Skip to main content
SpringerLink
Log in

Species-specific and generic biomass equations for seedlings and saplings of European tree species

European Journal of Forest Research volume 135, pages 313–329 (2016)Cite this article

Abstract

Biomass equations are a helpful tool to estimate the tree and stand biomass production and standing stock. Such estimations are of great interest for science but also of great importance for global reports on the carbon cycle and the global climate system. Even though there are various collections and generic meta-analyses available with biomass equations for mature trees, reports on biomass equations for juvenile trees (seedlings and saplings) are mainly missing. Against the background of an increasing amount of reforestation and afforestation projects and forests in young successional stages, such equations are required. In this study we have collected data from various studies on the aboveground woody biomass of 19 common tree species growing in Europe. The aim of this paper was to calculate species-specific biomass equations for the aboveground woody biomass of single trees in dependence of root-collar-diameter (RCD), height (H) and the combination of the two (RCD2 H). Next to calculating species-specific biomass equations for the species available in the dataset, we also calculated generic biomass equations for all broadleaved species and all conifer species. The biomass equations should be a contribution to the pool of published biomass equations, whereas the novelty is here that the equations were exclusively derived for young trees.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3

References

  • Alemdag IS, Stiell WM (1982) Spacing and age effects on biomass production in red pine plantations. For Chron 58:220–224

    Article  Google Scholar 

  • Annighöfer P, Mölder I, Zerbe S, Kawaletz H, Terwei A, Ammer C (2012) Biomass functions for the two alien tree species Prunus serotina Ehrh. and Robinia pseudoacacia L. in floodplain forests of Northern Italy. Eur J Forest Res 131:1619–1635

    Article  Google Scholar 

  • António N, Tomé M, Tomé J, Soares P, Fontes L (2007) Effect of tree, stand, and site variables on the allometry of Eucalyptus globulus tree biomass. Can J For Res 37:895–906

    Article  Google Scholar 

  • Anzola-Jürgenson GA (2002) Linking structural and process-oriented models of plant growth. Dissertation, Göttingen

  • Bartelink HH (1997) Allometric relationships for biomass and leaf area of beech (Fagus sylvatica L). Ann For Sci 54:39–50

    Article  Google Scholar 

  • Baskerville GL (1972) Use of logarithmic regression in the estimation of plant biomass. Can J For Res 2:49–53

    Article  Google Scholar 

  • Bates DM, Chambers JM (1992) Nonlinear models. In: Chambers JM, Hastie TJ (eds) Statistical models, vol 10. S. Wadsworth & Brooks, colo, Pacific Grove

    Google Scholar 

  • Bates DM, Watts DG (1988) Nonlinear regression analysis and its applications: wiley series in probability and statistics, 2nd edn. Wiley, New York

    Book  Google Scholar 

  • Baty F, Delignette-Muller M (2015) Tools for Nonlinear Regression Analysis: Package ‘nlstools’. https://cran.r-project.org/web/packages/nlstools/nlstools.pdf, 21 Oct 2015

  • Beauchamp JJ, Olson JS (1973) Corrections for bias in regression estimates after logarithmic transformation. Ecology 54:1403–1407

    Article  Google Scholar 

  • Berner LT, Alexander HD, Loranty MM, Ganzlin P, Mack MC, Davydov SP et al (2015) Biomass allometry for alder, dwarf birch, and willow in boreal forest and tundra ecosystems of far northeastern Siberia and north-central Alaska. For Ecol Manage 337:110–118

    Article  Google Scholar 

  • Bi H, Murphy S, Volkova L, Weston C, Fairman T, Li Y et al (2015) Additive biomass equations based on complete weighing of sample trees for open eucalypt forest species in south-eastern Australia. For Ecol Manage 349:106–121

    Article  Google Scholar 

  • Bjarnadottir B, Inghammar A-C, Brinker M-M, Sigurdsson BD (2007) Single tree biomass and volume functions for young Siberian larch trees (Larix sibirica) in eastern Iceland. Icel Agric Sci 20:125–135

    Google Scholar 

  • Bolte A, Czajkowski T, Bielefeldt J, Wolff B, Heinrichs S (2009) Schätzung der oberirdischen Biomassevorräte des Baum- und Strauchunterwuchses in Wäldern auf der Basis von Vegetationsaufnahmen. Forstarchiv 80

  • Brown JK (1976) Estimating shrub biomass from basal stem diameters. Can J For Res 6:153–158

    Article  Google Scholar 

  • Brown S (1997) Estimating biomass and biomass change of tropical forests: a primer: a forest resources assessment publication. FAO—Food and Agriculture Organization of the United Nations, Rome. FAO Forestry Paper 134

  • Brown S (2002) Measuring carbon in forests: current status and future challenges. Environ Pollut 116:363–372

    Article  CAS  PubMed  Google Scholar 

  • Buech RR, Rugg DJ (1989) Biomass relations of shrub components and their generality. For Ecol Manage 26:257–264

    Article  Google Scholar 

  • Carroll RJ, Ruppert D (1988) Transformation and weighting in regression: monographs on statistics and applied probability. Chapman and Hall, London

