Early ocean distribution of juvenile Chinook salmon in an upwelling ecosystem

Abstract

Extreme variability in abundance of California salmon populations is often ascribed to ocean conditions, yet relatively little is known about their marine life history. To investigate which ocean conditions influence their distribution and abundance, we surveyed juvenile Chinook salmon (Oncorhynchus tshawytscha) within the California Current (central California [37°30 0 N) to Newport, Oregon (44°00 0 N]) for a 2-week period over three summers (2010)(2011)(2012). At each station, we measured chlorophyll-a as an indicator of primary productivity, acoustic-based metrics of zooplankton density as an indicator of potential prey availability and physical characteristics such as bottom depth, temperature and salinity. We also measured fork lengths and collected genetic samples from each salmon that was caught. Genetic stock identification revealed that the majority of juvenile salmon were from the Central Valley and the Klamath Basin (91-98%). We constructed generalized logistic-linear negative binomial hurdle models and chose the best model(s) using Akaike's Information Criterion (AIC) to determine which covariates influenced the salmon presence and, at locations where salmon were present, determined the variables that influenced their abundance. The probability of salmon presence was highest in shallower waters with a high chlorophyll-a concentration and close to an individual's natal river. Catch abundance was primarily influenced by year, mean fork length and proximity to natal rivers. At the scale of sampling stations, presence and abundance were not related to acoustic indices of zooplankton density. In the weeks to months after ocean entry, California's juvenile Chinook salmon population appears to be primarily constrained to coastal waters near natal river outlets.

Spatial variability in ocean‐mediated growth potential is linked to Chinook salmon survival

“…These sea level anomalies are associated with alongshore geostrophic flow (Chelton et al., ), and, thus, poleward–equatorward reversals of alongshore velocities were most likely the cause for the observed spatial distribution in growth potential. Years with high values of NMS Axis 2 had increased growth potential in the areas just north of Point Reyes, an area where juvenile salmon are often captured during the summer and fall after ocean entry (Hassrick et al., ). Thus, it makes sense that the highest observed survival rates were in years when both Axis 1 (upwelling) and Axis 2 (alongshore flow) were above average.…”

“…The standardized covariate estimates show that interactions with NMS Axis 3 had the largest impact on juvenile salmon survival. This is counterintuitive since juvenile salmon have been primarily captured north of the GoF during surveys (Hassrick et al., ). One hypothesis is that increased production to the south results in greater recruitment of forage fish (e.g., juvenile rockfish [ Sebastes spp.…”

“…The area that was least favorable for growth was in the coastal waters north of Point Reyes. Trawl survey catches suggest that juvenile salmon are captured throughout the study area (Figure 2b), but are generally found distributed throughout the GoF and in nearshore waters north of Point Reyes(Hassrick et al, 2016); however, we note that trawls were not evenly distributed throughout the survey area and may not be a full representation of juvenile salmon distribution. Three NMS axes sufficiently described the variability among the spatial cells across years.…”

“…We used empirical observations to evaluate spatial occupancy of juvenile Chinook salmon throughout our study area. Juvenile salmon were collected during the summers of 1997-2005, 2007, and 2010-2013 from 36.5°N to 39.0°N following methods in Harding, Ammann, andMacFarlane (2011) andHassrick et al (2016). In summary, a Nordic 264 Rope Trawl with 14 m vertical and 27 m horizontal mouth dimensions was towed at the surface for an average of 22 min at an average speed of 6.5 km/hr.…”

Specific MHC class I supertype associated with parasite infection and color morph in a wild lizard population

“…Hurdle models include two components: First, a binomial model determines whether a zero or count (nonzero) outcome occurs (presence/absence). Second, a zerotruncated (excluding zeros) model (e.g., Poisson) analyses the count data (Dalrymple, Hudson, & Ford, 2003;Guo et al, 2016;Hassrick et al, 2016;Naimi et al, 2014;Welsh, Cunningham, Donnelly, & Lindenmayer, 1996;Xu, Paterson, Turpin, & Xu, 2015). The fit of both a negative binomial and Poisson distribution was considered for the zero-truncated (count) part of the hurdle models.…”