Continuous Cover Forestry pp 229-241

Part of the Managing Forest Ecosystems book series (MAFE, volume 23) | Cite as

Modelling Continuous Cover Forests

Chapter

Abstract

Many well established techniques such as yield tables and age-based growth models are not applicable in continuous cover forestry (CCF). A further complexity in CCF is the need to predict regeneration. It is possible to model CCF using stand-based approaches such as transition matrices and stand table projection but the utility of these approaches is limited. Individual-based approaches are the most promising and may take two forms: cohort-based and single-tree models. While the utility and limitations of site indices based on age and dominant height is well established for plantation situations, there is no dominant paradigm for dealing with site productivity in stands managed as CCF. Alternatives include dendrometric approaches, indicator species and site descriptors such as elevation, slope and soil depth. In even-aged forests, the self-thinning line provides an effective way to estimate mortality in forest stands, but the concept is of limited utility in CCF. With CCF, the best option is to predict individual-tree survival from the resources deemed to be available to each tree. Fertile areas for further research include site productivity assessment and the modelling of regeneration and species interactions.

References

  1. Almeida AC, Landsberg JJ, Sands PJ (2004) Parameterisation of 3-PG model for fast-growing Eucalyptus grandis plantations. For Ecol Manag 193:179–195CrossRefGoogle Scholar
  2. Arney JD (1985) A modeling strategy for the growth projection of managed stands. Can J For Res 15:511–518CrossRefGoogle Scholar
  3. Ball IR, Lindenmayer DB, Possingham HP (1999) A tree hollow dynamics simulation model. For Ecol Manag 123:179–194CrossRefGoogle Scholar
  4. Battaglia M, Sands P (1998) Process-based forest productivity models and their application in forest management. For Ecol Manag 102:13–32CrossRefGoogle Scholar
  5. Bergès L, Gégout J-C, Franc A (2006) Can understory vegetation accurately predict site index? A comparative study using floristic and abiotic indices in sessile oak (Quercus petraea Liebl.) stands in northern France. Ann For Sci 63:31–42CrossRefGoogle Scholar
  6. Bertalanffy Lv (1942) Theoretische Biologie, Band II. Borntraeger, 2nd edn.. Franke, Bern, 1951, BerlinGoogle Scholar
  7. Bigler C, Bugmann H (2004) Assessing the performance of theoretical and empirical tree mortality models using tree-ring series of Norway spruce. Ecol Model 174:225–239CrossRefGoogle Scholar
  8. Binkley CS (1980) Is succession in hardwood forests a stationary Markov process? For Sci 26: 566–570Google Scholar
  9. Blanco JA, Kimmins JP (2009) The simulation of allelopathy in ecosystem-level forest models: a study case in the Pacific Northwest. In: Dykstra DP, Monserud RA (eds) Forest growth and timber quality: crown models and simulation methods for sustainable forest management. Proceedings of an international conference, Portland, Oregon, 7–10 August 2007. General Technical Report – Pacific Northwest Research Station, USDA Forest Service 2009 No. PNW-GTR-791, pp 219–223Google Scholar
  10. Bosch CA (1971) Redwoods: a population model. Science 172:345–349PubMedCrossRefGoogle Scholar
  11. Botkin DB, Janak JF, Wallis JR (1972) Some ecological consequences of a computer model of forest growth. J Ecol 60:849–872CrossRefGoogle Scholar
  12. Bowman DMJS, Kirkpatrick JB (1986) Establishment, suppression and growth of eucalyptus delegatensis R.T. Baker in multiaged forests. II. Sapling growth and its environmental correlates. Aust J Bot 34:73–80CrossRefGoogle Scholar
  13. Bristow M, Vanclay JK, Brooks L, Hunt M (2006) Growth and species interactions of Eucalyptus pellita in a mixed and monoculture plantation in the humid tropics of north Queensland. For Ecol Manag 233:285–294CrossRefGoogle Scholar
