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Modelling forest regeneration strategies through the development of a spatio-temporal growth interaction model

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Abstract

Forest regeneration methods are an important factor in the generation of future forest spatial patterns. However, the difficulty of obtaining experimental data to study such interfering operations, mainly due the long life-span of tree species, has limited the analysis of these key silvicultural tools. Stochastic simulation of such real forest scenarios (i.e. simulated forest) is, therefore, a practical alternative to analyse such prescribed treatments. Here we adapt the Renshaw and Särkkä model to study forest regeneration strategies following the previous work of Renshaw et al. (Stoch Environ Res Risk Assess, 2008). We show that the highly flexible Renshaw and Särkkä algorithm to generate marked point configurations evolving through continuous time is easily adapted to enable the simulation and the comparative study of different forest regeneration methods. In particular, we consider two important regeneration strategies, namely, the shelterwood and the single-tree selection methods due to their forest importance. The results obtained strongly suggested that combining the birth-growth–spatial interaction model with a spatially explicit regeneration algorithm results in a flexible and realistic mechanism to mimic real forest dynamics.

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References

  • Augspurger CK, Franson SE (1987) Wind dispersal of artificial fruit varying in mass, area, and morphology. Ecology 68:27–42

    Article  Google Scholar 

  • Berger U, Hildenbrandt H (2000) A new approach to spatially explicit modelling of forest dynamics: spacing, ageing and neighbourhood competition of mangrove trees. Ecol Modell 132:287–302

    Article  Google Scholar 

  • Botkin DB, Janak JF, Wallis JR (1972) Some ecological consequences of a computer model of forest growth. J Ecol 60:849–873

    Article  Google Scholar 

  • Chapman DG (1961) Statistical problems in dynamics of exploited fisheries populations. In: Proceedings of the 4th Berkeley Symposium on Mathematics, Statistics and Probability. University of California Press, Berkeley

  • Comas C, Mateu J (2006) Spatial clustering based on structured parent configurations: a perspective from reproducing individuals. Technical Report 111. Universitat Jaume I, Castellon, Spain

  • Comas C, Palah M, Pukkala T, Mateu J (2008) Characterising forest spatial structure through inhomogeneous second order characteristics. Stoch Environ Res Risk Assess (in press)

  • Courbaud B, Goreaud F, Dreyfus Ph, Bonnet FR (2001) Evaluating thinning strategies using a tree distance dependent growth model: some examples based on the CAPSIS software “uneven-aged spruce forests” module. For Ecol Manag 145:15–28

    Article  Google Scholar 

  • Degenhardt A (1999) Description of tree distribution and their development through marked Gibbs processes. Biom J 41:457–470

    Article  Google Scholar 

  • Diggle PJ (2003) Statistical analysis of spatial point patterns. Hodder Arnold

  • Ek AR, Monserud RA (1974) FOREST: a computer model for simulating the growth and reproduction of mixed-species forest stands. Research Paper R2635. University of Wisconsin

  • Frampton VL, Linn MB, Hansing ED (1942) The spread of virus diseases of the yellow type under field conditions. Phytopathology 32:799–808

    Google Scholar 

  • Gerrard DJ (1969) Competition quotient—a new measure of the competition affecting individual forest trees. Michigan State University Agricultural Experimental Station Research Bulletin, No. 20

  • Gracia CC, Burriel JA, Ibànez JJ, Mata T, Vayreda J (2003) Inventari Ecològic Forestal de Catalunya 9. CREAF, Bellaterra

    Google Scholar 

  • Gregory PH (1968) Interpreting plant disease dispersal gradients. Annu Rev Phytopathol 6:189–212

    Article  Google Scholar 

  • Hasenauer H (1994) Ein Einzelbaumwachstumssimulator für ungleichaltrige Fichten-Kiefern- und Buchen-Fichtenmischbestde. Forstliche Schriftenreihe der Universitä für Bodenkultur. Wien. Band 8, p 152

  • Hawkes AG (1971a) Spectra and some self-exciting and mutually exciting point processes. Biometrika 58:83–211 1114

    Article  Google Scholar 

  • Hawkes AG (1971b) Point spectra of some mutually exciting point processes. J R Stat Soc B 20:1–43

    Google Scholar 

  • Hawkes AG (1972) Spectral of some mutually exciting point processes with associated variables. In: Lewis PAW (eds) 1118 Stochastic point processes. Wiley, New York, pp 261–271

    Google Scholar 

  • Kiyosawa S, Shiyomi M (1972) A theoretical evaluation of the effect of mixing resistant variety with susceptible variety for controlling plant diseases. Annu Rev Phytopathol Soc Jpn 38:41–51

    Google Scholar 

  • Lanner RM (1985) On the insensitivity of height growth to spacing. For Ecol Manag 13:143–148

    Article  Google Scholar 

  • Mateu J, Usó JL, Montes F (1998) The spatial pattern of forest ecosystems. Ecol Modell 108:163–174

