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  1. Free, publicly-accessible full text available April 1, 2023
  2. PREMISE The successful application of universal targeted sequencing markers, such as those developed for the Angiosperms353 probe set, within populations could reduce or eliminate the need for specific marker development, while retaining the benefits of full-gene sequences in population-level analyses. However, whether the Angiosperms353 markers provide sufficient variation within species to calculate demographic parameters is untested. METHODS Using herbarium specimens from a 50-year-old floristic survey in Texas, we sequenced 95 samples from 24 species using the Angiosperms353 probe set. Our data workflow calls variants within species and prepares data for population genetic analysis using standard metrics. In our case study, gene recovery was affected by genomic library concentration only at low concentrations and displayed limited phylogenetic bias. RESULTS We identified over 1000 segregating variants with zero missing data for 92% of species and demonstrate that Angiosperms353 markers contain sufficient variation to estimate pairwise nucleotide diversity (π)—typically between 0.002 and 0.010, with most variation found in flanking non-coding regions. In a subset of variants that were filtered to reduce linkage, we uncovered high heterozygosity in many species, suggesting that denser sampling within species should permit estimation of gene flow and population dynamics. DISCUSSION Angiosperms353 should benefit conservation genetic studies by providingmore »universal repeatable markers, low missing data, and haplotype information, while permitting inclusion of decades-old herbarium specimens.« less
  3. Abstract Machine learning (ML) has great potential to drive scientific discovery by harvesting data from images of herbarium specimens—preserved plant material curated in natural history collections—but ML techniques have only recently been applied to this rich resource. ML has particularly strong prospects for the study of plant phenological events such as growth and reproduction. As a major indicator of climate change, driver of ecological processes, and critical determinant of plant fitness, plant phenology is an important frontier for the application of ML techniques for science and society. In the present article, we describe a generalized, modular ML workflow for extracting phenological data from images of herbarium specimens, and we discuss the advantages, limitations, and potential future improvements of this workflow. Strategic research and investment in specimen-based ML methods, along with the aggregation of herbarium specimen data, may give rise to a better understanding of life on Earth.
  4. Summary Though substantial effort has gone into predicting how global climate change will impact biodiversity patterns, the scarcity of taxon‐specific information has hampered the efficacy of these endeavors. Further, most studies analyzing spatiotemporal patterns of biodiversity focus narrowly on species richness. We apply machine learning approaches to a comprehensive vascular plant database for the United States and generate predictive models of regional plant taxonomic and phylogenetic diversity in response to a wide range of environmental variables. We demonstrate differences in predicted patterns and potential drivers of native vs nonnative biodiversity. In particular, native phylogenetic diversity is likely to decrease over the next half century despite increases in species richness. We also identify that patterns of taxonomic diversity can be incongruent with those of phylogenetic diversity. The combination of macro‐environmental factors that determine diversity likely varies at continental scales; thus, as climate change alters the combinations of these factors across the landscape, the collective effect on regional diversity will also vary. Our study represents one of the most comprehensive examinations of plant diversity patterns to date and demonstrates that our ability to predict future diversity may benefit tremendously from the application of machine learning.
  5. Abstract Natural history collections (NHCs) are the foundation of historical baselines for assessing anthropogenic impacts on biodiversity. Along these lines, the online mobilization of specimens via digitization—the conversion of specimen data into accessible digital content—has greatly expanded the use of NHC collections across a diversity of disciplines. We broaden the current vision of digitization (Digitization 1.0)—whereby specimens are digitized within NHCs—to include new approaches that rely on digitized products rather than the physical specimen (Digitization 2.0). Digitization 2.0 builds on the data, workflows, and infrastructure produced by Digitization 1.0 to create digital-only workflows that facilitate digitization, curation, and data links, thus returning value to physical specimens by creating new layers of annotation, empowering a global community, and developing automated approaches to advance biodiversity discovery and conservation. These efforts will transform large-scale biodiversity assessments to address fundamental questions including those pertaining to critical issues of global change.