Abstract
Background and aims
Identifying root types helps understand the diversity of the morphogenetic capacities of root axes. Unfortunately, root types are generally defined arbitrarily. We used an unsupervised clustering method to define root types and examine the ontogeny of the Pinus pinaster root system.
Methods
K-clustering was first used on four root traits, including three geometric architectural traits: basal cross-sectional area, root tropism, parent root tropism, and branching angle. Based on these groups, we assigned all the root axes to a root type.
Results
Clustering yielded the same five groups of lateral roots and explained the same percentage of variance (70%) whatever the tree age. This way, 11,004 root axes from 69 excavated root systems ranging from 3 to 50 years were assigned in 5 types. P. pinaster root axes showed large differences based on root tropism and parent root tropism intensity. The larger horizontal shallow roots represented most of the woody volume throughout the life cycle of the trees. The frame of the central part of the root system was almost completed in 4-year-old trees.
Conclusions
In addition to the taproot, five types of lateral roots were identified in P. pinaster based on root geometry and size, and no consistent age effect was reported. This method could improve the connexion between root trait data and root modelling.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Abbreviations
- BA:
-
Branching Angle
- %CSAco:
-
Basal cross-sectional area taken 2 cm from the base of the root, divided by the cross-sectional area at the collar
- Hh:
-
Horizontal roots branching from horizontal roots (root type)
- OverSize:
-
Oversize roots (root type)
- Hv:
-
Horizontal roots branching from vertical roots (root type)
- PT:
-
Parent root tropism intensity
- T:
-
Tropism intensity
- SRL:
-
Specific root length
- Vh:
-
Vertical roots branching from horizontal roots (root type)
- Vv:
-
Vertical roots branching from vertical roots (root type)
- ZRT:
-
Zone of rapid taper
- Dataset names:
-
SA3, SA4, Picard6, Nezer13, Bilos50
References
Ahmed MA, Zarebanadkouki M, Meunier F, Javaux M, Kaestner A, Carminati A (2018) Root type matters: measurement of water uptake by seminal, crown, and lateral roots in maize. J Exp Bot 69:1199–1206. https://doi.org/10.1093/jxb/erx439
Arbelaitz O, Gurrutxaga I, Muguerza J, Pérez JM, Perona I (2013) An extensive comparative study of cluster validity indices. Pattern Recogn 46:243–256. https://doi.org/10.1016/j.patcog.2012.07.021
Atger C, Edelin C (1994) Premières données sur l’architecture comparée des systèmes racinaires et caulinaires. Can J Bot 72:963–975. https://doi.org/10.1139/b94-122
Atkinson JA, Pound MP, Bennett MJ, Wells DM (2019) Uncovering the hidden half of plants using new advances in root phenotyping. Curr Opin Biotechnol 55:1–8. https://doi.org/10.1016/j.copbio.2018.06.002
Augusto L, Bakker MR, Morel C, Meredieu C, Trichet P, Badeau V, Arrouays D, Plassard C, Achat DL, Gallet-Budynek A, Merzeau D, Canteloup D, Najar M, Ranger J (2010) Is ‘grey literature’ a reliable source of data to characterize soils at the scale of a region? A case study in a maritime pine forest in southwestern France. Eur J Soil Sci 61:807–822. https://doi.org/10.1111/j.1365-2389.2010.01286.x
Bailey PHJ, Currey JD, Fitter AH (2002) The role of root system architecture and root hairs in promoting anchorage against uprooting forces in Allium cepa and root mutants of Arabidopsis thaliana. J Exp Bot 53:333–340. https://doi.org/10.1093/jexbot/53.367.333
