Author
Lars Götzenberger
Other affiliations: Helmholtz Centre for Environmental Research - UFZ, Masaryk University, University of Tartu ...read more
Bio: Lars Götzenberger is an academic researcher from Academy of Sciences of the Czech Republic. The author has contributed to research in topics: Biodiversity & Trait. The author has an hindex of 23, co-authored 55 publications receiving 3461 citations. Previous affiliations of Lars Götzenberger include Helmholtz Centre for Environmental Research - UFZ & Masaryk University.
Papers
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University of Oldenburg1, University of Groningen2, University of Sheffield3, OFFIS4, University of Regensburg5, Radboud University Nijmegen6, Helmholtz Centre for Environmental Research - UFZ7, Katholieke Universiteit Leuven8, Ghent University9, Wageningen University and Research Centre10, VU University Amsterdam11, Stockholm University12, Centre national de la recherche scientifique13, Autonomous University of Madrid14
TL;DR: The LEDA Traitbase is useful for large-scale analyses of functional responses of communities to environmental change, effects of community trait composition on ecosystem properties and patterns of rarity and invasiveness, as well as linkages between traits as expressions of fundamental trade-offs in plants.
Abstract: Summary 1. An international group of scientists has built an open internet data base of life-history traits of the Northwest European flora (the LEDA-Traitbase) that can be used as a data source for fundamental research on plant biodiversity and coexistence, macro-ecological patterns and plant functional responses. 2. The species-trait matrix comprises referenced information under the control of an editorial board, for ca. 3000 species of the Northwest European flora, combining existing information and additional measurements. The data base currently contains data on 26 plant traits that describe three key features of plant dynamics: persistence, regeneration and dispersal. The LEDA-Traitbase is freely available at www.leda-traitbase.org. 3. We present the structure of the data base and an overview of the trait information available. 4. Synthesis. The LEDA Traitbase is useful for large-scale analyses of functional responses of communities to environmental change, effects of community trait composition on ecosystem properties and patterns of rarity and invasiveness, as well as linkages between traits as expressions of fundamental trade-offs in plants.
1,379 citations
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TL;DR: This work redefined the traditional concept of assembly rules in a more general framework where the co‐occurrence of species is a product of chance, historical patterns of speciation and migration, dispersal, abiotic environmental factors, and biotic interactions, with none of these processes being mutually exclusive.
Abstract: Understanding how communities of living organisms assemble has been a central question in ecology since the early days of the discipline. Disentangling the different processes involved in community assembly is not only interesting in itself but also crucial for an understanding of how communities will behave under future environmental scenarios. The traditional concept of assembly rules reflects the notion that species do not co-occur randomly but are restricted in their co-occurrence by interspecific competition. This concept can be redefined in a more general framework where the co-occurrence of species is a product of chance, historical patterns of speciation and migration, dispersal, abiotic environmental factors, and biotic interactions, with none of these processes being mutually exclusive. Here we present a survey and meta-analyses of 59 papers that compare observed patterns in plant communities with null models simulating random patterns of species assembly. According to the type of data under study and the different methods that are applied to detect community assembly, we distinguish four main types of approach in the published literature: species co-occurrence, niche limitation, guild proportionality and limiting similarity. Results from our meta-analyses suggest that non-random co-occurrence of plant species is not a widespread phenomenon. However, whether this finding reflects the individualistic nature of plant communities or is caused by methodological shortcomings associated with the studies considered cannot be discerned from the available metadata. We advocate that more thorough surveys be conducted using a set of standardized methods to test for the existence of assembly rules in data sets spanning larger biological and geographical scales than have been considered until now. We underpin this general advice with guidelines that should be considered in future assembly rules research. This will enable us to draw more accurate and general conclusions about the non-random aspect of assembly in plant communities.
719 citations
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TL;DR: It is demonstrated that only by estimating the species pool of a site is it possible to differentiate the patterns of trait dissimilarity produced by operating biotic processes, and a functional species pool framework is proposed, which enables a reinterpretation of community assembly processes.
Abstract: Functional trait differences among species are increasingly used to infer the effects of biotic and abiotic processes on species coexistence. Commonly, the trait diversity observed within communities is compared to patterns simulated in randomly generated communities based on sampling within a region. The resulting patterns of trait convergence and divergence are assumed to reveal abiotic and biotic processes, respectively. However, biotic processes such as competition can produce both trait divergence and convergence, through either excluding similar species (niche differences, divergence) or excluding dissimilar species (weaker competitor exclusion, convergence). Hence, separating biotic and abiotic processes that can produce identical patterns of trait diversity, or even patterns that neutralize each other, is not feasible with previous methods. We propose an operational framework in which the functional trait dissimilarity within communities (FDcomm) is compared to the corresponding trait dissimilarity expected from the species pool (i.e., functional species pool diversity, FDpool). FDpool includes the set of potential species for a site delimited by the operating environmental and dispersal limitation filters. By applying these filters, the resulting pattern of trait diversity is consistent with biotic processes, i.e., trait divergence (FDcomm > FDpool) indicates niche differentiation, while trait convergence (FDcomm < FDpool) indicates weaker competitor exclusion. To illustrate this framework, with its potential application and constraints, we analyzed both simulated and field data. The functional species pool framework more consistently detected the simulated trait diversity patterns than previous approaches. In the field, using data from plant communities of typical Northern European habitats in Estonia, we found that both niche-based and weaker competitor exclusion influenced community assembly, depending on the traits and community considered. In both simulated and field data, we demonstrated that only by estimating the species pool of a site is it possible to differentiate the patterns of trait dissimilarity produced by operating biotic processes. The framework, which can be applied with both functional and phylogenetic diversity, enables a reinterpretation of community assembly processes. Solving the challenge of defining an appropriate reference species pool for a site can provide a better understanding of community assembly.
