Pitfalls and potential of high-throughput plant phenotyping platforms.

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

Plants cope with fluctuating light by frequency-dependent nonphotochemical quenching and cyclic electron transport.

In natural environments, plants are exposed to rapidly changing light. Maintaining photosynthetic efficiency while avoiding photodamage requires equally rapid regulation of photoprotective mechanisms. We asked what the operation frequency range of regulation is in which plants can efficiently respond to varying light. Chlorophyll fluorescence, P700, plastocyanin, and ferredoxin responses of wild-types Arabidopsis thaliana were measured in oscillating light of various frequencies. We also investigated the npq1 mutant lacking violaxanthin de-epoxidase, the npq4 mutant lacking PsbS protein, and the mutants crr2-2, and pgrl1ab impaired in different pathways of the cyclic electron transport. The fastest was the PsbS-regulation responding to oscillation periods longer than 10 s. Processes involving violaxanthin de-epoxidase dampened changes in chlorophyll fluorescence in oscillation periods of 2 min or longer. Knocking out the PGR5/PGRL1 pathway strongly reduced variations of all monitored parameters, probably due to congestion in the electron transport. Incapacitating the NDH-like pathway only slightly changed the photosynthetic dynamics. Our observations are consistent with the hypothesis that nonphotochemical quenching in slow light oscillations involves violaxanthin de-epoxidase to produce, presumably, a largely stationary level of zeaxanthin. We interpret the observed dynamics of photosystem I components as being formed in slow light oscillations partially by thylakoid remodeling that modulates the redox rates.

Deep-water uptake under drought improved due to locally increased root conductivity in maize, but not in faba bean.

Moderate soil drying can cause a strong decrease in the soil-root system conductance. The resulting impact on root water uptake depends on the spatial distribution of the altered conductance relatively to remaining soil water resources, which is largely unknown. Here, we analyzed the vertical distribution of conductance across root systems using a novel, noninvasive sensor technology on pot-grown faba bean and maize plants. Withholding water for 4 days strongly enhanced the vertical gradient in soil water potential. Therefore, roots in upper and deeper soil layers were affected differently: In drier, upper layers, root conductance decreased by 66%-72%, causing an amplification of the drop in leaf water potential. In wetter, deeper layers, root conductance increased in maize but not in faba bean. The consequently facilitated deep-water uptake in maize contributed up to 21% of total water uptake at the end of the measurement. Analysis of root length distributions with MRI indicated that the locally increased conductance was mainly caused by an increased intrinsic conductivity and not by additional root growth. Our findings show that plants can partly compensate for a reduced root conductance in upper, drier soil layers by locally increasing root conductivity in wetter layers, thereby improving deep-water uptake.

Joint optimization of land carbon uptake and albedo can help achieve moderate instantaneous and long-term cooling effects.

Both carbon dioxide uptake and albedo of the land surface affect global climate. However, climate change mitigation by increasing carbon uptake can cause a warming trade-off by decreasing albedo, with most research focusing on afforestation and its interaction with snow. Here, we present carbon uptake and albedo observations from 176 globally distributed flux stations. We demonstrate a gradual decline in maximum achievable annual albedo as carbon uptake increases, even within subgroups of non-forest and snow-free ecosystems. Based on a paired-site permutation approach, we quantify the likely impact of land use on carbon uptake and albedo. Shifting to the maximum attainable carbon uptake at each site would likely cause moderate net global warming for the first approximately 20 years, followed by a strong cooling effect. A balanced policy co-optimizing carbon uptake and albedo is possible that avoids warming on any timescale, but results in a weaker long-term cooling effect.

Field phenotyping of ten wheat cultivars under elevated CO2 shows seasonal differences in chlorophyll fluorescence, plant height and vegetation indices.

