Multivariate Models of Adult Pacific Salmon Returns

Abstract

Most modeling and statistical approaches encourage simplicity, yet ecological processes are often complex, as they are influenced by numerous dynamic environmental and biological factors. Pacific salmon abundance has been highly variable over the last few decades and most forecasting models have proven inadequate, primarily because of a lack of understanding of the processes affecting variability in survival. Better methods and data for predicting the abundance of returning adults are therefore required to effectively manage the species. We combined 31 distinct indicators of the marine environment collected over an 11-year period into a multivariate analysis to summarize and predict adult spring Chinook salmon returns to the Columbia River in 2012. In addition to forecasts, this tool quantifies the strength of the relationship between various ecological indicators and salmon returns, allowing interpretation of ecosystem processes. The relative importance of indicators varied, but a few trends emerged. Adult returns of spring Chinook salmon were best described using indicators of bottom-up ecological processes such as composition and abundance of zooplankton and fish prey as well as measures of individual fish, such as growth and condition. Local indicators of temperature or coastal upwelling did not contribute as much as large-scale indicators of temperature variability, matching the spatial scale over which salmon spend the majority of their ocean residence. Results suggest that effective management of Pacific salmon requires multiple types of data and that no single indicator can represent the complex early-ocean ecology of salmon.

Winter ichthyoplankton biomass as a predictor of early summer prey fields and survival of juvenile salmon in the northern California Current

“…Additionally, rockfish growth and seabird reproductive success has been related to winter ocean conditions, particularly during February (Black et al 2010). Logerwell et al (2003) and Burke et al (2013) observed significant relationships between juvenile salmon marine survival and ocean conditions during the winter prior to ocean entry. The link between biological performance measures and winter ocean conditions has not been limited to predators; winter ocean conditions have also been associ- ated with the early life history stages of other fishes.…”

“…For juvenile salmon, ichthyoplankton biomass during the winter prior to ocean entry explains 34 to 85% of the variability in marine survival or adult returns. Burke et al (2013) used a multivariate model with 31 environmental and biological processes to predict spring Chinook salmon stock specific adult return and the most important indicators were bottom-up processes including the winter ichthyoplankton data set. Most of the indicators used in the Burke et al (2013) model relied on environmental or biological factors that are typically available or measured in late spring or summer after the hatchery salmon juveniles have been released.…”

“…Burke et al (2013) used a multivariate model with 31 environmental and biological processes to predict spring Chinook salmon stock specific adult return and the most important indicators were bottom-up processes including the winter ichthyoplankton data set. Most of the indicators used in the Burke et al (2013) model relied on environmental or biological factors that are typically available or measured in late spring or summer after the hatchery salmon juveniles have been released. However, the winter ichthyoplankton index is based on data collected only until the end of March and therefore can be calculated earlier in the year, potentially offering hatchery managers the ability to adjust their salmon release schedules.…”

Copepod Life Strategy and Population Viability in Response to Prey Timing and Temperature: Testing a New Model across Latitude, Time, and the Size Spectrum

“…Examples include interannual variation in pollock recruitment in the Eastern Bering Sea Eisner et al, 2014), interdecadal fluctuations in salmon marine survival across the Northeast Pacific (Mantua et al, 1997;Hooff and Peterson, 2006;Burke et al, 2013), and long-term trends in forage fish and seabird abundance in the North Sea (Beaugrand and Kirby, 2010;MacDonald et al, 2015). These cases can be all be schematized as following the "junk food" hypothesis (Österblom et al, 2008) in which the crucial axis of variation is not between high and low total prey productivity, but rather between high and low relative abundance of large, lipid-rich prey taxa.…”

Forecasting Pink Salmon Production in Southeast Alaska Using Ecosystem Indicators in Times of Climate Change

“…Most recently, an ecosystem metric approach using ranks and multiple indicators was developed to provide a qualitative pink salmon harvest outlook. This use of multiple ecosystem indicators (oceanographic and ecological) has been described previously to forecast returns of other salmonids such as Chinook salmon (O. tshawytscha) and coho salmon (O. kisutch) in the Pacific Northwest in multivariate or "stoplight" chart approaches (Logerwell et al 2003;Burke et al 2013;Peterson and Burke 2013).…”