Gulf of Alaska Indicators

Indicators presented in this section are intended to provide detailed information and updates on the status and trends of the Gulf of Alaska ecosystem components. These indicators are used to build the Ecosystem Status Report. 

Last updated: 2017

 
 
  • The weather on the Bering Sea shelf was generally warmer than normal, for the 4th year in a row. An exception was early 2017, which included the usual intermittent outbreaks of Arctic air. The fall of 2016 was stormier than normal; winter and spring were relatively calm. During the winter of 2016–2017, sea ice was present mostly between the coast and the 70-meter isobath. While ice reached the M2 mooring site on the southeast Bering Sea shelf, the water column did not fully mix. The result was moderate bottom temperatures (∼0 oC) for the summer cold pool in the middle domain of the southern Bering Sea shelf. In this region the thermal stratification was greater than usual in summer 2017, but the vertically integrated heat content was more typical, at least as compared with 2015 and 2016

    Contributed by Nick Bond (UW/JISAO) NOAA/PMEL, Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349
    Contact: nicholas.bond@noaa.gov
    Last updated: August 2017

  • Description of indices: The state of the North Pacific climate from autumn 2016 through summer 2017 is summarized in terms of seasonal mean sea surface temperature (SST) and sea level pressure (SLP) anomaly maps. The SST and SLP anomalies are relative to mean conditions over the period of 1981–2010. The SST data are from NOAA’s Optimum Interpolation Sea Surface Temperature (OISST) analysis; the SLP data are from the NCEP/NCAR Reanalysis project. Both data sets are made available by NOAA’s Earth System Research Laboratory (ESRL) at http: //www.esrl.noaa.gov/psd/cgi-bin/data/composites/printpage.pl.

    Status and trends: The eastern portion of the North Pacific ocean experienced during 2014–16 one of the most extreme marine heat waves in the observational record (Scannell et al., 2016); the interval summarized here can be considered a transition period between that event and a more climatologically normal SST distribution on the basin-scale. More detail on the evolution of the SST and SLP from a seasonal perspective is provided directly below.

    The SST in the North Pacific during the autumn (Sep–Nov) of 2016 (Figure 14a) was warmer than normal in the Gulf of Alaska (GOA) and much warmer than normal (>2 oC) in the northern and eastern Bering Sea. Most of the remainder of the North Pacific Ocean had SSTs that were near to slightly above normal, with the exception of a cold patch at the dateline between 40o and 50oN. The SST anomalies in the tropical Pacific were positive in the west, and negative in the east, with the latter implying weak La Ni˜na conditions. The pattern of anomalous SLP during autumn 2016 featured a pole of strongly negative anomalies over the western Bering Sea, and lower than normal SLP extending eastward to a secondary negative pole off the coast of the Pacific Northwest (Figure 15a). This SLP pattern implies wind anomalies from the west across the North Pacific between roughly 40o and 50oN, causing enhanced cooling.

    Figure 14

    The pattern of North Pacific SST during winter (Dec–Feb) of 2016–17 relative to the seasonal mean (Figure 14b) reflected cooling north of about 40oN relative to the previous fall season. This cooling was associated with anomalous winds out of the west across the middle latitudes of the North Pacific in fall, followed by anomalous winds during winter out of the west across the Bering Sea and out of the northwest in the GOA. The latter wind anomalies were due to a distribution of anomalous SLP during winter 2016–17 that featured much higher pressures than normal over a large portion of the eastern North Pacific, with a peak magnitude greater than 12 mb located south of the Alaska Peninsula (Figure 15b). This is the signature of a particularly weak Aleutian Low, and implies suppressed storminess for the southeastern Bering Sea and GOA. A weak Aleutian Low commonly occurs during La Ni˜na, but as shown in Figure 14b, the SST anomalies in the central and eastern tropical Pacific were not much cooler than normal. It is not known why there appears to have been such a disproportionate response in the atmospheric circulation over the North Pacific. The anomalous northerly flow on the east side of the positive SLP anomaly south of Alaska resulted in the coldest winter for the Pacific Northwest since 1992–93; the region of lower than normal pressure along the west coast of the US was also accompanied by higher than normal precipitation.
    The distribution of anomalous SST in the North Pacific during spring (Mar–May) of 2017 (Figure 14c) was similar to that during the previous winter season, with moderation in the magnitude of the anomalies north of 30oN and modest warming in the sub-tropical North Pacific. Moderate cooling occurred in the central North Pacific in the vicinity of 40oN, 170oW. The overall pattern projected on the positive phase of the Pacific Decadal Oscillation (PDO), but not as strong as during the past two years. The SST anomalies in the tropical Pacific were of minor amplitude. The SLP anomaly pattern (Figure 15c) for spring 2017 featured a band of lower than normal pressure from eastern Siberia to a negative center south of the Aleutian Islands, with an eastward extension to British Columbia. Above-normal SLP resulted in suppressed storminess for the eastern Bering Sea. The atmospheric circulation in the northeast Pacific promoted relatively downwelling-favorable winds in the coastal GOA and wet weather in the Pacific Northwest.
    The SST anomaly pattern in the North Pacific during summer (Jun–Aug) 2017 is shown in Figure 14d. It was warmer than normal north of 50oN, with the greatest positive anomalies of +2oC near Bering Strait into the southern Chukchi Sea. Warm SSTs were also present in a band between about 30o and 15oN across the entire North Pacific Ocean with the greatest anomalies located northeast of the Hawaiian Islands. Upper ocean temperatures in the tropical Pacific were quite close to their climatological norms. The distribution of anomalous SLP (Figure 15d) during summer 2017 included negative centers in the northwestern portion of the North Pacific basin and south of mainland Alaska straddling a region of slightly higher than normal SLP centered near 40oN and the dateline.

    Contributed by N. Bond (UW/JISAO) Pacific Marine Environmental Laboratory, NOAA, Seattle, WA
    Contact: nicholas.bond@noaa.gov
    Last updated: September 2017

  • Description of indices: Climate indices provide an alternative means of characterizing the state of the North Pacific atmosphere-ocean system. The focus here is on five commonly used indices: the NINO3.4 index for the state of the El Ni˜no/Southern Oscillation (ENSO) phenomenon, Pacific Decadal Oscillation (PDO) index (the leading mode of North Pacific SST variability), North Pacific Index (NPI), North Pacific Gyre Oscillation (NPGO) and Arctic Oscillation (AO). The time series of these indices from 2007 into summer 2017 are plotted in Figure 16.

    Figure 16

    Status and trends: The North Pacific atmosphere-ocean climate system, in an overall sense, was in a more moderate state during 2016–17 than during the previous two years. The NINO3.4 index ranged from slightly negative during late 2016 to slightly positive during spring of 2017, with little trend over the course of summer 2017 (Figure 16). This rather quiet state for the tropical Pacific is in contrast with the large swings that occurred in 2015–16. The PDO has been positive (indicating warmer than normal SST along the west coast of North America and cooler than normal in the central and western North Pacific) since early 2014. The magnitude of the PDO has generally decreased since early 2016. Much of this decline can probably be attributed to ENSO, and in particular the transition from a strong El Ni˜no to a weak La Ni˜na in 2016. The NPI was negative during the past fall and spring, implying a deeper than normal Aleutian Low, as indicated in Figures 15a and 15b. In contrast, the winter of 2016–17 included a large positive value for the NPI. While this sign of the NPI represents a typical atmospheric response to La Ni˜na, its magnitude is disproportionately large considering the weak amplitude of La Ni˜na in late 2016.

    The NPGO mostly declined from a small positive value in early 2016 to a small negative value in early 2017. This index has been shown to be positively correlated with nitrate concentrations on Line P extending from Vancouver Island to Station P at 50oN, 145oW. The AO represents a measure of the strength of the polar vortex, with positive values signifying anomalously low pressure over the Arctic and high pressure over the Pacific and Atlantic Ocean at a latitude of roughly 45oN. It has a weakly positive correlation with sea ice extent in the Bering Sea. The AO was positive during the winter of 2016–17, perhaps contributing to the anomalously weak Aleutian Low (Figure 15b), and otherwise in a mostly neutral state on seasonal time scales since early 2016.

    Contributed by N. Bond (UW/JISAO) Pacific Marine Environmental Laboratory, NOAA, Seattle, WA Contact: nicholas.bond@noaa.gov Last updated: September 2017

  • text and graph will go here

    Description of indicator: Seasonal projections of SST from the National Multi-Model Ensemble (NMME) are shown in Figure 17. An ensemble approach incorporating different models is particularly appropriate for seasonal and longer-term simulations; the NMME represents the average of eight climate models. The uncertainties and errors in the predictions from any single climate model can be substantial. More detail on the NMME, and projections of other variables, are available at the following website: http://www.cpc.ncep.noaa.gov/products/NMME/.

    Figure 17

    Status and trends: First, the projections from a year ago are reviewed qualitatively. The onemonth lead forecast for Oct–Dec 2016 was quite accurate, which is not surprising in that the upper ocean has a great deal of thermal inertia, i.e., persistence, with the initial state being a primary determinant of near-term future conditions. This influence lessens with time and indeed for the period considered here, the longer-range (3-month and 5-month) forecasts were not as skillful. The models as a group, as reflected in the ensemble averages, correctly predicted the signs and the magnitudes of the SST anomalies in the sub-tropical and tropical Pacific, with only minor discrepancies. The NMME forecasts at the 3-month and 5-month forecast horizons did not validate as well north of about 30oN, where the modeled SSTs were generally warmer than observed. The models simulated too-little moderation of the pre-existing warm anomalies in the GOA and Bering Sea, and also under-predicted the amount of cooling in the waters offshore of the Pacific Northwest. Nevertheless, the models did reproduce the overall patterns in anomalous SST that were observed, even in the longer-range projections; the positive skill in these forecasts discussed here (and found in other studies) suggest that the NMME SST output merits consideration.

    These NMME forecasts of three-month average SST anomalies indicate a continuation of warm conditions across most of the North Pacific through the end of the year (Oct–Dec 2017) with a reduction in the longitudinal extent of cooler than normal temperatures offshore of the Pacific Northwest (Figure 17a). The magnitude of the positive anomalies is projected to be greatest (exceeding 1oC) in the western Bering Sea. Negative SST anomalies are projected in the central and eastern equatorial Pacific. It is uncertain whether they will remain weak enough to constitute neutral conditions or become strong enough to constitute La Ni˜na. As of early September 2017, the probabilistic forecast provided by NOAA’s Climate Prediction Center (CPC) in collaboration with the International Research Institute for Climate and Society (IRI) for the upcoming fall through winter indicates about a 40% chance of neutral conditions and a 55% chance of a weak La Ni˜na. The overall pattern of SST anomalies across the North Pacific is maintained through the 3-month periods of December 2017–February 2018 (Figure 17b) and February–April 2018 (Figure 17c) with some slight cooling in the eastern Bering Sea, GOA, and nearshore waters of the Pacific Northwest.

    Implications The distribution of forecast SST anomalies projects on the positive phase of the PDO, but also exhibits some substantial differences with the characteristic pattern of the PDO. In particular, the positive phase of the PDO generally includes significantly warmer than normal water in the GOA, and only modest anomalies in the western Bering Sea, while just the reverse is shown in the forecasts. This discrepancy appears to be related to some of the individual NMME models forecasts of a relatively weak Aleutian low (not shown).

    Contributed by N. Bond (UW/JISAO) Pacific Marine Environmental Laboratory, NOAA, Seattle, WA
    Contact: nicholas.bond@noaa.gov
    Last updated: September 2017

  • Description of indicator: Eddies in the northern Gulf of Alaska have been shown to influence distributions of nutrients (Ladd et al., 2009, 2005; Ladd, 2007), phytoplankton (Brickley and Thomas, 2004), ichthyoplankton (Atwood et al., 2010), and the foraging patterns of fur seals (Ream et al., 2005). Eddies propagating along the slope in the northern and western Gulf of Alaska are generally formed in the eastern Gulf in autumn or early winter (Okkonen et al., 2001) sometimes associated with gap winds from Cross Sound (Ladd and Cheng, 2016). Using altimetry data from 1993 to 2001, Okkonen et al. (2003) found that strong, persistent eddies occurred more often after 1997 than in the period from 1993 to 1997. Ladd et al. (2007) extended that analysis and found that, in the region near Kodiak Island (Figure 18; region c), eddy energy in the years 2002–2004 was the highest in the altimetry record.
    Since 1992, a suite of satellite altimeters has been monitoring sea surface height. Eddy kinetic energy (EKE) can be calculated from gridded altimetry data (merged TOPEX/Poseidon, ERS1/2, Jason and Envisat; (Ducet et al., 2000), giving a measure of the mesoscale energy in the system. A map of eddy kinetic energy in the Gulf of Alaska averaged over the altimetry record (updated from Ladd et al. (2007)) shows four regions with local maxima (labeled a, b, c and d in Figure 18). The first two regions are associated with the formation of Haida (a) and Sitka (b) eddies. Eddies that move along the shelf-break often feed into the third and fourth high EKE regions (c and d; Figure 18). By averaging EKE over regions c and d (see boxes in Figure 18), we obtain an index of energy associated with eddies in these regions (Figure 19). The Ssalto/Duacs altimeter products were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (http://www.marine.copernicus.eu).

    Figure 18

    Status and trends: The seasonal cycle of EKE averaged over the two regions (c and d) are out of phase with each other. Region (c) exhibits high EKE in the spring (March–May) and lower EKE in the autumn (September–November) while region (d) exhibits high EKE in the autumn and low EKE in the spring. EKE was particularly high in region (c) in 2002–2004 when three large persistent eddies passed through the region. The highest EKE observed in region (c) occurred in 2016 when a strong persistent eddy remained in the region for multiple months. In region (d), high EKE was observed in 1993, 1995, 2000, 2002, 2004, 2006, 2007, 2010, 2012, 2013, and 2015. Near-real-time data suggests that EKE was low in both regions in spring and summer 2017. The high EKE values in spring 2016 in region (c) were due to a strong eddy that formed near Yakutat in January 2016.

    Figure 19

    Factors causing observed trends: In the eastern Gulf of Alaska, interannual changes in surface winds (related to the Pacific Decadal Oscillation, El Ni˜no), and the strength of the Aleutian Low modulate the development of eddies (Combes and Di Lorenzo, 2007; Di Lorenzo et al., 2013). Recent work suggests that regional scale gap-wind events may also play a role in eddy formation in the eastern Gulf of Alaska (Ladd and Cheng, 2016). In the western Gulf of Alaska, variability is related both to the propagation of eddies from their formation regions in the east and to intrinsic variability

    Implications: EKE may have implications for the ecosystem. Phytoplankton biomass was probably more tightly confined to the shelf during 2017 due to the absence of eddies, while in 2007, 2010, 2012, 2013, and 2015 (region (d)), phytoplankton biomass likely extended farther off the shelf. In addition, cross-shelf transport of heat, salinity, and nutrients were probably weaker in 2017 than in years with large persistent eddies. Eddies sampled in 2002–2004 were found to contain different ichthyoplankton assemblages than surrounding slope and basin waters indicating that eddies along the slope may influence the distribution and survival of fish (Atwood et al., 2010). In addition, carbon isotope values suggest that cross-shelf exchange due to eddies may be important to the marine survival rate of pink salmon (Kline, 2010).

    Contributed by Carol Ladd, Pacific Marine Environmental Laboratory, NOAA, Seattle, WA
    Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349
    Contact: carol.ladd@noaa.gov
    Last updated: August 2017

  • Description of indicator: The PAPA Trajectory Index (PTI) provides an annual index of nearsurface water movement variability, based on the trajectory of a simulated surface drifter released at Ocean Station PAPA (50oN, 145oW; Figure 20). The simulation for each year is conducted using the “Ocean Surface CURrent Simulator” (OSCURS; http://las.pfeg.noaa.gov/oscurs). Using daily gridded atmospheric pressure fields, OSCURS calculates the speed and direction of water movement at the ocean’s surface at the location of a simulated surface drifter. It uses this information to update the position of the simulated drifter on a daily basis over a specified time period. For the index presented here, OSCURS was run for 90 days to simulate a surface drifter released at Ocean Station PAPA on December 1 for each year from 1901 to 2016 (trajectory endpoints years 1902–2017).

    Figure 20

    Status and trends: In general, the trajectories fan out northeastwardly toward the North American continent (Figure 20). The 2009/2010 trajectory was an exception and resulted in the westernmost trajectory endpoint for the entire set of model runs (1902–2017 endpoints). Under the influence of contemporaneous El Ni˜no conditions, the Aleutian Low in the winter of 2009–2010 was anomalously deep and displaced to the southeast of its usual position in winter (Bond and Guy, 2010), resulting in anomalously high easterly (blowing west) wind anomalies north of Ocean Station PAPA. The 2011/2012 trajectory followed the general northeastwardly path of most drifters, but was notable because its ending latitude was the northernmost of all trajectories since 1994. The trajectory for 2012/13 was notable as ending up the furthest east among trajectories in recent years, driven by very strong westerly anomalies in the northeast Pacific. The trajectories for 2013/14, 2014/15, and 2015/16 trajectories were very similar to that for 2011/12, although these did not reach quite as as far north as in 2011/12. These trajectories coincided with the development (2013/14) and continuation (2014/15, 2015/16) of the “Blob” of warm surface waters along the eastern Pacific coast and the return of the Pacific Decadal Oscillation (PDO) to a warm, positive phase associated with winds from the south near the coast. The increased southerly winds contributed to well above-average sea surface temperatures in the Gulf of Alaska in 2015/16. The opposite was true for the 2016/17 winter, however, and strong northerly winds pushed the drifter trajectory to its most southerly latitude since the late 1930s.
    The PTI time series (Figure 21, black dotted line and points) indicates high interannual variation in the north/south component of drifter trajectories, with an average between-year change of >4 o and a maximum change of greater than 13o (between 1931–1932). The change in the PTI between 2010/11 and 2011/12 was the largest since 1994, while the changes between 2011/12 and 2012/13, and between 2012/13 and 2013/14, represented reversals with slightly less, but diminishing, magnitude. Such swings, however, were not uncommon over the entire time series. The changes from 2013/14 to 2015/16 constituted a relatively rare event when the index changed very little over three successive years.
    Over the past century, the filtered (5-year running average) PTI has undergone four complete oscillations with distinct crossings of the mean, although the durations of the oscillations are not identical: 26 years (1904–1930), 17 years (1930–1947), 17 years (1947–1964), and 41 years (1964– 2005). The filtered index indicates that a shift occurred in the mid 2000s to predominantly southerly anomalous flow following a 20+ year period of predominantly northerly anomalous flow. This was indicative of a return to conditions (at least in terms of surface drift) similar to those prior to the 1977 environmental regime shift. This part of the cycle apparently ended rather quickly, however, as it now appears the filtered PTI has crossed the mean in the opposite direction. The recent period of predominantly southern flow has been the shortest and weakest in the time series

    Figure 21

    Factors influencing observed trends: Filtered PTI values greater than the long-term mean are indicative of increased transport and/or a northerly shift in the Alaska Current, which transports warm water northward along the west coast of Canada and southeast Alaska from the south and consequently plays a major role in the Gulf of Alaskas heat budget. In addition, the PDO recently (July, 2014) shifted into a positive and warm phase, associated with warm SST anomalies near the coast in the eastern Pacific and low sea level pressures over the North Pacific, the latter of which contributes to southerly winds and northerly flows. Individual trajectories also reflect interannual variability in regional (northeast Pacific) wind patterns.

    Implications: The year-to-year variability in near-surface water movements in the North Pacific Ocean has been shown to have important effects on the survival of walleye pollock (Gadus chalcogrammus) by affecting its spatial overlap with predators (Wespestad et al., 2000), as well as to influence recruitment success of winter spawning flatfish in the eastern Bering Sea (EBS; Wilderbuer et al. (2002)). Interdecadal changes in the PTI reflect changes in ocean climate that appear to have widespread impacts on biological variability at multiple trophic levels (King, 2005). There is strong evidence that the productivity and possibly the carrying capacity of the Alaska Gyre and of the continental shelf were enhanced during the “warm” regime that began in 1977. Zooplankton production was positively affected after the 1977 regime shift (Brodeur and Ware, 1992), as were recruitment and survival of salmon and demersal fish species. Recruitment of rockfish (Pacific ocean perch) and flatfish (arrowtooth flounder, halibut, and flathead sole) also increased. However, shrimp and forage fish such as capelin were negatively affected by the 1977 shift (Anderson, 2003). The reduced availability of forage fish may have contributed to the decline in marine mammal and seabird populations observed after the 1977 shift (Piatt and Anderson, 1996).

