Abstract:Over 300 million arthropod specimens are housed in North American natural history collections. These collections represent a “vast hidden treasure trove” of biodiversity −95% of the specimen label data have yet to be transcribed for research, and less than 2% of the specimens have been imaged. Specimen labels contain crucial information to determine species distributions over time and are essential for understanding patterns of ecology and evolution, which will help assess the growing biodiversity crisis drive… Show more
“…Furthermore, additional curation and digitization of museum specimens will be critical in developing a historical backbone for analyses across time and space. Millions of specimens still remain undigitized in arthropod natural history collections (Cobb et al 2019), and the continuation of funding for museum staff and biodiversity informatics infrastructure will be critical in mobilizing these data needed for ecological research, especially potential for some kinds of temporal trend analyses (Soroye et al 2020) . Supporting digitization in tandem with concerted efforts to direct community science initiatives towards under-sampled regions will move us towards unlocking the full potential of these opportunistic data in an era of global change.…”
Aggregate biodiversity data from museum specimens and community observations have promise for macroscale ecological analyses. Despite this, many groups are under-sampled, and sampling is not homogeneous across space. Here we used butterflies, the best documented group of insects, to examine inventory completeness across North America. We separated digitally accessible butterfly records into those from natural history collections and burgeoning community science observations to determine if these data sources have differential spatio-taxonomic biases. When we combined all data, we found startling under-sampling in regions with the most dramatic trajectories of climate change and across biomes. We also found support for the hypothesis that community science observations are filling more gaps in sampling but are more biased towards areas with the highest human footprint. Finally, we found that both types of occurrences have familial-level taxonomic completeness biases, in contrast to the hypothesis of less taxonomic bias in natural history collections data. These results suggest that higher inventory completeness, driven by rapid growth of community science observations, is partially offset by higher spatio-taxonomic biases. We use the findings here to provide recommendations on how to alleviate some of these gaps in the context of prioritizing global change research.
“…Furthermore, additional curation and digitization of museum specimens will be critical in developing a historical backbone for analyses across time and space. Millions of specimens still remain undigitized in arthropod natural history collections (Cobb et al 2019), and the continuation of funding for museum staff and biodiversity informatics infrastructure will be critical in mobilizing these data needed for ecological research, especially potential for some kinds of temporal trend analyses (Soroye et al 2020) . Supporting digitization in tandem with concerted efforts to direct community science initiatives towards under-sampled regions will move us towards unlocking the full potential of these opportunistic data in an era of global change.…”
Aggregate biodiversity data from museum specimens and community observations have promise for macroscale ecological analyses. Despite this, many groups are under-sampled, and sampling is not homogeneous across space. Here we used butterflies, the best documented group of insects, to examine inventory completeness across North America. We separated digitally accessible butterfly records into those from natural history collections and burgeoning community science observations to determine if these data sources have differential spatio-taxonomic biases. When we combined all data, we found startling under-sampling in regions with the most dramatic trajectories of climate change and across biomes. We also found support for the hypothesis that community science observations are filling more gaps in sampling but are more biased towards areas with the highest human footprint. Finally, we found that both types of occurrences have familial-level taxonomic completeness biases, in contrast to the hypothesis of less taxonomic bias in natural history collections data. These results suggest that higher inventory completeness, driven by rapid growth of community science observations, is partially offset by higher spatio-taxonomic biases. We use the findings here to provide recommendations on how to alleviate some of these gaps in the context of prioritizing global change research.
“…Digitisation is a concept that, depending on the person, context or situation, may have different meanings (see also p. 3 in Cobb et al 2019). Generally, digitisation is the process of creating a virtual representation of physical objects.…”
Section: Digitisationmentioning
confidence: 99%
“…Using the term digitisation in some cases explicitly refers to making digital images whereas in others it is restricted solely to data. When using digitisation in combination with registering data, it may include all the data linked to a specimen or only part of the data (Saarenmaa et al 2019). By using the term 'digitisation' one also cannot assume that this also includes validation or georeferencing.…”
Section: Digitisationmentioning
confidence: 99%
“…Results from a survey carried out across Europe in 2018 as part of the ICEDIG project and presented as deliverable D2.2 of this project (van Egmond et al 2019) showed that most private collections hold less than 10,000 specimens, some 25% having more than 10,000 specimens. Private collections with more than 100,000 objects are exceptional but still form some 5% of the totalof private collections around.…”
Section: Private Collectionsmentioning
confidence: 99%
“…Whether data is derived from specimens kept in larger museums or small private collections is much less important than the certainty of high data quality and reliable identifications. The already mentioned survey carried out in the ICEDIG project (van Egmond et al 2019) in 2018 showed that the overall size of private collections and the data they contain is impressive and for specific groups or geographic areas unique. The research community is organised in a multitude of programs, platforms and projects both at the national and international level.…”
Results are presented of a study investigating solutions and procedures to incorporate private natural history collections into the international collections data infrastructure. Results are based on pilot projects carried out in three European countries aimed at approaches on how to best motivate and equip citizen collectors for digitisation:
1) In Estonia, the approach was to outline tools for registering, digitising and publishing private collection data in the biodiversity data management system PlutoF.
2) In Finland, the functionality of FinBIF, a portal offering a popular Notebook Service for citizens to store observations has been expanded to include collection specimens related to a field gathering event.
3) In the Netherlands private collection owners were approached directly and asked to start digitising their collection using dedicated software, either by themselves or with the help of volunteers who were recruited specifically for this task.
In addition to management tools, pilots also looked at motivation, persons undertaking the work, scope, planning, specific knowledge or skills required and the platform for online publication. Future ownership, legality of specimens residing in private collections and the use of unique identifiers are underexposed aspects effecting digitisation. Besides streamlining the overall process of digitising private collections and dealing with local, national or international challenges, developing a communication strategy is crucial in order to effectively distribute information and keep private collection owners aware of ongoing developments.
Besides collection owners other stakeholders were identified and for each of them a roadmap is outlined aimed at further streamlining the data from private collections into the international infrastructure.
In conclusion recommendations are presented based on challenges encountered during this task that are considered important to really make significant progress towards the overall accessibility of data stored in privately held natural history collections.
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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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