    Book  Google Scholar 

  • Chambers JM (1992) Linear models. Chapter 4. In: Chambers JM, Hastie TJ (eds) Statistical models. S. Wadsworth & Brooks, Cole, Pacific Grove

    Google Scholar 

  • Chave J, Riera B, Dubois MA (2001) Estimation of biomass in a neotropical forest of French Guiana: spatial and temporal variability. J Trop Ecol 17:79–96

    Article  Google Scholar 

  • Chroust L (1985) Above ground biomass of young pine forest (Pinus sylvestris) and its determination. Communicationes Instituti Forestalis Cechosloveniae 14:127–145

    Google Scholar 

  • Cienciala E, Apltauer J, Exnerová Z, Tatarinov FA (2008) Biomass functions applicable to oak trees grown in Central-European forestry. J For Sci 54:109–120

    Google Scholar 

  • Cifuentes Jara M, Henry M, Réjou Méchain M, Lopez OR, Wayson C, Fuentes Michel, María José et al (2015a) Overcoming obstacles to sharing data on tree allometric equations. Ann For Sci 72:789–794

    Article  Google Scholar 

  • Cifuentes Jara M, Henry M, Réjou-Méchain M, Wayson C, Zapata-Cuartas M, Piotto D et al (2015b) Guidelines for documenting and reporting tree allometric equations. Ann For Sci 72:763–768

    Article  Google Scholar 

  • Cleveland WS, Grosse E, Shyu WM (1992) Local regression models. Chapter 8. In: Chambers JM, Hastie TJ (eds) Statistical models in S. Wadsworth & Brooks, Cole: Pacific Grove

    Google Scholar 

  • Dixon RK, Solomon AM, Brown S, Houghton RA, Trexier MC, Wisniewski J (1994) Carbon pools and flux of global forest ecosystems. Science 263:185–190

    Article  CAS  PubMed  Google Scholar 

  • Djomo AN, Ibrahima A, Saborowski J, Gravenhorst G (2010) Allometric equations for biomass estimations in Cameroon and pan moist tropical equations including biomass data from Africa. For Ecol Manage 260:1873–1885

    Article  Google Scholar 

  • Enquist BJ, Niklas KJ (2001) Invariant scaling relations across tree-dominated communities. Nature 410:655–660

    Article  CAS  PubMed  Google Scholar 

  • Falster DS, Duursma RA, Ishihara MI, Barneche DR, FitzJohn RG, Vårhammar A et al (2015) BAAD: a biomass and allometry database for woody plants. Ecology 96:1445

    Article  Google Scholar 

  • Galik CS, Mobley ML, Richter D (2009) A virtual “field test” of forest management carbon offset protocols: the influence of accounting. Mitig Adapt Strat Glob Change 14:677–690

  • Gonzalez-Benecke CA, Gezan SA, Albaugh TJ, Allen HL, Burkhart HE, Fox TR et al (2014a) Local and general above-stump biomass functions for loblolly pine and slash pine trees. For Ecol Manage 334:254–276

    Article  Google Scholar 

  • Gonzalez-Benecke CA, Gezan SA, Martin TA, Cropper WP, Samuelson LJ, Leduc DJ (2014b) Individual tree diameter, height, and volume functions for longleaf pine. For Sci 60:43–56

    Google Scholar 

  • Heinrichs S, Bernhardt-Römermann M, Schmidt W (2010) The estimation of aboveground biomass and nutrient pools of understorey plants in closed Norway spruce forests and on clearcuts. Eur J For Res 129:613–624

    Article  Google Scholar 

  • Huang S, Titus SJ, Wiens DP (1992) Comparison of nonlinear height–diameter functions for major Alberta tree species. Can J For Res 22:1297–1304

    Article  Google Scholar 

  • IPCC (2013) Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA

  • Jenkins JC, Chojnacky DC, Heath LS, Birdsey RA (2003) National-scale biomass estimators for United States tree species. For Sci 49:12–35

    Google Scholar 

  • Kaitaniemi P (2004) Testing the allometric scaling laws. J Theor Biol 228:149–153

    Article  PubMed  Google Scholar 

  • Ketterings QM, Coe R, van Noordwijk M, Ambagau Y, Palm CA (2001) Reducing uncertainty in the use of allometric biomass equations for predicting above-ground tree biomass in mixed secondary forests. For Ecol Manage 146:199–209

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Nelson BW, Mesquita R, Pereira JL, Aquino Garcia, de Souza Silas, Teixeira Batista G, Bovino Couto L (1999) Allometric regressions for improved estimate of secondary forest biomass in the central Amazon. For Ecol Manage 117:149–167

    Article  Google Scholar 

  • Norgren O, Elfving B, Olsson O (1995) Non-destructive biomass estimation of tree seedlings using image analysis. Scand J For Res 10:347–352

    Article  Google Scholar 

  • Ott RL (1993) An introduction to statistical methods and data analysis. Duxbury press, California

    Google Scholar 

  • Pajtík J, Konôpka B, Lukac M (2008) Biomass functions and expansion factors in young Norway spruce (Picea abies [L.] Karst) trees. For Ecol Manage 256:1096–1103

