Diversity and inclusion in conservation: A proposal for a marine diversity network

Low diversity among scientists and practitioners is rampant in conservation. Currently, conservation professionals do not reflect the same diversity of perspectives and experiences of the world as the communities who bear the largest burden for implementing—or adverse consequences for failing to implement—conservation action. Acknowledging and describing the problem is important. But policies and programmes must also be put in place to correct it. Here, we highlight some measurable benefits of workforce diversity, and give an overview of some of the barriers to inclusion in marine conservation that help perpetuate low workforce diversity. Importantly, we underscore actions that both individuals and groups can take to alleviate such barriers. In particular, we describe the establishment of an online Marine Diversity Network, which conference participants proposed during a focus group meeting at the 4th International Marine Conservation Congress. The network will serve to bring together people from across the globe, from a variety of backgrounds, and from all career stages, to share knowledge, experiences and ideas, to provide and receive mentorship in marine conservation, and to forge new collaborations. Removing barriers to diverse participation requires coordinated, mindful actions by individuals and organizations. We hope that the proposed network and other actions presented in this paper find widespread support, and that they might serve both as inspiration and guide to other groups concerned with increasing diversity and inclusivity

αβ T cells and a mixed Th1/Th17 response are important in organic dust-induced airway disease

Abstract Background Organic dust exposure in agricultural environments induces an inflammatory response that attenuates over time, yet repetitive dust exposures result in chronic lung diseases. Animal models resembling this chronic lung inflammatory response have been developed, yet the underlying cellular mechanisms are not well defined. Objective Because mice repetitively exposed to organic dust extracts (DE) display increased CD3+ T cell lung infiltrates, we sought to determine the phenotype and importance of these cells. Methods Mice received swine confinement DE repetitively for 3 weeks by established intranasal inhalation protocol. Studies were conducted with peptidoglycan (PGN) because it is a major DE component in large animal farming environments and has shared similar biologic effects with DE. Enumeration of T cells and intracellular cytokine profiles were conducted by flow cytometry techniques. Whole lung homogenate cytokines were analyzed by multiplex immunoassay. T cell receptor (TCR) αβ knockouts were used to determine the functional importance of αβ-expressing T cells. Results DE increased lung-associated CD3+CD4+ T cells and interleukin (IL)-17 (but not IL-4, interferon [IFN]-γ, IL-10) producing CD4+ T cells. PGN treatment resulted in increased IL-17 and IFN-γ producing CD4+ T cells and IFN-γ producing CD8+ T cells. Both DE and PGN augmented expression of cytokines associated with Th1 and Th17 polarization in lung homogenates. DE-induced lung mononuclear aggregates and bronchiolar compartment inflammation were significantly reduced in TCR knockout animals; however, neutrophil influx and alveolar compartment inflammation were not affected. Conclusion Studies demonstrated that DE and PGN exposure promote a Th1/Th17 lung microenvironment and that αβ-expressing T cells are important in mediating DE-induced lung pathologic conditions

CD11c + /CD11b + Cells Are Critical for Organic Dust–Elicited Murine Lung Inflammation

Organic dust exposure in the agricultural industry results in significant lung disease. Macrophage infiltrates are increased in the lungs after organic dust exposures, yet the phenotype and functional importance of these cells remain unclear. Using an established intranasal inhalation murine model of dust-induced lung inflammation, animals were treated once or daily for 3 weeks with swine confinement organic dust extract (DE). Repetitive DE treatment for 3 weeks resulted in significant increases in CD11c+/CD11b+ macrophages in whole lung–associated tissue. These cells displayed increased costimulatory molecule (CD80 and CD86) expression, enhanced phagocytic ability, and an increased production of IL-6, CXCL1, and CXCL2. Similar findings were observed with the CD11c+/CD11b+ macrophage infiltrate after repetitive exposure to peptidoglycan, a major DE component. To determine the functional importance of macrophages in mediating DE-induced airway inflammation, lung macrophages were selectively depleted using a well-established intranasal clodronate liposome depletion/suicide strategy. First, macrophage depletion by clodronate liposomes resulted in significant reductions in airway neutrophil influx and TNF-α and IL-6 production after a single exposure to DE. In contrast, after repetitive 3-week exposure to DE, airway lavage fluid and lung tissue neutrophils were significantly increased in clodronate liposome–treated mice compared with control mice. A histological examination of lung tissue demonstrated striking increases in alveolar and bronchiolar inflammation, as well as in the size and distribution of cellular aggregates in clodronate–liposome versus saline–liposome groups repetitively exposed to DE. These studies demonstrate that DE elicits activated CD11c+/CD11b+ macrophages in the lung, which play a critical role in regulating the outcome of DE-induced airway inflammation

