Modeling the additive stand biomass of Larix spp. for Eurasia

• Autorzy: Usoltsev V.A. , Shobairi S.O.R. , Chasovskikh V.P.
• Języki publikacji: EN

Abstrakty

EN

When using the unique in terms of the volumes of database on the level of a stand of the genus Larix Mill., the trans-Eurasian additive allometric models of biomass for Eurasian larch forests are developed for the first time, and thereby the combined problem of model additivity and generality is solved. The additive model of forest biomass of Larix is harmonized in two levels, one of which provides the principle of additivity of biomass components, and the second one is associated with the introduction of dummy independent variables localizing model for eco-regions of Eurasia. Comparative analysis of the biomass structure of larch stands of different ecoregions at the age of 100 years shows, that the greatest values of biomass (210-450 t/ha) correspond to the regions adjacent to the Atlantic and Pacific coasts, as well as to the regions, located at the southern limit of larch growing area and the lowest – to northern taiga regions of Siberia, where larch grows on permafrost. The biomass indices of different ecoregions differed not only in absolute value but also in biomass ratios of different components; for example, the proportion of needles in the aboveground biomass is maximum (5.0-7.3%) in the northern taiga of Central Siberia and the Far East on permafrost and is minimum (1.4-1.9%) in larch forests of upper productivity having biomass values 210-450 t/ha. The proposed model and corresponding tables for estimating stand biomass makes them possible to calculate larch stand biomass on Eurasian forests when using measuring taxation.

• Wydawca: -
• Czasopismo: Ecological Questions
• Rocznik: 2019
• Tom: 30
• Numer: 1
• Opis fizyczny: p.35-46,fig.,ref.

Twórcy

autor

Usoltsev V.A.

• Botanical Garden, Ural Branch, Russian Academy of Sciences, 8 Marta 202a St, Yekaterinburg, 620144 Russia
• Ural State Forest Engineering University, Sibirskii Trakt 37 St, Yekaterinburg, 620100 Russia

autor

Shobairi S.O.R.

• Ural State Forest Engineering University, Sibirskii Trakt 37 St, Yekaterinburg, 620100 Russia

autor

Chasovskikh V.P.

• Ural State Forest Engineering University, Sibirskii Trakt 37 St, Yekaterinburg, 620100 Russia

