Mechanistic models using stomatal traits and leaf carbon isotope ratios to reconstruct atmospheric carbon dioxide (CO2) concentrations (ca) are important to understand the Phanerozoic paleoclimate. However, methods for preparing leaf cuticles to measure stomatal traits have not been standardized.
Three people measured the stomatal density and index, guard cell length, guard cell pair width, and pore length of leaves from the same Ginkgo biloba, Quercus alba, and Zingiber mioga leaves growing at known CO2 levels using four preparation methods: fluorescence on cleared leaves, nail polish, dental putty on fresh leaves, and dental putty on dried leaves.
There are significant differences between trait measurements from each method. Modeled ca calculations are less sensitive to method than individual traits; however, the choice of assumed carbon isotope fractionation also impacted the accuracy of the results.
We show that there is not a significant difference between ca estimates made using any of the four methods. Further study is needed on the fractionation due to carboxylation of ribulose bisphosphate (RuBP) in individual plant species before use as a paleo-CO2 barometer and to refine estimates based upon widely applied taxa (e.g., Ginkgo). Finally, we recommend that morphological measurements be made by multiple observers to reduce the effect of individual observational biases.
Orthology inference is crucial for comparative genomics, and multiple algorithms have been developed to identify putative orthologs for downstream analyses. Despite the abundance of proposed solutions, including publicly available benchmarks, it is difficult to assess which tool is most suitable for plant species, which commonly have complex genomic histories.
We explored the performance of four orthology inference algorithms—OrthoFinder, SonicParanoid, Broccoli, and OrthNet—on eight Brassicaceae genomes in two groups: one group comprising only diploids and another set comprising the diploids, two mesopolyploids, and one recent hexaploid genome.
The composition of the orthogroups reflected the species' ploidy and genomic histories, with the diploid set having a higher proportion of identical orthogroups. While the diploid + higher ploidy set had a lower proportion of orthogroups with identical compositions, the average degree of similarity between the orthogroups was not different from the diploid set.
Three algorithms—OrthoFinder, SonicParanoid, and Broccoli—are helpful for initial orthology predictions. Results produced using OrthNet were generally outliers but could still provide detailed information about gene colinearity. With our Brassicaceae dataset, slight discrepancies were found across the orthology inference algorithms, necessitating additional analyses such as tree inference to fine-tune results.
Seaweeds are gaining substantial research interest, particularly for genomic applications, where high-quality DNA is a prerequisite. Extracting DNA from these organisms presents challenges due to high levels of biomacromolecules resulting from their diverse cell structures. Existing protocols often lack versatility, leading to inconsistent outcomes across various materials and taxa, which highlights the need for a universal method for use with a variety of green seaweed samples.
We optimized the conventional cetyltrimethylammonium bromide (CTAB) protocol for green seaweed DNA extraction. Our method, involving an initial sample treatment, lysis buffer adjustment, and enzyme incubation alterations, outperformed the conventional CTAB and commercial kits in terms of DNA yield and purity. Notably, the protocol's effectiveness was demonstrated across various green algal materials and preservation methods, and was tested with downstream applications with satisfactory results.
Our optimized CTAB protocol offers a reliable solution for high-quality genomic DNA extraction from a wide variety of green seaweed samples.
Leveraging DNA markers, particularly single-nucleotide polymorphisms (SNPs), in parentage analysis, sib-ship reconstruction, and genomic relatedness analysis can enhance plant breeding efficiency. However, the limited availability of genomic information, confined to the most commonly used species, hinders the broader application of SNPs in species of lower economic interest (e.g., most tree species). We explored the possibility of using universal target capture probes, namely Angiosperms353, to identify SNPs and assess their effectiveness in genomic relatedness analysis.
We tested the approach in 11 tree species, six of which had a half-sib family structure. Variants were called within species, and genomic relatedness analysis was conducted in species with two or more families. Scalability via amplicon sequencing was tested by designing primers and testing them in silico.
Adequate SNPs for relatedness analysis were identified in all species. Relatedness values from Angiosperms353-based SNPs highly correlated with those from thousands of genome-wide DArTseq SNPs in Cordia africana, one of the species with a family structure. The in silico performance of designed primers demonstrated the potential for scaling up via amplicon sequencing.