    Article  Google Scholar 

  • Parresol BR (1999) Assessing tree and stand biomass: a review with examples and critical comparisons. Forest Science 45:573–593

    Google Scholar 

  • Parresol BR (2001) Additivity of nonlinear biomass equations. Can J For Res 31:865–878

    Article  Google Scholar 

  • Pilli R, Anfodillo T, Carrer M (2006) Towards a functional and simplified allometry for estimating forest biomass. For Ecol Manage 237:583–593

    Article  Google Scholar 

  • R Development Core Team (2013) R: a language and environment for statistical computing

  • Repola J (2008) Biomass equations for birch in Finland. Silva Fennica 42:605–624

    Article  Google Scholar 

  • Rojas-García F, Jong De, Bernardus HJ, Martínez-Zurimendí P, Paz-Pellat F (2015) Database of 478 allometric equations to estimate biomass for Mexican trees and forests. Ann For Sci 72:835–864

    Article  Google Scholar 

  • Sah JP, Ross MS, Koptur S, Snyder JR (2004) Estimating aboveground biomass of broadleaved woody plants in the understory of Florida keys pine forests. For Ecol Manage 203:319–329

    Article  Google Scholar 

  • Satoo T, Madgwick HA (1982) Forest biomass. Kluwer Academic Publishers Group, London

    Google Scholar 

  • Schroeder P, Brown S, Mo J, Birdsey R, Cieszewski C (1997) Biomass estimation for temperate broadleaf forests of the United States using inventory data. Forest Science 43:424–434

    Google Scholar 

  • Shinozaki K, Yoda K, Hozumi K, Kira T (1964a) A quantitative analysis of plant form—the pipe model theory: I Basic analyses. Jpn J Ecol 14:97–104

    Google Scholar 

  • Shinozaki K, Yoda K, Hozumi K, Kira T (1964b) A quantitative analysis of plant form—the pipe model theory: II. Further evidence of the theory and its application in forest ecology. Jpn J Ecol 14:133–139

    Google Scholar 

  • Snell O (1892) Die Abhängigkeit des Hirngewichtes von dem Körpergewicht und den geistigen Fähigkeiten. Archiv für Psychiatrie und Nervenkrankheiten 23:436–446

    Article  Google Scholar 

  • Sprugel DG (1983) Correcting for bias in log-transformed allometric equations. Ecology 64:209

    Article  Google Scholar 

  • Ter-Mikaelian MT, Korzukhin MD (1997) Biomass equations for sixty-five North American tree species. For Ecol Manage 97:1–24

    Article  Google Scholar 

  • Valentini R, Matteucci G, Dolman AJ, Schulze ED, Rebmann C, Moors EJ et al (2000) Respiration as the main determinant of carbon balance in European forests. Nature 404:861–865

    Article  CAS  PubMed  Google Scholar 

  • West GB, Brown JH, Enquist BJ (1999) A general model for the structure and allometry of plant vascular systems. Nature 400:664–667

    Article  CAS  Google Scholar 

  • Wirth C, Schumacher J, Schulze E-D (2004) Generic biomass functions for Norway spruce in Central Europe: a meta-analysis approach toward prediction and uncertainty estimation. Tree Physiol 24:121–139

    Article  PubMed  Google Scholar 

  • Yandle DO, Wiant HV (1981) Estimation of plant biomass based on the allometric equation. Can J For Res 11:833–834

    Article  Google Scholar 

  • Zianis D, Mencuccini M (2004) On simplifying allometric analyses of forest biomass. For Ecol Manage 187:311–332

    Article  Google Scholar 

  • Zianis D, Muukkonen P, Mäkipää R, Mencuccini M (2005) Biomass and Stem volume equations for tree species in Europe. Silva Fennica Monographs

Download references

Acknowledgments

We thank the national research project “Ecosystem Services of Natural Forests at Forestry and Climate Policy (FKZ 3511 84 0200),” from the Federal Agency for Nature Conservation (BfN) of the Federal Ministry for the Environment Nature Conservation and Nuclear Safety (BMU) for funding this project. We are also grateful for the technical assistances and support in the field and laboratory by (working group Ammer) Ulrike Westphal, Andreas Parth, and Michael Unger; (working group Löf and Bolte), Tomasz Czajkowski, Thomas Kompa, and Heiko Rubbert; (working group Scherer-Lorenzen) Sigrid Berger, Felix Berthold, Stephanie Kätsch, Joanna McMillan, Vlad Tataru, and Stefan Trogisch; (working group Balandier) Virginie Chirent and Ludivine Guinard for helping with the seedling excavation; (working group Kändler) wished to thank Rainer Kruse for conducting the field sampling. The BIOTREE sites Bechstedt and Kaltenborn are maintained by the Federal Forestry Office Thüringer Wald (Bundesforstamt Thuringer Wald), and we also wish to thank them.

