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Herbarium sheets present a unique view of the world's botanical history, evolution, and biodiversity. This makes them an all–important data source for botanical research. With the increased digitization of herbaria worldwide and advances in the domain of fine–grained visual classification which can facilitate automatic identification of herbarium specimen images, there are many opportunities for supporting and expanding research in this field. However, existing datasets are either too small, or not diverse enough, in terms of represented taxa, geographic distribution, and imaging protocols. Furthermore, aggregating datasets is difficult as taxa are recognized under a multitude of names and must be aligned to a common reference. We introduce the Herbarium 2021 Half–Earth dataset: the largest and most diverse dataset of herbarium specimen images, to date, for automatic taxon recognition. We also present the results of the Herbarium 2021 Half–Earth challenge, a competition that was part of the Eighth Workshop on Fine-Grained Visual Categorization (FGVC8) and hosted by Kaggle to encourage the development of models to automatically identify taxa from herbarium sheet images. 

Abstract The plant genus Bidens (Asteraceae or Compositae; Coreopsidae) is a species-rich and circumglobally distributed taxon. The 19 hexaploid species endemic to the Hawaiian Islands are considered an iconic example of adaptive radiation, of which many are imperiled and of high conservation concern. Until now, no genomic resources were available for this genus, which may serve as a model system for understanding the evolutionary genomics of explosive plant diversification. Here, we present a high-quality reference genome for the Hawaiʻi Island endemic species B. hawaiensis A. Gray reconstructed from long-read, high-fidelity sequences generated on a Pacific Biosciences Sequel II System. The haplotype-aware, draft genome assembly consisted of ~6.67 Giga bases (Gb), close to the holoploid genome size estimate of 7.56 Gb (±0.44 SD) determined by flow cytometry. After removal of alternate haplotigs and contaminant filtering, the consensus haploid reference genome was comprised of 15 904 contigs containing ~3.48 Gb, with a contig N50 value of 422 594. The high interspersed repeat content of the genome, approximately 74%, along with hexaploid status, contributed to assembly fragmentation. Both the haplotype-aware and consensus haploid assemblies recovered >96% of Benchmarking Universal Single-Copy Orthologs. Yet, the removal of alternate haplotigs did not substantially reduce the proportion of duplicated benchmarking genes (~79% vs. ~68%). This reference genome will support future work on the speciation process during adaptive radiation, including resolving evolutionary relationships, determining the genomic basis of trait evolution, and supporting ongoing conservation efforts. 

Summary Process‐based vegetation models attempt to represent the wide range of trait variation in biomes by grouping ecologically similar species into plant functional types (PFTs). This approach has been successful in representing many aspects of plant physiology and biophysics but struggles to capture biogeographic history and ecological dynamics that determine biome boundaries and plant distributions. Grass‐dominated ecosystems are broadly distributed across all vegetated continents and harbour large functional diversity, yet most Land Surface Models (LSMs) summarise grasses into two generic PFTs based primarily on differences between temperate C₃grasses and (sub)tropical C₄grasses. Incorporation of species‐level trait variation is an active area of research to enhance the ecological realism of PFTs, which form the basis for vegetation processes and dynamics in LSMs. Using reported measurements, we developed grass functional trait values (physiological, structural, biochemical, anatomical, phenological, and disturbance‐related) of dominant lineages to improve LSM representations. Our method is fundamentally different from previous efforts, as it uses phylogenetic relatedness to create lineage‐based functional types (LFTs), situated between species‐level trait data and PFT‐level abstractions, thus providing a realistic representation of functional diversity and opening the door to the development of new vegetation models.