Aleutian Islands Indicators

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

Last updated: 2017

 
 
  • Alaska Peninsula and Aleutian Islands. The waters of this region were relatively warm, especially in the fall of 2015 and summer of 2016. In part this can be attributed to the overall warmth of the North Pacific and in part to the weather, which featured persistently above normal air temperatures during the past year with only short and minor exceptions. Based on synthetic data from NOAA’s Global Ocean Data Assimilation System (GODAS), the Alaskan Stream appears to have had a relatively strong westward flow from late 2015 into 2016. The GODAS product suggests there were pulses in the strength of the eastward flow associated with the Aleutian North Slope Current.

    Contributed by Nick Bond, University of Washington, JISAO NOAA/PMEL, Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349
    Contact: nicholas.bond@noaa.gov
    Last updated: August 2016

  • Description of indices: The state of the North Pacific climate from autumn 2015 through summer 2016 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 Surfacae 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. Previous versions of this overview included SST anomaly distributions based on NOAA’s Extended Reconstructed Sea Surface Temperature (ERSST) V4; here the OISST analysis is used because of its finer-scale resolution, and incorporation of satellite data, which is valuable in regions where direct observations of SST by ships and buoys are sparse.

    Status and trends: The anomalies that occurred during the past year in the North Pacific beginning in autumn of 2015 reflect, to a large extent, the maintenance of conditions that developed during the previous 1-2 years. In particular, a leading large-scale climate index for the North Pacific, the Pacific Decadal Oscillation (PDO), remained positive, following a transition in sign early in 2014. 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 2015 (Figure 10a) was warmer than normal east of the dateline. The positive anomalies were especially prominent off southern and Baja California and in the eastern tropical Pacific, the latter in association with a strong El Ni˜no. The pattern of anomalous SLP during autumn 2015 featured strongly negative anomalies extending from Bering Strait into northwestern Canada with higher than normal pressure from the Kamchatka Peninsula into the central Gulf of Alaska (GOA). This SLP pattern implies wind anomalies from the west across the Bering Sea and anomalous upwelling in the coastal waters of the GOA.
    The pattern of North Pacific SST during winter (Dec-Feb) of 2015-16 relative to the seasonal mean (Figure 10b) resembled that of the preceding autumn with the exception of the western Bering Sea and Aleutian Islands, which cooled to near normal. The latter cooling was associated with anomalous winds out of the northwest in association with extremely low SLP (negative anomalies exceeding 12 mb) over the eastern Bering Sea and western GOA (Figure 11b). For the area of 50oN to 60oN, 170oW to 150oW, the SLP was more than 3 mb lower than that during any other December through February in the record back to 1949. This meant relatively frequent gale force winds and high wave heights for the region. A deeper than normal Aleutian Low commonly occurs during El Ni˜no (whose signature is prominent in Figure 10b) but the center of the anomalous SLP was displaced to the northwest from its usual position during winters with strong El Ni˜nos. The anomalous southerly flow to the east of the SLP anomaly minimum brought relatively warm air to the northern Gulf of Alaska, especially from late January into February during which surface air temperatures were about 6oC above normal. The coastal region of the GOA therefore received a greater proportion of rain versus snow than usual at lower elevations, but it is uncertain whether the GOA experienced significantly more freshwater runoff than typical for the season.
    The distribution of anomalous SST in the North Pacific during spring (Mar-May) of 2016 (Figure 10c) bore some resemblance to that of the season before, with an increase in the magnitude of the positive anomalies in the eastern Bering Sea and GOA. Moderate cooling occurred in the central North Pacific in the vicinity of 40oN, 170oW. The overall pattern projected strongly on the positive phase of the Pacific Decadal Oscillation (PDO) as will be discussed further below. The SST anomalies in the central and eastern tropical Pacific decreased as El Ni˜no wound down. The SLP anomaly pattern (Figure 11c) for spring 2016 was similar to that of the previous winter season, with a weaker negative anomaly shifted southeast of its previous location. Lower than normal SLP over a broad region extending from the southeastern Bering Sea towards the west coast of the lower 48 states often occurs in the springs following El Ni˜no winters.

