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European Journal of Forest Research

, Volume 124, Issue 2, pp 143–153 | Cite as

Modelling mortality of Scots pine (Pinus sylvestris L.) plantations in the northwest of Spain

  • Ulises Diéguez-ArandaEmail author
  • Fernando Castedo-Dorado
  • Juan Gabriel Álvarez-González
  • Roque  Rodríguez-Soalleiro
Original Paper

Abstract

Mortality is an important element of growth and yield models, especially if only low intensity silvicultural treatments are carried out. The objective of the present study was to develop a model for predicting tree number decline in planted even-aged stands of Scots pine (Pinus sylvestris L.) in Galicia (northwestern Spain). The model was constructed using data from two inventories of a trial network involving 68 permanent plots located in unthinned stands, or stands thinned lightly from below. Two alternatives were tested. In one alternative, a two-step modelling strategy was applied. First, a binary response function predicting the survival probability of all the trees in the stand was constructed, and an equation for reduction in tree number was developed, using only data where death had occurred over the period analyzed. Three different approaches were then used to compare the application of the above-mentioned functions together. In the other alternative, a mortality function for directly predicting the reduction in tree number was fitted, including all plots (with and without occurrence of mortality). Both alternatives provided similar results, showed logical behavior, and performed satisfactorily in evaluation tests. However, in choosing the best strategy for inclusion in a stand-level simulator, the use of the second alternative is recommended because it possesses the path invariance property required in a mortality model.

Keywords

Mortality Pinus sylvestris Logistic regression Tree-number reduction Even-aged forest stand 

Notes

Acknowledgements

Funding for this research was provided by the Ministry of Science and Technology through project AGL2001-3871-C02-01 “Crecimiento y evolución de masas de pinar en Galicia”. Helpful review comments were provided by three anonymous referees. Christine Francis checked the English.

