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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: La Cono, Violetta; Messina, Enzo; Reva, Olga; Smedile, Francesco; +14 Authors

    AbstractClimate change, desertification, salinisation of soils and the changing hydrology of the Earth are creating or modifying microbial habitats at all scales including the oceans, saline groundwaters and brine lakes. In environments that are saline or hypersaline, the biodegradation of recalcitrant plant and animal polysaccharides can be inhibited by salt‐induced microbial stress and/or by limitation of the metabolic capabilities of halophilic microbes. We recently demonstrated that the chitinolytic haloarchaeon Halomicrobium can serve as the host for an ectosymbiont, nanohaloarchaeon ‘Candidatus Nanohalobium constans’. Here, we consider whether nanohaloarchaea can benefit from the haloarchaea‐mediated degradation of xylan, a major hemicellulose component of wood. Using samples of natural evaporitic brines and anthropogenic solar salterns, we describe genome‐inferred trophic relations in two extremely halophilic xylan‐degrading three‐member consortia. We succeeded in genome assembly and closure for all members of both xylan‐degrading cultures and elucidated the respective food chains within these consortia. We provide evidence that ectosymbiontic nanohaloarchaea is an active ecophysiological component of extremely halophilic xylan‐degrading communities (although by proxy) in hypersaline environments. In each consortium, nanohaloarchaea occur as ectosymbionts of Haloferax, which in turn act as scavenger of oligosaccharides produced by xylan‐hydrolysing Halorhabdus. We further obtained and characterised the nanohaloarchaea–host associations using microscopy, multi‐omics and cultivation approaches. The current study also doubled culturable nanohaloarchaeal symbionts and demonstrated that these enigmatic nano‐sized archaea can be readily isolated in binary co‐cultures using an appropriate enrichment strategy. We discuss the implications of xylan degradation by halophiles in biotechnology and for the United Nation's Sustainable Development Goals.

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    ZENODO; Microbial Biotechnology
    Article . 2023 . Peer-reviewed
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      ZENODO; Microbial Biotechnology
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    Authors: Jingjing Liang; Javier G. P. Gamarra; Nicolas Picard; Mo Zhou; +219 Authors

    The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

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    Authors: Cazzolla Gatti, Roberto; Reich, Peter B; Gamarra, Javier GP; Crowther, Tom; +144 Authors

    One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness. Proceedings of the National Academy of Sciences of the United States of America, 119 (6) ISSN:1091-6490 ISSN:0027-8424

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    NARCIS; Research@WUR
    Article . 2022
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    Apollo
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    Repositori Obert UdL
    Article . 2022
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    Authors: W. Dorigo; I. Himmelbauer; D. Aberer; L. Schremmer; +68 Authors

    In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

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    Lirias
    Article . 2021
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    DOAJ
    Article . 2021
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    Authors: H. K. Lappalainen; H. K. Lappalainen; T. Petäjä; T. Vihma; +83 Authors

    Short summary We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis. The Pan-Eurasian Experiment (PEEX) Science Plan, released in 2015, addressed a need for a holistic system understanding and outlined the most urgent research needs for the rapidly changing Arctic-boreal region. Air quality in China, together with the long-range transport of atmospheric pollutants, was also indicated as one of the most crucial topics of the research agenda. These two geographical regions, the northern Eurasian Arctic-boreal region and China, especially the megacities in China, were identified as a “PEEX region”. It is also important to recognize that the PEEX geographical region is an area where science-based policy actions would have significant impacts on the global climate. This paper summarizes results obtained during the last 5 years in the northern Eurasian region, together with recent observations of the air quality in the urban environments in China, in the context of the PEEX programme. The main regions of interest are the Russian Arctic, northern Eurasian boreal forests (Siberia) and peatlands, and the megacities in China. We frame our analysis against research themes introduced in the PEEX Science Plan in 2015. We summarize recent progress towards an enhanced holistic understanding of the land–atmosphere–ocean systems feedbacks. We conclude that although the scientific knowledge in these regions has increased, the new results are in many cases insufficient, and there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures, especially the lack of coordinated, continuous and comprehensive in situ observations of the study region as well as integrative data analyses, hindering a comprehensive system analysis. The fast-changing environment and ecosystem changes driven by climate change, socio-economic activities like the China Silk Road Initiative, and the global trends like urbanization further complicate such analyses. We recognize new topics with an increasing importance in the near future, especially “the enhancing biological sequestration capacity of greenhouse gases into forests and soils to mitigate climate change” and the “socio-economic development to tackle air quality issues”.

