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Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.

Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land–climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles. ; TRY initiative on plant traits German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. European Union's Horizon 2020 project BACI 640176 University of Zurich University Research Priority Program on Global Change and Biodiversity National Science Foundation (NSF) 20-508 NOMIS grant of Remotely Sensing Ecological Genomics Max Planck Society via its fellowship programme German Research Foundation (DFG) RU 1536/3-1 project Resilient Forests of the Dutch Ministry of Economic Affairs KB-29-009-003 EU-FP7-KBBE project: BACCARA-Biodiversity and climate change, a risk analysis 226299 Australian Research Council DP170103410 European Research Council (ERC) ERC-SyG-2013-610028 IMBALANCE-P VIDI by the Netherlands Organization of Scientific Research 016.161.318 II. Oldenburgischer Deichband Wasserverbandstag e.V. NWS 10/05 Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPQ) 369617/2017-2 307689/2014-0 National Research Foundation of Korea (NRF) - Korea government (MSIT) 2018R1C1B6005351 Comision Nacional de Investigacion Cientifica y Tecnologica (CONICYT) CONICYT FONDECYT 11150835 1200468 Russian Science Foundation (RSF) 19-14-00038 Future Earth ; Versión publicada - versión final del editor

The authors investigate the broad-scale climatological and soil properties that co-vary with major axes of plant functional traits. They find that variation in plant size is attributed to latitudinal gradients in water or energy limitation, while variation in leaf economics traits is attributed to both climate and soil fertility including their interaction. Plant functional traits can predict community assembly and ecosystem functioning and are thus widely used in global models of vegetation dynamics and land-climate feedbacks. Still, we lack a global understanding of how land and climate affect plant traits. A previous global analysis of six traits observed two main axes of variation: (1) size variation at the organ and plant level and (2) leaf economics balancing leaf persistence against plant growth potential. The orthogonality of these two axes suggests they are differently influenced by environmental drivers. We find that these axes persist in a global dataset of 17 traits across more than 20,000 species. We find a dominant joint effect of climate and soil on trait variation. Additional independent climate effects are also observed across most traits, whereas independent soil effects are almost exclusively observed for economics traits. Variation in size traits correlates well with a latitudinal gradient related to water or energy limitation. In contrast, variation in economics traits is better explained by interactions of climate with soil fertility. These findings have the potential to improve our understanding of biodiversity patterns and our predictions of climate change impacts on biogeochemical cycles. ; The study was supported by the TRY initiative on plant traits (http://www.try-db. org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (MPI BGC, Germany) and supported by Future Earth and the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig. We would like to thank all PIs contributing to the TRY database, whose efforts allowed this analysis. In detail, we thank: J.H.C. Cornelissen, R. Milla, W. Cornwell, K. Kramer, S. Gachet, Ingolf Kühn, P. Poschlod, M. Scherer, J. Pausas, B. Sandal, K. Verheyen, J. Penuelas, N. Soudzilovskaia, P. Reich, J. Fang, S. Harrison, R. Gallagher, B. Hawkins, B. Finegan, J. Powers, F. Lenti, S. Higgins, B. Medlyn, H. Ford, V. Pillar, M. Bahn, E. Sosinski, T. He, B. Cerabolini, J. Cavender-Bares, I. J. Wright, F. Louault, B. Amiaud, G. Gonzalez-Melo, P. Adler, F. Schurr, J. Craine, Y. Niinemets, A. Zanne, H. Jactel, M. Harze, R. Montgomery, C. Römermann, T. Hickler, A. Pahl, M. Dainese, D. Kirkup, J. Dickie, W. Hattingh, P. Higuchi, T. Domingues, A. Araujo, M. Williams, C. Price, B. Shipley, L. Sack, B. Schamp, W. Han, Y. Onoda, K. Fleischer, J.P. Wright, G. Guerin, F. de Vries, D.D. Baldocchi, J. Kattge, B. Blonder, K. Brown, D. Campetella, G. Frechet, Q. Read, N. G. Swenson, V. Lanta, E. Weiher, M. Leishman, A. Siefert, M. Spasojevic, R. Jackson, J. Messier, S. J. Wright, D. Craven, J. Molofsky, P. Meir, E. Forey, A. Totte, C. Frenette Dussault, O. Atkin, F. Koike, D. Laughlin, S. Burrascano, K. Ollerer, N. Gross, A. Madhur, P. Begonna, B. Bond-Lamberty, B. von Holle, W. Green, B. Yguel, A. C. Malhado, P. Manning, G. Zotz, E. Lamb, J. Fagundez, Z. Wang, S. Diaz, C. Byun, W. Bond, B. Enquist, C. Baraloto, P. Manning, M. Kleyer, W. Ozinga, J. Ordonez, J. Lloyd, H. Poorter, E. Garnier, F. Valladares, C. Pladevall, G. Freschet, M. Moretti, H. Kurokawa, V. Minden, A. Demey, F. Férnandez-Méndez, J. Butterfield, T. Domingu, E. Swaine, L. Poorter, S. Shiodera, T. Chapin, M. Beckmann, J.A. Gutierrez, M. Mencuccini, S. Jansen, and N. J. B. Kraft. We appreciate the discussions at the MPI BGC. We thank F. Fazayeli for preparing the gap-filled trait data. We thank F. Gans and U. Weber for preparing ancillary data and B. Ahrens for pointing out some soil data availability. We acknowledge Environmental Systems Research Institute (ESRI) and its licensor(s) for the Geodata product of the Missions Database 'ArcWorld Supplement' (GMI), published by Global Mapping International and originated from Global Mapping International for producing Extended Data Fig. 1 and Supplementary Fig. 7 and available in ArcGIS software by ESRI. ArcGIS and ArcMap are the intellectual property of ESRI and are used herein under license. For more information about ESRI software, please visit www.esri.com. The authors affiliated with the MPI BGC acknowledge funding by the European Union's Horizon 2020 project BACI under grant agreement no. 640176. We are thankful to the data providers for the SoilGrids, hosted by ISRIC. J.S.J. acknowledges the International Max Planck Research School for global biogeochemical cycles. J.S.J., M.E.S. and M.C.S. acknowledge support from the University of Zurich University Research Priority Program on Global Change and Biodiversity. P.B.R., M.E.S. and M.C.S. acknowledge membership in the US NSF 20-508 BII-Implementation project, 'The causes and consequences of plant biodiversity across scales in a rapidly changing world'. M.E.S. acknowledges the NOMIS grant of Remotely Sensing Ecological Genomics that funds J.S.J. and M.C.S. C.W. acknowledges the support of the Max Planck Society via its fellowship programme. N.R. was funded by a research grant from Deutsche Forschungsgemeinschaft DFG (RU 1536/3-1). K.K. was supported by the project Resilient Forests (KB-29-009-003) of the Dutch Ministry of Economic Affairs. The trait data supplied were co-funded by the EU-FP7-KBBE project: BACCARA—Biodiversity and climate change, a risk analysis (project ID 226299). I.W. acknowledges support from the Australian Research Council (DP170103410). J.P. acknowledges financial support from the European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. N.A.S. is financed by a VIDI grant (016.161.318) issued by the Netherlands Organization of Scientific Research. The data V.M. provided were funded by II. Oldenburgischer Deichband and the Wasserverbandstag e.V. (NWS 10/05). We thank M. Kleyer for his critical input. P.H. and V.D.P. have been supported by CNPq (grant nos 369617/2017-2 and 307689/2014-0, respectively). C.B. was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2018R1C1B6005351). A.G.G. was funded by FONDECYT grant nos 11150835 and 1200468. V.O. thanks Russian science foundation (RSF, 19-14-00038) for financial support.

