Biodiversity researchers often need to answer the question: "Which species of taxon X have been documented in (or near) spatial polygon Y?" Online databases with billions of occurrence records, including vouchered specimens and citizen science records, can provide the answer; however, quick spatial processing of huge biodiversity datasets can be difficult, and many general-purpose tools are constrained by dataset size.

(Boraginaceae, subfamily Cynoglossoideae), a species native to the Sonoran Desert (North America), has served as a model system for a suite of ecological and evolutionary studies. However, no reference genomes are currently available in Cynoglossoideae. A high-quality reference genome for would be valuable for addressing questions in this system and across broader taxonomic scales.

Specialized metabolites serve various roles for plants and humans. Unlike core metabolites, specialized metabolites are restricted to certain plant lineages; thus, in addition to their ecological functions, specialized metabolites can serve as diagnostic markers of plant lineages.

Recently, plant science has seen transformative advances in scalable data collection for sequence and chemical data. These large datasets, combined with machine learning, have demonstrated that conducting plant metabolic research on large scales yields remarkable insights. A key next step in increasing scale has been revealed with the advent of accessible large language models, which, even in their early stages, can distill structured data from the literature. This brings us closer to creating specialized databases that consolidate virtually all published knowledge on a topic.

This study investigates advanced training techniques to improve the performance of convolutional neural networks for disease detection in cocoa, .

Current phylogenies of Amaranthaceae sensu stricto (s.s.) are inadequately sampled and resolved to reflect the entire evolutionary history of the lineage, which is likely complex due to at least three whole-genome duplication events, occasionally followed by subsequent additional polyploidization events and rapid diversification of individual sublineages. We designed a new target enrichment bait set to overcome these challenges when reconstructing a phylogeny and demonstrated its applicability to the entire Amaranthaceae s.s. lineage.

Leaf extracts are valuable sources of bioactive compounds. However, co-extracted chlorophylls interfere with analyses, including spectroscopic and biochemical assays. Existing methods for chlorophyll removal often have limitations, including the use of hazardous solvents, low specificity, or high costs.

Functional plant ecology seeks to understand how functional traits govern species distributions, community assembly, and ecosystem functions. While global trait datasets have advanced the field, substantial gaps remain, and extracting trait information from text in books, research articles, and online sources via machine learning offers a valuable complement to costly field campaigns.

Pollinator communities have been surveyed through direct observation, which is labor intensive and difficult for monitoring nocturnal pollinators. Interval photography surveys are increasingly used, although the resulting data from pollinator community surveys have rarely been validated.

(Fabaceae) is the largest genus of flowering plants, with about 3100 species. Most phylogenies of the genus are based on a few nuclear or plastid loci (with one exception that uses ~100 loci) and usually provide poorly resolved trees and even conflicting subgeneric classifications. Target enrichment can greatly improve phylogenetic resolution, even at recently diverged taxonomic units, by generating sequences for hundreds of phylogenetically informative, putative single-copy loci. However, a specific bait set for the Astragalean clade is still lacking.

This study capitalized on a library of single-nucleotide polymorphisms created via whole genome sequencing (WGS) to develop and test a PCR assay for detecting toxic species in food products. Complex foods can be difficult to analyze, but safeguarding consumer well-being and public health necessitates that products regulated by the U.S. Food and Drug Administration are contaminant free.

While controlled environments are desirable for growing and measuring plants, growth chambers and greenhouses typically have microclimates that impact plant growth, development, and stress responses. Furthermore, opening and closing the doors of a controlled environment introduces variation in the environment, especially at temperature extremes, affecting both the measurements and the organisms within. Using multiple temperature data loggers to normalize results can be cost-prohibitive and rarely offers real-time feedback on temperature status.

The increased online publication of data associated with Brazilian botanical collections and biodiversity information systems has significantly improved access to information on plant species occurring in Brazil. However, information about Brazilian flora within Protected Areas is fragmented.

Currently, there is a lack of controlled cultivation methods for cacao (), a plant species with high commercial value. One major concern is the tendency of cacao plants to accumulate high concentrations of cadmium (Cd), a heavy metal with high toxicity to living organisms.

Plant metabolomes are structurally diverse. One of the most popular techniques for sampling this diversity is liquid chromatography-mass spectrometry (LC-MS), which typically detects thousands of peaks from single organ extracts, many representing true metabolites. These peaks are usually annotated using in-house retention time or spectral libraries, in silico fragmentation libraries, and increasingly through computational techniques such as machine learning. Despite these advances, over 85% of LC-MS peaks remain unidentified, posing a major challenge for data analysis and biological interpretation. This bottleneck limits our ability to fully understand the diversity, functions, and evolution of plant metabolites. In this review, we first summarize current approaches for metabolite identification, highlighting their challenges and limitations. We further focus on alternative strategies that bypass the need for metabolite identification, allowing researchers to interpret global metabolic patterns and pinpoint key metabolite signals. These methods include molecular networking, distance-based approaches, information theory-based metrics, and discriminant analysis. Additionally, we explore their practical applications in plant science and highlight a set of useful tools to support researchers in analyzing complex plant metabolomics data. By adopting these approaches, researchers can enhance their ability to uncover new insights into plant metabolism.

Plant morphological traits, their observable characteristics, are fundamental to understanding the role played by each species within its ecosystem; however, compiling trait information for even a moderate number of species is a demanding task that may take experts years to accomplish. At the same time, online species descriptions contain massive amounts of information about morphological traits, but the lack of structure makes this source of data impossible to use at scale.

The model liverwort is an emerging testbed species for plant metabolic engineering but lacks well-characterized inducible promoters, which are necessary to minimize biochemical and physiological disruption when over-accumulating target products. Here, we demonstrate the functionality of the light-inducible plant-usable light-switch elements (PULSE) optogenetic system in and exemplify its use through the light-inducible overproduction of the bioplastic poly-3-hydroxybutyrate (PHB).

Here we demonstrate the application of interpretable machine learning methods to investigate intraspecific functional trait divergence using diverse genotypes of the wide-ranging sunflower occupying populations across two contrasting ecoregions-the Great Plains versus the North American Deserts.

With the advent of inexpensive nucleic acid sequencing and automated annotation at the level of basic functionality, the central problem of enzyme discovery is no longer finding active sequences, it is determining which ones are suitable for further study. This requires annotation that goes beyond sequence similarity to known enzymes and provides information at the sequence and structural levels.

The quantification of plant drought resistance, particularly embolism formation, within and across species, is critical for ecosystem management and agriculture. We developed a cost-effective protocol to measure the water potential at which 50% of hydraulic conductivity ( ) is lost in stems, using affordable and accessible materials in comparison to the traditional optical method.

Metabarcoding has become a successful tool for the identification of species in ecological assemblages. However, the usefulness of metabarcoding for identifying plant species has been hampered due to a lack of universal gene regions that work across all taxa, limiting the applications of metabarcoding in ecology.