Copepods and salmon: characterizing the spatial distribution of juvenile salmon along the Washington and Oregon coast, USA

Abstract

Yearling Chinook (Oncorhynchus tshawytscha) and coho salmon (Oncorhynchus kisutch) were sampled concurrently with physical variables (temperature, salinity, depth) and biological variables (chlorophyll a concentration and copepod abundance) along the Washington and Oregon coast in June 1998-2008. Copepod species were divided into four different groups based on their water-type affinities: cold neritic, subarctic oceanic, warm neritic, and warm oceanic. Generalized linear mixed models were used to quantify the relationship between the abundance of these four different copepod groups and the abundance of juvenile salmon. The relationships between juvenile salmon and different copepod groups were further validated using regression analysis of annual mean juvenile salmon abundance versus the mean abundance of the copepod groups. Yearling Chinook salmon abundance was negatively correlated with warm oceanic copepods, warm neritic copepods, and bottom depth, and positively correlated with cold neritic copepods, subarctic copepods, and chlorophyll a concentration. The selected habitat variables explained 67% of the variation in yearling Chinook abundance. Yearling coho salmon abundance was negatively correlated with warm oceanic copepods, warm neritic copepods, and bottom depth, and positively correlated with temperature. The selected habitat variables explained 40% of the variation in yearling coho abundance. Results suggest that copepod communities can be used to characterize spatio-temporal patterns of abundance of juvenile salmon, i.e., large-scale interannual variations in ocean conditions (warm versus cold years) and inshore-offshore (cross-shelf) gradients in the abundance of juvenile salmon can be characterized by differences in the abundance of copepod species with various water mass affinities.

Defining marine habitat of juvenile Chinook salmon, Oncorhynchus tshawytscha, and coho salmon, O. kisutch, in the northern California Current System

“…While our understanding of juvenile salmon marine habitat in the CCS is increasing, explanatory variables have generally been limited to a few physical characteristics (i.e., temperature, salinity, water depth) and chlorophyll a and copepod biomass as the only biological variables Bi et al 2007Bi et al , 2008Bi et al , 2011Peterson et al 2010). Information is lacking on whether other physical or ocean processes also play a role and to what extent they influence the relative abundance of juvenile salmon.…”

Coastal Distribution and Consequent Factors Influencing Production of Endangered Snake River Sockeye Salmon

“…There is an emerging understanding of the integrative oceanographic factors and food web features that favor the growth and survival of juvenile salmon (e.g., Brodeur et al 2004;Fisher et al 2007;Bi et al 2011a;Yu et al 2012). Conditions that lead to increased growth and energy storage during the first marine growing season are expected to decrease mortality rates during periods of potentially high and variable mortality and ultimately increase adult returns (Pearcy 1992;Beamish and Mahnken 2001;Beamish et al 2004).…”

Spatial variations in the distribution of yearling spring Chinook salmon off Washington and Oregon using COZIGAM analysis

“…The abundance of yearling Chinook salmon in trawl surveys also shows large interannual variation (Bi et al 2007, Peterson et al 2010. The spatial distribution pattern has been shown to correlate with large-scale ocean conditions and local environmental factors along the Washington and Oregon coasts, such as strong upwelling, the PDO, chl a concentration, copepod biomass, water temperature and bathymetry (Brodeur et al 2004, Bi et al 2008, 2011a, Peterson et al 2010. Understanding juvenile salmon spatial distribution in relation to its pelagic habitat is necessary for studying spatial variation of juvenile salmon growth and survival.…”

“…However, none of the previous studies have properly addressed spatial autocorrelation, except Bi et al (2011a) where random effects were considered as spatial structure. Without properly addressing spatial autocorrelation, models may produce dubious relationships (Latimer et al 2006, Beale et al 2010.…”

“…For example, sea surface temperature (SST) is correlated with growth rate (Hinch et al 1995), age of maturation (Friedland 1998), timing of migration (Met calfe 1998, Dahl et al 2004), marine production (Jonsson & Jonsson 2004) and habitat use (Hinke et al 2005). Chlorophyll a (chl a) concentration and zooplankton biomass also influence salmon distribution and production (Brodeur et al 2004, Bi et al 2007, 2011a. However, the preferences to the environmental factors are different among populations and even among different life stages (Kendall et al 2010).…”