The global distribution and drivers of wood density and their impact on forest carbon stocks
Jazyk angličtina Země Velká Británie, Anglie Médium print-electronic
Typ dokumentu časopisecké články
PubMed
39406932
PubMed Central
PMC11618071
DOI
10.1038/s41559-024-02564-9
PII: 10.1038/s41559-024-02564-9
Knihovny.cz E-zdroje
- MeSH
- biomasa MeSH
- dřevo * MeSH
- lesy * MeSH
- stromy * růst a vývoj metabolismus MeSH
- uhlík * metabolismus analýza MeSH
- Publikační typ
- časopisecké články MeSH
- Názvy látek
- uhlík * MeSH
The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.
Agricultural High School Polytechnic Institute of Viseu Viseu Portugal
AgroParisTech UMR AMAP Cirad CNRS INRA IRD Université de Montpellier Montpellier France
AMAP Univ Montpellier CIRAD CNRS INRAE IRD Montpellier France
AMAP Univ Montpellier Montpellier France
Andes to Amazon Biodiversity Program Madre de Dios Peru
Bavarian State Institute of Forestry Freising Germany
Center for Forest Ecology and Productivity Russian Academy of Sciences Moscow Russian Federation
Center for Natural Climate Solutions Conservation International Arlington TX USA
Center for Tropical Research Institute of the Environment and Sustainability UCLA Los Angeles CA USA
Centre for Agricultural Research in Suriname Paramaribo Suriname
Centre for Conservation Science The Royal Society for the Protection of Birds Sandy UK
Centre for Forest Research Université du Québec à Montréal Montréal Québec Canada
Centre for Integrative Biodiversity Research Halle Jena Leipzig Leipzig Germany
Centro Agricoltura Alimenti Ambiente University of Trento San Michele All'adige Italy
Centro de Ciências Biológicas e da Natureza Universidade Federal do Acre Rio Branco Brazil
Centro Multidisciplinar Universidade Federal do Acre Rio Branco Brazil
CIRAD CNRS INRAE IRD Montpellier France
Cirad UMR EcoFoG Campus Agronomique Kourou French Guiana
Cirad UPR Forêts et Sociétés University of Montpellier Montpellier France
Climate Fire and Carbon Cycle Sciences USDA Forest Service Durham NC USA
Colegio de Profesionales Forestales de Cochabamba Cochabamba Bolivia
Compensation International S A Ci Progress GreenLife Bogotá Colombia
CTFS ForestGEO Smithsonian Tropical Research Institute Panama City Panama
Departamento de Biología Universidad de la Serena La Serena Chile
Departamento de Ciências Biológicas Universidade do Estado de Mato Grosso Nova Xavantina Brazil
Departamento de Ecologia Universidade Federal do Rio Grande do Norte Natal Brazil
Departamento de Gestión Forestal y su Medio Ambiente Universidad de Chile Santiago Chile
Department of Agricultural and Forest Sciences and Engineering University of Lleida Lleida Spain
Department of Agricultural Food Environmental and Animal Sciences University of Udine Udine Italy
Department of Agriculture Food Environment and Forest University of Firenze Florence Italy
Department of Agriculture Forestry and Bioresources Seoul National University Seoul South Korea
Department of Biological Geological and Environmental Sciences University of Bologna Bologna Italy
Department of Biology Stanford University Stanford CA USA
Department of Biology University of Florence Florence Italy
Department of Biology University of Missouri St Louis St Louis MO USA
Department of Biology University of Oxford Oxford UK
Department of Biology West Virginia University Morgantown WV USA
Department of Botany Banaras Hindu University Varanasi India
Department of Botany Dr Harisingh Gour Vishwavidyalaya Sagar India
Department of Botany Faculty of Science University of South Bohemia České Budějovice Czech Republic
Department of Ecology and Environmental Sciences Pondicherry University Puducherry India
Department of Ecology and Evolutionary Biology University of Arizona Tucson AZ USA
Department of Ecology and Evolutionary Biology University of Connecticut Storrs CT USA
Department of Environment and Development Studies United International University Dhaka Bangladesh
Department of Environment and Geography University of York York UK
Department of Evolutionary Anthropology Duke University Durham NC USA
Department of Evolutionary Biology and Environmental Studies University of Zürich Zurich Switzerland
Department of Forest and Wood Science University of Stellenbosch Stellenbosch South Africa
Department of Forest Engineering Universidade Regional de Blumenau Blumenau Brazil
Department of Forest Resources University of Minnesota St Paul MN USA
Department of Forest Science Tokyo University of Agriculture Tokyo Japan
