang WJ, He HS, Spetich MA, Shifley SR, Thompson FR (2014b) Evaluating forest landscape model predictions using empirical data and knowledge. Environ Model Softw 62:230–239CrossRefGoogle Scholar
  136. Wang WJ, He HS, Spetich MA, Shifley SR, Thompson FR, Larsen DR, Fraser JS, Yang J (2013) A large-scale forest landscape model incorporating multi-scale processes and utilizing forest inventory data. Ecosphere 4(9):106–117CrossRefGoogle Scholar
  137. Wang WJ, He HS, Thompson FR, 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
  138. Wear DN (2011) Forecasts of county-level land uses under three future scenarios: a technical document supporting the Forest Service 2010 RPA Assessment. U.S. Forest Service, Southern Research Station, General Technical Report SRS-141. Asheville, NC, USAGoogle Scholar
  139. Wikipedia contributors (2016a) Moore’s law. Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Moore%27s_law&oldid=704579407. Accessed Feb 2016
  140. Wikipedia contributors (2016b) Transistor count. Wikipedia, The Free Encyclopedia. https://en.wikipedia.org/w/index.php?title=Transistor_count&oldid=704262444. Accessed Feb 2016
  141. 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
  142. Wykoff WR, Crookston NL, Stage AR (1982) User’s guide to the Stand Prognosis Model. U.S. Forest Service, Intermountain Forest and Range Experiment Station, General Technical Report INT-133. Ogden, UT, USAGoogle Scholar
  143. Yoda K, Kira T, Ogawa H, Hozumi K (1963) Self-thinning in overcrowded pure stands under cultivated and natural conditions. J Biol 14:107–129Google Scholar
  144. Zollner PA, Gustafson EJ, He HS, Radeloff VC, Mladenoff DJ (2005) Modeling the influence of dynamic zoning of forest harvesting on ecological succession in a Northern Hardwoods landscape. Environ Manag 35:410–425CrossRefGoogle Scholar
  145. Zurbriggen N (2013) Avalanche disturbance and regeneration in mountain forests under climate change: experimental and modeling approaches. PhD Dissertation. Swiss Federal Institute of Technology Zürich (ETHZ), Zürich. http://e-collection.library.ethz.ch/eserv/eth:7282/eth-7282-01.pdf#search=%22Zurbriggen%22
  146. Zurbriggen N, Nabel JEMS, Teich M, Bebi P, Lischke H (2014) Explicit avalanche-forest feedback simulations improve the performance of a coupled avalanche-forest model. Ecol Complex 17:56–66CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. (outside the USA) 2017

Authors and Affiliations

  • Stephen R. Shifley
    • 1
    Email author
  • Hong S. He
    • 2
  • Heike Lischke
    • 3
  • Wen J. Wang
    • 2
  • Wenchi Jin
    • 2
  • Eric J. Gustafson
    • 4
  • Jonathan R. Thompson
    • 5
  • Frank R. ThompsonIII
    • 1
  • William D. Dijak
    • 1
  • Jian Yang
    • 6
  1. 1.Northern Research StationUSDA Forest Service, University of MissouriColumbiaUSA
  2. 2.School of Natural ResourcesUniversity of MissouriColumbiaUSA
  3. 3.Dynamic Macroecology, Landscape DynamicsSwiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
  4. 4.Institute of Applied Ecosystem Studies, Northern Research StationUSDA Forest ServiceRhinelanderUSA
  5. 5.Harvard ForestPetershamUSA
  6. 6.Department of ForestryUniversity of KentuckyLexingtonUSA

Personalised recommendations