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

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.

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Correspondence to Peter Annighöfer.

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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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Keywords

  • Juvenile tree biomass
  • Allometric equations
  • Forest regeneration