  14. Buongiorno J, Michie BR (1980) A matrix model of uneven-aged forest management. For Sci 26(4):609–625Google Scholar
  15. Congleton WR, Pearce BR, Beal BF (1997) A C++ implementation of an individual/landscape model. Ecol Model 103:1–17CrossRefGoogle Scholar
  16. Coops NC, Waring RH, Beier C, Roy-Jauvin R, Wang T (2011) Modeling the occurrence of 15 coniferous tree species throughout the Pacific Northwest of North America using a hybrid approach of a generic process-based growth model and decision tree analysis. Appl Veg Sci. doi:10.1111/j.1654-109X.2011.01125.x
  17. Costanza R, Duplisea A, Kautsky U (1998) Ecological Modelling on modelling ecological and economic systems with STELLA. Ecol Model 110:1–4CrossRefGoogle Scholar
  18. Ek AR, Monserud RA (1974) Trials with program FOREST: growth and reproduction simulation for mixed species even- or uneven-aged forest stands. In: Fries J (ed) Growth Models for Tree and Stand Simulation. Proc. IUFRO Working Party S4.01-4 meetings, 1973. Dep. For. Yield Res., Royal Coll. For., Stockholm. Res. Note 30, pp 56–69Google Scholar
  19. Forrester DI, Bauhus J, Cowie A, Vanclay JK (2006) Mixed-species plantations of Eucalyptus with nitrogen fixing trees: a review. For Ecol Manag 233:211–230CrossRefGoogle Scholar
  20. Forrester DI, Vanclay JK, Forrester RI (2011) The balance between facilitation and competition interactions in mixtures of Eucalyptus and Acacia changes as stands develop. Oecologia 166:265–272PubMedCrossRefGoogle Scholar
  21. Garcia O (2003) Dimensionality reduction in growth models: an example. Forest Biometry Model Inform Sci 1(3):1–15Google Scholar
  22. Groot A (2004) A model to estimate light interception by tree crowns, applied to black spruce. Can J For Res 34(4):788–799CrossRefGoogle Scholar
  23. Hasenauer H (ed) (2006) Sustainable forest management: growth models for Europe. Springer, Berlin, 298 ppGoogle Scholar
  24. Hasenauer H, Merkl D, Weingartner M (2001) Estimating tree mortality of Norway spruce stands with neural networks. Adv Environ Res 5:405–414CrossRefGoogle Scholar
  25. Hawkes C (2000) Woody plant mortality algorithms: description, problems and progress. Ecol Model 126:225–248CrossRefGoogle Scholar
  26. Hegyi F (1974) A simulation model for managing jack-pine stands. In: Fries G (ed) Growth models for tree and stand simulation. Royal College of Forestry, Stockholm, Sweden. Res Note 30: 74–90Google Scholar
  27. Hool JN (1966) A dynamic programming Markov chain approach to forest production control. For Sci Monog 12, 26 pGoogle Scholar
  28. Kobziar L, Moghaddas J, Stephens SL (2006) Tree mortality patterns following prescribed fires in a mixed conifer forest. Can J For Res 36:3222–3238CrossRefGoogle Scholar
  29. Lacointe A (2000) Carbon allocation among tree organs: a review of basic processes and representation in functional-structural tree models. Ann For Sci 57:521–533CrossRefGoogle Scholar
  30. Landsberg JJ (1986) Physiological ecology of forest production. Academic Press, LondonGoogle Scholar
  31. Lemmon PE, Schumacher FX (1962) Stocking density around ponderosa pine trees. For Sci 8: 397–402Google Scholar
  32. Lhotka JM, Loewenstein EF (2011) An individual-tree diameter growth model for managed uneven-aged oak-shortleaf pine stands in the Ozark Highlands of Missouri, USA. For Ecol Manag 261:770–778CrossRefGoogle Scholar
  33. López I, Ortuñoa SF, Martína ÁJ, Fullana C (2007) Estimating the sustainable harvesting and the stable diameter distribution of European beech with projection matrix models. Ann For Sci 64:593–599CrossRefGoogle Scholar
  34. McMurtrie R, Wolf L (1983) Above- and below-ground growth of forest stands: a carbon budget model. Ann Bot 52(4):437–448Google Scholar
  35. Miina J, Heinonen J (2008) Stochastic simulation of forest regeneration establishment using a multilevel multivariate model. For Sci 54:206–219Google Scholar
  36. Miina J, Pukkala T (2000) Using numerical optimization for specifying individual-tree competition models. For Sci 46:277–283Google Scholar