Author information

Authors and Affiliations

  1. Department of Silviculture and Forest Ecology of the Temperate Zones, University of Göttingen, Büsgenweg 1, 37077, Göttingen, Germany

    Peter Annighöfer, Christian Ammer, Norbert Bartsch, Tsegay Gebereyesus, Peter Schall & Martina Mund

  2. Forest Sciences Centre of Catalonia (CEMFOR-CTFC), Ctra. Sant Llorenç de Morunys km. 2, 25280, Solsona, Catalonia, Spain

    Aitor Ameztegui & Lluís Coll

  3. Irstea, U.R. Forest Ecosystems (EFNO), Domaine des Barres, 45290, Nogent-sur-Vernisson, France

    Philippe Balandier

  4. Thünen Institute of Forest Ecosystems, A.-Möller-Str. 1, 16225, Eberswalde, Germany

    Andreas Bolte

  5. LERFoB, UMR 1092, INRA-AgroParisTech, 54280, Champenoux, France

    Catherine Collet

  6. Botany and Vegetation Science, University of Applied Science Weihenstephan-Triesdorf, Hans-Carl-von-Carlowitz-Platz 3, 85354, Freising, Germany

    Jörg Ewald

  7. Thüringen Forst, Forestry Research and Competence Centre, Jägerstraße 1, 99867, Gotha, Germany

    Nico Frischbier

  8. Institute of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstr. 190, 8057, Zurich, Switzerland

    Josephine Haase

  9. Institute of Silviculture and Forest Protection, University of Technology Dresden, Pienner Str. 8, 01737, Tharandt, Germany

    Tobias Hamm, Bastian Hirschfelder, Franka Huth, Johannes Weidig & Jörg Wollmerstädt

  10. Department for Biometry and Informatics, FVA Baden-Württemberg, Wonnhaldestraße 4, 79100, Freiburg, Germany

    Gerald Kändler

  11. Systematic Botany and Functional Biodiversity, University of Leipzig, Johannisallee 21, 04103, Leipzig, Germany

    Anja Kahl, Carolin Seele & Christian Wirth

  12. DBU Naturerbe GmbH, An der Bornau 2, 49090, Osnabrück, Germany

    Heike Kawaletz

  13. School of Forest Resources, University of Maine, 5755 Nutting Hall, Orono, ME, 04469, USA

    Christian Kuehne

  14. UMR547 PIAF, INRA, Domaine de Crouelle, 234 Avenue du Brezet, 63039, Clermont-Ferrand, France

    André Lacointe & André Marquier

  15. Research Institute of Tropical Forestry, Chinese Academy of Forestry, Guangzhou, 510520, China

    Na Lin

  16. Southern Swedish Forest Research Centre, Swedish University of Agricultural Science, Sundsvägen 3, Alnarp, Sweden

    Magnus Löf

  17. UMR547 PIAF, Clermont University, University Blaise Pascal, BP 10448, 63000, Clermont-Ferrand, France

    Philippe Malagoli

  18. Faculty of Biology, Geobotany, University of Freiburg, Schaenzlestr. 1, 79104, Freiburg, Germany

    Sandra Müller & Michael Scherer-Lorenzen

  19. Bavarian State Institute of Forestry, Hans-Carl-von-Carlowitz-Platz 1, 85354, Freising, Germany

    Susanne Promberger

  20. Plante and Cité, Maison du végétal 26 rue Jean Dixméras, 49066, Angers Cedex 1, France

    Damien Provendier

  21. Institute for Forest Growth and Biometrics, Technical University Dresden, Pienner Str. 8, 01737, Tharandt, Germany

    Heinz Röhle

  22. Department of Horticulture, Faculty of Agriculture, Kasetsart University, 50 Ngam Wong Wan Rd, Ladyaow Chatuchak, Bangkok, 10900, Thailand

    Jate Sathornkich

  23. Faculty of Forest and Environment, Eberswalde University for Sustainable Development, Alfred-Möller-Str. 1, 16225, Eberswalde, Germany

    Jens Schröder

  24. Department for Forest Genetics and Forest Tree Breeding, Staatsbetrieb Sachsenforst, Bonnewitzer Str. 34, 01796, Pirna, Germany

    Heino Wolf

Authors
  1. Peter Annighöfer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aitor Ameztegui
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christian Ammer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Philippe Balandier
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Norbert Bartsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Andreas Bolte
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lluís Coll
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Catherine Collet
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jörg Ewald
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Nico Frischbier
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Tsegay Gebereyesus
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Josephine Haase
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Tobias Hamm
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Bastian Hirschfelder
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Franka Huth
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Gerald Kändler
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Anja Kahl
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Heike Kawaletz
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Christian Kuehne
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. André Lacointe
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Na Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Magnus Löf
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Philippe Malagoli
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. André Marquier
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Sandra Müller
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Susanne Promberger
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Damien Provendier
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Heinz Röhle
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Jate Sathornkich
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Peter Schall
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Michael Scherer-Lorenzen
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Jens Schröder
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Carolin Seele
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Johannes Weidig
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Christian Wirth
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Heino Wolf
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Jörg Wollmerstädt
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Martina Mund
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter Annighöfer.

Additional information

Communicated by Miren del Rio.

Appendices

Appendix 1

Dataset references and responsible scientists. Presented are the names of the datasets as used in this study and the publication they refer to

No.

