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Premise of the Study A novel method of estimating phenology of herbarium specimens was developed to facilitate more precise determination of plant phenological responses to explanatory variables (e.g., climate).
Methods and Results Simulated specimen data sets were used to compare the precision of phenological models using the new method and two common, alternative methods (flower presence/absence and ≥50% flowers present). The new “estimated phenophase” method was more precise and extracted a greater number of significant species‐level relationships; however, this method only slightly outperformed the simple “binary” (e.g., flowers present/absent) method.
Conclusions The new method enables estimation of phenological trends with greater precision. However, when time and resources are limited, a presence/absence method may offer comparable results at lower cost. Using a more restrictive approach, such as only including specimens in a certain phenophase, is not advised given the detrimental effect of decreased sample size on resulting models.
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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.more » « less
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Premise Biological outliers (observations that fall outside of a previously understood norm, e.g., in phenology or distribution) may indicate early stages of a transformative change that merits immediate attention. Collectors of biodiversity specimens such as plants, fungi, and animals are on the front lines of discovering outliers, yet the role collectors currently play in providing such data is unclear.
Methods We surveyed 222 collectors of a broad range of taxa, searched 47 training materials, and explored the use of 170 outlier terms in 75 million specimen records to determine the current state of outlier detection and documentation in this community.
Results Collectors reported observing outliers (e.g., about 80% of respondents observed morphological and distributional outliers at least occasionally). However, relatively few specimen records include outlier terms, and imprecision in their use and handling in data records complicates data discovery by stakeholders. This current state appears to be at least partly due to the absence of protocols: only one of the training materials addressed documenting and reporting outliers.
Conclusions We suggest next steps to mobilize this largely untapped, yet ideally suited, community for early detection of biotic change in the Anthropocene, including community activities for building relevant best practices.
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Premise of the Study Phenological annotation models computed on large‐scale herbarium data sets were developed and tested in this study.
Methods Herbarium specimens represent a significant resource with which to study plant phenology. Nevertheless, phenological annotation of herbarium specimens is time‐consuming, requires substantial human investment, and is difficult to mobilize at large taxonomic scales. We created and evaluated new methods based on deep learning techniques to automate annotation of phenological stages and tested these methods on four herbarium data sets representing temperate, tropical, and equatorial American floras.
Results Deep learning allowed correct detection of fertile material with an accuracy of 96.3%. Accuracy was slightly decreased for finer‐scale information (84.3% for flower and 80.5% for fruit detection).
Discussion The method described has the potential to allow fine‐grained phenological annotation of herbarium specimens at large ecological scales. Deeper investigation regarding the taxonomic scalability of this approach is needed.