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    https://doi.org/10.5194/acp-20...
    Preprint . 2021 . Peer-reviewed
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    Atmospheric Chemistry and Physics (ACP)
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      https://doi.org/10.5194/acp-20...
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      MPG.PuRe
      Article . 2022
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      https://acp.copernicus.org/pre...
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      MPG.PuRe
      Article . 2022
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      CNR ExploRA
      Article . 2022
      Data sources: CNR ExploRA
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      MPG.PuRe
      Article . 2022
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      Atmospheric Chemistry and Physics (ACP)
      Article . 2022 . Peer-reviewed
      License: CC BY
      Data sources: Crossref
      MPG.PuRe
      Article . 2022
      Data sources: MPG.PuRe
      DOAJ
      Article . 2022
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    Authors: Telmo Morato; Telmo Morato; Christopher K. Pham; Christopher K. Pham; +35 Authors

    The identification of areas that fit the Food and Agriculture Organization of the United Nations (FAO) criteria to define what constitutes a Vulnerable Marine Ecosystem (VME) has been the main policy driver for the protection of deep-sea environments in Areas Beyond National Jurisdictions (United Nations General Assembly, 2006; FAO, 2009) in relation to bottom fisheries. At the same time, the Convention on Biological Diversity advocates for the implementation of representative networks of Marine Protected Areas (MPAs) in the open ocean and the deep sea, and calls for the identification of Ecologically or Biologically Significant marine Areas (EBSAs; Convention on Biological Diversity, 2008, Decision IX/20). Although VMEs and EBSAs are conceptually different, Ardron et al. (2014) argue that the designation of VMEs, EBSAs, and large open-ocean MPAs should be aligned to ensure that VMEs are incorporated within area-based management tools. The International Council for the Exploration of the Sea (ICES) adopted a Multi-Criteria Assessment (MCA) methodology for informing the identification of VMEs in the North-East Atlantic (ICES, 2016a,b; Morato et al., 2018). The MCA is a taxa-dependent spatial method that incorporates the fact that not all VME indicators are equally vulnerable to human impacts, and thus should not be weighted equally. By including a measure of the confidence associated with each VME record, this methodology also considers some of the uncertainties associated with the sampling methodologies, the reported taxonomy, and data quality issues. Equally important, it highlights areas in the North Atlantic that have been poorly sampled and that require further attention. Finally, this methodology also allows for the evaluation and comparison of VME index with spatial fisheries data that may directly generate significant adverse impacts on VMEs. Although the VME Index has been used since 2018 in ICES advice, several caveats and limitations have been identified (ICES, 2018, 2019, 2020). The main criticism refers to the fact that the VME index signals the presence of VME indicator taxa that are considered to be the most important rather than showing the likelihood of an area containing a spatially explicit VME. Also, concerns over the abundance scores adopted have been raised and it has been suggested that abundance thresholds should be defined for each VME indicator. It is, therefore, recognized that improvements of the VME index and the way actual VMEs are identified are still necessary. The identification of representative areas that can form a network of MPAs in the deep sea requires ocean basin-scale approaches grounded on ocean basin-scale datasets. In this regard, the H2020 ATLAS project (GA 678760) performed a unique trans-Atlantic assessment of deep-water ecosystems to inform Atlantic Ocean basin-scale governance. The ATLAS project compiled the best available data on VME indicator taxa for the North Atlantic (Ramiro-Sánchez et al., 2020) in order to assist with the identification of locations that may constitute VMEs and EBSAs, as a precursor to the development of a North Atlantic wide network of MPAs. Here, we applied the ICES MCA method to the ATLAS VME indicator taxa database to produce and make publicly available a new “North Atlantic Ocean basin-scale VME index dataset,” facilitating further consultation and use by scientists, managers, or other relevant stakeholders. SI

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    ZENODO; Frontiers in Marine Science
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: ZENODO; Crossref
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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    Frontiers in Marine Science
    Article
    License: CC BY
    Data sources: UnpayWall
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    DOAJ
    Article . 2021
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ArchiMer - Instituti...arrow_drop_down
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      ZENODO; Frontiers in Marine Science
      Article . 2021 . Peer-reviewed
      License: CC BY
      Data sources: ZENODO; Crossref
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      Frontiers in Marine Science
      Article
      License: CC BY
      Data sources: UnpayWall
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      DOAJ
      Article . 2021
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    Authors: Bagdevi Mishra; Bagdevi Mishra; Bartosz Ulaszewski; Joanna Meger; +27 Authors

