Co-developing an international TLS network for the 3D ecological understanding of global trees: System architecture, remote sensing models, and functional prospects

Environmental Science and Ecotechnology - Tập 16 - Trang 100257 - 2023
Yi Lin1, Sagi Filin2, Roland Billen3, Nobuya Mizoue4
1School of Earth and Space Sciences, Peking University, Beijing, 100871, China
2Technion – Israel Institute of Technology, Haifa IL, 32000, Israel
3Department of Geography, University of Liège, Liège, 4000, Belgium
4Faculty of Agriculture, Kyushu University, Fukuoka 819-0395, Japan

Tài liệu tham khảo

Wilmking, 2020, Global assessment of relationships between climate and tree growth, Global Change Biol., 26, 3212, 10.1111/gcb.15057 Ricklefs, 1999, Global patterns of tree species richness in moist forests: distinguishing ecological influences and historical contingency, Oikos, 86, 369, 10.2307/3546454 Achache, 2009, Keeping track of the Earth's carbon-cycle components, Nature, 461, 340, 10.1038/461340c Alessandrini, 2011, Tree size distribution at increasing spatial scales converges to the rotated sigmoid curve in two old-growth beech stands of the Italian Apennines, For. Ecol. Manag., 262, 1950, 10.1016/j.foreco.2011.08.025 Cavender, 2015, Strengthening the conservation value of ex situ tree collections, Oryx, 49, 416, 10.1017/S0030605314000866 Qian, 2019, Global and regional tree species diversity, J. Plant Ecol., 12, 210, 10.1093/jpe/rty013 Corvalan, 2004, Global warming kills trees, and people, Bull. World Health Organ., 82 Körner, 2010, Phenology under global warming, Science, 327, 1461, 10.1126/science.1186473 Ramirez, 2018, 1 Tejedor, 2020, A global perspective on the climate-driven growth synchrony of neighbouring trees, Global Ecol. Biogeogr., 29, 1114, 10.1111/geb.13090 Manzanedo, 2020, Evidence of unprecedented rise in growth synchrony from global tree ring records, Nat. Ecol. Evol., 4, 1622, 10.1038/s41559-020-01306-x Brundu, 2020, Global guidelines for the sustainable use of non-native trees to prevent tree invasions and mitigate their negative impacts, NeoBiota, 61, 65, 10.3897/neobiota.61.58380 Kendal, 2018, A global comparison of the climatic niches of urban and native tree populations, Global Ecol. Biogeogr., 27, 629, 10.1111/geb.12728 Bastin, 2019, The global tree restoration potential, Science, 369 Han, 2020, Forecasting of droughts and tree mortality under global warming: a review of causative mechanisms and modeling methods, J. Water Clim. Chang., 11, 600, 10.2166/wcc.2020.239 Adams, 2018, Grand challenges: forests and global change, Front. For. Glob. Chang., 1, 1, 10.3389/ffgc.2018.00001 Hartmann, 2018, Monitoring global tree mortality patterns and trends, New Phytol., 217, 984, 10.1111/nph.14988 Ratnam, 2020, Trees as nature-based solutions: a global south perspective, One Earth, 3, 140, 10.1016/j.oneear.2020.07.008 Nascimbene, 2009, Influence of tree age, tree size and crown structure on lichen communities in mature Alpine spruce forests, Biodivers. Conserv., 18, 1509, 10.1007/s10531-008-9537-7 Barker, 1973, Quantitative morphometry of branching structure of trees, J. Theor. Biol., 40, 33, 10.1016/0022-5193(73)90163-X Ingram, 2005, Tree structure and diversity in human-impacted littoral forests, Madagascar, Environ. Manag., 35, 779, 10.1007/s00267-004-0079-9 Boyden, 2012, Seeing the forest for the heterogeneous trees: stand-scale resource distributions emerge from tree-scale structure, Ecol. Appl., 22, 1578, 10.1890/11-1469.1 Bastin, 2018, Pan-tropical prediction of forest structure from the largest trees, Global Ecol. Biogeogr., 27, 1366, 10.1111/geb.12803 Herrero-Jauregui, 2012, Population structure of two low-density neotropical tree species under different management systems, For. Ecol. Manag., 280, 31, 10.1016/j.foreco.2012.06.006 Guilherme, 2013, Tree community structure in a neotropical swamp forest in southeastern Brazil, Biosci. J., 29, 1007 Wang, 