[en] Context:
Wood specific gravity is a key element in tropical forest ecology. It integrates many aspects of tree mechanical properties and functioning and is an important predictor of tree biomass. Wood specific gravity varies widely among and within species and also within individual trees. Notably, contrasted patterns of radial variation of wood specific gravity have been demonstrated and related to regeneration guilds (light demanding vs. shade-bearing). However, although being repeatedly invoked as a potential source of error when estimating the biomass of trees, both intraspecific and radial variations remain little studied. In this study we characterized detailed pith-to-bark wood specific gravity profiles among contrasted species prominently contributing to the biomass of the forest, i.e., the dominant species, and we quantified the consequences of such variations on the biomass.
Methods:
Radial profiles of wood density at 8% moisture content were compiled for 14 dominant species in the Democratic Republic of Congo, adapting a unique 3D X-ray scanning technique at very high spatial resolution on core samples. Mean wood density estimates were validated by water displacement measurements. Wood density profiles were converted to wood specific gravity and linear mixed models were used to decompose the radial variance. Potential errors in biomass estimation were assessed by comparing the biomass estimated from the wood specific gravity measured from pith-to-bark profiles, from global repositories, and from partial information (outer wood or inner wood).
Results:
Wood specific gravity profiles from pith-to-bark presented positive, neutral and negative trends. Positive trends mainly characterized light-demanding species, increasing up to 1.8 g.cm-3 per meter for Piptadeniastrum africanum, and negative trends characterized shade-bearing species, decreasing up to 1 g.cm-3 per meter for Strombosia pustulata. The linear mixed model showed the greater part of wood specific gravity variance was explained by species only (45%) followed by a redundant part between species and regeneration guilds (36%). Despite substantial variation in wood specific gravity profiles among species and regeneration guilds, we found that values from the outer wood were strongly correlated to values from the whole profile, without any significant bias. In addition, we found that wood specific gravity from the DRYAD global repository may strongly differ depending on the species (up to 40% for Dialium pachyphyllum).
Main conclusion:
Therefore, when estimating forest biomass in specific sites, we recommend the systematic collection of outer wood samples on dominant species. This should prevent the main errors in biomass estimations resulting from wood specific gravity and allow for the collection of new information to explore the intraspecific variation of mechanical properties of trees.
Agrawal A, Nepstad D, Chhatre A. Reducing Emissions from Deforestation and Forest Degradation. Annu Rev Environ Resour. Annual Reviews; 2011; 36: 373-396. doi: 10.1146/annurev-environ- 042009-094508
Chave J, Andalo C, Brown S, Cairns M a, Chambers JQ, Eamus D, et al. Tree allometry and improved estimation of carbon stocks and balance in tropical forests. Oecologia. 2005; 145: 87-99. doi: 10.1007/ s00442-005-0100-x
Chave J, Réjou-Méchain M, Búrquez A, Chidumayo E, Colgan MS, Delitti WBC, et al. Improved allometric models to estimate the aboveground biomass of tropical trees. Glob Chang Biol. 2014; 20: 3177-3190. doi: 10.1111/gcb.12629
Chave J, Condit R, Aguilar S, Hernandez A, Lao S, Perez R. Error propagation and scaling for tropical forest biomass estimates. Philos Trans R Soc Lond B Biol Sci. 2004; 359: 409-20. doi: 10.1098/rstb. 2003.1425
Molto Q, Rossi V, Blanc L. Error propagation in biomass estimation in tropical forests. Freckleton R, editor. Methods Ecol Evol. 2013; 4: 175-183. doi: 10.1111/j.2041-210x.2012.00266.x
Vieilledent G, Vaudry R, Andriamanohisoa SFD, Rakotonarivo OS, Randrianasolo HZ, Razafindrabe HN, et al. A universal approach to estimate biomass and carbon stock in tropical forests using generic allometric models. Ecol Appl. Ecological Society of America; 2012; 22: 572-83. Available: http://www. ncbi.nlm.nih.gov/pubmed/22611855
Zanne AE, Lopez-Gonzalez G, Coomes DA, Ilic J, Jansen S, Lewis SL, et al. Global wood density database. Dryad. Dryad. Identifier; 2009; 235: 33. Available: http://hdl.handle.net/10255/dryad.235.