  37. Mitchell KJ (1969) Simulation of the growth of even-aged stands of white spruce. Yale University School of Forestry Bull No. 75, 48 pGoogle Scholar
  38. Monserud RA, Ek AR (1977) Prediction of understorey tree height growth in northern hardwood stands. For Sci 23:391–400Google Scholar
  39. Muetzelfeldt R, Massheder J (2003) The simile visual modelling environment. Eur J Agron 18:345–358CrossRefGoogle Scholar
  40. Newnham RM, Smith JHG (1964) Development and testing of stand models for Douglas fir and lodgepole pine. For Chron 40:494–502Google Scholar
  41. Newton PF (1997) Stand density management diagrams: Review of their development and utility in stand-level management planning. For Ecol Manag 98:251–265CrossRefGoogle Scholar
  42. Ong RC, Kleine M (1995) DIPSIM: a dipterocarp forest growth simulation model for Sabah. FRC Research Papers, No. 2. Forest Research Center, Forestry Department, SabahGoogle Scholar
  43. Pacala SW, Canham CD, Silander JA Jr (1993) Forest models defined by field measurements: I. The design of a northeastern forest simulator. Can J For Res 23:1980–1988CrossRefGoogle Scholar
  44. Pretzsch H, Biber P (2005) A Re-evaluation of Reineke’s rule and stand density index. For Sci 51:304–320Google Scholar
  45. Pretzsch H, Grote R, Reineking B, Rotzer TH, Seifert ST (2008) Models for forest ecosystem management: a European perspective. Ann Bot 101:1065–1087PubMedCrossRefGoogle Scholar
  46. Pukkala T, Moykkynen T, Thor M, Ronnberg J, Stenlid J (2005) Modeling infection and spread of Heterobasidion annosum in even-aged Fennoscandian conifer stands. Can J For Res 35:74–84CrossRefGoogle Scholar
  47. Pukkala T, Lähde E, Laihoc O (2009) Growth and yield models for uneven-sized forest stands in Finland. For Ecol Manag 258(3):207–216CrossRefGoogle Scholar
  48. Ranius T, Niklasson M, Berg N (2009) Development of tree hollows in pedunculate oak (Quercus robur). For Ecol Manag 257:303–310CrossRefGoogle Scholar
  49. Ratkowsky DA (1983) Nonlinear regression modeling. Marcel Dekker, New York/Basel, 276 pGoogle Scholar
  50. Reineke LH (1933) Perfecting a stand density index for even-aged stands. J Agric Res 46:627–638Google Scholar
  51. Reynolds JF, Acock B (1997) Modularity and genericness in plant and ecosystem models. Ecol Model 94:7–16CrossRefGoogle Scholar
  52. Roberts MR, Hruska AJ (1986) Predicting diameter distributions: a test of the stationary Markov model. Can J For Res 16:130–135CrossRefGoogle Scholar
  53. Rojo JMT, Orois SS (2005) A decision support system for optimizing the conversion of rotation forest stands to continuous cover forest stands. For Ecol Manag 207(1–2):109–120CrossRefGoogle Scholar
  54. Rothe A, Binkley D (2001) Nutritional interactions in mixed species forests: a synthesis. Can J For Res 31:1855–1870CrossRefGoogle Scholar
  55. Roxburgh SH, Wood SW, Mackey BG, Woldendorp G, Gibbons P (2006) Assessing the carbon sequestration potential of managed forests: a case study from temperate Australia. J Appl Ecol 43:1149–1159CrossRefGoogle Scholar
  56. Salminen H, Lehtonen M, Hynynen J (2005) Reusing legacy FORTRAN in the MOTTI growth and yield simulator. Comput Electron Agric 49:103–113CrossRefGoogle Scholar
  57. Sands PJ (2004a) 3PGpjs vsn 2.4 – a user-friendly interface to 3-PG, the Landsberg and Waring model of forest productivity. CRC Forestry Technical Report 140Google Scholar
  58. Sands PJ (2004b) Adaptation of 3-PG to novel species: guidelines for data collection and parameter assignment. CRC Forestry Technical Report 141Google Scholar
  59. Sands PJ, Landsberg JJ (2002) Parameterisation of 3-PG for plantation grown Eucalyptus globulus. For Ecol Manag 163:273–292CrossRefGoogle Scholar
  60. Sequeira RA, Sharpe PJH, Stone ND, El-Zik KM, Makela ME (1991) Object-oriented simulation: plant growth and discrete organ to organ interactions. Ecol Model 58:55–89CrossRefGoogle Scholar