Bakker MR, Augusto L, Achat DL (2006) Fine root distribution of trees and understory in mature stands of maritime pine (Pinus pinaster) on dry and humid sites. Plant Soil 286:37–51. https://doi.org/10.1007/s11104-006-9024-4
Barczi J-F, Rey H, Griffon S, Jourdan C (2018) DigR: a generic model and its open source simulation software to mimic three-dimensional root-system architecture diversity. Ann Bot 121:1089–1104. https://doi.org/10.1093/aob/mcy018
Barthélémy D, Caraglio Y (2007) Plant architecture: a dynamic, multilevel and comprehensive approach to plant form, structure and ontogeny. Ann Bot 99:375–407. https://doi.org/10.1093/aob/mcl260
Bodner G, Leitner D, Nakhforoosh A, Sobotik M, Moder K, Kaul HP (2013) A statistical approach to root system classification. Front Plant Sci 4:292. https://doi.org/10.3389/fpls.2013.00292
Brown WGE, Lacate DS (1961) Rooting habits of white and red pine. Canada Dept Forestry Tech Note
Burbidge NT (1936) Root development in Pinus pinaster and the seasonal variation of its mycorrhizae. Aust For 10:32–40
Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Statist - Theory Methods 3:1–27. https://doi.org/10.1080/03610927408827101
Chambers JM, Cleveland WS, Kleiner B, Tukey PA (1983) Graphical methods for data analysis. Boston, Duxbury Press
Charles-Dominique T, Mangenet T, Rey H, Jourdan C, Edelin C (2009) Architectural analysis of root system of sexually vs. vegetatively propagated yam (Dioscorea rotundata Poir.), a tuber monocot. Plant Soil 317:61–77. https://doi.org/10.1007/s11104-008-9788-9
Charrad M, Ghazzali N, Boiteau V, Niknafs A (2014) NbClust : an R package for determining the relevant number of clusters in a data set. J Stat Softw. 61: https://doi.org/10.18637/jss.v061.i06
Chouikhi H, Charrad M, Ghazzali N (2015) A comparison study of clustering validity indices. In: 2015 Global summit on Computer & Information Technology (GSCIT). IEEE, Sousse, Tunisia, pp. 1–4
Collet C, Löf M, Pagès L (2006) Root system development of oak seedlings analysed using an architectural model. Effects of competition with grass. Plant Soil 279:367–383. https://doi.org/10.1007/s11104-005-2419-9
Colombi T, Kirchgessner N, Le Marié CA et al (2015) Next generation shovelomics: set up a tent and REST. Plant Soil 388:1–20. https://doi.org/10.1007/s11104-015-2379-7
Cottinet D (1974) Contribution à l’étude des fluctuations de la nappe du massif forestier landais. PhD Thesis, University of Bordeaux, France
Coutts MP (1983) Root architecture and tree stability. Plant Soil 71:171–188. https://doi.org/10.1007/BF02182653
Coutts MP (1987) Developmental processes in tree root systems. Can J For Res 17:761–767. https://doi.org/10.1139/x87-122
Coutts MP, Lewis GJ (1983) When is the structural root system determined in Sitka spruce? Plant Soil 71:155–160. https://doi.org/10.1007/BF02182650
Coutts MP, Nielsen CCN, Nicoll BC (1999) The development of symmetry, rigidity and anchorage in the structural root system of conifers. Plant Soil 217:1–15. https://doi.org/10.1023/A:1004578032481
Curtis J (1964) Roots of a ponderosa pine. U. S. Forest Service 1–30
Danjon F, Reubens B (2008) Assessing and analyzing 3D architecture of woody root systems, a review of methods and applications in tree and soil stability, resource acquisition and allocation. Plant Soil 303:1–34. https://doi.org/10.1007/s11104-007-9470-7
Danjon F, Fourcaud T, Bert D (2005) Root architecture and wind-firmness of mature Pinus pinaster. New Phytol 168:387–400. https://doi.org/10.1111/j.1469-8137.2005.01497.x