Predictors of rapid aortic root dilation and referral for aortic surgery in Marfan syndrome

Few data exist regarding predictors of rapid aortic root dilation and referral for aortic surgery in Marfan syndrome (MFS). To identify independent predictors of the rate of aortic root (AoR) dilation and referral for aortic surgery, we investigated the data from the Pediatric Heart Network randomized trial of atenolol versus losartan in young patients with MFS. Data were analyzed from the echocardiograms at 0, 12, 24, and 36months read in the core laboratory of 608 trial subjects, aged 6months to 25 years, who met original Ghent criteria and had an AoR z-score (AoRz)>3. Repeated measures linear and logistic regressions were used to determine multivariable predictors of AoR dilation. Receiver operator characteristic curves were used to determine cut-points in AoR dilation predicting referral for aortic surgery. Multivariable analysis showed rapid AoR dilation as defined by change in AoRz/year>90th percentile was associated with older age, higher sinotubular junction z-score, and atenolol use (R-2=0.01) or by change in AoR diameter (AoRd)/year>90th percentile with higher sinotubular junction z-score and non-white race (R-2=0.02). Referral for aortic root surgery was associated with higher AoRd, higher ascending aorta z-score, and higher sinotubular junction diameter:ascending aorta diameter ratio (R-2=0.17). Change in AoRz of 0.72 SD units/year had 42% sensitivity and 92% specificity and change in AoRd of 0.34cm/year had 38% sensitivity and 95% specificity for predicting referral for aortic surgery. In this cohort of young patients with MFS, no new robust predictors of rapid AoR dilation or referral for aortic root surgery were identified. Further investigation may determine whether generalized proximal aortic dilation and effacement of the sinotubular junction will allow for better risk stratification. Rate of AoR dilation cut-points had high specificity, but low sensitivity for predicting referral for aortic surgery, limiting their clinical use. Clinical Trial Number ClinicalTrials.gov number, NCT00429364

Validation of clinical acceptability of deep-learning-based automated segmentation of organs-at-risk for head-and-neck radiotherapy treatment planning

IntroductionOrgan-at-risk segmentation for head and neck cancer radiation therapy is a complex and time-consuming process (requiring up to 42 individual structure, and may delay start of treatment or even limit access to function-preserving care. Feasibility of using a deep learning (DL) based autosegmentation model to reduce contouring time without compromising contour accuracy is assessed through a blinded randomized trial of radiation oncologists (ROs) using retrospective, de-identified patient data.MethodsTwo head and neck expert ROs used dedicated time to create gold standard (GS) contours on computed tomography (CT) images. 445 CTs were used to train a custom 3D U-Net DL model covering 42 organs-at-risk, with an additional 20 CTs were held out for the randomized trial. For each held-out patient dataset, one of the eight participant ROs was randomly allocated to review and revise the contours produced by the DL model, while another reviewed contours produced by a medical dosimetry assistant (MDA), both blinded to their origin. Time required for MDAs and ROs to contour was recorded, and the unrevised DL contours, as well as the RO-revised contours by the MDAs and DL model were compared to the GS for that patient.ResultsMean time for initial MDA contouring was 2.3 hours (range 1.6-3.8 hours) and RO-revision took 1.1 hours (range, 0.4-4.4 hours), compared to 0.7 hours (range 0.1-2.0 hours) for the RO-revisions to DL contours. Total time reduced by 76% (95%-Confidence Interval: 65%-88%) and RO-revision time reduced by 35% (95%-CI,-39%-91%). All geometric and dosimetric metrics computed, agreement with GS was equivalent or significantly greater (p<0.05) for RO-revised DL contours compared to the RO-revised MDA contours, including volumetric Dice similarity coefficient (VDSC), surface DSC, added path length, and the 95%-Hausdorff distance. 32 OARs (76%) had mean VDSC greater than 0.8 for the RO-revised DL contours, compared to 20 (48%) for RO-revised MDA contours, and 34 (81%) for the unrevised DL OARs.ConclusionDL autosegmentation demonstrated significant time-savings for organ-at-risk contouring while improving agreement with the institutional GS, indicating comparable accuracy of DL model. Integration into the clinical practice with a prospective evaluation is currently underway