    AbstractThe European Beech is the dominant climax tree in most regions of Central Europe and valued for its ecological versatility and hardwood timber. Even though a draft genome has been published recently, higher resolution is required for studying aspects of genome architecture and recombination. Here we present a chromosome-level assembly of the more than 300 year-old reference individual, Bhaga, from the Kellerwald-Edersee National Park (Germany). Its nuclear genome of 541 Mb was resolved into 12 chromosomes varying in length between 28 Mb and 73 Mb. Multiple nuclear insertions of parts of the chloroplast genome were observed, with one region on chromosome 11 spanning more than 2 Mb of the genome in which fragments up to 54,784 bp long and covering the whole chloroplast genome were inserted randomly. Unlike inArabidopsis thaliana, ribosomal cistrons are present inFagus sylvaticaonly in four major regions, in line with FISH studies. On most assembled chromosomes, telomeric repeats were found at both ends, while centromeric repeats were found to be scattered throughout the genome apart from their main occurrence per chromosome. The genome- wide distribution of SNPs was evaluated using a second individual from Jamy Nature Reserve (Poland). SNPs, repeat elements and duplicated genes were unevenly distributed in the genomes, with one major anomaly on chromosome 4. The genome presented here adds to the available highly resolved plant genomes and we hope it will serve as a valuable basis for future research on genome architecture and for understanding the past and future of European Beech populations in a changing climate.

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    https://www.biorxiv.org/conten...
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    Frontiers in Genetics
    Article
    License: CC BY
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    Frontiers in Genetics
    Article . 2022
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      https://www.biorxiv.org/conten...
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      Frontiers in Genetics
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      Frontiers in Genetics
      Article . 2022
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    Authors: Martin Tetard; Laetitia Licari; Ekaterina Ovsepyan; Kazuyo Tachikawa; +1 Authors

    Abstract. Oxygen Minimum Zones (OMZs) are oceanic areas largely depleted in dissolved oxygen, nowadays considered in expansion in the face of global warming. Their ecological and economic consequences are being debated. The investigation of past OMZ conditions allows us to better understand biological and physical mechanisms responsible for their variability with regards to climate change, carbon pump and carbonate system. To investigate the relationship between OMZ expansion and global climate changes during the late Quaternary, quantitative oxygen reconstructions are needed, but are still in their early development. Here, past bottom water oxygenation (BWO) was quantitatively assessed through a new, fast, semi-automated, and taxonfree morphometric analysis of benthic foraminiferal tests, developed and calibrated using Eastern North Pacific (ENP) and the Eastern South Pacific (ESP) OMZs samples. This new approach is based on an average size and circularity index for each sample. This method, as well as two already published micropalaeontological approaches based on benthic foraminiferal assemblages variability and porosity investigation of a single species, were here calibrated based on availability of new data from 23 core tops recovered along an oxygen gradient (from 0.03 to 1.79 mL.L−1) from the ENP, ESP, AS (Arabian Sea) and WNP (Western North Pacific, including its marginal seas) OMZs. Global calibrated transfer functions are thus herein proposed for each of these methods. These micropalaeontological reconstruction approaches were then applied on a paleorecord from the ENP OMZ to examine the consistency and limits of these methods, as well as the relative influence of bottom and pore waters on these micropalaeontological tools. Both the assemblages and morphometric approaches (that is also ultimately based on the ecological response of the complete assemblage and faunal succession according to BWO) gave similar and consistent past BWO reconstructions, while the porosity approach (based on a single species and its unique response to a mixed signal of bottom and pore waters) shown ambiguous estimations.

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    Biogeosciences
    Article . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    https://doi.org/10.5194/bg-202...
    Preprint . 2021 . Peer-reviewed
    License: CC BY
    Data sources: Crossref
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    Biogeosciences
    Article . Preprint
    License: CC BY
    Data sources: UnpayWall
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    Biogeosciences (BG)
    Other literature type . 2021
    DOAJ
    Article . 2021
    Data sources: DOAJ
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    Authors: Erik Melén; Marie Standl; Ulrike Gehring; Hicran Altug; +18 Authors