Danjon F, Eveno E, Bernier F, et al (2009) Genetic variability in 3D coarse root architecture in Pinus pinaster. In Proceedings of Second International Conference on Wind Effects on Trees, eds 19:155–161
Danjon F, Stokes A, Bakker MR (2013a) Root Systems of Woody Plants. Plant Roots: The Hidden Half, Root Systems of Woody Plants
Danjon F, Caplan JS, Fortin M, Meredieu C (2013b) Descendant root volume varies as a function of root type: estimation of root biomass lost during uprooting in Pinus pinaster. Front Plant Sci 4. https://doi.org/10.3389/fpls.2013.00402
Danjon F, Saint Cast C, Meredieu C, et al (2020) Descriptive variables of 11,004 roots from 69 Pinus pinaster root systems excavated and digitized in 3D [dataset]. https://doi.org/10.15454/K9DQPA
Danquechin Dorval A (2015) Architecture racinaire et stabilité chez le pin maritime (Pinus pinaster Ait.) au stade jeune. PhD Thesis, University of Bordeaux, France
Danquechin Dorval A, Meredieu C, Danjon F (2016) Anchorage failure of young trees in sandy soils is prevented by a rigid central part of the root system with various designs. Ann Bot 118:747–762. https://doi.org/10.1093/aob/mcw098
Desgraupes B (2018) Package ‘clusterCrit.’ r-project 1–10
Dimitriadou E, Dolničar S, Weingessel A (2002) An examination of indexes for determining the number of clusters in binary data sets. Psychometrika 67:137–159. https://doi.org/10.1007/BF02294713
Dunbabin VM, Diggle AJ, Rengel Z, Van Hugten R (2002) Modelling the interactions between water and nutrient uptake and root growth. Plant Soil 239:19–38. https://doi.org/10.1023/A:1014939512104
Dunbabin VM, Armstrong RD, Officer SJ, Norton RM (2009) Identifying fertiliser management strategies to maximise nitrogen and phosphorus acquisition by wheat in two contrasting soils from Victoria, Australia. Aust J Soil Res 47:74. https://doi.org/10.1071/SR08107
Dupuy L, Fourcaud T, Stokes A, Danjon F (2005) A density-based approach for the modelling of root architecture: application to maritime pine (Pinus pinaster Ait.) root systems. J Theor Biol 236:323–334. https://doi.org/10.1016/j.jtbi.2005.03.013
Edwards AWF, Cavalli-Sforza LL (1965) A method for cluster analysis. Biometrics 21:362. https://doi.org/10.2307/2528096
Eis S (1974) Root system morphology of Western hemlock, Western red cedar, and Douglas-fir. Can J For Res 4:28–38. https://doi.org/10.1139/x74-005
Fayle DCF (1975) Extension and longitudinal growth during the development of red pine root systems. Can J For Res 5:109–121. https://doi.org/10.1139/x75-016
Fry EL, Evans AL, Sturrock CJ, Bullock JM, Bardgett RD (2018) Root architecture governs plasticity in response to drought. Plant Soil 433:189–200. https://doi.org/10.1007/s11104-018-3824-1
Giehl RFH, von Wiren N (2014) Root nutrient foraging. Plant Physiol 166:509–517. https://doi.org/10.1104/pp.114.245225
Godin C, Caraglio Y (1998) A multiscale model of plant topological structures. J Theor Biol 191:1–46. https://doi.org/10.1006/jtbi.1997.0561
Godin C, Costes E, Caraglio Y (1997) Exploring plant topological structure with the AMAPmod software: an outline. Silva Fennica 31:355–366. https://doi.org/10.14214/sf.a8533
Guo D, Li H, Mitchell RJ, Han W, Hendricks JJ, Fahey TJ, Hendrick RL (2008) Fine root heterogeneity by branch order: exploring the discrepancy in root turnover estimates between minirhizotron and carbon isotopic methods. New Phytol 177:443–456. https://doi.org/10.1111/j.1469-8137.2007.02242.x
Halkidi M, Vazirgiannis M (2001) Clustering validity assessment: finding the optimal partitioning of a data set. In: Proceedings 2001 IEEE international conference on data mining. IEEE Comput. Soc, San Jose, CA, USA, pp. 187–194. https://doi.org/10.1109/ICDM.2001.989517