Although satellite‐based variables have for long been expected to be key components to a unified and global biodiversity monitoring strategy, a definitive and agreed list of these variables still remains elusive. The growth of interest in biodiversity variables observable from space has been partly underpinned by the development of the essential biodiversity variable (EBV) framework by the Group on Earth Observations – Biodiversity Observation Network, which itself was guided by the process of identifying essential climate variables. This contribution aims to advance the development of a global biodiversity monitoring strategy by updating the previously published definition of EBV, providing a definition of satellite remote sensing (SRS) EBVs and introducing a set of principles that are believed to be necessary if ecologists and space agencies are to agree on a list of EBVs that can be routinely monitored from space. Progress toward the identification of SRS‐EBVs will require a clear understanding of what makes a biodiversity variable essential, as well as agreement on who the users of the SRS‐EBVs are. Technological and algorithmic developments are rapidly expanding the set of opportunities for SRS in monitoring biodiversity, and so the list of SRS‐EBVs is likely to evolve over time. This means that a clear and common platform for data providers, ecologists, environmental managers, policy makers and remote sensing experts to interact and share ideas needs to be identified to support long‐term coordinated actions. ; DSS, RS, DR and JP were financed by the EU BON project that is a Seventh Framework Programme funded by the European Union under Contract No. 308454. ; Peer reviewed