In the context of climate change and global sustainable development goals, future wheat cultivation has to master various challenges at a time, including the rising atmospheric carbon dioxide concentration ([CO2]). To investigate growth and photosynthesis dynamics under the effects of ambient (~434 ppm) and elevated [CO2] (~622 ppm), a Free-Air CO2 Enrichment (FACE) facility was combined with an automated phenotyping platform and an array of sensors. Ten modern winter wheat cultivars (Triticum aestivum L.) were monitored over a vegetation period using a Light-induced Fluorescence Transient (LIFT) sensor, ground-based RGB cameras and a UAV equipped with an RGB and multispectral camera. The LIFT sensor enabled a fast quantification of the photosynthetic performance by measuring the operating efficiency of Photosystem II (Fq'/Fm') and the kinetics of electron transport, i.e. the reoxidation rates Fr1' and Fr2'. Our results suggest that elevated [CO2] significantly increased Fq'/Fm' and plant height during the vegetative growth phase. As the plants transitioned to the senescence phase, a pronounced decline in Fq'/Fm' was observed under elevated [CO2]. This was also reflected in the reoxidation rates Fr1' and Fr2'. A large majority of the cultivars showed a decrease in the harvest index, suggesting a different resource allocation and indicating a potential plateau in yield progression under e[CO2]. Our results indicate that the rise in atmospheric [CO2] has significant effects on the cultivation of winter wheat with strong manifestation during early and late growth.

Sugarcane bagasse ash as fertilizer for soybeans: Effects of added residues on ash composition, mineralogy, phosphorus extractability and plant availability.

Sugarcane bagasse is commonly combusted to generate energy. Unfortunately, recycling strategies rarely consider the resulting ash as a potential fertilizer. To evaluate this recycling strategy for a sustainable circular economy, we characterized bagasse ash as a fertilizer and measured the effects of co-gasification and co-combustion of bagasse with either chicken manure or sewage sludge: on the phosphorus (P) mass fraction, P-extractability, and mineral P phases. Furthermore, we investigated the ashes as fertilizer for soybeans under greenhouse conditions. All methods in combination are reliable indicators helping to assess and predict P availability from ashes to soybeans. The fertilizer efficiency of pure bagasse ash increased with the ash amount supplied to the substrate. Nevertheless, it was not as effective as fertilization with triple-superphosphate and K2SO4, which we attributed to lower P availability. Co-gasification and co-combustion increased the P mass fraction in all bagasse-based ashes, but its extractability and availability to soybeans increased only when co-processed with chicken manure, because it enabled the formation of readily available Ca-alkali phosphates. Therefore, we recommend co-combusting biomass with alkali-rich residues to increase the availability of P from the ash to plants.

The global spectrum of plant form and function: enhanced species-level trait dataset.

Here we provide the 'Global Spectrum of Plant Form and Function Dataset', containing species mean values for six vascular plant traits. Together, these traits -plant height, stem specific density, leaf area, leaf mass per area, leaf nitrogen content per dry mass, and diaspore (seed or spore) mass - define the primary axes of variation in plant form and function. The dataset is based on ca. 1 million trait records received via the TRY database (representing ca. 2,500 original publications) and additional unpublished data. It provides 92,159 species mean values for the six traits, covering 46,047 species. The data are complemented by higher-level taxonomic classification and six categorical traits (woodiness, growth form, succulence, adaptation to terrestrial or aquatic habitats, nutrition type and leaf type). Data quality management is based on a probabilistic approach combined with comprehensive validation against expert knowledge and external information. Intense data acquisition and thorough quality control produced the largest and, to our knowledge, most accurate compilation of empirically observed vascular plant species mean traits to date.

Stomatal conductance tracks soil-to-leaf hydraulic conductance in faba bean and maize during soil drying.

Although regulation of stomatal conductance is widely assumed to be the most important plant response to soil drying, the picture is incomplete when hydraulic conductance from soil to the leaf, upstream of the stomata, is not considered. Here, we investigated to what extent soil drying reduces the conductance between soil and leaf, whether this reduction differs between species, how it affects stomatal regulation, and where in the hydraulic pathway it occurs. To this end, we noninvasively and continuously measured the total root water uptake rate, soil water potential, leaf water potential, and stomatal conductance of 4-week-old, pot-grown maize (Zea mays) and faba bean (Vicia faba) plants during 4 days of water restriction. In both species, the soil-plant conductance, excluding stomatal conductance, declined exponentially with soil drying and was reduced to 50% above a soil water potential of -0.1 MPa, which is far from the permanent wilting point. This loss of conductance has immediate consequences for leaf water potential and the associated stomatal regulation. Both stomatal conductance and soil-plant conductance declined at a higher rate in faba bean than in maize. Estimations of the water potential at the root surface and an incomplete recovery 22 h after rewatering indicate that the loss of conductance, at least partly, occurred inside the plants, for example, through root suberization or altered aquaporin gene expression. Our findings suggest that differences in the stomatal sensitivity among plant species are partly explained by the sensitivity of root hydraulic conductance to soil drying.

