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Research Methods of Timber-Yielding Plants (in the Example of Boreal Forests)

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Biology, Productivity and Bioenergy of Timber-Yielding Plants

Part of the book series: SpringerBriefs in Plant Science ((BRIEFSPLANT))

Abstract

Many methods for the study of timber-yielding plants exist. These methods encompass a variety of issues. They are based on traditional approaches and approaches of nonlinear dynamics. The greatest attention is paid to the study of forest stand biomass. Regression method is considered the most accurate and versatile. The set of functions is available to approximate tree biomass depending on its diameter. Allometric function is the most biologically driven. However, all these biomass functions belong to the regression method. They cannot claim to be a universal dependency, which can be used in a wide age range and geographic range. Therefore, prediction on this basis is not accurate. Logistics systems of equations are more versatile. Our research has shown that the use of systems of differential equations gives good results for the study of the joint growth of two wood species. This approach allows one to predict the role of valuable wood species in the forest stand structure in the process of reforestation. It is useful for the planning of forest management and environmental measures. However, the gap between the mathematical and experimental ecology continues to exist. The development of new universal methods of forecasting the state of timber-yielding plants and their ecosystems is still relevant.

The original version of this chapter was revised. An erratum to this chapter can be found at DOI 10.1007/978-3-319-61798-5_3

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References

  • Acevedo MA, Marcano M, Fletcher RJ (2012) A diffusive logistic growth model to describe forest recovery. Ecol Model 244:13–19. doi:10.1016/j.ecolmodel.2012.07.012

    Article  Google Scholar 

  • Arnold VI (1992) Catastrophe theory, 3rd edn. Springer, Berlin

    Book  Google Scholar 

  • Bartelink H (2000) A growth model for mixed forest stands. For Ecol Manag 134(1–3):29–43

    Article  Google Scholar 

  • Bazykin AP (1985) Mathematical biophysics of interacting populations. Nauka, Moscow

    Google Scholar 

  • Boisvenue C, Running SW (2006) Impacts of climate change on natural forest productivity—evidence since the middle of the 20th century. Glob Chang Biol 12(5):862–882. doi:10.1111/j.1365-2486.2006.01134.x

    Article  Google Scholar 

  • Bugmann H (2001) A review of forest gap models. Clim Chang 51(3–4):259–305

    Article  Google Scholar 

  • Bugmann H, Yan X, Sykes M, Martin P, Lindner M, Desanker P, Cumming S (1996) A comparison of forest gap models: model structure and behaviour. Clim Chang 34(2):289–313

    Google Scholar 

  • Bystrai GP, Ivanova NS (2010) Approaches to the modelling of forest vegetation on the base of the theory of catastrophes. Agrar Bull Ural 2:75–79

    Google Scholar 

  • Clark DB, Kellner JR (2012) Tropical forest biomass estimation and the fallacy of misplaced concreteness. J Veg Sci 23(6):1191–1196. doi:10.1111/j.1654-1103.2012.01471.x

    Article  Google Scholar 

  • Cobb L (1981) Parameter estimation for the cusp catastrophe model. Behav Sci 26(1):75–78

    Article  Google Scholar 

  • Cobb L, Koppstein P, Chen N (1983) Estimation and moment recursion relations for multimodal distributions of the exponential family. J Am Stat Assoc 78(381):124–130

    Article  Google Scholar 

  • Deakin M (1980) Applied catastrophe theory in the social and biological sciences. Bull Math Biol 42(5):647–679

    Article  Google Scholar 

  • Ermakova M (2008) Characteristics of scots pine trees (Pinus sylvestris L.) of the first age class with defects form the trunk. Agrar Bull Urals 12:81–84

    Google Scholar 

  • Evans AM, Perschel RT, Kittler BA (2013) Overview of forest biomass harvesting guidelines. J Sustain For 32(1–2):89–107. doi:10.1080/10549811.2011.651786

    Article  Google Scholar 

  • Forest Communities Study Methods (2002) Chemistry R&D Institute of St. Petersburg State University, St. Petersburg, p 240

    Google Scholar 

  • Gilmore R (1993) Catastrophe theory for scientists and engineers. Dover, New York

    Google Scholar 

  • Global Forest Resources Assessment (2005) Progress towards sustainable forest management. 2006. FAO Forestry 147. ISBN: 92-5-105481-9

    Google Scholar 

  • Grasman R, Van Der Maas H, Wagenmakers EJ (2009) Fitting the cusp catastrophe in R: a cusp package primer. J Stat Softw 32(8):1–27. Publisher: American Statistical Association

    Article  Google Scholar 

  • Guts AK, Volodchenkova LA (2012) Cybernetics of forest ecosystem catastrophes. KAN Publ, Omsk, p 220