  61. Skovsgaard JP, Vanclay JK (2008) Forest site productivity: review of the evolution of dendrometric concepts for even-aged stands. Forestry 81(1):13–31CrossRefGoogle Scholar
  62. Strub MR, Green EJ, Burkhart HE, Pirie WR (1986) Merchantability of loblolly pine – an application of nonlinear regression with a discrete dependent variable. For Sci 32:254–261Google Scholar
  63. Trasobares A, Pukkala T (2004) Using past growth to improve individual-tree diameter growth models for uneven-aged mixtures of Pinus sylvestris L. and Pinus nigra Arn. In Catalonia, north-east Spain. Ann For Sci 61:409–417CrossRefGoogle Scholar
  64. Turner DP, Ollinger SV, Kimball JS (2004) Integrating remote sensing and ecosystem process models for landscape- to regional-scale analysis of the carbon cycle. Bioscience 54:573–584CrossRefGoogle Scholar
  65. van Hulst R (1979) On the dynamics of vegetation: Markov chains as models of succession. Vegetatio 40:3–14CrossRefGoogle Scholar
  66. Vanclay JK (1989) Site productivity assessment in rainforests: an objective approach using indicator species. In: Wan Razali W, Appanah S (eds) Proceedings of the seminar on growth and yield in tropical mixed/moist forests, 20–24 June 1988, Kuala Lumpur. Forest Research Institute Malaysia, pp 225–241Google Scholar
  67. Vanclay JK (1991a) Compatible deterministic and stochastic predictions by probabilistic modelling of individual trees. For Sci 37:1656–1663Google Scholar
  68. Vanclay JK (1991b) Aggregating tree species to develop diameter increment equations for tropical rainforests. For Ecol Manag 42:143–168CrossRefGoogle Scholar
  69. Vanclay JK (1991c) Mortality functions for north Queensland rainforests. J Trop For Sci 4:15–36Google Scholar
  70. Vanclay JK (1991d) Modelling changes in the merchantability of individual trees in tropical rainforest. Commonwealth Forestry Rev 70:105–111Google Scholar
  71. Vanclay JK (1992) Modelling regeneration and recruitment in a tropical rainforest. Can J For Res 22:1235–1248CrossRefGoogle Scholar
  72. Vanclay JK (1994a) Modelling forest growth and yield: applications to mixed tropical forests. CAB International, WallingfordGoogle Scholar
  73. Vanclay JK (1994b) Sustainable timber harvesting: simulation studies in the tropical rainforests of north Queensland. For Ecol Manag 69:299–320CrossRefGoogle Scholar
  74. Vanclay JK (2003) The one-minute modeller: an introduction to simile. Ann Trop Res 25(1):31–44Google Scholar
  75. Vanclay JK (2006) Spatially-explicit competition indices and the analysis of mixed-species plantings with the simile modelling environment. For Ecol Manag 233:295–302CrossRefGoogle Scholar
  76. Vanclay JK (2010) Robust relationships for simple plantation growth models based on sparse data. Forest Ecol Manag 259:194–198CrossRefGoogle Scholar
  77. Vanclay JK, Preston, RA (1990) Utility of Landsat Thematic Mapper data for mapping site productivity in tropical moist forests. Photogramm Eng Rem Sens 56:1383–1388Google Scholar
  78. Vanclay, JK, Skovsgaard, JP (1997) Evaluating forest growth models. Ecol Model 98:1–12CrossRefGoogle Scholar
  79. Vega J, Jimenez E, Vega D, Ortiz L, Perez JR (2011) Pinus pinaster Ait. Tree mortality following wildfire in Spain. Forest Ecol Manag 261:2232–2242CrossRefGoogle Scholar
  80. Vickers LA, Fox TR, Loftis DL, Boucugnani DA (2011) Adaptation and validation of the REGEN expert system for the Central Appalachians. In: Fei S, Lhotka JM, Stringer JW, Gottschalk KW, Miller GW (eds) Proceedings, 17th Central Hardwood Forest Conference. USDA For Serv Gen Tech Rep NRS-P-78, pp 332–340Google Scholar
  81. Weiskittel AR, Hann DW, Kershaw JW, Vanclay JK (2011) Forest growth and yield modeling. Wiley, New YorkCrossRefGoogle Scholar
  82. Wykoff WR (1990) A basal area increment model for individual conifers in the northern Rocky Mountains. For Sci 36:1077–1104Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Southern Cross UniversityLismoreAustralia

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