BKH is a one-stop portal that allows users to access FAIR and interlinked biodiversity data and services in a few clicks. BKH was designed to support a new emerging community of users over time and across the entire biodiversity research cycle providing its services to anybody, anywhere and anytime.
The Knowledge Hub is the main product from our BiCIKL consortium, and we are delighted with the result!
BKH can easily be seen as the beginning of the major shift in the way we search interlinked biodiversity information.”
Biodiversity researchers, research infrastructures and publishers interested in fields ranging from taxonomy to ecology and bioinformatics can now freely use BKH as a compass to navigate the oceans of biodiversity data. BKH will do the linkages.
says Prof. Lyubomir Penev,BiCIKL’s Project coordinator and Founder of Pensoft Publishers.
The BKH is designed to serve a new emerging community of users over time and across the entire biodiversity research cycle.
We have invested our best energies and resources in the development of BKH and the Fair Data Place (FDP), which is the beating heart of the portal,”
BKH has been designed to support a new emerging community of users across the entire biodiversity research cycle.
Its purpose goes beyond the BiCIKL project itself: we are thrilled to say that BKH is meant to stay, aiming to reshape the way biodiversity knowledge is accessed and used.
The BKH outlines how users can navigate and access the linked data, tools and services of the infrastructures cooperating in BiCIKL.
By revealing how they harvest, liberate and reuse data, these increasingly integrated sources enable researchers in the natural sciences to move more seamlessly between specimens and material samples, genomic and metagenomic data, scientific literature, and taxonomic names and units.
In 2018, NHM London’s digitisation team started a project to digitise non-type herbarium material from the legume family. A recent data paper in the Biodiversity Data Journal reports on the outcomes.
You can find the original blog post by the Natural History Museum of London, reposted here with minor edits.
Legumes are a group of plants that include soybeans, peas, chickpeas, peanuts and lentils. They are a significant source of protein, fibre, carbohydrates, and minerals in our diet and some, like the cowpea, are resistant to droughts.
The project’s outcomes were published in a data paper in the Biodiversity Data Journal. Within the project, the digitisation team aimed to collectively digitise non-type herbarium material from the legume family. This includes rosewood trees (Dalbergia), padauk trees (Pterocarpus) and the Phaseolinae subtribe that contains many of the beans cultivated for human and animal food.
Guinea, Ethiopia, Sudan, Kenya, Uganda, Tanzania, Mozambique, Malawi and Madagascar
Asian
Bangladesh, Myanmar, Nepal, New Guinea and India
Southern and Central American
Guatemala, Honduras, El Salvador, Nicaragua, Bolivia, Argentina and Brazil
ODA-listed Countries
The legume groups: Dalbergia, Pterocarpus and Phaseolinae,were chosen for digitisation to support the development of dry beans as a sustainable and resilient crop, and to aid conservation and sustainable use of rosewood and padauk trees. Some of these beans, especially cow pea and pigeon pea, are sustainable and resilient crops, as they can be grown in poor-quality soils and are drought stress resistant. This makes them particularly suitable for agricultural production where the growing of other crops would be difficult.
Digitally discoverable herbarium specimens can provide important information about the distribution of individual species, as well as highlighting which species occur naturally together.
While there have been collaborative efforts between herbaria in the past, these have tended to prioritise digitisation of type specimens: the example specimens for which a species is named.
Types are important to identification, but being individual specimens, they don’t offer insights into species distribution over time. By focusing on the non-types across the world and over the last 200 years, we have released a brand-new resource to the global scientific community.
Searching for beans
This collection was digitised by creating an inventory record for each specimen, attaching images of each herbarium sheet, and then transcribing more data and georeferencing the specimens, providing an accurate locality in space and time for their collection.
We originally had four months and three members of staff to digitise over 11,000 specimens. The Covid-19 lockdown was ironically rather lucky for this project as it enabled us to have more time to transcribe and georeference all of the records.
say the researchers behind the digitisation project.
Map showing breakdown of records by country.
“We were able to assign country-level data to 10,857 out of the total number of 11,222 records. We were also able to transcribe the collectors’ names from the majority of our specimen labels (10,879 out of 11,222). Only 770 out of the 2,226 individuals identified during this project collected their specimens in ODA listed countries. The highest contributors were: Richard Beddome (130 specimens), Charles Clarke (110), Hans Schlieben (98) and Nathaniel Wallich (79). The breakdown of records by ODA country can be seen in the chart below. “
Map showing breakdown of records by country and pie chart showing distribution by ODA listed countries.
From our data, we can see the peak decade of collection was the 1930s, with almost half (4,583 specimens or 49,43%) collected between 1900 and 1950 (Fig. 10).
This peak can be attributed to three of our most prolific collectors: Arthur Kerr, John Gossweiler and Georges Le Testu, all of whom were most active in the 1930s. The oldest specimen (BM013713473) was collected by Mark Catesby (1683-1749) in the Bahamas in 1726.
they explain.
An interesting, but perhaps unsurprising, finding is that our collection is strongly male-dominated.
