Author
Valério D. Pillar
Other affiliations: Federal University of Rio Grande do Norte, Martin Luther University of Halle-Wittenberg
Bio: Valério D. Pillar is an academic researcher from Universidade Federal do Rio Grande do Sul. The author has contributed to research in topics: Vegetation & Grassland. The author has an hindex of 46, co-authored 201 publications receiving 10326 citations. Previous affiliations of Valério D. Pillar include Federal University of Rio Grande do Norte & Martin Luther University of Halle-Wittenberg.
Topics: Vegetation, Grassland, Biodiversity, Species richness, Trait
Papers published on a yearly basis
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Max Planck Society1, National University of Cordoba2, Centre national de la recherche scientifique3, Macquarie University4, University of Paris-Sud5, University of Minnesota6, University of Western Sydney7, VU University Amsterdam8, University of Arizona9, University of California, Berkeley10, University of Guelph11, Australian National University12, University of Innsbruck13, University of Leeds14, University of Groningen15, Universidade Federal do Rio Grande do Sul16, University of Cape Town17, University of Wollongong18, New Jersey Institute of Technology19, Centro Agronómico Tropical de Investigación y Enseñanza20, Lawrence Berkeley National Laboratory21, University of Alaska Fairbanks22, University of Cambridge23, Kansas State University24, Helmholtz Centre for Environmental Research - UFZ25, Arizona State University26, University of Giessen27, Autonomous University of Barcelona28, University of Maryland, College Park29, Universidad del Tolima30, University of São Paulo31, University of La Réunion32, University of York33, University of Sydney34, Harvard University35, Goethe University Frankfurt36, University of Sheffield37, University of Ulm38, State University of Campinas39, Kenyon College40, Royal Botanic Gardens41, University of Florida42, University of Oldenburg43, University of Nebraska–Lincoln44, Tohoku University45, Northern Arizona University46, University of Wisconsin–Eau Claire47, Naturalis48, James Cook University49, Institut national de la recherche agronomique50, Newcastle University51, University of New South Wales52, Leipzig University53, Columbia University54, Estonian University of Life Sciences55, Polish Academy of Sciences56, Moscow State University57, Kyushu University58, Wageningen University and Research Centre59, Spanish National Research Council60, University of Regensburg61, University of Rennes62, Université du Québec à Trois-Rivières63, Potsdam Institute for Climate Impact Research64, Technical University of Denmark65, University of California, Los Angeles66, Hokkaido University67, Université de Sherbrooke68, Syracuse University69, Empresa Brasileira de Pesquisa Agropecuária70, University of Aberdeen71, Michigan State University72, Oak Ridge National Laboratory73, University of Leicester74, Utah State University75, Smithsonian Institution76, University of Missouri77
TL;DR: TRY as discussed by the authors is a global database of plant traits, including morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs, which can be used for a wide range of research from evolutionary biology, community and functional ecology to biogeography.
Abstract: Plant traits – the morphological, anatomical, physiological, biochemical and phenological characteristics of plants and their organs – determine how primary producers respond to environmental factors, affect other trophic levels, influence ecosystem processes and services and provide a link from species richness to ecosystem functional diversity. Trait data thus represent the raw material for a wide range of research from evolutionary biology, community and functional ecology to biogeography. Here we present the global database initiative named TRY, which has united a wide range of the plant trait research community worldwide and gained an unprecedented buy-in of trait data: so far 93 trait databases have been contributed. The data repository currently contains almost three million trait entries for 69 000 out of the world's 300 000 plant species, with a focus on 52 groups of traits characterizing the vegetative and regeneration stages of the plant life cycle, including growth, dispersal, establishment and persistence. A first data analysis shows that most plant traits are approximately log-normally distributed, with widely differing ranges of variation across traits. Most trait variation is between species (interspecific), but significant intraspecific variation is also documented, up to 40% of the overall variation. Plant functional types (PFTs), as commonly used in vegetation models, capture a substantial fraction of the observed variation – but for several traits most variation occurs within PFTs, up to 75% of the overall variation. In the context of vegetation models these traits would better be represented by state variables rather than fixed parameter values. The improved availability of plant trait data in the unified global database is expected to support a paradigm shift from species to trait-based ecology, offer new opportunities for synthetic plant trait research and enable a more realistic and empirically grounded representation of terrestrial vegetation in Earth system models.
