By translating global food trade flows into biodiversity loss transfers, the dataset maps how, and through which trade links, ecological impacts shift across borders.
Global food trade is essential for food security but its ecological consequences often remain unseen. A new data paper published in One Ecosystem introduces a global long-term dataset, quantifying biodiversity loss embodied in the international trade of staple food crops. As such, this dataset offers a novel perspective on how food trade redistributes environmental pressures worldwide.
Developed by Dr Zhuofan Huang, Dr Zhenglei He and Zelin Xing of the Guangdong University of Technology in Guangzhou, China, the dataset spans 1995–2022 and focuses on four major staple crops: wheat, soybean, rice and maize. By integrating bilateral trade data from UN Comtrade with agricultural production statistics from FAOSTAT and biodiversity loss intensity factors expressed as the Potential Disappeared Fraction (PDF), the dataset translates food trade flows into quantifiable biodiversity loss transfers between countries.
The resulting global network includes 157 countries and up to 91,414 trade relationships, capturing the dynamic evolution of biodiversity loss embedded in staple food trade over nearly three decades. Unlike previous studies that examine agricultural biodiversity impacts at national or sectoral levels, this dataset explicitly maps how biodiversity loss is transferred across borders through international trade.
Global biodiversity loss embodied in staple food trade (1995–2022)
Initial analyses reveal a strong upward trend in biodiversity loss embodied in global staple food trade. Among the four crops, soybean trade shows the most rapid increase, with biodiversity loss rising more than sixfold from 1995 to 2022, and surpassing wheat as the dominant contributor in recent years. The findings also highlight the central role of major agricultural producers and traders (including the United States, Brazil, China, Australia and Argentina) in shaping global biodiversity loss patterns.
Major trade pathways transferring biodiversity loss in global staple food trade between 1995-2022 (Huang et al., 2026)
The authors have openly released this dataset, therefore providing a valuable resource for interdisciplinary research and policy analysis. The data can support assessments of environmental responsibility in food supply chains, help identify high-risk trade pathways, and inform the development of more sustainable and equitable global food trade policies – these factors will in turn contribute to biodiversity conservation and the achievement of the UN Sustainable Development Goals 15 (Life on Land).
Original source:
Huang Z, He Z, Xing Z (2026) Dataset of Biodiversity Loss in Global Staple Food Trade, 1995-2022. One Ecosystem 11: e159884. https://doi.org/10.3897/oneeco.11.e159884
It is nothing new that our planet is facing a number of serious threats: climate change, biodiversity loss, pandemics… If you have been watching the news, all this is probably familiar to you. The wealth of data hosted in Natural history collections can contribute to finding a response to these challenges.Alas, today’s practices of working with collected bio- and geodiversity specimens lack some efficiency, thus limiting what our scientists can achieve.
In particular, there is a rather serious absence of linkages between specimen data. Sure, each specimen in a collection usually has its own catalogue ID that is unique within that collection, but the moment collections attempt to work with other collections -as they should in the face of planetary threats- problems start to arise because usually, each collection has its own way of identifying their data, thus leading to confusion.
Persistent identifiers: the DOIs
To avoid this problem, several initiatives have been launched in recent years to establish a globally accepted system of persistent identifiers (PIDs) that guarantee the “uniqueness” of collection specimens—physical or digital—over time.
Digital specimen DOIs can point to individual specimens in a collections.
You can think of a PID as a marker, an identifier that points at a single individual object and only one, differentiating it from any other in the world. You must have heard of acronyms such as ISBN or ORCID. Those are PIDs for books and individual scholars, respectively. For digital research content, the most widely used PID is the DOI (Digital Object Identifier), proposed by the DOI Foundation.
A DOI is an alphanumeric code that looks like this: 10.prefix/sufix
For example, if you type https://doi.org/10.15468/w6ubjx in your browser, you will reach the Royal Belgian Institute of Natural Sciences’s mollusk collection database, accessed through GBIF. This specific DOI will never point at anything else, and the identifier will remain the same in the future, even if changes occur in the content of this particular database.
DiSSCo and the DOIs
The Distributed System of Scientific Collections (DiSSCo) aims to provide a DOI for all individual digital specimens in European natural history collections. The point is not only to accurately identify specimens. That is, of course, crucial, but the DOI of a digital specimen provides a number of other advantages that are extremely interesting for DiSSCo and natural history collections in general. Among them, two are simply revolutionary.
The digital specimen DOI stores quick-access, basic metadata about the specimen.