Halkidi M, Batistakis Y, Vazirgiannis M (2001) On Clustering Validation Techniques. J Intell Inf Syst 17:107–145. https://doi.org/10.1023/A:1012801612483
Hartigan JA (1975) Clustering algorithms. Wiley, New York
Hartigan JA, Wong MA (1979) Algorithm AS 136: a K-means clustering algorithm. Appl Stat 28:100. https://doi.org/10.2307/2346830
Heyward F (1933) The root system of longleaf pine on the Deep Sands of Western Florida. Ecology 14:136–148. https://doi.org/10.2307/1932880
Hishi T, Takeda H (2005) Dynamics of heterorhizic root systems: protoxylem groups within the fine-root system of Chamaecyparis obtusa. New Phytol 167:509–521. https://doi.org/10.1111/j.1469-8137.2005.01418.x
Hodge A (2004) The plastic plant: root responses to heterogeneous supplies of nutrients. New Phytologist 162:9–24. https://doi.org/10.1111/j.1469-8137.2004.01015.x
Horton KW (1958) Rooting habits of lodgepole pine. Forest Res Division Technical Note No 67
Janalipour M, Mohammadzadeh A (2019) A novel and automatic framework for producing building damage map using post-event LiDAR data. International Journal of Disaster Risk Reduction 39:101238. https://doi.org/10.1016/j.ijdrr.2019.101238
Jourdan C, Rey H (1997) Modelling and simulation of the architecture and development of the oil-palm (Elaeis guineensis Jacq.) root system. Plant Soil 190:217–233. https://doi.org/10.1023/A:1004270014678
Khuder H, Stokes A, Danjon F, Gouskou K, Lagane F (2007) Is it possible to manipulate root anchorage in young trees? Plant Soil 294:87–102. https://doi.org/10.1007/s11104-007-9232-6
Korndörfer CL, Mósena M, Dillenburg LR (2008) Initial growth of Brazilian pine (Araucaria angustifolia) under equal soil volumes but contrasting rooting depths. Trees 22:835–841. https://doi.org/10.1007/s00468-008-0244-5
Köstler JN, Brückner E, Bibelriether B (1968) Die Wurzeln der Waldbäume. Verlag Paul Parey 120:140–141. https://doi.org/10.1002/jpln.19681200210
Kutschera L (1960) Wurzelatlas mitteleuropäischer Ackerunkräuter und Kulturpflanzen. DLG-Verlags-G.m.b.h, Verlag
Laitakari E (1929) The root systems of pine. Acta Forestalia Fennica
Li X, Chen Z, Chen J, Zhu H (2019) Automatic characterization of rock mass discontinuities using 3D point clouds. Eng Geol 259:105131. https://doi.org/10.1016/j.enggeo.2019.05.008
Liu Y, Li Z, Xiong H, et al (2010) Understanding of internal clustering validation measures. In: 2010 IEEE international conference on data mining. IEEE, Sydney, Australia, pp. 911–916
Loades KW, Bengough AG, Bransby MF, Hallett PD (2013) Biomechanics of nodal, seminal and lateral roots of barley: effects of diameter, waterlogging and mechanical impedance. Plant Soil 370:407–418. https://doi.org/10.1007/s11104-013-1643-y
Lynch JP (2007) Roots of the second green revolution. Aust J Bot 55:493. https://doi.org/10.1071/BT06118
Lynch JP (2019) Root phenotypes for improved nutrient capture: an underexploited opportunity for global agriculture. New Phytol 223:548–564. https://doi.org/10.1111/nph.15738
Ma L, Shi Y, Siemianowski O, Yuan B, Egner TK, Mirnezami SV, Lind KR, Ganapathysubramanian B, Venditti V, Cademartiri L (2019) Hydrogel-based transparent soils for root phenotyping in vivo. Proc Natl Acad Sci U S A 116:11063–11068. https://doi.org/10.1073/pnas.1820334116
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In 5-th Berkeley symposium on mathematical statistics and Probabilit 281–297
Mauer O, Palátová E (2012) Root system development in Douglas fir (Pseudotsuga menziesii [Mirb.] Franco) on fertile sites. J Sci 58:400–409. https://doi.org/10.17221/94/2011-JFS