    Figure 10

    Figure 11

    The SST anomaly pattern in the North Pacific during summer (Jun-Aug) 2016 is shown in Figure 10d. It was warmer than normal in the north, with especially positive anomalies region exceeding 3 oC in the southeastern Bering Sea. Relatively cool water was present in a broad band between roughly 25oN and 40oN from the east coast of Asia to the central North Pacific, with the most negative anomalies located north of the Hawaiian Islands. Warm water persisted in the subtropical North Pacific. Finally, cold anomalies developed in a narrow strip along the equator in the eastcentral Pacific, signifying the demise of El Ni˜no and the potential for the development of La Ni˜na. The distribution of anomalous SLP (Figure 11d) during summer 2016 featured higher than normal pressure between the Alaska Peninsula and the Hawaiian Islands that was almost opposite to that of the previous season. The relatively high SLP extended into the Bering Sea and was associated with seasonally suppressed storminess and hence scant vertical mixing of the upper ocean, resulting in the very warm surface temperatures shown in Figure 10d. The higher than normal SLP off the coast of the Pacific Northwest and California brought about strong coastal upwelling, and a moderation of SST in the immediate vicinity of the coast.

     

    Contributed by Nick Bond, University of Washington, JISAO NOAA/PMEL, Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349
    Contact: nicholas.bond@noaa.gov
    Last updated: August 2016

  • Last updated: August 2016

    Description of indices: Climate indices provide a complementary perspective on the North Pacific atmosphere-ocean climate system to the SST and SLP anomaly maps presented above. The focus here is on five commonly used indices: the NINO3.4 index to characterize 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 2006 through early summer 2016 are plotted in Figure 12.

    Figure 12

    Status and trends: The North Pacific atmosphere-ocean climate system has been in a highly perturbed state recently. Specifically, NINO3.4 reached a peak value of 2.3 in December 2015 in association with the strong El Nino of 2015-16. This measure of ENSO has declined over the first 8 months of 2016 and is now slightly negative. 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) during the last 2 years. The magnitude of the PDO actually decreased in 2015 during the ramp-up of El Ni˜no, which is unusual. It generally tracks ENSO, with a lag of a few months, as illustrated here for the period of 2008-13 in Figure 12 The PDO did increase in early 2016 to a value exceeding +2, followed by a decrease in late spring/early summer 2015. The NPI was strongly negative during the past winter and spring, which implies a deeper than normal and often displaced Aleutian Low, as indicated in Figures 10b and 11b). This represents a typical atmospheric response to El Ni˜no. The deep Aleutian Low was accompanied by anomalous winds from the south and relatively warm air along the west of North America, i.e., atmospheric forcing favoring a positive trend in the PDO.

    The North Pacific Gyre Oscillation (NPGO) underwent a transition from negative in 2015 to a near-neutral state in 2016. A negative sense of this index, which is formally related to the 2nd mode of variability in sea surface height in the North Pacific, implies a reduced west wind drift and projects on weaker than normal flows in both the Alaska Current portion of the Subarctic Gyre and the California Current. 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 latter portion of 2015, and then mostly negative during early 2016. Most winters since 2009-10 have included relatively strong and persistent (multi-month) signals in the AO, in either the positive and negative sense, but that was not the case for the winter of 2015-16.

