Tag: natural history collections

Forgotten species: the crucial role of taxonomy and natural history collections in saving lost biodiversity

As a growing number of species face extinction, both researchers and the general public tend to focus on attractive, well-known and charismatic fauna and flora. But what about the species that have disappeared from scientific recognition altogether? 

Research published in our open-access journal Nature Conservation sheds light on how historic taxonomic errors and misinterpretations have led to the disappearance of many species from science’s radar, highlighting the crucial role that  taxonomy and natural history collections (NHCs) can play in rediscovering and conserving biodiversity.

Research paper: ‘Lost species, neglected taxonomy, and the role of natural history collections and synonymization in the identification of the World’s forgotten biodiversity’ by Spartaco Gippoliti, Simone Farina and Franco Andreone. 

Forgotten species and taxonomic inertia

Many species that were described long ago have been overlooked due to erroneous synonymisation, a process whereby one species is mistakenly classified under another’s name, generally because of the scarce number of specimens available. These species, the authors now refer to as ‘long-lost synonymised species,’ can fall out of awareness for decades, even centuries. 

The 20th century saw a general trend of ‘lumping’ species together, reducing the number of recognised taxa, especially within well-known vertebrate groups. Taxonomic inertia – the persistence of outdated classifications – has caused many species to remain under-recognised, with their conservation statuses too often overlooked. This problem is described among better-known vertebrates, but is also likely present in some of the best studied invertebrates.

The importance of natural history collections

More than simply relics of the past, natural history collections provide a contemporary and essential resource for taxonomists working to untangle these historical errors. Museum specimens allow scientists to re-examine old classifications, using modern tools and methods to correct mistakes and uncover new taxa. Recent advances in ‘museomics’ – the study of genetic material from museum specimens – have opened new possibilities for species identification and conservation.

A 'Geoffroy's cat' laying in grass.
Leopardus geoffroyi. Credit: diegocarau via iNaturalist.

Such breakthroughs have led to the revalidation of the Neotropical genus Leopardus and the African wolf, Canis anthus, which had been synonymised for decades. Without natural history collections and the associated holotypes, the nomenclature of these species might have remained obscured, and their conservation needs unmet or delayed.

Natural History Collections and Museomics

Pensoft recently launched a new journal titled Natural History Collections and Museomics (NHCM).The publication comes at a pivotal moment in which taxonomists face the challenges of dwindling resources and fewer scientists entering the field. Through the publication of important open-access research, the journal aims to play a crucial role in bridging the gap between traditional taxonomy and modern conservation efforts. 

New Diamond OA journal Natural History Collections and Museomics launches on ARPHA Platform

Furthermore, by highlighting the essential role of taxonomy and natural history collections, NHCM will support the rediscovery of species long lost to science and help to conserve the world’s forgotten biodiversity. As the field of museomics grows, so too does the hope of rediscovering species that have been hidden in plain sight. The new journal already benefits from a competent and varied editorial board, including two of the authors of the Nature Conservation paper, Franco Andreone and Spartaco Gippoliti.

If the scientific community rally behind taxonomy and natural history collections, ensuring these vital tools are integrated into future biodiversity assessments, we can hope to preserve not just the species we know, but those we have forgotten.

Original source:

Gippoliti S, Farina S, Andreone F (2024) Lost species, neglected taxonomy, and the role of natural history collections and synonymization in the identification of the World’s forgotten biodiversity. Nature Conservation 56: 119-126. https://doi.org/10.3897/natureconservation.56.132036

Artificial neural networks could power up curation of natural history collections

Deep learning techniques manage to differentiate between similar plant families with up to 99 percent accuracy, Smithsonian researchers reveal

Millions, if not billions, of specimens reside in the world’s natural history collections, but most of these have not been carefully studied, or even looked at, in decades. While containing critical data for many scientific endeavors, most objects are quietly sitting in their own little cabinets of curiosity.

Thus, mass digitization of natural history collections has become a major goal at museums around the world. Having brought together numerous biologists, curators, volunteers and citizens scientists, such initiatives have already generated large datasets from these collections and provided unprecedented insight.

Now, a study, recently published in the open access Biodiversity Data Journal, suggests that the latest advances in both digitization and machine learning might together be able to assist museum curators in their efforts to care for and learn from this incredible global resource.

A team of researchers from the Smithsonian Department of Botany, Data Science Lab, and Digitization Program Office recently collaborated with NVIDIA to carry out a pilot project using deep learning approaches to dig into digitized herbarium specimens.

Smithsonian researchers classifying digitized herbarium sheets.
Smithsonian researchers classifying digitized herbarium sheets.

Their study is among the first to describe the use of deep learning methods to enhance our understanding of digitized collection samples. It is also the first to demonstrate that a deep convolutional neural network–a computing system modelled after the neuron activity in animal brains that can basically learn on its own–can effectively differentiate between similar plants with an amazing accuracy of nearly 100%.

In the paper, the scientists describe two different neural networks that they trained to perform tasks on the digitized portion (currently 1.2 million specimens) of the United States National Herbarium.

The team first trained a net to automatically recognize herbarium sheets that had been stained with mercury crystals, since mercury was commonly used by some early collectors to protect the plant collections from insect damage. The second net was trained to discriminate between two families of plants that share a strikingly similar superficial appearance.

Sample herbarium specimen image of stained clubmoss
Sample herbarium specimen image of stained clubmoss.

The trained neural nets performed with 90% and 96% accuracy respectively (or 94% and 99% if the most challenging specimens were discarded), confirming that deep learning is a useful and important technology for the future analysis of digitized museum collections.

“The results can be leveraged both to improve curation and unlock new avenues of research,” conclude the scientists.

“This research paper is a wonderful proof of concept. We now know that we can apply machine learning to digitized natural history specimens to solve curatorial and identification problems. The future will be using these tools combined with large shared data sets to test fundamental hypotheses about the evolution and distribution of plants and animals,” says Dr. Laurence J. Dorr, Chair of the Smithsonian Department of Botany.

 

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Original source:

Schuettpelz E, Frandsen P, Dikow R, Brown A, Orli S, Peters M, Metallo A, Funk V, Dorr L (2017) Applications of deep convolutional neural networks to digitized natural history collections. Biodiversity Data Journal 5: e21139. https://doi.org/10.3897/BDJ.5.e21139