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Near-infrared spectroscopy (NIRS) has become a popular tool for investigating phenotypic variability in plants. We developed the Shiny NIRSpredict application to get predictions of 81 Arabidopsis thaliana phenotypic traits, including classical functional traits as well as a large variety of commonly measured chemical compounds, based from near-infrared spectroscopy values based on deep learning. It is freely accessible at the following URL: https://shiny.cefe.cnrs.fr/NirsPredict/ . NIRSpredict has three main functionalities. First, it allows users to submit their spectrum values to get the predictions of plant traits from models built with the hosted A. thaliana database. Second, users have access to the database of traits used for model calibration. Data can be filtered and extracted on user's choice and visualized in a global context. Third, a user can submit his own dataset to extend the database and get part of the application development. NIRSpredict provides an easy-to-use and efficient method for trait prediction and an access to a large dataset of A. thaliana trait values. In addition to covering many of functional traits it also allows to predict a large variety of commonly measured chemical compounds. As a reliable way of characterizing plant populations across geographical ranges, NIRSpredict can facilitate the adoption of phenomics in functional and evolutionary ecology., Competing Interests: Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Understanding trait-trait coordination is essential for successful plant breeding and crop modeling. Notably, plant size drives variation in morphological, physiological, and performance-related traits, as described by allometric laws in ecology. Yet, as allometric relationships have been limitedly studied in crops, how they influence and possibly limit crop performance remains unknown. Here, we review how an allometry perspective on crops gains insights into the phenotypic evolution during crop domestication, the breeding of varieties adapted to novel conditions, and the prediction of crop yields. As allometry is an active field of research, modeling and manipulating crop allometric relationships can help to develop more resilient and productive agricultural systems to face future challenges., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.)

Relationships between crop genetic and functional diversity are key to addressing contemporary agricultural challenges. Yet, there are few approaches for quantifying the relationship between genetic diversity and crop functional trait expression. Here, we introduce 'functional space accumulation curves' to analyze how trait space increases with the number of crop genotypes within a species. We explore the potential for functional space accumulating curves to quantify genotype-trait space relationships in four common annual crop species: barley (Hordeum vulgare), rice (Oryza sativa), soybean (Glycine max), and durum wheat (Triticum durum). We also employ these curves to describe genotype-trait space relationships in the wild annual Arabidopsis thaliana, which has not been subjected to artificial selection. All five species exhibited asymptotic functional space accumulation curves, suggesting a limit to intraspecific functional crop diversity, likely due to: dominant phenotypes represented by several genotypes; or functional redundancy that might exist among genotypes. Our findings indicate that there is a diminishing return of functional diversity with increasing number of genotypes. Our analysis demonstrates the efficacy of functional space accumulation curves in quantifying trait space occupancy of crops, with implications for managing crop diversity in agroecosystems, and genetic diversity in crop breeding programs., (© 2024 The Author(s). New Phytologist © 2024 New Phytologist Foundation.)

In natural systems, different plant species have been shown to modulate specific nitrogen (N) cycling processes so as to meet their N demand, thereby potentially influencing their own niche. This phenomenon might go beyond plant interactions with symbiotic microorganisms and affect the much less explored plant interactions with free-living microorganisms involved in soil N cycling, such as nitrifiers and denitrifiers. Here, we investigated variability in the modulation of soil nitrifying and denitrifying enzyme activities (NEA and DEA, respectively), and their ratio (NEA : DEA), across 193 Arabidopsis thaliana accessions. We studied the genetic and environmental determinants of such plant-soil interactions, and effects on plant biomass production in the next generation. We found that NEA, DEA, and NEA : DEA varied c. 30-, 15- and 60-fold, respectively, among A. thaliana genotypes and were related to genes linked with stress response, flowering, and nitrate nutrition, as well as to soil parameters at the geographic origin of the analysed genotypes. Moreover, plant-mediated N cycling activities correlated with the aboveground biomass of next-generation plants in home vs away nonautoclaved soil, suggesting a transgenerational impact of soil biotic conditioning on plant performance. Altogether, these findings suggest that nutrient-based plant niche construction may be much more widespread than previously thought., (© 2024 The Authors. New Phytologist © 2024 New Phytologist Foundation.)

Plant diversity is shaped by trade-offs between traits related to competitive ability, propagule dispersal, and stress resistance. However, we still lack a clear understanding of how these trade-offs influence species distribution and population dynamics. In Arabidopsis thaliana, recent genetic analyses revealed a group of cosmopolitan genotypes that successfully recolonized Europe from its center after the last glaciation, excluding older (relict) lineages from the distribution except for their north and south margins. Here, we tested the hypothesis that cosmopolitans expanded due to higher colonization ability, while relicts persisted at the margins due to higher tolerance to competition and/or stress. We compared the phenotypic and genetic differentiation between 71 European genotypes originating from the center, and the south and north margins. We showed that a trade-off between plant fecundity and seed mass shapes the differentiation of A. thaliana in Europe, suggesting that the success of the cosmopolitan groups could be explained by their high dispersal ability. However, at both north and south margins, we found evidence of selection for alleles conferring low dispersal but highly competitive and stress-resistance abilities. This study sheds light on the role of ecological trade-offs as evolutionary drivers of the distribution and dynamics of plant populations., (© 2024. The Author(s).)

Soil calcium carbonate (CaCO 3 ) impacts plant mineral nutrition far beyond Fe metabolism, imposing constraints for crop growth and quality in calcareous agrosystems. Our knowledge on plant strategies to tolerate CaCO 3 effects mainly refers to Fe acquisition. This review provides an update on plant cellular and molecular mechanisms recently described to counteract the negative effects of CaCO 3 in soils, as well as recent efforts to identify genetic bases involved in CaCO 3 tolerance from natural populations, that could be exploited to breed CaCO 3 -tolerant crops. Finally, we review the impact of environmental factors (soil water content, air CO 2 , and temperature) affecting soil CaCO 3 equilibrium and plant tolerance to calcareous soils, and we propose strategies for improvement in the context of climate change., Competing Interests: Declaration of interests The authors have no interests to declare., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Quantitative analyses and models are required to connect a plant's cellular organisation with its metabolism. However, quantitative data are often scattered over multiple studies, and finding such data and converting them into useful information is time-consuming. Consequently, there is a need to centralise the available data and to highlight the remaining knowledge gaps. Here, we present a step-by-step approach to manually extract quantitative data from various information sources, and to unify the data format. First, data from Arabidopsis leaf were collated, checked for consistency and correctness and curated by cross-checking sources. Second, quantitative data were combined by applying calculation rules. They were then integrated into a unique comprehensive, referenced, modifiable and reusable data compendium representing an Arabidopsis reference leaf. This atlas contains the metrics of the 15 cell types found in leaves at the cellular and subcellular levels., Competing Interests: The authors declare they have no competing interests., (© The Author(s) 2024.)