Landscape Ecology

, Volume 28, Issue 8, pp 1429–1437 | Cite as

When relationships estimated in the past cannot be used to predict the future: using mechanistic models to predict landscape ecological dynamics in a changing world



Researchers and natural resource managers need predictions of how multiple global changes (e.g., climate change, rising levels of air pollutants, exotic invasions) will affect landscape composition and ecosystem function. Ecological predictive models used for this purpose are constructed using either a mechanistic (process-based) or a phenomenological (empirical) approach, or combination. Given the accelerating pace of global changes, it is becoming increasingly difficult to trust future projections made by phenomenological models estimated under past conditions. Using forest landscape models as an example, I review current modeling approaches and propose principles for developing the next generation of landscape models. First, modelers should increase the use of mechanistic components based on appropriately scaled “first principles” even though such an approach is not without cost and limitations. Second, the interaction of processes within a model should be designed to minimize a priori constraints on process interactions and mimic how interactions play out in real life. Third, when a model is expected to make accurate projections of future system states it must include all of the major ecological processes that structure the system. A completely mechanistic approach to the molecular level is not tractable or desirable at landscape scales. I submit that the best solution is to blend mechanistic and phenomenological approaches in a way that maximizes the use of mechanisms where novel driver conditions are expected while keeping the model tractable. There may be other ways. I challenge landscape ecosystem modelers to seek new ways to make their models more robust to the multiple global changes occurring today.


Landscape modeling Forests Disturbances Climate change Global changes Mechanistic modeling Empirical modeling Phenomenological modeling 


