Measuring the use of natural history collections is essential to understand their past and present impact on science, to underpin decisions about their management and to assist with deploying them optimally to address societal challenges. Using the vast natural history collections of Naturalis Biodiversity Center as an example, this paper assesses the significance and relevance of quantifying collection use. Four aspects are discussed: 1. standardisation, 2. relevance of having standardised metrics on collection use, 3. the level of detail and completeness of the information and 4. the interactions between digitisation of collections and physical collection use. Based on a set of transparent and objective parameters to describe collection use, it is proposed to further develop these into international standards.

During an inventory of the Gelastocoridae in the Naturalis Biodiversity Center, Leiden, The Netherlands (formerly Rijksmuseum van Natuurlijke Historie, Leiden, The Netherlands), and the Nieser & Chen Collection in Tiel, The Netherlands, we came across an undescribed species of Nerthra Say, 1832 (Hemiptera: Heteroptera: Gelastocoridae: Nerthrinae). The specimens were collected from Bonaire and Curaçao, Kingdom of the Netherlands, and we describe them here as N. papaceki sp. nov. Nerthra lurida Todd, 1959, syn. nov., is synonymized with Nerthra occidua Todd, 1959 (both from Indonesia: Sulawesi). In addition, we provide new distributional records of Nerthra americana (Montandon, 1905) (Brazil: Paraná), Nerthra buenoi Todd, 1955 (Brazil: Pará), and Nerthra raptoria (Fabricius, 1803) (Suriname) and provide their differential diagnoses and illustrations.

During an inventory of the Gelastocoridae in the Naturalis Biodiversity Center, Netherlands), and the Nieser & Chen Collection in Tiel, The Netherlands, we came across an undescribed species of Nerthra Say, 1832 (Hemiptera: Heteroptera: Gelastocoridae: Nerthrinae). The specimens were collected from Bonaire and Cura & ccedil;ao, Kingdom of the Netherlands, and we describe them here as N. papaceki sp. nov. Nerthra lurida Todd, 1959, syn. nov., is synonymized with Nerthra occidua Todd, 1959 (both from Indonesia: Sulawesi). In addition, we provide new distributional records of Nerthra americana (Montandon, 1905) (Brazil: Paran & aacute;), Nerthra buenoi Todd, 1955 (Brazil: Par & aacute;), and Nerthra raptoria (Fabricius, 1803) (Suriname) and provide their differential diagnoses and illustrations.

Taxonomy of the genus Cordulegaster Leach in Brewster, 1815 in Greece is not completely understood. The taxonomic status of the subspecies C. helladica buchholzi (Lohmann, 1993), C. helladica kastalia (Lohmann, 1993), and C. heros pelionensis Theischinger, 1979 was still unclear. We applied a molecular genetic approach using sequences of mitochondrial and nuclear DNA fragmentscytochrome c oxidase I (COI) and Internal Transcribed Spacer 1 (ITS1). This approach revealed that specimens presently assigned to C. heros pelionensis should be considered as conspecific to the nominate subspecific taxon making C. heros a monotypic species. Two major monophyletic lines were found within the Greek representatives of the species grouped around C. bidentata Selys, 1843: the clade of the European endemic C. bidentata and the clade composed of three species: C. helladica (Lohmann, 1993), C. buchholzi (stat. nov., raised to species level), and C. insignis Schneider, 1845. Cordulegaster helladica is restricted to the Peloponnese. Cordulegaster buchholzi is not restricted to the Cyclades as previously thought, but widespread from the Cyclades over the island Euboea to south-east mainland Greece reaching in the west near Mount Parnassos, where it hybridize with C. bidentata. Hybridization between C. bidentata and C. buchholzi was detected at the Castalian Spring, where in ancient times the Oracle of Delphi was located, and some kilometres east of the Castalian Spring. These hybrids had been formerly named C. helladica kastalia. In the case of C. insignis montandoni St. Quentin, 1971 we have investigated specimens some kilometres away from the type locality in Romania, which all revealed hybrids between C. bidentata and C. insignis. However, we do not know if specimens phenotypically looking like C. insignis from further west in the SE Balkans represent isolated population of C. insignis within the range of C. bidentata or belong to a broader hybrid zone between C. bidentata and C. insignis.

Natural history collections play a vital role in biodiversity research and conservation by providing a window to the past. The usefulness of the vast amount of historical data depends on their quality, with correct taxonomic identifications being the most critical. The identification of many of the objects of natural history collections, however, is wanting, doubtful or outdated. Providing correct identifications is difficult given the sheer number of objects and the scarcity of expertise. Here we outline the construction of an ecosystem for the collaborative development and exchange of image recognition algorithms designed to support the identification of objects. Such an ecosystem will facilitate sharing taxonomic expertise among institutions by offering image datasets that are correctly identified by their in-house taxonomic experts. Together with openly accessible machine learning algorithms and easy to use workbenches, this will allow other institutes to train image recognition algorithms and thereby compensate for the lacking expertise.

