Contents Summary306I.The need to use phosphorus efficiently307II.P‐use efficiency and P dynamics in a growing crop307III.P pools in plants307IV.Phosphorus pools and growth rates310V.Are crops different from other plants in their P concentration?310VI.Phosphorus use and photosynthesis311VII.Crop development and canopy P distribution312VIII.Internal redistribution of P in a growing vegetative plant313IX.Allocation of P to reproductive structures314X.Constraints to P remobilisation315XI.Do physiological or phylogenetic trade‐offs constrain traits that could improve PUE?316XII.Identifying genetic loci associated with PUE316XIII.Conclusions317Acknowledgements317References317 Summary Limitation of grain crop productivity by phosphorus (P) is widespread and will probably increase in the future. Enhanced P efficiency can be achieved by improved uptake of phosphate from soil (P‐acquisition efficiency) and by improved productivity per unit P taken up (P‐use efficiency). This review focuses on improved P‐use efficiency, which can be achieved by plants that have overall lower P concentrations, and by optimal distribution and redistribution of P in the plant allowing maximum growth and biomass allocation to harvestable plant parts. Significant decreases in plant P pools may be possible, for example, through reductions of superfluous ribosomal RNA and replacement of phospholipids by sulfolipids and galactolipids. Improvements in P distribution within the plant may be possible by increased remobilization from tissues that no longer need it (e.g. senescing leaves) and reduced partitioning of P to developing grains. Such changes would prolong and enhance the productive use of P in photosynthesis and have nutritional and environmental benefits. Research considering physiological, metabolic, molecular biological, genetic and phylogenetic aspects of P‐use efficiency is urgently needed to allow significant progress to be made in our understanding of this complex trait.

AimIn recent years evidence has accumulated that plant species are differentially sorted from regional assemblages into local assemblages along local-scale environmental gradients on the basis of their function and abiotic filtering. The favourability hypothesis in biogeography proposes that in climatically difficult regions abiotic filtering should produce a regional assemblage that is less functionally diverse than that expected given the species richness and the global pool of traits. Thus it seems likely that differential filtering of plant traits along local-scale gradients may scale up to explain the distribution, diversity and filtering of plant traits in regional-scale assemblages across continents. The present work aims to address this prediction.Location North and South America. MethodsWe combine a dataset comprising over 5.5 million georeferenced plant occurrence records with several large plant functional trait databases in order to: (1) quantify how several critical traits associated with plant performance and ecology vary across environmental gradients; and (2) provide the first test of whether the woody plants found within 1°and 5°map grid cells are more or less functionally diverse than expected, given their species richness, across broad gradients. ResultsThe results show that, for many of the traits studied, the overall distribution of functional traits in tropical regions often exceeds the expectations of random sampling given the species richness. Conversely, temperate regions often had narrower functional trait distributions than their smaller species pools would suggest. Main conclusionThe results show that the overall distribution of function does increase towards the equator, but the functional diversity within regional-scale tropical assemblages is higher than that expected given their species richness. These results are consistent with the hypothesis that abiotic filtering constrains the overall distribution of function in temperate assemblages, but tropical assemblages are not as tightly constrained.