State of the climate in 2018

In 2018, the dominant greenhouse gases released into Earth’s atmosphere—carbon dioxide, methane, and nitrous oxide—continued their increase. The annual global average carbon dioxide concentration at Earth’s surface was 407.4 ± 0.1 ppm, the highest in the modern instrumental record and in ice core records dating back 800 000 years. Combined, greenhouse gases and several halogenated gases contribute just over 3 W m−2 to radiative forcing and represent a nearly 43% increase since 1990. Carbon dioxide is responsible for about 65% of this radiative forcing. With a weak La Niña in early 2018 transitioning to a weak El Niño by the year’s end, the global surface (land and ocean) temperature was the fourth highest on record, with only 2015 through 2017 being warmer. Several European countries reported record high annual temperatures. There were also more high, and fewer low, temperature extremes than in nearly all of the 68-year extremes record. Madagascar recorded a record daily temperature of 40.5°C in Morondava in March, while South Korea set its record high of 41.0°C in August in Hongcheon. Nawabshah, Pakistan, recorded its highest temperature of 50.2°C, which may be a new daily world record for April. Globally, the annual lower troposphere temperature was third to seventh highest, depending on the dataset analyzed. The lower stratospheric temperature was approximately fifth lowest. The 2018 Arctic land surface temperature was 1.2°C above the 1981–2010 average, tying for third highest in the 118-year record, following 2016 and 2017. June’s Arctic snow cover extent was almost half of what it was 35 years ago. Across Greenland, however, regional summer temperatures were generally below or near average. Additionally, a satellite survey of 47 glaciers in Greenland indicated a net increase in area for the first time since records began in 1999. Increasing permafrost temperatures were reported at most observation sites in the Arctic, with the overall increase of 0.1°–0.2°C between 2017 and 2018 being comparable to the highest rate of warming ever observed in the region. On 17 March, Arctic sea ice extent marked the second smallest annual maximum in the 38-year record, larger than only 2017. The minimum extent in 2018 was reached on 19 September and again on 23 September, tying 2008 and 2010 for the sixth lowest extent on record. The 23 September date tied 1997 as the latest sea ice minimum date on record. First-year ice now dominates the ice cover, comprising 77% of the March 2018 ice pack compared to 55% during the 1980s. Because thinner, younger ice is more vulnerable to melting out in summer, this shift in sea ice age has contributed to the decreasing trend in minimum ice extent. Regionally, Bering Sea ice extent was at record lows for almost the entire 2017/18 ice season. For the Antarctic continent as a whole, 2018 was warmer than average. On the highest points of the Antarctic Plateau, the automatic weather station Relay (74°S) broke or tied six monthly temperature records throughout the year, with August breaking its record by nearly 8°C. However, cool conditions in the western Bellingshausen Sea and Amundsen Sea sector contributed to a low melt season overall for 2017/18. High SSTs contributed to low summer sea ice extent in the Ross and Weddell Seas in 2018, underpinning the second lowest Antarctic summer minimum sea ice extent on record. Despite conducive conditions for its formation, the ozone hole at its maximum extent in September was near the 2000–18 mean, likely due to an ongoing slow decline in stratospheric chlorine monoxide concentration. Across the oceans, globally averaged SST decreased slightly since the record El Niño year of 2016 but was still far above the climatological mean. On average, SST is increasing at a rate of 0.10° ± 0.01°C decade−1 since 1950. The warming appeared largest in the tropical Indian Ocean and smallest in the North Pacific. The deeper ocean continues to warm year after year. For the seventh consecutive year, global annual mean sea level became the highest in the 26-year record, rising to 81 mm above the 1993 average. As anticipated in a warming climate, the hydrological cycle over the ocean is accelerating: dry regions are becoming drier and wet regions rainier. Closer to the equator, 95 named tropical storms were observed during 2018, well above the 1981–2010 average of 82. Eleven tropical cyclones reached Saffir–Simpson scale Category 5 intensity. North Atlantic Major Hurricane Michael’s landfall intensity of 140 kt was the fourth strongest for any continental U.S. hurricane landfall in the 168-year record. Michael caused more than 30 fatalities and $25 billion (U.S. dollars) in damages. In the western North Pacific, Super Typhoon Mangkhut led to 160 fatalities and$6 billion (U.S. dollars) in damages across the Philippines, Hong Kong, Macau, mainland China, Guam, and the Northern Mariana Islands. Tropical Storm Son-Tinh was responsible for 170 fatalities in Vietnam and Laos. Nearly all the islands of Micronesia experienced at least moderate impacts from various tropical cyclones. Across land, many areas around the globe received copious precipitation, notable at different time scales. Rodrigues and Réunion Island near southern Africa each reported their third wettest year on record. In Hawaii, 1262 mm precipitation at Waipā Gardens (Kauai) on 14–15 April set a new U.S. record for 24-h precipitation. In Brazil, the city of Belo Horizonte received nearly 75 mm of rain in just 20 minutes, nearly half its monthly average. Globally, fire activity during 2018 was the lowest since the start of the record in 1997, with a combined burned area of about 500 million hectares. This reinforced the long-term downward trend in fire emissions driven by changes in land use in frequently burning savannas. However, wildfires burned 3.5 million hectares across the United States, well above the 2000–10 average of 2.7 million hectares. Combined, U.S. wildfire damages for the 2017 and 2018 wildfire seasons exceeded $40 billion (U.S. dollars)