     

    Contributed by William T. Stockhausen
    Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National
    Marine Fisheries Service, NOAA
    Contact: william.stockhausen@noaa.gov
    Last updated: August 2017

  • Description of indicator: Since 1993, water column temperatures have been routinely recorded during Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Groundfish Assessment Program (RACE-GAP) GOA bottom trawl surveys using bathythermograph data loggers attached to the headrope of the bottom trawl net. In 2003, the SeaBird (SBE-39) microbathythermograph (Sea-Bird Electronics, Inc., Bellevue, WA) replaced the Brancker XL200 data logger (Richard Brancker Research, Ltd., Kanata, Ontario, Canada) which had been in use from 1993–2001 (Buckley et al., 2009). The analyses presented here combine these two types of bathythermic data; the downcast data from each RACE-GAP trawl haul were isolated and used to inform our models.
    The spatial and temporal coverage of the GOA RACE-GAP summer bottom trawl surveys has varied from year to year. Starting dates have ranged from the middle of May to the first week in June, and survey end dates ranged from the third week in July to the first week in September. The number of vessels employed, the areal extent, and the maximum depth of the GOA survey have all varied between survey years (e.g., water temperatures were not collected from the eastern GOA in 1993 and 2001, stations in the deepest GOA stratum [700–1000 m] have been sampled in just 5 of the last 12 surveys). Additionally, there is the expectation of an overall warming trend for water temperatures collected as the summer advances and the bottom trawl survey progresses from the western GOA into southeast Alaska; this should be particularly pronounced in the upper layers of the water column.
    To account for the spatio-temporal variation that were a consequence of the survey design we removed the effect of collection date on water temperature by standardizing all RACE-GAP GOA bottom trawl collection dates to a median survey date of July 10. We formulated a generalized additive model (GAM) to estimate the effects of collection date on temperature at depth across survey areas and years; the GAM accounted for 81% of the total deviance in the temperature data. The resulting model was used to predict water temperature on the median survey day at depth and station from survey trawl haul downcasts. Model residuals were added to the predicted median day temperature-at-depth to produce temperature anomaly estimates for each station and depth during each survey year. To facilitate visualization, these estimates were averaged over systematic depth bins in 1 ⁄2-degree longitude increments. Depth gradations were set finer in shallower depths (e.g., 5 m bins between 0 and 100 m, 10 m bins between 100 and 300 m) to capture the anticipated rapid water temperature changes in surface waters with increasing depth. To further stretch the color ramp and enhance the visual separation of the near-surface temperature anomalies (between ∼ 4 and 10oC and less than 100 m), predicted temperature anomalies ≥ 9.5oC and ≤ 3.5oC were fixed at 9.5 and 3.5oC (e.g., a 12.5oC temperature anomaly was recoded as 9.5oC for the graphic representation) and the y-axis (depth) was truncated (0–400 m) relative to the deepest depths sampled (ca. 1000 m).

    Status and trends: The thermal profile suggests that conditions in 2017 may have been slightly cooler compared to 2015 but still among the warmer years in our record (Figure 22). The warmest water did not penetrate as deeply into the upper 100 m in 2017. The warmer anomalies from this summers survey (≥ 7 oC) were generally constrained to depths less than 50 m whereas similar temperature anomaly values in 2015 were common between 50 and 100 m. However, there appeared to be warmer water deeper in the central Gulf (200–300 m) in 2017 compared to 2015. In general, the 2017 GOA thermal profile shares characteristics of the other warm years in our record (i.e., 2003, 2005, and 2015) and contrasts with the cooler years we have observed (e.g., 2007–2013).

    Factors influencing observed trends: Temperature data we collected during RACE-GAP bottom trawl surveys in the GOA represent a temporally brief and spatially constrained snapshot of water column conditions. In the data summarized in the figure, each temperature bin represents data that were collected over a relatively short time as the vessels moved through the area. It is difficult to draw general conclusions about longer term trends from this type of data, especially since these temperature observations can be affected by short term events such as storms, tidal currents, and changes in freshwater discharge. More persistent medium-term phenomena like sea71 sonal changes in solar heat flux, El Ni˜no Southern Oscillations, and shifts in the Alaska Coastal Current also play an important role in determining water column temperatures. In addition, the strength and persistence of eddies are believed to have a large impact on the transport of both heat and nutrients across the continental shelf in the GOA (Ladd et al., 2007). The data reported here depict the variation in water column temperatures observed amongst GOA bottom trawl survey years, but do not inform the mechanisms or processes driving these differences.

    Implications: Water column temperatures influence the distribution, assemblage membership, abundance, and growth rates of phytoplankton and zooplankton species. Ichthyoplankton distribution and growth rates are also related to location of the warm core eddies that are a prominent feature of the central GOA (Atwood et al., 2010). Adult and juvenile fish distribution can also be influenced by water temperatures (Kotwicki and Lauth, 2013; Rooney et al., in press). Interannual differences in water column temperatures, their implications, and their possible effects on fish populations in the GOA require more study to be better understood.

    Figure 22

    Contributed by Ned Laman, Resource Assessment and Conservation Engineering Division, Groundfish Assessment Program, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA Contact: ned.laman@noaa.gov Last updated: October 2017

  • Description of indicator: Temperature and salinity versus depth profiles have been taken since December 1970 at oceanographic station GAK1, located at the mouth of Resurrection Bay near Seward, Alaska. This 47-year time series is one of the longest running oceanographic time series in the North Pacific and the longest-running hydrographic profile dataset in Alaskan waters. The location is 59o 50.7’ N, 149o 28.0’ W and is located within the Alaska Coastal Current, so it is well “connected” with the shelf circulation. The sampling platform is normally R/V Little Dipper, a 28’ coastal research vessel operated by the University of Alaskas Seward Marine Center.
    The goal of the GAK1 project is to provide a long-term high-quality reference dataset for the coastal northern Gulf of Alaska that enables scientists, students, commercial and subsistence fishers and resource managers to better understand climatic and ecological conditions, their changes, and ramifications of change. Understanding, anticipating, and responding to change requires a stationary frame of reference in the form of long-term in situ observations. Such datasets are the best means to guide our assessments and interpretations of system variability. Untangling the relations between climatic and other drivers of change (e.g., oil spills or fishing regulations) similarly requires long reference time series. Environmental time series data can provide information valuable to the management of fish and shellfish populations and fisheries.
    The GAK1 dataset is collected under the fundamental hypothesis that oceanic conditions are important to the physical and biological functioning of the Prince William Sound and Gulf of Alaska ecosystems. To that end, many dozens of papers have examined this hypothesis from numerous perspectives (for a comprehensive listing, see the GAK1 home page at http://www.ims.uaf.edu/ gak1/). As the chemical and biological datasets begin to catch up (via quality of resolution, duration and frequency) to the physical measurements we expect that the insights gleaned through interdisciplinary analyses will grow in kind.

    Status and trends: Year 2016 was the warmest on record for the GAK1 time series. Trends shown in Figure 23 are averages over 0–50 m depth and 200–250 m depth. Table 2 shows regression statistics for the period of record and for 2009–2016. The recent 2013–2016 warm interval was preceded by relatively cool temperatures over 2007–2012 and as a consequence the 2009–2016 analysis period shows an increasing trend that is an order of magnitude larger than the 1970–2016 interval. While salinity has declined in the past decade in both the near-surface and near-bottom, salinity over the entire time period has increase slightly in the near-bottom.

    Factors influencing observed trends: The GAK1 record captures long-term (47-year) trends of warming throughout the water column, along with freshening in the 50 m closest to the surface and a slight salinization close to the seafloor. The observed warming is consistent with long-term climate trends of warming for the planet as a whole. Warming temperatures affect snow melt, net glacial ablation, coastal runoff, and by extension, these terrestrial discharges impact the surface salinity. The reasons for the near-seafloor salinity increases are presently unknown and are under investigation.

    Figure 23

    Implications: There are myriad consequences that may propagate through the ecosystem as a result of the observed temperature and salinity changes and many effects are difficult or impossible to anticipate. Some expected impacts include the following: 1. increasing coastal salinities could increase the along-shelf transport in the Alaska Coastal Current, altering the dispersal pathways and/or timing of water, heat, the coastal water chemical constituents, and plankton that are influenced by the coastal current; 2. warming will lead to increased metabolic rates and changes in biogeochemical cycling; 3. differential trends in the influence of temperature and salinity lead to increased levels of stratification on the Gulf of Alaska continental shelf; and 4. increasing stratification may alter (reduce) the transfer of nitrate from subsurface waters into the euphotic zone. The latter has implications for the amount of new production that the shelf may be able to sustain.

    Contributed by Seth Danielson, College of Fisheries and Ocean Sciences, University of Alaska, Fairbanks, AK
    Contact: sldanielson@alaska.edu
    Last updated: September 2017

  • Description of indicator: The Auke Creek Research Station has been in permanent operation since 1980 and provides a unique opportunity to study migratory salmonids due to the operation of a weir capable of the near-perfect capture of all migrating juvenile and adult salmon. In addition to the capture of migrating individuals, daily recordings of environmental variables are also collected. These variables include: creek temperature, and creek height. Creek temperature is collected using an in-creek probe that records temperature on an hourly basis and is located 25 meters upstream of the weir structure. Creek height is recorded using a staff gauge that is permanently installed directly downstream of the weir structure and approximately 7 meters above the average low tide line. Thirty-eight years of temperature data are available (1980–2017), and 12 years of creek height data (2006–2017). These variables provide a valuable addition to the fisheries data collected at the Auke Creek Research Station

    Table 2

    Status and trends: The historical trends of yearly average creek temperature in Auke Creek varies from 8.6oC to 11.6oC with an average temperature of 10.3oC from 1980–2017. The average temperature for 2016 was 11.5oC and 10.4oC for 2017. From 2006–2017, average yearly creek height varied from 21.6ft to 21.9ft, with an average of 21.7 ft. The average gauge height for 2017 was 21.6ft and 21.8ft for 2017. Historical trends and the most recent two years are shown for creek temperature (Figure 24) and gauge height (Figure 25).

    Factors influencing observed trends: The trends that we are observing in the Auke Creek watershed provide further evidence for the rapid climatic change that has been documented in this system. Due to recent fluctuations in winter snowfall, we are seeing shifts from a snowmeltdominated to a rainfall-dominated watershed at Auke Creek (Shanley et al., 2015)(Figure 25). This lack of snowfall, and subsequent lack of snowmelt, contribute to warmer creek temperatures earlier in the year (Figure 24).

    Implications: These changes in stream conditions and climate have been shown to have influence on the median migration date of juvenile and adult salmon in Auke Creek (Kovach et al., 2013). Additionally, changes in time of entry to the marine environment can effect marine survival (Weitkamp et al., 2011). Both of these can have impacts on groundfish and salmon productivity as juvenile salmon serve as an important food source in the early marine environment. (Landingham et al., 1998; Sturdevant et al., 2009, 2012). Additionally, shifts in the timing and magnitude of freshwater and associated nutrient input directly affects processes in the nearshore marine environment (e.g., salinity and temperature).

    Figure 24

    Figure 25

    Contributed by Scott C. Vulstek and Joshua R. Russell
    Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: scott.vulstek@noaa.gov
    Last updated: September 2017

 
  • Description of indicator: Structural epifauna groups considered to be Habitat Area of Particular Concern (HAPC) biota include sponges, corals (both hard and soft) and anemones. NOAA collects data on structural epifauna during the biennial RACE summer surveys in the Gulf of Alaska. For each species group, the catches for each year were scaled to the largest catch over the time series (which was arbitrarily scaled to a value of 100). The standard error (±1) was weighted proportionally to the CPUE to get a relative standard error. The percentage of positive catches in the survey bottom trawl hauls was also calculated

    Status and trends: A few general patterns are clearly discernible (Figure 26). Sponges are caught in about 50% of bottom trawl survey hauls in all areas of the GOA when combined across areas. This percentage has been increasing in the Yakutat (to about 30% of hauls) and Southeastern (to about 60% of hauls) regions. However, the CPUE is generally highest in the Shumagin area low to the east. Sponge CPUE has declined in the Kodiak area, while CPUE has remained fairly constant in the four other areas. Anemones are caught in low abundance in the eastern GOA, while they are common (occur in ∼50% of tows) at a relatively constant abundance in the Shumagin, Chirikof and Kodiak regions. Gorgonian corals show an opposite pattern, as they are in highest abundance in the southeastern GOA, although they are relatively uncommon in catches for all areas. The peak abundance occurred in 1999 in the eastern GOA, and catches have declined in recent surveys. The sea pen time series is dominated by large CPUEs in 2005 and 2015 in the Chirikof area, but they occur uncommonly in bottom trawl tows (< 10% occurrence). Stony coral catches are rare. Soft coral CPUE has been uniformly low with the exception of a large catch in the western GOA in the 1984 survey.

    Figure 26

    Factors influencing observed trends: The Gulf of Alaska survey does not sample any of these fauna well, so some caution is recommended in interpreting these trends in CPUE. Overall, most area-species combinations do not show trends in either catches or frequency of occurrence. However, the decline in sponge catches in the Kodiak area may indicate that sponges are decreasing here, whereas the increases in frequency of occurrence of sponges in the Yakutat and Southeastern areas may indicate an expansion of sponge populations.

    Implications: Changes in structural epifauna CPUE may indicate changes in habitat, but at present no research has demonstrated definitive links. In future ecosystem contributions an overall biomass for these species groups for the Gulf of Alaska will be computed using geospatial methods. Preliminary results (Figure 27) show that overall coral and sponge biomass estimates have remained highly variable, but relatively constant throughout the history of the bottom trawl survey, whereas pennatulaceans seemed to peak in the 2005 survey before falling off and recovering to a high level in 2017. These are preliminary results and need further examination and exploration.

    Contributed by Chris Rooper, Resource Assessment and Conservation Engineering Division, Alaska
    Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: Chris.Rooper@noaa.gov
    Last updated: October 2017

  • There are no updates to primary production indicators in this year’s report, except for the diatom trends in the Continuous Plankton Recorder contribution by Batten (p. 80. See the contribution archive for previous indicator submissions at: http://access.afsc.noaa.gov/reem/ecoweb/ index.php

  • Continuous Plankton Recorder Data from the Northeast Pacific: Lower Trophic Levels in 2016

    Description of indicator: Continuous Plankton Recorders (CPRs) have been deployed in the North Pacific routinely since 2000. Two transects are sampled seasonally, both originating in the Strait of Juan de Fuca, one sampled monthly (∼Apr–Sept) which terminates in Cook Inlet, the second sampled 3 times per year (in spring, summer and autumn) which follows a great circle route across the Pacific terminating in Asia. Several indicators are now routinely derived from the CPR data and updated annually. In this report we update three indices for three two regions (Figure 28); the abundance per sample of large diatoms (the CPR only retains large, hard-shelled phytoplankton so while a large proportion of the community is not sampled, the data are internally consistent and may reveal trends), mesozooplankton biomass (estimated from taxon-specific weights and abundance data) and mean Copepod Community Size (see Richardson et al., 2006 for details but essentially the length of an adult female of each species is used to represent that species and an average length of all copepods sampled calculated) as an indicator of community composition. Anomaly time series of each index have been calculated as follows: A monthly mean value for each region is first calculated. Each sampled months mean is then compared to the long-term mean of that month and an anomaly calculated (Log10). The mean anomaly of all sampled months in each year is calculated to give an annual anomaly.

    Figure 27

    The indices are calculated for the oceanic North-East Pacific and the Alaskan shelf SE of Cook Inlet (Figure 28). The oceanic NE Pacific region has the best sampling resolution as both transects intersect here. This region has been sampled up to 9 times per year with some months sampled twice. The Alaskan shelf region is sampled 5–6 times per year by the north-south transect.

    Figure 28

    Status and trends: Diatom abundance anomalies were very low on the Alaskan Shelf in 2016 (particularly in April and May) and lower than in 2015 in the oceanic NE Pacific (Figure 29). The mesozooplankton biomass anomaly was strongly positive on the Alaskan shelf, with positive anomalies in each sampled month. It was also positive overall for the NE Pacific region, although May and July values were slightly negative. The Copepod Community Size index saw negative anomalies for both the Alaskan Shelf and the NE Pacific regions meaning that the species were smaller then average. For the Alaskan Shelf region this was the 4th consecutive year of negative anomalies and while the anomaly for the NE Pacific was not as low as in 2015, it was again negative.

    Factors influencing observed trends: We have previously speculated that during the previous heat wave years of 2014–2015 nutrient conditions may have been poor since a higher than average contribution of smaller, pennate diatoms (that have an advantage over larger cells in low nutrient conditions) was evident in spring of those years in the Alaskan Shelf, as well as the NE Pacific region. This trend could have continued with even smaller cells present in 2016 that are not retained by the CPR. However, there was a very high zooplankton biomass in 2016 so it is also possible that the low numbers of larger diatoms was caused by high grazing pressure from zooplankton. Whatever the cause, the diatom anomaly in spring 2016 was the lowest of the time series so clearly something was unusual in the 2016 plankton community.
    The negative anomalies for the Copepod Community Size Index for both the Alaska Shelf and NE Pacific regions are consistent with the warmer water favoring the smaller-bodied species which generally have a more southerly center to their distribution and are more abundant in warm years. They were very abundant in 2016, since biomass anomalies were positive while mean size was small.
    Implications: Each of these variables is important to the way that ocean climate variability is passed though the phytoplankton to zooplankton and up to higher trophic levels. Changes in community composition (e.g., abundance and composition of large diatoms, prey size as indexed by mean copepod community size) may reflect changes in the nutritional quality of the organism to their predators. Changes in abundance or biomass, together with size, influences availability of prey to predators. For example, while mesozooplankton biomass anomalies were positive, the reduced average size of the copepod community suggests that the biomass was packaged into numerous, but small, prey items. This may require more work by predators to obtain their nutritional needs, and for the Alaskan Shelf region this is now a persistent feature occurring in 4 consecutive years.

    Figure 29

    Contributed by Sonia Batten, Sir Alister Hardy Foundation for Ocean Science, c/o 4737 Vista View Cr, Nanaimo, BC, V9V 1N8, Canada
    Contact: soba@sahfos.ac.uk
    Last updated: July 2017

  • Description of indicator: In 2015, EcoFOCI implemented a method for an at sea Rapid Zooplankton Assessment (RZA) to provide leading indicator information on zooplankton composition in Alaska’s Large Marine Ecosystems. The rapid assessment, which is a rough count of zooplankton (from paired 20/60 cm oblique bongo tows from 10 m from bottom or 300 m, whichever is shallower), provides preliminary estimates of zooplankton abundance and community structure. The method employed uses coarse categories and standard zooplankton sorting methods (Harris et al., 2005). The categories are small copepods (less than 2 mm; example species: Acartia spp., Pseudocalanus spp. and Oithona spp.), large copepods (> 2mm; example species: Calanus spp. and Neocalanus spp.), and euphausiids (less than 15 mm; example species: Thysanoessa spp.). Small copepods were counted from the 153 µm mesh, 20 cm bongo net. Large copepods and euphausiids were counted from the 505 µm mesh, 60 cm bongo net. In 2016, the method was refined and personnel counted a minimum of 100 organisms per sample at sea to improve zooplankton estimates. Other, rarer zooplankton taxa were present but were not sampled effectively with the on-board sampling method. Detailed information on these taxa is provided after in-lab processing protocols have been followed (1+ years post survey). The GOA RZA was conducted on two surveys: 1) from 12 May to 1 June 2017 (spring) and 2) from 19 Aug to 17 Sep 2017 (summer).
    In order to provide comparison to yearly RZA data, a long-term time series was developed from archived data. The mean, annual abundance of each RZA category was plotted for “Line 8” (an area approximately bounded by 57.46–57.73oN and 154.67–155.30oW) in the Shelikof Strait, Gulf of Alaska from 1990–2011 and represented May/June sampling (spring). For summer, mean, annual abundance of each RZA category was plotted for the primary grid area southwest of Kodiak Island (an area approximately bounded by 54–58oN and 155–160oW) from 2000–2015 (where data were available) and represented Aug/Sep sampling.