The West, Brown, and Enquist (WBE) theory for the origin of allometric scaling laws is centered on the idea that the geometry of the vascular network governs how a suite of organismal traits covary with each other and, ultimately, how they scale with organism size. This core assumption has been combined with other secondary assumptions based on physiological constraints, such as minimizing the scaling of transport and biomechanical costs while maximally filling a volume. Together, these assumptions give predictions for specific ''quarter-power'' scaling exponents in biology. Here we provide a strong test of the core assumption of WBE by examining how well it holds when the secondary assumptions have been relaxed. Our relaxed version of WBE predicts that allometric exponents are highly constrained and covary according to specific quantitative functions. To test this core prediction, we assembled several botanical data sets with measures of the allometry of morphological traits. A wide variety of plant taxa appear to obey the predictions of the model. Our results (i) underscore the importance of network geometry in governing the variability and central tendency of biological exponents, (ii) support the hypothesis that selection has primarily acted to minimize the scaling of hydrodynamic resistance, and (iii) suggest that additional selection pressures for alternative branching geometries govern much of the observed covariation in biological scaling exponents. Understanding how selection shapes hierarchical branching networks provides a general framework for understanding the origin and covariation of many allometric traits within a complex integrated phenotype.allometry ͉ fractal ͉ metabolism ͉ scaling ͉ traits S ince the pioneering work of Julian Huxley (1), questions concerning how selection influences specific traits within integrated phenotypes have been a prominent focus in comparative biology (2, 3). The phenotype is a constellation of traits that often covary with each other during ontogeny. Further, organism size is a central trait that influences how most biological structures, processes, and dynamics covary with each other (4, 5). The dependence of a given biological trait, Y, on organismal mass, M, is known as allometry. Allometric relationships are often characterized by power laws (1) of the form, Y ϭ Y 0 M , where is the scaling exponent and Y 0 is a normalization constant that may be characteristic of a given taxon. A sampling of intra-and interspecific data reveals that the central tendency of often approximates quarter-powers (4, 5) (e.g., 1/4, 3/4, 3/8, etc.), although for any given relationship considerable variability may exist (6).West, Brown, and Enquist (7-9) hypothesized that the value of for many biological allometries arises from the geometry of vascular networks and resource-exchange surfaces (e.g., cardiovascular or plant vascular systems). The core assumption of this theory is that many organismal, anatomical, and physiological traits are linked mechanistically by allometric scaling of...

BackgroundCharacterizing root system architecture (RSA) is essential to understanding the development and function of vascular plants. Identifying RSA-associated genes also represents an underexplored opportunity for crop improvement. Software tools are needed to accelerate the pace at which quantitative traits of RSA are estimated from images of root networks.ResultsWe have developed GiA Roots (General Image Analysis of Roots), a semi-automated software tool designed specifically for the high-throughput analysis of root system images. GiA Roots includes user-assisted algorithms to distinguish root from background and a fully automated pipeline that extracts dozens of root system phenotypes. Quantitative information on each phenotype, along with intermediate steps for full reproducibility, is returned to the end-user for downstream analysis. GiA Roots has a GUI front end and a command-line interface for interweaving the software into large-scale workflows. GiA Roots can also be extended to estimate novel phenotypes specified by the end-user.ConclusionsWe demonstrate the use of GiA Roots on a set of 2393 images of rice roots representing 12 genotypes from the species Oryza sativa. We validate trait measurements against prior analyses of this image set that demonstrated that RSA traits are likely heritable and associated with genotypic differences. Moreover, we demonstrate that GiA Roots is extensible and an end-user can add functionality so that GiA Roots can estimate novel RSA traits. In summary, we show that the software can function as an efficient tool as part of a workflow to move from large numbers of root images to downstream analysis.

Linking functional traits to plant growth is critical for scaling attributes of organisms to the dynamics of ecosystems and for understanding how selection shapes integrated botanical phenotypes. However, a general mechanistic theory showing how traits specifically influence carbon and biomass flux within and across plants is needed. Building on foundational work on relative growth rate, recent work on functional trait spectra, and metabolic scaling theory, here we derive a generalized trait-based model of plant growth. In agreement with a wide variety of empirical data, our model uniquely predicts how key functional traits interact to regulate variation in relative growth rate, the allometric growth normalizations for both angiosperms and gymnosperms, and the quantitative form of several functional trait spectra relationships. The model also provides a general quantitative framework to incorporate additional leaf-level trait scaling relationships and hence to unite functional trait spectra with theories of relative growth rate, and metabolic scaling. We apply the model to calculate carbon use efficiency. This often ignored trait, which may influence variation in relative growth rate, appears to vary directionally across geographic gradients. Together, our results show how both quantitative plant traits and the geometry of vascular transport networks can be merged into a common scaling theory. Our model provides a framework for predicting not only how traits covary within an integrated allometric phenotype but also how trait variation mechanistically influences plant growth and carbon flux within and across diverse ecosystems.