Global climatic drivers of leaf size.
Macquarie University1, University of Reading2, Université du Québec à Trois-Rivières3, Imperial College London4, National University of Cordoba5, Southern Methodist University6, University of Queensland7, Estonian University of Life Sciences8, University of Minnesota9, University of Sydney10, University of California, Los Angeles11, University of Córdoba (Spain)12, Northwest A&F University13, Pennsylvania State University14
01 Sep 2017-Science (American Association for the Advancement of Science)-Vol. 357, Iss: 6354, pp 917-921
TL;DR: It is shown that daytime and nighttime leaf-to-air temperature differences are key to geographic gradients in leaf size, which can enrich “next-generation” vegetation models in which leaf temperature and water use during photosynthesis play key roles.
Abstract: Leaf size varies by over a 100,000-fold among species worldwide. Although 19th-century plant geographers noted that the wet tropics harbor plants with exceptionally large leaves, the latitudinal gradient of leaf size has not been well quantified nor the key climatic drivers convincingly identified. Here, we characterize worldwide patterns in leaf size. Large-leaved species predominate in wet, hot, sunny environments; small-leaved species typify hot, sunny environments only in arid conditions; small leaves are also found in high latitudes and elevations. By modeling the balance of leaf energy inputs and outputs, we show that daytime and nighttime leaf-to-air temperature differences are key to geographic gradients in leaf size. This knowledge can enrich “next-generation” vegetation models in which leaf temperature and water use during photosynthesis play key roles.
Citations
More filters
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
Aarhus University1, University of Edinburgh2, Institute of Arctic and Alpine Research3, National Ecological Observatory Network4, University of Colorado Boulder5, Smithsonian Institution6, Lund University7, VU University Amsterdam8, University of Lapland9, Northern Arizona University10, Bigelow Laboratory For Ocean Sciences11, University of British Columbia12, University of Washington13, Grand Valley State University14, Swiss Federal Institute for Forest, Snow and Landscape Research15, Max Planck Society16, University of Zurich17, Université de Sherbrooke18, University of Greifswald19, University of Parma20, Memorial University of Newfoundland21, Université du Québec à Trois-Rivières22, University of Gothenburg23, Leiden University24, University of California, Riverside25, Qatar University26, Mississippi State University27, University of Barcelona28, Utrecht University29, Umeå University30, Adam Mickiewicz University in Poznań31, University of Alaska Anchorage32, Wageningen University and Research Centre33, Alaska Department of Fish and Game34, University of Tromsø35, University of Vienna36, University of Copenhagen37, University of Oulu38, Helmholtz Centre for Environmental Research - UFZ39, Spanish National Research Council40, Queen's University41, Saint Mary's University42, Oak Ridge National Laboratory43, University of Aberdeen44, University of Saskatchewan45, Vrije Universiteit Brussel46, University of Victoria47, Swiss Federal Institute of Aquatic Science and Technology48, Norwegian University of Science and Technology49, Research Institute for Nature and Forest50, Florida International University51, Moscow State University52, University of Alaska Fairbanks53, University of Waterloo54, Laval University55, Deakin University56, University of Bonn57, United States Forest Service58, Simon Fraser University59, University of Iceland60, University Centre in Svalbard61, United States Fish and Wildlife Service62, Colorado State University63, University of Texas at El Paso64, University of Stirling65, University of Innsbruck66, Rocky Mountain Biological Laboratory67, University of Oxford68, Pacific Northwest National Laboratory69, University of Camerino70, University of Insubria71, University of New South Wales72, University of Manchester73, National University of Cordoba74, Santa Fe Institute75, University of Arizona76, Harvard University77, King Juan Carlos University78, Estonian University of Life Sciences79, Kyoto University80, World Agroforestry Centre81, Radboud University Nijmegen82, Macquarie University83, Forschungszentrum Jülich84, University of Regensburg85, University of Minnesota86, University of Sydney87, Santa Clara University88, Algoma University89, Komarov Botanical Institute90, University of Wisconsin–Eau Claire91
TL;DR: Biome-wide relationships between temperature, moisture and seven key plant functional traits across the tundra and over time show that community height increased with warming across all sites, whereas other traits lagged behind predicted rates of change.