    Status and trends: Large copepods had high abundances in the SW area of the survey grid and near Kodiak Island in spring (Figure 30). Large copepod abundances declined in summer, and the SW area of the survey grid remained the area with the highest abundances (Figure 30). Compared to historical abundances of large copepods at Line 8, 2017 abundances appeared to be similar in magnitude to the long-term estimates and higher than 2015 (Figure 31). Small copepods were abundant throughout the sampling area in spring, and their abundances increased during summer (Figure 30). Small copepods showed very little interannual variability in spring and summer abundance, and 2017 values were similar to the long-term estimates (Figure 31). Euphausiid juvenile stages were abundant throughout the sampling region in spring and numbers reduced into summer (Figure 30). Euphausiid abundances appeared to be much higher than historical estimates in spring in general and 2015 in particular (Figure 31). Higher abundances of euphausiids were observed in summer of 2015 and 2017 compared to the most recent survey values (Figure 31).

    Factors influencing observed trends: Large copepods in spring include Neocalanus cristatus and N. plumchrus/flemingeri. These species enter diapause and disappear from the plankton in June, thus the decline of larger copepods into the summer (Figure 30) is partially explained by this life history event. The other large copepod species, Calanus marshallae, has been shown to increase in years with cold winters and a strong, spring phytoplankton bloom (Sousa et al., 2016). We observed evidence of a recent spring bloom and numerous Calanus copepodites during the spring survey, suggesting that winter conditions were favorable for Calanus. The long-term timesseries of large and small copepods showed little variability in either spring or summer (Figure 31) and this is likely due to the combination of multiple species into one, representative category.
    Examination of individual species shows that variability among the individual species of smaller copepods does occur (unpub. data). We also observed large numbers of euphausiid early life history stages in spring (Figure 30). Sousa et al. (2016) also report the conditions that increased Calanus abundances also appeared to favor the euphausiid Thysanoessa inermis. The significant decline in euphausiid numbers during the summer (Figure 30) can be partially explained by the development of euphausiids resulting in larger sized individuals that can effectively avoid the 60 cm bongo net. Long-term variability in mesozooplankton in this region is thought to be driven by PDO and ENSO cycles (Sousa et al., 2016). However, the euphausiid variability does show some patterns over time (Figure 31). The drops in euphausiid abundance corresponded with positive phases of the PDO (Figure 31) as suggested by (Sousa et al., 2016). However, considerable local variability within a given year may mask these longer-term trends in abundance.

    Figure 30

    Figure 31

    Implications: Zooplankton are an important prey base for larval and small juvenile pollock in spring. The increase in abundance of copepods and their presence at nearly all stations indicates significant secondary production present in the ecosystem during spring of 2017, particularly when compared to 2015 estimates (Figure 31). Furthermore, the higher abundances of early stage euphausiids present could bode well for higher abundances in fall when juvenile pollock diets shift to a primarily euphausiid-based diet prior to overwintering

     

     

     

     

     

    Contributed by Nissa Ferm, Jesse Lamb, David Kimmel, EcoFOCI Program, Resource Assessment
    and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: nissa.ferm@noaa.gov
    Last updated: September 2017

  • Description of indicator: The Gulf of Alaska survey of the abundance and distribution of euphausiids (“krill”, principally Thysanoessa spp.) has been developed (Simonsen et al., 2016) based on methods used by Ressler et al. (2012) in the Bering Sea. The survey incorporates both acoustic and Methot trawl data from summer Gulf of Alaska acoustic-trawl surveys for pollock conducted in 2003, 2005, 2011, and biennially since 2013 by NOAA-AFSC. Acoustic backscatter per unit area (sA at 120 kHz, m2 nmi-2) classified as euphausiids was integrated over the water column and then across the surveyed area to produce an annual estimate of acoustic abundance (sA * area, proportional to the total abundance of euphausiids). Approximate 95% confidence intervals on these estimates were computed from geostatistical estimates of relative estimation error (Petitgas, 1993). Though surveys since 2013 have covered the shelf from the Islands of Four Mountains to Yakutat (Figure 32), the index reported here is limited to areas around Kodiak that have been consistently sampled in all years of the time series (Figure 33). Net collections from euphausiid scattering layers in 2011, 2013, and 2015 have been numerically dominated by euphausiids and show that Thysanoessa inermis, T. spinifera, and Euphausia pacifica with average length ∼19 mm are the most commonly encountered species (Simonsen et al. 2016, Ressler unpublished data). Additional sampling during the summer 2017 survey was conducted in support of an NPRB-funded project focused upon measuring acoustic properties of and improving survey techniques for euphausiids (#1501, “How many krill are there in the eastern Bering Sea and Gulf of Alaska?”, PIs Warren, Ressler, Harvey, Gibson, underway through 2019).

    Status and trends: Results indicate that highest abundance of euphausiids in the time series was observed in 2011 and the lowest in 2003 (Figure 34). There was a decline in 2017 relative to the previous survey in 2015, to a value similar to 2003. Barnabas Trough appears to be a local hotspot (Figures 32, 33), as observed in previous surveys (Simonsen et al., 2016). Final species and length composition from summer 2017 Methot trawls are not yet available. These data are preliminary and will change.

    Factors influencing observed trends: Factors controlling annual changes in euphausiid abundance are not well understood; possible candidates include bottom-up forcing by temperature and food supply, and top-down control through predation (Hunt et al., 2016). When factors including temperature, pollock abundance, primary production, and spatial location have been considered in spatially-explicit multiple regression models, increases in euphausiid abundance have been strongly correlated with cold temperatures in the eastern Bering Sea (Ressler et al., 2014), but not in the GOA (Simonsen et al., 2016). Euphausiid abundance is not strongly correlated with the abundance of pollock (a major predator) in statistical models of observations from either system.

    Implications: The results presented here suggest a lower level of euphausiid prey availability in summer 2017. Euphausiids are a key prey species for fish species of both ecological and economic importance in the Gulf of Alaska, including walleye pollock (Gadus chalcogrammus), Pacific Ocean perch (Sebastes alutus), arrowtooth flounder (Atheresthes stomias), capelin (Mallotus villosus),eulachon (Thaleichthys pacificus), and as well as many species of seabirds and marine mammals.

    Figure 32

    Figure 33

    Figure 34

     

     

     

     

    Contributed by Patrick Ressler, Midwater Assessment and Conservation Engineering Program, Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, NOAA
    Contact: patrick.ressler@noaa.gov
    Last updated: October 2017

  • Description of indicator: In 2015, EcoFOCI implemented a method for an at sea Rapid Zooplankton Assessment (RZA) to provide leading indicator information on zooplankton composition in Alaskas Large Marine Ecosystems. The rapid assessment, which is a rough count of zooplankton (from paired 20/60 cm oblique bongo tows from 10m from bottom or 200 m, if bottom depth was >200 m), provides preliminary estimates of zooplankton abundance and community structure. Coarse species categories were assessed using standard zooplankton sorting methods (Harris et al., 2005). The categories were small copepods (less than 2 mm; example species: Acartia spp., Pseudocalanus spp., and Oithona spp.), large copepods (> 2mm; example species: Calanus spp. and Neocalanus spp.), and euphausiids (less than 15 mm; example species: Thysanoessa spp.). Small copepods were counted from the 153 µm mesh, 20 cm bongo net. Large copepods and euphausiids were counted from the 505 µm mesh, 60 cm bongo net. The method was refined in 2016, and personnel counted a minimum of 100 organisms per sample to improve zooplankton estimates. Other, less abundant zooplankton taxa were present but were not sampled effectively with this method. Detailed information on these taxa is provided after in-lab processing protocols have been followed (1+ years post survey). The eastern Gulf of Alaska was sampled from July 2 to July 25, 2017.

    Status and trends: This is the first year that the RZA has been performed on the eastern Gulf of Alaska Assessment survey. No information on trends in zooplankton abundance are available across years. However, we are able to provide a snap-shot of the large and small copepod and euphausiid (less than 15 mm) community in the eastern GOA during July. Large copepod abundance peaked in the coastal areas off Chichagof and Baranof Islands. They were also generally more abundant over the continental shelf and less abundant over continental slope and basin waters (Figure 35). Small copepod abundance decreased within the southern portion of the survey grid, especially in areas stretching from the coast to 80 nm offshore. Small copepod abundance peaked at survey stations located on the northern shelf (Figure 36). Euphausiids were most abundant at coastal nearshore stations (Figure 37). High catches at nearshore stations may have been due in part to shallow depths where the net was sampling closer to the substrate relative to deeper stations located further from shore

    Factors influencing observed trends: Large copepod species (e.g., Neocalanus spp.) enter diapause (descend) and become less abundant in zooplankton samples by June. Two hundred meters was the maximum depth sampled, and large copepods residing at slope and offshore stations would not be captured. The increase in large copepod abundance over nearshore stations (Figure 35) may thus be due to sampling, as gear was deployed to just above the bottom (stations less than 200 m) and may be more likely to sample copepods in or entering diapause. Another important large copepod species, Calanus marshallae/glacialis, is generally more abundant in years with cold winters and a strong spring phytoplankton bloom (Sousa et al., 2016). The EcoFOCI program observed evidence of a strong spring bloom and numerous Calanus spp. copepodites in the western Gulf of Alaska, spring 2017. This evidence suggests that winter conditions were favorable for Calanus spp.Copepod and euphausiid abundances in the eastern GOA were lower than those in the western GOA (p. 83). Large copepod densities in the western GOA during late summer typically ranged within 50 individuals m-3 (hereafter Im-3), whereas large copepod densities in the eastern GOA typically ranged between 10–20 Im-3. Small copepod densities in the western GOA ranged from 5,000– 15,000 Im-3, whereas in the eastern GOA they typically ranged from 2,500–7,500 Im-3. Euphausiid densities in the eastern GOA ranged from 2.5–7.5 Im-3 whereas those in the western GOA ranged from 10–40 Im-3 .

     

    Figure 35

    Figure 36

    Figure 37

    Implications: Zooplankton are an important prey base for age-0 and juvenile marine fish and some species of juvenile salmon. Increased abundance of copepods and euphausiids over the shelf and at coastal locations (within 3 miles from shore) likely served to benefit juvenile fishes and salmon inhabiting coastal waters during summer 2017.

     

     

     

    Contributed by Jamal Moss, Colleen Harpold, Melanie Paquin, and David Kimmel, Ecosystem Monitoring and Assessment Program, Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: jamal.moss@noaa.gov
    Last updated: October 2017

  • Description of indicator: The Southeast Coastal Monitoring (SECM) project of Auke Bay Laboratories, AFSC, has been investigating how climate change may affect Southeast Alaska (SEAK) nearshore ecosystems in relation to juvenile salmon and associated biophysical factors since 1997 (Fergusson et al., 2013; Orsi et al., 2015). Temperature and zooplankton data have been collected annually in Icy Strait during monthly (May to August) fisheries oceanography surveys.
    This report presents 2016 annual values of temperature and zooplankton in relation to the long-term trends in Icy Strait. The Icy Strait Temperature Index (ISTI, oC) is the average temperature of the upper 20-m integrated water column. Zooplankton density (number per m3 ) was computed from 333-µm bongo net samples (≤200 m depth) (Orsi et al., 2004; Park et al., 2004). Temperature and zooplankton anomalies were computed as deviations from the long-term annual mean values. The temperature and zooplankton measures were used to describe the nearshore environment utilized by many commercially and ecologically important forage fish in SEAK.

    Status and trends: The ISTI shows the annual temperature trend identifying warm and cool years, with 11 years warmer and 9 years cooler than the average (9.4oC). Overall, the ISTIs ranged from 8.3oC to 10.6oC, and anomalies did not exceed ±1.2oC (Figure 38). The ISTI in 2016 was anomalously warm by approximately 1.2oC, which made it the highest ISTI observed over the 20 year time series.
    The zooplankton density shows the trend in zooplankton abundance and also reflects the health of this important lower trophic level community. Overall, the long-term mean zooplankton density ranged from 891 to 3,192 organisms per m3 (Figure 39). The 2016 total density of zooplankton showed an increased from the 2015 density and was above average as the 6th highest measure in the 20-year time series. For all years, total zooplankton density and temperature were not significantly correlated, with positive and negative monthly anomalies occurring in both warm and cold years (r = 0.11, P = 0.64).
    Overall, the zooplankton community was numerically dominated by gastropods (≤ 36%) and small calanoid copepods (≤ 2.5 mm length; ≤ 35% composition). Three other taxa, important in fish diets (Sturdevant et al. 2012; Fergusson et al. 2013), contributed to the community in smaller percentages (large calanoid copepods (> 2.5 mm), ≤ 19%; hyperiid amphipods, ≤ 3%; and euphausiids, ≤ 1%). For 2016, densities of hyperiid amphipods and gastropods were anomalously high, showing a clear increase from the 2015 densities, furthermore, the densities of the hyperiids and gastropods are the 2nd and 1st highest densities, respectively, over the time series. Large calanoid copepods also showed an increase from 2015 densities but were still below the 20-year mean density. Euphausiid and small calanoid densities both declined from 2015 densities. The shifts in densities between species is indicative of differential responses to the drastic increase in the ISTI.

    Factors influencing observed trends: Subarctic zooplankton typically follow seasonal cycles of abundance, however as indicated here, responses to climate change may be species-specific. These species-specific differences may be based on life history, seasonal timing cues, physiology, and environmental parameters other than temperature (Mackas et al., 2012), and these responses could depend on the monthly timing, magnitude, and duration of temperature anomalies in warm or cold years. Therefore, the ISTI may not adequately explain shifts in abundance and composition of these prey fields, particularly at broader taxonomic scales. To more accurately reflect critical trophic interactions with respect to climate change, an analysis at the species level would be needed and should include a prey quality measure, such as % lipid.

    Implications: Climate change can have broad impacts on key trophic linkages in marine ecosystems by changing relationships of the biophysical environment with seasonal abundance, composition, timing, and utilization of prey (Mackas et al., 2004, 2012; Coyle et al., 2011). Our results suggest that such relationships are currently in flux with the perpetually increasing ISTIs and the evident increase in the density of some of the zooplankton groups. Likewise, the densities of euphausiids and small calanoid copepods are showing an opposite decreasing response to the warming temperatures, which could be trophically consequential for many planktivores. Additionally, shifts in the developmental timing of the zooplankton could lead to mismatched timing of favorable prey fields for planktivorous fish. These indices may help to explain climate-related variation in prey fields across the diverse range of fish communities (Sturdevant et al., 2012; Fergusson et al., 2013), which may directly or indirectly affect fish growth and recruitment (Beamish et al., 2004, 2012; Coyle et al., 2011).

    Figure 38

     

     

     

     

    Contributed by Emily Fergusson, Joseph Orsi, and Andrew Gray, Auke Bay Laboratories Division, Alaska Fisheries Science Center, NOAA Fisheries
    Contact: emily.fergusson@noaa.gov
    Last updated: August 2017

  • Description of indicator: Fisheries oceanography surveys were conducted by AFSC in the eastern Gulf of Alaska during July, 2010–2017. In 2017, we developed new indicators of zooplankton biomass in collaboration with the University of Alaska Fairbanks using 2017 data that will allow us to compare estimated total biomass for select zooplankton species in the future. A zooplankton net tow (60 cm bongo, 505 µm mesh) was used to collect mesozooplankton at each station. A Seabird Electronics FastCat (49) was mounted above the bongo net to provide real time depth, temperature, and salinity data. Casts were to “bottom” (5–10 m from bottom) or 200 m (if bottom depths were >200 m). Calibrated General Oceanics flowmeters were mounted inside the mouth of the nets to measure volume filtered. Volume estimates from flowmeters were compared with volumes estimates from the distance towed at each station to ensure net clogging was not a significant factor. All samples were preserved in 5% formalin, buffered with seawater for later processing.
    In the laboratory, each mesozooplankton sample was poured into a sorting tray and large organisms, such as shrimp and jellyfish, were removed and counted. The sample then was sequentially split using a Folsom splitter until the smallest subsample contained approximately 200 specimens of the most abundant taxa. All taxa in the smallest subsamples were identified, staged, counted, and weighed. Each larger subsample was examined to identify, count, and weigh the larger, less abundant taxa. Blotted wet weights for each taxa and stage were determined as outlined in earlier papers (Coyle et al., 2008, 2011). The coefficient of variation in the average wet weight was computed for each species. If the coefficient of variation for any given taxon and stage changed by less than 5% when additional weights were taken from subsequent samples, a mean wet weight was calculated, and wet weights were no longer measured for that taxon. The wet weight biomass for all subsequent samples was estimated by multiplying the specimen count by the mean wet weight. Multi-year (since 1997) average individual weights obtained from the Seward Line were applied to rare taxa. In practice, only calanoid copepods had consistent wet weights after weighing each taxon and stage in about 10–15 samples. Therefore, wet weights on chaetognaths, decapod larvae, and other larger and soft-bodied taxa were measured and recorded for each sample. Wet weight measurements were done on a Cahn Electrobalance or Mettler top loading balance, depending on the size of the animal. All animals in the samples were identified to the lowest taxonomic category possible. Copepodite stages were identified and recorded. Biomass values by station were computed for each species in mg m-3. In order to produce this volume of data within 2 months of the end of the survey, only data from the 60 cm bongo is being considered, and approximately every other station was processed.
    The depth of the pycnocline was computed for each station by locating the depth where dt/dz was at a maximum (σt = density - 1000, kg m-3; z = depth, m). The mean water-column temperature above and below the pycnocline were then computed. Interpolation of physical and biological values over the sampling area was done using kriging subroutines in SURFER 12 (Golden Software).

    Figure 39

    Status and trends: The 2017 eastern Gulf of Alaska shelf sea surface (12–14oC) and bottom (6–10oC) temperatures during July were above average for the survey area since 2014 (Figure 40). Salinity values ranged from 26–35 in the surface and 32–33 near the benthos (Figure 40). A freshwater plume emanated from the Alsek River, south of Yakutat (Figure 1). Total zooplankton biomass (Figure 41) had two large peaks, one oceanic and one over the shelf. The oceanic peak was due to a very high biomass of tunicates (salps, Figure 41), while the nearshore biomass peak was due to the shelled pteropod, Limacina helicina (Figure 3). Approximately 40% of the total zooplankton biomass was Cnidaria and Tunicata (hydrozoan jellyfish and salps) (Figure 42). The largest proportion of cnidarian (hydrozoan jellyfish; Figure 42) biomass was within and bordering the freshwater body near the Alsek River. Other selected zooplankton species were influenced by salinity above the pycnocline, with increased biomass in offshore and shelf areas along with intrusions of oceanic water (Figures 43, 44, 45).

    Factors influencing observed trends: Similar to previous work in the Gulf of Alaska (Coyle and Pinchuk, 2003), salinity appears to be the largest factor influencing distribution and biomass in the mesozooplankton community in the eastern Gulf of Alaska. Nearshore communities over the northern portion of the grid were shaped by a freshwater plume emanating from the Alsek River, south of Yakutat. Additionally, a lack of frontal structure and the ability of certain species to rapidly react to temperature and salinity differences appear to have shaped the zooplankton community in July of 2017. Some mixing of oceanic and shelf species assemblages occurred during July of 2017 and was likely due to a lack of an oceanic front (Mundy, 2005), and a weak Alaska Coastal Current.
    Asexual reproduction in hydrozoan jellyfishes and salps has been documented to increase under warmer or warming conditions (Purcell, 2005). In addition, we have qualitatively observed a large increase in the abundance and prevalence of pelagic tunicates (salps and doliolids) during the summer season in the past few years. Trawl samples (and other anecdotal evidence) indicate high numbers of pyrosomes and gymnosomes since 2014. This is likely due to seeding events from offshore during the “Warm Blob” (Bond et al., 2015) or from further south (Li et al., 2016)

    Figure 40

    Implications: Given the prevalence of gelatinous zooplankton in 2017, there is a high potential for the removal of a large fraction of primary productivity from the pelagic ecosystem (Li et al., 2016). As an example of critical species abundance reduction, the average abundance per cubic meter for Calanus marshallae in 2012 was nearly 500% more than the average abundance in 2017. Abundance of C. marshallae in 2012 was regularly above 100 individuals per cubic meter, 22 of 32 stations processed in 2017 had an abundance of less than 10 individuals per cubic meter. Removal of the base of the food chain has large implications for zooplankton, fish, seabirds, and marine mammals. The catch of juvenile salmon and age-0 marine groundfish in surface waters during this July 2017 survey was very low. The low pelagic fish catches were in part possibly due to a changes in trawl gear in 2017, and/or the result of shifts in prey fields and primary producers. In addition, gelatinous zooplankton are highly efficient filter feeders that shunt pelagic production to the benthos via fecal pellets and dead falls (Richardson et al., 2009) and may stimulate production and growth of benthic species.

