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6 results on '"Dray, Stéphane"'

1. Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity.

Author

Schmidt C, Dray S, and Garroway CJ

Subjects
Animals, Biological Evolution, Demography, Humans, Mammals genetics, Biodiversity, Ecosystem
Abstract

The processes that give rise to species richness gradients are not well understood, but may be linked to resource-based limits on the number of species a region can support. Ecological limits placed on regional species richness should also affect population demography, suggesting that these processes could also generate genetic diversity gradients. If true, we might better understand how broad-scale biodiversity patterns are formed by identifying the common causes of genetic diversity and species richness. We develop a hypothetical framework based on the consequences of regional variation in ecological limits set by resource availability and heterogeneity to simultaneously explain spatial patterns of species richness and neutral genetic diversity. Repurposing raw genotypic data spanning 38 mammal species sampled across 801 sites in North America, we show that estimates of genome-wide genetic diversity and species richness share spatial structure. Notably, species richness hotspots tend to harbor lower levels of within-species genetic variation. A structural equation model encompassing eco-evolutionary processes related to resource availability, habitat heterogeneity, and contemporary human disturbance supports the spatial patterns we detect. These results suggest broad-scale patterns of species richness and genetic diversity could both partly be caused by intraspecific demographic and evolutionary processes acting simultaneously across species., (© 2021 The Authors. Evolution © 2021 The Society for the Study of Evolution.)

Published
2022
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2. Statistical ecology comes of age

Author

Gimenez, Olivier, Buckland, Stephen T, Morgan, Byron JT, Bez, Nicolas, Bertrand, Sophie, Choquet, Rémi, Dray, Stéphane, Etienne, Marie-Pierre, Fewster, Rachel, Gosselin, Frédéric, Mérigot, Bastien, Monestiez, Pascal, Morales, Juan M, Mortier, Frédéric, Munoz, François, Ovaskainen, Otso, Pavoine, Sandrine, Pradel, Roger, Schurr, Frank M, Thomas, Len, Thuiller, Wilfried, Trenkel, Verena, de Valpine, Perry, and Rexstad, Eric

Subjects
Aetiology, 2.5 Research design and methodologies (aetiology), Animals, Biodiversity, Ecology, Models, Statistical, citizen science, hidden Markov model, hierarchical model, movement ecology, software package, spatially explicit capture-recapture, species distribution modelling, state-space model, spatially explicit capture–recapture, state–space model, Biological Sciences, Evolutionary Biology
Abstract

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

Published
2014

3. Spatial analyses of multi‐trophic terrestrial vertebrate assemblages in Europe.

Author

Braga, João, Pollock, Laura J., Barros, Ceres, Galiana, Núria, Montoya, José M., Gravel, Dominique, Maiorano, Luigi, Montemaggiori, Alessandro, Ficetola, Gentile Francesco, Dray, Stéphane, Thuiller, Wilfried, and McGill, Brain

Subjects
SPATIAL variation, GEOGRAPHIC spatial analysis, MULTICOLLINEARITY, SEASONAL temperature variations, WEBOMETRICS, STOCHASTIC processes, SPECIES diversity
Abstract

Aim: Although much has been said on the spatial distribution of taxonomic and phylogenetic diversity of vertebrates, how this diversity interacts in food webs and how these interactions change across space are largely unknown. Here, we analysed the spatial distribution of tetrapod food webs and asked whether the variation in local food web structure is driven by random processes or by natural and anthropogenic factors. Location: Europe. Time period: Present. Major taxa studied: Tetrapods. Methods: We combined an expert‐based food web (1,140 species and 70,601 links) of all European tetrapods with their respective spatial distributions. We mapped 17 different food web metrics representing complexity, chain length, vertical diversity and diet strategy across Europe and tested whether their distribution reflects the spatial structure of species richness using a null model of food web structure. To avoid multicollinearity issues, we defined composite descriptors of food web structure that we related to a set of environmental layers summarizing both natural and anthropogenic influences and tested their relative importance in explaining the spatial distribution of European terrestrial vertebrate food webs. Results: Of the 17 metrics, 10 showed a non‐random spatial distribution across Europe and could be summarized along two major axes of variation in food web structure. The first was related to species richness, mean trophic level and the proportion of intermediate species, whereas the second was related to the connectance and proximity of species within the web. Both descriptors varied with latitudinal gradient. The best descriptors of food web structure were mean annual temperature and seasonality (negatively correlated with the first axis), and human footprint (positively correlated with the second axis). Main conclusions: We demonstrate the importance of climate and anthropogenic pressure in shaping the spatial structure of European tetrapod food webs. [ABSTRACT FROM AUTHOR]

Published
2019
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4. Testing the Mantel statistic with a spatially‐constrained permutation procedure.