A meta-analysis of responses of C3 plants to atmospheric CO2 : dose-response curves for 85 traits ranging from the molecular to the whole-plant level.

Generalised dose-response curves are essential to understand how plants acclimate to atmospheric CO2 . We carried out a meta-analysis of 630 experiments in which C3 plants were experimentally grown at different [CO2 ] under relatively benign conditions, and derived dose-response curves for 85 phenotypic traits. These curves were characterised by form, plasticity, consistency and reliability. Considered over a range of 200-1200 µmol mol-1 CO2 , some traits more than doubled (e.g. area-based photosynthesis; intrinsic water-use efficiency), whereas others more than halved (area-based transpiration). At current atmospheric [CO2 ], 64% of the total stimulation in biomass over the 200-1200 µmol mol-1 range has already been realised. We also mapped the trait responses of plants to [CO2 ] against those we have quantified before for light intensity. For most traits, CO2 and light responses were of similar direction. However, some traits (such as reproductive effort) only responded to light, others (such as plant height) only to [CO2 ], and some traits (such as area-based transpiration) responded in opposite directions. This synthesis provides a comprehensive picture of plant responses to [CO2 ] at different integration levels and offers the quantitative dose-response curves that can be used to improve global change simulation models.

Leaf nitrogen from the perspective of optimal plant function.

Leaf dry mass per unit area (LMA), carboxylation capacity (V cmax) and leaf nitrogen per unit area (Narea) and mass (Nmass) are key traits for plant functional ecology and ecosystem modelling. There is however no consensus about how these traits are regulated, or how they should be modelled. Here we confirm that observed leaf nitrogen across species and sites can be estimated well from observed LMA and V cmax at 25°C (V cmax25). We then test the hypothesis that global variations of both quantities depend on climate variables in specific ways that are predicted by leaf-level optimality theory, thus allowing both Narea to be predicted as functions of the growth environment.A new global compilation of field measurements was used to quantify the empirical relationships of leaf N to V cmax25 and LMA. Relationships of observed V cmax25 and LMA to climate variables were estimated, and compared to independent theoretical predictions of these relationships. Soil effects were assessed by analysing biases in the theoretical predictions.LMA was the most important predictor of Narea (increasing) and Nmass (decreasing). About 60% of global variation across species and sites in observed Narea, and 31% in Nmass, could be explained by observed LMA and V cmax25. These traits, in turn, were quantitatively related to climate variables, with significant partial relationships similar or indistinguishable from those predicted by optimality theory. Predicted trait values explained 21% of global variation in observed site-mean V cmax25, 43% in LMA and 31% in Narea. Predicted V cmax25 was biased low on clay-rich soils but predicted LMA was biased high, with compensating effects on Narea. Narea was overpredicted on organic soils. Synthesis. Global patterns of variation in observed site-mean Narea can be explained by climate-induced variations in optimal V cmax25 and LMA. Leaf nitrogen should accordingly be modelled as a consequence (not a cause) of V cmax25 and LMA, both being optimized to the environment. Nitrogen limitation of plant growth would then be modelled principally via whole-plant carbon allocation, rather than via leaf-level traits. Further research is required to better understand and model the terrestrial nitrogen and carbon cycles and their coupling.

A starting guide to root ecology: strengthening ecological concepts and standardising root classification, sampling, processing and trait measurements.