    Background: Whether long-term exposure air to pollution has effects on allergic sensitization is controversial. Objective: Our aim was to investigate associations of air pollution exposure at birth and at the time of later biosampling with IgE sensitization against common food and inhalant allergens, or specific allergen molecules, in children aged up to 16 years. Methods: A total of 6163 children from 4 European birth cohorts participating in the Mechanisms of the Development of ALLergy [MeDALL] consortium were included in this meta-analysis of the following studies: Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) (Sweden), Influences of Lifestyle-Related Factors on the Human Immune System and Development of Allergies in Childhood (LISA)/German Infant Study on the Influence of Nutrition Intervention PLUS Environmental and Genetic Influences on Allergy Development (GINIplus) (Germany), and Prevention and Incidence of Asthma and Mite Allergy (PIAMA) (The Netherlands). The following indicators were modeled by land use regression: individual residential outdoor levels of particulate matter with aerodynamic diameters less than 2.5 μm, less than 10 μm, and between 2.5 and 10 μm; PM2.5 absorbance (a measurement of the blackness of PM2.5 filters); and nitrogen oxides levels. Blood samples drawn at ages 4 to 6 (n = 5989), 8 to 10 (n = 6603), and 15 to 16 (n = 5825) years were analyzed for IgE sensitization to allergen extracts by ImmunoCAP. Additionally, IgE against 132 allergen molecules was measured by using the MedALL microarray chip (n = 1021). Results: Air pollution was not consistently associated with IgE sensitization to any common allergen extract up to age 16 years. However, allergen-specific analyses suggested increased risks of sensitization to birch (odds ratio [OR] = 1.12 [95% CI = 1.01-1.25] per 10-μg/m3 increase in NO2 exposure). In a subpopulation with microarray data, IgE to the major timothy grass allergen Phleum pratense 1 (Phl p 1) and the cat allergen Felis domesticus 1 (Fel d 1) greater than 3.5 Immuno Solid-phase Allergen Chip standardized units for detection of IgE antibodies were related to PM2.5 exposure at birth (OR = 3.33 [95% CI = 1.40-7.94] and OR = 4.98 [95% CI = 1.59-15.60], respectively, per 5-μg/m3 increase in exposure). Conclusion: Air pollution exposure does not seem to increase the overall risk of allergic sensitization; however, sensitization to birch as well as grass pollen Phl p 1 and cat Fel d 1 allergen molecules may be related to specific pollutants. The research leading to these results has received funding from the European Community’s Seventh Framework Program under grant agreement numbers: 211250 (European Study of Cohorts for Air Pollution Effects [ESCAPE]), and 261357 (Mechanisms of the Development of ALLergy [MeDALL]). Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, Region Stockholm (ALF project, and database maintenance), the Strategic Research Programme in Epidemiology at Karolinska Institutet, the Swedish Research Council Formas and the Swedish Environment Protection Agency, the Swedish Asthma and Allergy Research Foundation, the Cancer and Allergy Foundation. E.M. is supported by a grant from the European Research Council (grant agreement 757919, TRIBAL). O.G. is supported by the Swedish Research Council for Health, Working Life and Welfare (FORTE 2017-01146).

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    UPF Digital Repository
    Article . 2021
    License: CC BY
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    Hal-Diderot
    Article . 2021
    Data sources: Hal-Diderot
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    Authors: Tavella, Teresa; Rampelli, Simone; Guidarelli, Giulia; Bazzocchi, Alberto; +16 Authors

    International audience; Aging is accompanied by physiological changes affecting body composition and functionality, including accumulation of fat mass at the expense of muscle mass, with effects upon morbidity and quality of life. The gut microbiome has recently emerged as a key environmental modifier of human health that can modulate healthy aging and possibly longevity. However, its associations with adiposity in old age are still poorly understood. Here we profiled the gut microbiota in a well-characterized cohort of 201 Italian elderly subjects from the NU-AGE study, by 16S rRNA amplicon sequencing. We then tested for association with body composition from dual-energy X-ray absorptiometry (DXA), with a focus on visceral and subcutaneous adipose tissue. Dietary patterns, serum metabolome and other health-related parameters were also assessed. This study identified distinct compositional structures of the elderly gut microbiota associated with DXA parameters, diet, metabolic profiles and cardio-metabolic risk factors.

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    Gut Microbes
    Article
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: La Cono, Violetta; Messina, Enzo; Reva, Olga; Smedile, Francesco; +14 Authors