Author

Crabot, Julie, Clappe, Sylvie, Dray, Stéphane, Datry, Thibault, and McCrea, Rachel

Subjects
AUTOCORRELATION (Statistics), BIODIVERSITY, DATA analysis, ALGORITHMS, NUMERICAL analysis
Abstract

Mantel tests are widely used in ecology to assess the significance of the relationship between two distance matrices computed between pairs of samples. However, recent studies demonstrated that the presence of spatial autocorrelation in both distance matrices induced inflations of parameter estimates and type I error rates. These results also hold for partial Mantel test which is supposed to control for the spatial structures.To address the issue of spatial autocorrelation in testing the Mantel statistic, we developed a new procedure based on spatially constrained randomizations using Moran spectral randomization. A simulation study was conducted to assess the performance of this new procedure. Different scenarios were considered by manipulating the number of variables, the number of samples, the regularity of the sampling design and the level of spatial autocorrelation.As identified by previous studies, we found that Mantel statistic and its associated type I error rate are inflated in simple and partial Mantel tests when both distances matrices are spatially structured. We showed that these biases increased with the number of variables, decreased with the number of samples and were slightly lower for regular than irregular sampling. The new procedure succeeded in correcting the spurious inflations of the parameter estimates and type I error rates in any of the presented scenarios.Our results suggest that studies from several fields (e.g. genetic or community ecology) could have been overestimating the relationship between two distances matrices when both presented spatial autocorrelation. We proposed an alternative solution applicable in every field to correctly compute Mantel statistic with a fair type I error rate. [ABSTRACT FROM AUTHOR]

Published
2019
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5. CONSISTENCY BETWEEN ORDINATION TECHNIQUES AND DIVERSITY MEASUREMENTS: TWO STRATEGIES FOR SPECIES OCCURRENCE DATA.

Author

Pélisier, Raphaël, Couteron, Pierre, Dray, Stéphane, and Sabatier, Daniel

Subjects
BIODIVERSITY, BIOLOGICAL classification
Abstract

Both the ordination of taxonomic tables and the measurements of species diversity aim to capture the prominent features of the species composition of a community. However, interrelations between ordination techniques and diversity measurements are seldom explicated and are mainly ignored by many field ecologists. This paper starts from the notion of the species occurrence table, which provides a unifying formulation for different kinds of taxonomic data. Here it is demonstrated that alternative species weightings can be used to equate the total inertia of a centered-by-species occurrence table with common diversity indices, such as species richness, Simpson diversity, or Shannon information. Such an equation defines two main ordination strategies related to two different but consistent measures of species diversity. The first places emphasis on scarce species and is based on Correspondence Analysis and species richness (CA-richness strategy). The second, in which abundant species are prominent, relies on Non-Symmetric Correspondence Analysis and Simpson diversity (NSCA-Simpson strategy). Both strategies are suitable for measuring α and β diversity by analyzing the centered-by-species occurrence table with respect to external environmental or instrumental variables. In this paper, these two strategies are applied to ecological data obtained in a Neotropical rainforest plot. The results are then discussed with respect to the intrinsic characteristics of the community under analysis, and also to the broad classes of floro-faunistic data used in ecology (i.e., data gathered from museum or herbarium collections, exhaustive inventories in a reference plot, or enumeration through species-by-relevés tables). The approach encompasses several well-known techniques such as Correspondence Analysis, Non-Symmetric Correspondence Analysis, Canonical Correspondence Analysis, and Redundancy Analysis, and provides greater insight into interrelations between... [ABSTRACT FROM AUTHOR]