206 citations
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TL;DR: This work used cross-validation techniques and a global data set to measure the predictive power of simple plant traits to estimate species' maximum dispersal distances and provided a function to be run in the software package R that enables researchers to estimate maximum disperseal distances with confidence intervals for plant species using measured traits as predictors.
Abstract: Many studies have shown plant species' dispersal distances to be strongly related to life-history traits, but how well different traits can predict dispersal distances is not yet known. We used cross-validation techniques and a global data set (576 plant species) to measure the predictive power of simple plant traits to estimate species' maximum dispersal distances. Including dispersal syndrome (wind, animal, ant, ballistic, and no special syndrome), growth form (tree, shrub, herb), seed mass, seed release height, and terminal velocity in different combinations as explanatory variables we constructed models to explain variation in measured maximum dispersal distances and evaluated their power to predict maximum dispersal distances. Predictions are more accurate, but also limited to a particular set of species, if data on more specific traits, such as terminal velocity, are available. The best model (R2 = 0.60) included dispersal syndrome, growth form, and terminal velocity as fixed effects. Reasonable predictions of maximum dispersal distance (R2 = 0.53) are also possible when using only the simplest and most commonly measured traits; dispersal syndrome and growth form together with species taxonomy data. We provide a function (dispeRsal) to be run in the software package R. This enables researchers to estimate maximum dispersal distances with confidence intervals for plant species using measured traits as predictors. Easily obtainable trait data, such as dispersal syndrome (inferred from seed morphology) and growth form, enable predictions to be made for a large number of species.
201 citations
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TL;DR: The rich empirical information on seed dispersal distances is synthesised to provide standardised dispersal kernels for 168 case studies and generalised kernels for plant growth form/dispersal mode combinations.
Abstract: 1. Dispersal is fundamental to ecological processes at all scales and levels of organisation but progress is limited by a lack of information about the general shape and form of plant dispersal kernels. We addressed this gap by synthesising empirical data describing seed dispersal and fitting general dispersal kernels representing major plant types and dispersal modes.
2. A comprehensive literature search resulted in 107 papers describing 168 dispersal kernels for 144 vascular plant species. The data covered 63 families, all the continents except Antarctica, and the broad vegetation types of forest, grassland, shrubland, and more open habitats (e.g. deserts). We classified kernels in terms of dispersal mode (ant, ballistic, rodent, vertebrates other than rodents, vehicle or wind), plant growth form (climber, graminoid, herb, shrub or tree), seed mass and plant height.
3. We fitted 11 widely-used probability density functions to each of the 168 datasets to provide a statistical description of the dispersal kernel. The Exponential Power (ExP) and Log-sech (LogS) functions performed best. Other 2-parameter functions varied in performance. For example, the Lognormal and Weibull performed poorly, while the 2Dt and Power law performed moderately well. Of the single-parameter functions, the Gaussian performed very poorly, while the Exponential performed better. No function was among the best-fitting for all datasets.
4. For 10 plant growth form/dispersal mode combinations for which we had >3 datasets, we fitted ExP and LogS functions across multiple datasets to provide generalised dispersal kernels. We also fitted these functions to sub-divisions of these growth form/dispersal mode combinations in terms of seed mass (for animal-dispersed seeds) or plant height (wind-dispersed) classes. These functions provided generally good fits to the grouped datasets, despite variation in empirical methods, local conditions, vegetation type and the exact dispersal process.
5. Synthesis. We synthesise the rich empirical information on seed dispersal distances to provide standardised dispersal kernels for 168 case studies and generalised kernels for plant growth form/dispersal mode combinations. Potential uses include: a) choosing appropriate dispersal functions in mathematical models; b) selecting informative dispersal kernels for one’s empirical study system; and c) using representative dispersal kernels in cross-taxon comparative studies.
168 citations
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TL;DR: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols used xiii 1.
Abstract: Preface to the Princeton Landmarks in Biology Edition vii Preface xi Symbols Used xiii 1. The Importance of Islands 3 2. Area and Number of Speicies 8 3. Further Explanations of the Area-Diversity Pattern 19 4. The Strategy of Colonization 68 5. Invasibility and the Variable Niche 94 6. Stepping Stones and Biotic Exchange 123 7. Evolutionary Changes Following Colonization 145 8. Prospect 181 Glossary 185 References 193 Index 201
14,171 citations
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National University of Cordoba1, SupAgro2, Joseph Fourier University3, University of Alaska Fairbanks4, VU University Amsterdam5, Kansas State University6, University of Western Australia7, University of Minnesota8, Wageningen University and Research Centre9, Macquarie University10, Stanford University11, Spanish National Research Council12, ETH Zurich13, University of Sheffield14, Utrecht University15, University of California, Los Angeles16, University of Arizona17, University of Regensburg18, Princeton University19, Centro Agronómico Tropical de Investigación y Enseñanza20
TL;DR: This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species’ effects on key ecosystem properties.