     

     

     

    Contributed by Nick Bond, University of Washington, JISAO NOAA/PMEL, Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349 Contact: nicholas.bond@noaa.gov

  • Description of indicator: Seasonal projections of SST from the National Multi-Model Ensemble (NMME) are shown in Figure 13. An ensemble approach incorporating different models is particularly appropriate for seasonal and longer-term simulations; the NMME represents the average of eight 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 13

    Status and trends: 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 (OctDec 2016) with a smaller region of near normal temperatures northwest of the Hawaiian Islands (Figure 13a). The magnitude of the positive anomalies is projected to be greatest (exceeding 1o ) in the GOA and eastern Bering Sea. Negative SST anomalies are projected in the central equatorial Pacific. The latter are associated with the potential for a weak La Ni˜na. As of August 2016, 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 a 55 to 60% chance of La Ni˜na by fall 2016. The overall pattern of SST anomalies across the North Pacific is maintained through the 3-month periods of December 2016 to February 2017 (Figure 13b) and February to April 2017 (Figure 13c) with a modest cooling in the central North Pacific and moderation of negative anomalies in the equatorial Pacific.

    Implications It is unclear whether the equatorial Pacific will be perturbed enough, particularly with respect to the intensity and distribution of deep atmospheric convection, to cause the usual response to La Ni˜na. Past La Ni˜na events have included a weaker than normal Aleutian low and a relatively cold winter for Alaska, western Canada and the Pacific Northwest. On the other hand, the models comprising the NMME are indicating remote responses to the equatorial Pacific that are relatively weak, and in consensus, slightly warmer than normal temperatures for western North America. These competing signals suggest that the North Pacific climate may be in a state of rather low predictability. That being said, it is unlikely that the upcoming winter in Alaska and western Canada will be as mild as those of the last three years.

    Also, the SST anomaly maps shown in Figure 13 share an unusual feature, and that is the coexistence of a relatively cold equatorial Pacific with a horseshoe-shaped pattern of warm water along the west coast of North America, a signature of the positive phase of the PDO. The closest analog to that situation in recent decades was from late 1980 into spring 1981. In that case, the PDO was not as strongly positive as predicted for the upcoming winter and spring, and the NINO3.4 anomalies were of modest amplitude (about -0.4 in early 1981). The maintenance of positive PDO conditions in the North Pacific during the upcoming year, despite an ENSO state that generally brings about an SST anomaly pattern associated with the negative phase of the PDO, could be a reflection of the enormous amount of extra heat in the upper ocean now present along most of the west coast of North America, and the model projections of a muted atmospheric response in the mid-latitudes to the equatorial Pacific during the next two seasons.

     

     

     

     

    Contributed by Nick Bond, University of Washington, JISAO NOAA/PMEL, Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349
    Contact: nicholas.bond@noaa.gov
    Last updated: August 2016

  • Description of indicator: Eddies in the Alaskan Stream south of the Aleutian Islands have been shown to influence flow into the Bering Sea through the Aleutian Passes (Okkonen, 1996). By influencing flow through the passes, eddies could impact flow in the Aleutian North Slope Current and Bering Slope Current as well as influencing the transports of heat, salt and nutrients (Mordy et al., 2005; Stabeno et al., 2005) into the Bering Sea.

    Since 1992, the Topex/Poseidon/Jason/ERS satellite altimetry system has been monitoring sea surface height. Eddy kinetic energy (EKE) can be calculated from gridded altimetry data (Ducet et al., 2000). Eddy kinetic energy (EKE) calculated from gridded altimetry data is particularly high in the Alaskan Stream from Unimak Pass to Amukta Pass (Figure 14) indicating the occurrence of frequent, strong eddies in the region. The average EKE in the region 171oW-169oW, 51.5o -52.5oN (Figure 15) provides an index of eddy energy likely to influence the flow through Amukta Pass. Numerical models have suggested that eddies passing near Amukta Pass may result in increased flow from the Pacific to the Bering Sea (Maslowski et al., 2008). The Ssalto/Duacs altimeter products were produced and distributed by the Copernicus Marine and Environment Monitoring Service (CMEMS) (http://www.marine.copernicus.eu).

    Figure 14

    Status and trends: Particularly strong eddies were observed south of Amukta Pass in 1997, 1999, 2004, 2006/2007, 2009/2010, and summer 2012. Eddy energy in the region has been low from the fall 2012 through June 2015. In early 2016, a small eddy was present in the region, resulting in slightly above average EKE.