2020, Do smaller trees easily form a ring structure around larger trees in temperate forests, Can. J. For. Res., 50, 542, 10.1139/cjfr-2019-0189 Paine, 2016, How mammalian predation contributes to tropical tree community structure, Ecol., 97, 3326, 10.1002/ecy.1586 Majasalmi, 2020, The impact of tree canopy structure on understory variation in a boreal forest, For. Ecol. Manag., 466, 10.1016/j.foreco.2020.118100 Tanaka, 2018, Effective tree distribution and stand structures in a forest for tsunami mitigation considering the different tree-breaking patterns of tree species, J. Environ. Manag., 223, 925 Osazuwa-Peters, 2015, Selective logging: does the imprint remain on tree structure and composition after 45 years, Conserv. Physiol., 3, 10.1093/conphys/cov012 Nascimbene, 2008, Influences of tree age and tree structure on the macrolichen Letharia vulpina: a case study in the Italian Alps, Ecosci, 15, 423, 10.2980/15-4-3154 Rogers, 2015, Influence of tree species on continental differences in boreal fires and climate feedbacks, Nat. Geosci., 8, 228, 10.1038/ngeo2352 Lutz, 2018, Global importance of large-diameter trees, Global Ecol. Biogeogr., 27, 849, 10.1111/geb.12747 Newton, 2015, Towards a global tree assessment, Oryx, 49, 410, 10.1017/S0030605315000137 Vicente-Serrano, 2016, Diverse relationships between forest growth and the Normalized Difference Vegetation Index at a global scale, Remote Sens. Environ., 187, 14, 10.1016/j.rse.2016.10.001 Lin, 2017, TLS-bridged co-prediction of tree-level multifarious stem structure variables from Worldview-2 panchromatic imagery: a case study of the boreal forest, Int. J. Digit. Earth, 10, 701, 10.1080/17538947.2016.1247473 Estoque, 2021, Remotely sensed tree canopy cover-based indicators for monitoring global sustainability and environmental initiatives, Environ. Res. Lett., 16, 10.1088/1748-9326/abe5d9 Jucker, 2017, Allometric equations for integrating remote sensing imagery into forest monitoring programmes, Global Change Biol., 23, 177, 10.1111/gcb.13388 Chadwick, 2018, Landscape evolution and nutrient rejuvenation reflected in Amazon forest canopy chemistry, Ecol. Lett., 21, 978, 10.1111/ele.12963 Lin, 2018, Recruiting conventional tree architecture models into state-of-the-art LiDAR mapping for investigating tree growth habits in structure, Front. Plant Sci., 9, 220, 10.3389/fpls.2018.00220 Schneider, 2020, Towards mapping the diversity of canopy structure from space with GEDI, Environ. Res. Lett., 15, 10.1088/1748-9326/ab9e99 Hansen, 2003, Global percent tree cover at a spatial resolution of 500 meters: first results of the MODIS vegetation continuous fields algorithm, Earth Interact., 7, 10, 10.1175/1087-3562(2003)007<0001:GPTCAA>2.0.CO;2 Funk, 2007, Meta-trees: grafting for a global perspective, Proc. Biol. Soc. Wash., 120, 232, 10.2988/0006-324X(2007)120[232:MGFAGP]2.0.CO;2 Crowther, 2016, Mapping tree density at a global scale, Nature, 532, 10.1038/nature16178 Kobayashi, 2016, A new global tree-cover percentage map using MODIS data, Int. J. Rem. Sens., 37, 969, 10.1080/01431161.2016.1142684 Beech, 2017, GlobalTreeSearch: the first complete global database of tree species and country distributions, J. Sustain. For., 36, 454, 10.1080/10549811.2017.1310049 Hewson, 2019, New 1 km resolution datasets of global and regional risks of tree cover loss, Land, 8, 14, 10.3390/land8010014 Stovall, 2019, Tree height explains mortality risk during an intense drought, Nat. Commun., 10, 4385, 10.1038/s41467-019-12380-6 Cáceres, 2012, The variation of tree beta diversity across a global network of forest plots, Global Ecol. Biogeogr., 21, 1191, 10.1111/j.1466-8238.2012.00770.x Verheyen, 2016, Contributions of a global network of tree diversity experiments to sustainable forest plantations, Ambio, 45, 29, 10.1007/s13280-015-0685-1 Prevedello, 2018, The importance of scattered trees for biodiversity conservation: a global