  1. Aber JD, Ollinger SV, Federer CA, Reich PB, Goulden ML, Kicklighter DW, Mellilo JM, Lathrop RG (1995) Predicting the effects of climate change on water yield and forest production in the northeastern US. Clim Change Res 5:207–222Google Scholar
  2. Allen TFH, Hoekstra TW (1992) Toward a unified ecology. Columbia University, New YorkGoogle Scholar
  3. Berliner LM (2003) Physical-statistical modeling in geophysics. J Geophys Res. doi:10.1029/2002JD002865 Google Scholar
  4. Boose ER, Chamberlin KE, Foster DR (2001) Landscape and regional impacts of hurricanes in New England. Ecol Monogr 71:27–48CrossRefGoogle Scholar
  5. Botkin DB, Janak JF, Wallis JR (1972) Some ecological consequences of a computer model of forest growth. J Ecol 60:849–873CrossRefGoogle Scholar
  6. Bugmann H, Lindner M, Lasch P, Flechsig M, Ebert B, Cramer W (2000) Scaling issues in forest succession modeling. Clim Change 44:265–289CrossRefGoogle Scholar
  7. Chew JD, Stalling C, Moeller K (2004) Integrating knowledge for simulating vegetation change at landscape scales. W J Appl For 19:102–108Google Scholar
  8. Crookston NL, Dixon GE (2005) The forest vegetation simulator: a review of its structure, content, and applications. Comp Electron Agric 49:60–80CrossRefGoogle Scholar
  9. Cuddington K, Fortin M-J, Gerber LR, Hastings A, Liebhold A, O’Connor M, Ray C (2013) Process-based models are required to manage ecological systems in a changing world. Ecosphere 4:20 Google Scholar
  10. Cullingham CI, Cooke JEK, Dang S, Davis CS, Cooke BJ, Coltman DW (2011) Mountain pine beetle host-range expansion threatens the boreal forest. Mol Ecol 20:2157–2171. doi:10.1111/j.1365-294X.2011.05086.x PubMedCrossRefGoogle Scholar
  11. Cushman SA, McKenzie D, Peterson DL, Littell J, McKelvey KS (2007) Research agenda for integrated landscape modeling. USDA Forest Service Gen. Tech. Rep. RMRS-194. Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  12. Cushman S, Tzeidle A, Wasserman N, McGarigal K (2011) Modeling landscape fire and wildlife habitat. In: McKenzie D, Miller C, Falk DA (eds), The landscape ecology of fire, Ecological Studies 213, Springer, New York, p 223–245 doi 10.1007/978-94-007-0301-8_9
  13. De Bruijn AMG, Gustafson EJ, Sturtevant B, Jacobs D (in prep) Merging PnET and LANDIS-II to model succession: mechanistic simulation of competition for water and light to project landscape forest dynamics. Ecol ModellingGoogle Scholar
  14. Ek AR, Monserud RA (1974) FOREST: computer model for the growth and reproduction simulation for mixed species forest stands. Research Report A2635, College of Agricultural and Life Sciences, University of Wisconsin, MadisonGoogle Scholar
  15. Friend AD, Schugart HH, Running SW (1993) A physiology-based model of forest dynamics. Ecology 74:792–797CrossRefGoogle Scholar
  16. Fries J (ed) (1974) Growth models for tree and stand simulation. Research Notes 30. Royal College of Forestry, Stockholm, p 397Google Scholar
  17. Grumbine RE (1994) What is Ecosystem Management? Cons Biol 8:27–38CrossRefGoogle Scholar
  18. Gustafson EJ, Crow TR (1996) Simulating the effects of alternative forest management strategies on landscape structure. J Environ Manage 46:77–94CrossRefGoogle Scholar
  19. Gustafson EJ, Shvidenko AZ, Sturtevant BR, Scheller RM (2010) Predicting global change effects on forest biomass and composition in south-central Siberia. Ecol Appl 20:700–715PubMedCrossRefGoogle Scholar
  20. He HS (2008) Forest landscape models: definitions, characterization, and classification. For Ecol Manage 254:484–498CrossRefGoogle Scholar
  21. IPCC (2007) Climate change 2007: the physical science basis. In: Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor Miller MHL (eds) Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University, CambridgeGoogle Scholar
  22. Johnson KN (1992) Consideration of watersheds in long-term forest planning models: the case of FORPLAN and it use on the national forests. In: Naiman RJ (ed) Watershed management: balancing sustainability and environmental change. Springer, New York, pp 347–360Google Scholar
  23. Keane RE, Arno SF, Brown JK (1989) FIRESUM—an ecological process model for fire succession in western conifer forests. USDA Forest Service Gen. Tech. Rep. INT-266. Intermountain Research Station, OgdenGoogle Scholar
  24. Keane RE, Holsinger LM, Pratt SD (2006) Simulating historical landscape dynamics using the landscape fire succession model LANDSUM version 4.0. USDA Forest Service General Tech Rep RMRS-171CD. Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  25. 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 Manage 254:375–389CrossRefGoogle Scholar
  26. Keane RE, Loehman RA, Holsinger LM (2011) The FireBGCv2 landscape fire and succession model: a research simulation platform for exploring fire and vegetation dynamics. USDA Forest Service Gen. Tech. Rep. RMRS-255. Rocky Mountain Research Station, Fort CollinsGoogle Scholar