Bibliografia

• Baskerville G.L., 1972, Use of Logarithmic Regression in the Estimation of Plant Biomass. Canadian Journal of Forest Research 2(1): 49-53.
• Bi H., Long Y., Turner J., Lei Y., Snowdon P., Li Y., Harper R., Zerihun A. & Ximenes F., 2010, Additive prediction of aboveground biomass for Pinus radiata (D. Don) plantations. Forest Ecology and Management 259: 2301-2314.
• Bi H., Murphy S., Volkova L., Weston Ch., Fairman T., Li Y., Law R., Norris J., Lei X. & Caccamo G., 2015, Additive biomass equations based on complete weighing of sample trees for open eucalypt forest species in south-eastern Australia. Forest Ecology and Management 349: 106-121.
• Bi H., Turner J. & Lambert M.J., 2004, Additive biomass equations for native eucalypt forest trees of temperate Australia. Trees 18: 467-479.
• Bobrov E.G., 1978, Forest-forming conifers of the USSR. Nauka Publishing, Leningrad (in Russian).
• Carvalho J.P. & Parresol B.R., 2003, Additivity in tree biomass components of Pyrenean oak (Quercus pyrenaica Willd.). Forest Ecology and Management 179: 269-276.
• Case B.S. & Hall R.J., 2008, Assessing prediction errors of generalized tree biomass and volume equations for the boreal forest region of west-central Canada. Canadian Journal of Forest Research 38: 878-889.
• Chave J., Andalo C., Brown S., Cairns M.A., Chambers J.Q., Eamus D., Folster H., Fromard F., Higuchi N., Kira T., Lescure J.P., Nelson B.W., Ogawa H., Puig H., Riera B. & Yamakura T., 2005, Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia 145: 87-99.
• Chave J., Réjou-Méchain M., Búrque, A., Chidumayo E., Colgan M.S., Delitti W.B.C., Duque A., Eid T., Fearnside P.M., Goodman R.C., Henry M., Martínez-Yrízar A., Mugasha W.A., Muller-Landau H.C., Mencuccini M., Nelson B.W., Ngomanda A., Nogueira E.M., Ortiz-Malavassi E., Pélissier R., Ploton P., Ryan C.M., Saldarriaga J.G. & Vieilledent G., 2014, Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology 20: 3177-3190.
• Crow T.R., 1978, Common regressions to estimate tree biomass in tropical stands. Forest Science 24(1): 110-114.
• Crow T.R., 1983, Comparing biomass regressions by site and stand age for red maple. Canadian Journal of Forest Research 13: 283-288.
• Crowther T.W., Glick H.B., Covey K.R., Bettigole C., Maynard D.S., Thomas S.M., Smith J.R., Hintler G., Duguid M.C., Amatulli G., Tuanmu M.-N., Jetz W., Salas C., Stam C., Piotto D., Tavani R., Green S., Bruce G., Williams S.J., Wiser S.K., Huber M.O., Hengeveld G.M., Nabuurs G.-J., Tikhonova E., Borchardt P., Li C.-F., Powrie L.W., Fischer M., Hemp A., Homeier J., Cho P., Vibrans A.C., Umunay P.M., Piao S.L., Rowe C.W., Ashton M.S., Crane P.R. & Bradford M.A., 2015, Mapping tree density at a global scale. Nature 525: 201-205.
• Cunia T. & Briggs R.D., 1984, Forcing additivity of biomass tables: some empirical results. Canadian Journal of Forest Research 14: 376-384.
• De-Miguel S., Mehtätalo L. & Durkaya A., 2014, Developing generalized, calibratable, mixed-effects meta-models for large-scale biomass prediction. Canadian Journal of Forest Research 44: 648-656.
• Dieguez-Aranda U., Burkhart H.E. & Amateis R.L., 2006, Dynamic site model for loblolly pine (Pinus taeda L.) plantations in the United States. Forest Science 52(3): 262–272.
• Dong L., Zhang L. & Li F., 2015a, Developing additive systems of biomass equations for nine hardwood species in Northeast China. Trees 29(4): 1149-1163. (https://doi.org/10.1007/s00468-015-1196-1).
• Dong L., Zhang L. & Li F., 2015b, A three-step proportional weighting system of nonlinear biomass equations. Forest Science 61(1): 35-45.
• Dong L., Zhang L. & Li F., 2016, Developing two additive biomass equations for three coniferous plantation species in Northeast China. Forests 7(7): 136.
• Fehrmann L., Lehtonen A., Kleinn C. & Tomppo R., 2008, Comparison of linear and mixed-effect regression models and a k-nearest neighbor approach for estimation of single tree biomass. Canadian Journal of Forest Research 38: 1-9.
• Fu L.Y., Zeng W.S., Tang S.Z., Sharma R.P. & Li H.K., 2012, Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales – A case study for Masson pine in Southern China. Journal of Forest Science 58(3): 101-115.
