[en] Wood density profiles reveal a tree's life strategy and growth. Density profiles are, however, rarely defined in terms of tissue fractions for wood of tropical angiosperm trees. Here, we aim at linking these fractions to corresponding density profiles of tropical trees from the Congo Basin. Cores of 8 tree species were scanned with X-ray Computed Tomography to calculate density profiles. Then, cores were sanded and the outermost 3 cm were used to semi-automatically measure vessel lumen, parenchyma and fibre fractions using theWeka segmentation tool in ImageJ. Fibre wall and lumen widths were measured using a newly developed semi-automated method. An assessment of density variation in function of growth ring boundary detection is done. A mixed regression model estimated the relative contribution of each trait to the density, with a species effect on slope and intercept of the regression. Position-dependent correlations were made between the fractions and the corresponding wood density profile. On average, density profile variation mostly reflects variations in fibre lumen and wall fractions, but these are species- and position-dependent: on some positions, parenchyma and vessels have a more pronounced effect on density. The model linking density to traits explains 92% of the variation, with 65% of the density profile variation attributed to the three measured traits. The remaining 27% is explained by species as a random effect. There is a clear variation between trees and within trees that have implications for interpreting density profiles in angiosperm trees: the exact driving anatomical fraction behind every density value will depend on the position within the core. The underlying function of density will thus vary accordingly.
Disciplines :
Agriculture & agronomy Environmental sciences & ecology Phytobiology (plant sciences, forestry, mycology...) Life sciences: Multidisciplinary, general & others
Author, co-author :
De Mil, Tom ; Université de Liège - ULiège > TERRA Research Centre > Gestion des ressources forestières ; Royal Museum for Central Africa, Wood Biology Service, Tervuren, Belgium ; UGCT-UGent-Woodlab, Laboratory ofWood Technology, Department of Environment, Ghent University, Gent, Belgium
Tarelkin, Yegor; Royal Museum for Central Africa, Wood Biology Service, Tervuren, Belgium ; UGCT-UGent-Woodlab, Laboratory ofWood Technology, Department of Environment, Ghent University, Gent, Belgium ; Landscape Ecology and Plant Production Systems Unit, Université Libre de Bruxelles, Bruxelles, Belgium
Hahn, Stephan ; Laboratory of Image, Signal processing and Acoustics-Brussels School of Engineering, Université libre de Bruxelles (ULB), Brussels, Belgium
Hubau, Wannes ; Royal Museum for Central Africa, Wood Biology Service, Tervuren, Belgium ; UGCT-UGent-Woodlab, Laboratory ofWood Technology, Department of Environment, Ghent University, Gent, Belgium
Deklerck, Victor ; UGCT-UGent-Woodlab, Laboratory ofWood Technology, Department of Environment, Ghent University, Gent, Belgium
Debeir, Olivier ; Laboratory of Image, Signal processing and Acoustics-Brussels School of Engineering, Université libre de Bruxelles (ULB), Brussels, Belgium
Van Acker, Joris; UGCT-UGent-Woodlab, Laboratory ofWood Technology, Department of Environment, Ghent University, Gent, Belgium
de Cannière, Charles; Landscape Ecology and Plant Production Systems Unit, Université Libre de Bruxelles, Bruxelles, Belgium
Beeckman, Hans ; Royal Museum for Central Africa, Wood Biology Service, Tervuren, Belgium
Van den Bulcke, Jan ; UGCT-UGent-Woodlab, Laboratory ofWood Technology, Department of Environment, Ghent University, Gent, Belgium
Language :
English
Title :
Wood density profiles and their corresponding tissue fractions in tropical angiosperm trees
BELSPO - Federaal Wetenschapsbeleid FRIA - Fonds pour la Formation à la Recherche dans l'Industrie et dans l'Agriculture Innoviris Brussels King Leopold III Fund for Nature Exploration and Conservation
Funding text :
Funding: T.D.M. and V.D. were funded by the HERBAXYLAREDD BELSPO Brain program (Federaal Wetenschapsbeleid) of the Belgian Federal Government (BR/143/A3/HERBAXYLAREDD). The PhD project of T.D.M and the tenure track of J.V.d.B. were supported by Ghent University Special Research Fund (BOF) (grant no. for T.D.M.: BOF.DOC.2014.0037.01). W.H. was funded by the BELSPO Brain program (Federaal Wetenschapsbeleid) of the Belgian Federal Government (BR/132/A1/AFRIFORD). Y.T. is funded by FRIA (FC 1371), as well as Jaumotte Demoulin Foundation and Van Buren Fund. Field missions were partly covered by the Leopold III fund for Nature Conservation. The PhD of S.H. was funded by the BRIDGE programme of Innoviris (2013-PFS-EH-7).