meta-analysis, J. Appl. Ecol., 55, 205, 10.1111/1365-2664.12943 Brazhnik, 2019, 3D simulation of boreal forests: structure and dynamics in complex terrain and in a changing climate, Environ. Res. Lett., 10 Hanan, 2020, Satellites could soon map every tree on Earth, Nature, 587, 42, 10.1038/d41586-020-02830-3 Disney, 2019, Terrestrial LiDAR: a three-dimensional revolution in how we look at trees, New Phytol., 222, 1736, 10.1111/nph.15517 Lin, 2016, Tree species classification based on explicit tree structure feature parameters derived from static terrestrial laser scanning data, Agric. For. Meteorol., 216, 105, 10.1016/j.agrformet.2015.10.008 Malhi, 2018, New perspectives on the ecology of tree structure and tree communities through terrestrial laser scanning, Interface Focus, 8, 10.1098/rsfs.2017.0052 Latifi, 2019, Current trends in forest ecological applications of three-dimensional remote sensing: transition from experimental to operational solutions?, Forests, 10, 891, 10.3390/f10100891 Ashton, 1998, A global network of plots for understanding tree species diversity in tropical forests, Forest Biodiver. Res. Monit. Modeling: Conceptual Background and Old World Case Studies, 20, 47 Schneider, 2019, Quantifying 3D structure and occlusion in dense tropical and temperate forests using close-range LiDAR, Agric. For. Meteorol., 268, 249, 10.1016/j.agrformet.2019.01.033 Maas, 2008, Automatic forest inventory parameter determination from terrestrial laser scanner data, Int. J. Rem. Sens., 29, 1579, 10.1080/01431160701736406 Moorthy, 2011, Field characterization of olive (Olea europaea L.) tree crown architecture using terrestrial laser scanning data, Agric. For. Meteorol., 151, 204, 10.1016/j.agrformet.2010.10.005 Lau, 2019, Estimating architecture-based metabolic scaling exponents of tropical trees using terrestrial LiDAR and 3D modelling, For. Ecol. Manag., 439, 132, 10.1016/j.foreco.2019.02.019 Zhu, 2015, 3D leaf water content mapping using terrestrial laser scanner backscatter intensity with radiometric correction, ISPRS J. Photogrammetry Remote Sens., 110, 14, 10.1016/j.isprsjprs.2015.10.001 Deere, 2020, Maximizing the value of forest restoration for tropical mammals by detecting three-dimensional habitat associations, Proc. Natl. Acad. Sci. U.S.A., 117, 26254, 10.1073/pnas.2001823117 Lin, 2015, LiDAR: an important tool for next-generation phenotyping technology of high potential for plant phenomics?, Comput. Electron. Agric., 119, 61, 10.1016/j.compag.2015.10.011 Cabo, 2018, Automatic dendrometry: tree detection, tree height and diameter estimation using terrestrial laser scanning, Int. J. Appl. Earth Obs. Geoinf., 69, 164 Liang, 2013, Automatic stem mapping by merging several terrestrial laser scans at the feature and decision levels, Sensors, 13, 1614, 10.3390/s130201614 de Tanago, 2018, Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR, Methods Ecol. Evol., 9, 223, 10.1111/2041-210X.12904 Calders, 2015, Nondestructive estimates of above-ground biomass using terrestrial laser scanning, Methods Ecol. Evol., 6, 198, 10.1111/2041-210X.12301 Zheng, 2016, Assessing the contribution of woody materials to forest angular gap fraction and effective leaf area index using terrestrial laser scanning data, IEEE Trans. Geosci. Rem. Sens., 54, 1475, 10.1109/TGRS.2015.2481492 Hancock, 2017, Measurement of fine-spatial-resolution 3D vegetation structure with airborne waveform lidar: calibration and validation with voxelised terrestrial lidar, Remote Sens. Environ., 188, 37, 10.1016/j.rse.2016.10.041 Pueschel, 2013, The influence of scan mode and circle fitting on tree stem detection, stem diameter and volume extraction from terrestrial laser scans, ISPRS J. Photogrammetry Remote Sens., 77, 44, 10.1016/j.isprsjprs.2012.12.001 Saarinen, 2017, Feasibility of terrestrial laser scanning for collecting stem volume information from single trees, ISPRS J. Photogrammetry Remote Sens., 123, 140, 