6533b7d8fe1ef96bd126b007

RESEARCH PRODUCT

The effect of automated taxa identification errors on biological indices

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In benthic macroinvertebrate biomonitoring systems, the target is to determine the status of ecosystems based on several biological indices. To increase cost-efficiency, computer-based taxa identification for image data has recently been developed. Taxa identification errors can, however, have strong effects on the indices and thus on the determination of the ecological status. In order to shift the biomonitoring process towards automated expert systems, we need a clear understanding on the bias caused by automation. In this paper, we examine eleven classification methods in the case of macroinvertebrate image data and show how their classification errors propagate into different biological indices. We evaluate 14 richness, diversity, dominance and similarity indices commonly used in biomonitoring. Besides the error rate of the classification method, we discuss the potential effect of different types of identification errors. Finally, we provide recommendations on indices that are least affected by the automatic identification errors and could be used in automated biomonitoring. We thank the Academy of Finland (projects 288584 (Kiranyaz), 295854 (Iosidis), 289364 (Gabbouj), 289076 (rje, Krkkinen) and 289104 (Meissner)) and the Ellen and Artturi Nyyssnen foundation for the grant of rje. The authors would like to thank Marko Vikstedt for the preparation of the monitoring data and Tuomas Turpeinen for the image data. We kindly thank Antti Penttinen for fruitful discussions and support. Scopus