Taxonomy of the genus Cordulegaster Leach in Brewster, 1815 in the Eastern part of the Western Palaearctic is poorly resolved. A two-step approach was applied: sequences of mitochondrial and nuclear DNA fragments were used to sort specimens; poorly known or new taxa with their phenotypic variation were described. The existence of two traditional groups (boltonii- and bidentata-group) was confirmed. Cordulegaster coronata Morton, 1916, however, belongs to a different group. Molecular-analysis supported three known and one new species (C. heros Theischinger, 1979, C. picta Selys, 1854, C. vanbrinkae Lohmann, 1993, and C. kalkmani sp. nov.) in the boltonii-group. In the bidentata-group, all specimens from West-Turkey belonged to C. insignis Schneider, 1845, all specimens further east to a complex of four closely related species, which we name charpentieri-complex (C. amasina Morton, 1916, stat. rev., C. mzymtae Bartenev, 1929 C. charpentieri (Kolenati, 1846), stat. rev. and C. cilicia sp. nov.). The following taxa: C. insignis nobilis Morton, 1916, syn. nov., C. nachitschevanica Skvortsov and Snegovaya, 2015, syn. nov. C. plagionyx Skvortsov and Snegovaya, 2015, syn. nov. and the Caucasian subspecies C. insignis lagodechica Bartenev, 1930, syn. nov., were synonymized with C. charpentieri. Finally, we provide a key for all Western Palaearctic Cordulegaster.

In terms of amateurs and professionals studying and collecting insects, Lepidoptera represent one of the most popular groups. It is this popularity, in combination with wings being routinely spread during mounting, which results in Lepidoptera often taking up the largest number of drawers and space in entomological collections. As resources grow increasingly scarce in natural history museums, any process that results in more efficient use of resources is a welcome addition to collection management practices. Therefore, we propose an alternative method to process papered Lepidoptera: a workflow to digitize (imaging and data registration) papered specimens and to store them (semi)permanently, still unmounted, in glassine envelopes. The mounting of specimens will be limited to those for which it is considered essential. The entire workflow of digitization and repacking can be carried out by non-expert volunteers. By releasing data and images on the internet, taxonomic experts worldwide can assist with identifications. This method was tested for Papilionidae. Results suggest that the workflow and permanent storage in glassine envelopes described here can be applied to most groups of Lepidoptera.

Butterflies are important ecosystem components. They play a major role in pollination, are preyed upon and parasitized by other species, and because of their specific habitat requirements, populations can change quickly and are widely regarded as sensitive environmental indicators, being used to assess factors ranging from climate change to land management. So in addition to their enormous aesthetic appeal and educational value to the layperson, they are important to the scientific community in investigating pressing climate change and biodiversity issues. While attention to and knowledge of butterflies in western countries is significant, this is not necessarily the case for species-rich tropical areas. Naturalis Biodiversity Center possesses a world-class collection of Southeast Asian butterflies, Indonesian specimens in particular, and would like to bridge this geographic gap in knowledge by embarking on a five-year project to establish an online presence of Southeast Asian butterflies. We hope to establish a consortium of interested international museums and institutes to join us in documenting species-level natural histories, distribution and occurrence data, and photos. The data we will be using will come from literature, digitized collections and observations. Ultimately, we hope to also develop a species identification app, provide links to Red List species protection data, serve as an online field guide for butterfly enthusiasts, and promote and stimulate European Union (EU) and Southeast Asian collection digitization. We will begin this year with a pilot project limited to swallowtails (Papilionidae) in our initial effort to provide an online resource of Southeast Asian butterflies for scientists, educators and laypersons alike.