Department of Forestry and Environment National Polytechnic Institute Yamoussoukro Côte d'Ivoire
Department of Forestry and Natural Resources Purdue University West Lafayette IN USA
Department of Game Management and Forest Protection Poznań University of Life Sciences Poznań Poland
Department of Genetics Evolution and Environment University College London London UK
Department of Geography Environment and Geomatics University of Guelph Guelph Ontario Canada
Department of Geography Remote Sensing Laboratories University of Zürich Zurich Switzerland
Department of Geography University College London London UK
Department of Geomatics Forest Research Institute Sękocin Stary Poland
Department of Natural Sciences Manchester Metropolitan University Manchester UK
Department of Physical and Biological Sciences The College of Saint Rose Albany NY USA
Department of Physical and Environmental Sciences Colorado Mesa University Grand Junction CO USA
Department of Plant Biology Institute of Biology University of Campinas Campinas Brazil
Department of Plant Systematics University of Bayreuth Bayreuth Germany
Department of Spatial Regulation GIS and Forest Policy Institute of Forestry Belgrade Serbia
Department of Wildlife Management College of African Wildlife Management Mweka Tanzania
Department of Zoology University of Oxford Oxford UK
Division of Forest and Forest Resources Norwegian Institute of Bioeconomy Research Ås Norway
Division of Forest Resources Information Korea Forest Promotion Institute Seoul South Korea
Division of Forestry and Natural Resources West Virginia University Morgantown WV USA
Ecole de Foresterie et Ingénierie du Bois Université Nationale d'Agriculture Kétou Benin
Embrapa Recursos Genéticos e Biotecnologia Brasilia Brazil
Environmental Change Institute School of Geography and the Environment Oxford UK
Environmental Studies and Research Center University of Campinas Campinas Brazil
European Commission Joint Research Center Ispra Italy
Faculty of Biology Białowieża Geobotanical Station University of Warsaw Białowieża Poland
Faculty of Forestry and Wood Sciences Czech University of Life Sciences Prague Czech Republic
Faculty of Forestry Qingdao Agricultural University Qingdao China
Faculty of Natural Resources Management Lakehead University Thunder Bay Ontario Canada
Field Museum of Natural History Chicago IL USA
Flamingo Land Ltd Kirby Misperton UK
Forest Research Institute Malaysia Kuala Lumpur Malaysia
Forest Research Institute University of the Sunshine Coast Sippy Downs Queensland Australia
Forest Science and Technology Centre of Catalonia Solsona Spain
Forestry Consultant Grosseto Italy
Forestry Division Food and Agriculture Organization of the United Nations Rome Italy
Forestry School Tecnológico de Costa Rica TEC Cartago Costa Rica
Fundacion Con Vida Universidad Nacional Abierta y a Distancia Medellin Colombia
Geobotany Faculty of Biology University of Freiburg Freiburg im Breisgau Germany
Geography Faculty of Environment Science and Economy University of Exeter Exeter UK
Glick Designs LLC Hadley MA USA
Global Change Research Institute CAS Brno Czech Republic
Graduate School of Agriculture Kyoto University Kyoto Japan
Guyana Forestry Commission Georgetown French Guiana
Hawkesbury Institute for the Environment Western Sydney University Penrith New South Wales Australia
IFER Institute of Forest Ecosystem Research Jilove u Prahy Czech Republic
Independent Researcher Sommersbergseestrasse Bad Aussee Austria
Institut Agronomique néo Calédonien Nouméa New Caledonia
Institute for World Forestry University of Hamburg Hamburg Germany
Institute of Botany The Czech Academy of Sciences Třeboň Czech Republic
Institute of Dendrology Polish Academy of Sciences Kórnik Poland
Institute of Environmental Sciences Leiden University Leiden the Netherlands
Institute of Forestry and Engineering Estonian University of Life Sciences Tartu Estonia
Institute of Forestry Belgrade Serbia
Institute of Integrative Biology ETH Zurich Zurich Switzerland
Institute of Plant Sciences University of Bern Bern Switzerland
Instituto de Investigaciones de la Amazonía Peruana Iquitos Peru
Instituto Nacional de Pesquisas da Amazônia Manaus Brazil
Instituto Nacional de Tecnología Agropecuaria Río Gallegos Argentina
International Institute for Applied Systems Analysis Laxenburg Austria
IRET Herbier National du Gabon Libreville Gabon
Isotope Bioscience Laboratory ISOFYS Ghent University Ghent Belgium
Iwokrama International Centre for Rainforest Conservation and Development Georgetown French Guiana
