Updated stock status indicators for silky sharks in the eastern Pacific Ocean (1994-2016), with oceanographic considerations

Indices of relative abundance for the silky shark in the eastern Pacific Ocean (EPO), developed from purse-seine catch-per-set, were updated with data from 2016. The index for all silky sharks north of the equator (north EPO) shows a large decrease in 2016 relative to 2015. In contrast, the index for all silky sharks south of the equator (south EPO) remains at about the 2014-2015 level. Some recent strong increasing trends in the indicators for silky sharks have been identified in previous reports, but they are not biologically plausible. To help further the understanding of potential processes driving the recent trends in the north EPO indices, silky shark indices by sub-region within the north EPO, and by shark size category, were compared to an index of variability in oceanographic conditions, and to a preliminary silky shark index for the Western and Central Pacific Ocean (WCPO) associated-set purse-seine fishery. Based on the preliminary results of these comparisons, it is hypothesized that the recent changes in the silky shark indices for the north EPO, particularly for small silky sharks, may be influenced by changing oceanographic conditions (e.g., El Niño and La Niña events), and thus the north EPO indices are potentially biased. Further analysis will be necessary to evaluate the magnitude of this bias quantitatively and, if the indices for large silky sharks are found to be less susceptible to bias caused by changing oceanographic conditions, they may be used exclusively as stock status indicators in the future. The IATTC staff reiterates its previous recommendation (SAC-07-06b(i), SAC-07-06b(iii)) that improving shark fishery data collection in the EPO is critical. This will facilitate the development of other stock status indicators and/or conventional stock assessments to better inform the management of the silky shark and other co-occuring shark species. Spatio-temporal models that combine data from multiple gear types to improve spatial coverage should also be explored in the future, to facilitate modeling efforts once data from other sources become available.