Computer-Assisted Bioidentification Using Freshwater Macroinvertebrates: A Scoping Review

Abstract

Background: Evaluation and prediction of the freshwater status based on freshwater macroinvertebrates (FwM) has become valuable in bioindication because they provide a more general and accurate picture of the ecological status of water bodies over time. Recent research on bioindication through FwM has increased the use of computational technologies, mainly in the classification and data analysis stages of water quality assessment and prediction. Objective: This scoping review aims to provide an overview of different approaches in computer-assisted bioindication with FwM. Particularly, the objective is to identify the techniques and strategies employed for FwM automatic classification or data treatment, characterize their use in recent years, and discuss gaps and challenges to broaden the scope of bioindication as a tool for understanding real conditions in a water body. Design: The scoping review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for scoping reviews (ScR). Scopus and Web of Science databases were used to identify articles published between 1999 and 2022. We selected 81 publications that used computational technology for automatic FwM classification or data analysis to predict water quality using biological indices. Results and conclusions: We identified two areas of applying computational technologies in bioindication studies with FwM. Firstly, computer-assisted technologies are used to evaluate water quality through samples already classified by human experts which correspond to 57% of the documents analyzed. The second application area is the automatic classification of FwM. In addition, we determined the main critical factors affecting strategy selection in each of the studies, such as taxonomic resolution, sample size and quality, image quality, data size, and complexity. Finally, we established the relationship between the strategies and algorithms employed in a timeline for automatic classification according to available FwM image databases. The research will allow taxonomic and related experts to better understand the role of computational technologies in FwM studies and thus increase confidence in these techniques to extend their use in bioassessment tasks.