10.1016/j.isprsjprs.2016.11.012 Liang, 2014, Automated stem curve measurement using terrestrial laser scanning, IEEE Trans. Geosci. Rem. Sens., 52, 1739, 10.1109/TGRS.2013.2253783 Kankare, 2013, Individual tree biomass estimation using terrestrial laser scanning, ISPRS J. Photogrammetry Remote Sens., 75, 64, 10.1016/j.isprsjprs.2012.10.003 Cote, 2011, An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR, Environ. Model. Software, 26, 761, 10.1016/j.envsoft.2010.12.008 Bayer, 2013, Structural crown properties of Norway spruce (Picea abies L. Karst.) and European beech (Fagus sylvatica L.) in mixed versus pure stands revealed by terrestrial laser scanning, Trees Struct. Funct., 27, 1035, 10.1007/s00468-013-0854-4 Bayer, 2018, Structural response of black locust (Robinia pseudoacacia L.) and small-leaved lime (Tilia cordata Mill.) to varying urban environments analyzed by terrestrial laser scanning: implications for ecological functions and services, Urban For. Urban Green., 35, 129, 10.1016/j.ufug.2018.08.011 Hackenberg, 2014, Highly accurate tree models derived from terrestrial laser scan data: a method description, Forests, 5, 1069, 10.3390/f5051069 Zhao, 2015, Terrestrial lidar remote sensing of forests: maximum likelihood estimates of canopy profile, leaf area index, and leaf angle distribution, Agric. For. Meteorol., 209, 100, 10.1016/j.agrformet.2015.03.008 Zheng, 2012, Computational-geometry-based retrieval of effective leaf area index using terrestrial laser scanning, IEEE Trans. Geosci. Rem. Sens., 50, 3958, 10.1109/TGRS.2012.2187907 Sanz, 2013, Relationship between tree row LIDAR-volume and leaf area density for fruit orchards and vineyards obtained with a LIDAR 3D Dynamic Measurement System, Agric. For. Meteorol., 171, 153, 10.1016/j.agrformet.2012.11.013 Béland, 2011, Estimating leaf area distribution in savanna trees from terrestrial LiDAR measurements, Agric. For. Meteorol., 151, 1252, 10.1016/j.agrformet.2011.05.004 Srinivasan, 2014, Multi-temporal terrestrial laser scanning for modeling tree biomass change, For. Ecol. Manag., 318, 304, 10.1016/j.foreco.2014.01.038 Jung, 2011, Estimating crown variables of individual trees using airborne and terrestrial laser scanners, Rem. Sens., 3, 2346, 10.3390/rs3112346 Hackenberg, 2015, SimpleTree-An efficient open source tool to build tree models from TLS clouds, Forests, 6, 4245, 10.3390/f6114245 Grau, 2017, Estimation of 3D vegetation density with Terrestrial Laser Scanning data using voxels. A sensitivity analysis of influencing parameters, Remote Sens. Environ., 191, 373, 10.1016/j.rse.2017.01.032 Takeda, 2008, Estimating the plant area density of a Japanese larch (Larix kaempferi Sarg.) plantation using a ground-based laser scanner, Agric. For. Meteorol., 148, 428, 10.1016/j.agrformet.2007.10.004 Ma, 2017, Retrieving forest canopy extinction coefficient from terrestrial and airborne lidar, Agric. For. Meteorol., 236, 1, 10.1016/j.agrformet.2017.01.004 Garcia, 2015, Canopy clumping appraisal using terrestrial and airborne laser scanning, Remote Sens. Environ., 161, 78, 10.1016/j.rse.2015.01.030 Ferrara, 2018, An automated approach for wood-leaf separation from terrestrial LIDAR point clouds using the density based clustering algorithm DBSCAN, Agric. For. Meteorol., 262, 434, 10.1016/j.agrformet.2018.04.008 Danson, 2007, Forest canopy gap fraction from terrestrial laser scanning, Geosci. Rem. Sens. Lett. IEEE, 4, 157, 10.1109/LGRS.2006.887064 Calders, 2015, Monitoring spring phenology with high temporal resolution terrestrial LiDAR measurements, Agric. For. Meteorol., 203, 158, 10.1016/j.agrformet.2015.01.009 Li, 2017, Retrieving the gap fraction, element clumping index, and leaf area index of individual trees using single-scan data from a terrestrial laser scanner, ISPRS J. Photogrammetry Remote Sens., 130, 308, 10.1016/j.isprsjprs.2017.06.006 Kattge, 2011, TRY – a global database of plant traits, Global