• Fu L., Sharma R.P., Hao K. & Tang S., 2017, A generalized interregional nonlinear mixed-effects crown width model for Prince Rupprecht larch in northern China. Forest Ecology and Management 389: 364-373.
• Fu L., Sun H., Sharma R.P., Lei Y., Zhang H. & Tang S., 2013, Nonlinear mixed-effects crown width models for individual trees of Chinese fir (Cunninghamia lanceolata) in south-central China. Forest Ecology and Management 302: 210-220.
• Gill R.A. & Jackson R.B., 2000, Global patterns of root turnover for terrestrial ecosystems. New Phytologist 147: 13-31.
• Grigal D.F. & Kernik L.K., 1984, Generality of black spruce biomass estimation equations. Canadian Journal of Forest Research 14: 468-470.
• Jacobs M.W. & Cunia T., 1980, Use of dummy variables to harmonize tree biomass tables. Canadian Journal of Forest Research 10(4): 483-490.
• Jucker T., Caspersen J., Chave J., Antin C., Barbier N., Bongers F., Dalponte M., van Ewijk K.Y., Forrester D.I., Heani M., Higgins S.I., Holdaway R.J., Iida Y., Lorimer C., Marshall P.M., Momo S., Moncrieff G.R., Ploton P., Poorter L., Rahman K.A., Schlund M., Sonké B., Sterck F.J., Trugman A.T., Usoltsev V.A., Vanderwel M.C., Waldner P., Wedeux B., Wirth C., Wöll H., Woods M., Xiang W., Zimmermann N. & Coomes D.A., 2017, Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Global Change Biology 23: 177-190.
• Lang P.M., 2008, Linear mixed model of aerial photo crown width and ground diameter. Scientia Silvae Sinicae 44: 41-44.
• Li L.X., Hao Y.H. & Zhang Y., 2006, The application of dummy variable in statistic analysis. The Journal of Mathematical Medicine 19: 51-52.
• Li C.M. & Zhang H.R., 2010, Modeling dominant height for Chinese fir plantation using a nonlinear mixed-effects modeling approach. Scientia Silvae Sinicae 46: 89-95.
• Liang J., Crowther T.W., Picard N., Wiser S., Zhou M., Alberti G., Schulze E.-D., McGuire A.D., Bozzato F., Pretzsch H., de-Miguel S., Paquette A., Hérault B., Scherer-Lorenzen M., Barrett C.B., Glick H.B., Hengeveld G.M., Nabuurs G.-J., Pfautsch S., Viana H., Vibrans A.C., Ammer C., Schall P., Verbyla D., Tchebakova N.M., Fischer M., Watson J.V., Chen H.Y.H., Lei X., Schelhaas M.-J., Lu H., Gianelle D., Parfenova E.I., Salas C., Lee E., Lee B., Kim H. S., Bruelheide H., Coomes D.A., Piotto D., Sunderland T., Schmid B., Gourlet-Fleury S., Sonké B., Tavani R., Zhu J., Brandl S., Vayreda J., Kitahara F., Searle E.B., Neldner V.J., Ngugi M.R., Baraloto C., Frizzera L., Bałazy R., Oleksyn J., Zawiła-Niedźwiecki T., Bouriaud O., Bussotti F., Finér L., Jaroszewicz B., Jucker T., Valladares F., Jagodzinski A.M., Peri P.L., Gonmadje C., Marthy W., O’Brien T., Martin E.H., Marshall A.R., Rovero F., Bitariho R., Niklaus P.A., Alvarez-Loayza P., Chamuya N., Valencia R., Mortier F., Wortel V., Engone-Obiang N.L., Ferreira L.V., Odeke D.E., Vasquez R.M., Lewis S.L. & Reich P.B., 2016, Positive biodiversity - productivity relationship predominant in global forests. Science 354(6309): 196-208.
• Nord-Larsen T., 2006, Developing dynamic site index curves for European beech (Fagus sylvatica L.) in Denmark. Forest Science 52(2): 173-181.
• Ogawa H., Yoda K., Ogino K. & Kira T., 1965, Comparative ecological studies on three main types of forest vegetation in Thailand. 2. Plant biomass. Nature and Life in Southeast Asia 4: 49-80.
• Parresol B.R., 2001, Additivity of nonlinear biomass equations. Canadian Journal of Forest Research 31(5): 865-878.
• Pastor J., Aber J.D. & Melillo J.M., 1984, Biomass prediction using generalized allometric regressions for some Northeast tree species. Forest Ecology and Management 7: 265-274.
• Poorter H., Jagodzinski A.M., Ruiz-Peinado R., Kuyah S., Luo Y., Oleksyn J., Usoltsev V.A., Buckley T.N., Reich P.B. & Sack L., 2015, How does biomass allocation change with size and differ among species? An analysis for 1200 plant species from five continents. New Phytologist 208(3): 736-749.
• Reed D.D. & Green E.J., 1985, A method of forcing additivity of biomass tables when using nonlinear models. Canadian Journal of Forest Research 15: 1184-1187.
