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10. sPlotOpen – An environmentally balanced, open‐access, global dataset of vegetation plots

Author

Ben Sparrow, V. B. Martynenko, Jonathan Lenoir, Eszter Ruprecht, Idoia Biurrun, Luzmila Arroyo, Borja Jiménez-Alfaro, Aníbal Pauchard, Roberto Venanzoni, Stephan M. Hennekens, Mohamed Z. Hatim, Cyrus Samimi, Arkadiusz Nowak, Gerhard E. Overbeck, Petr Sklenář, Renata Ćušterevska, Valentin Golub, Eduardo Vélez-Martin, Gwendolyn Peyre, Inger Greve Alsos, Ioannis Tsiripidis, Tarek Hattab, Andrey Yu. Korolyuk, Jutta Kapfer, Jörg Ewald, Donald M. Waller, Ute Jandt, Tetiana Dziuba, Marco Schmidt, Alvaro G. Gutiérrez, Thomas Wohlgemuth, Adrian Indreica, Zygmunt Kącki, Jürgen Dengler, Željko Škvorc, Dirk Nikolaus Karger, Panayotis Dimopoulos, Viktor Onyshchenko, Hanhuai Shan, John Janssen, Hua Feng Wang, Holger Kreft, Jérôme Munzinger, Brian J. Enquist, Frederic Lens, Wannes Hubau, Birgit Jedrzejek, Alexander Christian Vibrans, Miguel D. Mahecha, Emmanuel Garbolino, Sophie Gachet, Abel Monteagudo Mendoza, Josep Peñuelas, Melisa A. Giorgis, Svetlana Aćić, Débora Vanessa Lingner, Victor V. Chepinoga, Richard Field, Ladislav Mucina, Michele De Sanctis, Mohamed A. El-Sheikh, Isabelle Aubin, Hamid Gholizadeh, Fahmida Sultana, Fabio Attorre, Valerijus Rašomavičius, Cindy Q. Tang, Tomáš Černý, Gonzalo Rivas-Torres, Donald A. Walker, Alicia Teresa Rosario Acosta, Timothy J. Killeen, Francesco Maria Sabatini, Susan K. Wiser, Urban Šilc, Andraž Čarni, Florian Jansen, Valério D. Pillar, Jonas V. Müller, Aaron Pérez-Haase, Els De Bie, Antonio Galán-de-Mera, Zhiyao Tang, Anne D. Bjorkman, Sylvia Haider, Kiril Vassilev, Risto Virtanen, Henrik von Wehrden, Hjalmar S. Kühl, Manfred Finckh, Zvjezdana Stančić, Pavel Shirokikh, Elizabeth Kearsley, Petr Petřík, Yves Bergeron, Iva Apostolova, Emiliano Agrillo, Jozef Šibík, Norbert Jürgens, Marta Gaia Sperandii, Anna Kuzemko, Jens-Christian Svenning, Timothy J. S. Whitfeld, Michael Kessler, Bruno Hérault, John-Arvid Grytnes, Laura Casella, Tomáš Peterka, Miguel Alvarez, Tsipe Aavik, Gregory Richard Guerin, André Luis de Gasper, Corrado Marcenò, Luis Cayuela, Brody Sandel, Cyrille Violle, Jens Kattge, Guillermo Hinojos Mendoza, Anke Jentsch, Arindam Banerjee, Jesper Erenskjold Moeslund, Mohammed Abu Sayed Arfin Khan, Patrice de Ruffray, Milan Chytrý, S. M. Yamalov, Tatiana Lysenko, Meelis Pärtel, Viktoria Bondareva, Helge Bruelheide, John S. Rodwell, Jiri Dolezal, Oliver L. Phillips, Rasmus Revermann, Larisa Khanina, Erwin Bergmeier, Robert K. Peet, Jörg Brunet, Solvita Rūsiņa, Oliver Purschke, Gianmaria Bonari, Jürgen Homeier, Martin Zobel, János Csiky, Marijn Bauters, Jalil Noroozi, Karsten Wesche, Kim André Vanselow, Norbert Hölzel, Flavia Landucci, Farideh Fazayeli, Wolfgang Willner, Viktoria Wagner, Alireza Naqinezhad, Aurora Levesley, Vadim Prokhorov, Hongyan Liu, Ali Kavgaci, Rodolfo Vásquez Martínez, Franziska Schrodt, Attila Lengyel, Elise A. Arnst, Sabatini F.M., Lenoir J., Hattab T., Arnst E.A., Chytry M., Dengler J., De Ruffray P., Hennekens S.M., Jandt U., Jansen F., Jimenez-Alfaro B., Kattge J., Levesley A., Pillar V.D., Purschke O., Sandel B., Sultana F., Aavik T., Acic S., Acosta A.T.R., Agrillo E., Alvarez M., Apostolova I., Arfin Khan M.A.S., Arroyo L., Attorre F., Aubin I., Banerjee A., Bauters M., Bergeron Y., Bergmeier E., Biurrun I., Bjorkman A.D., Bonari G., Bondareva V., Brunet J., Carni A., Casella L., Cayuela L., Cerny T., Chepinoga V., Csiky J., Custerevska R., De Bie E., de Gasper A.L., De Sanctis M., Dimopoulos P., Dolezal J., Dziuba T., El-Sheikh M.A.E.-R.M., Enquist B., Ewald J., Fazayeli F., Field R., Finckh M., Gachet S., Galan-de-Mera A., Garbolino E., Gholizadeh H., Giorgis M., Golub V., Alsos I.G., Grytnes J.-A., Guerin G.R., Gutierrez A.G., Haider S., Hatim M.Z., Herault B., Hinojos Mendoza G., Holzel N., Homeier J., Hubau W., Indreica A., Janssen J.A.M., Jedrzejek B., Jentsch A., Jurgens N., Kacki Z., Kapfer J., Karger D.N., Kavgaci A., Kearsley E., Kessler M., Khanina L., Killeen T., Korolyuk A., Kreft H., Kuhl H.S., Kuzemko A., Landucci F., Lengyel A., Lens F., Lingner D.V., Liu H., Lysenko T., Mahecha M.D., Marceno C., Martynenko V., Moeslund J.E., Monteagudo Mendoza A., Mucina L., Muller J.V., Munzinger J., Naqinezhad A., Noroozi J., Nowak A., Onyshchenko V., Overbeck G.E., Partel M., Pauchard A., Peet R.K., Penuelas J., Perez-Haase A., Peterka T., Petrik P., Peyre G., Phillips O.L., Prokhorov V., Rasomavicius V., Revermann R., Rivas-Torres G., Rodwell J.S., Ruprecht E., Rusina S., Samimi C., Schmidt M., Schrodt F., Shan H., Shirokikh P., Sibik J., Silc U., Sklenar P., Skvorc Z., Sparrow B., Sperandii M.G., Stancic Z., Svenning J.-C., Tang Z., Tang C.Q., Tsiripidis I., Vanselow K.A., Vasquez Martinez R., Vassilev K., Velez-Martin E., Venanzoni R., Vibrans A.C., Violle C., Virtanen R., von Wehrden H., Wagner V., Walker D.A., Waller D.M., Wang H.