Combining empirical indicators and expert knowledge for surveillance of data-limited sea trout stocks. (September 2019) - Shephard, Samuel; Josset, Quentin; Davidson, Ian; Kennedy, Richard; Magnusson, Katarina; Gargan, Patrick G.; Walker, Alan M.; Poole, Russell
Highlights: Data-limited inland fish stocks can support important ecological, social and cultural functions. Empirical surveillance indicators provide a first-step to monitoring such stocks. These indicators could be selected, informed and interpreted via expert knowledge. Surveillance metrics for sea trout from European rivers were presented on a visual indicator plot. Expert knowledge was elicited to interpret the plots in light of historical pressure-state events. Abstract: Inland and diadromous fish stocks can support important ecological, social and cultural functions. However, many of these stocks are data-limited and do not have formal state assessments or management reference points. Empirical surveillance indicators of relative abundance and population size-structure may provide an accessible first-step for monitoring such stocks. These indicators could be informed and interpreted via expert knowledge of specific systems and stock histories. The current study focused on an international scientific working group that met in 2017–2019. The group collated long-term monitoring data for sea trout Salmo trutta from six salmonid 'index rivers' in France, Ireland, Wales, Northern Ireland and Sweden. Indicators were selected, and time series of abundance- and length-based metrics for each river were presented on a new visual indicator plot. Expert knowledge from each system was elicited through one-to-one interviews to provide a corresponding stock narrative, in which indicator trends reflected known historical pressure-state events. The accessible presentation of simple empirical indicators supports elicitation of local expert knowledge, and provides an accessible framework for surveillance and reference-direction assessment of data-limited sea trout stocks. This approach could be applied to inland or diadromous stocks where available data are restricted to estimates of relative abundance and size-structure.