    Article  Google Scholar 

  • Nathan R, Muller-Landau HC (2000) Spatial patterns of seed dispersal, their determinants and consequences for recruitment. TREE 15:7

    Google Scholar 

  • Okubo A, Levin SA (1989) A theoretical framework for data analysis of wind dispersal of seeds and pollen. Ecology 70:329–338

    Article  Google Scholar 

  • Oliver CD, Larson BC (1996) Forest stand dynamics (Update edition). Willey, NY

  • Pacala SW, Canham CD, Silander JA (1993) Forest models defined by field measurements: I. The design of a northeastern forest simulator. Can J For Res 23:1980–1988

    Article  Google Scholar 

  • Pasquill F (1962) Atmospheric diffusion. Van Nostrand, London

    Google Scholar 

  • Penttinen A, Stoyan D, Henttonen HM (1992) Marked point processes in forest statistics. For Sci 38:806–824

    Google Scholar 

  • Pienaar LV, Turnbull KJ (1973) The Chapman-Richards generalization of von Bertalanffy’s growth model for basal area growth and yield in even-aged stands. For Sci 34:804–808

    Google Scholar 

  • Pretzsch, H (1992) Konzeption und Konstruktion von Wuchsmodellem für Rein- und Mischbestände. Schriftenreihe d. Forstw. Fak. Univ. München, No. 115, p 332

  • Pretzsch H, Biber P, Ďursk J (2002) The single tree-based stand simulator Silva: construction, application and evaluation. For Ecol Manag 162:3–21

    Article  Google Scholar 

  • Prévosto B, Curt T, Gueugnot J, Coquillard P (2000) Modelling mid-elevation Scots pine growth on a volcanic substrate. For Ecol Manag 131:223–237

    Article  Google Scholar 

  • Pukkala T, Kolström T, Miina J (1994) A method for predicting tree dimensions in Scots pine and Norway spruce stands. For Ecol Manag 65:123–134

    Article  Google Scholar 

  • Rathbun SL, Cressie N (1994) A space–time survival point process for a Longleaf pine forest in Southern Georgia. J Am Stat Assoc 89:1164–1174

    Article  Google Scholar 

  • Renshaw E, Särkkä A (2001) Gibbs point processes for studying the development of spatial-temporal stochastic processes. Comput Stat Data Anal 36:85–105

    Article  Google Scholar 

  • Renshaw E, Comas C, Mateu J (2008) Analysis of forest thinning strategies through the development of space–time growth-interaction simulation models. Stoch Environ Res Risk Assess (in press)

  • Richards FJ (1959) A flexible growth function for empirical use. J Exp Bot 10:290–300

    Article  Google Scholar 

  • Särkkä A, Tomppo E (1998) Modelling interactions between trees by means of field observations. For Ecol Manag 108:57–62

    Article  Google Scholar 

  • Särkkä A, Renshaw E (2006) The analysis of marked point patterns evolving through space and time. Comput Stat Data Anal 51:1678–1718

    Article  Google Scholar 

  • Shugart HH (1984) A theory of forest dynamics. Springer, New York

    Google Scholar 

  • Shugart HH, West DC (1977) Development of an Appalachian deciduous forest succession models and its application to assessment of the impact of chestnut blight. J Environ Manag 5:161–179

    Google Scholar 

  • Smith DM, Larson BC, Kelty MJ, Ashton PMS (1997) The practice of silviculture; applied forest ecology. Wiley, New York

    Google Scholar 

  • Staebler GR (1951) Growth and spacing in an even-aged stand of Douglas-fir. Dissertation, University of Michigan

  • Stoyan D, Kendall WS, Mecke J (1995) Stochastic geometry and its applications. Wiley, New York

    Google Scholar 

  • Teck R, Moeur M, Eav B (1996) Forecasting ecosystems with the forest vegetation simulator. J For 94:7–10

    Google Scholar 

  • Tubbs CH, Oberg RR (1978) How to calculate size-class distribution for all-age forests. USFS, North Central Forest Experiment Station, St. Paul, p 5

    Google Scholar 

  • von Bertalanffy L (1949) Problems of organic growth. Nature 163:156–158

    Article  CAS  Google Scholar 

  • von Gadow K, Hui GY (1998) Modelling forest development. Forestry science. Kluwer, Dordrecht

Download references

Acknowledgments

We are grateful to the Editor, AE and two anonymous referees whose comments and suggestions have clearly improved an earlier version of the manuscript. C. Comas was supported during 2008 by a “Beatriu de Pinós” contract from the “Comissionat per a la Universitat i Recerca del Dep. d’ Innovació, Universitats i Empresa de la Generalitat de Catalunya”.

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  1. Department of Statistics and Operation Research, Universitat Politècnica de Catalunya, Barcelona, Spain

    Carlos Comas

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  1. Carlos Comas
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Correspondence to Carlos Comas.

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Comas, C. Modelling forest regeneration strategies through the development of a spatio-temporal growth interaction model. Stoch Environ Res Risk Assess 23, 1089–1102 (2009). https://doi.org/10.1007/s00477-008-0282-y

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