Developing an ocean best practice: A case study of marine sampling practices from Australia

Abstract

Since 2012, there has been a surge in the numbers of marine science publications that use the term ‘best practice’, yet the term is not often defined, nor is the process behind the best practice development described. Importantly a ‘best practice’ is more than a documented practice that an individual or institution uses and considers good. This article describes a rigorous process to develop an ocean best practice using examples from a case study from Australia in which a suite of nine standard operating procedures were released in 2018 and have since become national best practices. The process to develop a best practice includes three phases 1) scope and recruit, 2) develop and release, 3) revise and ratify. Each phase includes 2-3 steps and associated actions that are supported by the Ocean Best Practices System (www.oceanbestpractices.org). The Australian case study differs from many other practices, which only use the second phase (develop and release). In this article, we emphasize the value of the other phases to ensure a practice is truly a ‘best practice’. These phases also have other benefits, including higher uptake of a practice stemming from a sense of shared ownership (from scope and recruit phase) and currency and accuracy (from revise and ratify phase). Although the process described in this paper may be challenging and time-consuming, it optimizes the chance to develop a true best practice that is a) fit-for-purpose with clearly defined scope; b) representative and inclusive of potential users; c) accurate and effective, reflecting emerging technologies and programs; and d) supported and adopted by users.

Marine lebensspuren: improving the classification of seafloor traces from underwater imagery and observations

“…Unfortunately, there is currently no global repository for lebensspuren imagery, and images are instead scattered among existing institutional or regional repositories or inaccessably stored on researchers' cloud storage or hard drives network or hard drives. In order to make imagery accessible and discoverable to all researchers, the community must decide on and actively use an appropriate platform to store, share, and annotate imagery using a standard classifications system such as that proposed here (Przeslawski et al, 2023). The ideal platform for this will have the following characteristics: 1) Allow any researcher to upload imagery and share it, 2) Require meta data for each image, including geographic coordinates and depth, 3) Provide an annotation tool for researchers to extract data from images, including classification of lebensspuren, 4) Allow experts to comment on and validate images of lebensspuren to identify new morphologies and match behaviours and tracemakers to a given trace, and 5) Contain a search function for the whole imagery collection so that annotated images of particular traces can be readily found.…”