    Factors causing trends: The causes of variability in EKE are currently unclear and a subject of ongoing research.

    Implications: These trends indicate that higher than average volume, heat, salt, and nutrient fluxes to the Bering Sea through Amukta Pass may have occurred in 1997/1998, 1999, 2004, 2006/2007, 2009/2010, and summer 2012. These fluxes were likely smaller during the period from fall 2012 until early 2015 and may have been slightly enhanced in early 2016.

    Figure 15

    Contributed by Carol Ladd, NOAA/PMEL Building 3, 7600 Sand Point Way NE, Seattle, WA 98115-6349
    Contact: carol.ladd@noaa.gov
    Last updated: August 2016

  • Description of indicator: The oceanography of the Aleutian Islands (AI) is shaped by three major currents running along the archipelago and strong tidal forces in the passes between islands (Hunt and Stabeno, 2005). The Alaska Coastal Current (Schumacher and Reed, 1986; Reed, 1987) flows westward along the south side of the Aleutians from the Gulf of Alaska to Samalga Pass. The Alaskan Current also flows westward along the southern shelf break of the Aleutians to Amchitka Pass where some of the water flows northward to serve as source water for the Aleutian North Slope Current. The remainder of the Alaskan Current continues westward in a series of meanders and eddies throughout the western Aleutians. The Alaska Coastal Current is warmer and fresher than the Alaskan Current and these differences contribute greatly to the chemical and physical properties of the water flowing through the passes of the Aleutian Islands which are zones of strong vertical mixing (Ladd et al., 2005) The Aleutian North Slope Current originates at Amchitka Pass and flows eastward along the north side of the Aleutians.

    Water temperature data have been routinely collected during National Marine Fisheries Service (NMFS) Alaska Fisheries Science Center (AFSC) Resource Assessment and Conservation Engineering Division (RACE) AI bottom trawl surveys since 1994. Microbathythermographs attached to the headrope of the net measure and record temperature and depth during each trawl haul. In 2004, the SeaBird (SBE-39) microbathythermograph (Sea-Bird Electronics, Inc., Bellevue, WA) that is in use today replaced the Brancker XL200 data logger (Richard Brancker Research, Ltd., Kanata, Ontario, Canada) which had been in use since 1993. The analyses presented here utilize all available bathythermic data collected on AI bottom trawl surveys since 1994.

    The RACE AI bottom trawl survey typically begins in late May-early June and proceeds west over the next three months of the summer. The anticipation of increasing water temperature with advancing collection date during as the survey progresses westward over the summer leads to spatially and temporally confounded data that complicates inter-annual comparisons. Additionally, in 2002 and 2006, our typical sampling progression was partially reversed with the later season survey progressing from west to east. There were three triennial AI bottom trawl surveys between 1994 and 2000; since 2000 the surveys have been conducted biennially (except in 2008 when there was no AI bottom trawl survey).

    To account for the influence of changing day length on water temperatures over the course of the summer and to make inter-annual comparisons more meaningful, we used a generalized additive model (GAM) to assign a standardized collection day to water temperature measurements. The standard collection day was set to an approximate median date from all of our summer surveys (i.e., July 10). Collection day-standardized water temperatures from the trawl downcast (the period of time between when the trawl net is released to sink and the center of the footrope touches the bottom) were binned into depth intervals from a depth of 3 m to the deepest depth of the tow and averaged for each bin. Finer depth increments were employed nearer the surface to capture the rapid changes in water temperatures often seen in shallower depths; broader increments were used in deeper depths where changes are not as rapid. The resulting model was used to predict the temperature at depth on the standard date. Residuals from this GAM were added back to the predicted temperatures yielding an estimate of thermal anomaly from the model prediction. These median date-standardized temperature anomalies were then binned into 1 ⁄2 degree longitudeby-depth increments and the mean of each bin was reported. To enhance the visual separation of the mid-range temperature anomalies, we manipulated the color gradient in the plots so that predicted temperature anomalies > 7.5oC and less tahn 3.5oC were fixed at 7.5 and 3.5oC (e.g., a 12.5oC temperature anomaly was recoded as 7.5oC for the graphic representation).