    Figure 42

    Figure 43

    Figure 44

    Figure 45

     

     

     

    Contributed by Wesley Strasburger1 and Alexei Pinchuk2 textsuperscript1 Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    textsuperscript2 College of Fisheries and Ocean Sciences, University of Alaska Fairbanks, Juneau, AK
    Contact: wes.strasburger@noaa.gov
    Last updated: September 2017

     

     

     

     

  • Jellyfish – Gulf of Alaska Bottom Trawl Survey

    Description of indicator: RACE bottom trawl surveys in the Gulf of Alaska (GOA) are designed primarily to assess populations of commercially important fish and invertebrates. However many other species are identified, weighed and counted during the course of these surveys and these data may provide a measure of relative abundance for some of these species. For jellyfish, the catches for each year were scaled to the largest catch over the time series (which was arbitrarily scaled to a value of 100). The standard error (+/- 1) was weighted proportionally to the CPUE to get a relative standard error. The percentage of positive catches in the survey bottom trawl hauls was also calculated.

    Status and trends: Jellyfish mean catch per unit effort (CPUE) is typically higher in the Kodiak region than in other areas (Figure 46). The frequency of occurrence in trawl catches is generally high across all areas, except the Shumagins, but has been variable. Jellyfish catches in the western GOA (Chirikof and Shumagin areas) have been uniformly low. Jellyfish catch in the Kodiak area decreased from peaks during the 2013 and 2015 surveys. Jellyfish catches in the eastern GOA (Yakutat and Southeastern areas) have been low.

    Factors influencing observed trends: Jellyfish are probably not sampled well by the gear due to their fragility and potential for catch in the mid-water during net deployment or retrieval. Therefore jellyfish encountered in small numbers which may or may not reflect their true abundance in the GOA. The fishing gear used aboard the Japanese vessels that participated in all GOA surveys prior to 1990 was very different from the gear used by all vessels since. This gear difference almost certainly affected the catch rates for jellyfish.

    Implications: GOA survey results show very few trends in jellyfish abundance across time, with most of the trends being consistent within regions.

    Contributed by Chris Rooper, Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service,
    NOAA Contact: chris.rooper@noaa.gov
    Last updated: October 2017

  • Jellyfish – Gulf of Alaska Bottom Trawl Survey

    Description of indicator: Pelagic jellyfish were sampled using a rope trawl towed in the upper 20 m of the eastern Gulf of Alaska during the Alaska Fisheries Science Center’s Gulf of Alaska Assessment Survey, 2011–2017. Stations were approximately 10 nautical miles apart and a trawl was towed for approximately 30 minutes. Area swept was estimated from horizontal net opening and distance towed.
    Jellyfish catch was estimated in kilograms. Jellyfish distribution, abundance, and densities in these areas were estimated using geostatistical modeling methods (Thorson et al., 2015). All jellyfish medusae caught in the surface trawl (top 20 m of the water column) were sorted by species and subsampled for bell diameter and wet weight. Several gelatinous species were encountered with the surface trawl, the most common identifiably retained are: Aequorea sp., Aurelia sp., Chrysaora melanaster, Ctenophora (Hormiphora sp., Pleurobrachia bachei, Beroe sp.), Cyanea capillata, Phacellocephora camtschatica, and Staurophora mertensii. Biomass was calculated for each species and compared across species, and oceanographic domain in the eastern Gulf of Alaska. In 2017 a gear change was implemented, the Cantrawl was replaced with a Nordic 264 trawl net. For specific details on this gear change please reference cruise report (Strasburger et al. in review).
    Abundance and distribution (center of gravity and area occupied) were estimated for each jellyfish species using the VAST package for multispecies version 1.1.0 (Thorson et al., 2015; Thorson and Kristensen, 2016; Thorson et al., 2016a,b) in RStudio version 0.99.896 and R software version 3.3.0 (R Core Team 2016). The abundance index is a standardized geostatistical index developed by Thorson et al. (2015); Thorson and Kristensen (2016); Thorson et al. (2016a,b) to estimate indices of abundance for stock assessments. We specified a gamma distribution and estimated spatial and spatio-temporal variation for both encounter probability and positive catch rate components at a spatial resolution of 100 knots. Parameter estimates were within the upper and lower bounds.

    Figure 46

    Status and trends: Total biomass of jellyfish in the EGOA survey area was low during the cold years (2011–2013) and high during the warm years (2014–2017) with the exception of 2016 (Figure 47). The trend in biomass was dominated by Aequorea. Temporal trends in the estimated abundance of jellyfish indicated a recent increase in the productivity of smaller body-sized jellyfish (Aequorea, Ctenophora) and a decrease in the typically dominant larger jellyfish (Chrysaora). Cyanea and Phacellophora have also increased recently in the eastern Gulf of Alaska. The large body-sized jellyfish Chrysaora melanaster seems to be without favor in the Gulf of Alaska ecosystem and fails to dominant currently as they have in the past and in other ecosystems. Overall, Ctenophora and Aequorea seem to be the recent key players in Gulf of Alaska in terms of abundance Distribution of jellyfish varied among species and years. Yearly distributions throughout the sample grid for all species have been patchy and highly variable. Center of gravity plots indicate no warm and cold year trend in the distribution of jellyfish (Figure 48). Latitudinal (N-S) distribution was farther north during 2017 for Aequorea, Ctenophora, and Phacellophora, and slightly farther south for Aurelia, Chrysaora, and Staurophora, and no different for Cyanea relative to 2016. Longitudinal distribution was oriented farther east for Aequorea, Aurelia, Chrysaora, and Staurophora, and farther west for Ctenophora, Cyanea, and Phacellophora in 2017 relative to 2016. According to the estimated area occupied, the distribution was contracted for Aequorea, Chrysaora, Phacellophora, and Staurophora, and slightly expanded for Aurelia, Ctenophora, and Cyanea during 2017 relative to 2016 (Figure 49).for Aurelia, Chrysaora, and Staurophora, and no different for Cyanea relative to 2016. Longitudinal distribution was oriented farther east for Aequorea, Aurelia, Chrysaora, and Staurophora, and farther west for Ctenophora, Cyanea, and Phacellophora in 2017 relative to 2016. According to the estimated area occupied, the distribution was contracted for Aequorea, Chrysaora, Phacellophora, and Staurophora, and slightly expanded for Aurelia, Ctenophora, and Cyanea during 2017 relative to 2016 (Figure 49).

    Factors influencing observed trends: Shifts in abundance from single-body, large-sized jellyfish during cold years to multiple, smaller-sized species during warm years indicate that there could be a shift to multiple taxa in the future during warm stanzas. There is not enough eastern Gulf of Alaska data to determine if this is the trend. The cause for the shifts in biomass and distribution do not seem to rely solely on physical ocean factors (temperature and salinity). These shifts could also be a result of environmental forcing earlier in the growing season or during an earlier life history stage (polyp), which may influence large medusae biomasses and abundances (Purcell et al., 2009). The rise in Ctenophore catches could possibly be related to the change in gear but gear effects have not been determined at this time.

    Figure 47

    Implications: Significant increases in jellyfish biomass may redirect energy pathways, causing disruption to eastern Gulf of Alaska foodwebs by increased jellyfish predation pressure on zooplankton and larval fish, which could result in limiting carbon transfer to higher trophic levels (Condon et al., 2011).

    Figure 48

    Figure 49

     

     

     

     

     

     

     

     

    Contributed by Kristen Cieciel and Ellen Yasumiishi, Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: kristin.cieciel@noaa.gov
    Last updated: October 2016

  • Larval Walleye Pollock Assessment in the Gulf of Alaska, Spring 2017

    Description of indicator: The 2017 EcoFOCI spring larval survey was conducted from May 11 to June 2. A total of 267 stations were sampled using the 60 cm bongo with 0.505 mm mesh. From the 60 cm bongos, net 1 was preserved in 1.5% formaldehyde for later identification, quantification, and length measurement of all larval fish species. Net 2 was used to acquire a rough count of larval pollock while at sea to determine a rough estimate of abundance and geographic distribution. Rough counts were acquired by pouring the codend contents into a 4-liter beaker and removing a 10% subsample for sorting. If the larval pollock count was below 10, then the entire codend was sorted. The larval samples removed from net 2 were preserved in alcohol to determine age, growth, hatch date distributions, prey estimates, and condition by cell cycle analysis. The 20/60 bongo array with 0.153/0.505 mm mesh was used every other survey line to identify and quantify relative zooplankton abundance available for larval fish. A Sea-Bird FastCat was used in conjunction with the bongos to acquire temperature and salinity profiles. Argos satellite-tracked drifters drogued at 40 meters were released east of Kennedy Entrance and between the Semidi Islands and Chirikof Island (both areas of high larval pollock counts) to assess current strength and direction of larval transport.
    A time series of larval walleye pollock abundance has been developed using quantitative laboratory counts of samples from the spring larval survey from 1981–2015 (Rogers and Mier, 2016; Doyle et al., 2009); however, these data are not available until approximately 12 months after the survey. At-sea rough counts of larval walleye pollock provide a rapid indicator of larval abundance, and a time series was constructed for larval rough counts using the same methodology as for laboratorybased counts. Comparison of abundance estimates using at-sea rough counts and laboratory counts from 2000–2015 indicate a strong correlation (r = 0.99). At-sea rough counts are only available for walleye pollock.

    Status and trends: Larval walleye pollock rough counts from the 2017 western Gulf of Alaska survey were above average throughout the grid with few zero catches (Figure 50). This is in contrast to 2015 when catches were far below average and often zero across the survey grid (Dougherty, 2015). The rough-count abundance estimate indicates that larval walleye pollock abundance was similar to previous high-abundance years (2000, 2006, 2010) although not as high as 2013 (Figure 51).

    Factors influencing observed trends: All larval pollock caught were observed to be in excellent condition and feeding well. A rapid zooplankton assessment (RZA) was conducted at sea to determine prey quality and abundance (see Ferm et al., p. 83). Results suggest good conditions for pollock larvae and juvenile production. Temperature at 40 meters (larval pollock residence) ranged between 4–6oC, which is closer to the expected temperature range for late May in comparison to 2015. The Argos satellite-tracked drifter released east of Kennedy Entrance quickly entered Shelikof Strait and is currently traveling along the Alaska Peninsula between Sutwik Island and the Shumagin Islands. The drifter released between Chirikof Island and the Semidi Islands. has traveled inland to the Alaska Peninsula past the Shumagin Islands and is moving towards Pavlof Bay. The data from the drifter tracks will be useful to determine if mixing of larval pollock from geographically separated spawning stocks is occurring (i.e. Shelikof and Shumagin Islands).

    Figure 50

    Implications: Observed high abundance of larval walleye pollock indicates good survival through the first critical bottleneck period for walleye pollock. This suggests that the ecosystem has returned to a more productive state after the 2014–2016 warm anomaly. Juvenile walleye pollock are an important prey species in the Gulf of Alaska. Whether 2017 will be a strong year-class for the walleye pollock fishery remains to be seen, as high mortality at later stages can reduce even strong larval and juvenile cohorts, as was seen in 2013.

    Figure 51

    Contributed by Annette Dougherty and Lauren Rogers, EcoFOCI, Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: annette.dougherty@noaa.gov
    Last updated: August 2017

  • Small Neritic Fishes in Coastal Marine Ecosystems: Late-Summer Conditions in the Western Gulf of Alaska

    Description of indicator: The Ecosystems & Fisheries Oceanography Coordinated Investigations (EcoFOCI) Program monitors and researches small neritic fishes to improve our understanding and management of the Gulf of Alaska ecosystem and fisheries. Small neritic fishes include the juvenile stages of economically and ecologically important species (e.g., walleye pollock, Pacific cod, Pacific ocean perch and other rockfishes, sablefish, and arrowtooth flounder). They also include species managed exclusively as forage fishes (e.g., capelin and eulachon) that support the fishes, seabirds, and marine mammals that characterize the piscivore-dominated GOA ecosystem. Longstanding objectives of EcoFOCI late-summer field work in the western Gulf of Alaska are to extend a time series of age-0 walleye pollock abundance estimates, monitor the neritic environment including zooplankton and abiotic conditions, and collect samples for research (e.g., trophic and spatial ecology, bioenergetics, age and growth).
    During 21 August–15 September, the NOAA vessel Oscar Dyson sampled the western Gulf of Alaska from Cook Inlet to the Shumagin Islands, including Shelikof Strait, the east side of Kodiak Island, and spanning the shelf out to the shelf break. The survey grid west of the Shumagin Islands was truncated due to weather. At each of 130 stations, water temperature and salinity were profiled and zooplankton samples were collected with 20 cm (153 µm mesh) and 60-cm (505 µm mesh) bongos towed obliquely through the upper 200 m of the water column (see p.83). Target fishes were collected using a Stauffer trawl (aka anchovy trawl) equipped with a small-mesh (2x3 mm) codend liner towed obliquely to a maximum depth of 200 m.
    Time series of abundance for age-0 pollock and for capelin were constructed based on catches from late-summer surveys since 2000 (only odd years since 2001) for the consistently sampled region between Kodiak Island and the Shumagin Islands (Figure 52). Mean catch per unit area was calculated using an area-weighted mean. Due to significant differences in catches of capelin during day versus night, mean CPUE for the night stations only is also shown.

    Figure 52

    Status and trends: Catches of age-0 pollock were particularly high through Shelikof Strait and to the east of Kodiak Island (Figure 52). The mean CPUE estimate for 2017, which does not include the stations near Kodiak, suggests the second highest abundance of age-0 pollock in our time series (Figure 53), averaging 380,000 age-0 pollock per square kilometer (0.38 fish/m2 ). Pollock densities tapered off towards the Shumagin Islands in the southwest. This spatial distribution is in contrast to a previous high abundance year (2013) when catches were highest southwest of the Shumagin Islands. Capelin abundance remained low in 2017, continuing a trend of low abundance since 2011. However, note that no sampling occurred in even years and the time series estimate does not include catches from near Kodiak, where capelin catches are typically higher.

    Figure 53

    Factors influencing observed trends: The abundance of age-0 pollock in late summer reflects the number of surviving larvae from spawning in the spring and survival processes through the summer. In spring of 2017, catches of larval pollock were high (Dougherty and Rogers, p.107), and a rapid assessment of zooplankton in late summer (Ferm et al., p.83) suggested high abundance of euphausiids and large copepods, preferred prey for pollock. The observed spatial distribution of pollock may result from transport processes and/or reflect production from different spawning groups. Investigations into factors driving changes in the spatial distribution and abundance of capelin and juvenile pollock are underway.

    Implications: Capelin and young-of-year pollock are key forage fish species in the Gulf of Alaska, providing prey for seabirds, fishes, and mammals. This late-summer survey also provides an as sessment of the abundance, size, and condition of young-of-year pollock before entering their first winter, giving an early indicator of potential year class strength. Strong catches of juvenile pollock, together with previously observed high larval abundance, suggest a return to productive conditions in the Gulf of Alaska following the “Blob” warm anomaly in 2014–2016.

    Contributed by Lauren Rogers, Matthew Wilson, Steven Porter, EcoFOCI Program, Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, NOAA
    Contact: lauren.rogers@noaa.gov
    Last updated: September 2017

  • Description of indicator: Time series of seabird and forage fish monitoring at Middleton Island in the north-central Gulf of Alaska are among the longest available from any Alaska location. Being situated near the continental shelf edge (lat 59.4375, lon -146.3277), Middletons seabirds sample both neritic habitat and deep ocean waters beyond the shelf break. Consequently, certain species of ecological concern (myctophids) and/or economic concern (0-age group sablefish) figure prominently in seabird diets at Middleton, unlike anywhere else these prey and their seabird predators might be monitored.
    In most years since 2000, regurgitated food samples have been collected from adult and/or nestling black-legged kittiwakes (Rissa tridactyla) during all months April through August. From an evaluation of alternate methods of analyzing and reporting diet results, the preferred metric for kittiwakes is prey relative occurrence (Hatch, 2013), for which the relevant sample units are numbers of identified prey types in a given sample. Rhinoceros auklet (Cerorhinca monocerata) diets are monitored by collecting bill-loads from chick-provisioning adults, usually once or twice a week from early July through early or mid-August. Since 1978, more than 100 kg of auklet prey samples have been collected on Middleton, and auklet diet monitoring provides our single best indicator of forage fish dynamics in the region.

    Status and trends: On average, Middleton kittiwakes take about equal amounts of Pacific sand lance, capelin, and invertebrates, with lesser amounts of herring, sablefish, salmon, and myctophids, depending on stage of the season. The juxtaposition of time series for kittiwakes and rhinoceros auklets since 1978 (Figure 54) shows general agreement between a sustained decline of sand lance and, beginning in 2007, the emergence of capelin as a dominant forage species. In the last 4 years,however, when neither sand lance nor capelin were prevalent, the diets of surface-feeding kittiwakes and diving auklets diverged in respect to prey-switching to alternate species such as mytophids and salmon.
    Auklet data plotted separately by prey type highlight the interannual dynamics of individual species (Figure 55). By all appearances, sand lance were the overwhelmingly dominant forage species in the northern Gulf in the late 1970s through the early 1980s. Following a period of reduced availability in the mid 1990s, sand lance made a strong comeback by the end of that decade. However, sand lance steadily declined in importance after 2000 and for the most part have contributed little to seabird diets since 2008. However, the appearance of about 30% sand lance in the auklet diet in 2016 and 2017 is consistent with a known association of sand lance with warm-water conditions (Hatch, 2013). Pacific herring seem also to have benefited from recent warm surface conditions prevalent in the region

    Factors influencing observed trends: Seabird diets at Middleton reflect ecosystem shifts in the Gulf of Alaska. This includes specific events recorded and widely discussed in the ecological literature such as the notable shift from “warm” (positive Pacific Decadal Oscillation, PDO) conditions to cold (negative PDO) conditions around 1999–2000 (Greene, 2002; Peterson et al., 2003; Batten and Welch, 2004), a similar but stronger and more persistent shift in 2008 (Hatch, 2013), and a widely reported warm-water anomaly that has dominated the system since late winter 2013 (Bond et al., 2015)). A salient finding during a recent, anomalous warm-water event has been the virtual disappearance of capelin from the kittiwake diet on Middleton, following 6 prior years when capelin were predominant (Figure 54). An apparent trade-off between Pacific sand lance (warm conditions) and capelin (cold conditions) may be a benchmark of the forage fish community in the region.

    Implications: Seabird diets provide further evidence that capelin disappeared in the ecosystem during the recent warm years. Chick diets at Middleton may be informative for sablefish studies. In 2017, the Alaska Fisheries Science Center will begin using specimens from seabird diet sampling at Middleton for phenology and growth studies of first-year sablefish, which are difficult to sample directly. Seabird diet indicators are potentially applicable to other management concerns in the region, including Pacific herring stocks, which crashed and have not recovered after the ExxonValdez Oil Spill in 1989, and year-class strengths of pink and chum salmon, which appear regularly in Middleton seabird diets.