-F., Wesche K., Whitfeld T.J.S., Willner W., Wiser S.K., Wohlgemuth T., Yamalov S., Zobel M., Bruelheide H., Sabatini, Fm, Lenoir, J, Hattab, T, Arnst, Ea, Chytry, M, Dengler, J, De Ruffray, P, Hennekens, Sm, Jandt, U, Jansen, F, Jimenez-Alfaro, B, Kattge, J, Levesley, A, Pillar, Vd, Purschke, O, Sandel, B, Sultana, F, Aavik, T, Acic, S, Acosta, Atr, Agrillo, E, Alvarez, M, Apostolova, I, Khan, Masa, Arroyo, L, Attorre, F, Aubin, I, Banerjee, A, Bauters, M, Bergeron, Y, Bergmeier, E, Biurrun, I, Bjorkman, Ad, Bonari, G, Bondareva, V, Brunet, J, Carni, A, Casella, L, Cayuela, L, Cerny, T, Chepinoga, V, Csiky, J, Custerevska, R, De Bie, E, de Gasper, Al, De Sanctis, M, Dimopoulos, P, Dolezal, J, Dziuba, T, El-Sheikh, Mam, Enquist, B, Ewald, J, Fazayeli, F, Field, R, Finckh, M, Gachet, S, Galan-de-Mera, A, Garbolino, E, Gholizadeh, H, Giorgis, M, Golub, V, Alsos, Ig, Grytnes, Ja, Guerin, Gr, Gutierrez, Ag, Haider, S, Hatim, Mz, Herault, B, Mendoza, Gh, Holzel, N, Homeier, J, Hubau, W, Indreica, A, Janssen, Jam, Jedrzejek, B, Jentsch, A, Jurgens, N, Kacki, Z, Kapfer, J, Karger, Dn, Kavgaci, A, Kearsley, E, Kessler, M, Khanina, L, Killeen, T, Korolyuk, A, Kreft, H, Kuhl, H, Kuzemko, A, Landucci, F, Lengyel, A, Lens, F, Lingner, Dv, Liu, Hy, Lysenko, T, Mahecha, Md, Marceno, C, Martynenko, V, Moeslund, Je, Mendoza, Am, Mucina, L, Muller, Jv, Munzinger, Jm, Naqinezhad, A, Noroozi, J, Nowak, A, Onyshchenko, V, Overbeck, Ge, Partel, M, Pauchard, A, Peet, Rk, Penuelas, J, Perez-Haase, A, Peterka, T, Petrik, P, Peyre, G, Phillips, Ol, Prokhorov, V, Rasomavicius, V, Revermann, R, Rivas-Torres, G, Rodwell, J, Ruprecht, E, Rusina, S, Samimi, C, Schmidt, M, Schrodt, F, Shan, Hh, Shirokikh, P, Sibik, J, Silc, U, Sklenar, P, Skvorc, Z, Sparrow, B, Sperandii, Mg, Stancic, Z, Svenning, Jc, Tang, Zy, Tang, Cq, Tsiripidis, I, Vanselow, Ka, Martinez, Rv, Vassilev, K, Velez-Martin, E, Venanzoni, R, Vibrans, Ac, Violle, C, Virtanen, R, von Wehrden, H, Wagner, V, Walker, Da, Waller, Dm, Wang, Hf, Wesche, K, Whitfeld, Tj, Willner, W, Wiser, Sk, Wohlgemuth, T, Yamalov, S, Zobel, M, Bruelheide, H, Ecologie et Dynamique des Systèmes Anthropisés - UMR CNRS 7058 (EDYSAN), Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS), MARine Biodiversity Exploitation and Conservation (UMR MARBEC), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), 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), Centre de recherche sur les Risques et les Crises (CRC), Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Université de Montpellier (UM)-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), 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)-Université de Montpellier (UM)-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 SupAgro, 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), ANR-07-BDIV-0006,BIONEOCAL,L'endémisme en Nouvelle-Calédonie : étude phylogénétique et populationnelle des son émergence.(2007), ANR-07-BDIV-0008,INC,Incendies et biodiversité de écosystèmes en Nouvelle-Calédonie.(2007), ANR-07-BDIV-0010,ULTRABIO,Biodiversité et stratégies adaptatives végétales et microbiennes des écosystèmes ultramafiques en Nouvelle-Calédonie.(2007), European Project: 610028,EC:FP7:ERC,ERC-2013-SyG,IMBALANCE-P(2014), European Project: 291585,EC:FP7:ERC,ERC-2011-ADG_20110209,T-FORCES(2012), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut de Recherche pour le Développement (IRD), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paul-Valéry - Montpellier 3 (UPVM)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), and 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)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
0106 biological sciences, Biome, Bos- en Landschapsecologie, Biodiversity, DIVERSITY, FOREST VEGETATION, 01 natural sciences, purl.org/becyt/ford/1 [https], Abundance (ecology), big data, Vegetation type, PHYTOSOCIOLOGICAL DATABASE, parcelle, Forest and Landscape Ecology, functional traits, vascular plants, biodiversity, biogeography, database, macroecology, vegetation plots, Macroecology, [SDV.EE]Life Sciences [q-bio]/Ecology, environment, Global and Planetary Change, Ecology, vascular plant, Vegetation, F70 - Taxonomie végétale et phytogéographie, PE&RC, Vegetation plot, Geography, 580: Pflanzen (Botanik), Ecosystems Research, Diffusion de l'information, Plantenecologie en Natuurbeheer, Vegetatie, Bos- en Landschapsecologie, Biodiversité, ARCHIVE, Communauté végétale, Evolution, [SDE.MCG]Environmental Sciences/Global Changes, Biogéographie, GRASSLAND VEGETATION, Plant Ecology and Nature Conservation, [SDV.BID]Life Sciences [q-bio]/Biodiversity, 010603 evolutionary biology, Behavior and Systematics, Couverture végétale, 577: Ökologie, PLANT, purl.org/becyt/ford/1.6 [https], functional trait, Biology, Ecology, Evolution, Behavior and Systematics, Vegetatie, 010604 marine biology & hydrobiology, Impact sur l'environnement, DRY GRASSLANDS, Plant community, 15. Life on land, Végétation, WETLAND VEGETATION, Earth and Environmental Sciences, UNIVERSITY, Physical geography, Vegetation, Forest and Landscape Ecology, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, données ouvertes
Abstract

