Pacific Salmon Environmental and Life History Models: Advancing Science for Sustainable Salmon in the Future

<em>Abstract.</em>—There is a great opportunity to advance our understanding of salmon life history modeling by expanding the use of quantitative data thereby improving model efficacy and precision. However, a lack of basic and consistent data documentation frustrates secondary researchers’ attempts to identify extant data and evaluate its suitability for use. We apply preliminary results of State of the Salmon’s <em>North Pacific Salmon Monitoring Activity Inventory</em>, to demonstrate the potential of simple metadata (data about data) to rapidly appraise data deficiencies. We focus on sockeye salmon <em>Oncorhynchus nerka </em>in Bristol Bay, Alaska using elementary but standardized information about long term, freshwater adult and juvenile abundance and age composition monitoring efforts in the region. We classify monitoring into either that of a metapopulation (Tier 2) or individual populations (Tier 3). To accommodate data on catch or harvest from coastal fisheries (e.g., test fisheries) that are often used as a measure of abundance or run timing, we established a Tier 1 (regional grouping); however, in this chapter we do not consider Tier 1 activities. At the Tier 2 level, spawner-to-spawner ratios can be developed for every one of the nine Bristol Bay sockeye stocks and stage-specific life tables, including juvenile stages, can be populated for two out of the nine—the Wood and Kvichak river systems. Each of these drainages has historic or contemporary, long term abundance and biological surveys for fry/parr, smolts, and adults. Moreover, routine adult estimates and biological sampling occurs at the Tier 3 level in these areas, largely due to the long standing research activities of the University of Washington’s Alaska Salmon Program. Given our current understanding of data needs in a variety of research areas, we also present a recommended set of ‘core’ metadata elements to facilitate evaluation of primary data for use by secondary researchers. Ultimately, it is hoped that this exercise will help generate more and improved documentation among those who conduct salmon monitoring. With concerted attention to documentation throughout the data life cycle, time and costs associated with salmon modeling science and other secondary research activities can be reduced and, accordingly, advance the scientific community’s contribution to salmon conservation.