There are only two women (Caroline Whitefoord and Ynes Mexia) in the list of our top 50 plant collectors and they are not close to the most prolific collectors.
We identified more women in the rest of our records, but their contribution is on average less than 25 specimens per person in the dataset consisting of more than 10,000 specimens. In contrast, the top five male collectors contributed 10% of our collection.
they continued
Releasing Rosewoods
Both the Pterocarpus and Dalbergia genera include species that are used as expensive good quality timber that is prone to illegal logging. Many species such as Pterocarpus tinctorius are also listed on the International Union for Conservation of Nature (IUCN) Red List of Threatened Species. By releasing this new resource of information on all these plants from three of the biggest herbaria in the world, we can share this datа with the people who are taking care of biodiversity in these countries. The data can be used to identify hotspots, where the tree is naturally growing and protect these areas. These data would also allow much closer attention to be paid to areas that could be targets for illegal logging activity.
Pterocarpus tinctorius is a species of padauk tree that is listed as endangered on the IUCN Red List.
Cowpea (Vigna unguiculata) is a food and animal feed crop grown in the semi-arid tropics.
The ODA-listed countries are economically impoverished and disproportionately prone to be disadvantaged with the changing climate whether from flood or drought or increase in temperature.
Using data to identify good, nutritious plant species that can be grown in such conditions can therefore benefit local communities, potentially reducing dependence on imports, aid and on less resilient crops.
the team adds in conclusion.
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This dataset is now openly available on the Museum’s Data Portal and a data paper about this work has been released in the Biodiversity Data Journal.
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New Research Idea, published in RIO Journal presents a promising machine-learning ecosystem to unite experts around the world and make up for lacking taxonomic expertise.
In their Research Idea, published in Research Ideas and Outcomes (RIO Journal), Swiss-Dutch research team present a promising machine-learning ecosystem to unite experts around the world and make up for lacking expert staff
Guest blog post by Luc Willemse, Senior collection manager at Naturalis Biodiversity Centre (Leiden, Netherlands)
Imagine the workday of a curator in a national natural history museum. Having spent several decades learning about a specific subgroup of grasshoppers, that person is now busy working on the identification and organisation of the holdings of the institution. To do this, the curator needs to study in detail a huge number of undescribed grasshoppers collected from all sorts of habitats around the world.
The problem here, however, is that a curator at a smaller natural history institution – is usually responsible for all insects kept at the museum, ranging from butterflies to beetles, flies and so on. In total, we know of around 1 million described insect species worldwide. Meanwhile, another 3,000 are being added each year, while many more are redescribed, as a result of further study and new discoveries. Becoming a specialist for grasshoppers was already a laborious activity that took decades, how about knowing all insects of the world? That’s simply impossible.
Then, how could we expect from one person to sort and update all collections at a museum: an activity that is the cornerstone of biodiversity research? A part of the solution, hiring and training additional staff, is costly and time-consuming, especially when we know that experts on certain species groups are already scarce on a global scale.
We believe that automated image recognition holds the key to reliable and sustainable practises at natural history institutions.
Today, image recognition tools integrated in mobile apps are already being used even by citizen scientists to identify plants and animals in the field. Based on an image taken by a smartphone, those tools identify specimens on the fly and estimate the accuracy of their results. What’s more is the fact that those identifications have proven to be almost as accurate as those done by humans. This gives us hope that we could help curators at museums worldwide take better and more timely care of the collections they are responsible for.
However, specimen identification for the use of natural history institutions is still much more complex than the tools used in the field. After all, the information they store and should be able to provide is meant to serve as a knowledge hub for educational and reference purposes for present and future generations of researchers around the globe.
This is why we propose a sustainable system where images, knowledge, trained recognition models and tools are exchanged between institutes, and where an international collaboration between museums from all sizes is crucial. The aim is to have a system that will benefit the entire community of natural history collections in providing further access to their invaluable collections.
We propose four elements to this system:
A central library of already trained image recognition models (algorithms) needs to be created. It will be openly accessible, so any other institute can profit from models trained by others.
Mock-up of a Central Library of Algorithms.
A central library of datasets accessing images of collection specimens that have recently been identified by experts. This will provide an indispensable source of images for training new algorithms.
Mock-up of a Central Library of Datasets.
A digital workbench that provides an easy-to-use interface for inexperienced users to customise the algorithms and datasets to the particular needs in their own collections.
As the entire system depends on international collaboration as well as sharing of algorithms and datasets, a user forum is essential to discuss issues, coordinate, evaluate, test or implement novel technologies.
How would this work on a daily basis for curators? We provide two examples of use cases.
First, let’s zoom in to a case where a curator needs to identify a box of insects, for example bush crickets, to a lower taxonomic level. Here, he/she would take an image of the box and split it into segments of individual specimens. Then, image recognition will identify the bush crickets to a lower taxonomic level. The result, which we present in the table below – will be used to update object-level registration or to physically rearrange specimens into more accurate boxes. This entire step can also be done by non-specialist staff.
