Using Life-Cycle Models to Identify Monitoring Gaps for Central Valley Spring-Run Chinook Salmon

摘要

Life cycle models (LCMs) provide a quantitative framework that allows evaluation of how management actions targeting specific life stages can have population-level impacts on a species. The LCM building process is also a powerful tool that can be used to identify data gaps existing in the knowledge of the target species, and that might strongly influence overall population dynamics. LCMs are particularly useful for species such as salmon that are highly migratory and use multiple aquatic ecosystems throughout their life. Furthermore, they are lacking for threatened Central Valley spring-run Chinook (Oncorhynchus tshawytscha; CVSC). Here, we developed a CVSC LCM to describe the dynamics of Mill, Deer and Butte Creek CVSC populations. We used model construction, calibration and a global sensitivity analysis to highlight important data gaps in the monitoring of those populations. In particular, we found strong model sensitivity and high uncertainty in various egg, juvenile and adult ocean life stages’ biological processes. We concluded that the current CVSC monitoring network is insufficient to support using a LCM to inform how future management actions (e.g., hydrology and habitat restoration) influence CVSC dynamics. We propose a series of monitoring recommendations, such as the development of an enhanced juvenile tracking monitoring program and the implementation of juvenile trapping efficiency methodology combined with genetic identification tools, to help fill highlighted data gaps. These additional data collection efforts will provide critical quantitative information about the status of this imperiled species at key life stages (e.g., CVSC juvenile abundance estimates), and create a more comprehensive monitoring framework fundamental for working on the recovery of the entire stock. Furthermore, additional data collection will strengthen the LCM parameterization and calibration process, and ultimately improve the model’s predictive performance.