    Figure 54

    Figure 55

    Contributed by Scott A. Hatch1 , Mayumi Arimitsu2 , John F. Piatt2 1 Institute for Seabird Research and Conservation, Anchorage, AK 2 Seabird and Forage Fish Ecology Program, Marine Ecosystems Office, Alaska Science Center U.S.
    Geological Survey, Anchorage, AK
    Contact: shatch.isrc@gmail.com
    Last updated: August 2017

  • Description of indicator: Pelagic fish were sampled using a rope trawl towed in the upper 20 m during the Alaska Fisheries Science Center’s eastern Gulf of Alaska Assessment Survey, summer 2010–2017. Stations were approximately 10 nautical miles apart and hauls were standardized to approximately 30 minutes. Area swept was estimated from horizontal net opening and distance towed. The Nordic 264 trawl was used in 2010 and 2017, and a CanTrawl 400/601 was used in 2011–2016.
    Fish catch was estimated in kilograms. Young-of-the-year (YOY) marine fish weight was estimated by multiplying the grand mean weight in a given year by the number captured at a station. Four YOY groundfish species were captured in the trawl, including: Pacific cod, walleye pollock, arrowtooth flounder, and rockfish (Sebastes spp.).
    Abundance and distribution (center of gravity and area occupied) were estimated using the VAST package for multispecies version 1.1.0 (Thorson et al., 2015; Thorson and Kristensen, 2016; Thorson et al., 2016a,b). This package generates a standardized geostatistical index, used for estimating abundance for stock assessments. We specified a gamma distribution and estimated spatial and spatio-temporal variation for both encounter probability and positive catch rate components at a spatial resolution of 100 knots (100 prediction units). Parameter estimates were within the upper and lower bounds and final gradients were less than 0.0005. If no positive catches occurred for a species within a year, an artificial minimum value was inserted, allowing the model to run. It is important to note that no YOY Pacific cod were sampled in 2016. This resulted in very large confidence intervals on a low point. The overall trend is still what would be expected.

    Status and trends: YOY biomass estimates indicate high levels of arrowtooth flounder, and low levels of Pacific cod, pollock, and rockfish in 2017. Temporal trends in the estimated abundance of these groundfish species indicated above average abundance of arrowtooth flounder in 2012, above average abundance of Pacific cod in 2014, above average abundance of pollock during 2014, and an above average abundance of rockfish in 2016 (Figure 1). In 2016, rockfish were the most abundant YOY marine fish species in 2016 followed by pollock, and arrowtooth flounder (Figure 56). Distribution of groundfish in pelagic waters varied among species and years. Pacific cod were commonly predicted to be over the shelf (50–200 m bottom depth) and within 20 nm from shore. Pollock were more widely distributed, occupying shelf, slope, and basin domains (50–2000 m bottom depth). Rockfish were the most widely distributed species in the eastern Gulf of Alaska; occupying shelf, slope, and basin domains up to 100 nm from shore. Arrowtooth flounder were typically found offshore, with the exception of in 2012. In 2017, Pacific cod were found off of Yakutat Bay and Baranof Island, pollock were found north and offshore, arrowtooth flounder in the northern region of the survey area, and rockfish fairly evenly distributed across the shelf. Center of gravity indicated that all species were distributed farther north in 2017. Pacific cod were distributed farther north during recent warm years (2014–2015), whereas pollock, arrowtooth flounder, and rockfish were not until 2017 (Figure 2). Range expansion or contraction occurred for all species in 2017 relative to 2016, except for pollock (Figure 3).

    Figure 56

    Factors influencing observed trends: Lower groundfish abundances in pelagic waters during 2017 are believed to be in response to poor primary production (Strom et al., 2016) and an increased abundance of salps (Li et al., 2016), which further reduced the amount of plankton available transfer energy to upper trophic levels. Piscivorous predators not common to the eastern Gulf of Alaska (e.g., Pacific pomfret) were present in the eastern Gulf of Alaska during 2014, and 2015, presumably in response to unprecedented warming in the eastern Pacific Ocean commonly referred to as the “Warm Blob” (Bond et al., 2015). Additional predation pressure by these warm water predators may have reduced the amount of YOY marine fish that would have otherwise been present.

    Implications: Lower groundfish abundances in surface waters during 2017 indicate a change in productivity in pelagic waters that affected many species of YOY marine fish. Warm conditions during 2014 and 2015 appeared to initially benefit pollock and Pacific cod and in more recent years benefited arrowtooth flounder and rockfish. No Pacific cod were sampled during 2016. Additionally, Pacific cod were low for 2010–2013 year classes in the survey area, a period of cool temperatures.

    Figure 57

    Figure 58

    Contributed by Wes Strasburger, Jamal H. Moss and Ellen Yasumiishi, Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: wes.strasburger@noaa.gov
    Last updated: October 2017

  • Description of indicator: Pelagic fish were sampled using a trawl net towed in the upper 20 m of the eastern Gulf of Alaska (EGOA) during the Alaska Fisheries Science Centers Gulf of Alaska Assessment Surveys during summer, 2010–2017. Stations were approximately 10 nautical miles apart and a trawl was towed for approximately 30 minutes. The area swept by the trawl was estimated from horizontal net opening and distance towed. Fish catch was estimated in kilograms. Juvenile salmon weight was estimated by multiplying the grand mean weight in a given year by the number captured at a station. A CanTrawl was used to sample fish during 2011–2016 and Nordic trawl was used to sample fish in 2017.
    Abundance and distribution (center of gravity and area occupied) were estimated for juvenile Chinook (Oncorhynchus tshawytscha), chum (O. keta), pink (O. gorbuscha), and sockeye (O. nerka) salmon using the VAST package for multispecies version 1.1.0 (Thorson et al., 2015; Thorson and Kristensen, 2016; Thorson et al., 2016a,b) in RStudio version 1.0.136 and R software version 3.3.0 (R Core Team 2016). The abundance index is a standardized geostatistical index developed by Thorson et al. (2015); Thorson and Kristensen (2016); Thorson et al. (2016a,b) to estimate indices of abundance for stock assessments. We specified a gamma distribution and estimated spatial and spatio-temporal variation for both encounter probability and positive catch rate components at a spatial resolution of 100 knots. Parameter estimates were within the upper and lower bounds and final gradients were less than 0.0005.

    Status and trends: Temporal trends in the estimated biomass of juvenile salmon in the EGOA shelf survey area indicates a decrease in the productivity of juvenile salmon during 2017 (Figure 59). Abundances were low for Chinook, coho, pink, and sockeye salmon, and moderate for chum salmon. Both juvenile pink and chum salmon had an alternating year pattern with higher abundances in even-numbered years. Juvenile salmon were distributed nearshore in waters above the continental shelf. Juvenile salmon were distributed farther south and east in 2017 relative to 2016 (Figure 59). In 2017, area occupied was contracted for Chinook, coho, and sockeye, and expanded for pink and chum salmon (Figure 59).

    Factors influencing observed trends: Lower abundances of juvenile salmon during 2017 was likely due to a combination of lower odd-brood year pink salmon production and due to the continuation of warm waters and low and patchy ocean productivity.

    Implications: Recent decreases in the abundance of juvenile salmon in our survey area during summer implies a decline in conditions for growth and survival of salmon from southeast Alaska, British Columbia and the Pacific Northwest lakes and rivers and/or a change in the distribution of juvenile salmon into our survey area during July. The size of juvenile salmon in the surveys were also observed as smaller than usual. Juvenile indices may be an early indication for the numbers of returning adults to the region of origin.

    Figure 59

    Figure 60

    Figure 61

    Contributed by Jamal Moss, Wesley Strasburger, and Ellen Yasumiishi, Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: jamal.moss@noaa.gov
    Last updated: October 2017

  • Description of indicator: Squid were sampled using a trawl net towed in the upper 20 m of the eastern Gulf of Alaska (EGOA) during the Alaska Fisheries Science Center’s Gulf of Alaska Assessment Surveys during summer, 2011–2017. Stations were approximately 10 nautical miles apart and a trawl was towed for approximately 30 minutes at each station. A CanTrawl was used from 2011–2016 and a Nordic trawl in 2017. The area swept by the trawl was estimated from horizontal net opening and distance towed. Squid catch was estimated in kilograms by year and station. Squid weight at each station was estimated by multiplying the grand mean weight in a given year by the number captured at a station.
    Abundance and distribution (center of gravity and area occupied) were estimated using the VAST package for multispecies version 1.1.0 (Thorson et al., 2015; Thorson and Kristensen, 2016; Thorson et al., 2016a,b) in RStudio version 1.0.136 and R software version 3.3.0 (R Core Team 2016). The abundance index is a standardized geostatistical index developed by Thorson et al. (2015, 2016a) to estimate indices of abundance for stock assessments. We specified a gamma distribution and estimated spatial and spatio-temporal variation for both encounter probability and positive catch rate components at a spatial resolution of 100 knots. Parameter estimates were within the upper and lower bounds and final gradients were less than 0.0005.

    Status and trends: Squid were most abundant in the EGOA during 2014 (Figure 62). Temporal trends in squid abundances indicate that squid has steadily increased since 2011 with the exception of a large spike in abundance in 2014 and slight decline in 2017. Squid were distributed across the shelf but farther offshore during 2011–2015 and nearshore during 2016 and 2017 (Figure 63). Squid were distributed farther northwest during 2017 relative to 2016. During the 2014–2017 warm years, squid were generally distributed farther north relative to the prior cold years 2011–2013 (Figure 64). The area that squid occupied in the survey area declined over the 2011–2017 year period (Figure 65).

    Factors influencing observed trends: Squid abundance was lowest during 2011 and may be correlated with poor primary production. Abundances of squid in the EGOA during 2014 may have been in response to unprecedented warming in the eastern Pacific Ocean and influx of the water mass referred to as the “Warm Blob” (Bond et al., 2015).

    Implications: Predators not common to the eastern Gulf of Alaska were present in the EGOA during 2014, and 2015, presumably in response to the “Warm Blob” (Bond et al. 2015). We speculate that forage such as squid may have attracted predators to the EGOA shelf. Squid is a high energy prey item for marine species. The high abundances of squid in 2014 may had increased the quality and quantity of prey for predators and subsequent survival for species such as salmon. Good feeding conditions during the early marine and pre- and post-winter life stages of juvenile salmon is thought to improve body condition and survival of salmon to adulthood. The high abundances of squid in 2014 corresponded with the 2014 juvenile year for salmon that had higher returns of chum salmon (that typically mature after 3 winters in the ocean) to Alaska in 2017 and high returns of pink salmon (that mature after one winter in the ocean) to Alaska in 2015.

    Figure 62

    Figure 63

    Figure 64

    Figure 65

    Contributed by Jamal Moss, Wesley Strasburger, and Ellen Yasumiishi, Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: jamal.moss@noaa.gov
    Last updated: October 2017

  • Southeastern Alaska Herring

    Description of indicator: Pacific herring (Clupea pallasi) stocks that reside in southeastern Alaskan waters are defined on a spawning area basis. In recent decades there have been about nine spawning areas where spawning events have typically been annual and meaningful in size relative to potential for commercial exploitation. These areas include Sitka Sound, Craig, Seymour Canal, Hoonah Sound Hobart Bay-Port Houghton, Tenakee Inlet, Ernest Sound, West Behm Canal, and Kah Shakes-Cat Island (Figure 66). Stock assessments have been conducted at these areas for most years since at least the 1980s by the Alaska Department of Fish and Game, primarily through modeling that relies on indices of egg deposition, age, and size (Hebert, 2017). Although spawning at these areas accounts for a large proportion of the spawning biomass in southeastern Alaska in any given year, other areas with more limited spawning also exist throughout southeastern Alaska. However, little or no stock assessment activity occurs at these minor locations other than occasional and opportunistic aerial surveys to document the miles of spawn along shoreline. The herring that spawn in all areas of Southeast Alaska are believed to be affected by the physical and chemical characteristics of Gulf of Alaska waters, though the spawning areas directly exposed to the open coast (Sitka Sound, Craig, Kah Shakes-Cat Island) may be affected the greatest or the most immediately

    Status and trends: Since peaking around the early 2010s, several stocks have decreased substantially. The current biomass level for Southeast Alaskan herring in aggregate is below the mean level over the base period 2009–2016 (Figure 67). This also holds true for every individual stock in the region. Biomass levels prior to the base period were also below the mean level of the base period, for both combined biomass and for most years of individual stocks. Biomass levels, both current and historical, are low relative to the base period because the base period partially coincides with the most productive period for herring in Southeast Alaska that has been documented since at least 1980. Although the two largest and most consistent stocks (Sitka Sound and Craig), have declined substantially from their peaks around 2011, they continue to be at levels above the thresholds necessary to allow commercial fisheries. Other, smaller stocks in the region have declined to much lower levels over the past few years and in some cases to small fractions of their peaks a few years ago (e.g., Hoonah Sound, Seymour Canal, Ernest Sound). Age-structured stock assessment modeling indicates that the decline can be attributed at least in part to lower survival rates over the past few years.

    Factors influencing observed trends: The underlying cause for the recent decline in herring survival and biomass in the region remains unknown. Multiple plausible factors may be contributing, including increasing populations of predatory marine mammals, such as humpback whales and Stellar sea lions (Sigler et al., 2009; Muto et al.; Fritz et al., 2016), high levels of predatory fish such as salmon, or the recent shift to warmer sea surface temperatures as reflected in the PDO, which could affect herring prey or metabolism.

    Figure 66

    Implications: Although it is possible that lower abundance of herring in the region may have short-term deleterious effects on predators that rely on herring, there is not enough information about populations of other forage species to understand the broader net impact on predators. The short life-span of herring and the natural volatility of stock levels, particularly of smaller-sized stocks, make it difficult to speculate on long-term implications to the ecosystem.

    Figure 67

    Contributed by Kyle Hebert and Sherri Dressel, Alaska Department of Fish and Game, Commerical Fisheries Division, P. O. Box 110024, Juneau, AK 99811-0024
    Contact: kyle.hebert@alaska.gov
    Last updated: July 2017

  • Salmon Trends in the Southeast Coastal Monitoring (SECM) Survey

    Description of indicator: The Southeast Coastal Monitoring (SECM) program has collected fish, zooplankton, and oceanographic samples in southeast Alaska since 1997 (Fergusson et al., 2013; Orsi et al., 2014, 2015). Sampling has been focused most consistently in Icy Strait, the primary northern migratory pathway to the Gulf of Alaska for juvenile salmon originating from over 2000 southeast Alaska streams and rivers. Research objectives of the SECM program are to provide insight into the production dynamics and early ocean ecology of Southeast Alaska salmon.
    Surface trawls (0–20m) are used to sample epipelagic fish species, including all five commercial species of Pacific salmon (Oncorhynchus sp.) in southeast Alaska. Juvenile pink salmon (O. gorbuscha) are, on average, the most abundant species in the epipelagic habitat in SECM surveys. In addition to juvenile salmon, SECM surveys catch a suite of non-salmonid fish species, including occasional large numbers of walleye pollock (Gadus chalcogrammus), capelin (Mallotus villosus), and Pacific herring (Clupea pallasii). We provide summaries of the annual catch rates for the five salmon species (pink; coho O. kisutch; sockeye, O. nerka; chum, O. keta; and Chinook, O. tshawytscha).

    Status and trends: In 2017, juvenile salmon catch rates were among the lowest of the time series for all five salmon species (Figure 68). Meanwhile, adult chum salmon catch rates continued an upward trend, though these rates remain nearly an order of magnitude lower than those of adult pink salmon.

    Factors influencing observed trends: Ocean conditions in 2017 were preceded by several anomalously warm years. Warm ocean conditions are likely to have influenced recruitment patterns through multiple years of altered community structure and stock dynamics. We continue to seek relationships between observed trends and environmental covariates (e.g., sea surface temperature).

    Implications: Understanding recruitment processes of fish stocks is an important aspect of managing fish stocks, particularly during periods of substantial climate change. Juvenile abundance and oceanographic data collected during SECM have provided reliable forecasts of pink salmon returns to Southeast Alaska (Orsi et al., 2015) and are used for pre-season fisheries management decisions in the purse seine and drift gillnet fisheries of Southeast Alaska (Wertheimer et al., 2015). By extending the application of SECM fish catches beyond pink salmon, we are poised to better resolve the relationships between other salmon species and ecosystem indicators that help to describe their production dynamics. Furthermore, as SECM surveys continue annually, they fill a valuable gap in data that occurs during off-years for the Gulf of Alaska Ecosystem Surveys.

    Figure 68

    Contributed by Jordan T. Watson, Andy Gray, Emily Fergusson, James M. Murphy, Auke Bay Laboratories Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: stephani.zador@noaa.gov
    Last updated: August 2017

  • Gulf of Alaska Groundfish Condition

    Description of indicator: Length-weight residuals are an indicator of somatic growth (Brodeur et al., 2004) and, therefore, a measure of fish condition. Fish condition is an indicator of how heavy a fish is per unit body length, and may be an indicator of ecosystem productivity. Positive length-weight residuals indicate fish are in better condition (i.e., heavier per unit length); whereas, negative residuals indicate fish are in poorer condition (i.e., lighter per unit length). Fish condition may affect fish growth and subsequent survival (Paul et al., 1997; Boldt and Haldorson, 2004). The AFSC Gulf of Alaska bottom trawl survey data were utilized to acquire lengths and weights of individual fish for walleye pollock, Pacific cod, arrowtooth flounder, southern rock sole, dusky rockfish, northern rockfish, and Pacific ocean perch. Only standard survey stations were included in analyses. Data were combined by INPFC area; Shumagin, Chirikof, Kodiak, Yakutat and Southeastern. Length-weight relationships for each of the seven species were estimated with a linear regression of log-transformed values over all years where data were available (during 1984–2017). Additionally, length-weight relationships for age 1+ walleye pollock (length from 100–250 mm) were also calculated independent from the adult life history stage. Predicted log-transformed weights were calculated and subtracted from measured log-transformed weights to calculate residuals for each fish. Length-weight residuals were averaged for the entire GOA and for the 5 INPFC areas sampled in the standard summer survey. Temporal and spatial patterns in residuals were examined.

    Status and trends: Length-weight residuals varied over time for all species with a few notable patterns (Figure 71). Residuals for most species where there were data were positive in the first three years of the survey (1985–1990). The residuals have been mixed for all species since then, generally smaller and varying from year to year. Age-1 pollock have generally been at or near the mean condition since the 1990 survey (although 2017 was a negative condition year). In 2017 condition was below average for all species except Pacific cod. Fish condition for northern rockfish and arrowtooth flounder was the lowest on record and Pacific Ocean perch and southern rock sole were the second lowest on record. In general, for all species except the gadids there has been a general decrease in body condition since 1990.
    Spatial trends in residuals were also apparent for some species (Figure 72). Most species were generally in better condition in the Kodiak area, especially southern rock sole. The southeastern area was an area where fish condition was generally worse than other areas of the GOA, except for northern rockfish. For Pacific Ocean perch, the Kodiak and Shumagin areas generally had positive length-weight residuals. Arrowtooth flounder and age-1 pollock are the only species with consistently higher residuals in the Yakutat area.

    Factors influencing observed trends: One potential factor causing the observed temporal variability in length-weight residuals may be temperature and local production. The lack of consistent trends in any of the species and any of the areas suggests that local conditions that vary from year to year might be driving condition trends in the Gulf of Alaska.
    Other factors that could affect length-weight residuals include survey sampling timing and fish migration. The date of the first length-weight data collected is generally in the beginning of June and the bottom trawl survey is conducted sequentially throughout the summer months from west to east. Therefore, it is impossible to separate the in-season time trend from the spatial trend in these data.

    Implications: A fish’s condition may have implications for its survival. For example, in Prince William Sound, the condition of herring prior to the winter may in part determine their survival (Paul and Paul 1999). The condition of Gulf of Alaska groundfish may partially contribute to their survival and recruitment. In the future, as years are added to the time series, the relationship between length-weight residuals and subsequent survival can be examined further. It is likely, however, that the relationship is more complex than a simple correlation. Also important to consider is that condition of all sizes of fish were examined and used to predict survival. Perhaps, it would be better to examine the condition of juvenile fish, not yet recruited to the fishery, or the condition of adult fish and correlations with survival. This work has not yet been done for the 2017 bottom trawl survey data, but we are preparing a manuscript describing the juvenile-adult condition correlation and further splitting of juvenile and adult fishes and anticipate including it in the 2018 ecosystem contributions.