McQuilkin WE (1935) Root development of pitch pine with some comparative observations on shortleaf pine. J Agric Res, Washington, D C 51:983–1016
Milligan GW (1980) An examination of the effect of six types of error perturbation on fifteen clustering algorithms. Psychometrika 45:325–342. https://doi.org/10.1007/BF02293907
Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika 50:159–179. https://doi.org/10.1007/BF02294245
Mohamad IB, Usman D (2013) Standardization and its effects on K-means clustering algorithm. Res J Appl Sci, Eng Technol 6:3299–3303. https://doi.org/10.19026/rjaset.6.3638
Muller B, Guédon Y, Passot S, Lobet G, Nacry P, Pagès L, Wissuwa M, Draye X (2019) Lateral roots: random diversity in adversity. Trends Plant Sci 24:810–825. https://doi.org/10.1016/j.tplants.2019.05.011
Nicoll BC, Coutts MP (1998) Timing of root dormancy and tolerance to root waterlogging in clonal Sitka spruce. Trees 12:241. https://doi.org/10.1007/s004680050147
Nicoll BC, Easton EP, Milner AD, Walker C, Coutts MP (1995) Wind stability factors in tree selection: distribution of biomass within root systems of Sitka spruce clones. In: Coutts MP, Grace J (eds) Wind and trees. Cambridge University Press, Cambridge, pp 276–292
Nielsen CC (1990) Einflüsse von Pflanzenabstand und Stammzahlhaltung auf Wurzelform, Wurzelbiomasse, Verankerung sowie auf die Biomassenverteilung im Hinblick auf die Sturmfestigkeit der Fichte. Sauerländer, Frankfurt am Main
Oppelt AL, Kurth W, Godbold DL (2001) Topology, scaling relations and Leonardo’s rule in root systems from African tree species. Tree Physiol 21:117–128. https://doi.org/10.1093/treephys/21.2-3.117
Pagès L (1993) Development of the root system of young peach trees (Prunus persia L. Batsch): a Morphometrical analysis. Ann Bot 71:369–375. https://doi.org/10.1006/anbo.1993.1046
Pagès L, Le Roux Y, Thaler P (1995) Modélisation de l’architecture racinaire. Plantations, Recherche, Développement 2:19–34
Pagès L, Vercambre G, Drouet J-L, Lecompte F, Collet C, le Bot J (2004) Root Typ: a generic model to depict and analyse the root system architecture. Plant Soil 258:103–119. https://doi.org/10.1023/B:PLSO.0000016540.47134.03
Passot S, Gnacko F, Moukouanga D, Lucas M, Guyomarc’h S, Ortega BM, Atkinson JA, Belko MN, Bennett MJ, Gantet P, Wells DM, Guédon Y, Vigouroux Y, Verdeil JL, Muller B, Laplaze L (2016) Characterization of pearl millet root architecture and anatomy reveals three types of lateral roots. Front Plant Sci 7. https://doi.org/10.3389/fpls.2016.00829
Passot S, Moreno-Ortega B, Moukouanga D, Balsera C, Guyomarc’h S, Lucas M, Lobet G, Laplaze L, Muller B, Guédon Y (2018) A new phenotyping pipeline reveals three types of lateral roots and a random branching pattern in two cereals. Plant Physiol 177:896–910. https://doi.org/10.1104/pp.17.01648
Pavlis J, Jeník J (2000) Roots of pioneer trees in the Amazonian rain forest. Trees 14:442–455. https://doi.org/10.1007/s004680000049
Pregitzer KS, DeForest JL, Burton AJ et al (2002) Fine root architecture of nine north american trees. Ecol Monogr 72:293–309. https://doi.org/10.1890/0012-9615(2002)072[0293:FRAONN]2.0.CO;2
R Development Core Team (2019) R: A Language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org
Puhe J (2003) Growth and development of the root system of Norway spruce (Picea abies) in forest stands - a review. For Ecol Manag 175:253–273. https://doi.org/10.1016/S0378-1127(02)00134-2
Ratkowsky DA, Lance GN (1978) Criterion for determining the number of groups in a classification. Aust Comput J 10:115–117