    Google Scholar 

  • Hoffmann CW, Usoltsev VA (2002) Tree-crown biomass estimation in forest species of the Ural and of Kazakhstan. For Ecol Manag 158(1–3):59–69. doi:10.1016/S0378-1127(00)00669-1

    Article  Google Scholar 

  • Hope JCE (2003) Modelling forest landscape dynamics in Glen Affric, northern Scotland. Ph.D. Thesis. University of Stirling, 317 p

    Google Scholar 

  • Ivanova NS, Bystrai GP (2010) Model of the formation of the tree layer structure on cuttings. Part 1. The control parameters. Agrar Bull Urals 5:85–89

    Google Scholar 

  • Ivanova NS (2014) Recovery of tree stand after clear-cutting in the Ural Mountains. Int J Bioresour Stress Manag 5(1):90–92

    Article  Google Scholar 

  • Ivanova NS, Zolotova ES (2014) Development of forest typology in Russia. Int J Bioresour Stress Manag 5(2):298–303

    Article  Google Scholar 

  • Ivanova NS (2016) Catastrophe theory. In: Maiti R, Rodriguez HG, Kumari A (eds) Applied biology of woody plants. American Academic Press, New York, pp 339–363

    Google Scholar 

  • Iziumsky PP (1972) Valuation of the thin wood. Forest industry. Moscow, 88 p

    Google Scholar 

  • Kern C, Montgomery R, Reich P, Strong T (2013) Canopy gap size influences niche partitioning of the ground-layer plant community in a northern temperate forest. J Plant Ecol 6(1):101–112

    Article  Google Scholar 

  • Kolesnikov BP, Zubareva RS, Smolonogov EP (1973) Forest vegetation conditions and forest types of the Sverdlovsk region. UNTS of Academy of Science of the USSR, Sverdlovsk, p 176

    Google Scholar 

  • Laubhann D, Sterba H, Reinds G, De Vries W (2009) The impact of atmospheric deposition and climate on forest growth in European monitoring plots: an individual tree growth model. For Ecol Manag 258:1751–1761

    Article  Google Scholar 

  • Lischke H, Bolliger J, Seppelt R (2007) Dynamic spatio-temporal landscape models. In: A changing world, pp 273–296

    Google Scholar 

  • Lischke H, Zimmermann N, Bolliger J, Rickebusch S, Löffler T (2006) TreeMig: a forest-landscape model for simulating spatio-temporal patterns from stand to landscape scale. Ecol Model 199:409–420

    Article  Google Scholar 

  • Lotka AJ (1925) Elements of physical biology. Williams and Wilkins, Baltimore

    Google Scholar 

  • Lu D (2006) The potential and challenge of remote sensing‐based biomass estimation. Int J Remote Sens 27(7):1297–1328. doi:10.1080/01431160500486732

    Article  Google Scholar 

  • Madgwick HAI, Satoo T (1975) On estimating the aboveground weights of tree stands. Ecology 56(6):1446–1450

    Article  Google Scholar 

  • Maiti R, Rodriguez H, Ivanova N (2016) Autoecology and ecophysiology of woody shrubs and trees: concepts and applications. Wiley, Chichester, p 352

    Book  Google Scholar 

  • Mladenoff D (2004) LANDIS and forest landscape models. Ecol Model 180:7–19

    Article  Google Scholar 

  • Mugasha WA, Mwakalukwa EE, Luoga E, Malimbwi RE, Zahabu E, Silayo DS, Sola G, Crete P, Henry M, Kashindye A (2016) Allometric models for estimating tree volume and aboveground biomass in lowland forests of Tanzania. Int J For Res. 1–13. doi: 10.1155/2016/8076271

  • Munro DD (1974) Forest growth models: a prognosis. In: Fries J (ed) Growth models for tree and stand simulation. Royal College of Forestry, Stockholm, pp 7–21

    Google Scholar 

  • Næsset E, Gobakken T (2008) Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser. Remote Sens Environ 112(6):3079–3090. doi:10.1016/j.rse.2008.03.004

    Article  Google Scholar 

  • Neeff T, Dos Santos J (2005) A growth model for secondary forest in Central Amazonia. For Ecol Manag 216:270–282

    Article  Google Scholar 

  • Newnham RM (1964) The development of a stand model for Douglas fir. Unpublished Ph.D. Thesis, The University of British Columbia, Vancouver, Canada

    Google Scholar 

  • Oliva T, Desarbo W, Day D, Jedidi K (1987) GEMCAT: a general multivariate methodology for estimating catastrophe models. Behav Sci 32(2):121–137

