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Abstract Biodiversity scientists must be fluent across disciplines; they must possess the quantitative, computational, and data skills necessary for working with large, complex data sets, and they must have foundational skills and content knowledge from ecology, evolution, taxonomy, and systematics. To effectively train the emerging workforce, we must teach science as we conduct science and embrace emerging concepts of data acumen alongside the knowledge, tools, and techniques foundational to organismal biology. We present an open education resource that updates the traditional plant collection exercise to incorporate best practices in twenty-first century collecting and to contextualize the activities that build data acumen. Students exposed to this resource gained skills and content knowledge in plant taxonomy and systematics, as well as a nuanced understanding of collections-based data resources. We discuss the importance of the extended specimen in fostering scientific discovery and reinforcing foundational concepts in biodiversity science, taxonomy, and systematics. 

Abstract The early twenty-first century has witnessed massive expansions in availability and accessibility of digital data in virtually all domains of the biodiversity sciences. Led by an array of asynchronous digitization activities spanning ecological, environmental, climatological, and biological collections data, these initiatives have resulted in a plethora of mostly disconnected and siloed data, leaving to researchers the tedious and time-consuming manual task of finding and connecting them in usable ways, integrating them into coherent data sets, and making them interoperable. The focus to date has been on elevating analog and physical records to digital replicas in local databases prior to elevating them to ever-growing aggregations of essentially disconnected discipline-specific information. In the present article, we propose a new interconnected network of digital objects on the Internet—the Digital Extended Specimen (DES) network—that transcends existing aggregator technology, augments the DES with third-party data through machine algorithms, and provides a platform for more efficient research and robust interdisciplinary discovery. 

1. The Poweshiek skipperling [Oarisma poweshiek(Parker, 1870; Lepidoptera: Hesperiidae)] is a federally endangered butterfly that was historically common in prairies of the upper Midwestern United States and Southern Manitoba, Canada. Rapid declines over the last 20 years have reduced the population numbers to four verified extant sites. The causes of Poweshiek skipperling decline are unknown. 2. We aggregated all known Poweshiek skipperling occurrence records to examine the spatiotemporal patterns of Poweshiek skipperling decline. Ecological niche models were developed for five time frames (1985, 1990, 1995, 2000 and 2005) and three spatial extents (eastern occupied range, western occupied range and total occupied range). We used a backward elimination method to investigate the effects of climate and land use on the ecological niche of Poweshiek skipperling. 3. Predictors of occurrence changed over time and across the geographical extent of Poweshiek skipperling. Land use covariates were retained in east models. In the west and total extent, climate variables contributed the most to model predictive power for the 1985, 1990 and 1995 models; land use variables contributed the most to model predictive power in the 2000 and 2005 models. 4. During the rapid decline in Poweshiek skipperling population numbers occurring at the turn of the century, probability of Poweshiek skipperling presence was being driven by proportion of natural land cover and distance to nearest grassland/wetland. Our results suggest that these land use variables are important landscape‐level variables to consider when developing risk assessments of extant populations and potential reintroduction sites.

 

With digitization and data sharing initiatives underway over the last 15 years, an important need has been prioritizing specimens to digitize. Because duplicate specimens are shared among herbaria in exchange and gift programs, we investigated the extent to which unique biogeographic data are held in small herbaria vs. these data being redundant with those held by larger institutions. We evaluated the unique specimen contributions that small herbaria make to biogeographic understanding at county, locality, and temporal scales.

We sampled herbarium specimens of 40 plant taxa from each of eight states of the United States of America in four broad status categories: extremely rare, very rare, common native, and introduced. We gathered geographic information from specimens held by large (≥100,000 specimens) and small (<100,000 specimens) herbaria. We built generalized linear mixed models to assess which features of the collections may best predict unique contributions of herbaria and used an Akaike information criterion‐based information‐theoretic approach for our model selection to choose the best model for each scale.

Small herbaria contributed unique specimens at all scales in proportion with their contribution of specimens to our data set. The best models for all scales were the full models that included the factors of species status and herbarium size when accounting for state as a random variable.

We demonstrated that small herbaria contribute unique information for research. It is clear that unique contributions cannot be predicted based on herbarium size alone. We must prioritize digitization and data sharing from herbaria of all sizes.