Rose DA (1983) The description of the growth of root systems. Plant Soil 75:405–415. https://doi.org/10.1007/BF02369974
Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53–65. https://doi.org/10.1016/0377-0427(87)90125-7
Rowe J (1964) Studies in the rooting of White spruce. Project H-131, 2nd Prog rep, Mimeo Dep For Can
Ruedt C, Gibis M, Weiss J (2020) Quantification of surface iridescence in meat products by digital image analysis. Meat Sci 163:108064. https://doi.org/10.1016/j.meatsci.2020.108064
Saint Cast C, Meredieu C, Défossez P, Pagès L, Danjon F (2019) Modelling root system development for anchorage of forest trees up to the mature stage, including acclimation to soil constraints: the case of Pinus pinaster. Plant Soil 439:405–430. https://doi.org/10.1007/s11104-019-04039-4
Schnepf A, Leitner D, Landl M, Lobet G, Mai TH, Morandage S, Sheng C, Zörner M, Vanderborght J, Vereecken H (2018) CRootBox: a structural–functional modelling framework for root systems. Ann Bot 121:1033–1053. https://doi.org/10.1093/aob/mcx221
Scott AJ, Symons MJ (1971) Clustering methods based on likelihood ratio criteria. Biometrics 27:387. https://doi.org/10.2307/2529003
Shim Y, Chung J, Choi I-C (2005) A comparison study of cluster validity indices using a nonhierarchical clustering algorithm. In: International conference on computational intelligence for Modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). IEEE, Vienna, Austria, pp 199–204
Soethe N, Lehmann J, Engels C (2007) Root tapering between branching points should be included in fractal root system analysis. Ecol Model 207:363–366. https://doi.org/10.1016/j.ecolmodel.2007.05.007
Strong WL, Roi GHL (1983) Root-system morphology of common boreal forest trees in Alberta, Canada. Can J For Res 13:1164–1173. https://doi.org/10.1139/x83-155
Tai H, Lu X, Opitz N, Marcon C, Paschold A, Lithio A, Nettleton D, Hochholdinger F (2016) Transcriptomic and anatomical complexity of primary, seminal, and crown roots highlight root type-specific functional diversity in maize (Zea mays L.). J Exp Bot 67:1123–1135. https://doi.org/10.1093/jxb/erv513
Tian K, Li J, Zeng J, Evans A, Zhang L (2019) Segmentation of tomato leaf images based on adaptive clustering number of K-means algorithm. Comput Electron Agric 165:104962. https://doi.org/10.1016/j.compag.2019.104962
Tobin B, Čermák J, Chiatante D, Danjon F, di Iorio A, Dupuy L, Eshel A, Jourdan C, Kalliokoski T, Laiho R, Nadezhdina N, Nicoll B, Pagès L, Silva J, Spanos I (2007) Towards developmental modelling of tree root systems. Plant Biosyst - Int J Dealing all Asp Plant Biol 141:481–501. https://doi.org/10.1080/11263500701626283
Tracy SR, Nagel KA, Postma JA, Fassbender H, Wasson A, Watt M (2020) Crop improvement from Phenotyping roots: highlights reveal expanding opportunities. Trends Plant Sci 25:105–118. https://doi.org/10.1016/j.tplants.2019.10.015
Vercambre G, Pagès L, Doussan C, Habib R (2003) Architectural analysis and synthesis of the plum tree root system in an orchard using a quantitative modelling approach. Plant Soil 251:1–11. https://doi.org/10.1023/A:1022961513239
Wang M, Abrams ZB, Kornblau SM, Coombes KR (2018) Thresher: determining the number of clusters while removing outliers. BMC Bioinform 19. https://doi.org/10.1186/s12859-017-1998-9
Watt M, Magee LJ, McCully ME (2008) Types, structure and potential for axial water flow in the deepest roots of field-grown cereals. New Phytol 178:135–146. https://doi.org/10.1111/j.1469-8137.2007.02358.x