2,017 citations
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to 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.
Abstract: 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.
882 citations
Centre national de la recherche scientifique1, University of Grenoble2, Austral University of Chile3, Queen's University4, Universidade Federal de Goiás5, Universidade Federal do Rio Grande do Sul6, Coordenadoria de Aperfeiçoamento de Pessoal de Nível Superior7, State University of Campinas8, Sewanee: The University of the South9, Academy of Sciences of the Czech Republic10, Federal University of Rio Grande do Norte11, University of Montpellier12, Swedish University of Agricultural Sciences13, Institut national de la recherche agronomique14, Tohoku University15, United Nations University16, University of Florida17, Université du Québec à Montréal18, University of Maryland, College Park19, Université de Sherbrooke20, Landcare Research21, Spanish National Research Council22, Lund University23, University of Tokyo24, Algoma University25, Umeå University26, Natural History Museum27
TL;DR: This paper conducted a meta-analysis of the relative extent of ITV within and among plant communities worldwide, using a data set encompassing 629 communities (plots) and 36 functional traits.
Abstract: Recent studies have shown that accounting for intraspecific trait variation (ITV) may better address major questions in community ecology. However, a general picture of the relative extent of ITV compared to interspecific trait variation in plant communities is still missing. Here, we conducted a meta-analysis of the relative extent of ITV within and among plant communities worldwide, using a data set encompassing 629 communities (plots) and 36 functional traits. Overall, ITV accounted for 25% of the total trait variation within communities and 32% of the total trait variation among communities on average. The relative extent of ITV tended to be greater for whole-plant (e.g. plant height) vs. organ-level traits and for leaf chemical (e.g. leaf N and P concentration) vs. leaf morphological (e.g. leaf area and thickness) traits. The relative amount of ITV decreased with increasing species richness and spatial extent, but did not vary with plant growth form or climate. These results highlight global patterns in the relative importance of ITV in plant communities, providing practical guidelines for when researchers should include ITV in trait-based community and ecosystem studies.
653 citations
TL;DR: Urgent needs are identified to create more conservation units in different regions, including different grassland types throughout southern Brazil, to develop proper management strategies where grasslands are subject to shrub encroachment and forest expansion and to raise public awareness of the value and vulnerability of this vegetation type.
Abstract: The South Brazilian grasslands occupy some 13.7 million ha and support very high levels of biodiversity. This paper reviews the current state of ecological knowledge on South Brazilian Campos and of threats and challenges associated with their conservation. The principal factors shaping grassland physiognomy and diversity are discussed, and information is presented on diversity of plant species; best estimates suggest that 3000–4000 phanerophytes occur in the South Brazilian grasslands.
It is argued that, despite their high species richness, Campos vegetation is not adequately protected under current conservation policies. In the past three decades, approximately 25% of the grassland area has been lost due to land use changes, and this trend continues. However, representation of Campos grasslands in conservation units is extremely low (less than 0.5%), and the management in most of these is inadequate to preserve the grasslands, as grazing and fire are important factors for their persistence. In conclusion, the following urgent needs are identified: (1) to create more conservation units in different regions, including different grassland types throughout southern Brazil, (2) to develop proper management strategies where grasslands are subject to shrub encroachment and forest expansion, (3) to conduct research on biodiversity and ecological processes in the Campos region and (4) to raise public awareness of the value and vulnerability of this vegetation type.