  27. Keane RE, Miller C, Smithwick E, McKenzie D, Falk D, Kellogg L (in review) Representing climate, disturbance, and vegetation interactions in landscape simulation models. Ecol ModellingGoogle Scholar
  28. Kennedy MC, Ford ED (2011) Using multicriteria analysis of simulation models to understand complex biological systems. Bioscience 61:994–1004CrossRefGoogle Scholar
  29. Kimmins JP, Blanco JA (2011) Issues facing forest management in Canada, and predictive ecosystem management tools for assessing possible futures. In: Li C, Lafortezza R, Chen J (eds) Landscape ecology in forest management and conservation. Higher Education Press/Springer, Bejing/Berlin, pp 46–72CrossRefGoogle Scholar
  30. Kimmins JP, Blanco JA, Seely B, Welham C, Scoullar K (2008) Complexity in modeling forest ecosystems: how much is enough? For Ecol Manage 256:1646–1658CrossRefGoogle Scholar
  31. Korzukhin MD, Ter-Mikaelian MT, Wagner RG (1996) Process versus empirical models: which approach for forest ecosystem management? Can J For Res 26:879–887CrossRefGoogle Scholar
  32. McGarigal K, Romme WH (2012) Modeling historical range of variation at a range of scales: example application. In: Wiens J, Regan C, Hayward G, Safford H (eds) Historical environmental variation in conservation and natural resource management. Wiley, New York, pp 128–145CrossRefGoogle Scholar
  33. Mladenoff DJ (2004) LANDIS and forest landscape models. Ecol Modelling 180:7–19CrossRefGoogle Scholar
  34. Mladenoff DJ, Baker WL (1999) Development of forest and landscape modeling approaches. In: Mladenoff DJ, Baker WL (eds) Spatial modeling of forest landscape change: approaches and applications. Cambridge University, Cambridge UK, pp 1–13Google Scholar
  35. Parslow J, Cressie N, Campbell EP, Jones E, Murray L (2013) Bayesian learning and predictability in a stochastic nonlinear dynamical model. Ecol Appl 23:679–698PubMedCrossRefGoogle Scholar
  36. Perera AH, Yemshanov D, Schnekenburger F, Baldwin DJB, Boychuk D, Weaver K (2004) Spatial simulation of broad-scale fire regimes as a tool for emulating natural forest landscape disturbance. In: Perera AH, Buse LJ, Weber MG (eds) Emulating natural forest landscape disturbances: concepts and applications. Columbia University, New York, pp 112–122Google Scholar
  37. Risser PG, Iverson LR (2013) 30 years later—landscape ecology: directions and approaches. Landscape Ecol 28:367–369CrossRefGoogle Scholar
  38. Running SW, Coughlan JC (1988) A general model of forest ecosystem processes for regional applications I. Hydrologic balance, canopy gas exchange and primary production processes. Ecol Modelling 42:125–154CrossRefGoogle Scholar
  39. Running SW, Hunt ER Jr (1993) Generalization of a forest ecosystem process model for other biomes, BIOME-BGC, and an application for global-scale models. In: Ehleringer JR, Field CB, Roy J (eds) Scaling physiological processes: leaf to globe. Academic Press, San Diego, pp 141–157CrossRefGoogle Scholar
  40. Scheller RM, Mladenoff DJ (2004) A forest growth and biomass module for a landscape simulation model, LANDIS: design, validation, and application. Ecol Modelling 180:211–229CrossRefGoogle Scholar
  41. Scheller RM, Mladenoff DJ (2007) An ecological classification of forest landscape simulation models: tools and strategies for understanding broad-scale forested ecosystems. Landscape Ecol 22:491–505CrossRefGoogle Scholar
  42. Schulte LA, Mladenoff DJ (2005) Severe wind and fire regimes in northern forests; historical variability at the regional scale. Ecology 86:431–445CrossRefGoogle Scholar
  43. Shugart HH, Woodward FI (2011) Global change and the terrestrial biosphere: achievements and challenges. Wiley–Blackwell, Oxford UK 242 pGoogle Scholar
  44. Shugart HH Jr, Crow TR, Hett JM (1973) Forest succession models: a rationale and methodology for modeling forest succession over large regions. For Sci 19:203–212Google Scholar
  45. Suffling R, Perera AH (2004) Characterizing natural forest disturbance regimes. In: Perera AH, Buse LJ, Weber MG (eds) Emulating natural forest landscape disturbances: concepts and applications. Columbia University, New York, pp 43–54Google Scholar
  46. Turner MG (2005) Landscape ecology in North America: past present, and future. Ecology 86:1967–1974CrossRefGoogle Scholar
  47. Urban DL, O’Neill RV, Shugart HH Jr (1987) Landscape ecology. Bioscience 37:119–127CrossRefGoogle Scholar
  48. Urban DL, Bonan GB, Smith TM, Schugart HH (1991) Spatial applications of gap models. For Ecol Manag 42:95–110CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht (outside the USA) 2013

Authors and Affiliations

  1. 1.Institute for Applied Ecosystem Studies, Northern Research StationUSDA Forest ServiceRhinelanderUSA

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