    AbstractClimate change, desertification, salinisation of soils and the changing hydrology of the Earth are creating or modifying microbial habitats at all scales including the oceans, saline groundwaters and brine lakes. In environments that are saline or hypersaline, the biodegradation of recalcitrant plant and animal polysaccharides can be inhibited by salt‐induced microbial stress and/or by limitation of the metabolic capabilities of halophilic microbes. We recently demonstrated that the chitinolytic haloarchaeon Halomicrobium can serve as the host for an ectosymbiont, nanohaloarchaeon ‘Candidatus Nanohalobium constans’. Here, we consider whether nanohaloarchaea can benefit from the haloarchaea‐mediated degradation of xylan, a major hemicellulose component of wood. Using samples of natural evaporitic brines and anthropogenic solar salterns, we describe genome‐inferred trophic relations in two extremely halophilic xylan‐degrading three‐member consortia. We succeeded in genome assembly and closure for all members of both xylan‐degrading cultures and elucidated the respective food chains within these consortia. We provide evidence that ectosymbiontic nanohaloarchaea is an active ecophysiological component of extremely halophilic xylan‐degrading communities (although by proxy) in hypersaline environments. In each consortium, nanohaloarchaea occur as ectosymbionts of Haloferax, which in turn act as scavenger of oligosaccharides produced by xylan‐hydrolysing Halorhabdus. We further obtained and characterised the nanohaloarchaea–host associations using microscopy, multi‐omics and cultivation approaches. The current study also doubled culturable nanohaloarchaeal symbionts and demonstrated that these enigmatic nano‐sized archaea can be readily isolated in binary co‐cultures using an appropriate enrichment strategy. We discuss the implications of xylan degradation by halophiles in biotechnology and for the United Nation's Sustainable Development Goals.

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    ZENODO; Microbial Biotechnology
    Article . 2023 . Peer-reviewed
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      ZENODO; Microbial Biotechnology
      Article . 2023 . Peer-reviewed
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    Authors: Jingjing Liang; Javier G. P. Gamarra; Nicolas Picard; Mo Zhou; +219 Authors

    The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0.025° × 0.025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1.3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB). However, MTB underestimated LDG in the tropics, where high species richness was also moderated by topographic, soil and anthropogenic factors operating at local scales. Given that local landscape variables operate synergistically with bioclimatic factors in shaping the global LDG pattern, we suggest that MTB be extended to account for co-limitation by subordinate drivers. The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108- 00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)— ‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007– 2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109– 00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/ NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10- 2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/ PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2.

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    Authors: Cazzolla Gatti, Roberto; Reich, Peter B; Gamarra, Javier GP; Crowther, Tom; +144 Authors

    One of the most fundamental questions in ecology is how many species inhabit the Earth. However, due to massive logistical and financial challenges and taxonomic difficulties connected to the species concept definition, the global numbers of species, including those of important and well-studied life forms such as trees, still remain largely unknown. Here, based on global ground-sourced data, we estimate the total tree species richness at global, continental, and biome levels. Our results indicate that there are ∼73,000 tree species globally, among which ∼9,000 tree species are yet to be discovered. Roughly 40% of undiscovered tree species are in South America. Moreover, almost one-third of all tree species to be discovered may be rare, with very low populations and limited spatial distribution (likely in remote tropical lowlands and mountains). These findings highlight the vulnerability of global forest biodiversity to anthropogenic changes in land use and climate, which disproportionately threaten rare species and thus, global tree richness. Proceedings of the National Academy of Sciences of the United States of America, 119 (6) ISSN:1091-6490 ISSN:0027-8424

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    Authors: W. Dorigo; I. Himmelbauer; D. Aberer; L. Schremmer; +68 Authors

    In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonises them in terms of units and sampling rates, applies advanced quality control, and stores them in a database. Users can freely retrieve the data from this database through an online web portal (https://ismn.earth/en/, last access: 28 October 2021). Meanwhile, the ISMN has evolved into the primary in situ soil moisture reference database worldwide, as evidenced by more than 3000 active users and over 1000 scientific publications referencing the data sets provided by the network. As of July 2021, the ISMN now contains the data of 71 networks and 2842 stations located all over the globe, with a time period spanning from 1952 to the present. The number of networks and stations covered by the ISMN is still growing, and approximately 70 % of the data sets contained in the database continue to be updated on a regular or irregular basis. The main scope of this paper is to inform readers about the evolution of the ISMN over the past decade, including a description of network and data set updates and quality control procedures. A comprehensive review of the existing literature making use of ISMN data is also provided in order to identify current limitations in functionality and data usage and to shape priorities for the next decade of operations of this unique community-based data repository.