630 citations
Martin Luther University of Halle-Wittenberg1, University of Bayreuth2, Zürcher Fachhochschule3, University of Picardie Jules Verne4, University of Oviedo5, Wageningen University and Research Centre6, Masaryk University7, University of Nottingham8, University of Rostock9, Max Planck Society10, Universidade Federal do Rio Grande do Sul11, University of North Carolina at Chapel Hill12, Santa Clara University13, Leiden University14, Academy of Sciences of the Czech Republic15, Shahjalal University of Science and Technology16, Sapienza University of Rome17, Natural Resources Canada18, Florida International University19, Universidade Federal do Acre20, Ghent University21, University of Göttingen22, University of the Basque Country23, Aarhus University24, Environmental Change Institute25, Rocky Mountain Biological Laboratory26, University of Nova Gorica27, Slovenian Academy of Sciences and Arts28, King Juan Carlos University29, Czech University of Life Sciences Prague30, VU University Amsterdam31, University of Würzburg32, National University of Cordoba33, Wake Forest University34, National University of Saint Anthony the Abbot in Cuzco35, University of Exeter36, Université du Québec en Abitibi-Témiscamingue37, University of Montpellier38, University of Adelaide39, University of Chile40, IFREMER41, University of British Columbia42, Universidade do Estado de Santa Catarina43, University of Münster44, University of Hamburg45, University of Wrocław46, University of Zurich47, Swiss Federal Institute for Forest, Snow and Landscape Research48, University of Oldenburg49, University of Waikato50, University of Wyoming51, Institut national de la recherche agronomique52, Sofia University53, Royal Botanic Gardens54, University of Edinburgh55, Landcare Research56, Radboud University Nijmegen57, Spanish National Research Council58, University of Barcelona59, University of Leeds60, University of Tartu61, University of Minnesota62, University of Sydney63, University of Jena64, University of La Serena65, Peking University66, Iwokrama International Centre for Rain Forest Conservation and Development67, Aristotle University of Thessaloniki68, Bulgarian Academy of Sciences69, University of Oulu70, University of Wisconsin–Eau Claire71, International Institute of Minnesota72, American Museum of Natural History73, Leipzig University74
TL;DR: It is shown that global trait composition is captured by two main dimensions that are only weakly related to macro-environmental drivers, which reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale.
Abstract: Plant functional traits directly affect ecosystem functions. At the species level, trait combinations depend on trade-offs representing different ecological strategies, but at the community level trait combinations are expected to be decoupled from these trade-offs because different strategies can facilitate co-existence within communities. A key question is to what extent community-level trait composition is globally filtered and how well it is related to global versus local environmental drivers. Here, we perform a global, plot-level analysis of trait-environment relationships, using a database with more than 1.1 million vegetation plots and 26,632 plant species with trait information. Although we found a strong filtering of 17 functional traits, similar climate and soil conditions support communities differing greatly in mean trait values. The two main community trait axes that capture half of the global trait variation (plant stature and resource acquisitiveness) reflect the trade-offs at the species level but are weakly associated with climate and soil conditions at the global scale. Similarly, within-plot trait variation does not vary systematically with macro-environment. Our results indicate that, at fine spatial grain, macro-environmental drivers are much less important for functional trait composition than has been assumed from floristic analyses restricted to co-occurrence in large grid cells. Instead, trait combinations seem to be predominantly filtered by local-scale factors such as disturbance, fine-scale soil conditions, niche partitioning and biotic interactions.
349 citations
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
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14,171 citations
Journal Article•
TL;DR: This book by a teacher of statistics (as well as a consultant for "experimenters") is a comprehensive study of the philosophical background for the statistical design of experiment.
Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON
13,333 citations
TL;DR: In this article, a non-parametric method for multivariate analysis of variance, based on sums of squared distances, is proposed. But it is not suitable for most ecological multivariate data sets.
Abstract: Hypothesis-testing methods for multivariate data are needed to make rigorous probability statements about the effects of factors and their interactions in experiments. Analysis of variance is particularly powerful for the analysis of univariate data. The traditional multivariate analogues, however, are too stringent in their assumptions for most ecological multivariate data sets. Non-parametric methods, based on permutation tests, are preferable. This paper describes a new non-parametric method for multivariate analysis of variance, after McArdle and Anderson (in press). It is given here, with several applications in ecology, to provide an alternative and perhaps more intuitive formulation for ANOVA (based on sums of squared distances) to complement the description pro- vided by McArdle and Anderson (in press) for the analysis of any linear model. It is an improvement on previous non-parametric methods because it allows a direct additive partitioning of variation for complex models. It does this while maintaining the flexibility and lack of formal assumptions of other non-parametric methods. The test- statistic is a multivariate analogue to Fisher's F-ratio and is calculated directly from any symmetric distance or dissimilarity matrix. P-values are then obtained using permutations. Some examples of the method are given for tests involving several factors, including factorial and hierarchical (nested) designs and tests of interactions.
12,328 citations
01 Jan 2016
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