Chave J, Coomes D, Jansen S, Lewis SL, Swenson NG, Zanne AE. Towards a worldwide wood economics spectrum. Ecol Lett. 2009; 12: 351-66. doi: 10.1111/j.1461-0248.2009.01285.x
Détienne P, Chanson B. L'éventail de la densité du bois des feuillus : Comparaison entre différentes régions du monde. Bois forêts des Trop. CIRAD-Forêt; 1996; 19-30. Available: http://cat.inist.fr/? aModele=afficheN&cpsidt=2472307
Turner IM. The Ecology of Trees in the Tropical Rain Forest. Cambridge: Cambridge University Press; 2001.
Swenson NG, Enquist BJ. Ecological and evolutionary determinants of a key plant functional trait: wood density and its community-wide variation across latitude and elevation. Am J Bot. 2007; 94: 451-9. doi: 10.3732/ajb.94.3.451
Slik JWF. Estimating species-specific wood density from the genus average in Indonesian trees. J Trop Ecol. 2006; 22: 481. doi: 10.1017/S0266467406003324
Maniatis D, Saint André L, Temmerman M, Malhi Y, Beeckman H. The potential of using xylarium wood samples for wood density calculations: a comparison of approaches for volume measurement. iForest -Biogeosciences For. 2011; 4: 150-159. doi: 10.3832/ifor0575-004
Chave J, Muller-Landau HC, Baker TR, Easdale T a, ter Steege H, Webb CO. Regional and phylogenetic variation of wood density across 2456 Neotropical tree species. Ecol Appl. 2006; 16: 2356-67. Available: http://www.ncbi.nlm.nih.gov/pubmed/17205910
King DA, Davies SJ, Supardi MNN, Tan S. Tree growth is related to light interception and wood density in two mixed dipterocarp forests of Malaysia. Funct Ecol. 2005; 19: 445-453. doi: 10.1111/j.1365-2435. 2005.00982.x
Panshin AJ, de Zeeuw C. textbook of wood technology. McGraw-Hill Book Co.; 1980.
Nock CA, Geihofer D, Grabner M, Baker PJ, Bunyavejchewin S, Hietz P. Wood density and its radial variation in six canopy tree species differing in shade-tolerance in western Thailand. Ann Bot. 2009; 104: 297-306. doi: 10.1093/aob/mcp118
Woodcock DW, Shier a. D. Wood specific gravity and its radial variations: the many ways to make a tree. Trees. 2002; 16: 437-443. doi: 10.1007/s00468-002-0173-7
Wiemann M, Williamson G. Extreme Radial Changes in Wood Specific Gravity in Some Tropical Pioneers. Wood Fiber Sci. 1988; 20: 344-349. Available: http://swst.metapress.com/content/ QR6U1843MR344J38
Chao K-J, Phillips OL, Gloor E, Monteagudo A, Torres-Lezama A, Martínez RV. Growth and wood density predict tree mortality in Amazon forests. J Ecol. 2008; 96: 281-292. doi: 10.1111/j.1365-2745. 2007.01343.x
King DA, Davies SJ, Tan S, Noor NSM. The role of wood density and stem support costs in the growth and mortality of tropical trees. J Ecol. 2006; 94: 670-680. doi: 10.1111/j.1365-2745.2006.01112.x
McCulloh KA, Meinzer FC, Sperry JS, Lachenbruch B, Voelker SL, Woodruff DR, et al. Comparative hydraulic architecture of tropical tree species representing a range of successional stages and wood density. Oecologia. 2011; 167: 27-37. doi: 10.1007/s00442-011-1973-5
Fournier M, Dlouhá J, Jaouen G, Almeras T. Integrative biomechanics for tree ecology: beyond wood density and strength. J Exp Bot. 2013; 64: 4793-815. doi: 10.1093/jxb/ert279
Urquiza-Haas T, Dolman PM, Peres CA. Regional scale variation in forest structure and biomass in the Yucatan Peninsula, Mexico: Effects of forest disturbance. For Ecol Manage. 2007; 247: 80-90. doi: 10. 1016/j.foreco.2007.04.015