Published
2003

6. The global spectrum of plant form and function: enhanced species-level trait dataset

Author

Sandra Díaz, Jens Kattge, Johannes H. C. Cornelissen, Ian J. Wright, Sandra Lavorel, Stéphane Dray, Björn Reu, Michael Kleyer, Christian Wirth, I. Colin Prentice, Eric Garnier, Gerhard Bönisch, Mark Westoby, Hendrik Poorter, Peter B. Reich, Angela T. Moles, John Dickie, Amy E. Zanne, Jérôme Chave, S. Joseph Wright, Serge N. Sheremetiev, Hervé Jactel, Christopher Baraloto, Bruno E. L. Cerabolini, Simon Pierce, Bill Shipley, Fernando Casanoves, Julia S. Joswig, Angela Günther, Valeria Falczuk, Nadja Rüger, Miguel D. Mahecha, Lucas D. Gorné, Bernard Amiaud, Owen K. Atkin, Michael Bahn, Dennis Baldocchi, Michael Beckmann, Benjamin Blonder, William Bond, Ben Bond-Lamberty, Kerry Brown, Sabina Burrascano, Chaeho Byun, Giandiego Campetella, Jeannine Cavender-Bares, F. Stuart Chapin, Brendan Choat, David Anthony Coomes, William K. Cornwell, Joseph Craine, Dylan Craven, Matteo Dainese, Alessandro Carioca de Araujo, Franciska T. de Vries, Tomas Ferreira Domingues, Brian J. Enquist, Jaime Fagúndez, Jingyun Fang, Fernando Fernández-Méndez, Maria T. Fernandez-Piedade, Henry Ford, Estelle Forey, Gregoire T. Freschet, Sophie Gachet, Rachael Gallagher, Walton Green, Greg R. Guerin, Alvaro G. Gutiérrez, Sandy P. Harrison, Wesley Neil Hattingh, Tianhua He, Thomas Hickler, Steven I. Higgins, Pedro Higuchi, Jugo Ilic, Robert B. Jackson, Adel Jalili, Steven Jansen, Fumito Koike, Christian König, Nathan Kraft, Koen Kramer, Holger Kreft, Ingolf Kühn, Hiroko Kurokawa, Eric G. Lamb, Daniel C. Laughlin, Michelle Leishman, Simon Lewis, Frédérique Louault, Ana C. M. Malhado, Peter Manning, Patrick Meir, Maurizio Mencuccini, Julie Messier, Regis Miller, Vanessa Minden, Jane Molofsky, Rebecca Montgomery, Gabriel Montserrat-Martí, Marco Moretti, Sandra Müller, Ülo Niinemets, Romà Ogaya, Kinga Öllerer, Vladimir Onipchenko, Yusuke Onoda, Wim A. Ozinga, Juli G. Pausas, Begoña Peco, Josep Penuelas, Valério D. Pillar, Clara Pladevall, Christine Römermann, Lawren Sack, Norma Salinas, Brody Sandel, Jordi Sardans, Brandon Schamp, Michael Scherer-Lorenzen, Ernst-Detlef Schulze, Fritz Schweingruber, Satomi Shiodera, Ênio Sosinski, Nadejda Soudzilovskaia, Marko J. Spasojevic, Emily Swaine, Nathan Swenson, Susanne Tautenhahn, Ken Thompson, Alexia Totte, Rocío Urrutia-Jalabert, Fernando Valladares, Peter van Bodegom, François Vasseur, Kris Verheyen, Denis Vile, Cyrille Violle, Betsy von Holle, Patrick Weigelt, Evan Weiher, Michael C. Wiemann, Mathew Williams, Justin Wright, Gerhard Zotz, Biology, General Botany and Nature Management, Instituto Multidisciplinario de Biología Vegetal [Córdoba] (IMBIV), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET)-Facultad de Ciencias Exactas, Físicas y Naturales [Córdoba], Universidad Nacional de Córdoba [Argentina]-Universidad Nacional de Córdoba [Argentina], Universidad Nacional de Córdoba [Argentina], Ecologie quantitative et évolutive des communautés, Département écologie évolutive [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Montpellier (UM), Biodiversité, Gènes & Communautés (BioGeCo), Université de Bordeaux (UB)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Etude et Compréhension de la biodiversité (ECODIV), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU), Unité Mixte de Recherche sur l'Ecosystème Prairial - UMR (UREP), VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), TRY initiative on plant traits (https://www.try-db.org).TRY is an initiative of the Max Planck Institute for Biogeochemistry, bioDISCOVERY/Future Earth (ICSU), the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and Nucleo DiverSus (CONICET-Universidad Nacional de Cordoba, Argentina)., The Global Spectrum of Plant Form and Function study has been supported by the European BACI project (Towards a Biosphere Atmosphere change Index, EU grant ID 640176), FONCyT, CONICET, Universidad Nacional de Cordoba, the Inter-American Institute for Global Change Research, and The Newton Fund (NERC UK -CONICET ARG), Díaz, Sandra [0000-0003-0012-4612], Kattge, Jens [0000-0002-1022-8469], Wright, Ian J [0000-0001-8338-9143], Lavorel, Sandra [0000-0002-7300-2811], Dray, Stéphane [0000-0003-0153-1105], Wirth, Christian [0000-0003-2604-8056], Garnier, Eric [0000-0002-9392-5154], Westoby, Mark [0000-0001-7690-4530], Reich, Peter B [0000-0003-4424-662X], Moles, Angela T [0000-0003-2041-7762], Zanne, Amy E [0000-0001-6379-9452], Chave, Jérôme [0000-0002-7766-1347], Wright, S Joseph [0000-0003-4260-5676], Sheremetiev, Serge N [0000-0002-0318-6766], Baraloto, Christopher [0000-0001-7322-8581], Cerabolini, Bruno EL [0000-0002-3793-0733], Casanoves, Fernando [0000-0001-8765-9382], Joswig, Julia S [0000-0002-7786-1728], Mahecha, Miguel D [0000-0003-3031-613X], Atkin, Owen K [0000-0003-1041-5202], Bahn, Michael [0000-0001-7482-9776], Bond, William [0000-0002-3441-2084], Bond-Lamberty, Ben [0000-0001-9525-4633], Byun, Chaeho [0000-0003-3209-3275], Campetella, Giandiego [0000-0001-6126-522X], Cavender-Bares, Jeannine [0000-0003-3375-9630], Chapin, F Stuart [0000-0002-2558-9910], Choat, Brendan [0000-0002-9105-640X], Coomes, David Anthony [0000-0002-8261-2582], Cornwell, William K [0000-0003-4080-4073], Craine, Joseph [0000-0001-6561-3244], Craven, Dylan [0000-0003-3940-833X], Dainese, Matteo [0000-0001-7052-5572], Domingues, Tomas Ferreira [0000-0003-2857-9838], Enquist, Brian J [0000-0002-6124-7096], Gallagher, Rachael [0000-0002-4680-8115], Harrison, Sandy P [0000-0001-5687-1903], Hattingh, Wesley Neil [0000-0002-3626-5137], He, Tianhua [0000-0002-0924-3637], Higuchi, Pedro [0000-0002-3855-555X], Jackson, Robert B [0000-0001-8846-7147], Jansen, Steven [0000-0002-4476-5334], Kreft, Holger [0000-0003-4471-8236], Kühn, Ingolf [0000-0003-1691-8249], Kurokawa, Hiroko [0000-0001-8778-8045], Laughlin, Daniel C [0000-0002-9651-5732], Manning, Peter [0000-0002-7940-2023], Mencuccini, Maurizio [0000-0003-0840-1477], Müller, Sandra [0000-0003-4289-755X], Pausas, Juli G [0000-0003-3533-5786], Penuelas, Josep [0000-0002-7215-0150], Pillar, Valério D [0000-0001-6408-2891], Sack, Lawren [0000-0002-7009-7202], Salinas, Norma [0000-0001-9941-2109], Sardans, Jordi [0000-0003-2478-0219], Scherer-Lorenzen, Michael [0000-0001-9566-590X], Sosinski, Ênio [0000-0001-6310-9474], Spasojevic, Marko J [0000-0003-1808-0048], Weigelt, Patrick [0000-0002-2485-3708], Williams, Mathew [0000-0001-6117-5208], Zotz, Gerhard [0000-0002-6823-2268], Apollo - University of Cambridge Repository, Institut méditerranéen de biodiversité et d'écologie marine et continentale (IMBE), Avignon Université (AU)-Aix Marseille Université (AMU)-Institut de recherche pour le développement [IRD] : UMR237-Centre National de la Recherche Scientifique (CNRS), Diaz, S, Kattge, J, Cornelissen, JHC, Wright, IJ, Lavorel, S, Dray, S, Reu, B, Kleyer, M, Wirth, C, Prentice, IC, Garnier, E, Bonisch, G, Westoby, M, Poorter, H, Reich, PB, Moles, AT, Dickie, J, Zanne, AE, Chave, J, Wright, SJ, Sheremetiev, SN, Jactel, H, Baraloto, C, Cerabolini, BEL, Pierce, S, Shipley, B, Casanoves, F, Joswig, JS, Gunther, A, Falczuk, V, Ruger, N, Mahecha, MD, Gorne, LD, Amiaud, B, Atkin, OK, Bahn, M, Baldocchi, D, Beckmann, M, Blonder, B, Bond, W, Bond-Lamberty, B, Brown, K, Burrascano, S, Byun, C, Campetella, G, Cavender-Bares, J, Chapin, FS, Choat, B, Coomes, DA, Cornwell, WK, Craine, J, Craven, D, Dainese, M, de Araujo, AC, de Vries, FT, Domingues, TF, Enquist, BJ, Fagundez, J, Fang, J, Fernandez-Mendez, F, Fernandez-Piedade, MT, Ford, H, Forey, E, Freschet, GT, Gachet, S, Gallagher, R, Green, W, Guerin, GR, Gutierrez, AG, Harrison, SP, Hattingh, WN, He, T, Hickler, T, Higgins, SI, Higuchi, P, Ilic, J, Jackson, RB, Jalili, A, Jansen, S, Koike, F, Konig, C, Kraft, N, Kramer, K, Kreft, H, Kuhn, I, Kurokawa, H, Lamb, EG, Laughlin, DC, Leishman, M, Lewis, S, Louault, F, Malhado, ACM, Manning, P, Meir, P, Mencuccini, M, Messier, J, Miller, R, Minden, V, Molofsky, J, Montgomery, R, Montserrat-Marti, G, Moretti, M., Muller, S, Niinemets, U, Ogaya, R, Ollerer, K, Onipchenko, V, Onoda, Y, Ozinga, WA, Pausas, JG, Peco, B, Penuelas, J, Pillar, VD, Pladevall, C, Romermann, C, Sack, L, Salinas, N, Sandel, B, Sardans, J, Schamp, B, Scherer-Lorenzen, M, Schulze, ED, Schweingruber, F, Shiodera, S, Sosinski, E, SOUDZILOVSKAIA, Nadia, Spasojevic, MJ, Swaine, E, Swenson, N, Tautenhahn, S, Thompson, K, Totte, A, Urrutia-Jalabert, R, Valladares, F, van Bodegom, P, Vasseur, F, Verheyen, K, Vile, D, Violle, C, von Holle, B, Weigelt, P, Weiher, E, Wiemann, MC, Williams, M, Wright, J, Zotz, G, and Systems Ecology

