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Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy's ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.

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.

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.

Circadian resonance, whereby a plant's endogenous rhythms are tuned to match environmental cues, has been repeatedly shown to be adaptive, although the underlying mechanisms remain elusive. Concomitantly, the adaptive value of nocturnal transpiration in C3 plants remains unknown because it occurs without carbon assimilation. These seemingly unrelated processes are interconnected because circadian regulation drives temporal patterns in nocturnal stomatal conductance, with maximum values occurring immediately before dawn for many species. We grew individuals of six Eucalyptus camaldulensis genotypes in naturally lit glasshouses and measured sunset, predawn and midday leaf gas exchange and whole-plant biomass production. We tested whether sunrise anticipation by the circadian clock and subsequent increases in genotype predawn stomatal conductance led to rapid stomatal opening upon illumination, ultimately affecting genotype differences in carbon assimilation and growth. We observed faster stomatal responses to light inputs at sunrise in genotypes with higher predawn stomatal conductance. Moreover, early morning and midday stomatal conductance and carbon assimilation, leaf area and total plant biomass were all positively correlated with predawn stomatal conductance across genotypes. Our results lead to the novel hypothesis that genotypic variation in the circadian-regulated capacity to anticipate sunrise could be an important factor underlying intraspecific variation in tree growth.

Climate change threatens the ability of agriculture and forestry to meet growing global demands for food, fibre and wood products. Information gathered from genotype-by-environment interactions (G × E), which demonstrate intraspecific variation in phenotypic plasticity (the ability of a genotype to alter its phenotype in response to environmental change), may prove important for bolstering agricultural and forest productivity under climate change. Nonetheless, very few studies have explicitly quantified genotype plasticity-productivity relationships in agriculture or forestry. Here, we conceptualize the importance of intraspecific variation in agricultural and forest species plasticity, and discuss the physiological and genetic factors contributing to intraspecific variation in phenotypic plasticity. Our discussion highlights the need for an integrated understanding of the mechanisms of G × E, more extensive assessments of genotypic responses to climate change under field conditions, and explicit testing of genotype plasticity-productivity relationships. Ultimately, further investigation of intraspecific variation in phenotypic plasticity in agriculture and forestry may prove important for identifying genotypes capable of increasing or sustaining productivity under more extreme climatic conditions.

Climate change threatens the ability of agriculture and forestry to meet growing global demands for food, fibre and wood products. Information gathered from genotype-by-environment interactions (G × E), which demonstrate intraspecific variation in phenotypic plasticity (the ability of a genotype to alter its phenotype in response to environmental change), may prove important for bolstering agricultural and forest productivity under climate change. Nonetheless, very few studies have explicitly quantified genotype plasticity-productivity relationships in agriculture or forestry. Here, we conceptualize the importance of intraspecific variation in agricultural and forest species plasticity, and discuss the physiological and genetic factors contributing to intraspecific variation in phenotypic plasticity. Our discussion highlights the need for an integrated understanding of the mechanisms of G × E, more extensive assessments of genotypic responses to climate change under field conditions, and explicit testing of genotype plasticity-productivity relationships. Ultimately, further investigation of intraspecific variation in phenotypic plasticity in agriculture and forestry may prove important for identifying genotypes capable of increasing or sustaining productivity under more extreme climatic conditions.

C4 perennial bioenergy grasses are an economically and ecologically important group whose responses to climate change will be important to the future bioeconomy. These grasses are highly productive and frequently possess large geographic ranges and broad environmental tolerances, which may contribute to the evolution of ecotypes that differ in physiological acclimation capacity and the evolution of distinct functional strategies. C4 perennial bioenergy grasses are predicted to thrive under climate change—C4 photosynthesis likely evolved to enhance photosynthetic efficiency under stressful conditions of low [CO2], high temperature, and drought—although few studies have examined how these species will respond to combined stresses or to extremes of temperature and precipitation. Important targets for C4 perennial bioenergy production in a changing world, such as sustainability and resilience, can benefit from combining knowledge of C4 physiology with recent advances in crop improvement, especially genomic selection. [ABSTRACT FROM AUTHOR]