Chave, J.; Coomes, D.; Jansen, S.; Lewis, S.L.; Swenson, N.G.; Zanne, A.E. Towards a worldwide wood economics spectrum. Ecol. Lett. 2009, 12, 351-366
Lachenbruch, B.; Mcculloh, K.A. Traits, properties, and performance: How woody plants combine hydraulic and mechanical functions in a cell, tissue, or whole plant. New Phytol. 2014, 204, 747-764
Baker, T.R.; Phillips, O.L.; Malhi, Y.; Almeida, S.; Arroyo, L.; Di Fiore, A.; Erwin, T.; Killeen, T.J.; Laurance, S.G.; Laurance, W.F.; et al. Variation in wood density determines spatial patterns in Amazonian forest biomass. Glob. Chang. Biol. 2004, 10, 545-562
Hietz, P.; Valencia, R.; Joseph Wright, S. Strong radial variation in wood density follows a uniform pattern in two neotropical rain forests. Funct. Ecol. 2013, 27, 684-692
Bastin, J.F.; Fayolle, A.; Tarelkin, Y.; Van Den Bulcke, J.; De Haulleville, T.; Mortier, F.; Beeckman, H.; Van Acker, J.; Serckx, A.; Bogaert, J.; et al. Wood specific gravity variations and biomass of central African tree species: The simple choice of the outer wood. PLoS ONE 2015, 10, e0142146
Bouriaud, O.; Leban, J.-M.; Bert, D.; Deleuze, C. Intra-annual variations in climate influence growth and wood density of Norway spruce. Tree Physiol. 2005, 25, 651-660
Björklund, J.A.; Gunnarson, B.E.; Seftigen, K.; Esper, J.; Linderholm, H.W. Blue intensity and density from northern Fennoscandian tree rings, exploring the potential to improve summer temperature reconstructions with earlywood information. Clim. Past 2014, 10, 877-885
Polge, H. établissement des courbes de variation de la densité du bois par exploration densitométrique de radiographies d'échantillons prélevés à la tarière sur des arbres vivants. Applications dans les domaines Technologique et Physiologique. Ann. Sci. For. 1966, 23, 215
Evans, R. Rapid measurement of the transverse Dimensions of Tracheids in radial wood sections from Pinus radiata. Holzforschung 1994, 48, 168-172
Chen, F.F.; Evans, R. Automated measurement of vessel properties in birch and poplar wood. Holzforschung 2010, 64, 369-374
Jacquin, P.; Longuetaud, F.; Leban, J.M.; Mothe, F. X-ray microdensitometry of wood: A review of existing principles and devices. Dendrochronologia 2017, 42, 42-50
Bergsten, U.; Lindeberg, J.; Rindby, A.; Evans, R. Batch measurements of wood density on intact or prepared drill cores using x-ray microdensitometry. Wood Sci. Technol. 2001, 35, 435-452
De Mil, T.; Vannoppen, A.; Beeckman, H.; Van Acker, J.; Van den Bulcke, J. A field-to-desktop toolchain for X-ray CT densitometry enables tree ring analysis. Ann. Bot. 2016, 117, 1187-1196
Van den Bulcke, J.; Boone, M.N.; Van Acker, J.; Stevens, M.; Van Hoorebeke, L. X-ray tomography as a tool for detailed anatomical analysis. Ann. For. Sci. 2009, 66, 1-12
Van den Bulcke, J.;Wernersson, E.L.G.; Dierick, M.; Van Loo, D.; Masschaele, B.; Brabant, L.; Boone, M.N.; Van Hoorebeke, L.; Haneca, K.; Brun, A.; et al. 3D tree-ring analysis using helical X-ray tomography. Dendrochronologia 2014, 32, 39-46