Firstly, using DOIs allows linking the digital specimen to all other relevant information about the same specimen that might be hosted in other repositories (e.g. ecological data, genomic data, etc.). In creating this extended digital specimen that links different data types, digital specimen DOIs make a huge contribution to inter-institutional scientific work, filling the gap that is described at the beginning of this piece. Now scientists will be in a much better position to really exchange and link data across institutions.
Second, in contrast to most other persistent identifiers, the DOI of a digital specimen stores additional metadata (e.g. name, catalogue number) beyond the URL to which it redirects. This allows access to some information about the specimen without having to retrieve the full data object, i.e. without having to be redirected to the specimen HTML page. This metadata facilitates AI systems to quickly navigate billions of digital specimens and perform different automated work on them, saving us (humans) precious time.
Use of DOIs in publications
With all this in mind, it is easier to understand why being able to cite digital specimens in scholarly publications using DOIs is an important step. So far, the only DOIs that we could use in publications were those at the dataset level, not at the individual specimen level. In the example above, if a scientist were to publish an article about a specific type of bivalve in the Belgian collection, the only DOI that she or he would have available for citation in the article would be that of the entire mollusk database -containing hundreds or thousands of specimens- not the one of the specific oyster or scallop that might be the focus of the publication.
Main page of DiSSCo’s sandbox, the future DiSSCover service.
The publication in Biodiversity Data Journalabout the Chrysilla and Phintelloides genera is the first of its kind and opens the door to citing not only dataset-level objects but also individual specimens in publications using DOIs. You can try it yourself: Hover over the DOIs that are cited in the publication and you will get some basic information that might save you the time of visiting the page of the institution where the specimen is. Click on it and you will be taken to DiSSCo’s sandbox -the future DiSSCover service- where you will find all the information about the digital specimen. There you will also be able to comment, annotate the specimen, and more, thus making science in a more dynamic and efficient way than until now.
A note about Christa Deeleman-Reinhold
At 94 years old, the Dutch arachnologist Christa Deeleman-Reinhold is not only one of the authors of the Chrysilla and Phintelloides article but also one of the most important arachnologists in the world. Born in 1930 on the island of Java -then part of the Dutch East Indies- Christa gained her PhD from Leiden University in 1978. Since then, she has developed a one-of-a-kind scientific career, mainly focused on spider species from South Asia. In her Forest Spiders of South East Asia (2001), Dr. Deeleman-Reinhold revised six spider families, describing 18 new genera and 115 new species. The Naturalis Biodiversity Center hosts the Christa Laetitia Deeleman-Reinhold collection, with more than 20,000 specimens.
Text and images provided by DiSSCo RI.
Research article:
Deeleman-Reinhold CL, Addink W, Miller JA (2024) The genera Chrysilla and Phintelloides revisited with the description of a new species (Araneae, Salticidae) using digital specimen DOIs and nanopublications. Biodiversity Data Journal 12: e129438. https://doi.org/10.3897/BDJ.12.e129438
A post on social media asked about plant genera named for women and sparked a lively discussion with many contributors. This simple question was not as easily answered as initially thought. The resulting informal working group tackled this topic remotely during the COVID-19 pandemic and beyond. The team was motivated by the desire to amplify the contribution of women to botany through eponymy. The work of this team has so far resulted in a paper in Biodiversity Data Journal, presentations at several conferences, and a linked open dataset.
Prior to our international collaboration, no dataset was available to answer these simple questions and the required information was scattered in many different data sources. We set out to bring these data together and in doing so developed and refined our workflow. Our data paper documents this innovative workflow bringing together the various data elements needed to answer our research questions. Ultimately we created a Linked Open Data (LOD) dataset that amplified the names of women and female mythological beings celebrated through generic names of flowering plants.
🙋🏻♀️Inspired by the melastome plant genus 𝘔𝘦𝘳𝘪𝘢𝘯𝘪𝘢: which plant genera do you know that honor women? Who were/are they? 🌺 ¿Qué géneros de plantas dedicados a mujeres conoces? ¿Quienes fueron/son? 🧵👇🏽 #WomenInSTEMhttps://t.co/QWpyaMfihTpic.twitter.com/XBCYD6hmx1
During our research process we focused on pulling data from a wide variety of sources while at the same time proactively sharing the data generated as widely as possible. This was done by adding and linking it to multiple public databases and sources (push-pull) including the International Plant Name Index (hereafter IPNI), Tropicos®, Wikidata, Bionomia and the Biodiversity Heritage Library (hereafter BHL).
Visualisation of our workflow to create a working list of flowering plant genera named for women.