Dataset

Region

Sampling year

Species

Bibliographic references

(1)

AME2013

Catalonia, Spain

2011

Abies alba (48), Betula pendula (47), Pinus sylvestris (45), Pinus uncinata (46)

Ameztegui, A., Coll, L. (2013) Unraveling the role of light and biotic interactions on seedling performance of four Pyrenean species along environmental gradients. Forest Ecology and Management 303: 25–34, DOI 10.1016/j.foreco.2013.04.011

(2)

AMM2003

Freising, Germany

1999

Fagus sylvatica (107), Quercus robur (107)

Ammer C (2003) Growth and biomass partitioning of Fagus sylvatica L. and Quercus robur L. seedlings in response to shading and small changes in the R/FR-ratio of radiation. Annals of Forest Science 60: 163–171, DOI 10.1051/forest:2003009

(3)

ANN2012

Ticino, Italy

2010

Prunus serotina (35)

Annighöfer et al. (2012) Biomass functions for the two alien tree species Prunus serotina Ehrh. And Robinia pseudoacacia L. in floodplain forests of Northern Italy. European Journal of Forest Research 131:1619–1635, DOI 10.1007/s10342-012-0629-2

(4)

BAL2007

Fontfreyde, France

2007

Fagus sylvatica (10)

Balandier (2007) Unpublished data

(5)

BAL2009

Fontfreyde, France

2009

Fagus sylvatica (9)

Balandier (2009) Unpublished data

(6)

BAL2011

Clermont-Ferrand, France (Greenhouse)

2011

Quercus petraea (24)

Balandier (2011) Unpublished data

(7)

CAQ2010

Graoully Forest, France

2005, 2006, 2007

Acer pseudoplatanus (40), Fagus sylvatica (176)

Caquet B, Montpied P, Dreyer E, Epron D, Collet C 2010 Response to canopy opening does not act as a filter to Fagus sylvatica and Acer sp. Advance regeneration in a mixed temperate forest. Ann For Sci 67:105

Caquet B, Barigah T, Cochard H, Montpied P, Collet C, Dreyer E, Epron D 2009 Hydraulic properties of naturally regenerated beech saplings respond to canopy opening. Tree Physiol. 29:1395–1405.

(8)

COL1996

Champenoux, France

1993

Quercus petraea (197)

Collet C, Guehl JM, Frochot H, Ferhi A 1996 Effect of two forest grasses differing in their growth dynamics on the water relations and the growth of Quercus petraea seedlings. Can J Bot, 74: 1562–1571

(9)

COL2006

Champenoux, France

2000

Quercus petraea (229)

Collet C, Löf M, Pagès L 2006 Root system development of oak seedlings analyzed using a root architectural model. Effects of competition with grass. Plant and Soil, 279: 367–383.

(10)

DIR2010

Bayerischer Wald, Germany

2009

Abies alba (40), Fagus sylvatica (40), Picea abies (40), Sorbus aucuparia (40)

Dirnberger (2010) Unpublished data, Diploma thesis: Biomasse und sommerliches Äsungsangebot von Jungbäumen im Nationalpark Bayerischer Wald. University of applied Sciences, Weihenstephan

(11)

GEB2013

Göttingen, Germany greenhouse experiment

2013

Acer pseudoplatanus (12), Fagus sylvatica (6), Fraxinus excelsior (12)

Gebereyesus (2013) Unpublished data, Master thesis: Biomass estimations of regeneration trees (DBH < 7 cm) in temperate forests. Georg-August-University, Göttingen

(12)

GEL2001

Graupa, Germany

2001

Fagus sylvatica (32)

Gellrich M, Steinke C, Schröder J (2001) Ergebnisse der Biomasseuntersuchungen an ausgewählten Probebäumen des Rotbuchen-Herkunftsversuches 1990, Versuchsfläche RBU-V03 Graupa, Nordteil, Staatsbetrieb Sachsenforst, Ergebnisbericht Technische Universität Dresden für Probebäume des Buchenprovenienzversuches auf der Versuchsfläche “Pflanzgarten,” LAF Graupa. University of Technology Dresden, Tharandt

(13)

HAM2014

Sachsen, Germany

2010

Abies alba (194)

Hamm T, Weidig J, Huth F, Kuhlisch W, Wagner S et al. (2014) Wachstumsreaktionen junger Weißtannen-Voraussaaten auf Begleitvegetation und Strahlungskonkurrenz. AFJZ 185:45–59

(14)

HIR2011

Sachsen, Germany

2010

Fagus sylvatica (88)

Hirschfelder (2011) Unpublished data, Master thesis: Die Untersuchung der Wachstumsparameter und der Wurzeldeformationen von Rotbuchen-Voranbauten (Fagus sylvatica L.) aus Saat und Pflanzung unter einem Fichtenschirm (Picea abies [L.] KARST.) im Tharandter Wald. University of Technology Dresden, Tharandt

(15)

HOF2008

Freising, Landshut Germany

2004

Fagus sylvatica (289)

Hofmann R, Ammer C (2008) Biomass partitioning of beech seedlings under the canopy of spruce. Austrian Journal of forest science (1):51–66

(16)