References

  1. Alenius V, Hökkä H, Salminen H, Jutras S (2003) Evaluating estimation methods for logistic regression in modelling individual-tree mortality. In: Amaro A, Reed D, Soares P (eds) Modelling forest systems. CAB International, Wallingford, pp 225–236Google Scholar
  2. Álvarez González JG, Castedo Dorado F, Ruiz González AD, López Sánchez CA, Gadow Kv (2004) A two-step mortality model for even-aged stands of Pinus radiata D. Don in Galicia (northwestern Spain). Ann For Sci 61:439–448Google Scholar
  3. Amateis RL, Burkhart HE, Jiping L (1997) Modeling survival in juvenile and mature loblolly pine plantations. For Ecol Manage 90:51–58Google Scholar
  4. Avila OB, Burkhart HE (1992) Modeling survival of loblolly pine trees in thinned and unthinned plantations. Can J For Res 22:1878–1882Google Scholar
  5. Bailey RL, Borders BE, Ware KD, Jones EP (1985) A compatible model for slash pine plantation survival to density, age, site index, and type and intensity of thinning. For Sci 31:180–189Google Scholar
  6. Botkin DB (1993) Forest dynamics: an ecological model. Oxford University Press, OxfordGoogle Scholar
  7. Buchman RG (1979) Mortality functions. In: A generalized forest growth projection system applied to the Lake States region, USDA For Serv Gen Tec Rep NC-49, pp 47–55Google Scholar
  8. Burgman M, Incoll W, Ades P, Ferguson I, Fletcher T, Wholers A (1994) Mortality models for mountain and alpine ash. For Ecol Manage 67:319–327Google Scholar
  9. Burnham KP, Anderson DR (1998) Model selection and inference: a practical information-theoretic approach. Springer, Berlin Heidelberg New YorkGoogle Scholar
  10. Clutter JL, Jones EP (1980) Prediction of growth after thinning in old-field slash pine plantations. USDA Forest Service Research Paper SE-217Google Scholar
  11. Clutter JL, Fortson JC, Pienaar LV, Brister GH, Bailey RL (1983) Timber management: a quantitative approach. Krieger, New YorkGoogle Scholar
  12. Clutter JL, Harms WR, Brister GH, Rheney JW (1984) Stand structure and yields of site-prepared loblolly pine plantations in the lower coastal plain of the Carolinas, Georgia, and north Florida. USDA Forest Service General Technical Report SE-27Google Scholar
  13. Crookston NL (1990) User’s guide to the event monitor: part of prognosis model version 6. USDA Forest Service General Technical Report INT-275Google Scholar
  14. Davis LS, Johnson KN, Bettinger PS, Howard TE (2001) Forest management: to sustain ecological, economic, and social values. McGraw-Hill, New YorkGoogle Scholar
  15. Dick MG (2001) Keyword reference guide for the forest vegetation simulator. USDA Forest Service, Forest Management Service Center, Ft. Collins, COGoogle Scholar
  16. Diéguez-Aranda U, Álvarez González JG, Barrio Anta M, Rojo Alboreca A (2004) Site index equations for Pinus sylvestris L. plantations in Galicia (north-western Spain). Ann For Sci (in press) Google Scholar
  17. Eid T, Øyen B-H (2003) Models for prediction of mortality in even-aged forest. Scand J For Res 18:64–77Google Scholar
  18. Eid T, Tuhus E (2001) Models for individual tree mortality in Norway. For Ecol Manage 154:69–84Google Scholar
  19. Gadow Kv (1987) Untersuchungen zur Konstruktion von Wuchsmodellen für schnellwüchsige Plantagenbaumarten. Schr Forstwissensch Fak Univ München Bayer Forstl Versuchs Forschungsanst 77:147Google Scholar
  20. Gadow Kv, Hui G (1999) Modelling forest development. Kluwer, DordrechtGoogle Scholar
  21. Hamilton DA (1986) A logistic model of mortality in thinned and unthinned mixed conifer stands in northern Idaho. For Sci 32:989–1000Google Scholar
  22. Hamilton DA, Brickell JE (1983) Modeling methods for a two-stage system with continuous responses. Can J For Res 13:1117–1121Google Scholar
  23. Hamilton DA, Edwards BM (1976) Modeling the probability of individual tree mortality. USDA Forest Service Research Paper INT-185Google Scholar
  24. Hartley HO (1961) The modified Gauss-Newton method for the fitting of nonlinear regression functions by least squares. Technometrics 3:269–280 Google Scholar
  25. Hosmer DW, Lemeshow S (2000) Applied logistic regression. In: Wiley series in probability and statistics. Wiley, New YorkGoogle Scholar
  26. Huang S, Yang Y, Wang Y (2003) A critical look at procedures for validating growth and yield models. In: Amaro A, Reed D, Soares P (eds) Modelling forest systems. CAB International, Wallingford, pp 271–293Google Scholar
  27. Hynynen J (1993) Self-thinning models for even-aged stands of Pinus sylvestris, Picea abies and Betula pendula. Scand J For Res 8:326–336Google Scholar
  28. Jutras S, Hökkä H, Alenius V, Salminen H (2003) Modeling mortality of individual trees in drained peatland sites in Finland. Silva Fennica 37:235–251Google Scholar
  29. Laar Av, Akça A (1997) Forest mensuration. Cuvillier Verlag, GöttingenGoogle Scholar
