Filling the gap in functional trait databases: Use of ecological hypotheses to replace missing data

Functional trait databases are powerful tools in ecology, though most of them contain large amounts of missing values. The goal of this study was to test the effect of imputation methods on the evaluation of trait values at species level and on the subsequent calculation of functional diversity indices at community level using functional trait databases. Two simple imputation methods (average and median), two methods based on ecological hypotheses, and one multiple imputation method were tested using a large plant trait database, together with the influence of the percentage of missing data and differences between functional traits. At community level, the complete-case approach and three functional diversity indices calculated from grassland plant communities were included. At the species level, one of the methods based on ecological hypothesis was for all traits more accurate than imputation with average or median values, but the multiple imputation method was superior for most of the traits. The method based on functional proximity between species was the best method for traits with an unbalanced distribution, while the method based on the existence of relationships between traits was the best for traits with a balanced distribution. The ranking of the grassland communities for their functional diversity indices was not robust with the complete-case approach, even for low percentages of missing data. With the imputation methods based on ecological hypotheses, functional diversity indices could be computed with a maximum of 30% of missing data, without affecting the ranking between grassland communities. The multiple imputation method performed well, but not better than single imputation based on ecological hypothesis and adapted to the distribution of the trait values for the functional identity and range of the communities. Ecological studies using functional trait databases have to deal with missing data using imputation methods corresponding to their specific needs and making the most out of the information available in the databases. Within this framework, this study indicates the possibilities and limits of single imputation methods based on ecological hypothesis and concludes that they could be useful when studying the ranking of communities for their functional diversity indices. (Résumé d'auteur

Cartographie des forêts anciennes de France : objectifs, bilan et perspectives

Il y a dix ans démarraient les premiers travaux de vectorisation, à l'échelle régionale, des forêts de la carte d'Etat-Major, en vue de l'établissement d'une carte nationale des forêts à longue continuité de l'état boisé. Où en est-on aujourd'hui ? Nous faisons le point de l'avancement des travaux et en tirons les premiers enseignements, en répondant aux questions suivantes : Quels sont les définitions et concepts sous-jacents à ces travaux ? Pourquoi cartographier les forêts dites "anciennes" ou "récentes" ? L'analyse des institutions ayant réalisé le travail montre que ce sont principalement les milieux de la conservation qui ont été moteurs dans ces travaux. Mais la production et la qualité des produits bois sont aussi concernés par cette cartographie. Le rôle actuel de puits de carbone des forêts françaises ne peut par exemple se comprendre qu'au travers de cette dynamique forestière ancienne. Pourquoi une focalisation sur la première moitié du XIXe siècle comme date de référence ? Que signifie la notion de minimum forestier ? Quelles en sont les limites ? Quels sont les supports de données les plus intéressantes pour cette cartographie ? Pourquoi la carte d'Etat-Major est une source particulière d'information, dans l'objectif de la cartographie des forêts anciennes, parmi la multitude de cartes ou statistiques disponibles à différentes dates et échelles ? Quelles sont les méthodes d'acquisition de la donnée ? Quelle est la précision spatiale des cartes d'occupation du sol obtenues ? Les principaux problèmes posés par l'utilisation de la carte d'Etat-Major seront présentés, ainsi que la façon dont différents projets y ont répondu. Quels résultats ont été obtenus ? Nous reviendrons entre autres sur l'estimation de la surface forestière française à la date de son minimum. Les cartes déjà réalisées, sur 33% du territoire, permettent de dessiner avec précision et de comparer les changements d'occupation du sol dans différentes régions de France, en termes de pourcentage de déboisement, reboisement et taux de forêt ancienne dans la forêt actuelle. Les évolutions du couvert forestier issues d'autres sources non cartographiques sont-elles confirmées ? Le lien avec le type de propriété foncière est particulièrement intéressant à analyser. Dans plusieurs zones de France (Pyrénées, Luberon, Alpes, Lorraine, Nord-Pas-de-Calais...) ont été réalisés des croisements entre ces cartes et les bases de données régionales de relevés floristiques (Inventaire forestier national, conservatoires botaniques). Ce nouveau type d'analyse permet d'identifier rapidement les espèces végétales liées à la continuité de l'état boisé, dites espèces de forêts anciennes, et les traits de vie qui leur sont associés. Nous présenterons une synthèse de ces résultats. Dans la moitié des zones déjà cartographiées, ce sont toutes les occupations du sol anciennes qui ont été numérisées et non seulement les forêts. Nous évoquerons l'intérêt de ce cadastre ancien, au-delà des seules questions forestières, pour le suivi de la dynamique à long terme des prairies, des milieux humides, des vignes ou des milieux urbanisés. Les techniques de vectorisation des occupations anciennes du sol évoluent vers une simplification et une accélération qui laisse présager une fin du travail plus rapide que prévue initialement, parfois au détriment de la qualité. L'extension à la France entière permettra une vision à la fois à petite échelle mais localement précise des mouvements des masses forestières. Nous discuterons les perspectives de recherche et les développements en cours, ouverts par ces progrès

Mapping local and global variability in plant trait distributions

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

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

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

The relationships between soil seed bank, aboveground vegetation and disturbances in old embanked marshlands of Western France