Jardín Botánico de Medellín Medellin Colombia
Jardín Botánico de Missouri Oxapampa Peru
LINCGlobal Museo Nacional de Ciencias Naturales CSIC Madrid Spain
Manaaki Whenua Landcare Research Lincoln New Zealand
Museo de Historia natural Noel kempff Mercado Santa Cruz Bolivia
Museu Paraense Emílio Goeldi Coordenação de Ciências da Terra e Ecologia Belém Brazil
National Biodiversity Future Center Palermo Italy
National Center for Agro Meteorology Seoul South Korea
National Forest Centre Forest Research Institute Zvolen Zvolen Slovakia
National Institute of Amazonian Research Manaus Brazil
Natural Resources Institute Finland Joensuu Finland
Natural Science Department Universidade Regional de Blumenau Blumenau Brazil
Naturalis Biodiversity Center Leiden the Netherlands
Negaunee Integrative Research Center Field Museum of Natural History Chicago IL USA
Nicholas School of the Environment Duke University Durham NC USA
Peoples Friendship University of Russia Moscow Russian Federation
Plant Ecology and Nature Conservation Group Wageningen University Wageningen the Netherlands
Polish State Forests Coordination Center for Environmental Projects Warsaw Poland
Pontificia Universidad Católica del Ecuador Quito Ecuador
Proceedings of the National Academy of Sciences Washington DC USA
Quantitative Biodiversity Dynamics Department of Biology Utrecht University Utrecht the Netherlands
Research and Innovation Center Fondazione Edmund Mach San Michele All'adige Italy
Research Institute for Agriculture and Life Sciences Seoul National University Seoul South Korea
Rhino and Forest Fund e 5 Kehl Germany
Royal Botanic Garden Edinburgh Edinburgh UK
Santa Catarina State University Lages Brazil
School of Biological and Behavioural Sciences Queen Mary University of London London UK
School of Biological Sciences University of Bristol Bristol UK
School of Forestry and Environmental Studies Yale University New Haven CT USA
School of Geography University of Leeds Leeds UK
School of Social Sciences Western Sydney University Penrith New South Wales Australia
Section for Ecoinformatics and Biodiversity Department of Biology Aarhus University Aarhus Denmark
Siberian Federal University Krasnoyarsk Russian Federation
Silviculture and Forest Ecology of the Temperate Zones University of Göttingen Göttingen Germany
Silviculture Research Institute Vietnamese Academy of Forest Sciences Hanoi Vietnam
Ștefan cel Mare University of Suceava Suceava Romania
Sustainable Forest Management Research Institute iuFOR University Valladolid Valladolid Spain
Swiss Federal Institute for Forest Snow and Landscape Research WSL Birmensdorf Switzerland
TERRA Teach and Research Centre Gembloux Agro Bio Tech University of Liege Liege Belgium
The Nature Conservancy Boulder CO USA
The Santa Fe Institute Santa Fe NM USA
Theoretical Ecology Unit African Institute for Mathematical Sciences Cape Town South Africa
Tropenbos International Wageningen the Netherlands
Tropical Biodiversity MUSE Museo delle Scienze Trento Italy
UFR Biosciences University Félix Houphouët Boigny Abidjan Côte d'Ivoire
UNELLEZ Guanare Programa de Ciencias del Agro y el Mar Herbario Universitario Guanare Venezuela
Universidad del Tolima Ibagué Colombia
Universidad Estatal Amazónica Puyo Ecuador
Universidad Nacional de la Amazonía Peruana Iquitos Peru
Universidad Nacional de San Antonio Abad del Cusco Cusco Peru
Université de Lorraine AgroParisTech INRAE Silva Nancy France
Vicerrectoría de Investigación y Postgrado Universidad de La Frontera Temuco Chile
Wageningen University and Research Wageningen the Netherlands
Zobrazit více v PubMed
Chave, J. et al. Towards a worldwide wood economics spectrum. Ecol. Lett.12, 351–366 (2009). PubMed
Swenson, N. G. & Enquist, B. J. Ecological and evolutionary determinants of a key plant functional trait: wood density and its community-wide variation across latitude and elevation. Am. J. Bot.94, 451–459 (2007). PubMed
Kraft, N. J. B., Metz, M. R., Condit, R. S. & Chave, J. The relationship between wood density and mortality in a global tropical forest data set. New Phytol.188, 1124–1136 (2010). PubMed
Pérez-Ramos, I. M., Matías, L., Gómez-Aparicio, L. & Godoy, Ó. Functional traits and phenotypic plasticity modulate species coexistence across contrasting climatic conditions. Nat. Commun.10, 2555 (2019). PubMed PMC
Reich, P. B. et al. The evolution of plant functional variation: traits, spectra and strategies. Int. J. Plant Sci.164, S143–S164 (2003).
