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Whole-genome duplication (WGD), or polyploidy, is a major evolutionary force throughout the tree of life. Ancient WGDs are commonly inferred from the distribution of synonymous substitutions per synonymous site (𝑲𝒔) between duplicated genes. Here, we argue that the standard interpretation of these distributions is incomplete. Using haplotype-phased genome assemblies spanning canonical autopolyploid and allopolyploid systems, we find that 𝑲𝒔 accumulates primarily between gene copies on non-recombining chromosomes. Thus, 𝑲𝒔-based WGD detection depends not simply on WGD, but on whether duplicated copies evolve independently. This distinction reframes many inferences of ancient WGD as signatures of hybridization and the evolution of meiotic isolation and alters how we interpret the origin, detectability, and evolutionary consequences of polyploidy across the tree of life. 

Ancestral state reconstruction (ASR) is a foundational tool in comparative biology, offering insights into the evolutionary history of lineages. With each new evolutionary model, our ability to estimate ancestral states with increased biological realism has improved. However, the field has primarily relied on marginal reconstructions, which focus on individual nodes. This framework is analytically tractable and appropriate for node-specific hypotheses, but it is not designed to identify the most probable sequence of evolutionary events across a tree. We argue that for researchers interested in evolutionary trajectories, joint reconstructions provide a more effective way to characterize the full history of transitions. Traditionally, joint reconstruction algorithms focused only on the single most likely sequence, but here we use conditional probabilities derived from stochastic mapping to sample the distribution of plausible ancestral histories efficiently. Furthermore, we provide tools to quantify and summarize this joint uncertainty. Through simulations and an empirical case study, we demonstrate that joint reconstructions more effectively recover simulated trait histories than node-wise marginal estimates and that the uncertainty surrounding these histories can be biologically meaningful. We apply our methods to epidemic multidrug-resistantKlebsiella pneumoniaeand find that the evolution of antibiotic resistance is not a single narrative but a series of competing histories. Each of these histories exhibits distinct phenotype–genotype transitions that node-wise approaches would struggle to identify, yet have critical implications for predicting and understanding resistance evolution. 

Abstract Climate change is shifting the flowering phenology of wind-pollinated trees, leading to longer pollen seasons and heightened allergy risks. Accurately forecasting these phenological shifts requires a clear understanding of the underlying mechanisms and variations in the sensitivity of phenology to temperature. Here, we investigated the temperature sensitivity of flowering phenology in 74 wind-pollinated tree species across the US using long-term herbarium records spanning over a century. We found consistent phenological responses to spring temperature variations over space and over time in 77% of species, indicating that these long-lived trees can rely primarily on plasticity to cope with temperature variation across different regions. Sensitivity estimates were consistent across both century-scale and decadal time scales, challenging the previous understanding of declines. Interspecific variations in sensitivity exhibited a strong phylogenetic signal, indicating evolutionary conservatism in phenological responses among related species. Together, these findings shed light on the magnitude, mechanism, trend, and variability of temperature sensitivity of wind-pollinated tree flowering phenology. Our results improve the mechanistic basis for forecasting phenological shifts and their downstream effects on airborne pollen and allergen exposure under ongoing climate change. 

Summary Accurate divergence times are essential for interpreting and understanding the context in which lineages have evolved. Over the past several decades, debates have surrounded the discrepancies between the inferred molecular ages of crown angiosperms, often estimated from the Late Jurassic into the Permian, and the fossil record, placing angiosperms in the Early Cretaceous. That crown angiosperms could have emerged as early as the Permian or even the Triassic would have major implications for the paleoecological context of the origin of one of the most consequential clades in the tree of life. Here, we argue, and demonstrate through simulations, that the older ages inferred from molecular data and relaxed‐clock models are misled by lineage‐specific rate heterogeneity resulting from life history changes that occurred several times throughout the evolution of vascular plants. To overcome persistent discrepancies in age estimates, more biologically informed and realistic models should be developed, and our results should be considered in the context of their biological implications before we accept inferences that are a major departure from our strongest evidence. 

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 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. 

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.