Imaging technologies that capture three-dimensional (3D) variation in floral morphology at micro- and nano-resolutions are increasingly accessible. In herkogamous flowers, such as those of Theobroma cacao, structural barriers between anthers and stigmas represent bottlenecks that restrict pollinator size and access to reproductive organs. To study the unresolved pollination biology of cacao, we present a novel application of micro-computed tomography (micro-CT) using floral dimensions to quantify pollinator functional size limits.
We generated micro-CT data sets from field-collected flowers and museum specimens of potential pollinators. To compare floral variation, we used 3D Slicer to place landmarks on the surface models and performed a geometric morphometric (GMM) analysis using geomorph R. We identified the petal side door (an opening between the petal hoods and filament) as the main bottleneck for pollinator access. We compared its mean dimensions with proposed pollinators to identify viable candidates.
We identified three levels of likelihood for putative pollinators based on the number of morphological (body) dimensions that fit through the petal side door. We also found floral reward microstructures whose presence and location were previously unclear.
Using micro-CT and GMM to study the 3D pollination biology of cacao provides new evidence for predicting unknown pollinators. Incorporating geometry and floral rewards will strengthen plant–pollinator trait matching models for cacao and other species.
Quantitative plant traits play a crucial role in biological research. However, traditional methods for measuring plant morphology are time consuming and have limited scalability. We present LeafMachine2, a suite of modular machine learning and computer vision tools that can automatically extract a base set of leaf traits from digital plant data sets.
LeafMachine2 was trained on 494,766 manually prepared annotations from 5648 herbarium images obtained from 288 institutions and representing 2663 species; it employs a set of plant component detection and segmentation algorithms to isolate individual leaves, petioles, fruits, flowers, wood samples, buds, and roots. Our landmarking network automatically identifies and measures nine pseudo-landmarks that occur on most broadleaf taxa. Text labels and barcodes are automatically identified by an archival component detector and are prepared for optical character recognition methods or natural language processing algorithms.
LeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel-to-metric conversion factors for 26 commonly used ruler types.
LeafMachine2 is a highly efficient tool for generating large quantities of plant trait data, even from occluded or overlapping leaves, field images, and non-archival data sets. Our project, along with similar initiatives, has made significant progress in removing the bottleneck in plant trait data acquisition from herbarium specimens and shifted the focus toward the crucial task of data revision and quality control.
We recognized the need for a customized imaging protocol for plant specimens at the time of collection for the purpose of three-dimensional (3D) modeling, as well as the lack of a broadly applicable photogrammetry protocol that encompasses the heterogeneity of plant specimen geometries and the challenges introduced by processes such as wilting.
We developed an equipment list and set of detailed protocols describing how to capture images of plant specimens in the field prior to their deformation (e.g., with pressing) and how to produce a 3D model from the image sets in Agisoft Metashape Professional.
The equipment list and protocols represent a foundation on which additional improvements can be made for specimen geometries outside of the range of the six types considered, and an easy entry into photogrammetry for those who have not previously used it.
Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers.
Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi–based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device.
FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.
Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) is a chemical imaging method that can visualize spatial distributions of particular molecules. Plant tissue imaging has so far mostly used cryosectioning, which can be impractical for the preparation of large-area imaging samples, such as full flower petals. Imaging unsectioned plant tissue presents its own difficulties in extracting metabolites to the surface due to the waxy cuticle.
We address this by using established delipidation techniques combined with a solvent vapor extraction prior to applying the matrix with many low-concentration sprays.
Using this procedure, we imaged tissue from three different plant species (two flowers and one carnivorous plant leaf). Material factorization analysis of the resulting data reveals a wide range of plant-specific small molecules with varying degrees of localization to specific portions of the tissue samples, while facilitating detection and removal of signal from background sources.
This work demonstrates applicability of MALDI-MSI to press-dried plant samples without freezing or cryosectioning, setting the stage for spatially resolved molecule identification. Increased mass resolution and inclusion of tandem mass spectrometry are necessary next steps to allow more specific and reliable compound identification.
A comparison of methods using different materials to exclude light from stems to prevent stem CO2 exchange (i.e., photosynthesis), without affecting stem conductance to water vapor, surface temperature, and relative humidity, was conducted on stems of avocado trees in California.
The experiment featured three materials: aluminum foil, paper-based wrap, and mineral-based paint. We examined stem CO2 exchange with and without the light exclusion treatments. We also examined stem surface temperature, relative humidity, and photosynthetic active radiation (PAR) under the cover materials. All materials reduced PAR and stem CO2 exchange. However, aluminum foil reduced stem surface temperature and increased relative humidity.
Methods used to study stem CO2 exchange through light exclusion have historically relied on methods that may induce experimental artifacts. Among the methods tested here, mineral-based paint effectively reduced PAR without affecting stem surface temperature and relative humidity around the stem.
Sessa, E. B., R. R. Masalia, N. Arrigo, M. S. Barker, and J. A. Pelosi. 2023. GOgetter: A pipeline for summarizing and visualizing GO slim annotations for plant genetic data. Applications in Plant Sciences 11(4): e11536.
In the Acknowledgments, a grant number was left out of the sentence “Funding was provided by the National Science Foundation (DEB #1844930 to E.B.S.).” This should have read “Funding was provided by the National Science Foundation (DEB #1844930 and IOS #2310485 to E.B.S.).”
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