For our list of genera, each of the protologues were reviewed to confirm the etymology or eponymy. To find the generic prologues, we searched botanical databases such as IPNI and Tropicos, openly accessible providers of digital publications and other digital libraries and websites that provide free access to such publications. Here the BHL was invaluable as the majority of protologues and many other relevant publications were openly accessible through this digital library. Where no digital publication was available we accessed scientific literature through our affiliated institutions.
For the women, our starting point was the “Index of Eponymic Plant Names – Extended Edition” by Lotte Burkhardt (2018). We manually extracted all genera honouring women. This dataset was supplemented with other sources including IPNI (2023), Mari Mut (2017-2021), a 2022 updated version of Burkhardt’s document (Burkhardt 2022), as well as suggestions received from colleagues and generated from our own research.
We collected the following information as structured data: information on the woman honoured, the genera named in honour of the woman, the year and place of the protologue or original publication (the nomenclatural reference), the author(s) of the genus name, and the link to the protologue or original publication if available online.
Wikidata
Wikidata was the central data repository and linking mechanism for this project as it provided structured data that can be read and edited by humans and machines and it acts as a hub for other identifiers. As such Wikidata played a central role in semantic linking and enriching of our data.
Wikidata items for the plant genera were created or enriched with information about the name, the author(s) of the genus and the year of publication. Those statements were referenced using the original publication. If the protologue was available on BHL, the BHL bibliographic or page number was added to that reference, thus creating a digital link improving access to the protologue. While undertaking this work we also collated a list of all those public domain publications that appeared to be absent from BHL. We passed on this list to BHL and requested these texts be scanned and added to BHL for the benefit of everyone.
We then added a named afterstatement to the Wikidata item for the appropriate plant genera linking that item to the Wikidata item for the woman honoured. Wikidata items for the women honoured were newly created or enriched. We researched each person and her contributions, plus information on mythological figures where necessary, and added this information to Wikidata items. Our work also included disambiguating the woman from other people with identical or similar names.
To amplify the women’s contributions to science and to enrich the wider (biodiversity) data ecosystem, we linked to other Wikidata items and websites or databases by adding other relevant identifiers. For example if the women were botanists, botanical collectors or other naturalists, we used the author property to link the women to publications written by them. In addition, we added the women to Bionomia and attributed specimens collected or identified by them to their profiles.
Our work also included enriching Wikidata items of taxon authors. IPNI and Tropicos were searched for these author names, and websites such as BHL, the Global Biodiversity Information Facility (GBIF) or other specialist databases were consulted. Corrections or newly researched information on taxon authors was placed not just in Wikidata but was also sent together with the corresponding references to IPNI and Tropicos. This information was then used by those organizations to update these databases accordingly.
As a result of our data being placed in Wikidata it is available to be queried via the Wikidata Query Service.
Our Goal Achieved
As a result of our project, we published a dataset of 728 genera honouring women or female beings. This was a nearly twenty-fold increase in the number of genera linked to women in Wikidata. Our analysis paper on this data is forthcoming.
Notable Women
Monsonia L.
All of us came away from this research with a favourite story. One that stood out was Ann Monson, for whom Linnaeus named Monsonia. Linnaeus wrote a delightful letter to her about their creating, platonically of course, a kind of plant love-child between them, in the form of this new genus.
Translated from Latin: “….Lock these [seeds] in a pot, and place them in the window of the chamber towards the sun, when it bursts forth in February, and in the first summer the sun blooms and lasts the most beautiful Alstromeria, which no one has seen in England, and you bring forth no flowers. If it should come to pass, as I wish, if you offer our flames, I would only wish to beget with you an only child, as a pledge of my love, little Monsonia, by which you may perpetuate the fame of Lady in the kingdom of Flora, who was the Queen of Women.”
Fittonia Coem.
Two eponymous women with an interesting story are Sarah Mary Fitton and her sister Elizabeth. They wrote Conversations on Botany in 1817 accompanied by colour engravings of flowers which popularised botany with women. The genus Fittonia was named in their honour.
Chanekia Lundell
Another woman honoured in a plant genus was Mercedes Chanek, a Mayan plant collector who worked in the 1930’s for Cyrus Longworth Lundell and collected for the University of Michigan in British Honduras, today Belize. Very little is known about her life and work. However, her collections are detailed in Tropicos and Bionomia, and you can see the genus named for her by Lundell in IPNI under Chanekia.
An example of a mythological female being honoured in several plant names is that of Medusa, who has the most genera named after her, six, more than any real woman!