KAE2006

Baden-Württemberg, Germany

2005, 2006

Abies alba (117), Acer pseudoplatanus (51), Fagus sylvatica (149), Fraxinus excelsior (63), Picea abies (156), Pinus sylvestris (40), Quercus robur (44)

Kändler et al. (2006) Herleitung von Biomassefunktionen für Verjüngungsbäume (“Nicht Derbholz”-Kollektiv)—erste Ergebnisse. DVFFA—Sektion Ertragskunde, Jahrestagung 2006

(17)

KAW2013

Göttingen, Germany

2011

Carpinus betulus (296), Prunus serotina (176), Quercus robur (288), Robinia pseudoacacia (238)

Kawaletz et al. (2013) Exotic tree seedlings are much more competitive than natives but show underyielding when growing together. J Plant Eco 6:305–315, DOI 10.1093/jpe/rts044

(18)

KUE2011

Freiburg, Germany

2008, 2012

Pseudotsuga menziesii (48)

Kühne et al. (2011) Einfluss von Überschirmung, Dichtstand und Pflanzengröße auf die Wurzelentwicklung natürlich verjüngter Douglasien. (Effects of canopy closure, crowding and plant size on root system development in Douglas-fir seedlings). Forstarchiv 82, 184–194, DOI 10.4432/0300-4112-82-184 Kuehne et al. (2015) Root system development in naturally regenerated Douglas-fir saplings as influenced by canopy closure and crowding. Journal of Forest Science 61, 406–415, DOI: 10.17221/53/2015-JFS

Merkel (2009) Unpublished data, Diploma thesis: Zur Ästigkeit von Douglasie unter Schirm. Rottenburg University of Applied Forest Sciences, Rottenburg

(19)

KUE2014

Freiburg, Germany

2012

Acer pseudoplatanus (15), Carpinus betulus (15), Quercus robur (15), Quercus rubra (15)

Kühne et al. (2014) A comparative study of physiological and morphological seedling traits associated with shade tolerance in introduced red oak (Quercus rubra) and native hardwood tree species in southwestern Germany. Tree Physiology 34, 184–193, DOI 10.1093/treephys/tpt124

(20)

LIN2014

Solling, Germany

2012

Fagus sylvatica (30)

Lin N, Bartsch N, Vor T (2014) Long-term effects of gap creation and liming on understory vegetation with a focus on tree regeneration in a European beech (Fagus sylvatica L.) forest. Annals of forest science 57(2): 249–262, DOI 10.15287/afr.2014.274

(21)

LOE2006

Skarhul, Sweden

2004

Quercus robur (48)

Löf M, Rydberg D, Bolte A (2006): Mounding site preparation for forest restoration: Survival and growth response in Quercus robur L. seedlings. For. Ecol. Manage. 232: 19–25, DOI 10.1016/j.foreco.2006.05.003

Bolte A, Löf M (2010): Root spatial distribution and biomass partitioning in Quercus robur L. seedlings: the effects of mounding site preparation. Eur. J. Forest Res. 129, 4: 603–612, DOI 10.1007/s10342-010-0360-9

(22)

MUE2014

Bechstedt, Kaltenborn, Germany

2010, 2011

Betula pendula (11), Fagus sylvatica (3), Pinus sylvestris (10), Salix spec. (10), Tilia cordata (9)

Müller S (2014) Unpublished data, Dissertation: Architectural light foraging syndromes of juvenile temperate broad leaved trees. Albert-Ludwigs Universität Freiburg.

(23)

PRO2008

Charensat, France

2004

Fagus sylvatica (54)

Provendier D, Balandier P (2008) Compared effects of competition by grasses (Graminoids) and broom (Cytisus scoparius) on growth and functional traits of beech saplings (Fagus sylvatica). Ann For Sci (65) 510, DOI 10.1051/forest:2008028

(24)

SCH2012

Göttingen, Germany greenhouse experiment

2008

Fagus sylvatica (184), Picea abies (172)

Schall P, Lödige C, Beck M., Ammer C (2012) Biomass allocation to roots and shoots is more sensitive to shade and drought in European beech than in Norway spruce seedlings. For Eco Manage 266:246–253, DOI 10.1016/j.foreco.2011.11.017

(25)

SEE2011

Hainich, Thuringia, Germany

2008

Acer pseudoplatanus (80), Fagus sylvatica (43), Fraxinus excelsior (70)

Seele (2008) Unpublished data, Dissertation: The influence of deer browsing on natural forest regeneration. Friedrich-Schiller-University, Jena

(26)

SLO2003

Bechstedt, Germany

2003

Acer pseudoplatanus (5), Betula pendula (5), Carpinus betulus (5), Fagus sylvatica (5), Fraxinus excelsior (5), Pinus sylverstris (5), Prunus avium (5), Quercus petraea (5), Sorbus aucuparia (5), Tilia cordata (5)

Scherer-Lorenzen (2003) Unpublished data

(27)

WAK2009

Bechstedt, Kaltenborn Germany

2009

Acer pseudoplatanus (12), Fagus sylvatica (5), Fraxinus excelsior (15), Prunus avium (7), Quercus petraea (15), Tilia cordata (1)