Westoby, M. & Wright, I. J. Land-plant ecology on the basis of functional traits. Trends Ecol. Evol.21, 261–268 (2006). PubMed
Bouchard, E. et al. Global patterns and environmental drivers of forest functional composition. Glob. Ecol. Biogeogr.33, 303–324 (2024).
Reis, S. M. et al. Climate and crown damage drive tree mortality in southern Amazonian edge forests. J. Ecol.110, 876–888 (2022).
Poorter, L. et al. Wet and dry tropical forests show opposite successional pathways in wood density but converge over time. Nat. Ecol. Evol.3, 928–934 (2019). PubMed
Chave, J. et al. Regional and phylogenetic variation of wood density across 2456 neotropical tree species. Ecol. Appl.16, 2356–2367 (2006). PubMed
Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA108, 9899–9904 (2011). PubMed PMC
Thurner, M. et al. Carbon stock and density of northern boreal and temperate forests. Glob. Ecol. Biogeogr.23, 297–310 (2014).
Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data13, 3927–3950 (2021).
Baker, T. R. et al. Variation in wood density determines spatial patterns inAmazonian forest biomass. Glob. Change Biol.10, 545–562 (2004).
Preston, K. A., Cornwell, W. K. & DeNoyer, J. L. Wood density and vessel traits as distinct correlates of ecological strategy in 51 California coast range angiosperms. New Phytol.170, 807–818 (2006). PubMed
Swenson, N. G. & Zambrano, J. Why wood density varies across communities. J. Veg. Sci.28, 4–6 (2017).
Slik, J. W. F. et al. Environmental correlates of tree biomass, basal area, wood specific gravity and stem density gradients in Borneo’s tropical forests. Glob. Ecol. Biogeogr.19, 50–60 (2010).
Crivellaro, A., Piermattei, A., Dolezal, J., Dupree, P. & Büntgen, U. Biogeographic implication of temperature-induced plant cell wall lignification. Commun. Biol.5, 767 (2022). PubMed PMC
Gleason, S. M. et al. Weak tradeoff between xylem safety and xylem‐specific hydraulic efficiency across the world’s woody plant species. New Phytol.209, 123–136 (2016). PubMed
Choat, B. et al. Global convergence in the vulnerability of forests to drought. Nature491, 752–755 (2012). PubMed
Johnson, D. M., Katul, G. & Domec, J. Catastrophic hydraulic failure and tipping points in plants. Plant Cell Environ.45, 2231–2266 (2022). PubMed PMC
McDowell, N. G. et al. Mechanisms of woody-plant mortality under rising drought, CO2 and vapour pressure deficit. Nat. Rev. Earth Environ.3, 294–308 (2022).
Johnson, D. M. et al. Co‐occurring woody species have diverse hydraulic strategies and mortality rates during an extreme drought. Plant Cell Environ.41, 576–588 (2018). PubMed
Hacke, U. G., Sperry, J. S., Pockman, W. T., Davis, S. D. & McCulloh, K. A. Trends in wood density and structure are linked to prevention of xylem implosion by negative pressure. Oecologia126, 457–461 (2001). PubMed
Sperry, J. S., Hacke, U. G. & Pittermann, J. Size and function in conifer tracheids and angiosperm vessels. Am. J. Bot.93, 1490–1500 (2006). PubMed
Larjavaara, M. & Muller-Landau, H. C. Rethinking the value of high wood density. Funct. Ecol.24, 701–705 (2010). PubMed
Niklas, K. J. & Spatz, H. Worldwide correlations of mechanical properties and green wood density. Am. J. Bot.97, 1587–1594 (2010). PubMed
Köhler, P. & Huth, A. Towards ground-truthing of spaceborne estimates of above-ground life biomass and leaf area index in tropical rain forests. Biogeosciences7, 2531–2543 (2010).