Change Biol., 17, 2905, 10.1111/j.1365-2486.2011.02451.x Zellweger, 2019, Advances in microclimate ecology arising from remote sensing, Trends Ecol. Evol., 34, 327, 10.1016/j.tree.2018.12.012 Xu, 2019, Linkages between tree architectural designs and life-history strategies in a subtropical montane moist forest, For. Ecol. Manag., 438, 1, 10.1016/j.foreco.2019.01.047 Jackson, 2019, Finite element analysis of trees in the wind based on terrestrial laser scanning data, Agric. For. Meteorol., 265, 137, 10.1016/j.agrformet.2018.11.014 Heinzel, 2019, A single-tree processing framework using terrestrial laser scanning data for detecting forest regeneration, Rem. Sens., 11, 60, 10.3390/rs11010060 Singh, 2018, Variability in fire-induced change to vegetation physiognomy and biomass in semi-arid savanna, Ecosphere, 9, 10.1002/ecs2.2514 Decuyper, 2018, Assessing the structural differences between tropical forest types using terrestrial laser scanning, For. Ecol. Manag., 429, 327, 10.1016/j.foreco.2018.07.032 Georgi, 2018, Long-term abandonment of forest management has a strong impact on tree morphology and wood volume allocation pattern of European Beech (Fagus sylvatica L.), Forests, 9, 704, 10.3390/f9110704 Lau, 2018, Quantifying branch architecture of tropical trees using terrestrial LiDAR and 3D modelling, Trees (Berl.), 32, 1219, 10.1007/s00468-018-1704-1 Li, 2018, On the utilization of novel spectral laser scanning for three-dimensional classification of vegetation elements, Interface Focus, 8, 10.1098/rsfs.2017.0039 Wagner, 2018, An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data, Trees (Berl.), 32, 125, 10.1007/s00468-017-1618-3 Blakey, 2017, Terrestrial laser scanning reveals below-canopy bat trait relationships with forest structure, Remote Sens. Environ., 198, 40, 10.1016/j.rse.2017.05.038 Nölke, 2015, On the geometry and allometry of big-buttressed trees - a challenge for forest monitoring: new insights from 3D-modeling with terrestrial laser scanning, iForest, 8, 574, 10.3832/ifor1449-007 Smith, 2014, Tree root system characterization and volume estimation by terrestrial laser scanning and quantitative structure modeling, Forests, 5, 3274, 10.3390/f5123274 Liu, 2019, Simulating solar-induced chlorophyll fluorescence in a boreal forest stand reconstructed from terrestrial laser scanning measurements, Remote Sens. Environ., 232, 10.1016/j.rse.2019.111274 Martin-Sanz, 2019, How does water availability affect the allocation to bark in a Mediterranean conifer?, Front. Plant Sci., 10, 607, 10.3389/fpls.2019.00607 Jacquemoud, 1996, Estimating leaf biochemistry using the PROSPECT leaf optical properties model, Remote Sens. Environ., 56, 194, 10.1016/0034-4257(95)00238-3 Bye, 2017, Estimating forest canopy parameters from satellite waveform LiDAR by inversion of the FLIGHT three-dimensional radiative transfer model, Remote Sens. Environ., 188, 177, 10.1016/j.rse.2016.10.048 Iglhaut, 2019, Structure from motion photogrammetry in forestry: a review, Curr. For. Rep., 5, 155, 10.1007/s40725-019-00094-3 Ciesielski, 2019, Accuracy of determining specific parameters of the urban forest using remote sensing, iForest, 12, 498, 10.3832/ifor3024-012 Woodgate, 2015, Understanding the variability in ground-based methods for retrieving canopy openness, gap fraction, and leaf area index in diverse forest systems, Agric. For. Meteorol., 205, 83, 10.1016/j.agrformet.2015.02.012 Tang, 2019, Definition and measurement of tree cover: a comparative analysis of field-, lidar- and landsat-based tree cover estimations in the Sierra national forests, USA, Agric. For. Meteorol., 268, 258, 10.1016/j.agrformet.2019.01.024 Ilangakoon, 2015, Estimating leaf area index by bayesian linear regression using terrestrial LiDAR, LAI-2200 plant canopy analyzer, and Landsat TM spectral indices, Can. J. Rem. Sens., 41, 315, 10.1080/07038992.2015.1102629 Stovall, 2018, Improved biomass calibration and