Annual Survey of Juvenile Salmon Ecologically-Related Species and Biophysical Factors in the Marine Waters of Southeastern Alaska

Introduction

Juvenile Pacific salmon (Oncorhynchus spp.), ecologically-related species, and associated biophysical data were collected from the marine waters of the northern region of southeastern Alaska (SEAK) in 2016. This annual survey, conducted by the Southeast Coastal Monitoring (SECM) project, marks 20 consecutive years of systematically monitoring how juvenile salmon utilize marine ecosystems during a period of climate change. The survey was implemented to identify the relationships between year-class strength of juvenile salmon and biophysical parameters that influence their habitat use, marine growth, prey fields, predation, and stock interactions.

Methods

Up to 13 stations were sampled monthly in epipelagic waters from May to August (total of 23 sampling days). Fish, zooplankton, surface water samples, and physical profile data were collected during daylight at each station using a surface rope trawl, bongo nets, a water sampler, and a conductivity-temperature-depth profiler. Surface (3-m) temperatures and salinities ranged from approximately 9 to 16 ºC and 16 to 32 PSU across inshore, strait, and coastal habitats for the four months. Integrated (top 20-m) temperatures and salinities ranged from approximately 8 to 15 ºC and 24 to 31 PSU, notably the warmest 20-m integrated temperatures recorded by the SECM project.

A total of 72,073 fish and squid, representing 27 taxa, were captured in 89 rope trawl hauls fished from June to August. Juvenile salmon comprised approximately 49% of the catch. For all months and habitats, juvenile pink (O. gorbuscha), chum (O. keta), sockeye (O. nerka),and coho (O. kisutch) salmon occurred in 58-87% of the hauls, while juvenile Chinook salmon (O. tshawytscha) occurred in about 18% of the hauls. Abundance of juvenile salmon was high in 2016; peak CPUE occurred in June strait and coastal habitats. Coded-wire tags were recovered from 28 juvenile coho, that primarily originated from hatchery and wild stocks in SEAK sampled in the strait habitat; an additional 17adipose-clipped juvenile coho and Chinook salmon without tags were present. The only non-Alaskan stocks were juvenile coho salmon recovered off Icy Point, one from the Solduc River, WA and the other from the Methow River, Washington. Of the juvenile salmon examined for otolith marks, Alaska enhanced stocks comprised 69% of the juvenile chum (503 of 726) and 18% of the juvenile sockeye salmon (107 of 489). Of the 96 potential predators of juvenile salmon, predation on juvenile salmon was observed in three of eight fish species examined. The long term seasonal time series of SECM juvenile salmon stock assessment and biophysical data is used in conjunction with basin-scale ecosystem metrics to annually forecast pink salmon harvest in SEAK. Long term seasonal monitoring of key stocks of juvenile salmon and associated ecologically-related species, including fish predators and prey, permits researchers to understand how growth, abundance, and interactions affect year-class strength of salmon in marine ecosystems during a period of rapid climate change.

The Southeast Coastal Monitoring Project

The Southeast Coastal Monitoring (SECM) project, an ecosystem study in the northern region of southeastern Alaska (SEAK), was initiated in 1997 to annually study the early marine ecology of Pacific salmon (Oncorhynchus spp.) and associated epipelagic ichthyofauna and to better understand effects of climate change on salmon production. Salmon are a keystone species in SEAK whose role in marine ecosystems remains poorly understood. Fluctuations in the survival of this important living marine resource have broad ecological and socio-economic implications for coastal localities throughout the Pacific Rim.

Relationships between climate shifts and production have impacted year-class strength of Pacific salmon throughout their distribution (Beamish et al. 2010a, b). In particular, climate variables such as temperature have been associated with freshwater production (Bryant 2009; Taylor 2008) and ocean production and survival of both wild and hatchery salmon (Wertheimer et al. 2001; Beauchamp et al. 2007). Biophysical attributes of climate may influence trophic linkages and lead to variable growth and survival of salmon (Francis et al. 1998; Brodeur et al. 2007; Coyle et al. 2011). However, research is lacking on the links between salmon production and climate variability, intra-and interspecific competition and carrying capacity, and biological interactions among stock groups (Beamish et al. 2010a). In addition, past research has not provided adequate time series data to explain these links (Pearcy 1997; Beamish et al. 2008). Increases in salmon production throughout the Pacific Rim in recent decades has elevated the need to understand the consequences of population changes and potential interactions on the growth, distribution, migratory rates, timing, and survival of all salmon species and stock groups (Rand et al. 2012). Furthermore, region-scale spatial effects that are important to salmon production (Pyper et al. 2005) may be linked to local dynamics in complex marine ecosystems like SEAK (Weingartner et al. 2008).

