A suite of standard post-tagging evaluation metrics can help assess tag retention for field-based fish telemetry research

Telemetry can inform many scientific and research questions if a context exists for integrating individual studies into the larger body of literature. Creating cumulative distributions of post-tagging evaluation metrics would allow individual researchers to relate their telemetry data to other studies. Widespread reporting of standard metrics is a precursor to the calculation of benchmarks for these distributions (e.g., mean, SD, 95% CI). Here we illustrate five types of standard post-tagging evaluation metrics using acoustically tagged Blue Catfish (Ictalurus furcatus) released into a Kansas reservoir. These metrics included: (1) percent of tagged fish detected overall, (2) percent of tagged fish detected daily using abacus plot data, (3) average number of (and percent of available) receiver sites visited, (4) date of last movement between receiver sites (and percent of tagged fish moving during that time period), and (5) number (and percent) of fish that egressed through exit gates. These metrics were calculated for one to three time periods: early (<10 d), during (weekly), and at the end of the study (5 months). Over three-quarters of our tagged fish were detected early (85%) and at the end (85%) of the study. Using abacus plot data, all tagged fish (100%) were detected at least one day and 96% were detected for > 5 days early in the study. On average, tagged Blue Catfish visited 9 (50%) and 13 (72%) of 18 within-reservoir receivers early and at the end of the study, respectively. At the end of the study, 73% of all tagged fish were detected moving between receivers. Creating statistical benchmarks for individual metrics can provide useful reference points. In addition, combining multiple metrics can inform ecology and research design. Consequently, individual researchers and the field of telemetry research can benefit from widespread, detailed, and standard reporting of post-tagging detection metrics.