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Liu S, Brandt M, Nord-Larsen T, Chave J, Reiner F, Lang N, Tong X, Ciais P, Igel C, Pascual A, Guerra-Hernandez J, Li S, Mugabowindekwe M, Saatchi S, Yue Y, Chen Z, Fensholt R. The overlooked contribution of trees outside forests to tree cover and woody biomass across Europe. Sci Adv 2023; 9:eadh4097. [PMID: 37713489 PMCID: PMC10881069 DOI: 10.1126/sciadv.adh4097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 08/15/2023] [Indexed: 09/17/2023]
Abstract
Trees are an integral part in European landscapes, but only forest resources are systematically assessed by national inventories. The contribution of urban and agricultural trees to national-level carbon stocks remains largely unknown. Here we produced canopy cover, height and above-ground biomass maps from 3-meter resolution nanosatellite imagery across Europe. Our biomass estimates have a systematic bias of 7.6% (overestimation; R = 0.98) compared to national inventories of 30 countries, and our dataset is sufficiently highly resolved spatially to support the inclusion of tree biomass outside forests, which we quantify to 0.8 petagrams. Although this represents only 2% of the total tree biomass, large variations between countries are found (10% for UK) and trees in urban areas contribute substantially to national carbon stocks (8% for the Netherlands). The agreement with national inventory data, the scalability, and spatial details across landscapes, including trees outside forests, make our approach attractive for operational implementation to support national carbon stock inventory schemes.
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Affiliation(s)
- Siyu Liu
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Martin Brandt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Nord-Larsen
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Jerome Chave
- Laboratoire Evolution et Diversité Biologique, CNRS, UPS, IRD, Université Paul Sabatier, Toulouse, France
| | - Florian Reiner
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Nico Lang
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Xiaoye Tong
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Philippe Ciais
- Laboratoire des Sciences du Climat et de l’Environnement, CEA/CNRS/UVSQ/Université Paris Saclay, Gif-sur-Yvette, France
| | - Christian Igel
- Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Adrian Pascual
- Department of Geographical Sciences, University of Maryland, College Park, MD, USA
| | - Juan Guerra-Hernandez
- Forest Research Center, School of Agriculture, University of Lisbon, Lisbon, Portugal
| | - Sizhuo Li
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Maurice Mugabowindekwe
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
| | - Sassan Saatchi
- Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
| | - Yuemin Yue
- Key Laboratory for Agro-ecological Processes in Subtropical Region, Institute of Subtropical Agriculture, Chinese Academy of Sciences, Changsha, China
| | - Zhengchao Chen
- Airborne Remote Sensing Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
| | - Rasmus Fensholt
- Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark
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Horlacher M, Wagner N, Moyon L, Kuret K, Goedert N, Salvatore M, Ule J, Gagneur J, Winther O, Marsico A. Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning. Genome Biol 2023; 24:180. [PMID: 37542318 PMCID: PMC10403857 DOI: 10.1186/s13059-023-03015-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Accepted: 07/17/2023] [Indexed: 08/06/2023] Open
Abstract
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and miCLIP assays, outperforming state-of-the-art classifiers. RBPNet performs bias correction by modeling the raw signal as a mixture of the protein-specific and background signal. Through model interrogation via Integrated Gradients, RBPNet identifies predictive sub-sequences that correspond to known and novel binding motifs and enables variant-impact scoring via in silico mutagenesis. Together, RBPNet improves imputation of protein-RNA interactions, as well as mechanistic interpretation of predictions.
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Affiliation(s)
- Marc Horlacher
- Computational Health Center, Helmholtz Center Munich, Munich, Germany.
- Department of Biology, University of Copenhagen, Copenhagen, Denmark.
- Department of Informatics, Technical University of Munich, Garching, Germany.
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany.
| | - Nils Wagner
- Department of Informatics, Technical University of Munich, Garching, Germany
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
| | - Lambert Moyon
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Klara Kuret
- National Institute of Chemistry, Ljubljana, Slovenia
- The Francis Crick Institute, London, UK
- Jozef Stefan International Postgraduate School, Jamova cesta 39, 1000, Ljubljana, Slovenia
| | - Nicolas Goedert
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Marco Salvatore
- Department of Biology, University of Copenhagen, Copenhagen, Denmark
| | - Jernej Ule
- National Institute of Chemistry, Ljubljana, Slovenia
- The Francis Crick Institute, London, UK
| | - Julien Gagneur
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
- Department of Informatics, Technical University of Munich, Garching, Germany
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany
| | - Ole Winther
- Department of Biology, University of Copenhagen, Copenhagen, Denmark.
| | - Annalisa Marsico
- Computational Health Center, Helmholtz Center Munich, Munich, Germany.
- Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany.
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