Subjects
Statistics and Probability, Data Descriptor, [SDV]Life Sciences [q-bio], Bos- en Landschapsecologie, Library and Information Sciences, Education, SIZE-REDUCTION, QUERCUS-ILEX, WIDE-RANGE, Life Science, Forest and Landscape Ecology, Macroecology, Vegetatie, Vegetation, ENVIRONMENT RELATIONSHIPS, 3103 Ecology, Biology and Life Sciences, Biodiversity, 3108 Plant Biology, Computer Science Applications, Biogeography, 631/158/852, FOLIAR NITROGEN ISOTOPES, 631/158/851, [SDE]Environmental Sciences, Vegetatie, Bos- en Landschapsecologie, Vegetation, Forest and Landscape Ecology, LEAF ECONOMICS SPECTRUM, Statistics, Probability and Uncertainty, data-descriptor, ELEVATED CO2, WOODY-PLANTS, PHOTOSYNTHETIC CAPACITY, 631/158/670, RELATIVE GROWTH-RATE, Information Systems, 31 Biological Sciences
Abstract

[Abstract] Here we provide the ‘Global Spectrum of Plant Form and Function Dataset’, containing species mean values for six vascular plant traits. Together, these traits –plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass – define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date. The study has been supported by the TRY initiative on plant traits (https://www.try-db.org). TRY is an initiative of the Max Planck Institute for Biogeochemistry, bioDISCOVERY/Future Earth (ICSU), the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig and Núcleo DiverSus (CONICET- Universidad Nacional de Córdoba, Argentina). The Global Spectrum of Plant Form and Function study has been supported by the European BACI project (Towards a Biosphere Atmosphere change Index, EU grant ID 640176), and grants to SD by FONCyT, CONICET, Universidad Nacional de Córdoba, the Inter-American Institute for Global Change Research, and The Newton Fund (NERC UK – CONICET ARG). VO thanks RSF (#19-14-00038p). Open Access funding enabled and organized by Projekt DEAL

Published
2022

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