Digitally improving the identification of aquatic macroinvertebrates for indices used in biomonitoring

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dc.creatorKoen, R. C. J.
dc.creatorKoen, F. J.
dc.creatorPattinson, N. B.
dc.creatorDickens, Chris W. S.
dc.creatorGraham, P. M.
dc.date2023-12-31
dc.date2024-01-22T10:05:54Z
dc.date2024-01-22T10:05:54Z
dc.date.accessioned2026-06-27T18:26:24Z
dc.descriptionThis report provides an overview of the mini Stream Assessment Scoring System (miniSASS) and South African Scoring System Version 5 (SASS5) as biomonitoring techniques for assessing the ecological condition of streams and rivers based on the identification of aquatic macroinvertebrates. While miniSASS relies on minimally trained citizen scientists to identify macroinvertebrates at the Order-level, SASS5 utilizes expertly accredited practitioners for finer resolution, even up to the family-level. However, the reliance on citizen scientists for miniSASS identification introduces limitations in terms of precision, accuracy, and reliability. To address these limitations, ongoing developments within the CGIAR Initiative on Digital Innovation include the creation of a miniSASS smartphone application, an upgraded website, an interactive online course, and a machine-learning identification algorithm to assist with photo identification. Additionally, a revised dichotomous key has been developed to improve operator identification during miniSASS surveys. Furthermore, the potential for upscaling the machine-learning identification algorithm to assist in identifying the 91 family-level taxa used in SASS5 assessments has been explored. The outcomes of these developments and explorations presented in this paper aim to enhance the overall effectiveness and reliability of both the miniSASS and SASS5 techniques. By leveraging digital innovation and incorporating machine-learning technology, we anticipate the efficiency, accuracy, and accessibility of biomonitoring assessments will significantly improve, ultimately contributing to a better understanding and management of our aquatic ecosystems.
dc.formatapplication/pdf
dc.identifierhttps://hdl.handle.net/10568/138246
dc.identifier.urihttp://hdl.handle.net/123456789/155897
dc.languageen
dc.publisherInternational Water Management Institute
dc.rightsOpen Access
dc.sourceKoen, R. C. J.; Koen, F. J.; Pattinson, N. B.; Dickens, Chris W. S.; Graham, P. M. 2023. Digitally improving the identification of aquatic macroinvertebrates for indices used in biomonitoring. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 10p.
dc.subjectcitizen science
dc.subjectdata collection
dc.subjectcommunity involvement
dc.subjectbiomonitoring
dc.subjectmacroinvertebrates
dc.subjectsustainable development
dc.subjectdigital innovation
dc.subjectmachine learning
dc.titleDigitally improving the identification of aquatic macroinvertebrates for indices used in biomonitoring
dc.typeReport

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Instituto Internacional para el Manejo del Agua (IWMI)