Vibrans, A. C. et al. Unprecedented large-area turnover estimates for the subtropical Brazilian Atlantic Forest based on systematically-gathered data. Ecol. Manag.505, 119902 (2022).
Rodrigues, A. V. et al. A test of the fast–slow plant economy hypothesis in a subtropical rain forest. Plant Ecol. Divers.14, 267–277 (2021).
Pyles, M. V. et al. Human impacts as the main driver of tropical forest carbon. Sci. Adv.8, eabl7968 (2022). PubMed PMC
Haddad, N. M. et al. Species’ traits predict the effects of disturbance and productivity on diversity. Ecol. Lett.11, 348–356 (2008). PubMed
Sommerfeld, A. et al. Patterns and drivers of recent disturbances across the temperate forest biome. Nat. Commun.9, 4355 (2018). PubMed PMC
Martin, A. R., Erickson, D. L., Kress, W. J. & Thomas, S. C. Wood nitrogen concentrations in tropical trees: phylogenetic patterns and ecological correlates. New Phytol.204, 484–495 (2014). PubMed
Liang, X., Ye, Q., Liu, H. & Brodribb, T. J. Wood density predicts mortality threshold for diverse trees. New Phytol.229, 3053–3057 (2021). PubMed
Macdonald, E. & Hubert, J. A review of the effects of silviculture on timber quality of Sitka spruce. Forestry75, 107–138 (2002).
Barlow, J. et al. Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature535, 144 (2016). PubMed
Wang, J. A., Baccini, A., Farina, M., Randerson, J. T. & Friedl, M. A. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Change11, 435–441 (2021).
Mack, M. C. et al. Carbon loss from boreal forest wildfires offset by increased dominance of deciduous trees. Science372, 280–283 (2021). PubMed
Slik, J. W. F. et al. Wood density as a conservation tool: quantification of disturbance and identification of conservation-priority areas in tropical forests. Conserv. Biol.22, 1299–1308 (2008). PubMed
Berenguer, E. et al. Seeing the woods through the saplings: using wood density to assess the recovery of human-modified Amazonian forests. J. Ecol.106, 2190–2203 (2018).
Feeley, K. J., Davies, S. J., Perez, R., Hubbell, S. P. & Foster, R. B. Directional changes in the species composition of a tropical forest. Ecology92, 871–882 (2011). PubMed
Lewis, S. L. et al. Above-ground biomass and structure of 260 African tropical forests. Philos. Trans. R. Soc. B368, 20120295 (2013). PubMed PMC
Carreño-Rocabado, G. et al. Effects of disturbance intensity on species and functional diversity in a tropical forest. J. Ecol.100, 1453–1463 (2012).
Bunker, D. E. et al. Species loss and aboveground carbon storage in a tropical forest. Science310, 1029–1031 (2005). PubMed
Yuan, Z. et al. Multiple metrics of diversity have different effects on temperate forest functioning over succession. Oecologia182, 1175–1185 (2016). PubMed
Gourlet-Fleury, S. et al. Environmental filtering of dense-wooded species controls above-ground biomass stored in African moist forests. J. Ecol.99, 981–990 (2011).
Lohbeck, M. et al. Successional changes in functional composition contrast for dry and wet tropical forest. Ecology94, 1211–1216 (2013). PubMed
van der Sande, M. T. et al. A 7000-year history of changing plant trait composition in an Amazonian landscape; the role of humans and climate. Ecol. Lett.22, 925–935 (2019). PubMed PMC
Poorter, L. et al. The importance of wood traits and hydraulic conductance for the performance and life history strategies of 42 rainforest tree species. New Phytol.185, 481–492 (2010). PubMed
Chaturvedi, R. K., Raghubanshi, A. S., Tomlinson, K. W. & Singh, J. S. Impacts of human disturbance in tropical dry forests increase with soil moisture stress. J. Veg. Sci.28, 997–1007 (2017).