We hope that our data paper inspires others to use the methodology and workflow described to create other linked open datasets, e.g. celebrating and amplifying the contributions of underrepresented or marginalised groups in science.
Data paper:
von Mering S, Gardiner LM, Knapp S, Lindon H, Leachman S, Ulloa Ulloa C, Vincent S, Vorontsova MS (2023) Creating a multi-linked dynamic dataset: a case study of plant genera named for women. Biodiversity Data Journal 11: e114408. https://doi.org/10.3897/BDJ.11.e114408
Spectacular subtropical montane forest scenery in Yushan National Park. Credit: Ms. Wen-Ling Tsai
Montane forests, known as biodiversity hotspots, are among the ecosystems facing threats from climate change. To comprehend potential impacts of climate change on birds in these forests, researchers set up automatic recorders in Yushan National Park, Taiwan, and developed an AI tool for species identification using bird sounds. Their goal is to analyze status and trends in animal activity through acoustic data.
Compared to traditional observation-based methods, passive acoustic monitoring using automatic recorders to capture wildlife sounds provides cost-effective, long-term, and systematic alternative for long-term biodiversity monitoring.
The authors deployed six recorders in Yushan National Park, Taiwan, a subtropical montane forest habitat with elevations ranging from 1,200 to 2,800 meters. From 2020 to 2021, they recorded nearly 30,000 hours of audio files with abundant biological information.
An automatic recorder was installed on a tree to capture the surrounding soundscape. Credit: Ph.D. Candidate Shih-Hung Wu
However, analyzing this vast dataset is challenging and requires more than human effort alone.
To tackle this challenge, the authors utilized deep learning technology to develop an AI tool called SILIC that can identify species by sound.
SILIC can quickly pinpoint the precise timing of each animal call within the audio files. After several optimizations, the tool is now capable of recognizing 169 species of wildlife native to Taiwan, including 137 bird species, as well as frogs, mammals, and reptiles.
In this study, authors used SILIC to extract 6,243,820 vocalizations from seven montane forest bird species with a high precision of 95%, creating the first open-access AI-analyzed species occurrence dataset available on the Global Biodiversity Information Facility. This is the first open-access dataset with species occurrence data extracted from sounds in soundscape recordings by artificial intelligence.
The Gray-chinned Minivet (left) displays a secondary non-breeding season peak (right) which is possibly related to flocking behavior. Credit: Shih-Hung Wu, Ph.D. Candidate
The dataset unveils detailed acoustic activity patterns of wildlife across both short and long temporal scales. For instance, in diel patterns, the authors identify a morning vocalization peak for all species. On an annual basis, most species exhibit a single breeding season peak; however, some, like the Gray-chinned Minivet, display a secondary non-breeding season peak, possibly related to flocking behavior.
As the monitoring projects continue, the acoustic data may help to understand changes and trends in animal behavior and population across years in a cost-effective and automated manner.
The sound of Gray-chinned Minivet. Credit: Ph.D. Candidate Shih-Hung Wu
The authors anticipate that this extensive wildlife vocalization dataset will not be valuable only for the National Park’s headquarters in decision-making.
“We expect our dataset will be able to help fill the data gaps of fine-scale avian temporal activity patterns in montane forests and contribute to studies concerning the impacts of climate change on montane forest ecosystems,”
they say.
Original source:
Wu S-H, Ko JC-J, Lin R-S, Tsai W-L, Chang H-W (2023) An acoustic detection dataset of birds (Aves) in montane forests using a deep learning approach. Biodiversity Data Journal 11: e97811. https://doi.org/10.3897/BDJ.11.e97811
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For the first time, the satellite tracks of two Antarctic blue whales, tagged a decade ago, have been published in the open-access Biodiversity Data Journal.
Ten years ago, Dr Virginia Andrews-Goff was riding the bowsprit of a six-metre boat, as a 30-metre, 120-tonne Antarctic blue whale surfaced alongside.
That day in the Southern Ocean, she became the first and, so far, the only person, to deploy satellite tags on two of these critically endangered and rarely sighted giants.
At the time, her success added weight to a case in the United Nations International Court of Justice, demonstrating that scientific research on whales could be conducted without killing them.
Dr Andrews-Goff and her colleagues at the Australian Antarctic Division have now published the two satellite tracks generated by that 2013 work, in the open-access Biodiversity Data Journal.
This is a unique data set that was incredibly challenging to get.
Dr Virginia Andrews-Goff
The tracks give an insight into the animals’ movement and behaviour on their feeding grounds, and illustrate the significant logistical challenges needed to successfully locate, tag, and track Antarctic blue whales.