Abstract: Plant functional traits are the features (morphological, physiological, phenological) that represent ecological strategies and determine how plants respond to environmental factors, affect other trophic levels and influence ecosystem properties. Variation in plant functional traits, and trait syndromes, has proven useful for tackling many important ecological questions at a range of scales, giving rise to a demand for standardised ways to measure ecologically meaningful plant traits. This line of research has been among the most fruitful avenues for understanding ecological and evolutionary patterns and processes. It also has the potential both to build a predictive set of local, regional and global relationships between plants and environment and to quantify a wide range of natural and human-driven processes, including changes in biodiversity, the impacts of species invasions, alterations in biogeochemical processes and vegetation–atmosphere interactions. The importance of these topics dictates the urgent need for more and better data, and increases the value of standardised protocols for quantifying trait variation of different species, in particular for traits with power to predict plant- and ecosystem-level processes, and for traits that can be measured relatively easily. Updated and expanded from the widely used previous version, this handbook retains the focus on clearly presented, widely applicable, step-by-step recipes, with a minimum of text on theory, and not only includes updated methods for the traits previously covered, but also introduces many new protocols for further traits. This new handbook has a better balance between whole-plant traits, leaf traits, root and stem traits and regenerative traits, and puts particular emphasis on traits important for predicting species’ effects on key ecosystem properties. We hope this new handbook becomes a standard companion in local and global efforts to learn about the responses and impacts of different plant species with respect to environmental changes in the present, past and future.
2,744 citations
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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
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National University of Cordoba1, Max Planck Society2, VU University Amsterdam3, Macquarie University4, University of Grenoble5, University of Lyon6, Industrial University of Santander7, Leipzig University8, University of Oldenburg9, Imperial College London10, University of Montpellier11, Forschungszentrum Jülich12, University of Minnesota13, University of Western Sydney14, University of New South Wales15, Royal Botanic Gardens16, Missouri Botanical Garden17, George Washington University18, Paul Sabatier University19, Smithsonian Tropical Research Institute20, Komarov Botanical Institute21, University of Bordeaux22, Institut national de la recherche agronomique23, Florida International University24, University of Insubria25, University of Milan26, Université de Sherbrooke27, Centro Agronómico Tropical de Investigación y Enseñanza28
TL;DR: Analysis of worldwide variation in six major traits critical to growth, survival and reproduction within the largest sample of vascular plant species ever compiled found that occupancy of six-dimensional trait space is strongly concentrated, indicating coordination and trade-offs.
Abstract: The authors found that the key elements of plant form and function, analysed at global scale, are largely concentrated into a two-dimensional plane indexed by the size of whole plants and organs on the one hand, and the construction costs for photosynthetic leaf area, on the other.
1,814 citations
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TL;DR: It is shown that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents, and is called for for accelerated collection of spatially and temporally explicit species data.
Abstract: Predicting which species will occur together in the future, and where, remains one of the greatest challenges in ecology, and requires a sound understanding of how the abiotic and biotic environments interact with dispersal processes and history across scales. Biotic interactions and their dynamics influence species' relationships to climate, and this also has important implications for predicting future distributions of species. It is already well accepted that biotic interactions shape species' spatial distributions at local spatial extents, but the role of these interactions beyond local extents (e.g. 10 km2 to global extents) are usually dismissed as unimportant. In this review we consolidate evidence for how biotic interactions shape species distributions beyond local extents and review methods for integrating biotic interactions into species distribution modelling tools. Drawing upon evidence from contemporary and palaeoecological studies of individual species ranges, functional groups, and species richness patterns, we show that biotic interactions have clearly left their mark on species distributions and realised assemblages of species across all spatial extents. We demonstrate this with examples from within and across trophic groups. A range of species distribution modelling tools is available to quantify species environmental relationships and predict species occurrence, such as: (i) integrating pairwise dependencies, (ii) using integrative predictors, and (iii) hybridising species distribution models (SDMs) with dynamic models. These methods have typically only been applied to interacting pairs of species at a single time, require a priori ecological knowledge about which species interact, and due to data paucity must assume that biotic interactions are constant in space and time. To better inform the future development of these models across spatial scales, we call for accelerated collection of spatially and temporally explicit species data. Ideally, these data should be sampled to reflect variation in the underlying environment across large spatial extents, and at fine spatial resolution. Simplified ecosystems where there are relatively few interacting species and sometimes a wealth of existing ecosystem monitoring data (e.g. arctic, alpine or island habitats) offer settings where the development of modelling tools that account for biotic interactions may be less difficult than elsewhere.
1,297 citations