Datos disponibles en https://github.com/fmsabatini/sPlotOpen_Code, EU H2020 project BACI, Grant No. 640176 (...), Sabatini, F.M., Lenoir, J., Hattab, T., Arnst, E.A., Chytrý, M., Dengler, J., De Ruffray, P., Hennekens, S.M., Jandt, U., Jansen, F., Jiménez-Alfaro, B., Kattge, J., Levesley, A., Pillar, V.D., Purschke, O., Sandel, B., Sultana, F., Aavik, T., Aćić, S., Acosta, A.T.R., Agrillo, E., Alvarez, M., Apostolova, I., Arfin Khan, M.A.S., Arroyo, L., Attorre, F., Aubin, I., Banerjee, A., Bauters, M., Bergeron, Y., Bergmeier, E., Biurrun, I., Bjorkman, A.D., Bonari, G., Bondareva, V., Brunet, J., Čarni, A., Casella, L., Cayuela, L., Černý, T., Chepinoga, V., Csiky, J., Ćušterevska, R., De Bie, E., de Gasper, A.L., De Sanctis, M., Dimopoulos, P., Dolezal, J., Dziuba, T., El-Sheikh, M.A.E.-R.M., Enquist, B., Ewald, J., Fazayeli, F., Field, R., Finckh, M., Gachet, S., Galán-de-Mera, A., Garbolino, E., Gholizadeh, H., Giorgis, M., Golub, V., Alsos, I.G., Grytnes, J.-A., Guerin, G.R., Gutiérrez, A.G., Haider, S., Hatim, M.Z., Hérault, B., Hinojos Mendoza, G., Hölzel, N., Homeier, J., Hubau, W., Indreica, A., Janssen, J.A.M., Jedrzejek, B., Jentsch, A., Jürgens, N., Kącki, Z., Kapfer, J., Karger, D.N., Kavgacı, A., Kearsley, E., Kessler, M., Khanina, L., Killeen, T., Korolyuk, A., Kreft, H., Kühl, H.S., Kuzemko, A., Landucci, F., Lengyel, A., Lens, F., Lingner, D.V., Liu, H., Lysenko, T., Mahecha, M.D., Marcenò, C., Martynenko, V., Moeslund, J.E., Monteagudo Mendoza, A., Mucina, L., Müller, J.V., Munzinger, J., Naqinezhad, A., Noroozi, J., Nowak, A., Onyshchenko, V., Overbeck, G.E., Pärtel, M., Pauchard, A., Peet, R.K., Peñuelas, J., Pérez-Haase, A., Peterka, T., Petřík, P., Peyre, G., Phillips, O.L., Prokhorov, V., Rašomavičius, V., Revermann, R., Rivas-Torres, G., Rodwell, J.S., Ruprecht, E., Rūsiņa, S., Samimi, C., Schmidt, M., Schrodt, F., Shan, H., Shirokikh, P., Šibík, J., Šilc, U., Sklenář, P., Škvorc, Ž., Sparrow, B., Sperandii, M.G., Stančić, Z., Svenning, J.-C., Tang, Z., Tang, C.Q., Tsiripidis, I., Vanselow, K.A., Vásquez Martínez, R., Vassilev, K., Vélez-Martin, E., Venanzoni, R., Vibrans, A.C., Violle, C., Virtanen, R., von Wehrden, H., Wagner, V., Walker, D.A., Waller, D.M., Wang, H.-F., Wesche, K., Whitfeld, T.J.S., Willner, W., Wiser, S.K., Wohlgemuth, T., Yamalov, S., Zobel, M., Bruelheide, H.