    Figure 71

    Figure 72

    Figure 49

    Contributed by Jennifer Boldt1 , Chris Rooper2 , and Jerry Hoff2 1Fisheries and Oceans Canada, Pacific Biological Station, 3190 Hammond Bay Rd, Nanaimo, BC, Canada V9T 6N7 2Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: chris.rooper@noaa.gov
    Last updated: October 2015

  • Miscellaneous Species – Gulf of Alaska Bottom Trawl Survey

    Description of indicator: RACE bottom trawl surveys in the Gulf of Alaska (GOA) are designed primarily to assess populations of commercially important fish and invertebrates. However many other species are identified, weighed and counted during the course of these surveys, and these data may provide a measure of relative abundance for some of these species. For each species group, the catches for each year were scaled to the largest catch over the time series (which was arbitrarily scaled to a value of 100). The standard error (+/- 1) was weighted proportionally to the CPUE to get a relative standard error. The percentage of positive catches in the survey bottom trawl hauls was also calculated.

    Status and trends: Echinoderm catches have been highest in the Kodiak and Chirikof regions of the GOA, and they are consistently captured in ∼80% of bottom trawl hauls in all areas (Figure 79. Echinoderms have been declining in both of these areas over the last few surveys. Shrimp CPUE has been increasing in the Kodiak and Chirikof areas over the last few surveys, while remaining fairly constant and low in the other areas. Eelpout CPUE has been variable, with peak abundances occurring in 1993, 2001 and 2009 in the Shumagin area, 2003 and 2013–2015 in the central GOA (Kodiak and Chirikof areas) and peak catches after 1999 in the eastern GOA. Poacher CPUEs peaked in 1993 and 2015 in the Shumagin area. Poachers have been uniformly in low abundance in the Chirikof, Yakutat and Southeastern areas and have been variable, but somewhat higher in the Kodiak areas.

    Figure 79

    Factors influencing observed trends: Many of these species are not sampled well by the gear or occur in areas that are not well sampled by the survey (hard, rough areas, mid-water etc.) and are therefore encountered in small numbers which may or may not reflect their true abundance in the GOA. The fishing gear used aboard the Japanese vessels that participated in all GOA surveys prior to 1990 was very different from the gear used by all vessels since. This gear difference almost certainly affected the catch rates for some of these species groups.

    Implications: The trends in other species in the bottom trawl survey do not appear consistent over time, with the possible exception of decreases in recent echinoderm catches.

    Contributed by Chris Rooper, Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: Chris.Rooper@noaa.gov
    Last updated: October 2017

  • Seabird Monitoring Summary for the Western Gulf of Alaska

    Description of indicator: The Alaska Maritime National Wildlife has monitored seabirds at colonies around Alaska in most years since the early- to mid-1970’s. Time series of annual breeding success and phenology (and other parameters) are available from over a dozen species at eight Refuge sites in the Gulf of Alaska, Aleutian Islands, and Bering and Chukchi Seas. Monitored colonies in the Gulf of Alaska include Aiktak (in Unimak Pass), Chowiet (Semidi Islands), East Amatuli (Barren Islands), and St. Lazaria (Southeast Alaska; not monitored in 2017) islands. Reproductive success is defined as the proportion of nest sites with eggs (or just eggs for murres that do not build nests) that fledged a chick.

    Status and trends: Several fish-eating seabirds had unusually low reproductive success in 2017. At Chowiet Island in the Semidis, this included tufted and horned (not shown) puffins (Figure 80). Common murres, which showed rare widespread reproductive failure in 2015–2016, generally had better colony attendance and fledging rates in 2017 but still the number of birds breeding was low. In 2017, tufted puffin productivity was >1 standard deviation (SD) below the long term mean at all monitored sites except at East Amatuli (where they were observed carrying large amounts of capelin). Black-legged kittiwakes and storm-petrels (which consume a mix of fish and invertebrates) showed fledging rates within 1 SD of the mean, as did planktivorous auklets. Timing of breeding was within typical ranges for most species (although horned puffins and ancient murrelets were early at Aiktak and murres and kittiwakes were late at East Amatuli).

    Factors influencing observed trends: In general, murres appear to have been negatively affected during the marine heat wave of the past few years, with widespread reproductive failures, die-offs, and low attendance at breeding colonies. Other species did not show broad-scale failures during this period; planktivorous seabirds were generally successful. Despite overall low reproductive success of murres in 2017, some improvement in murre attendance and fledging success may reflect environmental changes returning back to more neutral conditions (see Bond, p. 56).

    Implications: Reproductive activity of central-place foraging seabirds can reflect ecosystem conditions at multiple spatial and temporal scales. For piscivorous species that feed at higher trophic levels, continued reduced reproductive success may indicate that the ecosystem has not yet shifted back from warm conditions and/or there is a lagged response of the prey. However, the improvement in attendance and minimal reproductive activity among murres during 2017 indicates some improvement in foraging conditions for those species.

    Figure 80

    Contributed by Heather Renner, Nora Rojek, Arthur Kettle, Alaska Maritime National Wildlife Refuge, Homer, AK
    Contact: heather renner@fws.gov
    Last updated: October 2017

  • Humpback Whale Calving and Juvenile Return Rates in Glacier Bay and Icy Strait

    Description of indicator: From 1985–2017, we used consistent methods and levels of effort to monitor humpback whales annually from June 1–August 31 in Glacier Bay and Icy Strait (Gabriele et al., 2017). We calculated the crude birth rate as an annual index of reproduction by dividing the number of calves by the total whale count each year. We also documented the return and recruitment of calves into the population as juveniles. Humpback whales and groundfish target the same lipid-rich prey (i.e., forage fish and euphausiids) and trends in humpback whale reproductive success and juvenile survival may indicate changes in prey availability and/or quality in the eastern Gulf of Alaska.

    Status and trends: There is mounting evidence that humpback whale calving and juvenile return rates in Glacier Bay and Icy Strait have declined substantially in recent years. In 1985–2013, we observed 2–21 calves per year (mean = 9.3) and a crude birth rate ranging 3.3%–18.2% (Figure 81). Females almost always remained with their calves for the entire summer although we documented a missing calf in eight cases over the 1985–2013 time period, with no more than one case per year (Neilson et al., 2015). In 2014, an unprecedented number of calves (n = 5) went missing between early August and late October (Neilson et al., 2015) and none of the remaining calves (n = 9) have been resighted in subsequent years. In 2015, we documented relatively few calves (n = 5) (Neilson and Gabriele, 2016) and none have been resighted. In 2016, we documented only one mother/calf pair (both appeared to be abnormally thin), which led to the lowest crude birth rate (0.6%) since monitoring began in 1985 (Neilson et al. 2017) (Figure 81). In 2017, we documented only two mother-calf pairs, which we expect will lead to an anomalously low crude birth rate for the second year in a row (the total whale count for 2017 is pending data analysis). Furthermore, one of the mothers in 2017 appeared to be abnormally thin and the other lost her calf by mid-July
    Humpback whales that summer in southeastern Alaska exhibit strong maternally-directed site fidelity that has driven population growth over time (Pierszalowski et al., 2016). We did not document any one or two-year-old whales in 2016 and we observed very few small whales in 2017. Out of 29 calves that we documented in the study area from 2013–2015, only two individuals (7%) are known to have survived to be juveniles in the study. While the mean age at which calves return to the study area is 3.2 years (Gabriele et al., 2017) and juvenile whales can be difficult to track and photo-identify based on their small size and often erratic behavior, it is notable that before 2016, the last time we documented no one- or two-year-old whales in the study area was 2003 (Figure 82).

    Factors influencing observed trends: We hypothesize that these changes in calving and juvenile return rates may be related to recent changes in whale prey availability and/or quality, which may in turn be negatively affecting maternal body condition and therefore reproductive success and/or overall juvenile survival (Bradford et al., 2012; Fuentes et al., 2016; Seyboth et al., 2016). This hypothesis is supported by observed declines in the body condition of humpback whales throughout northern Southeast Alaska in recent years (see Moran et al. this report).

    Figure 81

    Figure 82

    Implications: Recruitment from local populations (vs. immigration from outside populations) has been a key driver of humpback whale population growth over the past 30 years in Glacier Bay and Icy Strait (Pierszalowski et al., 2016), therefore sustained declines in calving and/or recruitment will have long-term effects on the humpback whale population in this area. If humpback whales are currently food-limited in northern Southeast Alaska, this might indicate that groundfish (which prey on the same species) may also be food-limited.

    Contributed by Janet Neilson, Christine Gabriele, Louise Taylor-Thomas, Glacier Bay National Park and Preserve, P.O. Box 140, Gustavus, AK 99826
    Contact: janet neilson@nps.gov
    Last updated: September 2017

  • Aggregated Catch-Per-Unit-Effort of Fish and Invertebrates in Bottom Trawl Surveys in the Gulf of Alaska, 1993–2017

    Description of indicator: This index provides a measure of the overall biomass of benthic, demersal, and semi-demersal fish and invertebrate species. We obtained catch-per-unit-effort (CPUE in kg ha-1) of fish and major invertebrate taxa for each successful haul completed during standardized bottom trawl surveys on the Gulf of Alaska shelf (GOA), 1993–2017. Total CPUE for each haul was computed as the sum of the CPUEs of all fish and invertebrate taxa. To obtain an index of average CPUE by year, we modeled log-transformed total CPUE (N = 6333 and 1561 hauls in the western (west of 147oW) and eastern GOA, respectively) as smooth functions of depth, alongshore distance and sampling stratum with year-specific intercepts using Generalized Additive Models following Mueter et al. (2002). Hauls were weighted based on the area represented by each stratum. To avoid biases due to gear and vessel issues, data prior to the 1993 survey was not included in the analysis.

    Status and trends: Total log(CPUE) in both the eastern and western GOA decreased from recent high values to their lowest (west) and second lowest (east) value in 2017 (Figure 84). There was no significant long-term trend over time from 1993 to 2017 in either region, but total CPUE decreased significantly by 30–40% since 2009 and 2013, respectively, in the eastern and western GOA. The decrease in CPUE was widespread among species, affecting commercial and non-commercial species. Species that showed the largest absolute declines in biomass since 2013 included walleye pollock, Pacific cod, arrowtooth flounder and northern rockfish in the western GOA; and arrowtooth flounder, shortraker rockfish, Pacific cod and spiny dogfish in the eastern GOA.

    Factors influencing observed trends: Commercially harvested species account for over 70% of total survey catches. Fishing is expected to be a major factor determining trends in survey CPUE, but environmental variability is likely to account for a substantial proportion of the observed variability in CPUE through variations in recruitment, growth, and distribution. Substantial declines in many species in recent years may be associated with the unusual warm conditions in the GOA in 2014–2016, which appeared to affect prey availability and were associated with unusual mortality events in seabirds and marine mammals.

    Implications: This indicator can help address concerns about maintaining adequate prey for upper trophic level species and other ecosystem components. A sharp drop in total biomass of demersal fish and invertebrates affecting commercial and non-commercial species, suggests poor availability of zooplankton prey for these species and a reduced prey base for upper trophic level species following the 2014/15 warm event.

    Figure 84

    Contributed by Franz Mueter, University of Alaska Fairbanks, 17101 Point Lena Road, Juneau, AK 99801
    Contact: fmueter@alaska.edu
    Last updated: October 2017

  • “Mushy” Halibut Syndrome Occurrence

    Description of indicator: The condition was first detected in Gulf of Alaska halibut in 1998. Increased prevalence occurred in 2005, 2011, and 2012, while it was apparently absent in 2013 and 2014. It is most often observed in smaller halibut of 15–20 lbs in the Cook Inlet area, but has also been noted in Kodiak, Seward, and Yakutat. Alaska Department of Fish and Game (ADF&G) describes the typical condition consisting of fish having large areas of body muscle that are abnormally opaque and flaccid or jelly-like. The overall body condition of these fish is usually poor, and often they are released because of the potential inferior meat quality. Incidence of mushy halibut is reported opportunistically in recreational fishing reports, but may not represent true trends.

    Status and trends: ADF&G post samplers have reported relatively few “mushy” halibut during the 2017 sport fishing season (http://www.adfg.alaska.gov/sf/fishingreports/).

    Factors influencing observed trends: The condition is considered a result of nutritional myopathy/deficiency, and thus may be indicative of poor prey conditions for halibut. According to ADF&G, the Cook Inlet and Homer/Seward areas are nursery grounds for large numbers of young halibut that feed primarily on forage fish that have recently declined in numbers. Stomach contents of smaller halibut now contain mostly small crab species. Whether this forage is deficient, either in quantity or in essential nutrients is not known. However, mushy halibut syndrome is similar to that described for other animals with nutritional deficiencies in vitamin E and selenium. This muscle atrophy would further limit the ability of halibut to capture prey possibly leading to further malnutrition and increased severity of the primary nutritional deficiency.

    Implications: The relatively few reports of “mushy” halibut, particularly relative to its recent prevalence in 2015 and 2016, may indicate that foraging conditions for young halibut were more favorable during the past year. However, reporting is opportunistic and may not reflect true prevalence.

    Contributed by Stephani Zador Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: stephani.zador@noaa.gov
    Last updated: October 2016

    Ichthyophonus Parasite

    Description of indicator: Ichthyophonus (spp.) is a globally distributed mesomycetozoan fish parasite, which has caused epizootic events among economically important fish stocks, including herring and salmon. The parasite has been documented in at least 145 fish species, and infection can result in reduced growth, stamina, and overall health. In some cases, individuals show gross clinical signs including black papules, white nodules on heart tissue, muscle ulcers, and roughening of the skin. The FAST Lab has conducted Ichthyophonus surveys since 2011 that have focused on common sport-caught fishes throughout the marine waters of southcentral Alaska.
    Current work is focused on surveying Ichthyophonus prevalence in Pacific halibut for 2017 in Homer. This work uses a fish length-based sampling design and also examines physiological components to gain more information on how the parasite affects halibut condition. Bioelectric impedance analysis (BIA) and Fultons Condition Factor (K) are being used to assess muscle condition and length/weight ratio. In addition, host immune response to the parasite is being measured by histolopathological methods, and qPCR is being used to quantify parasite load. As with our earlier research, we are working cooperatively with the ADF&G port sampling program and the charter halibut fleets.

    Status and trends: Initial work in August of 2011 resulted in the first documentation of natural Ichthyophonus infections in lingcod (Ophiodon elongates), yelloweye rockfish (Sebastes ruberrimus) and Pacific cod (Gadus macrocephalus), along with the expansion of the geographic range of Ichthyophonus in black rockfish (Sebastes melanops) northward to include southcentral Alaska (Harris et al., InReview).
    Subsequent work has focused on Pacific halibut (Hippoglossus stenolepis) in Homer, Alaska. The most recent survey was performed in 2016, and found 57% prevalence (n=335). Previous surveys reported 26% (n=126, 2011), 29% (n=248, 2012) and 23% (n=315, 2013)(Grenier, 2014).

    Factors influencing observed trends: The 2012 and 2013 FAST Lab Pacific halibut research found that the parasite infected heart tissues; was never found in liver, spleen, or kidney tissues; and was more prevalent in older fish. A pepsin digestion assay was developed to assess the degree of infection, and found that load varied widely among infected fish with 6 to 1,245 Ichthyophonus schizonts per gram of heart tissue. Findings did not support the hypothesis that reduced halibut size-at-age may be caused by Ichthyophonus.

    Implications: This research has found no indication of high intensity infections or clinically diseased individuals. These results support the hypothesis that under typical conditions, Ichthyophonus can occur at high infection prevalence with concomitant low infection intensities. This project lays important methodological groundwork for the expansion of groundfish condition research to the Bering Sea, Aleutian Islands and Gulf of Alaska.

    Contributed by the Fisheries, Aquatic Science, and Technology (FAST) Lab, Alaska Pacific University. Contact: bharris@alaskapacific.edu Last updated: August 2017

 
  • Time Trends in Groundfish Discards

    Description of indicator: Estimates of groundfish discards for 1993–2002 are sourced from NMFS Alaska Regions blend data, while estimates for 2003 and later come from the Alaska Region’s Catch Accounting System. These sources, which are based on observer data in combination with industry landing and production reports, provide the best available estimates of groundfish discards. Discard rates as shown in Figure 87 below are calculated as the weight of groundfish discards divided by the total (i.e., retained and discarded) catch weight for the relevant area-gear-target sector. Where rates are described below for species or species groups, they represent the total discarded weight of the species/species group divided by the total catch weight of the species/species group for the relevant area-gear-target sector. These estimates include only catch of Fishery Management Plan (FMP)-managed groundfish species on FMP-managed groundfish targets: not included are groundfish discards in the halibut fishery and discards of non-FMP groundfish species, such as forage fish and species managed under prohibited species catch limits

    Figure 87

    Status and trends: Since 1993 discard rates of groundfish species in federally-managed Alaskan groundfish fisheries have generally declined in both pollock and non-pollock trawl fisheries in the Gulf of Alaska (GOA)(Figure 87). One exception is in the GOA fixed gear sector, where rates have varied between a low of 6% in 2012 and a high of 14% in 1993 and have trended upward since 2012 following a downward decline during the preceding 4 year period. In the non-pollock trawl sector, discard rates dropped from 40% in 1994 to 21% in 1998, trended upwards from 1998 to 2003, and have generally declined over the last ten years to a low of 8% in 2015 and 2016. Discard rates in the GOA pollock trawl sectors declined from over 10% in 1993 to about 1% in 1998 and have since fluctuated between 1% and 3%.

    Factors influencing observed trends: Since the early 1990s fisheries managers in North Pacific groundfish fisheries have employed various measures to address the problem of discards, including:

    Limited access privilege programs (LAPPs) that reduce economic discards by removing the 166 race for fish In-season closure of fisheries once target or bycatch species quotas are reached Minimum retention and utilization standards for certain fisheries Maximum retainable amounts (MRAs) specifying the amounts of “bycatch only” species that harvesters may retain relative to other groundfish species that remain open to directed fishing. MRAs reduce regulatory discards by allowing for limited retention of species harvested incidentally in directed fisheries.

    In the Gulf of Alaska ecosystem management and conservation measures aimed at reducing bycatch have contributed to an overall decline in groundfish discards over time. Pollock roe stripping, wherein harvesters extract only the highest value pollock product and discard all of the remaining fish, was prohibited in 1991. In 1997 arrowtooth flounder was added as a basis species for retention of pollock and Pacific cod, and in 1998 full retention requirements for pollock and cod were implemented for all vessels fishing for groundfish, leading to overall declines in pollock and cod discards in the GOA. Additional retention requirements went into effect in 2003 for shallow-water flatfish species across the entire GOA and in 2004 for demersal shelf rockfish in the Southeast Outside district of the Eastern Gulf. Under the GOA groundfish FMP, fisheries with discard rates over 5% for shallow-water flatfish are subject to annual review by the North Pacific Council. Reductions in flatfish discards account for most of the general decline in discards and discard rates for the non-pollock trawl sector.
    Implementation of the Pacific Halibut and Sablefish Individual Fishing Quota (IFQ) Program in 1995 led to an overall decline in groundfish discards in the GOA longline sablefish fishery. The IFQ program includes a number of measures to minimize discards of both target and bycatch species. Retention of sablefish and halibut is required as long as the harvester has IFQ catch quota available, which restricts the practice of high grading (discard of lower quality fish). Additionally, all Pacific cod and rockfish must be retained when IFQ halibut or sablefish are on board, subject to other regulations.
    Another LAPP, the Central Gulf of Alaska Rockfish Program, rationalizes allocations of rockfish species targeted with trawl gear in the Central GOA (northern rockfish, Pacific ocean perch, and dusky rockfish) along with species harvested incidentally in the fishery (Pacific cod, rougheye rockfish, shortraker rockfish, and sablefish). Vessels fishing in cooperatives with Rockfish Program quota are prohibited from discarding catch of rationalized species. Discards and discard rates of sablefish and rockfish across the entire GOA non-pollock trawl sector have generally declined since the Rockfish Program was piloted in 2007.
    In recent years the species historically comprising the “other groundfish” assemblage (skate, sculpin, shark, squid, octopus) have overtaken flatfish as the largest source of discards in the GOA. Most discards of these species currently occur in the longline fishery, although greater observer coverage on smaller hook and line vessels beginning in 2013 likely accounts for some of the recent increase in fixed gear sector discards. Retention rates for skate and octopus have fluctuated over time, in part due to changing market conditions for these species. Interest in retention of skates and directed fishing for skates (despite their management under “bycatch-only” status beginning in 2005) resulted in annual overages of longnose and big skate TACs from 2007 to 2013. Discards of skate have increased since 2012 as NMFS has taken action to prevent such overages, including regulatory discard requirements for big skate in the Central GOA, imposed at progressively earlier dates from 2013 to 2015, and, in 2016, a reduction in the MRA for GOA skates from 20% to 5%.

