Biology subjects

Earles, J. M.

Publications and source records attributed to Earles, J. M..

4 recordsLinked to original sources

bioRxiv2026

Genetic mapping and genomic prediction for agronomic, grain compositional, and sensing-enabled traits in a cowpea MAGIC population along an environmental gradient

Cowpea (Vigna unguiculata [L.] Walp.) is a resilient grain legume and an important global source of dietary protein, yet the genetic and environmental basis of phenological and canopy development, as well as grain composition, remains incompletely characterized across production environments. In this study, we evaluated a cowpea multi-parent advanced generation intercross (MAGIC) population along an environmental gradient in California (with contrasting daylengths, temperatures, and soil types) using agronomic, grain compositional, and uncrewed aerial vehicle (UAV) and rover-enabled phenotyping. Near-infrared spectroscopy (NIRS) enabled assessment of grain compositional traits, while sensing-enabled time-series imaging captured canopy and reproductive dynamics. Quantitative trait locus (QTL) mapping identified 267 QTL, and genome-wide association studies (GWAS) detected 1,973 marker-trait associations. Integrating QTL mapping and GWAS results identified two major genomic hotspots affecting multiple traits. A chromosome 9 hotspot (5.8-6.0 Mb) was associated with flowering time and co-localized with sensing-enabled measures of flower and pod counts, plant height, and vegetation fraction, indicating broad effects on phenological and canopy development. A chromosome 8 hotspot (37.3-37.9 Mb) contained co-localized signals for seed weight, protein, starch, phytate, and moisture. A total of 22 prioritized candidate genes were identified within these and other loci with multi-environment QTL and GWAS support. Genomic predictive abilities were moderate to high for most traits and scenarios, with multi-trait MegaLMM outperforming RR-BLUP. Together, these results define major genomic regions controlling cowpea phenology, canopy development, and grain composition, and provide targets and strategies for breeding cowpea cultivars with favorable and environmentally resilient productivity and grain composition. Significance StatementTo dissect the genetic basis of cowpea productivity, adaptation, and grain composition, and how performance for these traits varies and can be predicted across environments, we combined multi-environment phenotyping, including sensing of canopy and reproductive traits, with quantitative genetic analyses in a multi-parental population. We identified genomic hotspots for seed size/composition and reproductive phenology and an across-environment predictive advantage for multi-trait vs. single-trait genomic prediction. Overall, these findings support the comprehensive improvement of cowpea.

genetics↗

bioRxiv2021

The 3D construction of leaves is coordinated with water use efficiency in conifers

O_LIConifers prevail in the canopies of many terrestrial biomes, holding a great ecological and economic importance globally. Current increases in temperature and aridity are imposing high transpirational demands and resulting in conifer mortality. Therefore, identifying leaf structural determinants of water use efficiency is essential in predicting physiological impacts due to environmental variation. C_LIO_LIUsing synchrotron-generated microCT imaging, we extracted leaf volumetric anatomy and stomatal traits in 34 species across conifers with a special focus on Pinus, the richest conifer genus. C_LIO_LIWe show that intrinsic water use efficiency (WUEi) is positively driven by leaf vein volume. Needle-like leaves of Pinus, as opposed to flat leaves or flattened needles of other genera, showed lower mesophyll porosity, decreasing the relative mesophyll volume. This led to increased ratios of stomatal pore number per mesophyll or intercellular airspace volume, which emerged as powerful explanatory variables, predicting both stomatal conductance and WUEi. C_LIO_LIOur results clarify how the three-dimensional organization of tissues within the leaf has a direct impact on plant water use and carbon uptake. By identifying a suit of structural traits that influence important physiological functions, our findings can help to understand how conifers may respond to the pressures exerted by climate change. C_LI

plant biology↗

bioRxiv2020

Maximum CO2 diffusion inside leaves is limited by the scaling of cell size and genome size

Maintaining high rates of photosynthesis in leaves requires efficient movement of CO2 from the atmosphere to the chloroplasts inside the leaf where it is converted into sugar. Throughout the evolution of vascular plants, CO2 diffusion across the leaf surface was maximized by reducing the sizes of the guard cells that form stomatal pores in the leaf epidermis1,2. Once inside the leaf, CO2 must diffuse through the intercellular airspace and into the mesophyll cells where photosynthesis occurs3,4. However, the diffusive interface defined by the mesophyll cells and the airspace and its coordinated evolution with other leaf traits are not well described5. Here we show that among vascular plants variation in the total amount of mesophyll surface area per unit mesophyll volume is driven primarily by cell size, the lower limit of which is defined by genome size. The higher surface area enabled by smaller cells allows for more efficient CO2 diffusion into photosynthetic mesophyll cells. Our results demonstrate that genome downsizing among the flowering plants6 was critical to restructuring the entire pathway of CO2 diffusion, facilitating high rates of CO2 supply to the leaf mesophyll cells despite declining atmospheric CO2 levels during the Cretaceous.

plant biology↗

bioRxiv2019

Digitally Deconstructing Leaves in 3D Using X-ray Microcomputed Tomography and Machine Learning

Premise of the studyX-ray microcomputed tomography (microCT) can be used to measure 3D leaf internal anatomy, providing a holistic view of tissue organisation. Previously, the substantial time needed for segmenting multiple tissues limited this technique to small datasets, restricting its utility for phenotyping experiments and limiting our confidence in the conclusion of these studies due to low replication numbers. Methods and ResultsWe present a Python codebase for random-forest machine learning segmentation and 3D leaf anatomical trait quantification which dramatically reduces the time required to process single leaf microCT scans into detailed segmentations. By training the model on each scan using 6 hand segmented image slices out of >1500 in the full leaf scan, it achieves >90% accuracy in background and tissue segmentation. ConclusionOverall, this 3D segmentation and quantification pipeline can reduce one of the major barriers to using microCT imaging in high-throughput plant phenotyping.

plant biology↗