In the context of a recent massive increase in research on plant root functions and their impact on the environment, root ecologists currently face many important challenges to keep on generating cutting-edge, meaningful and integrated knowledge. Consideration of the below-ground components in plant and ecosystem studies has been consistently called for in recent decades, but methodology is disparate and sometimes inappropriate. This handbook, based on the collective effort of a large team of experts, will improve trait comparisons across studies and integration of information across databases by providing standardised methods and controlled vocabularies. It is meant to be used not only as starting point by students and scientists who desire working on below-ground ecosystems, but also by experts for consolidating and broadening their views on multiple aspects of root ecology. Beyond the classical compilation of measurement protocols, we have synthesised recommendations from the literature to provide key background knowledge useful for: (1) defining below-ground plant entities and giving keys for their meaningful dissection, classification and naming beyond the classical fine-root vs coarse-root approach; (2) considering the specificity of root research to produce sound laboratory and field data; (3) describing typical, but overlooked steps for studying roots (e.g. root handling, cleaning and storage); and (4) gathering metadata necessary for the interpretation of results and their reuse. Most importantly, all root traits have been introduced with some degree of ecological context that will be a foundation for understanding their ecological meaning, their typical use and uncertainties, and some methodological and conceptual perspectives for future research. Considering all of this, we urge readers not to solely extract protocol recommendations for trait measurements from this work, but to take a moment to read and reflect on the extensive information contained in this broader guide to root ecology, including sections I-VII and the many introductions to each section and root trait description. Finally, it is critical to understand that a major aim of this guide is to help break down barriers between the many subdisciplines of root ecology and ecophysiology, broaden researchers' views on the multiple aspects of root study and create favourable conditions for the inception of comprehensive experiments on the role of roots in plant and ecosystem functioning.

An integrated framework of plant form and function: the belowground perspective.

Plant trait variation drives plant function, community composition and ecosystem processes. However, our current understanding of trait variation disproportionately relies on aboveground observations. Here we integrate root traits into the global framework of plant form and function. We developed and tested an overarching conceptual framework that integrates two recently identified root trait gradients with a well-established aboveground plant trait framework. We confronted our novel framework with published relationships between above- and belowground trait analogues and with multivariate analyses of above- and belowground traits of 2510 species. Our traits represent the leaf and root conservation gradients (specific leaf area, leaf and root nitrogen concentration, and root tissue density), the root collaboration gradient (root diameter and specific root length) and the plant size gradient (plant height and rooting depth). We found that an integrated, whole-plant trait space required as much as four axes. The two main axes represented the fast-slow 'conservation' gradient on which leaf and fine-root traits were well aligned, and the 'collaboration' gradient in roots. The two additional axes were separate, orthogonal plant size axes for height and rooting depth. This perspective on the multidimensional nature of plant trait variation better encompasses plant function and influence on the surrounding environment.

Root traits explain plant species distributions along climatic gradients yet challenge the nature of ecological trade-offs.

Ecological theory is built on trade-offs, where trait differences among species evolved as adaptations to different environments. Trade-offs are often assumed to be bidirectional, where opposite ends of a gradient in trait values confer advantages in different environments. However, unidirectional benefits could be widespread if extreme trait values confer advantages at one end of an environmental gradient, whereas a wide range of trait values are equally beneficial at the other end. Here, we show that root traits explain species occurrences along broad gradients of temperature and water availability, but model predictions only resembled trade-offs in two out of 24 models. Forest species with low specific root length and high root tissue density (RTD) were more likely to occur in warm climates but species with high specific root length and low RTD were more likely to occur in cold climates. Unidirectional benefits were more prevalent than trade-offs: for example, species with large-diameter roots and high RTD were more commonly associated with dry climates, but species with the opposite trait values were not associated with wet climates. Directional selection for traits consistently occurred in cold or dry climates, whereas a diversity of root trait values were equally viable in warm or wet climates. Explicit integration of unidirectional benefits into ecological theory is needed to advance our understanding of the consequences of trait variation on species responses to environmental change.

Global patterns of biomass allocation in woody species with different tolerances of shade and drought: evidence for multiple strategies.

The optimal partitioning theory predicts that plants of a given species acclimate to different environments by allocating a larger proportion of biomass to the organs acquiring the most limiting resource. Are similar patterns found across species adapted to environments with contrasting levels of abiotic stress? We tested the optimal partitioning theory by analysing how fractional biomass allocation to leaves, stems and roots differed between woody species with different tolerances of shade and drought in plants of different age and size (seedlings to mature trees) using a global dataset including 604 species. No overarching biomass allocation patterns at different tolerance values across species were found. Biomass allocation varied among functional types as a result of phenological (deciduous vs evergreen broad-leaved species) and broad phylogenetical (angiosperms vs gymnosperms) differences. Furthermore, the direction of biomass allocation responses between tolerant and intolerant species was often opposite to that predicted by the optimal partitioning theory. We conclude that plant functional type is the major determinant of biomass allocation in woody species. We propose that interactions between plant functional type, ontogeny and species-specific stress tolerance adaptations allow woody species with different shade and drought tolerances to display multiple biomass partitioning strategies.

