The sea-urchins (class Echinoidea) of South Africa have received little taxonomic attention since the last comprehensive regional monographic guide, which was published in 1976 and is thus now severely outdated. That account was also poorly illustrated, making it difficult to use. Recent work by the authors has also added some 19 additional species to the known fauna, which now stands at 70 species. The aims of this study are thus to revise and update the South African echinoid fauna list, and to present a new, easy to use and fully-illustrated guide to all known regional species. The analysis is based mainly on examination of samples from the Iziko South African Museum collections, although additional records from other museum collections, the published literature, photographic images submitted to the EchinoMap Virtual Museum Database and any other reliable records are also included. A short account of each species is given, including relevant synonymy and literature, and brief notes on identification, size, and global distribution. Photographs that best represent the diagnostic features of each species and a map showing its known records within the region accompany each species account. A binary key to species is also provided.
A minuscule fraction of the deep sea has been scientifically explored and characterized due to several constraints, including expense, inefficiency, exclusion, and the resulting inequitable access to tools and resources around the world. To meet the demand for understanding the largest biosphere on our planet, we must accelerate the pace and broaden the scope of exploration by adding low-cost, scalable tools to the traditional suite of research assets. Exploration strategies should increasingly employ collaborative, inclusive, and innovative research methods to promote inclusion, accessibility, and equity to ocean discovery globally. Here, we present an important step toward this new paradigm: a collaborative design study on technical capacity needs for equitable deep-sea exploration. The study focuses on opportunities and challenges related to low-cost, scalable tools for deep-sea data collection and artificial intelligence-driven data analysis. It was conducted in partnership with twenty marine professionals worldwide, covering a broad representation of geography, demographics, and domain knowledge within the ocean space. The results of the study include a set of technical requirements for low-cost deep-sea imaging and sensing systems and automated image and data analysis systems. As a result of the study, a camera system called Maka Niu was prototyped and is being field-tested by thirteen interviewees and an online AI-driven video analysis platform is in development. We also identified six categories of open design and implementation questions highlighting participant concerns and potential trade-offs that have not yet been addressed within the scope of the current projects but are identified as important considerations for future work. Finally, we offer recommendations for collaborative design projects related to the deep sea and outline our future work in this space.
Globally, South Africa ranks in the top five countries regarding marine species richness per unit area. Given the high diversity, it is not surprising that many invertebrate taxa in the region are poorly characterised. The South African azooxanthellate Scleractinia (Anthozoa) is one such taxonomic group, and was last reviewed by Boshoff in 1980. Although more recent regional publications have reported on some species, there has not been a faunistic review that accounts for the country’s species diversity since then. Moreover, numerous unidentified specimens representing more than three decades of sampling effort have accumulated. In this study the authors update the state of knowledge of South African azooxanthellate coral species. Specimens, particularly those within the extensive collections of the Iziko South African and Smithsonian museums, were morphologically examined and identified. Other data considered included historic data represented as imagery data, associated species data from recent research surveys, and the scientific literature. To date, the study has increased the total number of known species from 77 to 108 across eleven families, 28 new South African records, and three are new species with one new genus.
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