Plourde BT, Boukili VK, Chazdon RL. Radial changes in wood specific gravity of tropical trees: interand intraspecific variation during secondary succession. Anten N, editor. Funct Ecol. 2015; 29: 111-120. doi: 10.1111/1365-2435.12305
Slik JWF, Aiba S-I, Brearley FQ, Cannon CH, Forshed O, Kitayama K, et al. Environmental correlates of tree biomass, basal area, wood specific gravity and stem density gradients in Borneo's tropical forests. Glob Ecol Biogeogr. 2010; 19: 50-60. doi: 10.1111/j.1466-8238.2009.00489.x
Fayolle A, Picard N, Doucet J-L, Swaine M, Bayol N, Bénédet F, et al. A new insight in the structure, composition and functioning of central African moist forests. For Ecol Manage. 2014; 329: 195-205. doi: 10.1016/j.foreco.2014.06.014
Wassenberg M, Chiu H-S, Guo W, Spiecker H. Analysis of wood density profiles of tree stems: incorporating vertical variations to optimize wood sampling strategies for density and biomass estimations. Trees. 2014; 29: 51-561. doi: 10.1007/s00468-014-1134-7
Osazuwa-Peters OL, Wright SJ, Zanne AE. Radial variation in wood specific gravity of tropical tree species differing in growth-mortality strategies. Am J Bot. 2014; 101: 803-811. doi: 10.3732/ajb.1400040
Brienen RJW, Zuidema PA. Lifetime growth patterns and ages of Bolivian rain forest trees obtained by tree ring analysis. J Ecol. 2006; 94: 481-493. doi: 10.1111/j.1365-2745.2005.01080.x
Slik JWF, Paoli G, McGuire K, Amaral I, Barroso J, Bastian M, et al. Large trees drive forest aboveground biomass variation in moist lowland forests across the tropics. Glob Ecol Biogeogr. 2013; 22: 1261-1271. doi: 10.1111/geb.12092
Bastin J-F, Barbier N, Réjou-Méchain M, Fayolle A, Gourlet-Fleury S, Maniatis D, et al. Seeing Central African forests through their largest trees. Sci Rep. Nature Publishing Group; 2015; 5: 13156. doi: 10. 1038/srep13156
Gourlet-Fleury S, Rossi V, Rejou-Mechain M, Freycon V, Fayolle A, Saint-André L, et al. Environmental filtering of dense-wooded species controls above-ground biomass stored in African moist forests. J Ecol. 2011; 99: 981-990. doi: 10.1111/j.1365-2745.2011.01829.x
Gourlet-Fleury S, Mortier F, Fayolle A, Baya F, Ouédraogo D, Bénédet F, et al. Tropical forest recovery from logging: a 24 year silvicultural experiment from Central Africa. Philos Trans R Soc Lond B Biol Sci. 2013; 368: 20120302. doi: 10.1098/rstb.2012.0302
Bastin J-F, Barbier N, Couteron P, Adams B, Shapiro A, Bogaert J, et al. Aboveground biomass mapping of African forest mosaics using canopy texture analysis: toward a regional approach. Ecol Appl. 2014; 24: 1984-2001. Available: http://www.esajournals.org/doi/abs/10.1890/13-1574.1
Ramananantoandro T, Rafidimanantsoa HP, Ramanakoto MF. Forest aboveground biomass estimates in a tropical rainforest in Madagascar: new insights from the use of wood specific gravity data. J For Res. 2015; 26: 47-55. doi: 10.1007/s11676-015-0029-9
Chave J, Riera B, Dubois M-A, Riéra B. Estimation of biomass in a neotropical forest of French Guiana : spatial and temporal variability. J Trop Ecol. CUP; 2001; 17: 79-96. doi: 10.1017/S0266467401001055
Fauset S, Johnson O., Gloor M, Baker TRTR, Monteagudo A, Brienen RJW, et al. Hyperdominance in Amazonian forest carbon cycling. Nat Commun. Nature Publishing Group; 2015; 6: 6857. doi: 10.1038/ ncomms7857
Vancutsem C, Pekel J, Kibambe L, Blaes X, de Waseige C, Defourny F. République démocratique du Congo-occupation du sol. Carte Géographique. Presses Universitaire de Louvain. Bruxelles, Belgique. 2006.