The completeness and quality of the information in natural history museum collections is essential to support its use, such as in collection management. Currently, the accuracy of the taxonomic information largely depends on expert provided metadata, such as species identification. At present an increase in the use of digitization techniques coincides with a dwindling of the number of taxonomic specialists, creating a growing backlog in specimen identifications. We are investigating the role of artificial intelligence for automatic species identification in supporting collection management. When identifying collection specimens, common species are predominantly present, taking up a large amount of the expert’s time, who has to deal with a relatively easy, repetitive task. Therefore, one of our aims is to use human expertise where it is most needed, for complex tasks, and use properly validated computational methods for repetitive, less difficult identifications. To this end, we demonstrate the use of automatic species identification in digitization workflows, using deep learning based image recognition. We investigated potential gains in the identification process of a large digitization project of papered Lepidoptera (>500,000 specimens). In this ongoing project, volunteers unpack, register and photograph the unmounted butterflies and repack them sustainably, still unmounted. Using only the individual images made by volunteers, taxonomic experts identify the specimens. Considering that the speed of digitization currently exceeds that of identification, a growing backlog of yet-to-be-identified specimens has formed, limiting the speed of publication of this biodiversity information. The test case for image recognition concerns specimens of the families Papilionidae and Lycaenidae, mostly collected in Indonesia. By allowing the volunteers to provide an automatically generated identification with each image, we enable the taxonomic specialists to quickly validate the more easily identifiable specimens. This reduces their workload, allows them to focus on the more demanding specimens and increases the rate of specimen identification. We demonstrate how to combine computer and human decisions to ensure both high data quality standards and reduction of expert time.

A never ending and universal challenge in the management of biodiversity collections is to find a balance between on the one hand creating optimal conditions for conservation and maximizing accessibility and on the other, achieving this with limited resources, i.e. funding, time and space. If for instance available resources do not allow storage under the conditions required for optimal preservation and accessibility, what compromises and solutions can be made or found? Finding solutions and making compromises is far from easy, differs in each situation and per collection and is by and large carried out independently and single-handedly by each facility. In this presentation elements that are decisive in collection development are reviewed, starting from strategic choices regarding acquisition up to deaccession. Some examples of compromises and solutions are provided regarding collection acquisition, deselection and efficient storage. A typical phenomenon in natural history collections is asymmetrical space requirements per species: common species take up (a lot) more space in collections than rare species. As a potential solution, this presentation explores the idea of establishing a national or transnational centralized storage facility for 'common species' in combination with digitization and discusses its advantages and disadvantages.

By the summer of 2015 Naturalis Biodiversity Center had come to the end of a five-year digitization programme that aimed at digitally disclosing the entire collection of, at the time, 38 million objects. The result was a vast amount of collections data being made available to researchers, collection managers and the public. In order to utilize these data to their full extent, Naturalis has in the past few years been developing the Netherlands Biodiversity Data Services (NBDS). These services “speak” not only to our digitized collection, but to other sources of information as well and lets us query and use these data in a centralized manner. While the NBDS open up a lot of possibilities for i.e. communication, exhibition, education, policy making, etc., a very important field for its application is collection management. Instead of managing (at this point) 41 million individual objects, the NBDS could provide insight into custom aggregations of data to further professionalize decision making. Not only detailed information about taxonomy, gathering events and collection history can be provided, one can also think about quantifying use, conservation status, change in collection-size over time, etc. Some examples of application for collections management will be given during the presentation and illustrated with a collections dashboard. Even though we have made great progress in digitization, certain parts of our collection are not digitized to specimen-level and to various degrees of completeness, parts of the physical collection are not identified to species level, not all data are consistent or properly validated, etc. But instead of this limiting the applicability of the NBDS, the data service can be used as a tool to pinpoint these areas for improvement and to allow collection management to properly address and prioritize them. This presentation ultimately deals with the potential the NBDSNBA has for managing collections, both physical as digital, and enhancing their quality and value.

Techniques for image recognition through machine learning have advanced rapidly over recent years and applications using this technique are becoming increasingly common.. Applications using image recognition have enormous potential not only for research, education, conservation and capacity-building but certainly also for collections management. Perhaps by now an even bigger challenge than the technological one is supplying content in the form of large amounts of validated images. With an estimated 44 million objects, the collection of Naturalis Biodiversity Center has plenty of physical source material. During a five-year digitization programme (2010–2015) at Naturalis, 4.4 million herbarium sheets were imaged and since the start of the “Butterflies in Bags” project, 50,000 papered butterflies (out of more than 500,000) have been digitized and photographed by volunteers in a standardized manner. Still there are large parts of our collection that are not digitized at specimen level, let alone imaged, but hold great potential for collections work. This poster presents a workflow for efficient scanning of insect drawers and automated segmentation of those images to “feed” deep learning-based image recognition with images of individual insects. It will also demonstrate how this will aid in enhancing the value of our collections. With proper expert validation early on in the process, the software could mature and become more independent in such a way that ultimately, it could be used by non-specialist professionals to identify the majority of common species. The technique would pinpoint anomalies based on self-learned patterns, both in unidentified and in already identified specimens, and link those back to the taxonomic specialist. Not only does image recognition aid taxonomy, it may also hold potential for conservation and management by, for example, detecting damaged specimens or managing space utilization of drawers.