• Rutishauser E., Noor’an F., Laumonier Y., Halperin J., Rufi’ie, Hergoualch K. & Verchot L., 2013, Generic allometric models including height best estimate forest biomass and carbon stocks in Indonesia. Forest Ecology and Management 307: 219-225.
• Sanquetta C.R., Behling A., Corte1 A.P.D., Netto S.P., Schikowski A.B. & do Amaral M.K., 2015, Simultaneous estimation as alternative to independent modeling of tree biomass. Annals of Forest Science 72: 1099-1112.
• Schenk H.J. & Jackson R.B., 2002, The global biogeography of roots. Ecological Monographs 72(3): 311-328.
• Schenk H.J. & Jackson R.B., 2003, A global database of ecosystem root profiles (ERP). California State University, Fullerton.
• Schmitt M.D.C. & Grigal D.F., 1981, Generalized biomass estimation equations for Betula papyrifera Marsh. Canadian Journal of Forest Research 11: 837-840.
• Sokolov S.Y., Svyazeva О.А. & Kubli V.А., 1977, Areas of trees and shrubs of the USSR. Vol. 1. Nauka Publishing, Leningrad (in Russian).
• Stas S.M., Rutishauser E., Chave J., Anten N.P.R. & Laumonier Y., 2017, Estimating the aboveground biomass in an old secondary forest on limestone in the Moluccas, Indonesia: Comparing locally developed versus existing allometric models. Forest Ecology and Management 389: 27-34.
• Tang S., Zhang H. & Xu H., 2000, Study on establish and estimate method of compatible biomass model. Scientia Silvae Sinica 36: 19-27 (in Chinese with English abstract).
• Tang S.Z., Lang K.J. & Li H.K., 2008, Statistics and computation of biomathematical models (ForStat Course). Science Press, Beijing.
• Tritton L.M. & Hornbeck J.W., 1981, Biomass estimation for northeastern forests. Bulletin of the Ecological Society of America 62: 106-107.
• Usoltsev V.A., 2010, Eurasian forest biomass and primary production data. Ural Branch of Russian Academy of Sciences, Yekaterinburg. (http://elar.usfeu.ru/handle/123456789/2606).
• Usoltsev V.A., 2013, Forest biomass and primary production database for Eurasia. CD-version. The second edition, enlarged and re-harmonized. Ural State Forest Engineering University, Yekaterinburg. (http://elar.usfeu.ru/handle/123456789/3059).
• Usoltsev V.A., Koltunova A.I., Kajimoto T., Osawa A. & Koike T., 2002, Geographical gradients of annual biomass production from larch forests in Northern Eurasia. Eurasian Journal of Forest Research 5: 55-62.
• Usoltsev V.А., Voronov М.P., Kolchin К.V., Malenko А.А. & Коkh Е.V., 2017a. Transcontinental additive model and weight table for estimating spruce-fir forests biomass on the area of Eurasia. Bulletin of Altai State Agricultural University 9(155): 91-100. (http://www.asau.ru/vestnik/2017/9/091-100.pdf).
• Usoltsev V.А., Voronov М.P., Shobairi S.O.R., Dar J.A., Kolchin К.V., Chasovskikh V.P., Markovskaya E.V., 2017b. Comparative analysis of ordinary and additive models of component composition of tree and forest biomass (on the example of Picea and Abies spp.). Èko-potencial 3(19): 9-31. (http://elar.usfeu.ru/bitstream/123456789/6638/1/eko_17-3_01.pdf).
• Vieilledent G., Vaudry R., Andriamanohisoa S.F.D., Rakotonarivo O.S., Randrianasolo Z.H., Razafindrabe H.N., Bidaud Rakotoarivony C., Ebeling J. & Rasamoelina M., 2012, A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecological Applications 22(2): 572-583.
• Wang M., Borders B.E. & Zhao D.H., 2007, Parameter estimation of base-age invariant site index models: which data structure to use?. Forest Science 53(5): 541-551.
• Wang M., Borders B.E. & Zhao D., 2008, An empirical comparison of two subject-specific approaches to dominant heights modeling: The dummy variable method and the mixed model method. Forest Ecology and Management 255: 2659-2669.
• Zeng W.S., 2015, Using nonlinear mixed model and dummy variable model approaches to construct origin-based single tree biomass equations. Trees 29(1): 275-283.
• Zeng W.S., Tang S.Z., Xia Z.S., Zhu S. & Luo H.Z., 2011, Using linear mixed model and dummy variable model approaches to construct generalized single-tree biomass equations in Guizhou. Forest Research 24(3): 285-291.
• Zianis D. & Mencuccini M., 2003, Aboveground biomass relationships for beech (Fagus moesiaca Cz.) trees in Vermio Mountain, Northern Greece, and generalized equations for Fagus sp. Annals of Forest Science 60: 439-448.

Identyfikatory

DOI

10.12775/EQ.2019.002