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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.
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Finding biotic anomalies described in specimen label text is a challenge that artificial intelligence can address
Biodiversity specimen collectors are on the front lines of observing biotic anomalies, some of which herald early stages of significant changes (e.g., the arrival of a new disease; Pearson and Mast 2019). Online data sharing has opened new possibilities for the discovery of anomaly descriptions on collectors’ labels, but it remains a challenge to find these needles in the haystack of many millions of specimen records available at aggregators like iDigBio and Global Biodiversity Information Facility. In a recent community survey, over 200 collectors identified 170 unique words and phrases (e.g., atypical) that they would use to describe six types of anomaly (Pearson and Mast 2019). Left unanswered was the relative efficiency with which anomaly descriptions can be found using the simple presence of these words. Here, we address that question with a focus on one type of anomaly (phenological; related to the timing of life historyevents) and ask a second question: can we further improve the efficiency of anomaly description discovery by engaging artificial intelligence (AI)?
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- Award ID(s):
- 2027654
- PAR ID:
- 10518707
- Publisher / Repository:
- NSF Public Access Repository (NSF-PAR)
- Date Published:
- Journal Name:
- BAUHINIA – Zeitschrift der Basler Botanischen Gesellschaft
- Volume:
- 29
- ISSN:
- 0067-4605
- Subject(s) / Keyword(s):
- Anomaly detection Artificial intelligence Biodiversity specimens Global change Phenology
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Summary Natural history collections (NHCs) are essential for studying biodiversity. Although spatial, temporal, and taxonomic biases in NHCs affect analyses, the influence of collector practices on biases remains largely unexplored.We utilized one million digitized specimens collected in the northeastern United States byc.10 000 collectors to investigate how collector practices shape spatial, temporal, and taxonomic biases in NHCs; and similarities and differences between practices of more‐ and less‐prolific collectors.We identified six common collector practices, or collection norms: collectors generally collected different species, from multiple locations, from sites sampled by others, during the principal growing season, species identifiable outside peak collecting months, and species from species‐poor families and genera. Some norms changed over decades, with different taxa favored during different periods. Collection norms have increased taxonomic coverage in NHCs; however, collectors typically avoided large, taxonomically complex groups, causing their underrepresentation in NHCs. Less‐prolific collectors greatly enhanced coverage by collecting during more months and from less‐sampled locations.We assert that overall collection biases are shaped by shared predictable collection norms rather than random practices of individual collectors. Predictable biases offer an opportunity to more effectively address biases in future biodiversity models.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.12685/bauhinia.1374