    Implications: Characterizing fishery bycatch, which includes discards of groundfish, is an important component of ecosystem-based management. Discards add to the total human impact on biomass without providing a benefit to the Nation and as such are seen as “contrary to responsible stewardship and sustainable utilization of marine resources” (Kelleher, 2005). Bycatch in general constrains the utilization of commercial species (resulting in forgone income) and increases the uncertainty around total fishing-related mortality, making it more difficult to assess stocks, define overfishing levels, and monitor fisheries for overfishing. Although ecosystem effects of discards are not fully understood, discards of whole fish and offal have the potential to alter energy flow within ecosystems and have been observed to result in changes to habitat (e.g., oxygen depletion in the benthic environment) and community structure (e.g., increases in scavenger populations).
    Minimizing fishery discards is recognized as an ecological, economic, and moral imperative in various multilateral initiatives and in National Standard 9 of the Magnuson-Stevens Fishery Conservation and Management Act. Over the last three decades, management measures in North Pacific groundfish fisheries have generally been effective in increasing groundfish retention and utilization and reducing discards. Monitoring discards and discard rates provides a way to assess the continuing efficacy of such measures.

    Figure 49

    Contributed by Jean Lee, Resource Ecology and Fisheries Management Division, AFSC, NMFS, NOAA, and Alaska Fisheries Information Network, Pacific States Marine Fisheries Commission Contact: jean.lee@noaa.gov Last updated: September 2017

  • There are no updated or new indicators in this section this year

  • Fish Stock Sustainability Index and Status of Groundfish, Crab, Salmon, and Scallop Stocks

    Description of indicator: The Fish Stock Sustainability Index (FSSI) is a performance measure for the sustainability of fish stocks selected for their importance to commercial and recreational fisheries (http://www.nmfs.noaa.gov/sfa/fisheries_eco/status_of_fisheries). The FSSI will increase as overfishing is ended and stocks rebuild to the level that provides maximum sustainable yield. The FSSI is calculated by awarding points for each fish stock based on the following rules: 1. Stock has known status determinations:
    (a) overfishing level is defined = 0.5 (b) overfished level is defined = 0.5 2. Fishing mortality rate is below the “overfishing” level defined for the stock = 1.0 3. Biomass is above the “overfished” level defined for the stock = 1.0 4. Biomass is at or above 80% of the biomass that produces maximum sustainable yield (BMSY) = 1.0 (this point is in addition to the point awarded for being above the “overfished” level) The maximum score for each stock is 4.
    In the Alaska Region, there are 36 FSSI stocks and an overall FSSI of 144 would be achieved if every stock scored the maximum value, 4. Over time, the number of stocks included in the FSSI has changed as stocks have been added and removed from Fishery Management Plans (FMPs). Prior to 2015 there were 35 FSSI stocks and maximum possible score of 140. To keep FSSI scores for Alaska comparable across years we report the total Alaska FSSI as a percentage of the maximum possible score (i.e., 100%). Additionally, there are 29 non-FSSI stocks, two ecosystem component species complexes, and Pacific halibut which are managed under an international agreement. None of the non-FSSI stocks are known to be overfished, approaching an overfished condition, or subject to overfishing. For more information on non-FSSI stocks see the Status of U.S. Fisheries webpage. Within the GOA region there are 14 FSSI stocks. The assessment for sablefish is based on aggregated data from the GOA and BSAI regions. In previous FSSI contributions, the sablefish FSSI score was included among BSAI species. Starting with this years contribution sablefish has been removed from the BSAI contribution and is now included in the GOA FSSI.

    Table 6

    Status and trends: As of June 30, 2017, no BSAI or GOA groundfish stock or stock complex is subjected to overfishing, and no BSAI or GOA groundfish stock or stock complex is considered to be overfished or to be approaching an overfished condition (Table 6).
    The current overall Alaska FSSI is 132.5 out of a possible 144, or 92%, based on updates through June 2017 and is unchanged from last year (Figure 92). The overall Alaska FSSI has generally trended upwards from 80% in 2006 to 92% in 2017. The GOA FSSI is 51 out of a maximum possible 56 (Table 6). Two and a half points are deducted from both the Demersal Shelf Rockfish Complex and the Thornyhead Rockfish complex for unknown status determinations and not estimating B/BMSY. Since 2006 the GOA FSSI has been generally steady, increasing from 90% in 2006 up to 91% in 2017 (Figure 93). There were minor drops in the FSSI in 2008–2009 and again in 2012–2013. In 2008 and 2009 a point was lost each year for B/BMSY walleye pollock in the western/central GOA dropping below 0.8. In 2009 an additional 2.5 points were lost for the Rex sole stock having unknown status determinations and for not estimating B/BMSY. In 2012 and 2013 2.5 points were lost for having unknown status determinations and not estimating B/BMSY for the deep water flatfish complex.

    Factors influencing observed trends: The overall Alaska FSSI and the GOA FSSI is unchanged from last year. GOA stocks that had low FSSI scores (1.5) are the thornyhead rockfish complex (shortspine thornyhead rockfish as the indicator species) and the demersal shelf rockfish complex (yelloweye rockfish as the indicator species). The low scores for these groups are because the overfished status determinations are not defined and it is therefore unknown if the biomass is above the overfished level or if biomass is at or above 80% of BMSY.

    Implications: The majority of Alaska groundfish fisheries appear to be sustainably managed, including GOA groundfish fisheries. Until the overfished status determinations are defined for the Demersal Shelf Rockfish complex and the Thornyhead Rockfish complex, it will be unknown whether these stocks are overfished or approaching an overfished condition.

    Figure 92

    Figure 93

    Table 6

    Contributed by Andy Whitehouse, Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington, Seattle, WA
    Contact: andy.whitehouse@noaa.gov
    Last updated: September 2017

  • Economic Indicators in the Gulf of Alaska Ecosystem – Landings

    Description of indicator: Landings are a baseline metric for characterizing commercial economic production in the Gulf of Alaska. Landings are the retained catch of fish, calculated as in Fissel et al. (2016), and plotted here by functional group. While many species comprise a functional group, it is the handful of species that fishermen target that dominate the economic metrics in each group. The primary target species in the apex predators functional group are Pacific cod, Pacific halibut, sablefish, and arrowtooth flounder. The primary target species in the pelagic foragers functional group are walleye pollock, Pacific ocean perch, northern rockfish and dusky rockfish. The primary species caught in the benthic foragers functional group are flathead sole, and rex sole. The primary target species in the salmonid functional group are chinook, sockeye, and pink salmon. The primary species caught in the motile epifauna functional group are tanner and dungeness crab. Because of significant differences in the relative scale of landings across functional group landings are plotted on a log scale.

    Status and trends: Landings in the Gulf of Alaska are primarily comprised of catch from three functional groups: salmon, pelagic foragers, and apex predators (Figure 94). Salmon landings display a stable cycle driven by large returning year classes in odd years. The primary species landed within the pelagic forager functional group is pollock, whose landings have been fairly stable until 2012 when they began to increase with the Total Allowable Catch (TAC). Pacific ocean perch, northern rockfish and dusky rockfish are also caught in significant quantities in the Gulf of Alaska, but landings are roughly one half to one fifth the volume of pollock landings. Within the apex predator functional group, Pacific cod, arrowtooth flounder, halibut and sablefish all have significant target fisheries. Landings have been stable for the apex predator functional group as a whole, but the distribution of landings across species within this group has changed over time. Halibut and sablefish landings have declined significantly over roughly the last decade. Pacific cod landings decreased in 2016, a trend which is expected to continue through 2018. Relative to the preceding three functional groups, benthic forager and motile epifauna are caught in significantly smaller quantities. Rex sole and flathead sole have target fisheries and total landings are well below the annual TACs for these species. State managed fisheries exist for tanner and dungeness crab. Landings of both of these functional groups have remained fairly stable over time.

    Factors influencing observed trends: For species with limiting TACs, trends in landing follows that of the TACs. The decline in halibut and sablefish landings follows the conservation reductions in the TAC. Pacific cod landings are also determined by the TAC, which had been significantly higher since 2010 than before, until the decrease in 2016.

    Implications: Landings depict one aspect of the raw stresses from harvesting imposed on the Gulf of Alaska ecosystem’s functional group through fishing. This information can be useful in identifying areas where harvesting may be impacting different functional groups in times where the functional groups within the ecosystem might be constrained. Salmonids have on average been the largest functional group landed over this period, followed by apex predators and pelagic foragers which have been roughly equivalent over time but have experienced some divergence in recent years with pelagic foragers being the largest component of landings in 2016. Relative to other functional groups, benthic foragers and motile epifauna make up a smaller share of total landings in the Gulf of Alaska. Monitoring the trends in landings stratified by ecosystem functional group provides insight on the fishing related stresses on ecosystems. The ultimate impact that these stresses have on the ecosystem cannot be discerned from these metrics alone and must be viewed within the context of what the ecosystem can provide

    Figure 94

    Contributed by Benjamin Fissel1 , Jean Lee1,2, and Steve Kasperski1 1Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    2Alaska Fisheries Information Network, Pacific States Marine Fisheries Commission
    Contact: Ben.Fissel@noaa.gov
    Last updated: October 2017

    Halibut and Salmon Subsistence Trends in the Gulf of Alaska

    Description of indicator: Subsistence uses of wild resources are defined as “noncommercial, customary and traditional uses” for a variety purposes including, nutritional, trade, and cultural purposes (ADF&G, http://www.adfg.alaska.gov). Following the IPHC and NMFS regulations in 2003, the subsistence halibut fishery allows the use of halibut by rural residents and members of federally-recognized Alaska native tribes for non-commercial use, for food, or customary trade (Gilroy). Subsistence fishery harvests produces an average of 155 pounds of food per person per year in rural Alaska. In the GOA, all five salmon species are important subsistence fisheries, as well as halibut, shellfish, and other finfish (ADF; Fall et al., 2017). In addition to subsistence, the Gulf of Alaska also supports personal-use fisheries. For these reasons, subsistence harvests of two focal species, salmon and halibut, were considered informative.
    Harvest data were collected from the ADF&G Division of Subsistence for years 1994 to 2014 (ADF&G: http://www.adfg.alaska.gov/index.cfm?adfg=subsistence.harvest). ADF&G reports that 1994 was the first year data from all subsistence fisheries was available and comparable to current collections. Subsistence data are largely collected from household surveys.

    Status and trends: Records indicate a significant increase in household subsistence use permits in the GOA for salmon along with an increase in total salmon harvest (Figure 95). The vast majority of total harvest is sockeye salmon. In 2014, 94% of personal-use salmon harvested was sockeye salmon. The harvest data for other salmon species is less consistent (Figure 96). The data reflect a downward trend in Chinook harvest Alaska-wide, while Coho and chum salmon show considerable variation in harvest rates. The historical average since 1994 is 940,444 salmon, with the 2014 harvest estimated at 932,596 fish. Approximately half of state-wide subsistence sockeye harvests (47%) took place in GOA communities (Fall et al., 2017). In 2014, 728,225 salmon were harvested from personal-use fisheries, with 404,867 fish (56%) harvested from the Kenai River dip net fishery. Personal-use harvests have increased in the GOA since 1994, largely due to increased harvests in the Upper Cook Inlet dip net fisheries (Fall et al., 2017).
    According to the Alaska Department of Fish and Game, statewide subsistence halibut harvest (in pounds) declined between the years 2004 to 2012, with a slight uptick in 2014 (Figure 95). There were approximately 4,506 subsistence fishermen throughout Alaska who harvested an estimated 40,698 halibut in 2014. In comparison, the IPHC estimated that the total halibut harvest in Alaska in 2014 was 33,804 million pounds, with total subsistence harvests representing 2.3% of the total harvest. Subsistence halibut are largely harvested in GOA communities. The data illustrate 56% of the subsistence halibut harvest occurred in Area 2C (Southeast Alaska), and 32% in Area 3A (Southcentral Alaska). Research indicates that Kodiak and Sitka regions have the largest subsistence halibut harvests (Fall and Lemons, 2016). Setline gear is most commonly used (71%) while 29 percent of harvests are conducted with hand-operated gear.

    Factors influencing observed trends: The reasons for the decline in subsistence halibut harvest are complex, and in large part related to participation in the survey and methodology (Fall and Lemons, 2015). Due to budgetary constraints, data collection efforts were reduced in size and scope, which is consistent with the decrease in reported harvests, suggesting that some of the decrease in halibut harvest is a result of a lower participation in the survey. In certain regulatory areas, there is a downturn in renewal of Halibut permits (SHARCs) after the initial rise in participation after the start of the SHARC program (Fall and Lemons, 2015). Postal survey methodology differed in some regions. The decrease could suggest survey fatigue. In 2014, an effort was made to follow up with non-participants to complete the survey, increasing the reported harvest estimates. After the fieldwork, concerns were raised in certain areas about the decrease in available harvest, suggesting one cause may be bycatch from commercial cod fishing

    Figure 95

    Figure 96

    Implications: Subsistence fishing and hunting represent a major source of food security and cultural identity for rural Alaskans. Rural households rely on subsistence resources to supplement food during the winter when other sources of food may be unavailable or prohibitively expensive (Loring and Gerlach, 2009). In addition, gathering of subsistence resources represents a way of life to many rural Alaskans that connects them to their land, heritage and establishes community bonds of sharing and networking (Picou et al., 1992). The decline in halibut subsistence may indicate increased vulnerability for subsistence communities.

    Contributed by Sarah P. Wise1 and Kim Sparks2
    1 Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    2 Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA; and Alaska Fisheries Information Network, Pacific States Marine Fisheries Commission
    Contact: sarah.wise@noaa.gov
    Last updated: September 2017

  • Economic Indicators in the Gulf of Alaska Ecosystem – Value and Unit Value

    Description of indicator: Three metrics are used to characterize economic value in an ecosystem context for the Gulf of Alaska: ex-vessel value, first-wholesale value, and ratio of first-wholesale value to total catch. Ex-vessel value is the un-processed value of the retained catch. Ex-vessel value can informally be thought of as the revenue that fishermen receive from the catch. First-wholesale value is the revenue from the catch after primary processing by a processor. First-wholesale value is thus a more comprehensive measure of value to the fishing industry as it includes ex-vessel value as well as the value-added revenue from processing which goes to processing sector. The first-wholesale value to total catch unit value is the ratio of value to biomass extracted as a result of commercial fish harvesting. The measure of biomass included in this index includes retained catch, discards, and prohibited species catch. This metric answers the question: “how much revenue is the fishing industry receiving per-unit biomass extracted from the ecosystem?”
    The first two metrics are plotted by functional group. While many species comprise a functional group, it is the handful of species that fishermen target that dominate the economic metrics in each group. The primary target species in the apex predator functional group are Pacific cod, Pacific halibut, sablefish, and arrowtooth. The primary target species in the pelagic forager functional group are walleye pollock, Pacific ocean perch, northern rockfish and dusky rockfish. The primary species caught in the benthic forager functional group are flathead sole, and rex sole. The primary target species in the salmonid functional group are Chinook, sockeye, and pink salmon. The primary species caught in the motile epifauna functional group are tanner and dungeness crab. Because of significant differences in the relative scale of value across functional group value is plotted in logs. Ex-vessel value and first-wholesale value have been adjusted for inflation using the GDP chain-type deflator.

    Status and trends: Ex-vessel value is the revenue from landings, so trends in ex-vessel value and landings are closely connected. Ex-vessel value is highest in the salmon and apex predator functional groups (Figure 97). Ex-vessel revenues have remained fairly stable over time but have been lower since 2013 as the relative share of landings have shifted away from the more highly priced sablefish and halibut species towards the more moderately priced Pacific cod. Despite large catch volumes pollock prices are comparatively lower than apex predators or salmon. A combination of catch and price increases account for the increasing trend in up to 2012. Since 2013 depressed pollock prices have resulted in flat or decreasing revenue despite increased landings. Changes in benthic forager flatfish revenues have largely tracked changes in landings of rex sole and flathead sole. Value in the motile epifauna group has generally increased with crab ex-vessel prices.
    First-wholesale value is the revenue from the sale of processed fish. Some fish, in particular pollock and Pacific cod, are processed in a numerous product forms which can influence the generation of revenue by the processing sector. First-wholesale was generally increasing for each of the functional groups up to about 2008–2010 with stable or increasing landings and gradually increasing prices (Figure 98). After 2010, variation in landings or in prices have had differential impacts. Over the long-term both salmon prices and revenue show an increasing trend. First-wholesale value in the apex predator group decreased with Pacific cod prices in 2009 and declined after 2011. The value of the pelagic forager group shows a gradual increasing trend up to 2012 when prices for pollock decreased with high global pollock supply. Benthic forager first-wholesale value has remained fairly stable and changes in value largely reflect changes in landings. First-wholesale value in the motile epifauna group has remained fairly stable, as crab prices increased through 2012, dipped in 2013– 2014 and have been increasing through 2016.
    The first-wholesale to total catch unit value is analogous to a volumetrically weighted average price across functional groups which is inclusive of discards. However, discards represent a relatively small fraction of total catch. Because of the comparatively larger value of salmon and apex predators the unit value index is more heavily weighted towards these groups. The unit value index increased from 2003–2008 with generally increasing prices across all functional groups (Figure 99). After 2008 shifts in the relative share of landings from halibut and sablefish to the more moderately-priced cod resulted in a decrease in the average price of the apex predator group. Salmon prices continued to rise through 2012. The net effect of these changes is that the trend in the aggregate unit value index leveled out from 2009–2012. Pollock prices fell somewhat starting in 2013. Apex predator prices continued to decline after 2013.

    Figure 97

    Factors influencing observed trends: Sablefish and halibut are high valued whitefish, and price increases resulting from the reduced supply of these species have helped to offset the impact on revenues from reduced landings. Differences in the relative level of the indices between the landings and ex-vessel value in Figure 1 reflects differences in the average prices of the species that make up the functional group. Hence, landings of benthic forager flatfish may be larger than those of the motile epifauna group, but motile epifauna ex-vessel value is higher because it commands a higher price. Ex-vessel prices are influenced by a multitude of potential factors including demand for processed products, the volume of supply (both from the fishery and globally), the first-wholesale price, inflation, fishing costs, and bargaining power between processors and fishermen. However, annual variation in the ex-vessel prices tends to be smaller than variations in catch and short to medium term variation in the landings and ex-vessel revenue indices appear similar.
    Level shifts in the relative location of the first-wholesale indices compared to the ex-vessel indices are influenced by differences in the amount and types of value-added processing in each functional group. Salmon first-wholesale prices are affected by the annual cycles in landings and tend to display a counter-cyclic relationship with lower prices when landings volumes are high and higher prices when volumes are low. This relationship tends to smooth out revenues over time. Declines since 2011 are largely the result of a shift in the relative share of landings from the more highly priced sablefish and halibut species towards the more moderately priced Pacific cod.
    Significant global pollock supply contributed to the decline in pollock prices starting in 2013. The decline in apex predator prices after 2013 occurred with shifts in catch composition. These features combined with volatility in salmon prices account for the decreasing unit value trend since 2012.