Goals

A goal of the SECM project is to identify mechanisms linking salmon production to climate change using a time series of synoptic data related to ocean conditions and salmon, including stock-specific life history characteristics. The SECM project obtains stock information from coded-wire tags (CWT; Jefferts et al. 1963) or otolith thermal marks (Hagen and Munk 1994; Courtney et al. 2000) from all five Pacific salmon species: pink (O. gorbuscha), chum (O. keta), sockeye (O. nerka), coho (O. kisutch), and Chinook (O. tshawytscha). Portions of wild and hatchery salmon stocks are tagged or marked prior to ocean entry by enhancement facilities or state and federal agencies in SEAK, Canada, and the Pacific Northwest states. Catches of these marked fish by the SECM project in the northern, southern, and coastal regions of SEAK have provided information on habitat use, migration rates, and timing (e.g., Orsi et al. 2004, 2007, 2008); in addition, interceptions in the regional common property fisheries have documented substantial contributions of enhanced fish to commercial harvests (White 2011). Therefore, examining trends in early marine ecology and potential interactions of these marked stock groups provides an opportunity to link increasing wild and hatchery salmon production to climate change (Ruggerone and Nielsen 2009; Rand et al. 2012 and papers in Special Volume).

A goal of the SECM project is to identify mechanisms linking salmon production to climate change using a time series of synoptic data related to ocean conditions and salmon, including stock-specific life history characteristics. The SECM project obtains stock information from coded-wire tags (CWT; Jefferts et al. 1963) or otolith thermal marks (Hagen and Munk 1994; Courtney et al. 2000) from all five Pacific salmon species: pink (O. gorbuscha), chum (O. keta), sockeye (O. nerka), coho (O. kisutch), and Chinook (O. tshawytscha). Portions of wild and hatchery salmon stocks are tagged or marked prior to ocean entry by enhancement facilities or state and federal agencies in SEAK, Canada, and the Pacific Northwest states. Catches of these marked fish by the SECM project in the northern, southern, and coastal regions of SEAK have provided information on habitat use, migration rates, and timing (e.g., Orsi et al. 2004, 2007, 2008); in addition, interceptions in the regional common property fisheries have documented substantial contributions of enhanced fish to commercial harvests (White 2011). Therefore, examining trends in early marine ecology and potential interactions of these marked stock groups provides an opportunity to link increasing wild and hatchery salmon production to climate change (Ruggerone and Nielsen 2009; Rand et al. 2012 and papers in Special Volume).

Zooplankton prey fields are more likely to be cropped by the more abundant planktivorous forage fish, including walleye pollock (Gadus chalcogrammus) and Pacific herring (Clupea pallasi) (Orsi et al. 2004; Sigler and Csepp 2007), than by juvenile salmon. Seasonal and interannual changes in abundance of planktivorous jellyfish, another potential competitor with juvenile salmon, have been reported by SECM (Orsi et al. 2009). Therefore, monitoring abundance of jellyfish may be an important indicator of potential “bottom-up” trophic interactions(Purcell and Sturdevant 2001), particularly during periods of environmental change (Brodeur et al. 2008; Cieciel et al. 2009). Therefore, monitoring the composition, abundance, energetic content, and timing of zooplankton taxa with different life history strategies may permit the detection of climate-related changes in the seasonality and interannual abundance of prey fields (Coyle and Paul 1990; Park et al. 2004; Coyle et al. 2011; Fergusson et al. 2013, 2015, 2016). In contrast, “top-down” predation events can also influence salmon year-class strength (Sturdevant et al. 2009, 2012b, 2013). Highly abundant smaller juvenile salmon species, such as wild pink salmon, may be a predation buffer for less abundant, larger species, such as juvenile coho salmon (LaCroix et al. 2009; Weitkamp et al. 2011). These findings also stress the need to examine the entire epipelagic community in the context of trophic interactions (Cooney et al. 2001; Sturdevant et al. 2012b) and to compare ecological processes, community structure, and life history strategies among salmon production areas (Brodeur et al. 2007; Orsi et al. 2007, 2013).

Summary

In 2016, SECM sampling was conducted in the northern region of SEAK for the 20th consecutive year to continue annual ecosystem and climate monitoring, to document juvenile salmon abundance in relation to biophysical parameters, and to support models to forecast adult pink salmon returns. This document summarizes data collected by the SECM project in 2016 on juvenile salmon, ecologically-related species, and associated biophysical parameters. Subsets of the long term time series are examined in several recent documents (e.g., Fergusson et al. 2015, 2016; Orsi et al. 2015a, b, 2016a, b).