Abstract: The tundra is warming more rapidly than any other biome on Earth, and the potential ramifications are far-reaching because of global feedback effects between vegetation and climate. A better understanding of how environmental factors shape plant structure and function is crucial for predicting the consequences of environmental change for ecosystem functioning. Here we explore the biome-wide relationships between temperature, moisture and seven key plant functional traits both across space and over three decades of warming at 117 tundra locations. Spatial temperature-trait relationships were generally strong but soil moisture had a marked influence on the strength and direction of these relationships, highlighting the potentially important influence of changes in water availability on future trait shifts in tundra plant communities. Community height increased with warming across all sites over the past three decades, but other traits lagged far behind predicted rates of change. Our findings highlight the challenge of using space-for-time substitution to predict the functional consequences of future warming and suggest that functions that are tied closely to plant height will experience the most rapid change. They also reveal the strength with which environmental factors shape biotic communities at the coldest extremes of the planet and will help to improve projections of functional changes in tundra ecosystems with climate warming.
425 citations
Martin Luther University of Halle-Wittenberg1, Zürcher Fachhochschule2, University of Bayreuth3, 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, Slovenian Academy of Sciences and Arts27, University of Nova Gorica28, King Juan Carlos University29, Czech University of Life Sciences Prague30, VU University Amsterdam31, University of Würzburg32, National University of Cordoba33, National University of Saint Anthony the Abbot in Cuzco34, Wake Forest University35, 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 Wyoming50, University of Waikato51, 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 Sydney62, University of Minnesota63, 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, American Museum of Natural History72, International Institute of Minnesota73, 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
TL;DR: A natural leaf assembled triboelectric nanogenerator (Leaf‐TENG) is designed by utilizing the green leaf as an electrification layer and electrode to effectively harvest environmental mechanical energy.
207 citations
Helge Bruelheide1, Jürgen Dengler2, Jürgen Dengler3, Borja Jiménez-Alfaro4 +181 more•Institutions (100)
TL;DR: The sPlot database as mentioned in this paper contains 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015.
Abstract: Aims :Vegetation-plot records provide information on the presence and cover or abundance of plants co-occurring in the same community. Vegetation-plot data are spread across research groups, environmental agencies and biodiversity research centers and, thus, are rarely accessible at continental or global scales. Here we present the sPlot database, which collates vegetation plots worldwide to allow for the exploration of global patterns in taxonomic, functional and phylogenetic diversity at the plant community level. Results: sPlot version 2.1 contains records from 1,121,244 vegetation plots, which comprise 23,586,216 records of plant species and their relative cover or abundance in plots collected worldwide between 1885 and 2015. We complemented the information for each plot by retrieving climate and soil conditions and the biogeographic context (e.g., biomes) from external sources, and by calculating community-weighted means and variances of traits using gap-filled data from the global plant trait database TRY. Moreover, we created a phylogenetic tree for 50,167 out of the 54,519 species identified in the plots. We present the first maps of global patterns of community richness and community-weighted means of key traits. Conclusions: The availability of vegetation plot data in sPlot offers new avenues for vegetation analysis at the global scale.
160 citations
Cites methods from "Global climatic drivers of leaf siz..."
...Nevertheless, only the gridbased approach has been used to date in studies of the geographic distribution of trait values (e.g., Swenson et al., 2012, 2017; Wright et al., 2017)....
[...]
References
More filters
Book•
01 Jan 1998
TL;DR: In this paper, an updated procedure for calculating reference and crop evapotranspiration from meteorological data and crop coefficients is presented, based on the FAO Penman-Monteith method.
Abstract: (First edition: 1998, this reprint: 2004). This publication presents an updated procedure for calculating reference and crop evapotranspiration from meteorological data and crop coefficients. The procedure, first presented in FAO Irrigation and Drainage Paper No. 24, Crop water requirements, in 1977, allows estimation of the amount of water used by a crop, taking into account the effect of the climate and the crop characteristics. The publication incorporates advances in research and more accurate procedures for determining crop water use as recommended by a panel of high-level experts organised by FAO in May 1990. The first part of the guidelines includes procedures for determining reference crop evapotranspiration according to the FAO Penman-Monteith method. These are followed by updated procedures for estimating the evapotranspiration of different crops for different growth stages and ecological conditions.
21,958 citations
TL;DR: In this paper, the authors developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution).