    Article  Google Scholar 

  • Poorter H, Jagodzinski AM, Ruiz-Peinado R, Kuyah S, Luo Y (2015) How does biomass distribution change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytol 208(3):736–749. doi:10.1111/nph.13571

    Article  PubMed  PubMed Central  Google Scholar 

  • Poston T, Stewart I (1996) Catastrophe theory and its applications. Dover, New York

    Google Scholar 

  • Risch A, Heiri C, Bugmann H (2005) Simulating structural forest patterns with a forest gap model: a model evaluation. Ecol Model 181:161–172

    Article  Google Scholar 

  • Schlicht R, Iwasa Y (2004) Forest gap dynamics and the Ising model. J Theor Biol 230(1):65–75

    Article  PubMed  Google Scholar 

  • Siry JP, Cubbage FW, Ahmed MR (2005) Sustainable forest management: global trends and opportunities. Forest Policy Econ 7(4):551–561. doi:10.1016/j.forpol.2003.09.003

    Article  Google Scholar 

  • Somogyi Z, Cienciala E, Mäkipää R, Muukkonen P, Lehtonen A, Weiss P (2007) Indirect methods of large-scale forest biomass estimation. Eur J For Res 126(2):197–207. doi:10.1007/s10342-006-0125-7

    Article  Google Scholar 

  • Stegen JC, Swenson NG, Enquist BJ, White EP, Phillips OL, Jørgensen PM, Weiser MD, Mendoza AM, Vargas PN (2011) Variation in above-ground forest biomass across broad climatic gradients. Glob Ecol Biogeogr 20(5):744–754. doi:10.1111/j.1466-8238.2010.00645.x

    Article  Google Scholar 

  • Stupak I, Lattimore B, Titus BD, Smith CT (2011) Criteria and indicators for sustainable forest fuel production and harvesting: a review of current standards for sustainable forest management. Biomass Bioenergy 35(8):3287–3308. doi:10.1016/j.biombioe.2010.11.032

    Article  Google Scholar 

  • Teobaldelli M, Somogyi Z, Migliavacca M, Usoltsev VA (2009) Generalized functions of biomass expansion factors for conifers and broadleaved by stand age, growing stock and site index. For Ecol Manag 257(3):1004–1013. doi:10.1016/j.foreco.2008.11.002

    Article  Google Scholar 

  • Thom R, Zeeman EC (1975) Catastrophe theory: its present state and future perspectives. In: Manning A (ed) Dynamical systems: lecture notes in mathematics, vol 468(4). Springer, Berlin, pp 366–389

    Google Scholar 

  • Usoltsev VA (1997) Bioecological aspects of valuation of trees phytomass. Ural Branch of the Russian Academy of Sciences, Yekaterinburg, p 216

    Google Scholar 

  • Usoltsev VA (2001) Forest biomass of northern Eurasia: database and geography. Ural Branch of the Russian Academy of Sciences, Yekaterinburg, p 541

    Google Scholar 

  • Usoltsev VA (2007) Biological productivity of northern Eurasia’s forests: methods, datasets, applications. Ural Branch of the Russian Academy of Sciences, Yekaterinburg, p 637

    Google Scholar 

  • Vanclay J (1994) Modelling forest growth and yield: applications to mixed tropical forests. CAB International, Wallingford, p 312

    Google Scholar 

  • Wagenmakers EJ, Molenaar PCM, Grasman RPPP, Hartelman PAI, van der Maas HLJ (2005) Transformation invariant stochastic catastrophe theory. Physica 211:263–276

    Google Scholar 

  • Xi W et al (2009) Review of forest landscape models: types, methods, development and applications. Acta Ecol Sin 2(1):69–78

    Article  Google Scholar 

  • Wintle B, Bekessy S, Venier L, Pearce J, Chisholm R (2005) Utility of dynamic-landscape metapopulation models for sustainable forest management. Conserv Biol 19(6):1930–1943

    Article  Google Scholar 

  • Yamamoto S (2000) Forest gap dynamics and tree regeneration. J For Res 5(4):223–229

    Article  Google Scholar 

  • Zeeman EC (1976) Catastrophe theory. Sci Am 234:65–83

    Article  Google Scholar 

  • Zianis D, Mencuccini M (2003) On simplifying allometric analyses of forest biomass. For Ecol Manag 187(2–3):311–332. doi:10.1016/j.foreco.2003.07.007

    Google Scholar 

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  1. Botanical Garden of the Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia

    Natalya Ivanova

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Ivanova, N. (2017). Research Methods of Timber-Yielding Plants (in the Example of Boreal Forests). In: Biology, Productivity and Bioenergy of Timber-Yielding Plants. SpringerBriefs in Plant Science. Springer, Cham. https://doi.org/10.1007/978-3-319-61798-5_2

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