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Rest of authors: Decky Junaedi, Robert R. Junker, Eric Justes, Richard Kabzems, Jeffrey Kane, Zdenek Kaplan, Teja Kattenborn, Lyudmila Kavelenova, Elizabeth Kearsley, Anne Kempel, Tanaka Kenzo, Andrew Kerkhoff, Mohammed I. Khalil, Nicole L. Kinlock, Wilm Daniel Kissling, Kaoru Kitajima, Thomas Kitzberger, Rasmus Kjøller, Tamir Klein, Michael Kleyer, Jitka Klimešová, Joice Klipel, Brian Kloeppel, Stefan Klotz, Johannes M. H. Knops, Takashi Kohyama, Fumito Koike, Johannes Kollmann, Benjamin Komac, Kimberly Komatsu, Christian König, Nathan J. B. Kraft, Koen Kramer, Holger Kreft, Ingolf Kühn, Dushan Kumarathunge, Jonas Kuppler, Hiroko Kurokawa, Yoko Kurosawa, Shem Kuyah, Jean-Paul Laclau, Benoit Lafleur, Erik Lallai, Eric Lamb, Andrea Lamprecht, Daniel J. Larkin, Daniel Laughlin, Yoann Le Bagousse-Pinguet, Guerric le Maire, Peter C. le Roux, Elizabeth le Roux, Tali Lee, Frederic Lens, Simon L. Lewis, Barbara Lhotsky, Yuanzhi Li, Xine Li, Jeremy W. Lichstein, Mario Liebergesell, Jun Ying Lim, Yan-Shih Lin, Juan Carlos Linares, Chunjiang Liu, Daijun Liu, Udayangani Liu, Stuart Livingstone, Joan Llusià, Madelon Lohbeck, Álvaro López-García, Gabriela Lopez-Gonzalez, Zdeňka Lososová, Frédérique Louault, Balázs A. Lukács, Petr Lukeš, Yunjian Luo, Michele Lussu, Siyan Ma, Camilla Maciel Rabelo Pereira, Michelle Mack, Vincent Maire, Annikki Mäkelä, Harri Mäkinen, Ana Claudia Mendes Malhado, Azim Mallik, Peter Manning, Stefano Manzoni, Zuleica Marchetti, Luca Marchino, Vinicius Marcilio-Silva, Eric Marcon, Michela Marignani, Lars Markesteijn, Adam Martin, Cristina Martínez-Garza, Jordi Martínez-Vilalta, Tereza Mašková, Kelly Mason, Norman Mason, Tara Joy Massad, Jacynthe Masse, Itay Mayrose, James McCarthy, M. Luke McCormack, Katherine McCulloh, Ian R. McFadden, Brian J. McGill, Mara Y. McPartland, Juliana S. Medeiros, Belinda Medlyn, Pierre Meerts, Zia Mehrabi, Patrick Meir, Felipe P. L. Melo, Maurizio Mencuccini, Céline Meredieu, Julie Messier, Ilona Mészáros, Juha Metsaranta, Sean T. Michaletz, Chrysanthi Michelaki, Svetlana Migalina, Ruben Milla, Jesse E. D. Miller, Vanessa Minden, Ray Ming, Karel Mokany, Angela T. Moles, Attila Molnár V, Jane Molofsky, Martin Molz, Rebecca A. Montgomery, Arnaud Monty, Lenka Moravcová, Alvaro Moreno-Martínez, Marco Moretti, Akira S. Mori, Shigeta Mori, Dave Morris, Jane Morrison, Ladislav Mucina, Sandra Mueller, Christopher D. Muir, Sandra Cristina Müller, François Munoz, Isla H. Myers-Smith, Randall W. Myster, Masahiro Nagano, Shawna Naidu, Ayyappan