Published
2021

11. Robustness of trait connections across environmental gradients and growth forms

Author

Owen K. Atkin, Rhiannon L. Dalrymple, J. Hans C. Cornelissen, Farideh Fazayeli, Vladimir G. Onipchenko, Chaeho Byun, Andrés González-Melo, Ülo Niinemets, Habacuc Flores-Moreno, Ming Chen, Kirk R. Wythers, Madhur Anand, Ethan E. Butler, Joseph M. Craine, Jens Kattge, Wesley N. Hattingh, Abhirup Datta, Vanessa Minden, Steven Jansen, Nathan J. B. Kraft, Arindam Banerjee, Peter B. Reich, Josep Peñuelas, Nadejda A. Soudzilovskaia, Michael Bahn, Daniel C. Laughlin, Koen Kramer, Systems Ecology, and Biology

Subjects
0106 biological sciences, leaf traits, Specific leaf area, plant strategy integration, seed traits, Leaf morphology, trait networks, Competition (ecology), Biology, 010603 evolutionary biology, 01 natural sciences, Functional response, Resource Acquisition Is Initialization, Temperate climate, Environmental gradient, Growth form, Ecology, Evolution, Behavior and Systematics, 2. Zero hunger, Global and Planetary Change, plant functional traits, Ecology, Seed, 010604 marine biology & hydrobiology, Reproductive strategy, fungi, Robustness (evolution), food and beverages, 15. Life on land, Arid, stem traits, Technologie and Innovatie, Trait, Knowledge Technology and Innovation, Kennis, Embryophyta, Kennis, Technologie and Innovatie, Biological network, trait interdependence
Abstract

Aim: Plant trait databases often contain traits that are correlated, but for whom direct (undirected statistical dependency) and indirect (mediated by other traits) connections may be confounded. The confounding of correlation and connection hinders our understanding of plant strategies, and how these vary among growth forms and climate zones. We identified the direct and indirect connections across plant traits relevant to competition, resource acquisition and reproductive strategies using a global database and explored whether connections within and between traits from different tissue types vary across climates and growth forms. Location: Global. Major taxa studied: Plants. Time period: Present. Methods: We used probabilistic graphical models and a database of 10 plant traits (leaf area, specific leaf area, mass- and area-based leaf nitrogen and phosphorous content, leaf life span, plant height, stem specific density and seed mass) with 16,281 records to describe direct and indirect connections across woody and non-woody plants across tropical, temperate, arid, cold and polar regions. Results: Trait networks based on direct connections are sparser than those based on correlations. Land plants had high connectivity across traits within and between tissue types; leaf life span and stem specific density shared direct connections with all other traits. For both growth forms, two groups of traits form modules of more highly connected traits; one related to resource acquisition, the other to plant architecture and reproduction. Woody species had higher trait network modularity in polar compared to temperate and tropical climates, while non-woody species did not show significant differences in modularity across climate regions. Main conclusions: Plant traits are highly connected both within and across tissue types, yet traits segregate into persistent modules of traits. Variation in the modularity of trait networks suggests that trait connectivity is shaped by prevailing environmental conditions and demonstrates that plants of different growth forms use alternative strategies to cope with local conditions. © 2019 John Wiley and Sons Ltd