    Figure 98

    Figure 99

    Implications: The economic metrics displayed here provide perspective on how the human component of the ecosystem feeds off of and receives value from the Gulf of Alaska and the fish species within that ecosystem. Ex-vessel and first-wholesale value metrics area measure of the ultimate value from the raw resources extracted and how humans add value to the harvest for their own uses. While salmon and apex predators are relatively equally important to the ex-vessel sector, the salmonid functional group makes up a larger share of first-wholesale revenue. Pelagic foragers also make up a relatively similar share of landings as apex predators, but are substantially lower in terms of ex-vessel and first-wholesale revenue due to their high volume and relatively low prices. Situations in which the value of a functional group is decreasing but catches are increasing indicate that the per-unit value of additional catch to humans is declining. This information can be useful in identifying areas where fishing effort could be reallocated across functional groups in times where the functional groups within the ecosystem might be constrained while maintaining value to the human component of the ecosystem. Monitoring the economic trends stratified by ecosystem functional group provides insight on the fishing related stresses on ecosystems and the economic factors that influence observed fishing patterns. The ultimate impact that these stresses have on the ecosystem cannot be discerned from these metrics alone and must be viewed within the context of what the ecosystem can provide.

    buted by Benjamin Fissel1 , Jean Lee1,2, and Steve Kasperski1 1Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    2Alaska Fisheries Information Network, Pacific States Marine Fisheries Commission
    Contact: Ben.Fissel@noaa.gov
    Last updated: October 2017

     

  • Saltwater Recreational Fishing Participation in the Gulf of Alaska: Number of Anglers and Fishing Days

    Description of indicator: Federal fisheries management objectives include managing healthy ecosystems in part to provide recreational fishing opportunities. We use saltwater fishing participation to represent trends in recreational fishing in Alaska. The magnitude of recreational saltwater fishing participation is captured by (a) the days fished and (b) the number of anglers. The Alaska Department of Fish and Game (ADF&G) conducts an annual survey of anglers to collect information on participation, catch, and harvest (Jennings et al., 2015; Romberg, 2016). Annual estimates of the total number of saltwater anglers are available from 1996 to 2015. Estimates of the total number of saltwater fishing days are available from 1981 through 2015. For the purposes of this indicator, ADF&G Sport Fishing Areas A to H and J to Q correspond to the GOA, while Areas R–Z comprise the EBS (see http://www.adfg.alaska.gov/sf/sportfishingsurvey/index.cfm? ADFG=main.home).

    Status and trends: In the GOA the total number of days fished in saltwater has increased since the early 1980s when almost a half million fishing days were taken (Figure 100). Annual saltwater fishing days reached its peak in 1995 at about 1.06 million fishing days. In recent years the annual number of fishing days has been just shy of 1 million. The annual number of saltwater anglers fishing in the GOA has fluctuated since the mid-1990s between 350,000 and 442,000 anglers (Figure 101). Since 2009, the annual number of saltwater anglers has generally been below 400,000.

    Figure 100

    Figure 101

    Factors influencing observed trends: Saltwater recreational fishing participation in Alaska is influenced by a number of factors, including fishing regulations for target species, social and economic factors affecting the angler and the angler’s household, and expected fishing conditions (e.g., stock size, timing and size of runs, weather, etc.). Pacific halibut and Pacific salmon (Chinook, coho, chum, sockeye, and pink salmon) are the most common target species, with other species less frequently being the principal target but being caught on trips targeting these species. Fishing regulations for these fish influence decisions about whether or not to fish, where to fish, what species to fish for, and by what means to fish (e.g., unguided or guided fishing).
    Fishing regulations in the Pacific halibut sport fishery were first established in 1973, but have changed significantly over the years in the GOA (Meyer, 2010). Starting in 2007, more restrictive bag and size limit regulations were imposed for halibut caught on charter boat fishing trips in Southeast Alaska (74 Federal Register 21194). Beginning in 2014, Southcentral Alaska charter boat anglers began facing the same types of charter-specific bag and size limit and other restrictions (see https://alaskafisheries.noaa.gov/fisheries/2c-3a-halibut-regs). Under the Halibut Catch Sharing Plan (CSP), which went into effect during 2014, the management tools used to regulate harvest of Pacific halibut in the recreational sport sector are evaluated annually (79 Federal Register 13906).
    ADF and G manages Pacific salmon in Alaska primarily through a policy that involves maintaining spawning habitats and ensuring escapement levels (Heard, 2009). Allocation between the commercial and recreation sectors is set by the Alaska Board of Fish and can have a profound influence on observed trends. In recent years, there has been concern over declining Chinook salmon levels, leading to area closures.
    Macroeconomic factors such as economy-wide recessions likely affect participation patterns in saltwater fishing in Alaska. Due to the expense of traveling to and from Alaska, it is likely that during times of economic hardship, there will be fewer non-resident saltwater anglers, resulting in fewer trips and days fished. Dips in annual saltwater fishing days and number of anglers during the period 2001 and 2002 and the period 2008 and 2012 can be seen, which may be a result of the brief 2001 Recession and the Great Recession (that began at the end of 2007). The increasing trend in the numbers of anglers and fishing days in recent years (2013–2015) may be related to an improving national economy. Population growth in Alaska and the U.S. may also impact fishing trends. While conditions in the larger state, national, and international economy are likely to explain some of the observed trends, the statistics generally reflect micro-level decisions made by individual anglers (Lew and Larson, 2011, 2012, 2015).

    Implications: Monitoring the number of saltwater anglers and fishing days provides a general measure of fishing effort and participation in the saltwater sport fishery and can reflect changes in ecosystem conditions, target stock status, management, economic factors, demographic trends, and other economic, social, and cultural factors. Alaska is well-known for its sport fishing opportunities and draws anglers both from within and from outside Alaska. Saltwater recreational fishing can be a non-trivial source of extraction of several species (including Pacific halibut, Pacific salmon, and rockfish). Studies have indicated saltwater fishing in Alaska is valuable to anglers (Lew and Larson, 2011, 2012, 2015) and contributes to the economy by creating jobs and generating sales to fishing and non-fishing businesses and income to households (Lovell et al., 2013). Although there has been some variation over the past 15 years in annual fishing days and total saltwater anglers, the overall trends in recent years appear to be relatively stable. Thus, without significant changes in the ecological, economic, management, or socio-cultural factors that are likely to influence GOAlevel participation in saltwater recreational fishing, it is likely that saltwater recreational fishing will remain at or near currently observed levels.

    Contributed by Daniel K. Lew1 , Jean Lee2 1 Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA 2 Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA; and Alaska Fisheries Information Network, Pacific States Marine Fisheries Commission
    Contact: dan.lew@noaa.gov
    Last updated: August 2017

  • Trends in Unemployment in the Gulf of Alaska

    Description of indicator: Unemployment is a significant factor in the Gulf of Alaska (GOA) ecosystem, as it is an important indicator of community viability (Rasmussen et al., 2015). Advancements in socio-ecological systems research has demonstrated the importance of incorporating social variables in ecosystem management and monitoring, and unemployment reflects economic settings of a socio-ecological system (Turner et al., 2003; Ostrom, 2007).
    This section summarizes trends in unemployment rates over time in the Gulf of Alaska (including Southeast Alaska, Cook Inlet, and Prince William Sound). The 98 GOA fishing communities included in analysis comprise most of the population that resides along Gulf of Alaska coast. Communities were included if they are within 25 miles of the coast, and/or based on their historical involvement in Gulf of Alaska fisheries, or if they were included in one of the North Pacific Fishery Management Councils GOA fishery programs, such as the Community Quota Entity program. Unemployment data were aggregated and weighted to account for varying community populations across Alaska Boroughs. Estimates are presented annually from 1990–2017 (ADLWD, 2017). Population was calculated by aggregating community level data between 1890 and 1990 (DCCED, 2016) and annually from 1990–2015 (ADLWD, 2017).

    Status and trends: Unemployment rates in the GOA from 1990 to 2016 were lower than state and national rates overall (Figure 102) with one exception in 2000 when the GOA unemployment rate was 4.5%, which was higher than the national rate of 4.0%. As of 2016, the GOA unemployment rate was slightly higher than the national rate (4.80 and 4.60 respectively). Spanning the years of 1990 to 2016 the GOA employment rate including Anchorage is higher than when Anchorage is excluded, with the exception of the years 1994-1998 where the rates are almost equal. GOA unemployment rates reflect state and national trends overall as unemployment was highest in 1992 and peaked in 2003 and 2010. The unemployment rate in GOA communities increased from 4.59% in 2015 to 4.80% in 2016.

    Factors influencing observed trends: Alaska has experienced several boom and bust economic cycles. Peaks in employment occurred during the construction of the Alaska pipeline in the 1970s and oil boom of the 1980s. In contrast, unemployment peaked following completion of the pipeline, during the oil bust of the late 1980s, and during the great recession of 2007–2009 (ADLWD, 2016a). However, during the great recession, Alaskas employment decreased only 0.4%, whereas the national drop was 4.3%, in part because of the jobs provided by the oil industry (ADLWD, 2016b). With the oil industry headquarters mainly located in Anchorage, the GOA region would be most impacted by job loss in the industry. The GOA region had the second highest unemployment rates (Arctic region had highest) between 1990 and 2015 (Figure 102). In the GOA, seafood processing is a major contributor of jobs, despite being mainly comprised of low-wage, non-resident labor (ADLWD, 2016a).

    Figure 102

    Implications: Fisheries contribute to community vitality of the GOA, and reduced fishing opportunities and employment may lead to out-migration and population decline, particularly in small communities with few job alternatives (Donkersloot and Carothers, 2016). Many larger communities of the GOA region are highly engaged in fisheries and depend upon fish processing industries to support their economies, such as in Kodiak, with both a resident and transient labor force. Changes in groundfish policy and management may have implications for GOA community economies in both remote and urban areas.

    Contributed by Anna Lavoie, Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: anna.santos@noaa.gov
    Last updated: September 2017

  • Trends in Human Population in the Gulf of Alaska

    Description of indicator: Human population is a significant factor in the Gulf of Alaska (GOA) ecosystem, as many communities in the region rely upon fisheries to support their economies and to meet subsistence and cultural needs. As with areas neighboring the Arctic, population is an important indicator of community viability (Rasmussen et al., 2015). Advancements in socioecological systems research have demonstrated the importance of incorporating social variables in ecosystem management and monitoring (Turner et al., 2003; Ostrom, 2007). For example, variation in resource access or availability or employment opportunities may influence human migration patterns, which in turn may decrease human activity in one area of an ecosystem while increasing activity in another. This section summarizes trends in human population over time in the Gulf of Alaska (GOA) (including Southeast Alaska, Cook Inlet, and Prince William Sound). The 98 GOA fishing communities included in analysis comprise most of the population that resides along Gulf of Alaska coast. Communities were included if they are within 25 miles of the coast, and/or based on their historical involvement in Gulf of Alaska fisheries, or if they were included in one of the North Pacific Fishery Management Councils GOA fishery programs, such as the Community Quota Entity program. Also, as of 2015 there was no population data for several communities that were previously included in this report. They were not included in analysis because of insufficient data, however, they are mentioned below. Population was calculated by summing community level data at decadal scales from 1890–1990 (DCCED, 2016) and annually from 1990–2016 (ADLWD, 2017).

    Status and trends: As of 2016 the population of GOA was 456,556 or 157,519 excluding Anchorage. The population of small communities (population less than 1,500) was 26,000. The population of all GOA communities has increased steadily since 1880 with the greatest population increase of 194.2% occurring between 1950 and 1960 (Table 7). This figure includes Anchorage, the largest major city of Alaska, where the majority of population increase has occurred and where 40% of Alaskas population currently resides (ADLWD2017a). With Anchorage excluded, and for small communities, the greatest population increase of 50.2% occurred between 1980 and 1990 in the GOA (decadal increments). This is consistent with state trends as population change peaked during these periods (over 75% by 1960 and 36.9% by 1990). Population increase leveled off after 1990 with lower rates in the following decades in the GOA and Alaska state. Between 1990 and 2016, the population of GOA increased 32.0% (31.8% excluding Anchorage) which is consistent with, yet lower than, state trends across this time period (34.5%)(Figure 103).
    Despite the general population trend in the GOA as a whole, 41.2% of communities experienced population decline between 1990 and 2016. The communities of Hobart Bay, Annette, Meyers Chuck and Cube Cove had no population data as of 2010. Communities of Ivanof Bay, Elfin Cove, Karluk, Pelican City, Point Baker, and Cold Bay also had decreases in population ranging from 60.1% to 80.0%. In contrast, the community of Kalifornsky experience a population increase of over 2,900% during this time period.
    Indigenous Americans comprise up to 82% of the population of small communities in remote areas, and more Native Americans reside in Alaska than any U.S. state (Goldsmith et al., 2004). As of 2014, 15% of Alaskas population was Alaska Native or Native American (ADLWD, 2016c), and as of 2015, 28% of the population in the GOA identified as Native American alone or combination with another race (DCCED, 2016). In addition, there has been increased migration of Alaska Natives from rural to urban areas (Goldsmith et al., 2004; Williams, 2004). The majority of population growth that has occurred in Alaska and the GOA is of the Caucasian demographic (ADLWD, 2016c).

    Table 7

    Figure 103

    Factors influencing observed trends: Overall population increase in GOA between 1990 and 2016 (31.8%) was consistent with state trends (34.5%). Alaska has high rates of population turnover because of migration, and population growth has occurred mainly in urban areas (ADLWD, 2016c). The main factors that affect population growth are natural increase (births minus deaths) and migration, with the latter being the most unpredictable aspect of population change (Williams, 2004; ADLWD, 2016c). In 2010, 61% of Alaskas population was born out of state (Rasmussen et al. 2015). In terms of natural growth, from 2010 to 2014 the average annual birth rate in Alaska was 1.6 per 100 people which was higher than the national rate of 1.3 (ADLWD, 2016c). From 2010–2014 the Aleutian chain and Southeast Alaska had the lowest natural increase (0.0–1.0%) whereas the Northern Bering Sea area had the highest (1.5–3.0%). The natural growth rates of the GOA had a range of 0.0–1.5% (ADLWD, 2016c). The the GOA region has the highest net migration in the state, and the Matanuska-Susitna Borough has the highest growth rate (ADLWD, 2016c).
    Population trends in Alaska and the GOA region are the result of changes in resource extraction and military activity (Williams, 2004). Historically, the gold rush of the late 19th century doubled the states population by 1900, and later WWII activity and oil development fueled the population growth (ADLWD2016c). However, certain areas have experienced population shifts at various periods, particularly those with military bases. For example, the population of Kodiak declined in the 1990s because of Coast Guard cut-backs (Williams 2006). The fishing industry also influences community population. Kodiak and the Aleutian Islands have the most transient populations because of the seafood processing industry (Williams, 2004). Some GOA communities that experienced fishery permit loss subsequently experienced population decline (Donkersloot and Carothers, 2016). Also, reduction of jobs in the lumber industry have caused population decrease. For example, the Whitestone Logging Camp population fluctuated from 164 to 0 between 1990 and 2006, increased to 17 in 2010 and was zero in subsequent years (ADLWD, 2017).

    Implications: Population shifts can affect pressures on fisheries resources, however inferences about human impacts on resources should account for economic shifts and global market demand for seafood and other extractive resources of the ecoregion. As stated earlier, the majority of population increases in the GOA are due to increased net migration rather than natural increase, and they have mainly occurred in urban areas as populations in many small communities are declining. Fisheries contribute to community vitality of the GOA. Reduced fishing opportunities and employment may lead to out-migration and population decline, particularly in small communities with few job alternatives (Donkersloot and Carothers, 2016). Many larger communities of the GOA region are highly engaged in fisheries and depend upon fish processing industries to support their economies, such as in Kodiak, with both a resident and transient labor force. Changes in groundfish policy and management, such as increased regulations, may have implications for GOA community economies in both remote and urban areas.
    With a large concentration of Alaskas population in Anchorage, it has become the major hub for goods and services, trade, and travel. Services such as medical, business and technology support and entertainment attract people to the area seeking services, and employment and education opportunities. The population growth of Anchorage has also contributed to sprawl into the Matanuska-Susitna valley. According to the U.S. Census Bureau of 2010, the population density of the Matanuska-Susitna borough was 3.6, whereas the state as a whole was 1.2. This regional growth has increased regional hunting and fishing pressures, recreational demand, and reduced available agricultural land because of high speculative land values (Fischer, 1976). Rapid development of the Matanuska-Susitna valley may have impacts on the local watersheds fish stocks and habitat, which should be monitored over time.

    Contributed by Anna Lavoie, Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: anna.santos@noaa.gov
    Last updated: September 2017

    Trends in School Enrollment in the Gulf of Alaska

    Description of indicator: Ensuring the productivity and sustainability of fishing communities is a core mandate of Federal fisheries management. One indicator to evaluate community vitality is K-12 public school enrollment. Enrollments trends are of particular relevance due to the value of schools to community cohesion and identity.
    Public school enrollment was analyzed in the Gulf of Alaska (GOA) by borough and community level in order to examine broader regional trends as well as the social and economic vitality of individual rural communities. Fishing communities were defined as in Lavoie p. 196. Enrollment statistics for K-12 grades by school and region were compiled for the years 1996–2014 from The National Center for Educational Statistics (https://nces.ed.gov/ccd/elsi/tableGenerator. aspx). More recent enrollment data were available for years 2014–2017 from the Alaska Department of Education and Early Development (http://www.eed.state.ak.us/stats/). Current school locations and names were verified using the EPA EJ mapping tool (https://ejscreen.epa.gov/ mapper/). Municipalities or boroughs with enrollment over 4,500 were excluded from the analysis in order to avoid skewing the results (these included were the Matanuska-Susitna Borough, Juneau Borough, and Anchorage Municipality).

    Status and trends: School enrollment patterns vary considerably in the GOA by rural and urban areas and by population of the municipality. Within municipalities where school enrollment is over 500 students, school enrollment remains fairly stable, showing a general slight decrease in enrollment. Overall, there is a general decrease in enrollment. The exception to this is Kenai City which has an increase in enrollment from 1424 students in 1996 to 1787 students in 2017, an increase of 26 percent. In contrast school enrollment in Homer decreased nearly 50 percent among those schools with over 500 students with 2302 students in 1996 and 1186 in currently (Figure 104).
    In municipalities with school enrollment between 100 and 500 students, there is a downward trend for several schools including Hoonah City which decreased 60 percent, from 273 students to 109 students since 1996, and Petersburg City and Angoon City, which both decreased 50% (Figure 105).

    Figure 104

    Figure 105

    A majority of schools have enrollment under 100 students. Schools in smaller communities tend to have more variable enrollment trends. To illustrate, Figure 106 depicts Kodiak Island Borough schools, with enrollments currently ranging from10 to 29. There is some fluctuation in enrollment, but an overall downward trend. Two schools dropped in enrollment forcing periodic school closures. The Karluk School first opened in 2001 with 10 students, closed in 2003, and re-opened in 2006 with 10. Chiniak School closed in 2009 when enrollment dipped below the 10 student threshold, and re-opening in 2011. As of 2017, 27 schools have enrollment under 30, and 12 schools have enrollments under 15 students (Table 8).

    Table 8

    Figure 106

    Factors influencing observed trends: The GOA ecoregion varies substantially in population and community structure and vitality. The GOA holds several larger municipalities, with larger school enrollment, compared to other regions of Alaska. As people migrate to other areas, populations increase in adjacent communities. It is possible that enrollment may shift to the larger communities as more convenient schools open. However other factors must be considered including existing infrastructure such as functional ports, airports, or medical facilities to provide support for a viable community structure. Those schools with enrollment under 30 students experience the greatest uncertainty in terms of educational stability. With greater fluctuation in school enrollment, rural area schools are particularly vulnerable to closure and possible community disruption. The reasons for decreasing enrollment likely involve complex social and economic drivers including migratory patterns, resource availability, and employment. Additional research into the specific reasons for diminishing school enrollment in rural areas, as well as the impacts on these communities would inform and benefit management decisions.

    Implications: Community residents are closely tied to the ecosystem through sense of place and daily experience and activity. Schools are cultural centers and serve as important indicators of social and economic viability, and community well-being (Lyson, 2002, 2005). Within rural communities in particular, schools are valuable symbols for community identity, autonomy, and shared social values (Peshkin, 1978, 1982; Lyson, 2005). Research indicates that school closures negatively affect communities (Buzzard, 2016). Patterns of diminishing enrollment and school consolidation suggest a decrease in property values and taxes, fragmented community, and lost business, as well as declines in reported quality of life scores (Sell and Leistritz, 1997; Lyson, 2002). Some research finds the rate of participation in community organizations decreases in communities experiencing school closures (Oncescu and Giles, 2014; Sell and Leistritz, 1997). These finding suggests that reduced enrollments and school closures may flag disruptions in social cohesion and viability, possibly leading to less vibrant and sustainable communities.

    Contributed by Sarah P. Wise1 and Kim Sparks2 1 Resource Ecology and Fishery Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA 2 Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA; and Alaska Fisheries Information Network, Pacific States Marine Fisheries Commission Contact: sarah.wise@noaa.gov Last updated: September 2017

 

Gulf of Alaska