Abstract: We developed interpolated climate surfaces for global land areas (excluding Antarctica) at a spatial resolution of 30 arc s (often referred to as 1-km spatial resolution). The climate elements considered were monthly precipitation and mean, minimum, and maximum temperature. Input data were gathered from a variety of sources and, where possible, were restricted to records from the 1950–2000 period. We used the thin-plate smoothing spline algorithm implemented in the ANUSPLIN package for interpolation, using latitude, longitude, and elevation as independent variables. We quantified uncertainty arising from the input data and the interpolation by mapping weather station density, elevation bias in the weather stations, and elevation variation within grid cells and through data partitioning and cross validation. Elevation bias tended to be negative (stations lower than expected) at high latitudes but positive in the tropics. Uncertainty is highest in mountainous and in poorly sampled areas. Data partitioning showed high uncertainty of the surfaces on isolated islands, e.g. in the Pacific. Aggregating the elevation and climate data to 10 arc min resolution showed an enormous variation within grid cells, illustrating the value of high-resolution surfaces. A comparison with an existing data set at 10 arc min resolution showed overall agreement, but with significant variation in some regions. A comparison with two high-resolution data sets for the United States also identified areas with large local differences, particularly in mountainous areas. Compared to previous global climatologies, ours has the following advantages: the data are at a higher spatial resolution (400 times greater or more); more weather station records were used; improved elevation data were used; and more information about spatial patterns of uncertainty in the data is available. Owing to the overall low density of available climate stations, our surfaces do not capture of all variation that may occur at a resolution of 1 km, particularly of precipitation in mountainous areas. In future work, such variation might be captured through knowledgebased methods and inclusion of additional co-variates, particularly layers obtained through remote sensing. Copyright 2005 Royal Meteorological Society.
17,977 citations
Macquarie University1, University of Minnesota2, Stanford University3, Simón Bolívar University4, Wageningen University and Research Centre5, Smithsonian Environmental Research Center6, University of Alaska Fairbanks7, VU University Amsterdam8, University of Zurich9, Centre national de la recherche scientifique10, Curtin University11, Tohoku University12, University of Wisconsin–Eau Claire13, Landcare Research14, University of Concepción15, University of Cape Town16, University of Tartu17, Polish Academy of Sciences18, University of Tokyo19, Utrecht University20, University of Western Australia21, Charles Darwin University22, Ural State University23, University of Toronto24, Texas A&M University25, University of Córdoba (Spain)26
TL;DR: Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.
Abstract: Bringing together leaf trait data spanning 2,548 species and 175 sites we describe, for the first time at global scale, a universal spectrum of leaf economics consisting of key chemical, structural and physiological properties. The spectrum runs from quick to slow return on investments of nutrients and dry mass in leaves, and operates largely independently of growth form, plant functional type or biome. Categories along the spectrum would, in general, describe leaf economic variation at the global scale better than plant functional types, because functional types overlap substantially in their leaf traits. Overall, modulation of leaf traits and trait relationships by climate is surprisingly modest, although some striking and significant patterns can be seen. Reliable quantification of the leaf economics spectrum and its interaction with climate will prove valuable for modelling nutrient fluxes and vegetation boundaries under changing land-use and climate.
6,360 citations
TL;DR: In this article, the large-scale parameterization of the surface fluxes of sensible and latent heat is properly expressed in terms of energetic considerations over land while formulas of the bulk aerodynamic type are most suitahle over the sea.
Abstract: In an introductory review it is reemphasized that the large-scale parameterization of the surface fluxes of sensible and latent heat is properly expressed in terms of energetic considerations over land while formulas of the bulk aerodynamic type are most suitahle over the sea. A general framework is suggested. Data from a number of saturated land sites and open water sites in the absence of advection suggest a widely applicable formula for the relationship between sensible and latent heat fluxes. For drying land surfaces, we assume that the evaporation rate is given by the same formula for evaporation multiplied by a factor. This factor is found to remain at unity while an amount of water, varying from one site to another, is evaporated. Following this a linear decrease sets in, reducing the evaporation rate to zero after a further 5 cm of evaporation, the same at several sites examined.
5,918 citations
TL;DR: In this paper, an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas is presented.
Abstract: This paper describes the construction of an updated gridded climate dataset (referred to as CRU TS3.10) from monthly observations at meteorological stations across the world's land areas. Station anomalies (from 1961 to 1990 means) were interpolated into 0.5° latitude/longitude grid cells covering the global land surface (excluding Antarctica), and combined with an existing climatology to obtain absolute monthly values. The dataset includes six mostly independent climate variables (mean temperature, diurnal temperature range, precipitation, wet-day frequency, vapour pressure and cloud cover). Maximum and minimum temperatures have been arithmetically derived from these. Secondary variables (frost day frequency and potential evapotranspiration) have been estimated from the six primary variables using well-known formulae. Time series for hemispheric averages and 20 large sub-continental scale regions were calculated (for mean, maximum and minimum temperature and precipitation totals) and compared to a number of similar gridded products. The new dataset compares very favourably, with the major deviations mostly in regions and/or time periods with sparser observational data. CRU TS3.10 includes diagnostics associated with each interpolated value that indicates the number of stations used in the interpolation, allowing determination of the reliability of values in an objective way. This gridded product will be publicly available, including the input station series (http://www.cru.uea.ac.uk/ and http://badc.nerc.ac.uk/data/cru/). © 2013 Royal Meteorological Society
5,552 citations