“This is a unique data set that was incredibly challenging to get, and, unfortunately, for 10 years no-one has been able to generate more data,” Dr Andrews-Goff said.
“We know very little about the movement and distribution of Antarctic blue whales, where they migrate, where they forage and breed, and we don’t understand the threats they might face as they recover from whaling.”
Part of the issue is that the animals are incredibly difficult to find. Commercial whaling in the 1960s and ‘70s killed about 290,000 Antarctic blue whales, accounting for 90% of the population. By the late 1990s, the world’s population of Antarctic blue whales was estimated at 2280 animals.
Back in 2013, the research team used novel acoustic tracking techniques to detect blue whale calls and hone in on their location from up to 1000 kilometres away. Once the whales were in sight (in two separate locations), an expert crew manoeuvred close to their fast-moving targets.
The satellite tags showed that the whales travelled 1390 kilometres in 13 days and 5550 kilometres in 74 days, with an average distance of more than 100 kilometres per day.
“The two whales did entirely different things, but what became obvious is that these animals can travel really quickly,” Dr Andrews-Goff said.
“If you consider how far and fast these animals moved, protecting the broader population against potential threats will be tricky because they could potentially circumnavigate Antarctica within a single feeding season.”
Since the tracks were obtained, new analytical methods have added some behavioural context to the data.
Two movement rates were observed – a faster ‘in transit’ speed averaging 4.2 km/hr and a slower speed of 2.5 km/hr, thought to correspond with searching or foraging.
“It looks like the whales might hang around in one area to feed and then move quickly to another area and hang around there for another feed,” Dr Andrews-Goff said.
“There may be certain areas that are better feeding grounds than others. From a management perspective, it would be good to understand what is it that makes these areas important?”
Even at a sample size of two, Dr Andrews-Goff said the satellite tracks will assist the International Whaling Commission’s management of Antarctic blue whales, by providing initial insights into blue whale foraging ecology, habitat preferences, distribution, movement rates, and feeding. These will inform an in-depth assessment of Antarctic blue whales due to begin in 2024.
Original source:
Andrews-Goff V, Bell EM, Miller BS, Wotherspoon SJ, Double MC (2022). Satellite tag derived data from two Antarctic blue whales (Balaenoptera musculus intermedia) tagged in the east Antarctic sector of the Southern Ocean. Biodviersity Data Journal 10: e94228 https://doi.org/10.3897/BDJ.10.e94228
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Between now and 15 September 2021, the article processing fee (normally €550) will be waived for the first 36 papers, provided that the publications are accepted and meet the following criteria that the data paper describes a dataset:
The manuscript must be prepared in English and is submitted in accordance with BDJ’s instructions to authors by 15 September 2021. Late submissions will not be eligible for APC waivers.
Sponsorship is limited to the first 36 accepted submissions meeting these criteria on a first-come, first-served basis. The call for submissions can therefore close prior to the stated deadline of 15 September 2021. Authors may contribute to more than one manuscript, but artificial division of the logically uniform data and data stories, or “salami publishing”, is not allowed.
BDJ will publish a special issue including the selected papers by the end of 2021. The journal is indexed by Web of Science (Impact Factor 1.331), Scopus (CiteScore: 2.1) and listed in РИНЦ / eLibrary.ru.
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Datasets with more than 5,000 records that are new to GBIF.org
Datasets should contain at a minimum 5,000 new records that are new to GBIF.org. While the focus is on additional records for the region, records already published in GBIF may meet the criteria of ‘new’ if they are substantially improved, particularly through the addition of georeferenced locations.” Artificial reduction of records from otherwise uniform datasets to the necessary minimum (“salami publishing”) is discouraged and may result in rejection of the manuscript. New submissions describing updates of datasets, already presented in earlier published data papers will not be sponsored.
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Authors should start by publishing a dataset comprised of data and metadata that meets GBIF’s stated data quality requirement. This effort will involve work on an installation of the GBIF Integrated Publishing Toolkit.
Only when the dataset is prepared should authors then turn to working on the manuscript text. The extended metadata you enter in the IPT while describing your dataset can be converted into manuscript with a single-click of a button in the ARPHA Writing Tool (see also Creation and Publication of Data Papers from Ecological Metadata Language (EML) Metadata. Authors can then complete, edit and submit manuscripts to BDJ for review.
Datasets with geographic coverage in Russia
In correspondence with the funding priorities of this programme, at least 80% of the records in a dataset should have coordinates that fall within the priority area of Russia. However, authors of the paper may be affiliated with institutions anywhere in the world.
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