Dataset Information

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.

ABSTRACT: Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can facilitate this effort. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., presence/absence of flowers). Here, we use crowd-sourced phenological data of buds, flowers, and fruits from >3,000 specimens of six common wildflower species of the eastern United States (Anemone canadensis L., A. hepatica L., A. quinquefolia L., Trillium erectum L., T. grandiflorum (Michx.) Salisb., and T. undulatum Wild.) to train models using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of each of the three reproductive stages with >87% accuracy. We also successfully estimated the relative abundance of each reproductive structure on a specimen with ?90% accuracy. Precise counting of features was also successful, but accuracy varied with phenological stage and taxon. Specifically, counting flowers was significantly less accurate than buds or fruits likely due to their morphological variability on pressed specimens. Moreover, our Mask R-CNN model provided more reliable data than non-expert crowd-sourcers but not botanical experts, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, thus providing high-quality data with which to investigate plant responses to ongoing climatic change.

SUBMITTER: Davis CC

PROVIDER: S-EPMC7411132 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.

Davis Charles C CC Champ Julien J Park Daniel S DS Breckheimer Ian I Lyra Goia M GM Xie Junxi J Joly Alexis A Tarapore Dharmesh D Ellison Aaron M AM Bonnet Pierre P

Frontiers in plant science 20200731

Phenology-the timing of life-history events-is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current practice of manually harvesting data from individual specimens, however, greatly restricts our ability to scale-up data collection. Recent investigations have demonstrated that machine-learning approaches can f ...[more]

PMID: 32849691

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Project description:Whole genome duplication (WGD) events are common in many plant lineages, but the ploidy status and possible occurrence of intraspecific ploidy variation are unknown for most species. Standard methods for ploidy determination are chromosome counting and flow cytometry approaches. While flow cytometry approaches typically use fresh tissue, an increasing number of studies have shown that recently dried specimens can be used to yield ploidy data. Recent studies have started to explore whether high-throughput sequencing (HTS) data can be used to assess ploidy levels by analyzing allelic frequencies from single copy nuclear genes. Here, we compare different approaches using a range of yam (Dioscorea) tissues of varying ages, drying methods and quality, including herbarium tissue. Our aims were to: (1) explore the limits of flow cytometry in estimating ploidy level from dried samples, including herbarium vouchers collected between 1831 and 2011, and (2) optimize a HTS-based method to estimate ploidy by considering allelic frequencies from nuclear genes obtained using a target-capture method. We show that, although flow cytometry can be used to estimate ploidy levels from herbarium specimens collected up to fifteen years ago, success rate is low (5.9%). We validated our HTS-based estimates of ploidy using 260 genes by benchmarking with dried samples of species of known ploidy (Dioscorea alata, D. communis, and D. sylvatica). Subsequently, we successfully applied the method to the 85 herbarium samples analyzed with flow cytometry, and successfully provided results for 91.7% of them, comprising species across the phylogenetic tree of Dioscorea. We also explored the limits of using this HTS-based approach for identifying high ploidy levels in herbarium material and the effects of heterozygosity and sequence coverage. Overall, we demonstrated that ploidy diversity within and between species may be ascertained from historical collections, allowing the determination of polyploidization events from samples collected up to two centuries ago. This approach has the potential to provide insights into the drivers and dynamics of ploidy level changes during plant evolution and crop domestication.

Dataset Information

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.

Publications

A New Method for Counting Reproductive Structures in Digitized Herbarium Specimens Using Mask R-CNN.

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