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We employ photothermally driven self-assembly of colloidal particles to design microscopic structures with programmable size and tunable order. The experimental system is based on a binary mixture of “plasmonic heater” gold nanoparticles and “assembly building block” microparticles. Photothermal heating of the gold nanoparticles under visible light causes a natural convection flow that efficiently assembles the microscale building block particles (diameter 1–10 μm) into a monolayer. We identify the onset of active Brownian motion of colloidal particles under this convective flow by varying the conditions of light intensity, gold nanoparticle concentration, and sample height. We realize a crowded assembly of microparticles around the center of illumination and show that the size of the particle crowd can be programmed using patterned light illumination. In a binary mixture of gold nanoparticles and polystyrene microparticles, we demonstrate the formation of rapid and large-scale crystalline monolayers, covering an area of 0.88 mm2 within 10 min. We find that the structural order of the assembly can be tuned by varying the surface charge of the nanoparticles and the size of the microparticles, giving rise to the formation of different phases–colloidal crystals, crowds, and gels. Using Monte Carlo simulations, we explain how the phases emerge from the interplay between hydrodynamic and electrostatic interactions, as well as the assembly kinetics. Our study demonstrates the promise of self-assembly with programmable shapes and structural order under nonequilibrium conditions using an accessible setup comprising only binary mixtures and LED light. 

Abstract PremiseThe Caryophyllaceae (the carnation family) have undergone multiple transitions into colder climates and convergence on cushion plant adaptation, indicating that they may provide a natural system for cold adaptation research. Previous research has suggested that putative ancient whole‐genome duplications (WGDs) are correlated with niche shifts into colder climates across the Caryophyllales. Here, we explored the genomic changes potentially involved in one of these discovered shifts in the Caryophyllaceae. MethodsWe constructed a data set combining 26 newly generated transcriptomes with 45 published transcriptomes, including 11 cushion plant species across seven genera. With this data set, we inferred a dated phylogeny for the Caryophyllaceae and mapped ancient WGDs and gene duplications onto the phylogeny. We also examined functional groups enriched for gene duplications related to the climatic shift. ResultsThe ASTRAL topology was mostly congruent with the current consensus of relationships within the family. We inferred 15 putative ancient WGDs in the family, including eight that have not been previously published. The oldest ancient WGD (ca. 64.4–56.7 million years ago), WGD1, was found to be associated with a shift into colder climates by previous research. Gene regions associated with ubiquitination were overrepresented in gene duplications retained after WGD1 and those convergently retained by cushion plants inColobanthusandEremogone, along with other functional annotations. ConclusionsGene family expansions induced by ancient WGDs may have contributed to the shifts to cold climatic niches in the Caryophyllaceae. Transcriptomic data are crucial resources that help unravel heterogeneity in deep‐time evolutionary patterns in plants. 

Abstract PremiseQuantitative 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. MethodsLeafMachine2 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. ResultsLeafMachine2 can extract trait data from at least 245 angiosperm families and calculate pixel‐to‐metric conversion factors for 26 commonly used ruler types. DiscussionLeafMachine2 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. 

Abstract PremiseField 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. Methods and ResultsOur 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. ConclusionsFieldPrism 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. 

Complex patterns of genome evolution associated with the end-Cretaceous [Cretaceous-Paleogene (K–Pg)] mass extinction limit our understanding of the early evolutionary history of modern birds. Here, we analyzed patterns of avian molecular evolution and identified distinct macroevolutionary regimes across exons, introns, untranslated regions, and mitochondrial genomes. Bird clades originating near the K–Pg boundary exhibited numerous shifts in the mode of molecular evolution, suggesting a burst of genomic heterogeneity at this point in Earth’s history. These inferred shifts in substitution patterns were closely related to evolutionary shifts in developmental mode, adult body mass, and patterns of metabolic scaling. Our results suggest that the end-Cretaceous mass extinction triggered integrated patterns of evolution across avian genomes, physiology, and life history near the dawn of the modern bird radiation. 

Abstract Distribution models are widely used to understand landscape biodiversity patterns, facilitate evolutionary and ecological studies, and for making informed conservation decisions. While it is common to examine consequences of climate change, impacts of land use on distributions, a major factor in limiting ranges and corridors between populations, are less well understood. Here, we use distribution models to quantify changes in biodiversity due to land use for Michigan floral assemblages. We leveraged a distribution model dataset (1930 species) integrated with dated phylogenetic information and USGS land use maps to parse Michigan areas with unsuitable habitat. Additionally, we quantify the degree of high-quality habitat lost for each species, identifying those most strongly impacted by land use changes. Approx. 39% of Michigan terrestrial habitat fell within “unsuitable” land use categories. Sites predicted to harbor the most species based on climatic variables were those sites that lost the greatest proportion due to land use changes. Further, excluded sites were preferentially those composed of more phylogenetically even communities. Overall, the impact of land use changes on community species richness was the preferential loss of sites with the predicted highest biodiversity. For phylodiversity metrics, land use changes increased the degree of community phylogenetic clustering. This results in overall decreased phylodiversity, leading to assemblages less equipped to respond to rapid climatic changes. Our results confirm land use to be a major, but somewhat overlooked, factor impacting local diversity dynamics and illustrate how local-scale land use impacts regional-scale richness and phylodiversity patterns, likely leading to increased community fragility.