Utilizing universal target capture probes for SNP identification can help overcome the limitations of genomic information availability, thereby enhancing the application of genomic markers in breeding plant species with lower economic interest.
One of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.
We first showcase the development of a rule-based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule-based approaches often have high commission error rates.
Our results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors.
This work shows that an ensemble approach has particular value for creating high-quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.
Controlling light flux density during carbon dioxide assimilation measurements is essential in photosynthesis research. Commercial lamps are expensive and are based on monochromatic light-emitting diodes (LEDs), which deviate significantly in their spectral distribution compared to sunlight.
Using LED-emitted white light with a color temperature similar to sunlight, we developed a cost-effective lamp compatible with the LI-6800 Portable Photosynthesis System. When coupled with customized software, the lamp can be controlled via the LI-6800 console by a user or Python scripts. Testing and calibration show that the lamp meets the quality needed to estimate photosynthesis parameters.
The lamp can be built using a basic electronics lab and a 3D printer. Calibration instructions are supplied and only require equipment commonly available at plant science laboratories. The lamp is a cost-effective alternative to perform photosynthesis research coupled with the popular LI-6800 photosynthesis measuring system.
Automated disease, weed, and crop classification with computer vision will be invaluable in the future of agriculture. However, existing model architectures like ResNet, EfficientNet, and ConvNeXt often underperform on smaller, specialised datasets typical of such projects.
We address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. Data collection was informed by spectroscopy data, which provided useful insights into the spectral characteristics of cocoa trees. Cocoa was chosen as a focal species due to the diverse pathology of its diseases, which pose significant challenges for detection.
ResNet18 showed some signs of overfitting, while EfficientNet variants showed distinct signs of overfitting. By contrast, PhytNet displayed excellent attention to relevant features, almost no overfitting, and an exceptionally low computation cost of 1.19 GFLOPS.
We show that PhytNet is a promising candidate for rapid disease or plant classification and for precise localisation of disease symptoms for autonomous systems. We also show that the most informative light spectra for detecting cocoa disease are outside the visible spectrum and that efforts to detect disease in cocoa should be focused on local symptoms, rather than the systemic effects of disease.
The selection of Arabidopsis as a model organism played a pivotal role in advancing genomic science. The competing frameworks to select an agricultural- or ecological-based model species were rejected, in favor of building knowledge in a species that would facilitate genome-enabled research.
Here, we examine the ability of models based on Arabidopsis gene expression data to predict tissue identity in other flowering plants. Comparing different machine learning algorithms, models trained and tested on Arabidopsis data achieved near perfect precision and recall values, whereas when tissue identity is predicted across the flowering plants using models trained on Arabidopsis data, precision values range from 0.69 to 0.74 and recall from 0.54 to 0.64.
The identity of belowground tissue can be predicted more accurately than other tissue types, and the ability to predict tissue identity is not correlated with phylogenetic distance from Arabidopsis. k-nearest neighbors is the most successful algorithm, suggesting that gene expression signatures, rather than marker genes, are more valuable to create models for tissue and cell type prediction in plants.
Our data-driven results highlight that the assertion that knowledge from Arabidopsis is translatable to other plants is not always true. Considering the current landscape of abundant sequencing data, we should reevaluate the scientific emphasis on Arabidopsis and prioritize plant diversity.
Heterostyly in plants promotes pollen transfer between floral morphs, because female and male sex organs are located at roughly reciprocal heights within the flowers of each morph. Reciprocity indices, which assess the one-dimensional variation in the height of sex organs, are used to define the phenotypic structure of heterostyly in plant populations and to make inferences about selection. Other reciprocal stylar polymorphisms (e.g., enantiostyly) may function in a similar manner to heterostyly. In-depth assessment of their potential fit with pollinators requires accounting for the multidimensional variation in the location of sex organs.
We have adapted the existing reciprocity indices used for heterostylous plant populations to incorporate multidimensional data. We illustrate the computation of the adapted and original indices in the freely available R package FlowerMate.
FlowerMate provides fast computation of reliable indices to facilitate understanding of the evolution and function of the full diversity of reciprocal polymorphisms.
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