Liang, J. et al. Positive biodiversity–productivity relationship predominant in global forests. Science354, 6309 (2016). PubMed
Brown, S. Estimating Biomass and Biomass Change of Tropical Forests: A Primer (FAO, 1997).
Falster, D. S. et al. BAAD: a biomass and allometry database for woody plants. Ecology96, 1445–1445 (2015).
Vieilledent, G. et al. New formula and conversion factor to compute basic wood density of tree species using a global wood technology database. Am. J. Bot.105, 1653–1661 (2018). PubMed
Zhang, S.-B., Slik, J. W. F., Zhang, J.-L. & Cao, K.-F. Spatial patterns of wood traits in China are controlled by phylogeny and the environment. Glob. Ecol. Biogeogr.20, 241–250 (2011).
Zanne, A. E. et al. Data from: Towards a worldwide wood economics spectrum. Dryad10.5061/dryad.234 (2009). PubMed
Schepaschenko, D. et al. A dataset of forest biomass structure for Eurasia. Sci. Data4, 170070 (2017). PubMed PMC
Kattge, J. et al. TRY plant trait database–enhanced coverage and open access. Glob. Change Biol.26, 119–188 (2020). PubMed
Henry, M. et al. GlobAllomeTree: international platform for tree allometric equations to support volume, biomass and carbon assessment. Iforest6, 326–330 (2013).
Kennedy, C. M., Oakleaf, J. R., Theobald, D. M., Baruch-Mordo, S. & Kiesecker, J. Managing the middle: a shift in conservation priorities based on the global human modification gradient. Glob. Change Biol.25, 811–826 (2019). PubMed
Giglio, L. MOD14A1 MODIS/Terra thermal anomalies/fire daily L3 global 1 km SIN grid V006. USGS10.5067/MODIS/MOD14A1.061 (2015).
Santoro, M. et al. GlobBiomass—global datasets of forest biomass [dataset]. PANGAEA10.1594/PANGAEA.894711 (2018).
Santoro, M. et al. A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. Geophys. Res. Abstr.20, EGU2018-18932 (2018).
Ma, H. et al. The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nat. Ecol. Evol.5, 1110–1122 (2021). PubMed
Pagel, M. Inferring the historical patterns of biological evolution. Nature401, 877–884 (1999). PubMed
Blomberg, S. P., Garland, T. Jr & Ives, A. R. Testing for phylogenetic signal in comparative data: behavioral traits are more labile. Evolution57, 717–745 (2003). PubMed
Li, F. et al. Evolutionary history shapes variation of wood density of tree species across the world. Plant Divers.46, 283–293 (2024). PubMed PMC
Webb, C. O., Ackerly, D. D. & Kembel, S. W. Phylocom: software for the analysis of phylogenetic community structure and trait evolution. Bioinformatics24, 2098–2100 (2008). PubMed
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun.11, 4540 (2020). PubMed PMC
Batjes, N. H. Harmonized soil property values for broad-scale modelling (WISE30sec) with estimates of global soil carbon stocks. Geoderma269, 61–68 (2016).
Asner, G. P., Scurlock, J. M. O. & Hicke, J. A. Global synthesis of leaf area index observations: implications for ecological and remote sensing studies. Glob. Ecol. Biogeogr.12, 191–205 (2003).
Kerfriden, B., Bontemps, J.-D. & Leban, J.-M. Variations in temperate forest stem biomass ratio along three environmental gradients are dominated by interspecific differences in wood density. Plant Ecol.222, 289–303 (2021).
Pellegrini, A. F. A. et al. Decadal changes in fire frequencies shift tree communities and functional traits. Nat. Ecol. Evol.5, 504–512 (2021). PubMed
Snorrason, A., Kjartansson, B., Gunnarsson, E. & Eysteinsson, T.H. Global Forest Resources Assessment Update 2005 (FAO, 2005).
Araza, A. et al. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps. Remote Sens. Environ.272, 112917 (2022).
Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data7, 112 (2020). PubMed PMC
Ruesch, A. & Gibbs, H. K. New IPCC Tier-1 Global Biomass Carbon Map for the Year 2000. ESS-DIVE10.15485/1463800 (2008).
