In recognition of the love and devotion that Terry expressed for the study of the World’s biodiversity, ZooKeys invites contributions to this memorial issue, covering all subjects falling within the area of systematic zoology. Titled “Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020)”.
In tribute to our beloved friend and founding Editor-in-Chief, Dr Terry
Erwin, who passed away on 11th May 2020, we are planning a special
memorial volume to be published on 11 May 2021, the date Terry left us. Terry
will be remembered by all who knew him for his radiant spirit, charming
enthusiasm for carabid beetles and never-ceasing exploration of the world of
In recognition of the love and devotion that Terry expressed for study of the World’s biodiversity, ZooKeys invites contributions to this memorial issue, titled “Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020)”, to all subjects falling within the area of systematic zoology. Of special interest are papers recognising Terry’s dedication to collection based research, massive biodiversity surveys and origin of biodiversity hot spot areas. The Special will be edited by John Spence, Achille Casale, Thorsten Assmann, James Liebherr and Lyubomir Penev.
Article processing charges (APCs) will be waived for: (1) Contributions
to systematic biology and diversity of carabid beetles, (2) Contributions from
Terry’s students and (3) Contributions from his colleagues from the Smithsonian
Institution. The APC for articles which do not fall in the above categories
will be discounted at 30%.
The submission deadline is 31st December 2020.
Contributors are also invited to send memories and photos which shall be
published in a special addendum to the volume.
The memorial volume will also include a joint project of Plazi, Pensoft and the Biodiversity Literature Repository aimed at extracting of taxonomic data from Terry Erwin’s publications and making it easily accessible to the scientific community.
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.
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.
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.
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