Serckx A, Huynen M-C, Bastin J-F, Hambuckers A, Beudels-Jamar RC, Vimond M, et al. Nest grouping patterns of bonobos (Pan paniscus) in relation to fruit availability in a forest-savannah mosaic. PLoS One. 2014; 9: e93742. doi: 10.1371/journal.pone.0093742
Fayolle A, Swaine MD, Bastin J-F, Bourland N, Comiskey JA, Dauby G, et al. Patterns of tree species composition across tropical African forests. J Biogeogr. 2014; 41: 2320-2331.
Hawthorne WD. Ecological Profiles of Ghanaian Forest Trees [Internet]. Oxford Forestry Institute, Department of Plant Sciences, University of Oxford; 1995. Available: http://books.google.be/books/ about/Ecological-Profiles-of-Ghanaian-Forest-T.html?id=qYvKAAAACAAJ&pgis=1
De Ridder M, Van den Bulcke J, Vansteenkiste D, Van Loo D, Dierick M, Masschaele B, et al. High-resolution proxies for wood density variations in Terminalia superba. Ann Bot. 2011; 107: 293-302. doi: 10.1093/aob/mcq224
Zuur A, Ieno E, Walker N, Saveliev A, Smith G. Mixed effects models and extensions in ecology with R [Internet]. Survival Analysis, Edition . .. Springer Science & Business Media; 2009. Available: http:// link.springer.com/content/pdf/10.1007/978-1-4757-2739-5.pdf
Nakagawa S, Schielzeth H. A general and simple method for obtaining R 2 from generalized linear mixed-effects models. O'Hara RB, editor. Methods Ecol Evol. 2013; 4: 133-142. doi: 10.1111/j.2041- 210x.2012.00261.x
Pinheiro J, Bates D. Mixed-Effects Models in S and S-PLUS. Springer-V. New York; 2000.
Pinheiro J, Bates D. Mixed effects models in S and S-PLUS [Internet]. Springer-V. New York; 2000. Available: http://books.google.com/books?hl=en&lr=&id=RFDe-BKxvRIC&oi=fnd&pg=PR7&dq= Mixed+effect+models+in+S+and+S-PLUS&ots=mOsYED2TK5&sig=kxgG9b-1AAQnWTvjP6- S705WFHc
Williamson GB, Wiemann MCM. Measuring wood specific gravity correctly. Am J Bot. 2010; 97: 519-524. doi: 10.3732/ajb.0900243
Sallenave P. Propriétés Physiques et Mécaniques des Bois Tropicaux. Deuxième Supplément. CTFT. Nogent sur Marne, France.; 1971.
Sallenave P. Propriétés Physiques et Mécaniques des Bois Tropicaux de l'Union Française. CTFT. Nogent sur Marn, France; 1955.
Sallenave P. Propriétés Physiques et Mécaniques des Bois Tropicaux. Premier Supplément. CTFT. Nogent sur Marne, France.; 1964.
Nogueira EM, Fearnside PM, Nelson BW. Normalization of wood density in biomass estimates of Amazon forests. For Ecol Manage. 2008; 256: 990-996. doi: 10.1016/j.foreco.2008.06.001
Bates D. Fitting linear mixed models in R. R News. 2005; 5: 27-30. doi: 10.1159/000323281
Onoda Y, Richards AE, Westoby M. The relationship between stem biomechanics and wood density is modified by rainfall in 32 Australian woody plant species. New Phytol. 2010; 185: 493-501. doi: 10. 1111/j.1469-8137.2009.03088.x
Morse DR, Lawton JH, Dodson MM, Williamson MH. Fractal dimension of vegetation and the distribution of arthropod body lengths. Nature. 1985; 314: 731-733.
Stephenson NL, Das AJ, Condit R, Russo SE, Baker PJ, Beckman NG, et al. Rate of tree carbon accumulation increases continuously with tree size. Nature. Nature Publishing Group; 2014; doi: 10.1038/nature12914
Van den Bulcke J, Wernersson ELG, Dierick M, Van Loo D, Masschaele B, Brabant L, et al. 3D treering analysis using helical X-ray tomography. Dendrochronologia. 2014; 32: 39-46. doi: 10.1016/j. dendro.2013.07.001
Ter Steege H, Pitman NCA, Sabatier D, Baraloto C, Salomão RP, Guevara JE, et al. Hyperdominance in the Amazonian tree flora. Science (80-). 2013; 342: 1243092. Available: http://www.sciencemag.org/ content/342/6156/1243092.short doi: 10.1126/science.1243092