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    Authors: H. K. Lappalainen; H. K. Lappalainen; T. Petäjä; T. Vihma; +83 Authors

    Short summary We summarize results during the last 5 years in the northern Eurasian region, especially from Russia, and introduce recent observations of the air quality in the urban environments in China. Although the scientific knowledge in these regions has increased, there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures and integrative data analyses, hindering a comprehensive system analysis. The Pan-Eurasian Experiment (PEEX) Science Plan, released in 2015, addressed a need for a holistic system understanding and outlined the most urgent research needs for the rapidly changing Arctic-boreal region. Air quality in China, together with the long-range transport of atmospheric pollutants, was also indicated as one of the most crucial topics of the research agenda. These two geographical regions, the northern Eurasian Arctic-boreal region and China, especially the megacities in China, were identified as a “PEEX region”. It is also important to recognize that the PEEX geographical region is an area where science-based policy actions would have significant impacts on the global climate. This paper summarizes results obtained during the last 5 years in the northern Eurasian region, together with recent observations of the air quality in the urban environments in China, in the context of the PEEX programme. The main regions of interest are the Russian Arctic, northern Eurasian boreal forests (Siberia) and peatlands, and the megacities in China. We frame our analysis against research themes introduced in the PEEX Science Plan in 2015. We summarize recent progress towards an enhanced holistic understanding of the land–atmosphere–ocean systems feedbacks. We conclude that although the scientific knowledge in these regions has increased, the new results are in many cases insufficient, and there are still gaps in our understanding of large-scale climate–Earth surface interactions and feedbacks. This arises from limitations in research infrastructures, especially the lack of coordinated, continuous and comprehensive in situ observations of the study region as well as integrative data analyses, hindering a comprehensive system analysis. The fast-changing environment and ecosystem changes driven by climate change, socio-economic activities like the China Silk Road Initiative, and the global trends like urbanization further complicate such analyses. We recognize new topics with an increasing importance in the near future, especially “the enhancing biological sequestration capacity of greenhouse gases into forests and soils to mitigate climate change” and the “socio-economic development to tackle air quality issues”.

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    https://doi.org/10.5194/acp-20...
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    Authors: Telmo Morato; Telmo Morato; Christopher K. Pham; Christopher K. Pham; +35 Authors

    The identification of areas that fit the Food and Agriculture Organization of the United Nations (FAO) criteria to define what constitutes a Vulnerable Marine Ecosystem (VME) has been the main policy driver for the protection of deep-sea environments in Areas Beyond National Jurisdictions (United Nations General Assembly, 2006; FAO, 2009) in relation to bottom fisheries. At the same time, the Convention on Biological Diversity advocates for the implementation of representative networks of Marine Protected Areas (MPAs) in the open ocean and the deep sea, and calls for the identification of Ecologically or Biologically Significant marine Areas (EBSAs; Convention on Biological Diversity, 2008, Decision IX/20). Although VMEs and EBSAs are conceptually different, Ardron et al. (2014) argue that the designation of VMEs, EBSAs, and large open-ocean MPAs should be aligned to ensure that VMEs are incorporated within area-based management tools. The International Council for the Exploration of the Sea (ICES) adopted a Multi-Criteria Assessment (MCA) methodology for informing the identification of VMEs in the North-East Atlantic (ICES, 2016a,b; Morato et al., 2018). The MCA is a taxa-dependent spatial method that incorporates the fact that not all VME indicators are equally vulnerable to human impacts, and thus should not be weighted equally. By including a measure of the confidence associated with each VME record, this methodology also considers some of the uncertainties associated with the sampling methodologies, the reported taxonomy, and data quality issues. Equally important, it highlights areas in the North Atlantic that have been poorly sampled and that require further attention. Finally, this methodology also allows for the evaluation and comparison of VME index with spatial fisheries data that may directly generate significant adverse impacts on VMEs. Although the VME Index has been used since 2018 in ICES advice, several caveats and limitations have been identified (ICES, 2018, 2019, 2020). The main criticism refers to the fact that the VME index signals the presence of VME indicator taxa that are considered to be the most important rather than showing the likelihood of an area containing a spatially explicit VME. Also, concerns over the abundance scores adopted have been raised and it has been suggested that abundance thresholds should be defined for each VME indicator. It is, therefore, recognized that improvements of the VME index and the way actual VMEs are identified are still necessary. The identification of representative areas that can form a network of MPAs in the deep sea requires ocean basin-scale approaches grounded on ocean basin-scale datasets. In this regard, the H2020 ATLAS project (GA 678760) performed a unique trans-Atlantic assessment of deep-water ecosystems to inform Atlantic Ocean basin-scale governance. The ATLAS project compiled the best available data on VME indicator taxa for the North Atlantic (Ramiro-Sánchez et al., 2020) in order to assist with the identification of locations that may constitute VMEs and EBSAs, as a precursor to the development of a North Atlantic wide network of MPAs. Here, we applied the ICES MCA method to the ATLAS VME indicator taxa database to produce and make publicly available a new “North Atlantic Ocean basin-scale VME index dataset,” facilitating further consultation and use by scientists, managers, or other relevant stakeholders. SI