    Status and trends: The temperature anomaly profiles from the 2016 AI survey data appear to be some of the warmest in our record (Figure 16). These warm anomalies are also some of the most pervasive (vertically and longitudinally) recorded to date. The profiles from 2016 are visually similar to those of 2014 and share the characteristics of widely distributed warm surface waters along with greater thermal stratification although the 2016 anomalies are more broadly dispersed and penetrate deeper. By contrast, the 2000 AI survey remains one of the coldest years in the record. These marked differences amongst survey years illustrate the highly variable and dynamic oceanographic environment found in the Aleutian archipelago.

    Most survey years share common thermal profile features (Figure 16). These include warmer surface temperatures east of Amukta Pass (170o 30’W), between Seguam Pass (173o W) and Amchitka Pass (179o W), and west of Buldir Pass (175o E). The influence of these warmer surface temperatures generally extends to around 100 m depth, although in the warmest years it can be detected at deeper depths. Cooler temperatures at depths > 100 m consistently occur around Seguam Island (172o 30’W) and this seems to be a particularly striking feature in colder years (e.g., 2000, 2012). Cooler temperatures at depths ¿ 100 m are frequently a dominant feature west of 175o E, although in colder years this area of cooler water mass extends as far east as Amchitka Pass. Strong vertical mixing, indicated by relatively homogenous thermal profiles, dominate the Aleutian passes and, in cooler years, much of the region. During warmer years, mixing in the passes appears to weaken, resulting in more pronounced thermal stratification of the water column (e.g., 1997, 2014, and now,2016). In the warmest years, these thermally stratified waters can be observed across the region.

    Factors influencing observed trends: Water temperature data collected during RACE AI bottom trawl surveys are brief snapshots taken by our vessels as they move through a very broad area. Since each temperature-depth bin represents data collected over brief temporal (e.g., minutes) but broad spatial (i.e., nautical miles) scales, our ability to draw conclusions from these models can be greatly affected by short-term phenomena such as storm events, tidal current velocity, and/or direction and persistence of eddies. More recent and larger scale phenomena may have longer-lasting implications on water temperatures in the region. The thermal signal caused by the Ridiculously Resilient Ridge of atmospheric high pressure that helped to establish the persistent warm water Blob in the Northeast Pacific in 2014 2015 (Bond et al., 2015; Di Lorenzo and Mantua, 2016) and which likely intensified the El Ni˜no Southern Oscillation (ENSO) event of 2015-16 (Levine and McPhaden, 2016) probably influenced the temperatures observed on our 2016 survey. Daily plots of sea surface temperature anomalies (SST) show warmer surface waters extending from east to west during the summer of 2016. Due to these and other sources of variation not accounted for in the temperature model presented here, caution should be exercised when interpreting these results.

    Implications: There are no obvious trends across survey years when visually comparing the water temperatures modeled here. However, there are notable similarities within classes of colder or warmer years. During colder years (e.g., 2000 and 2012), the relatively homogeneous profiles suggest limited vertical thermal stratification and deeper penetration of the mixed layer. Increased thermal stratification and shallower mixed-layer-depths during warmer years appear to form a relatively consistent pattern amongst warm years. The persistence of a well-defined thermocline has important implications for oceanographic processes in the AI.

    The strength and persistence of eddies is believed to play a major role in mediating the transport of both heat and nutrients into the Bering Sea through the Aleutian passes (Maslowski et al., 2008). The formation and intensification of the warm blob in 2014 and 2015 followed by the ENSO in 2015-16 almost certainly influenced the temperatures observed during the 2016 RACE AI bottom trawl survey. Phenomena like these influence both Aleutian Islands and Bering Sea ecosystems and fish populations.