White AJ, Keller JP, Zhao S et al (2019) Air pollution, clustering of particulate matter components, and breast Cancer in the sister study: a U.S.-wide cohort. Environ Health Perspect 127:107002. https://doi.org/10.1289/EHP5131
Yang M, Défossez P, Danjon F, Dupont S, Fourcaud T (2017) Which root architectural elements contribute the best to anchorage of Pinus species? Insights from in silico experiments. Plant Soil 411:275–291. https://doi.org/10.1007/s11104-016-2992-0
York LM (2019) Functional phenomics: an emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. J Exp Bot 70:379–386. https://doi.org/10.1093/jxb/ery379
Yu P, Wang C, Baldauf JA, Tai H, Gutjahr C, Hochholdinger F, Li C (2018) Root type and soil phosphate determine the taxonomic landscape of colonizing fungi and the transcriptome of field-grown maize roots. New Phytol 217:1240–1253. https://doi.org/10.1111/nph.14893
Zadworny M, Eissenstat DM (2011) Contrasting the morphology, anatomy and fungal colonization of new pioneer and fibrous roots. New Phytol 190:213–221. https://doi.org/10.1111/j.1469-8137.2010.03598.x
Zobel RW (2011) A developmental genetic basis for defining root classes. Crop Sci 51:1410–1413. https://doi.org/10.2135/cropsci2010.11.0652
Zobel RW, Waisel Y (2010) A plant root system architectural taxonomy: a framework for root nomenclature. Plant Biosyst - Int J Dealing All Aspects Plant Biol 144:507–512. https://doi.org/10.1080/11263501003764483
Acknowledgements
We thank Raphaël Segura, Charlotte Swahn, Chantal Giroux, Antoine Danquechin Dorval, Alexandre Collin (INRAE, UMR1202, BIOGECO) and Bernard Issenhuth, Nicolas Cheval, Laurent Severin, William Oliva, Camille Guillem, Jean-Paul Chambon (INRAE, UE0570, UEFP - https://doi.org/10.15454/1.5483264699193726E12) for their technical support in data acquisition, the editor and the two anonymous reviewers for their valuable comments and suggestions. We also thank the Conseil Régional d’Aquitaine and the French Ministère de l’Agriculture, de l’Agroalimentaire et de la Forêt (Fortius project – 13001087) [grant number 13001087] for providing funds for the measurements and the framework of the Cluster of Excellence COTE (ANR-10-LABX-45) for additional funds.
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: Andrea Schnepf.
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
For each of the three traits %CSAco, T and PT, two clusters were discriminated in all datasets. We set limits between groups as the means of the 90th or 10th centiles of the two groups. For example, for tropism (T), in Bilos50, we got either vertical or horizontal roots, the 90th centile of the roots partitioned as horizontal roots was 0.38 and the 10th centile of vertical roots was 0.47. The limit between vertical and horizontal roots was therefore set to 0.43. Limits were then averaged across the five datasets. These fixed delimitations between the three aforementioned root traits allowed defining what we then called “root types”. We then assigned all the root axes of the datasets to root types using these new defined limits, with the following procedure: we first allocated them based on their %CSAco, then their tropism intensity (T), and finally their parent root tropism intensity (PT). Branching angle (BA) was not used for this secondary assignment
Rights and permissions
About this article
Cite this article
Saint Cast, C., Meredieu, C., Défossez, P. et al. Clustering of Pinus pinaster coarse roots, from juvenile to mature stage. Plant Soil 457, 185–205 (2020). https://doi.org/10.1007/s11104-020-04736-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11104-020-04736-5