Published
2019

12. Mapping local and global variability in plant trait distributions

Author

Fernando Valladares, Thomas Hickler, Benjamin Blonder, Kirk R. Wythers, Peter B. Reich, Kerry A. Brown, Ethan E. Butler, Andrés González-Melo, Enio E. Sosinski, Vanessa Minden, Nicolas Gross, Josep Peñuelas, Peter M. van Bodegom, Steven Jansen, Daniel C. Laughlin, Yusuke Onoda, Giandiego Campetella, Nathan J. B. Kraft, Bruno Enrico Leone Cerabolini, Ming Chen, Patrick Meir, Peter E. Thornton, Farideh Fazayeli, Tomas F. Domingues, Lawren Sack, Bernard Amiaud, Sandra Díaz, Dylan Craven, Ben Bond-Lamberty, Quentin D. Read, Ülo Niinemets, Gerhard Boenisch, Habacuc Flores-Moreno, Franciska T. de Vries, Chaeho Byun, Christian Wirth, Abhirup Datta, Marko J. Spasojevic, Koen Kramer, Brandon S. Schamp, Wenxuan Han, Arindam Banerjee, Joseph M. Craine, Mathew Williams, Jens Kattge, Nadejda A. Soudzilovskaia, Wesley N. Hattingh, Johannes H. C. Cornelissen, Estelle Forey, Owen K. Atkin, Hiroko Kurokawa, Department of Forest Resources, University of Minnesota, St. Paul, MN, USA, Department of Biostatistics [Baltimore], Johns Hopkins University (JHU), University of Minnesota [Twin Cities] (UMN), University of Minnesota System, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences [Beijing] (CAS), University of Minnesota, Department of Computer Science and Engineering, Australian National University (ANU), German Centre for Integrative Biodiversity Research, Ecologie et Ecophysiologie Forestières [devient SILVA en 2018] (EEF), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Environmental Change Institute, University of Oxford, Oxford, UK, Max Planck Institute for Biogeochemistry (MPI-BGC), Max-Planck-Gesellschaft, Joint Global Change Research Institute, University Research Court, Department of Geography and Geology, Kingston University, Kingston upon Thames, UK, Seoul National University [Seoul] (SNU), University of Camerino, Department of Theoretical and Applied Sciences [Insubria], University of Insubria, Varese, Jonah Ventures, School of Earth and Environmental Sciences [Manchester] (SEES), University of Manchester [Manchester], Insituto Multidisciplinario de Biologia Vegetal, Universidad Nacional de Córdoba [Argentina], Faculdade de Filosofia, Ciências e Letras de Ribeirão Preto, Étude et compréhension de la biodiversité (ECODIV), Université de Rouen Normandie (UNIROUEN), Normandie Université (NU)-Normandie Université (NU), Programa de Biología, Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario, Centre d'Études Biologiques de Chizé - UMR 7372 (CEBC), Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d'Etudes Biologiques de Chizé [France] (USC 1339 INRA), Institut National de la Recherche Agronomique (INRA), College of Resources and Environmental Sciences, Ministry of Agriculture, Key Laboratory of Arable Land Conservation (North China), China Agricultural University (CAU), Key Laboratory of Biogeography and Bioresource in Arid Land, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences [Changchun Branch] (CAS), School of Animal, Plant & Environmental Sciences, University of the Witwatersrand [Johannesburg] (WITS), Department of Physical Geography and Ecosystem Science [Lund], Lund University [Lund], Universität Ulm - Ulm University [Ulm, Allemagne], Alterra - Green World Research, Wageningen University and Research [Wageningen] (WUR), Department of Ecology and Evolutionary Biology, University of California, Los Angeles, California, Tohoku University [Sendai], Department of Botany, University of Wyoming, Laramie, WY, USA, University of Wyoming, Laramie, Research School of Biology, Institute of Biology and Environmental Science, Carl von Ossietzky University Oldenburg, Kyoto University [Kyoto], Global Ecology Unit CREAF-CEAB-CSIC, Universitat Autònoma de Barcelona (UAB), Department of Forestry, Michigan State University, East Lansing, Department of Ecology and Evolutionary Biology, University of California, Department of Biology, Algoma University, Marie, OA, Canada, Algoma University, Institute of Environmental Sciences [Leiden] (CML), Leiden University, Laboratorio de Planejamento Ambiental, Oak Ridge National Laboratory [Oak Ridge] (ORNL), UT-Battelle, LLC, Climate Change Science Institute [Oak Ridge] (CCSI), UT-Battelle, LLC-UT-Battelle, LLC, Escuela Superior de Ciencias Experimentales y Tecnológicas, Departamento de Biología y Geología, Universidad Rey Juan Carlos [Madrid] (URJC), School of Geosciences [Edinburgh], University of Edinburgh, Department of Systematic Botany and Functional Biodiversity, Universität Leipzig [Leipzig], Department of Forest Resources, University of Minnesota System-University of Minnesota System, European Project: 609398,EC:FP7:PEOPLE,FP7-PEOPLE-2013-COFUND,AGREENSKILLSPLUS(2014), Biology, Butler, Ethan E., Datta, Abhirup, Systems Ecology, Environmental Change Institute, University of Oxford, Università degli Studi di Camerino = University of Camerino (UNICAM), Universitá degli Studi dell’Insubria = University of Insubria [Varese] (Uninsubria), Institut National de la Recherche Agronomique (INRA)-La Rochelle Université (ULR)-Centre National de la Recherche Scientifique (CNRS), Kyoto University, University of California (UC), Universiteit Leiden, Universität Leipzig, INRA - CEBC, Institut National de la Recherche Agronomique (INRA)-Université de La Rochelle (ULR)-Centre National de la Recherche Scientifique (CNRS), Wageningen University and Research Centre [Wageningen] (WUR), Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, Universitat Autònoma de Barcelona [Barcelona] (UAB), University of Minnesota [Twin Cities], Department of Energy (US), National Science Foundation (US), University of Minnesota, European Commission, Natural Environment Research Council (UK), Generalitat de Catalunya, Wageningen University and Research Centre, National Natural Science Foundation of China, and Australian Research Council