Steffenrem, A.; Kvaalen, H.; Dalen, K.S.; Høibø, O.A. A high-throughput X-ray-basedmethod formeasurements of relative wood density from unprepared increment cores from Picea abies. Scand. J. For. Res. 2014, 29, 506-514
Briffa, K.R. Trees tell of past climates: But are they speaking less clearly today? Philos. Trans. R. Soc. Lond. B Biol. Sci. 1998, 353, 65-73
Björklund, J.; Seftigen, K.; Schweingruber, F.; Fonti, P.; von Arx, G.; Bryukhanova, M.V.; Cuny, H.E.; Carrer, M.; Castagneri, D.; Frank, D.C. Cell size and wall dimensions drive distinct variability of earlywood and latewood density in Northern Hemisphere conifers. New Phytol. 2017, 216, 728-740
Goldstein, G.; Santiago, S.L. Tropical Tree Physiology; Springer: Berlin, Germany, 2016; p. 467. ISBN9783319274201
Mariaux, A. Les cernes dans les bois tropicaux africains, nature et périodicité: Peuvent-ils révéler l'âge des arbres? Bois For. Trop. 1967, 113, 3-14
Nepveu, G. Croissance et qualité du bois de framiré. Evolution de la largeur de cerne et des composantes densitométriques en fonction de l'âge. Bois For. Trop. 1976, 165, 39-58
Pagotto, M.A.; DeSoto, L.; Carvalho, A.; Nabais, C.; Filho, M.T.; Ribeiro, A.; Lisi, C.S. Evaluation of X-ray densitometry to identify tree-ring boundaries of two deciduous species from semi-arid forests in Brazil. Dendrochronologia 2017, 42, 94-103
Guilley, E.; Mothe, F.; Nepveu, G. A procedure based on conditional probabilities to estimate proportions and densities of tissues from X-ray images of Quercus petraea samples. IAWA J. 2002, 23, 235-252
Roque, R.M.; Tomazelo-Filho, M. Relationships between anatomical features and intra-ring wood density profiles in Gmelina arborea applying X-ray densitometry. Cerne 2007, 13, 384-392
Fichtler, E.; Worbes, M. Wood anatomical variables in tropical trees and their relation to site conditions and individual tree morphology. IAWA J. 2012, 33, 119-140
Leclercq, A. Influence of beechwood anatomical features upon its physico-mechanical properties. Mitt. Bundesforsch. Forst-Holzw. Hamburg Reinbek 1980, 131, 33-47
Benedet, F.; Doucet, J.; Fayolle, A.; Gourlet-Fleury, S.; Vincke, D. Cofortraits, African Plant Traits Information Database. Version 1.0. Available online: http://coforchange.cirad.fr/african_plant_trait (accessed on 12 October 2018)
Dierick, M.; Van Loo, D.; Masschaele, B.; Van den Bulcke, J.; Van Acker, J.; Cnudde, V.; Van Hoorebeke, L. Recent micro-CT scanner developments at UGCT. Nucl. Instrum. Methods Phys. Res. Sect. B Beam Interact. Mater. Atoms 2014, 324, 35-40
Dierick, M.; Masschaele, B.; Van Hoorebeke, L. Octopus, a fast and user-friendly tomographic reconstruction package developed in LabView®. Meas. Sci. Technol. 2004, 15, 1366-1370
Vlassenbroeck, J.; Dierick, M.; Masschaele, B.; Cnudde, V.; Van Hoorebeke, L.; Jacobs, P. Software tools for quantification of X-ray microtomography at the UGCT. Nucl. Instrum. Methods Phys. Res. Sect. A Accel. Spectrom. Detect. Assoc. Equip. 2007, 580, 442-445