Narayanan, Balachandran Natesan, Luka Negoita, Andrew S. Nelson, Eike Lena Neuschulz, Jian Ni, Georg Niedrist, Jhon Nieto, Ülo Niinemets, Rachael Nolan, Henning Nottebrock, Yann Nouvellon, Alexander Novakovskiy, The Nutrient Network, Kristin Odden Nystuen, Anthony O'Grady, Kevin O'Hara, Andrew O'Reilly-Nugent, Simon Oakley, Walter Oberhuber, Toshiyuki Ohtsuka, Ricardo Oliveira, Kinga Öllerer, Mark E. Olson, Vladimir Onipchenko, Yusuke Onoda, Renske E. Onstein, Jenny C. Ordonez, Noriyuki Osada, Ivika Ostonen, Gianluigi Ottaviani, Sarah Otto, Gerhard E. Overbeck, Wim A. Ozinga, Anna T. Pahl, C. E. Timothy Paine, Robin J. Pakeman, Aristotelis C. Papageorgiou, Evgeniya Parfionova, Meelis Pärtel, Marco Patacca, Susana Paula, Juraj Paule, Harald Pauli, Juli G. Pausas, Begoña Peco, Josep Penuelas, Antonio Perea, Pablo Luis Peri, Ana Carolina Petisco-Souza, Alessandro Petraglia, Any Mary Petritan, Oliver L. Phillips, Simon Pierce, Valério D. Pillar, Jan Pisek, Alexandr Pomogaybin, Hendrik Poorter, Angelika Portsmuth, Peter Poschlod, Catherine Potvin, Devon Pounds, A. Shafer Powell, Sally A. Power, Andreas Prinzing, Giacomo Puglielli, Petr Pyšek, Valerie Raevel, Anja Rammig, Johannes Ransijn, Courtenay A. Ray, Peter B. Reich, Markus Reichstein, Douglas E. B. Reid, Maxime Réjou-Méchain, Victor Resco de Dios, Sabina Ribeiro, Sarah Richardson, Kersti Riibak, Matthias C. Rillig, Fiamma Riviera, Elisabeth M. R. Robert, Scott Roberts, Bjorn Robroek, Adam Roddy, Arthur Vinicius Rodrigues, Alistair Rogers, Emily Rollinson, Victor Rolo, Christine Römermann, Dina Ronzhina, Christiane Roscher, Julieta A. Rosell, Milena Fermina Rosenfield, Christian Rossi, David B. Roy, Samuel Royer-Tardif, Nadja Rüger, Ricardo Ruiz-Peinado, Sabine B. Rumpf, Graciela M. Rusch, Masahiro Ryo, Lawren Sack, Angela Saldaña, Beatriz Salgado-Negret, Roberto Salguero-Gomez, Ignacio Santa-Regina, Ana Carolina Santacruz-García, Joaquim Santos, Jordi Sardans, Brandon Schamp, Michael Scherer-Lorenzen, Matthias Schleuning, Bernhard Schmid, Marco Schmidt, Sylvain Schmitt, Julio V. Schneider, Simon D. Schowanek, Julian Schrader, Franziska Schrodt, Bernhard Schuldt, Frank Schurr, Galia Selaya Garvizu, Marina Semchenko, Colleen Seymour, Julia C. Sfair, Joanne M. Sharpe, Christine S. Sheppard, Serge Sheremetiev, Satomi Shiodera, Bill Shipley, Tanvir Ahmed Shovon, Alrun Siebenkäs, Carlos Sierra, Vasco Silva, Mateus Silva, Tommaso Sitzia, Henrik Sjöman, Martijn Slot, Nicholas G. Smith, Darwin Sodhi, Pamela Soltis, Douglas Soltis, Ben Somers, Grégory Sonnier, Mia Vedel Sørensen, Enio Egon Sosinski