The study deals with the influence of land use and abandonment on species composition of vegetation and seed bank in grasslands and oldfields. We wanted to explore: (1) How the seed bank changes when agricultural practices cease? In convergence with proposals in the literature, we addressed in particular the following two questions that have been proposed by Symonides (1986), Pickett & McDonnell (1989) and Roberts & Vankat (1991) for seed bank characteristics under secondary succession: (i) Does species richness and species diversity in the soil seed bank decrease during succession? (ii) Does the density of buried seed decline during succession? (2) What is the role played by seed bank in the recolonisation of plots disturbed by experimental disturbances ? We studied species composition of vegetation and seed bank in an experiment with grassland and oldfield plots in old embanked marshlands (called "Marais Poitevin"). In these wetlands, artificial disturbances (mowing) and natural disturbances (cattle, roebucks, coypus, voles) are very frequent. In order to mimic disturbances, experimental disturbances were generated in spring after the end of the winter flooding and emerged seedlings counted three months later. Data about the seed bank, the undisturbed vegetation and seedlings emerging in disturbed quadrats were sampled. Detrended Correspondence Analysis (DCA) of the undisturbed quadrats, disturbed quadrats and seed bank samples showed significant differences of species composition. Similarity between seed bank and undisturbed aboveground vegetation was low and not very different between grassland and oldfield. Very few seedlings emerged in the undisturbed vegetation both in grassland and oldfield, which potentially indicates the importance of gaps for seed bank expression. In Marais Poitevin, the seed bank contributed very little to the seedling flora, and vegetative regrowth clearly predominated recolonisation after disturbances. In the seed bank, few species lost after succession from grassland to oldfield vegetation were still present as seeds in the soil, but in most cases species lost were not recorded in the seed bank. The results have shown that species richness and species diversity in the seed bank decrease during succession. On the other hand, the density of buried seeds did not decrease significantly from grassland to oldfiel

INDIBIO : Élaborer des indicateurs relatifs aux effets des pratiques agricoles sur la biodiversité dans les systèmes d’exploitation d’élevage

La biodiversité ordinaire correspond aux espèces communes et aux paysages qui contribuent au bon fonctionnement des écosystèmes en fournissant des services à l’homme. L’intensification des pratiques agricoles de ces 50 dernières années a contribué à la destruction des équilibres écosystémiques. La disponibilité d’indicateurs pertinents est implicitement nécessaire pour une prise en compte de la biodiversité ordinaire dans le processus agricole. Les composantes paysagères sont identifiées entre autres par une diversité d’infrastructures agroécologiques (IAE) mais aussi par leur organisation spatiale dans le territoire agricole et leur densité. Ces IAE sont des éléments fixes du paysage faisant partie intégrante de l’exploitation agricole mais non-productifs et pouvant fournir de nombreux services écosystémiques. L’étude a été réalisée sur trois régions contrastées par leurs conditions pédoclimatiques, leur position géographique, des systèmes d’élevage et des paysages : la Lorraine/Champagne-Ardenne (climat semi-continental), la Basse-Normandie (climat océanique) et l’Auvergne (climat montagnard). Dans chacune des régions, les exploitations ont été choisies selon deux axes : l’intensité des pratiques agricoles appliquées sur la prairie permanente et l’importance des IAE sur leur territoire. L’analyse de ces données montre que la richesse spécifique des espèces floristique et faunistique est dépendante à la fois des pratiques agricoles mais aussi des composantes du territoire et de la structure même du parcellaire. Le premier enseignement apporté par le traitement statistique des données nous montre que les facteurs influençant la richesse spécifique les espèces étudiées sont différents d’une région à l’autre, ce qui montre que chaque zone a sa particularité. Ainsi, le modèle de prédiction de la biodiversité ordinaire doit être utilisé en dehors des zones étudiées dans le projet avec précaution. Enfin, une évaluation globale (BioTEX) à trois échelles spatiales a été mise au point pour identifier les composantes nécessaires de l’accueil des espèces et pour qualifier les pratiques dans le but d’agir en faveur de la biodiversité.Ordinary biodiversity corresponds to common species and landscapes that contribute to the proper functioning of ecosystems by providing services to humans. Intensification of agricultural practices from the last 50 years has contributed to destroy the ecosystem balance. Availability of relevant indicators is implicitly required for taking account of ordinary biodiversity in the agricultural process. Landscape components are identified by a variety of agro-ecological infrastructures (AEI) but also by their spatial organization in agricultural land and their density. These AEIs are fixed elements of the landscape as non-productive part of the agricultural exploitation and can provide many ecosystem services. The study was conducted in three regions contrasted by their soil, their geographical position of farming systems conditions and landscapes: the Lorraine/Champagne-Ardenne (semi-continental climate), BasseNormandie (oceanic climate) and Auvergne (mountain climate). In each region, farms were selected according to two axes: intensity of agricultural practices on permanent grassland and importance of AEI on their territory. Analysis of these data shows that the species richness of flora and fauna species is dependent on both agricultural practices but also components of the territory and the very structure of the parcel. The first lesson brought by the statistical analysis shows us that species, especially fauna do not response to the same factors. Thus, ordinary biodiversity prediction model offers possible extension outside the areas studied in the project with many precaution. Finally, an overall assessment (BioTEX) at three spatial scales was developed to identify the necessary components to host species and practices with positive impact on biodiversity

The relationships between soil seed bank, aboveground vegetation and disturbances in an eutrophic alluvial wetland of Western France

International audienc