Applying the economic concept of profitability to leaves.

Economic principles can be extended to biological organisms as they optimize the use of resources, but their use in biology has been limited. We applied concepts from traditional economics to the main production unit of plants, the leaf. We quantified the profitability (profit/cost of investment) of leaves from seven biomes worldwide and compared those to the profitability of companies. Here we demonstrate for the first time key similarities and differences between leaf and human economics. First, there was a weak, but positive relationship between profitability and size, both for leaves and companies. Second, environment has a strong effect on profitability, with high values in leaves from biomes with short growth periods and, for companies associated with innovation. Third, shorter longevity of productive units was related to higher profitability. In summary, by comparing economic behaviours of plants and humans there is potential to develop new perspectives on plant ecological strategies and plant evolution.

Root traits as drivers of plant and ecosystem functioning: current understanding, pitfalls and future research needs.

The effects of plants on the biosphere, atmosphere and geosphere are key determinants of terrestrial ecosystem functioning. However, despite substantial progress made regarding plant belowground components, we are still only beginning to explore the complex relationships between root traits and functions. Drawing on the literature in plant physiology, ecophysiology, ecology, agronomy and soil science, we reviewed 24 aspects of plant and ecosystem functioning and their relationships with a number of root system traits, including aspects of architecture, physiology, morphology, anatomy, chemistry, biomechanics and biotic interactions. Based on this assessment, we critically evaluated the current strengths and gaps in our knowledge, and identify future research challenges in the field of root ecology. Most importantly, we found that belowground traits with the broadest importance in plant and ecosystem functioning are not those most commonly measured. Also, the estimation of trait relative importance for functioning requires us to consider a more comprehensive range of functionally relevant traits from a diverse range of species, across environments and over time series. We also advocate that establishing causal hierarchical links among root traits will provide a hypothesis-based framework to identify the most parsimonious sets of traits with the strongest links on functions, and to link genotypes to plant and ecosystem functioning.

Dividing the pie: A quantitative review on plant density responses.

Plant population density is an important variable in agronomy and forestry and offers an experimental way to better understand plant-plant competition. We made a meta-analysis of responses of even-aged mono-specific stands to population density by quantifying for 3 stand and 33 individual plant variables in 334 experiments how much both plant biomass and phenotypic traits change with a doubling in density. Increasing density increases standing crop per area, but decreases the mean size of its individuals, mostly through reduced tillering and branching. Among the phenotypic traits, stem diameter is negatively affected, but plant height remains remarkably similar, partly due to an increased stem length-to-mass ratio and partly by increased allocation to stems. The reduction in biomass is caused by a lower photosynthetic rate, mainly due to shading of part of the foliage. Total seed mass per plant is also strongly reduced, marginally by lower mass per seed, but mainly because of lower seed numbers. Plants generally have fewer shoot-born roots, but their overall rooting depth seems hardly affected. The phenotypic plasticity responses to high densities correlate strongly with those to low light, and less with those to low nutrients, suggesting that at high density, shading affects plants more than nutrient depletion.

Enabling reusability of plant phenomic datasets with MIAPPE 1.1.

Enabling data reuse and knowledge discovery is increasingly critical in modern science, and requires an effort towards standardising data publication practices. This is particularly challenging in the plant phenotyping domain, due to its complexity and heterogeneity. We have produced the MIAPPE 1.1 release, which enhances the existing MIAPPE standard in coverage, to support perennial plants, in structure, through an explicit data model, and in clarity, through definitions and examples. We evaluated MIAPPE 1.1 by using it to express several heterogeneous phenotyping experiments in a range of different formats, to demonstrate its applicability and the interoperability between the various implementations. Furthermore, the extended coverage is demonstrated by the fact that one of the datasets could not have been described under MIAPPE 1.0. MIAPPE 1.1 marks a major step towards enabling plant phenotyping data reusability, thanks to its extended coverage, and especially the formalisation of its data model, which facilitates its implementation in different formats. Community feedback has been critical to this development, and will be a key part of ensuring adoption of the standard.