Jr, Nadejda A. Soudzilovskaia, Alexandre F. Souza, Marko Spasojevic, Marta Gaia Sperandii, Amanda B. Stan, James Stegen, Klaus Steinbauer, Jörg G. Stephan, Frank Sterck, Dejan B. Stojanovic, Tanya Strydom, Maria Laura Suarez, Jens-Christian Svenning, Ivana Svitková, Marek Svitok, Miroslav Svoboda, Emily Swaine, Nathan Swenson, Marcelo Tabarelli, Kentaro Takagi, Ulrike Tappeiner, Rubén Tarifa, Simon Tauugourdeau, Cagatay Tavsanoglu, Mariska te Beest, Leho Tedersoo, Nelson Thiffault, Dominik Thom, Evert Thomas, Ken Thompson, Peter E. Thornton, Wilfried Thuiller, Lubomír Tichý, David Tissue, Mark G. Tjoelker, David Yue Phin Tng, Joseph Tobias, Péter Török, Tonantzin Tarin, José M. Torres-Ruiz, Béla Tóthmérész, Martina Treurnicht, Valeria Trivellone, Franck Trolliet, Volodymyr Trotsiuk, James L. Tsakalos, Ioannis Tsiripidis, Niklas Tysklind, Toru Umehara, Vladimir Usoltsev, Matthew Vadeboncoeur, Jamil Vaezi, Fernando Valladares, Jana Vamosi, Peter M. van Bodegom, Michiel van Breugel, Elisa Van Cleemput, Martine van de Weg, Stephni van der Merwe, Fons van der Plas, Masha T. van der Sande, Mark van Kleunen, Koenraad Van Meerbeek, Mark Vanderwel, Kim André Vanselow, Angelica Vårhammar, Laura Varone, Maribel Yesenia Vasquez Valderrama, Kiril Vassilev, Mark Vellend, Erik J. Veneklaas, Hans Verbeeck, Kris Verheyen, Alexander Vibrans, Ima Vieira, Jaime Villacís, Cyrille Violle, Pandi Vivek, Katrin Wagner, Matthew Waldram, Anthony Waldron, Anthony P. Walker, Martyn Waller, Gabriel Walther, Han Wang, Feng Wang, Weiqi Wang, Harry Watkins, James Watkins, Ulrich Weber, James T. Weedon, Liping Wei, Patrick Weigelt, Evan Weiher, Aidan W. Wells, Camilla Wellstein, Elizabeth Wenk, Mark Westoby, Alana Westwood, Philip John White, Mark Whitten, Mathew Williams, Daniel E. Winkler, Klaus Winter, Chevonne Womack, Ian J. Wright, S. Joseph Wright, Justin Wright, Bruno X. Pinho, Fabiano Ximenes, Toshihiro Yamada, Keiko Yamaji, Ruth Yanai, Nikolay Yankov, Benjamin Yguel, Kátia Janaina Zanini, Amy E. Zanne, David Zelený, Yun-Peng Zhao, Jingming Zheng, Ji Zheng, Kasia Ziemińska, Chad R. Zirbel, Georg Zizka, Irié Casimir Zo-Bi, Gerhard Zotz, Christian Wirth.Max Planck Institute for Biogeochemistry; Max Planck Society; German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig; International Programme of Biodiversity Science (DIVERSITAS); International Geosphere-Biosphere Programme (IGBP); Future Earth; French Foundation for Biodiversity Research (FRB); GIS ‘Climat, Environnement et Société'.http://wileyonlinelibrary.com/journal/gcbhj2021Plant Production and Soil Scienc