Wirth and Kahl (2009) Unpublished data

Appendix 2

Parameters of the biomass equations, estimating aboveground biomass (AGB) from the predictor variable root-collar-diameter (RCD)

Species

n

β1

β2

se (β1)

se (β2)

p (β1)

p (β2)

CF

exp (β1)

R2

RSE

Abies alba

399

−3.489

2.854

0.034

0.016

<0.001

<0.001

1.089

0.033

0.988

0.413

Acer pseudoplatanus

215

−3.196

2.707

0.092

0.033

<0.001

<0.001

1.089

0.045

0.969

0.412

Betula pendula

63

−3.647

2.72

0.175

0.086

<0.001

<0.001

1.162

0.03

0.943

0.548

Carpinus betulus

316

−3.593

2.731

0.15

0.058

<0.001

<0.001

1.103

0.03

0.876

0.443

Fagus sylvatica

1230

−3.512

2.835

0.042

0.016

<0.001

<0.001

1.101

0.033

0.964

0.438

Fraxinus excelsior

165

−3.352

2.775

0.145

0.048

<0.001

<0.001

1.133

0.04

0.953

0.499

Picea abies

368

−3.084

2.676

0.085

0.029

<0.001

<0.001

1.091

0.05

0.959

0.418

Pinus sylvestris

100

−3.575

2.738

0.104

0.038

<0.001

<0.001

1.101

0.031

0.981

0.439

Pinus uncinata

46

−2.595

1.958

0.392

0.274

<0.001

<0.001

1.066

0.08

0.537

0.358

Prunus avium

12

−2.892

2.509

0.235

0.07

<0.001

<0.001

1.03

0.057

0.992

0.244

Prunus serotina

211

−3.748

2.902

0.195

0.06

<0.001

<0.001

1.052

0.025

0.919

0.317

Pseudotsuga menziesii

48

−2.408

2.522

0.22

0.07

<0.001

<0.001

1.032

0.093

0.966

0.25

Quercus petraea

470

−3.902

2.561

0.101

0.039

<0.001

<0.001

1.139

0.023

0.904

0.51

Quercus robur

502

−3.286

2.612

0.092

0.037

<0.001

<0.001

1.134

0.042

0.907

0.501

Quercus rubra

15

−1.595

1.929

0.515

0.207

<0.05

<0.001

1.035

0.21

0.869

0.261

Robinia pseudoacacia

238

−2.083

2.325

0.22

0.073

<0.001

<0.001

1.064

0.133

0.813

0.352

Salix spec

10

−3.299

2.686

0.402

0.111

<0.001

<0.001

1.029

0.038

0.986

0.239

Sorbus aucuparia

45

−2.598

2.305

0.345

0.146

<0.001

<0.001

1.156

0.086

0.853

0.539

Tilia cordata

15

−4.823

2.882

0.364

0.109

<0.001

<0.001

1.06

0.009

0.982

0.341

  1. All models were significant (p < 0.001). Biomass equations took the form of Eq. (5). Parameters are: n = number of observations for each species (total = 4468 single observations); β1 and β2 = estimated model coefficients; se = standard error of the regression coefficients; p = significance values of coefficients; CF = correction factor for back-transformation of β1 (Eq. 8); exp(β1) = back-transformed anti-log of β1 multiplied with CF; R 2 = multiple R-squared of the model; RSE residual standard error

Appendix 3

Parameters of the biomass equations, estimating aboveground biomass (AGB) from the predictor variable height (H)

Species

n

β1

β2

se (β1)

se (β2)

p (β1)

p (β2)

CF

exp (β1)

