Automated recording units are frequently used for passive acoustic monitoring of taxa, including bats. Detection and species-level identification of bat echolocation calls using manual techniques is a common practice, often supplementing automated analysis by software. However, few standardized protocols exist for manual analysis, which is challenging for novices and impedes comparisons among research groups. In this two-part review, I first summarize and synthesize current approaches to manual call analysis. Three observations about the processes used to conduct manual call identification emerge: (1) there are significant knowledge gaps and few comparisons of interoperator variability; (2) they are individual- and location-specific, with no standardized underlying framework; and (3) they are often not well-described in the peer-reviewed literature. In response to these observations, I then conduct a comparative analysis of the fields of clinical reasoning (the study of medical decision-making) and the identification of bat echolocation calls. Clinical reasoning is a mature area of research and findings from this field may inform practices and instructional strategies for manually identifying echolocation calls. I demonstrate similarities between clinical reasoning and call identification processes and then make recommendations on how to apply findings from the clinical reasoning literature to call identification practices and training.
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Erin E. Fraser (2018) studied this question.
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