Subjects
0106 biological sciences, Bayes theorem, Data base, 010504 meteorology & atmospheric sciences, Biodiversité et Ecologie, répartition territoriale, échelle globale, statistique spatiale, 01 natural sciences, Filosofie, spatial statistics, Leaf area, Bayes' theorem, Models, échelle locale, ComputingMilieux_MISCELLANEOUS, Priority journal, 2. Zero hunger, changement climatique, Multidisciplinary, Geography, Phosphorus, Vegetation, Plants, Statistical, PE&RC, global, Bayesian modeling, Climate, Global, Plant traits, Spatial statistics, PNAS Plus, [SDE]Environmental Sciences, Trait, Centre for Crop Systems Analysis, Statistical model, Cartography, CIENCIAS NATURALES Y EXACTAS, Nitrogen, Otras Ciencias Biológicas, Bayesian probability, plant traits, climate, Quantitative trait locus, Environment, 010603 evolutionary biology, Article, Ciencias Biológicas, Biodiversity and Ecology, Quantitative Trait, Quantitative Trait, Heritable, Spatial analysis, Heritable, Ecosystem, 0105 earth and related environmental sciences, Spatial Analysis, Models, Statistical, Plant Dispersal, diversité végétale fonctionnelle, Leaf litter, Plant, 15. Life on land, Nonhuman, modèle bayésien, Philosophy, Evergreen, 13. Climate action, Concentration (parameters), Prediction, Scale (map), Bayesian modelling, Model
Abstract

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (Nm) and phosphorus (Pm), we characterize how traits vary within and among over 50,000 ∼50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means., This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the US Department of Energy, Office of Science, Office of Biological and Environmental Research (Grant DE-SC0012677 to P.B.R. and A.B.). O.K.A. acknowledges the support of the Australian Research Council (CE140100008). This research was also funded by programs from the NSF Long-Term Ecological Research (Grant DEB-1234162) and Long-Term Research in Environmental Biology (Grant DEB-1242531). A.B., F.F., and P.B.R. acknowledge funding from NSF Grant IIS-1563950. P.B.R. also acknowledges support from two University of Minnesota Institute on the Environment discovery grants. This study has been supported by the TRY initiative on plant traits (www.try-db.org). The TRY database is hosted at the Max Planck Institute for Biogeochemistry (Jena, Germany) and supported by DIVERSITAS/Future Earth, the German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, and the EU H2020 project BACI (Grant 640176). B.B. acknowledges a Natural Environment Research Council (NERC) independent research fellowship NE/M019160/1. J.P. acknowledges the financial support from the European Research Council Synergy Grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government Grant CGL2013-48074-P, and the Catalan Government Grant SGR 2014-274. B.B.-L. was supported by the Earth System Modeling program of the US Department of Energy, Office of Science, Office of Biological and Environmental Research. K.K. acknowledges the contribution of the Wageningen University and Research Investment theme Resilience for the project Resilient Forest (KB-29-009-003). P.M. acknowledges support from ARC Grant FT110100457 and NERC Grant NE/F002149/1. W.H. acknowledges support from the National Natural Science Foundation of China (Grant 41473068) and the “Light of West China” Program of the Chinese Academy of Sciences.

Published
2017

13. BHPMF - a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

Author

Anuj Karpatne, Sandra Díaz, Paul Leadley, S. Joseph Wright, Peter B. Reich, Franziska Schrodt, Farideh Fazayeli, Ian J. Wright, Gerhard Bönisch, Hanhuai Shan, Markus Reichstein, Christian Wirth, Andy Gillison, Julia Joswig, Arindam Banerjee, Jens Kattge, John B. Dickie, and Sandra Lavorel

Subjects
Global and Planetary Change, Ecology, Computer science, business.industry, Bayesian probability, Probabilistic logic, Machine learning, computer.software_genre, Matrix decomposition, Trait, Bayesian hierarchical modeling, Artificial intelligence, business, computer, Ecology, Evolution, Behavior and Systematics, Macroecology, Imputation (genetics), Sparse matrix
Abstract

Aim Functional traits of organisms are key to understanding and predicting biodiversity and ecological change, which motivates continuous collection of traits and their integration into global databases. Such trait matrices are inherently sparse, severely limiting their usefulness for further analyses. On the other hand, traits are characterized by the phylogenetic trait signal, trait–trait correlations and environmental constraints, all of which provide information that could be used to statistically fill gaps. We propose the application of probabilistic models which, for the first time, utilize all three characteristics to fill gaps in trait databases and predict trait values at larger spatial scales. Innovation For this purpose we introduce BHPMF, a hierarchical Bayesian extension of probabilistic matrix factorization (PMF). PMF is a machine learning technique which exploits the correlation structure of sparse matrices to impute missing entries. BHPMF additionally utilizes the taxonomic hierarchy for trait prediction and provides uncertainty estimates for each imputation. In combination with multiple regression against environmental information, BHPMF allows for extrapolation from point measurements to larger spatial scales. We demonstrate the applicability of BHPMF in ecological contexts, using different plant functional trait datasets, also comparing results to taking the species mean and PMF. Main conclusions Sensitivity analyses validate the robustness and accuracy of BHPMF: our method captures the correlation structure of the trait matrix as well as the phylogenetic trait signal – also for extremely sparse trait matrices – and provides a robust measure of confidence in prediction accuracy for each missing entry. The combination of BHPMF with environmental constraints provides a promising concept to extrapolate traits beyond sampled regions, accounting for intraspecific trait variability. We conclude that BHPMF and its derivatives have a high potential to support future trait-based research in macroecology and functional biogeography.