  30. Lynch TB, Hitch KL, Huebschmann MM, Murphy PA (1999) An individual-tree growth and yield prediction system for even-aged natural shortleaf forests. South J Appl For 23:203–211Google Scholar
  31. McDill ME, Amateis RL (1992) Measuring forest site quality using the parameters of a dimensionally compatible height growth function. For Sci 38:409–429Google Scholar
  32. Monserud RA (1976) Simulation of forest tree mortality. For Sci 22:438–444Google Scholar
  33. Monserud RA, Sterba H (1999) Modeling individual tree mortality for Austrian forest species. For Ecol Manage 113:109–123Google Scholar
  34. Murty D, McMurtrie RE (2000) The decline of forest productivity as stands age: a model-based method for analysing causes for the decline. Ecol Model 134:185–205Google Scholar
  35. Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78:691–692Google Scholar
  36. Neter J, Maynes SE (1970) On the appropriateness of the correlation coefficient with 0, 1 dependent variable. J Am Stat Assoc 65:501–509Google Scholar
  37. Neter J, Wasserman W, Kutner MH (1989) Applied linear regression models. Richard D. Irwin, BostonGoogle Scholar
  38. Øyen B-H (2000) Mortality of Norwegian spruce and pine forests. Skogforsk Res Pap 3/00Google Scholar
  39. Peet RK, Christensen NL (1987) Competition and tree death. Bioscience 37:586–595Google Scholar
  40. Peschel W (1938) Die mathematischen Methoden zur Herleitung der Wachstumsgesetze von Baum und Bestand und die Ergebnisse ihrer Anwendung. Tharandt Forstl Jahrb 89:169–247Google Scholar
  41. Pienaar LV, Shiver BD (1981) Survival functions for site-prepared slash pine plantations in the flatwoods of Georgia and northern Florida. South J Appl For 5:59–62Google Scholar
  42. Pienaar LV, Page H, Rheney JW (1990) Yield prediction for mechanically site-prepared slash pine plantations. South J Appl For 14:104–109Google Scholar
  43. Reynolds MRJr (1984) Estimating the error in model predictions. For Sci 30:454–469Google Scholar
  44. Ryan TP (1997) Modern regression methods. Wiley, New YorkGoogle Scholar
  45. SAS Institute Inc. (1999) SAS OnLineDoc Version 8, http://v8doc.sas.com/sashtml/. Cited 10 November 2003
  46. SAS Institute Inc. (2000) SAS/STAT User’s Guide, Version 8. Cary, NCGoogle Scholar
  47. Shen G, Moore JA, Hatch CR (2000) The effect of nitrogen fertilization, rock type, and habitat type on individual tree mortality. For Sci 47:203–213Google Scholar
  48. Smith DM, Larson BC, Kelty MJ, Ashton PMS (1997) The practice of silviculture: applied forest ecology, 9th edn. Wiley, New YorkGoogle Scholar
  49. Tomé M, Falcao A, Amaro A (1997) Globulus v.1.0.0: a regionalized growth model for Eucalyptus plantations in Portugal. In: Ortega A, Gezan S (eds) IUFRO conference: modelling growth of fast-grown tree species, 5–7 September Google Scholar
  50. Vanclay JK (1991) Mortality functions for north Queensland rain forests. J Trop For Sci 4:15–36Google Scholar
  51. Vanclay JK (1994) Modelling forest growth and yield. Applications to mixed tropical forests. CAB International, WallingfordGoogle Scholar
  52. Vanclay JK (1995) Growth models for tropical forests: a synthesis of models and methods. For Sci 41:7–42Google Scholar
  53. Weber R (1891) Lehrbuch der Forsteinrichtung mit besonderer Berücksichtigung der Zuwachsgesetze der Waldbäume. Springer, Berlin Heidelberg New YorkGoogle Scholar
  54. Weber L, Ek A, Droessler T (1986) Comparison of stochastic and deterministic mortality estimation in an individual tree based stand growth model. Can J For Res 16:1139–1141Google Scholar
  55. Woollons RC (1998) Even-aged stand mortality estimation through a two-step regression process. For Ecol Manage 105:189–195Google Scholar
  56. Yao X, Titus S, MacDonald SE (2001) A generalized logistic model of individual tree mortality for aspen, white spruce, and lodgepole pine in Alberta mixedwood forests. Can J For Res 31:283–291Google Scholar
  57. Zunino CA, Ferrando MT (1997) Modelación del crecimiento y rendimiento de plantaciones de Eucalyptus en Chile. Una primera etapa. In: Ortega A, Gezan S (eds) IUFRO conference: modelling growth of fast-grown tree species, 5–7 September Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • Ulises Diéguez-Aranda
    • 1
    Email author
  • Fernando Castedo-Dorado
    • 2
  • Juan Gabriel Álvarez-González
    • 1
  • Roque  Rodríguez-Soalleiro
    • 3
  1. 1.Departamento de Ingeniería AgroforestalUniversidad de Santiago de Compostela, Escuela Politécnica SuperiorLugoSpain
  2. 2.Departamento de Ingeniería AgrariaUniversidad de León, Escuela Superior y Técnica de Ingeniería AgrariaPonferradaSpain
  3. 3.Departamento de Producción Vegetal Universidad de Santiago de Compostela, Escuela Politécnica SuperiorLugoSpain

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