BAAD: A biomass and allometry database for woody plants.

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01– 100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub-sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross-section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world’s vegetation.EEA Santa CruzFil: Falster, Daniel S. Macquarie University. Biological Sciences; Australia.Fil: Duursma, Remko A. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Ishihara, Masae I. Hiroshima University. Graduate School for International Development and Cooperation; Japón.Fil: Barneche, Diego R. Macquarie University. Biological Sciences; Australia.Fil: FitzJohn, Richard G. Macquarie University. Biological Sciences; Australia.Fil: Vårhammar, Angelica. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Aiba, Masahiro. Tohoku University. Graduate School of Life Sciences; Japón.Fil: Ando, Makoto. Kyoto University. Field Science Education and Research Center; JapónFil: Anten, Niels. Centre for Crop Systems Analysis; Países BajosFil: Aspinwall, Michael J. University of Western Sydney. Hawkesbury Insitute for the Environment; Australia.Fil: Gargaglione Verónica Beatriz. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Gargaglione Verónica Beatriz. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Santa Cruz; Argentina.Fil: Peri, Pablo Luis. Universidad Nacional de la Patagonia Austral; Argentina.Fil: Peri, Pablo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina.Fil: York, Robert A. University of California Berkeley. Center for Forestry; Estados Unido

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

Trees tolerate an extreme heatwave via sustained transpirational cooling and increased leaf thermal tolerance

© 2018 John Wiley & Sons Ltd Heatwaves are likely to increase in frequency and intensity with climate change, which may impair tree function and forest C uptake. However, we have little information regarding the impact of extreme heatwaves on the physiological performance of large trees in the field. Here, we grew Eucalyptus parramattensis trees for 1 year with experimental warming (+3°C) in a field setting, until they were greater than 6 m tall. We withheld irrigation for 1 month to dry the surface soils and then implemented an extreme heatwave treatment of 4 consecutive days with air temperatures exceeding 43°C, while monitoring whole-canopy exchange of CO2 and H2O, leaf temperatures, leaf thermal tolerance, and leaf and branch hydraulic status. The heatwave reduced midday canopy photosynthesis to near zero but transpiration persisted, maintaining canopy cooling. A standard photosynthetic model was unable to capture the observed decoupling between photosynthesis and transpiration at high temperatures, suggesting that climate models may underestimate a moderating feedback of vegetation on heatwave intensity. The heatwave also triggered a rapid increase in leaf thermal tolerance, such that leaf temperatures observed during the heatwave were maintained within the thermal limits of leaf function. All responses were equivalent for trees with a prior history of ambient and warmed (+3°C) temperatures, indicating that climate warming conferred no added tolerance of heatwaves expected in the future. This coordinated physiological response utilizing latent cooling and adjustment of thermal thresholds has implications for tree tolerance of future climate extremes as well as model predictions of future heatwave intensity at landscape and global scales

Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale.

The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO2 response curves, including data from 141 C3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate

Acclimation and adaptation components of the temperature dependence of plant photosynthesis at the global scale

The temperature response of photosynthesis is one of the key factors determining predicted responses to warming in global vegetation models (GVMs). The response may vary geographically, owing to genetic adaptation to climate, and temporally, as a result of acclimation to changes in ambient temperature. Our goal was to develop a robust quantitative global model representing acclimation and adaptation of photosynthetic temperature responses. We quantified and modelled key mechanisms responsible for photosynthetic temperature acclimation and adaptation using a global dataset of photosynthetic CO 2 response curves, including data from 141 C 3 species from tropical rainforest to Arctic tundra. We separated temperature acclimation and adaptation processes by considering seasonal and common-garden datasets, respectively. The observed global variation in the temperature optimum of photosynthesis was primarily explained by biochemical limitations to photosynthesis, rather than stomatal conductance or respiration. We found acclimation to growth temperature to be a stronger driver of this variation than adaptation to temperature at climate of origin. We developed a summary model to represent photosynthetic temperature responses and showed that it predicted the observed global variation in optimal temperatures with high accuracy. This novel algorithm should enable improved prediction of the function of global ecosystems in a warming climate