Published
2015

14. The Matrix Generalized Inverse Gaussian Distribution: Properties and Applications

Author

Arindam Banerjee and Farideh Fazayeli

Subjects
Generalized inverse Gaussian distribution, Generalized inverse, Distribution (number theory), Monte Carlo method, Markov chain Monte Carlo, 010103 numerical & computational mathematics, 01 natural sciences, Algebraic Riccati equation, 010104 statistics & probability, Matrix (mathematics), symbols.namesake, symbols, Applied mathematics, 0101 mathematics, Importance sampling, Mathematics
Abstract

While the Matrix Generalized Inverse Gaussian $$\mathcal {MGIG}$$ distribution arises naturally in some settings as a distribution over symmetric positive semi-definite matrices, certain key properties of the distribution and effective ways of sampling from the distribution have not been carefully studied. In this paper, we show that the $$\mathcal {MGIG}$$ is unimodal, and the mode can be obtained by solving an Algebraic Riccati Equation ARE equationi¾ź[7]. Based on the property, we propose an importance sampling method for the $$\mathcal {MGIG}$$ where the mode of the proposal distribution matches that of the target. The proposed sampling method is more efficient than existing approachesi¾ź[32, 33], which use proposal distributions that may have the mode far from the $$\mathcal {MGIG}$$'s mode. Further, we illustrate that the the posterior distribution in latent factor models, such as probabilistic matrix factorization PMFi¾ź[24], when marginalized over one latent factor has the $$\mathcal {MGIG}$$ distribution. The characterization leads to a novel Collapsed Monte Carlo CMC inference algorithm for such latent factor models. We illustrate that CMC has a lower log loss or perplexity than MCMC, and needs fewer samples.

Published
2016

15. Uncertainty Quantified Matrix Completion Using Bayesian Hierarchical Matrix Factorization

Author

Jens Kattge, Peter B. Reich, Arindam Banerjee, Farideh Fazayeli, and Franziska Schrodt

Subjects
Matrix completion, Computer science, business.industry, Hierarchical matrix, Bayesian probability, Machine learning, computer.software_genre, Directed acyclic graph, Tree (graph theory), symbols.namesake, Factorization, symbols, Point estimation, Artificial intelligence, Uncertainty quantification, business, computer, Sparse matrix, Gibbs sampling
Abstract

Low-rank matrix completion methods have been successful in a variety of settings such as recommendation systems. However, most of the existing matrix completion methods only provide a point estimate of missing entries, and do not characterize uncertainties of the predictions. In this paper, we propose a Bayesian hierarchical probabilistic matrix factorization (BHPMF) model to 1) incorporate hierarchical side information, and 2) provide uncertainty quantified predictions. The former yields significant performance improvements in the problem of plant trait prediction, a key problem in ecology, by leveraging the taxonomic hierarchy in the plant kingdom. The latter is helpful in identifying predictions of low confidence which can in turn be used to guide field work for data collection efforts. A Gibbs sampler is designed for inference in the model. Further, we propose a multiple inheritance BHPMF (MI-BHPMF) which can work with a general directed acyclic graph (DAG) structured hierarchy, rather than a tree. We present comprehensive experimental results on the problem of plant trait prediction using the largest database of plant traits, where BHPMF shows strong empirical performance in uncertainty quantified trait prediction, outperforming the state-of-the-art based on point estimates. Further, we show that BHPMF is more accurate when it is confident, whereas the error is high when the uncertainty is high.

Published
2014

16. HiTEC: accurate error correction in high-throughput sequencing data

Author

Lucian Ilie, Farideh Fazayeli, and Silvana Ilie

Subjects
Statistics and Probability, Source code, Genome, Models, Genetic, Computer science, Sequence analysis, media_common.quotation_subject, Reproducibility of Results, Sequence Analysis, DNA, computer.software_genre, Biochemistry, DNA sequencing, Computer Science Applications, Computational Mathematics, Computational Theory and Mathematics, Data mining, Error detection and correction, Molecular Biology, Algorithm, computer, Algorithms, Software, media_common
Abstract

Motivation: High-throughput sequencing technologies produce very large amounts of data and sequencing errors constitute one of the major problems in analyzing such data. Current algorithms for correcting these errors are not very accurate and do not automatically adapt to the given data. Results: We present HiTEC, an algorithm that provides a highly accurate, robust and fully automated method to correct reads produced by high-throughput sequencing methods. Our approach provides significantly higher accuracy than previous methods. It is time and space efficient and works very well for all read lengths, genome sizes and coverage levels. Availability: The source code of HiTEC is freely available at www.csd.uwo.ca/~ilie/HiTEC/. Contact: ilie@csd.uwo.ca

