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

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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.

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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
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  2. Aitor Ameztegui
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  3. Christian Ammer
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  4. Philippe Balandier
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  5. Norbert Bartsch
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  6. Andreas Bolte
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  7. Lluís Coll
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  8. Catherine Collet
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  9. Jörg Ewald
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  10. Nico Frischbier
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  11. Tsegay Gebereyesus
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  12. Josephine Haase
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  13. Tobias Hamm
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  14. Bastian Hirschfelder
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  15. Franka Huth
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  16. Gerald Kändler
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  17. Anja Kahl
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  18. Heike Kawaletz
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  19. Christian Kuehne
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  20. André Lacointe
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  21. Na Lin
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  22. Magnus Löf
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  23. Philippe Malagoli
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  24. André Marquier
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  25. Sandra Müller
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  26. Susanne Promberger
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  27. Damien Provendier
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  28. Heinz Röhle
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  29. Jate Sathornkich
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  30. Peter Schall
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  31. Michael Scherer-Lorenzen
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  32. Jens Schröder
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  33. Carolin Seele
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  34. Johannes Weidig
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  35. Christian Wirth
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  36. Heino Wolf
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  37. Jörg Wollmerstädt
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  38. Martina Mund
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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

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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

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