Charles A. Price

Opportunities for improving phosphorus‐use efficiency in crop plants

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.

A general integrative model for scaling plant growth, carbon flux, and functional trait spectra

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.

GiA Roots: software for the high throughput analysis of plant root system architecture

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.

A general model for allometric covariation in botanical form and function.

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.

Testing the metabolic theory of ecology

The metabolic theory of ecology (MTE) predicts the effects of body size and temperature on metabolism through considerations of vascular distribution networks and biochemical kinetics. MTE has also been extended to characterise processes from cellular to global levels. MTE has generated both enthusiasm and controversy across a broad range of research areas. However, most efforts that claim to validate or invalidate MTE have focused on testing predictions. We argue that critical evaluation of MTE also requires strong tests of both its theoretical foundations and simplifying assumptions. To this end, we synthesise available information and find that MTEs original derivations require additional assumptions to obtain the full scope of attendant predictions. Moreover, although some of MTEs simplifying assumptions are well supported by data, others are inconsistent with empirical tests and even more remain untested. Further, although many predictions are empirically supported on average, work remains to explain the often large variability in data. We suggest that greater effort be focused on evaluating MTEs underlying theory and simplifying assumptions to help delineate the scope of MTE, generate new theory and shed light on fundamental aspects of biological form and function.

Biological scaling: Does the exception prove the rule?

Arising from: P. B. Reich, M. G. Tjoelker, J.-L. Machado & J. Oleksyn 439, 457–461 (2006)10.1038/nature04282; Reich et al. reply, Hedin replyReich et al. report that the whole-plant respiration rate, R, in seedlings scales linearly with plant mass, M, so that when θ ≈ 1, in which cR is the scaling normalization and θ is the scaling exponent. They also state that because nitrogen concentration (N) is correlated with cR, variation in N is a better predictor of R than M would be. Reich et al. and Hedin incorrectly claim that these “universal” findings question the central tenet of metabolic scaling theory, which they interpret as predicting θ = ¾, irrespective of the size of the plant. Here we show that these conclusions misrepresent metabolic scaling theory and that their results are actually consistent with this theory.

Scaling mass and morphology in leaves: an extension of the WBE model.

Recent advances in metabolic scaling theory have highlighted the importance of exchange surfaces and vascular network geometry in understanding the integration and scaling of whole-plant form and function. Additional work on leaf form and function has also highlighted general scaling relationships for many leaf traits. However, it is unclear if a common theoretical framework can reveal the general rules underlying much of the variation observed in scaling relationships at the whole-plant and leaf level. Here we present an extension of the general model introduced by G. B. West, J. H. Brown, and B. J. Enquist that has previously been applied to scaling phenomena for whole plants to predict scaling relationships in leaves. Specifically, the model shows how the exponents that describe the scaling of leaf surface area, length, and petiole diameter should change with increasing leaf mass (or with one another) and with variation in leaf dimensionality. The predictions of the model are tested and found to be in general agreement with a large data set of leaves collected from both temperate and arid sites. Our results demonstrate that a general model based on the scaling properties of biological distribution networks can also be successfully applied to understand the diversity of leaf form and function.

Leaf Extraction and Analysis Framework Graphical User Interface: Segmenting and Analyzing the Structure of Leaf Veins and Areoles

Interest in the structure and function of physical biological networks has spurred the development of a number of theoretical models that predict optimal network structures across a broad array of taxonomic groups, from mammals to plants. In many cases, direct tests of predicted network structure are impossible given the lack of suitable empirical methods to quantify physical network geometry with sufficient scope and resolution. There is a long history of empirical methods to quantify the network structure of plants, from roots, to xylem networks in shoots and within leaves. However, with few exceptions, current methods emphasize the analysis of portions of, rather than entire networks. Here, we introduce the Leaf Extraction and Analysis Framework Graphical User Interface (LEAF GUI), a user-assisted software tool that facilitates improved empirical understanding of leaf network structure. LEAF GUI takes images of leaves where veins have been enhanced relative to the background, and following a series of interactive thresholding and cleaning steps, returns a suite of statistics and information on the structure of leaf venation networks and areoles. Metrics include the dimensions, position, and connectivity of all network veins, and the dimensions, shape, and position of the areoles they surround. Available for free download, the LEAF GUI software promises to facilitate improved understanding of the adaptive and ecological significance of leaf vein network structure.

The metabolic theory of ecology: prospects and challenges for plant biology

The metabolic theory of ecology (MTE) as applied to the plant sciences aims to provide a general synthesis for the structure and functioning of plants from organelles to ecosystems. MTE builds from simple assumptions of individual metabolism to make predictions about phenomena across a wide range of scales, from individual plant structure and function to community dynamics and global nutrient cycles. The scope of its predictions include morphological allometry, biomass partitioning, vascular network design, and life history phenomena at the individual level; size-frequency distributions, population growth rates, and energetic equivalence at the community level; and the flux, turnover and storage of nutrients at the ecosystem level. Here, we provide an overview of MTE, by considering its assumptions and predictions at these different levels of organization and explaining how the model integrates phenomena among all of these scales. We highlight the models many successes in predicting novel patterns and draw attention to areas in which gaps remain between observations and MTEs assumptions and predictions. Considering the theory as a whole, we argue that MTE has made a significant contribution in furthering our understanding of those unifying aspects of the structure and function of plants, populations, communities, and ecosystems.

Evaluating scaling models in biology using hierarchical Bayesian approaches

Theoretical models for allometric relationships between organismal form and function are typically tested by comparing a single predicted relationship with empirical data. Several prominent models, however, predict more than one allometric relationship, and comparisons among alternative models have not taken this into account. Here we evaluate several different scaling models of plant morphology within a hierarchical Bayesian framework that simultaneously fits multiple scaling relationships to three large allometric datasets. The scaling models include: inflexible universal models derived from biophysical assumptions (e.g. elastic similarity or fractal networks), a flexible variation of a fractal network model, and a highly flexible model constrained only by basic algebraic relationships. We demonstrate that variation in intraspecific allometric scaling exponents is inconsistent with the universal models, and that more flexible approaches that allow for biological variability at the species level outperform universal models, even when accounting for relative increases in model complexity.