R 2

RSE

Abies alba

399

−8.072

2.829

0.089

0.025

<0.001

<0.001

1.236

0.000386

0.97

0.651

Acer pseudoplatanus

175

−7.21

2.331

0.237

0.047

<0.001

<0.001

1.213

0.000896

0.934

0.621

Betula pendula

63

−10.348

2.858

0.399

0.095

<0.001

<0.001

1.181

0.000038

0.937

0.577

Carpinus betulus

316

−5.932

2.171

0.35

0.081

<0.001

<0.001

1.271

0.003374

0.695

0.693

Fagus sylvatica

1190

−7.308

2.377

0.099

0.021

<0.001

<0.001

1.254

0.000841

0.917

0.673

Fraxinus excelsior

165

−7.521

2.411

0.317

0.062

<0.001

<0.001

1.29

0.000699

0.903

0.714

Picea abies

368

−5.486

2.316

0.128

0.029

<0.001

<0.001

1.122

0.004653

0.946

0.481

Pinus sylvestris

100

−8.319

2.75

0.316

0.073

<0.001

<0.001

1.393

0.00034

0.936

0.814

Pinus uncinata

46

−5.879

1.997

1.075

0.354

<0.001

<0.001

1.084

0.00303

0.42

0.401

Prunus avium

12

−11.382

3.335

0.779

0.155

<0.001

<0.001

1.085

0.000012

0.979

0.405

Prunus serotina

211

−5.448

2.175

0.313

0.061

<0.001

<0.001

1.091

0.004696

0.859

0.418

Pseudotsuga menziesii

48

−7.99

2.583

0.786

0.15

<0.001

<0.001

1.132

0.000384

0.865

0.497

Quercus petraea

470

−6.479

2.318

0.199

0.05

<0.001

<0.001

1.275

0.001959

0.82

0.697

Quercus robur

454

−6.007

2.213

0.197

0.048

<0.001

<0.001

1.285

0.003163

0.822

0.708

Quercus rubra

15

−8.935

2.646

5.563

1.217

0.13

< 0.05

1.21

0.000159

0.267

0.617

Robinia pseudoacacia

238

−7.493

2.488

0.536

0.107

<0.001

<0.001

1.106

0.000616

0.695

0.449

Salix spec

10

−16.01

3.876

2.353

0.409

<0.001

<0.001

1.189

0.0000001

0.918

0.588

Sorbus aucuparia

45

−2.591

1.209

0.982

0.221

< 0.05

<0.001

1.79

0.134188

0.411

1.079

Tilia cordata

15

−9.128

2.946

0.578

0.123

<0.001

<0.001

1.073

0.000117

0.978

0.375

  1. All models were significant (p < 0.001), except for Q. rubra (p = 0.049). Biomass equations took the form of Eq. (6). Parameters are: n = number of observations for each species (total = 4097 single observations); β1 and β2 = estimated model coefficients; se = standard error of the regression coefficients; p = significance values of coefficients; CF = correction factor for back-transformation of β1 (Eq. 8); exp(β1) = back-transformed anti-log of β1 multiplied with CF; R 2 = multiple R-squared of the model; RSE residual standard error

Appendix 4

Parameters of the biomass equations, estimating aboveground biomass (AGB) from the predictor variable RCD2 H (both in cm)

Species

n

β1

β2

se (β1)

se (β2)

p (β1)

p (β2)

CF

exp (β1)

R 2

RSE

Abies alba

399

−0.672

0.956

0.022

0.005

<0.001

<0.001

1.076

0.549

0.99

0.383

Acer pseudoplatanus

175

−0.786

0.873

0.049

0.008

<0.001

<0.001

1.039

0.473

0.987

0.277

Betula pendula

63

−1.652

0.948

0.089

0.022

<0.001

<0.001

1.087

0.208

0.968

0.408

Carpinus betulus

316

−1.187

0.954

0.08

0.016

<0.001

<0.001

1.068

0.326

0.917

0.362

Fagus sylvatica

1190

−1.019

0.921

0.022

0.004

<0.001

<0.001

1.054

0.38

0.981

0.323

Fraxinus excelsior

165

−1.052

0.918

0.078

0.012

<0.001

<0.001

1.07

0.373

0.974

0.367

Picea abies

368

−0.164

0.868

0.042

0.007

<0.001

<0.001

1.052

0.892

0.976

0.317

Pinus sylvestris

100

−1.042

0.936

0.056

0.01

<0.001

<0.001

1.057

0.373

0.989

0.332

Pinus uncinata

46

−0.828

0.798

0.132

0.097

<0.001

<0.001

1.056

0.461

0.606

0.331

Prunus avium

12

−1.065

0.919

0.123

0.017

<0.001

<0.001

1.013

0.349

0.997

0.161

Prunus serotina

211

−0.774

0.921

0.107

0.015

<0.001

<0.001

1.033

0.476

0.947

0.256

Pseudotsuga menziesii

48

−0.626

0.89

0.132

0.019

<0.001

<0.001

1.019

0.545

0.98

0.194

Quercus petraea

470

−1.34

0.898

0.045

0.009

<0.001

<0.001

1.068

0.28

0.951

0.364

Quercus robur

454

−0.772

0.893

0.047

0.011

<0.001

<0.001

1.088

0.503

0.941

0.41

Quercus rubra

15

−1.397

0.931

0.342

0.069

<0.05

<0.001

1.018

0.252

0.933

0.186

Robinia pseudoacacia

238

−0.622

0.865

0.155

0.024

<0.001

<0.001

1.052

0.565

0.846

0.319

Salix spec

10

−2.103

1.013

0.387

0.046

<0.05

<0.001

1.035

0.126

0.984

0.262

Sorbus aucuparia

45

−0.474

0.726

0.273

0.058

0.09

<0.001

1.238

0.77

0.784

0.653

Tilia cordata

15

−1.84

0.977

0.191

0.028

<0.001

<0.001

1.033

0.164

0.99

0.255

  1. All models were significant (p < 0.001). Biomass equations took the form of Eq. (7). Parameters are: n = number of observations for each species (total = 4340 single observations); β1 and β2 = estimated model coefficients; se = standard error of the regression coefficients; p = significance values of coefficients; CF = correction factor for back-transformation of β1 (Eq. 8); exp(β1) = back-transformed anti-log of β1 multiplied with CF; R 2 = multiple R-squared of the model; RSE residual standard error

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Annighöfer, P., Ameztegui, A., Ammer, C. et al. Species-specific and generic biomass equations for seedlings and saplings of European tree species. Eur J Forest Res 135, 313–329 (2016). https://doi.org/10.1007/s10342-016-0937-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10342-016-0937-z

Keywords

search

Navigation