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    ZENODO; Frontiers in Marine Science
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    Frontiers in Marine Science
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ArchiMer - Instituti...arrow_drop_down
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      ZENODO; Frontiers in Marine Science
      Article . 2021 . Peer-reviewed
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      Frontiers in Marine Science
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      Article . 2021
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    Authors: Bagdevi Mishra; Bagdevi Mishra; Bartosz Ulaszewski; Joanna Meger; +27 Authors

    AbstractThe European Beech is the dominant climax tree in most regions of Central Europe and valued for its ecological versatility and hardwood timber. Even though a draft genome has been published recently, higher resolution is required for studying aspects of genome architecture and recombination. Here we present a chromosome-level assembly of the more than 300 year-old reference individual, Bhaga, from the Kellerwald-Edersee National Park (Germany). Its nuclear genome of 541 Mb was resolved into 12 chromosomes varying in length between 28 Mb and 73 Mb. Multiple nuclear insertions of parts of the chloroplast genome were observed, with one region on chromosome 11 spanning more than 2 Mb of the genome in which fragments up to 54,784 bp long and covering the whole chloroplast genome were inserted randomly. Unlike inArabidopsis thaliana, ribosomal cistrons are present inFagus sylvaticaonly in four major regions, in line with FISH studies. On most assembled chromosomes, telomeric repeats were found at both ends, while centromeric repeats were found to be scattered throughout the genome apart from their main occurrence per chromosome. The genome- wide distribution of SNPs was evaluated using a second individual from Jamy Nature Reserve (Poland). SNPs, repeat elements and duplicated genes were unevenly distributed in the genomes, with one major anomaly on chromosome 4. The genome presented here adds to the available highly resolved plant genomes and we hope it will serve as a valuable basis for future research on genome architecture and for understanding the past and future of European Beech populations in a changing climate.

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    https://www.biorxiv.org/conten...
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    Frontiers in Genetics
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    Frontiers in Genetics
    Article . 2022
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      https://www.biorxiv.org/conten...
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      Frontiers in Genetics
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      Frontiers in Genetics
      Article . 2022
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    Authors: Martin Tetard; Laetitia Licari; Ekaterina Ovsepyan; Kazuyo Tachikawa; +1 Authors

    Abstract. Oxygen Minimum Zones (OMZs) are oceanic areas largely depleted in dissolved oxygen, nowadays considered in expansion in the face of global warming. Their ecological and economic consequences are being debated. The investigation of past OMZ conditions allows us to better understand biological and physical mechanisms responsible for their variability with regards to climate change, carbon pump and carbonate system. To investigate the relationship between OMZ expansion and global climate changes during the late Quaternary, quantitative oxygen reconstructions are needed, but are still in their early development. Here, past bottom water oxygenation (BWO) was quantitatively assessed through a new, fast, semi-automated, and taxonfree morphometric analysis of benthic foraminiferal tests, developed and calibrated using Eastern North Pacific (ENP) and the Eastern South Pacific (ESP) OMZs samples. This new approach is based on an average size and circularity index for each sample. This method, as well as two already published micropalaeontological approaches based on benthic foraminiferal assemblages variability and porosity investigation of a single species, were here calibrated based on availability of new data from 23 core tops recovered along an oxygen gradient (from 0.03 to 1.79 mL.L−1) from the ENP, ESP, AS (Arabian Sea) and WNP (Western North Pacific, including its marginal seas) OMZs. Global calibrated transfer functions are thus herein proposed for each of these methods. These micropalaeontological reconstruction approaches were then applied on a paleorecord from the ENP OMZ to examine the consistency and limits of these methods, as well as the relative influence of bottom and pore waters on these micropalaeontological tools. Both the assemblages and morphometric approaches (that is also ultimately based on the ecological response of the complete assemblage and faunal succession according to BWO) gave similar and consistent past BWO reconstructions, while the porosity approach (based on a single species and its unique response to a mixed signal of bottom and pore waters) shown ambiguous estimations.