Published
2010

17. Assisting Cancer Diagnosis with Fuzzy Neural Networks

Author

Feng Chu, Wei Xie, Farideh Fazayeli, and Lipo Wang

Subjects
Artificial neural network, Fuzzy neural, Microarray analysis techniques, Computer science, business.industry, Pattern recognition, Feature selection, Machine learning, computer.software_genre, Fuzzy logic, Microarray databases, Artificial intelligence, business, Classifier (UML), computer, Membership function
Abstract

Cancer diagnosis from huge microarray gene expression data is an important and challenging bioinformatics research topic. We used a fuzzy neural network (FNN) proposed earlier for cancer classification. This FNN contains three valuable aspects i.e., automatically generating fuzzy membership functions, parameter optimization, and rule-base simplification. One major obstacle in microarray data set classifier is that the number of features (genes) is much larger than the number of objects. We therefore used a feature selection method based on t-test to select more significant genes before applying the FNN. In this work we used three well-known microarray databases, i.e., the lymphoma data set, the small round blue cell tumor (SRBCT) data set, and the ovarian cancer data set. In all cases we obtained 100% accuracy with fewer genes in comparison with previously published results. Our result shows the FNN classifier not only improves the accuracy of cancer classification problem but also helps biologists to find a better relationship between important genes and development of cancers.

Published
2009

18. Back-propagation with chaos

Author

Farideh Fazayeli, Wen Liu, and Lipo Wang

Subjects
Maxima and minima, Error function, Function approximation, Artificial neural network, Computer science, business.industry, Computer Science::Neural and Evolutionary Computation, Ergodicity, Feedforward neural network, Minification, Artificial intelligence, business, Backpropagation
Abstract

Multilayer feed-forward neural networks are widely used based on minimization of an error function. Back-propagation is a famous training method used in the multilayer networks but it often suffers from a local minima problem. To avoid this problem, we propose a new back-propagation training based on chaos. We investigate whether randomicity and ergodicity property of chaos can enable the learning algorithm to escape from local minima. Validity of the proposed method is examined by performing simulations on three real classification tasks, namely, the Ionosphere, the Wincson Breast Cancer (WBC), and the credit-screening datasets. The algorithm is shown to work better than the original back-propagation and is comparable with the Levenberg-Marquardt algorithm, but simpler and easier to implement comparing to Levenberg-Marquardt algorithm.

Published
2008

19. Feature Selection Based on the Rough Set Theory and Expectation-Maximization Clustering Algorithm

Author

Jacek Mańdziuk, Lipo Wang, and Farideh Fazayeli

Subjects
business.industry, Dominance-based rough set approach, Pattern recognition, Feature selection, Mixture model, computer.software_genre, Fuzzy logic, Feature (computer vision), Minimum redundancy feature selection, Data mining, Rough set, Artificial intelligence, business, Cluster analysis, computer, Mathematics
Abstract

We study the Rough Set theory as a method of feature selection based on tolerant classes that extends the existing equivalent classes. The determination of initial tolerant classes is a challenging and important task for accurate feature selection and classification. In this paper the Expectation-Maximization clustering algorithm is applied to determine similar objects. This method generates fewer features with either a higher or the same accuracy compared with two existing methods, i.e., Fuzzy Rough Feature Selection and Tolerance-based Feature Selection, on a number of benchmarks from the UCI repository.

Published
2008

20. Mapping local and global variability in plant trait distributions

Author

BUTLER, E. E., DATTA, A., FLORES-MORENO, H., CHEN, M., WYTHERS, K. R., FAZAYELI, F., BANERJEE, A., ATKIN, O. K., KATTGE, J., AMIAUD, B., BLONDER, B., BOENISCH, G., BOND-LAMBERTY, B., BROWN, K. A., BYUN, C., CAMPETELLA, G., CERABOLINI, B. E. L., CORNELISSEN, J. H. C., CRAINE, J. M., CRAVEN, D., DE VRIES, F. T., DIAZ, S., DOMINGUES, T. F., FOREY, E., GONZALEZ-MELO, A., GROSS, N., HAN, W., HATTINGH, W. N., HICKLER, T., JANSEN, S., SOSINSKI JUNIOR, E. E., KRAMER, K., ETHAN E. BUTLER, ABHIRUP DATTA, HABACUC FLORES-MORENO, MING CHEN, KIRK R. WYTHERS, FARIDEH FAZAYELI, ARINDAM BANERJEE, OWEN K. ATKIN, JENS KATTGE, BERNARD AMIAUD, BENJAMIN BLONDER, GERHARD BOENISCH, BEN BOND-LAMBERTY, KERRY A. BROWN, CHAEHO BYUN, GIANDIEGO CAMPETELLA, BRUNO E. L. CERABOLINI, JOHANNES H. C. CORNELISSEN, JOSEPH M. CRAINE, DYLAN CRAVEN, FRANCISKA T. DE VRIES, SANDRA DIAZ, TOMAS F. DOMINGUES, ESTELLE FOREY, ANDR?ES GONZALEZ-MELO, NICOLAS GROSS, WENXUAN HAN, WESLEY N. HATTINGH, THOMAS HICKLER, STEVEN JANSEN, ENIO EGON SOSINSKI JUNIOR, CPACT, and KOEN KRAMER.

Subjects
Clima
Abstract

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Published
2017

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