validation with terrestrial LiDAR: implications for future LiDAR and SAR missions, IEEE J. Sel. Top. Appl. Earth Obs. Rem. Sens., 11, 3527, 10.1109/JSTARS.2018.2803110 Bazezew, 2018, Integrating airborne LiDAR and terrestrial laser scanner forest parameters for accurate above-ground biomass/carbon estimation in Ayer Hitam tropical forest, Malaysia, Int. J. Appl. Earth Obs. Geoinf., 73, 638 Iizuka, 2020, Integration of multi-sensor data to estimate plot-level stem volume using machine learning algorithms – case study of evergreen conifer planted forests in Japan, Rem. Sens., 12, 1649, 10.3390/rs12101649 Wu, 2020, Inter-comparison of remote sensing platforms for height estimation of mango and avocado tree crowns, Int. J. Appl. Earth Obs. Geoinf., 89 Mulatu, 2019, Linking terrestrial LiDAR scanner and conventional forest structure measurements with multi-modal satellite data, Forests, 10, 291, 10.3390/f10030291 Kato, 2019, Relationships between satellite-based spectral burned ratios and terrestrial laser scanning, Forests, 10, 444, 10.3390/f10050444 Urban, 2018, Surface moisture and vegetation cover analysis for drought monitoring in the Southern Kruger national Park using sentinel-1, sentinel-2, and landsat-8, Rem. Sens., 10, 1482, 10.3390/rs10091482 Lin, 2012, Multiecho-recording mobile laser scanning for enhancing individual tree crown reconstruction, IEEE Trans. Geosci. Rem. Sens., 50, 4323, 10.1109/TGRS.2012.2194503 Elseberg, 2013, One billion points in the cloud - an octree for efficient processing of 3D laser scans, ISPRS J. Photogrammetry Remote Sens., 76, 76, 10.1016/j.isprsjprs.2012.10.004 Calders, 2020, Terrestrial laser scanning in forest ecology: expanding the horizon, Remote Sens. Environ., 251, 10.1016/j.rse.2020.112102 Krooks, 2014, Predicting tree structure from tree height using terrestrial laser scanning and quantitative structure models, Silva Fenn., 48, 1125, 10.14214/sf.1125 Wilkes, 2021, Terrestrial laser scanning to reconstruct branch architecture from harvested branches, Methods Ecol. Evol., 12, 2487, 10.1111/2041-210X.13709 Bentley, 2013, An empirical assessment of tree branching networks and implications for plant allometric scaling models, Ecol. Lett., 16, 1069, 10.1111/ele.12127 Brummer, 2017, A general model for metabolic scaling in self-similar asymmetric networks, PLoS Comput. Biol., 13, 10.1371/journal.pcbi.1005394 Craine, 2013, Mechanisms of plant competition for nutrients, water and light, Funct. Ecol., 27, 833, 10.1111/1365-2435.12081 Hale, 2015, Comparison and validation of three versions of a forest wind model, Environ. Model. Software, 67, 27, 10.1016/j.envsoft.2015.01.016 Pivato, 2014, A simple tree swaying model for forest motion in windstorm conditions, Trees (Berl.), 28, 281, 10.1007/s00468-013-0948-z Jackson, 2019, An architectural understanding of natural sway frequencies in trees, J. R. Soc. Interface, 16, 10.1098/rsif.2019.0116 Spatz, 2013, Oscillation damping in trees, Plant Sci., 207, 66, 10.1016/j.plantsci.2013.02.015 Muller-Landau, 2006, Testing metabolic ecology theory for allometric scaling of tree size, growth and mortality in tropical forests, Ecol. Lett., 9, 575, 10.1111/j.1461-0248.2006.00904.x Coomes, 2006, Challenges to the generality of WBE theory, Trends Ecol. Evol., 21, 593, 10.1016/j.tree.2006.09.002 Malhi, 2015, The linkages between photosynthesis, productivity, growth and biomass in lowland Amazonian forests, Global Change Biol., 21, 2283, 10.1111/gcb.12859 Metcalfe, 2013, Effects of nitrogen fertilization on the forest floor carbon balance over the growing season in a boreal pine forest, Biogeosciences, 10, 8223, 10.5194/bg-10-8223-2013 Sánchez-Robles, 2014, Effects of tree architecture on pollen dispersal and mating patterns in Abies pinsapo Boiss., Pinaceae), Mol. Ecol., 23, 6165, 10.1111/mec.12983 Lin, 2022, Towards 3D basic theories of plant forms, Commun. Biol., 5, 703, 10.1038/s42003-022-03652-x