Mock-up of box with grasshoppers mentioned in the above table
Results of automated image recognition identify specimens to a lower taxonomic level.
Another example is to incorporate image recognition tools into digitisation processes that include imaging specimens. In this case, image recognition tools can be used on the fly to check or confirm the identifications and thus improve data quality.
Mock-up of an interface for automated taxon identification.
Using image recognition tools to identify specimens in museum collections is likely to become common practice in the future. It is a technical tool that will enable the community to share available taxonomic expertise.
Using image recognition tools creates the possibility to identify species groups for which there is very limited to none in-house expertise. Such practises would substantially reduce costs and time spent per treated item.
Image recognition applications carry metadata like version numbers and/or datasets used for training. Additionally, such an approach would make identification more transparent than the one carried out by humans whose expertise is, by design, in no way standardised or transparent.
Greeff M, Caspers M, Kalkman V, Willemse L, Sunderland BD, Bánki O, Hogeweg L (2022) Sharing taxonomic expertise between natural history collections using image recognition. Research Ideas and Outcomes 8: e79187. https://doi.org/10.3897/rio.8.e79187
A myriad of species and genera new to science, including economically important wasps drawing immediate attention because of their amusing names and remarkable physical characters, in addition to work set to lay the foundations for future taxonomic and conservation research, together comprise the latest 64th issue of Journal of Hymenoptera Research (JHR).
The species Qrocodiledundee outbackense
Two genera (Qrocodiledundee and Tobleronius) named after the action comedy Crocodile Dundee and the chocolate brand Toblerone are only a couple of the 14 new genera from the monograph of the microgastrine wasps of the world’s tropical regions, authored by Dr Jose Fernandez-Triana and Caroline Boudreault of the Canadian National Collection of insects in Ottawa. In their article, the team also describes a total of 29 new species, where five of them carry the names of institutions holding some of the most outstanding wasp collections.
Another curiously named species of microgastrine wasp described in the new JHR issue, is called Eadya daenerys in reference to Daenerys Targaryen, a fictional character known from the best-selling book series A Song of Ice and Fire by George R. R. Martin, and the blockbuster TV show Game of Thrones. Discovered by University of Central Florida‘s Ryan Ridenbaugh, Erin Barbeau and Dr Barbara Sharanowski as a result of a collaboration between biocontrol researchers and taxonomists, the new species might not be in control of three dragons, nor a ruler or protector of whole nations. However, by being a potential biocontrol agent against a particular group of leaf beetle pests, it could spare the lives of many eucalyptus plantations around the world.
The species Tobleronius orientalis
Furthermore, a wasp named Dolichogenidea xenomorph, which parasitises other eucalyptus pests, is also named after a character from a sought-after franchise. The scriptwriters of the horror sci-fi movie series Alien are thought to have been thinking of parasitic wasps when they came up with the character Xenomorph, remind authors Erinn Fagan-Jeffries, Dr Steven Cooper and Dr Andrew Austin. Additionally, the team from University of Adelaide and the South Australian Museum point out that the species name translates to ‘strange form’ in Greek, which perfectly suits the characteristic remarkably long ovipositor of the new wasp.
Tomanovic, University of Belgrade, studies ways to extract DNA from dry parasitoid wasps from the natural history archives decades after their preservation. In their work, they make it clear that such projects are of great importance for future taxonomic and conservation research, as well as agriculture.
Boeve; J, Dominguez D, Smith D (2018) Sawflies from northern Ecuador and a checklist for the country (Hymenoptera: Argidae, Orussidae, Pergidae, Tenthredinidae, Xiphydriidae). Journal of Hymenoptera Research 64: 1-24. https://doi.org/10.3897/jhr.64.24408
Ridenbaugh RD, Barbeau E, Sharanowski BJ (2018) Description of four new species of Eadya (Hymenoptera, Braconidae), parasitoids of the Eucalyptus Tortoise Beetle (Paropsis charybdis) and other Eucalyptus defoliating leaf beetles. Journal of Hymenoptera Research 64: 141-175. https://doi.org/10.3897/jhr.64.24282
Fagan-Jeffries EP, Cooper SJB, Austin AD (2018) Three new species of Dolichogenidea Viereck (Hymenoptera, Braconidae, Microgastrinae) from Australia with exceptionally long ovipositors. Journal of Hymenoptera Research 64: 177-190. https://doi.org/10.3897/jhr.64.25219
Boeve; J, Dominguez D, Smith D (2018) Sawflies from northern Ecuador and a checklist for the country (Hymenoptera: Argidae, Orussidae, Pergidae, Tenthredinidae, Xiphydriidae). Journal of Hymenoptera Research 64: 1-24. https://doi.org/10.3897/jhr.64.24408
Mitrovic M, Tomanovic Z (2018) New internal primers targeting short fragments of the mitochondrial COI region for archival specimens from the subfamily Aphidiinae (Hymenoptera, Braconidae). Journal of Hymenoptera Research 64: 191-210. https://doi.org/10.3897/jhr.64.25399
