Dataset Information

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

ABSTRACT: Herbarium specimens provide a robust record of historical plant phenology (the timing of seasonal events such as flowering or fruiting). However, the difficulty of aggregating phenological data from specimens arises from a lack of standardized scoring methods and definitions for phenological states across the collections community.To address this problem, we report on a consensus reached by an iDigBio working group of curators, researchers, and data standards experts regarding an efficient scoring protocol and a data-sharing protocol for reproductive traits available from herbarium specimens of seed plants. The phenological data sets generated can be shared via Darwin Core Archives using the Extended MeasurementOrFact extension.Our hope is that curators and others interested in collecting phenological trait data from specimens will use the recommendations presented here in current and future scoring efforts. New tools for scoring specimens are reviewed.

SUBMITTER: Yost JM

PROVIDER: S-EPMC5851559 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

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Publications

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

Yost Jennifer M JM Sweeney Patrick W PW Gilbert Ed E Nelson Gil G Guralnick Robert R Gallinat Amanda S AS Ellwood Elizabeth R ER Rossington Natalie N Willis Charles G CG Blum Stanley D SD Walls Ramona L RL Haston Elspeth M EM Denslow Michael W MW Zohner Constantin M CM Morris Ashley B AB Stucky Brian J BJ Carter J Richard JR Baxter David G DG Bolmgren Kjell K Denny Ellen G EG Dean Ellen E Pearson Katelin D KD Davis Charles C CC Mishler Brent D BD Soltis Pamela S PS Mazer Susan J SJ

Applications in plant sciences 20180228 2

<h4>Premise of the study</h4>Herbarium specimens provide a robust record of historical plant phenology (the timing of seasonal events such as flowering or fruiting). However, the difficulty of aggregating phenological data from specimens arises from a lack of standardized scoring methods and definitions for phenological states across the collections community.<h4>Methods and results</h4>To address this problem, we report on a consensus reached by an iDigBio working group of curators, researchers ...[more]

PMID: 29732253

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Project description: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.

Dataset Information

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

Publications

Digitization protocol for scoring reproductive phenology from herbarium specimens of seed plants.

Similar Datasets

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