Baraloto, C. et al. Disentangling stand and environmental correlates of aboveground biomass in Amazonian forests. Glob. Change Biol.17, 2677–2688 (2011).
Jenkins, J. C., Chojnacky, D. C., Heath, L. S. & Birdsey, R. A. National-scale biomass estimators for United States tree species. For. Sci.49, 12–35 (2003).
Yang, H. et al. Global patterns of tree wood density. Glob. Change Biol.30, e17224 (2024). PubMed
Markesteijn, L., Poorter, L., Paz, H., Sack, L. & Bongers, F. Ecological differentiation in xylem cavitation resistance is associated with stem and leaf structural traits. Plant Cell Environ.34, 137–148 (2011). PubMed
Zheng, J., Zhao, X., Morris, H. & Jansen, S. Phylogeny best explains latitudinal patterns of xylem tissue fractions for woody angiosperm species across China. Front. Plant Sci.10, 556 (2019). PubMed PMC
Ibanez, T. et al. Community variation in wood density along a bioclimatic gradient on a hyper-diverse tropical island. J. Veg. Sci.28, 19–33 (2017).
Enrique, G. et al. A multidimensional functional trait approach reveals the imprint of environmental stress in Mediterranean woody communities. Ecosystems21, 248–262 (2018).
de la Riva, E. G. et al. Disentangling the relative importance of species occurrence, abundance and intraspecific variability in community assembly: a trait-based approach at the whole-plant level in Mediterranean forests. Oikos125, 354–363 (2016).
Serra‐Maluquer, X. et al. Wood density and hydraulic traits influence species’ growth response to drought across biomes. Glob. Change Biol.28, 3871–3882 (2022). PubMed
Muller-Landau, H. C. Interspecific and inter-site variation in wood specific gravity of tropical trees. Biotropica36, 20–32 (2004).
Ter Steege, H. et al. Continental-scale patterns of canopy tree composition and function across Amazonia. Nature443, 444–447 (2006). PubMed
LeBauer, D. S. & Treseder, K. K. Nitrogen limitation of net primary productivity in terrestrial ecosystems is globally distributed. Ecology89, 371–379 (2008). PubMed
Ziter, C., Bennett, E. M. & Gonzalez, A. Temperate forest fragments maintain aboveground carbon stocks out to the forest edge despite changes in community composition. Oecologia176, 893–902 (2014). PubMed
Morreale, L. L., Thompson, J. R., Tang, X., Reinmann, A. B. & Hutyra, L. R. Elevated growth and biomass along temperate forest edges. Nat. Commun.12, 7181 (2021). PubMed PMC
Smith, I. A., Hutyra, L. R., Reinmann, A. B., Marrs, J. K. & Thompson, J. R. Piecing together the fragments: elucidating edge effects on forest carbon dynamics. Front. Ecol. Environ.16, 213–221 (2018).
Zanne, A. E. et al. Angiosperm wood structure: global patterns in vessel anatomy and their relation to wood density and potential conductivity. Am. J. Bot.97, 207–215 (2010). PubMed
Muñoz, G. R., Encinas, J. I. & de Paula, J. E. Wood density as an auxiliary classification criterion for botanical identification of 241 tree species in the order Sapindales. Eur. J. Res.138, 583–594 (2019).
Slik, J. W. F. Estimating species-specific wood density from the genus average in Indonesian trees. J. Trop. Ecol.22, 481–482 (2006).
Boyle, B. L. et al. The taxonomic name resolution service: an online tool for automated standardization of plant names. BMC Bioinformatics14, 16 (2013). PubMed PMC
Jin, Y. & Qian, H. V. PhyloMaker: an R package that can generate very large phylogenies for vascular plants. Ecography42, 1353–1359 (2019). PubMed PMC
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol.3, 217–223 (2012).
Ooms, J. & Chamberlain, S. phylocomr: Interface to ‘Phylocom’. R package version 0.3.4 (2019).
Panchen, Z. A. et al. Leaf out times of temperate woody plants are related to phylogeny, deciduousness, growth habit and wood anatomy. New Phytol.203, 1208–1219 (2014). PubMed
Poorter, L. et al. Biomass resilience of neotropical secondary forests. Nature530, 211 (2016). PubMed
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. Bioscience51, 933–938 (2001).