    Thermal regime and mixed-layer-depth differences are known to influence regional biological processes and impact fish populations. In the AI, the magnitude of primary production depends on mixed-layer-depth (Mordy et al., 2005) while ontogenesis of Atka mackerel eggs and larvae is temperature dependent (Lauth et al., 2007). In addition, shifting summer temperature regimes in the eastern Bering Sea have resulted in lower pollock catches there (Stevenson and Lauth, 2012). Recent investigations into habitat-based definitions of essential fish habitat (EFH) in the AI demonstrate that water temperature can be an important determinant of EFH for many groundfish species (Rooper et al., in prep.). By considering interannual differences in water column temperatures and their implications, we can better utilize our survey data to understand the state of fish populations in the Aleutian Islands.

    Figure 16

     

     

     

     

    Contributed by Ned Laman, Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: ned.laman@noaa.gov
    Last updated: October 2016

  • Description of indicator: In 2012 the RACE Division purchased four SeaGuard CTD units (funded by the North Pacific Research Board and Deep Sea Coral Research and Technology Program). These units were purchased to increase the oceanographic data collections during bottom trawl surveys of the eastern Bering Sea slope, Gulf of Alaska and Aleutian Islands.

    The CTD units collect concurrent depth, temperature, salinity, pH, oxygen and turbidity data. The units are deployed on the headrope of the AFSC bottom trawls during most survey hauls. To date, the data has been collected on the 2012 and 2016 EBS slope, the 2013 and 2015 GOA, and the 2014 and 2016 Aleutian Islands bottom trawl surveys.

    The data are presented here as a series of maps of bottom variables (the average value of each variable during the on-bottom period of the bottom trawl haul). The data have been interpolated to a 1 km by 1 km raster using R software. For temperature, salinity, pH and oxygen kriging with a fitted exponential semi-variance model was used based on the spatial pattern in semi-variance plots. The turbidity data exhibited a linear decrease in semi-variance with distance, so inverse distance weighting was used for this variable. In the Aleutian Islands in 2014, there was no data collected east of Seguam Island, while in 2016 there is a gap in data collection between Samalga Pass and Petrel Bank (Figure 17). There were more than twice as many samples (n = 127) collected in 2014 than in 2016 (n = 52). The Aleutian Islands data were not corrected for time of the year, so some within-season temporal effects could be present because of the prosecution of the survey from east to west in the AI from June to August.

    Status and trends: Bottom temperature appeared to be higher in 2016 than 2014 in areas where measurements were collected in both seasons (Figure 18). Consistent spatial patterns in the temperature and salinity data across were not apparent. However, salinities measured in both years ranged only from 32-35 ppt. Oxygen concentrations were similar between 2014 and 2016 in the western Aleutian Islands, where there were some areas of low oxygen concentration in the farthest western areas of the survey. The central AI in 2014 had higher oxygen concentrations than other areas of the survey, with the exception of Unimak Pass in 2016. pH was not collected in 2016 due to equipment failure. pH and oxygen varied spatially in the Aleutian Islands and also changed with depth. Both variables exhibited lower values on underwater banks (such as Petrel Bank) and generally the two values appeared to be correlated in 2014 and 2016. There were very low values of turbidity in 2014. This is very suspicious and may be the result of instrument failure. Values of turbidity were highest in 2016 in the southern Bering Sea and near Buldir Strait.

    Factors influencing observed trends: The observed spatial trends in near bottom temperature and salinity are likely due to the relatively oceanic regime in the Aleutians west of Samalga Pass. The warmest and freshest water was found in the eastern Aleutian Islands and southern Bering Sea where Gulf of Alaska oceanography may have higher influence on water properties than in the central and western AI. The observed trends in oxygen and pH in the Aleutian Islands are probably a result of the interaction between depth and currents moving through the passes. The turbidity is suspicious given the magnitude of the difference between the two years (all values less than 1 in 2014 and up to ∼20 in 2016).

    Figure 17

    Implications: As more of this data are collected relationships between fish and invertebrate distributions will be explored. When multiple years of data have been collected for each area, variability of spatial patterns may be important.