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    Biogeosciences
    Article . 2021 . Peer-reviewed
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    https://doi.org/10.5194/bg-202...
    Preprint . 2021 . Peer-reviewed
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    Biogeosciences
    Article . Preprint
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    Biogeosciences (BG)
    Other literature type . 2021
    DOAJ
    Article . 2021
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    Authors: Erik Melén; Marie Standl; Ulrike Gehring; Hicran Altug; +18 Authors

    Background: Whether long-term exposure air to pollution has effects on allergic sensitization is controversial. Objective: Our aim was to investigate associations of air pollution exposure at birth and at the time of later biosampling with IgE sensitization against common food and inhalant allergens, or specific allergen molecules, in children aged up to 16 years. Methods: A total of 6163 children from 4 European birth cohorts participating in the Mechanisms of the Development of ALLergy [MeDALL] consortium were included in this meta-analysis of the following studies: Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) (Sweden), Influences of Lifestyle-Related Factors on the Human Immune System and Development of Allergies in Childhood (LISA)/German Infant Study on the Influence of Nutrition Intervention PLUS Environmental and Genetic Influences on Allergy Development (GINIplus) (Germany), and Prevention and Incidence of Asthma and Mite Allergy (PIAMA) (The Netherlands). The following indicators were modeled by land use regression: individual residential outdoor levels of particulate matter with aerodynamic diameters less than 2.5 μm, less than 10 μm, and between 2.5 and 10 μm; PM2.5 absorbance (a measurement of the blackness of PM2.5 filters); and nitrogen oxides levels. Blood samples drawn at ages 4 to 6 (n = 5989), 8 to 10 (n = 6603), and 15 to 16 (n = 5825) years were analyzed for IgE sensitization to allergen extracts by ImmunoCAP. Additionally, IgE against 132 allergen molecules was measured by using the MedALL microarray chip (n = 1021). Results: Air pollution was not consistently associated with IgE sensitization to any common allergen extract up to age 16 years. However, allergen-specific analyses suggested increased risks of sensitization to birch (odds ratio [OR] = 1.12 [95% CI = 1.01-1.25] per 10-μg/m3 increase in NO2 exposure). In a subpopulation with microarray data, IgE to the major timothy grass allergen Phleum pratense 1 (Phl p 1) and the cat allergen Felis domesticus 1 (Fel d 1) greater than 3.5 Immuno Solid-phase Allergen Chip standardized units for detection of IgE antibodies were related to PM2.5 exposure at birth (OR = 3.33 [95% CI = 1.40-7.94] and OR = 4.98 [95% CI = 1.59-15.60], respectively, per 5-μg/m3 increase in exposure). Conclusion: Air pollution exposure does not seem to increase the overall risk of allergic sensitization; however, sensitization to birch as well as grass pollen Phl p 1 and cat Fel d 1 allergen molecules may be related to specific pollutants. The research leading to these results has received funding from the European Community’s Seventh Framework Program under grant agreement numbers: 211250 (European Study of Cohorts for Air Pollution Effects [ESCAPE]), and 261357 (Mechanisms of the Development of ALLergy [MeDALL]). Children, Allergy, Milieu, Stockholm, Epidemiology (BAMSE) was supported by The Swedish Research Council, The Swedish Heart-Lung Foundation, Region Stockholm (ALF project, and database maintenance), the Strategic Research Programme in Epidemiology at Karolinska Institutet, the Swedish Research Council Formas and the Swedish Environment Protection Agency, the Swedish Asthma and Allergy Research Foundation, the Cancer and Allergy Foundation. E.M. is supported by a grant from the European Research Council (grant agreement 757919, TRIBAL). O.G. is supported by the Swedish Research Council for Health, Working Life and Welfare (FORTE 2017-01146).

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    UPF Digital Repository
    Article . 2021
    License: CC BY
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    Hal-Diderot
    Article . 2021
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    Authors: Tavella, Teresa; Rampelli, Simone; Guidarelli, Giulia; Bazzocchi, Alberto; +16 Authors

    International audience; Aging is accompanied by physiological changes affecting body composition and functionality, including accumulation of fat mass at the expense of muscle mass, with effects upon morbidity and quality of life. The gut microbiome has recently emerged as a key environmental modifier of human health that can modulate healthy aging and possibly longevity. However, its associations with adiposity in old age are still poorly understood. Here we profiled the gut microbiota in a well-characterized cohort of 201 Italian elderly subjects from the NU-AGE study, by 16S rRNA amplicon sequencing. We then tested for association with body composition from dual-energy X-ray absorptiometry (DXA), with a focus on visceral and subcutaneous adipose tissue. Dietary patterns, serum metabolome and other health-related parameters were also assessed. This study identified distinct compositional structures of the elderly gut microbiota associated with DXA parameters, diet, metabolic profiles and cardio-metabolic risk factors.

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