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It has long been anticipated that relating functional traits to species demography would be a cornerstone for achieving large-scale predictability of ecological systems. If such a relationship existed, species demography could be modeled only by measuring functional traits, transforming our ability to predict states and dynamics of species-rich communities with process-based community models. Here, we introduce a new method that links empirical functional traits with the demographic parameters of a process-based model by calibrating a transfer function through inverse modeling. As a case study, we parameterize a modified Lotka-Volterra model of a high-diversity mountain grassland with static plant community and functional trait data only. The calibrated trait-demography relationships are amenable to ecological interpretation, and lead to species abundances that fit well to the observed community structure. We conclude that our new method offers a general solution to bridge the divide between trait data and process-based models in species-rich ecosystems.
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http://dx.doi.org/10.1038/s41467-021-22630-1 | DOI Listing |
J Plant Physiol
September 2025
Department of Agricultural Sciences, University of Naples Federico II, Portici, Italy.
Weeds are one of the major constraints for wheat productivity, causing significant yield losses worldwide. While chemical control is the most used practice to overcome weed damage, its efficacy is challenged by increasing weed resistance to most used herbicides, which is an expanding phenomenon caused by herbicide overuse/misuse. Modern wheat varieties are less able to perceive the presence of weeds than old varieties and are therefore less competitive against them and require chemical control to ensure adequate yields.
View Article and Find Full Text PDFPlant J
September 2025
Rice Research Institute of Shenyang Agricultural University, Shenyang, 110 866, China.
Grain size is a crucial determinant of rice yield, yet the molecular mechanisms controlling this trait remain only partially understood. Here, we identified the JMJ720 locus as a key regulator of grain size through map-based cloning. The jmj720 mutant was found to exhibit significantly larger grains when compared to the wild type (WT).
View Article and Find Full Text PDFTree Physiol
September 2025
Linze Inland River Basin Research Station, State Key Laboratory of Ecological Safety and Sustainable Development in Arid Lands, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China.
Leaves constitute a vital bottleneck in whole-plant water transport, and their water strategies are key determinants of plant competition and productivity. Nonetheless, our knowledge of leaf water strategies predominantly stems from single perspectives (i.e.
View Article and Find Full Text PDFNew Phytol
September 2025
Horticulture and Product Physiology, Department of Plant Sciences, Wageningen University & Research, Wageningen, 6708 PB, the Netherlands.
Thermal imaging is a key plant phenotyping and monitoring technique but faces major bottlenecks in accurately and efficiently inferring stomatal conductance (g) from leaf temperature. The conductance index (I) was previously proposed to estimate g from thermography by linking temperature differences between real and artificial leaves (ALs) based on the leaf energy balance. However, I is highly sensitive to environmental fluctuations, hampering interpretation and reducing reproducibility.
View Article and Find Full Text PDFNew Phytol
September 2025
Systems Biology and Mathematical Modeling Group, Max Planck Institute of Molecular Plant Physiology, 14476, Potsdam, Germany.
Comparative molecular and physiological analyses of organisms from one taxonomic group grown under similar conditions offer a strategy to identify gene targets for trait improvement. While this strategy can also be performed in silico using genome-scale metabolic models for the compared organisms, we continue to lack solutions for the de novo generation of such models, particularly for eukaryotes. To facilitate model-driven identification of gene targets for growth improvement in green algae, here we present a semiautomated platform for de novo generation of genome-scale algal metabolic models.
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