    Figure 18

     

    Contributed by Chris Rooper, Pamela Goddard, Jerry Hoff Resource Assessment and Conservation Engineering Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA
    Contact: chris.rooper@noaa.gov
    Last updated: October 2016

 
  • Description of indicator: Groups considered to be structural epifauna, formerly known as Habitat Area of Particular Concern (HAPC) biota, include seapens/seawhips, corals, anemones, and sponges. The biennial survey in the Aleutian Islands (AI) does not sample estimate the density of HAPC fauna well, but does seem to capture spatial trends in presence or absence Rooper et al. 2016, Rooper et al. in review). However, survey effort in rough or rocky areas where these groups are likely to be more abundant and survey effort is quite limited. The gears used by the Japanese vessels in the surveys prior to 1991 were quite different from the survey gear used aboard U.S. vessels in subsequent surveys and likely resulted in different catch rates for many of these groups. For each species group, the largest catch over the time series was arbitrarily scaled to a value of 100 and all other values were similarly scaled. The standard error (±1) was weighted proportionally to the CPUE to get a relative standard error.

    Sponges include unidentified porifera, calcareous sponges, hexactinellid sponges and demosponges, which are the dominant group. Gorgonians include families of upright branching coral (primnoidae, plexauridae, isididae, etc.). Hydrocorals include stylasterid corals and stony corals. Soft corals are uncommon in the Aleutian Islands bottom trawl survey, but are represented by species such as gersemia. Sea anemones include all sea anemones captured in the bottom trawl surveys and pennatulaceans include sea pens and sea whips.

    Status and trends: A few general patterns are clearly discernible (Figure 19). Sponges are caught in most tows (>80%) in the Aleutians west of the southern Bering Sea. Interestingly, the frequency of occurrence of sponges in the southern Bering Sea is relatively high, but sponge abundance is much lower than other areas. The sponge estimates for the 1983 and 1986 surveys are much lower than other years, probably due to the use of different gear, including large tire gear that limited the catch of most sponges and possibly recording inconsistencies. In recent years, the abundance of sponges in the western and central Aleutian Islands and the frequency of occurrence have been declining.

    Gorgonian corals occur in about 20-40% of bottom trawl survey tows. Abundance of coral in all areas has declined since about 1991-1993 surveys and is at generally low levels in all areas, but the frequency of occurrence has remained steady. Hydrocorals are commonly captured, except in the southern Bering Sea. They typically occur in about 20-40% of tows in other areas. Similar to sponges, hydrocoral frequency of occurrence and abundance has decreased in the western and central Aleutian Islands over recent surveys (from a peak in the 2000 survey).

    Soft corals occur in relatively few tows, except in the eastern Aleutian Islands where they occur in about 20% of tows. Their abundance time series is dominated by a couple of years (1986 in the western Aleutians and 1991 in the central Aleutians).

    Sea anemones are also common in survey catches (∼20-40% of tows) but abundance trends are not clear for most areas. In the Southern Bering Sea abundance and frequency of occurrence have been increasing during recent surveys.

    Sea pens are much more likely to be encountered in the southern Bering Sea and eastern AI than in areas further west. Abundance estimates are low across the survey area and large apparent increases in abundance, such as that seen in the eastern AI in 1997, are typically based on a single large catch.

    Figure 19

    Factors influencing observed trends: The two major threats to populations of benthic invertebrates in the Aleutian Islands have been identified as fishing impacts and impacts of climate change. Both of these processes are occurring in the Aleutian Islands. Much of the benthic habitat in the Aleutians (∼50% of the shelf and slope to depths of 500 m) has been protected from mobile fishing gear since 2006, however, no studies have been conducted to determine potential recovery or expansion of populations due to the closures. As indicated by the 2016 bottom trawl survey temperature time series (p. 51), temperatures for the last two biennial surveys have been warmer than historical records. Non-motile organisms are sensitive to these changes in the benthic environment as well.