De Ridder, M.; Van den Bulcke, J.; Vansteenkiste, D.; Van Loo, D.; Dierick, M.; Masschaele, B.; DeWitte, Y.; Mannes, D.; Lehmann, E.; Beeckman, H.; et al. High-resolution proxies for wood density variations in Terminalia superba. Ann. Bot. 2011, 107, 293-302
Schneider, C.A.; Rasband, W.S.; Eliceiri, K.W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 2012, 9, 671-675
Kennel, P.; Subsol, G.; Guéroult, M.; Borianne, P. Automatic identification of cell files in light microscopic images of conifer wood. In Proceedings of the 2010 2nd International Conference on Image Processing Theory, Tools and Applications, Paris, France, 7-10 July 2010; pp. 98-103
Wu, K.; Otoo, E.; Shoshani, A. Optimizing connected component labeling algorithms. In Proceedings of the Medical Imaging 2005: Image Processing, San Diego, CA, USA, 12-17 February 2005
Fiorio, C.; Gustedt, J. Two linear time Union-Find strategies for image processing. Theor. Comput. Sci. 1996, 154, 165-181
Van Der Walt, S.; Schönberger, J.L.; Nunez-Iglesias, J.; Boulogne, F.; Warner, J.D.; Yager, N.; Gouillart, E.; Yu, T. Scikit-image: Image processing in Python. PeerJ 2014, 2, e453
Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Müller, A.; Nothman, J.; Louppe, G.; et al. Scikit-learn: Machine Learning in Python. J. Mach. Learn. Res. 2012, 12, 2825-2830
Ziemińska, K.; Butler, D.W.; Gleason, S.M.;Wright, I.J.;Westoby, M. Fibre wall and lumen fractions drive wood density variation across 24 Australian angiosperms. AoB Plants 2013, 5
Dadzie, P.K.; Amoah, M.; Frimpong-Mensah, K.; Shi, S.Q. Comparison of density and selected microscopic characteristics of stem and branch wood of two commercial trees in Ghana. Wood Sci. Technol. 2016, 50, 91-104
Worbes, M. One hundred years of tree-ring research in the tropics-a brief history and an outlook to future challenges. Dendrochronologia 2002, 20, 217-231
Worbes, M. How to Measure Growth Dynamics in Tropical Trees. IAWA J. 1995, 16, 337-351
Worbes, M. Structural and other adaptations to long-term flooding by trees in Central Amazonia. Amazoniana 1985, 9, 459-484
Coster, C. Zur anatomie und physiologie der zuwachszonen-und jahresringbildung in den tropen. Ann. Jard. Bot. Buitenzorg 1927, 37, 49-161
Tarelkin, Y.; Delvaux, C.; De Ridder, M.; El Berkani, T.; De Cannière, C.; Beeckman, H. Growth-ring distinctness and boundary anatomy variability in tropical trees. IAWA J. 2016, 37, S1-S7
Spicer, R. Symplasmic networks in secondary vascular tissues: Parenchyma distribution and activity supporting long-distance transport. J. Exp. Bot. 2014, 65, 1829-1848
Morris, H.; Plavcová, L.; Cvecko, P.; Fichtler, E.; Gillingham, M.A.F.; Martínez-Cabrera, H.I.; Mcglinn, D.J.; Wheeler, E.; Zheng, J.; Ziemiska, K.; et al. A global analysis of parenchyma tissue fractions in secondary xylem of seed plants. New Phytol. 2016, 209, 1553-1565
Zheng, J.; Martínez-Cabrera, H.I. Wood anatomical correlates with theoretical conductivity and wood density across China: Evolutionary evidence of the functional differentiation of axial and radial parenchyma. Ann. Bot. 2013, 112, 927-935
Beeckman, H. Wood anatomy and trait-based ecology. IAWA J. 2016, 37, 127-151