Karger, D. N. et al. Climatologies at high resolution for the Earth’s land surface areas. Sci. Data4, 170122 (2017). PubMed PMC
Amatulli, G. et al. A suite of global, cross-scale topographic variables for environmental and biodiversity modeling. Sci. Data5, 180040 (2018). PubMed PMC
Wilson, A. M. & Jetz, W. Remotely sensed high-resolution global cloud dynamics for predicting ecosystem and biodiversity distributions. PLoS Biol.14, e1002415 (2016). PubMed PMC
Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science339, 940–943 (2013). PubMed
Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol.37, 4302–4315 (2017).
Shangguan, W., Hengl, T., de Jesus, J. M., Yuan, H. & Dai, Y. Mapping the global depth to bedrock for land surface modeling. J. Adv. Model Earth Syst.9, 65–88 (2017).
Rodell, M. et al. The global land data assimilation system. Bull. Am. Meteorol. Soc.85, 381–394 (2004).
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc.146, 1999–2049 (2020).
Gelaro, R. et al. The modern-era retrospective analysis for research and applications, version 2 (MERRA-2). J. Clim.30, 5419–5454 (2017). PubMed PMC
Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS Vegetation Index User’s Guide (MOD13 Series) (Univ. of Arizona, 2015).
Myneni, R., Knyazikhin, Y. & Park, T. MOD15A2H MODIS/terra leaf area index/FPAR 8-day L4 global 500 m SIN grid V006. USGS10.5067/MODIS/MYD15A2H.006 (2015).
Zhao, M., Running, S., Heinsch, F. A. & Nemani, R. in Land Remote Sensing and Global Environmental Change (eds Ramachandran, B. et al.) 635–660 (Springer, 2010).
Trabucco, A. & Zomer, R. J. Global Soil Water Balance Geospatial Database (CGIAR-CSI, 2010).
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ.126, 67–80 (2008).
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science342, 850–853 (2013). PubMed
Crowther, T. W. et al. Mapping tree density at a global scale. Nature525, 201–205 (2015). PubMed
Simard, M., Pinto, N., Fisher, J. B. & Baccini, A. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. Biogeosci. 10.1029/2011JG001708 (2011).
Besnard, S. et al. Mapping global forest age from forest inventories, biomass and climate data. Earth Syst. Sci. Data13, 4881–4896 (2021).
Tuanmu, M.-N. & Jetz, W. A global 1-km consensus land-cover product for biodiversity and ecosystem modelling. Glob. Ecol. Biogeogr.23, 1031–1045 (2014).
Klein Goldewijk, K., Beusen, A. & Janssen, P. Long-term dynamic modeling of global population and built-up area in a spatially explicit way: HYDE 3.1. Holocene20, 565–573 (2010).
Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr.20, 73–86 (2011).
Gorelick, N. et al. Google Earth Engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27 (2017).
Van Den Hoogen, J. et al. Soil nematode abundance and functional group composition at a global scale. Nature572, 194–198 (2019). PubMed
LeDell, E. et al. h2o: R interface for the ‘H2O’ scalable machine learning platform. R package version 3.44.0 (2020).
Li, J. Assessing the accuracy of predictive models for numerical data: not r nor r2, why not? Then what? PLoS ONE12, e0183250 (2017). PubMed PMC
Sagi, O. & Rokach, L. Ensemble learning: a survey. WIREs Data Min. Knowl. Discov.8, e1249 (2018).
Phillips, O. L. et al. Species matter: wood density influences tropical forest biomass at multiple scales. Surv. Geophys. 40, 913–935 (2019). PubMed PMC
Heiberger, R. M. & Holland, B. Statistical Analysis and Data Display: An Intermediate Course with Examples in R (Springer, 2019).
Hothorn, T. & Zeileis, A. partykit: a modular toolkit for recursive partytioning in R. J. Mach. Learn. Res.16, 3905–3909 (2015).
Borkovec, M. & Madin, N. ggparty: ‘ggplot’ visualizations for the ‘partykit’ package. R package version 1.0.0 (2019).
Braatz, S. M. State of the World’s Forests, 1997 (FAO, 1997).
Mo, L. The global distribution and drivers of wood density across angiosperms and gymnosperms and their impact on forest carbon stocks (Version Ver01). Zenodo10.5281/zenodo.13331493 (2024). PubMed PMC
The global distribution and drivers of wood density and their impact on forest carbon stocks