    Implications: The Aleutian Islands bottom trawl survey is not particularly good at measuring the abundance trends of structural epifauna. However, the bottom trawl surveys are reasonably adept at capturing presence or absence trends as indicated by recent distribution model validation studies for the species groups. The recent declines in sponge, gorgonians and hydrocorals in the western and central Aleutian Islands should continue to be monitored.

    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 2016

  • There are no updates to primary production indicators in this year’s report. 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 2015

    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 is sampled monthly (∼Apr-Sept) and terminates in Cook Inlet; the second is sampled 3 times per year and follows a great circle route across the Pacific, terminating in Japan. Several indicators are now routinely derived from the CPR data and updated annually. In this report we update three indices for three regions (Figure 20); 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 (Richardson et al., 2006) as an indicator of community composition. Anomaly time series of each index have been calculated as follows: a monthly mean value (geometric mean) is first calculated. Each sampled month is then compared to the 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

    The indices are calculated for three regions; the oceanic North-East Pacific, the Alaskan shelf SE of Cook Inlet and the deep waters of the southern Bering Sea (Figure 20). The oceanic NE Pacific region has the best temporal sampling resolution as both transects intersect here. This region has been sampled up to 9 times per year with some months sampled twice. The southern Bering Sea is sampled only 3 times per year by the east-west transect while the Alaskan shelf region is sampled 5-6 times per year by the north-south transect. Note that in 2015 the Bering Sea region was only sampled in the fall owing to a ship change in the spring so that the transect was cancelled, and a severe storm in the summer causing the ship to divert south away from the region.

    Status and trends: Ocean conditions in 2015 were warm across much of the north Pacific, with strongly positive values of the Pacific Decadal Oscillation (PDO) through the year, and continued influence from the warm Blob first noted in 2014 (Bond et al., 2015) plus a strong El Ni˜no that developed during the year. The lower trophic level indices showed some similarities to what was reported for 2014, driven largely by the warmth (Figure 21).

    Figure 20

    Diatom abundance anomalies were higher in 2015 on the Alaskan shelf and the oceanic region than they were in 2014. However, spring abundances were still low, and it was increased abundances later in the year which caused the overall anomalies to be more positive.

    The Copepod Community Size index saw negative anomalies for all three regions. While the Alaska Shelf region had seen a bias towards smaller species since 2013, this was the first year since 2010 that the oceanic NE Pacific region had shown a negative anomaly. The Bering Sea data are only represented by the fall sampling but 2015 values were the smallest since 2009 at this time of year.

    The mesozooplankton biomass anomalies were neutral in the oceanic NE Pacific region and Bering Sea region. For the Alaskan shelf region the value was quite high and similar to that of 2014, but it was the late summer/fall values that were unusually high with spring and summer values near average.

    Factors influencing observed trends: Spring diatom abundances for the Alaskan Shelf and oceanic NE Pacific regions were low, and these communities contained a higher than usual proportion of pennate-type taxa. These taxa generally do better in lower nutrient conditions as their high surface area to volume ratio facilitates nutrient uptake compared to centric taxa. Diatom numbers had increased by the summer and fall, leading to positive anomalies in both regions and suggesting a change in the ocean conditions mid-way through the year.

    The negative anomalies for the Copepod Community Size Index are consistent with the warmer water favoring the smaller-bodied species which generally have a more southerly center to their distribution. It is interesting that on the shelf this switch to smaller species occurred in 2013 when the warmth first became apparent, while in the oceanic region it was not until 2015 that the anomaly became negative. Abundance of zooplankton organisms was generally higher than average so that biomass anomalies remained neutral despite smaller organisms.

    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, influence availability of prey to predators. For example, while mesozooplankton biomass anomalies remained neutral or positive, the reduced average size of the copepod community suggests that the biomass was packaged into numerous, but smaller, prey items. This may require more work by predators to obtain their nutritional needs.

    Figure 21

    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 2016

  • Jellyfish – Bottom Trawl Survey

    Description of indicator: RACE bottom trawl surveys in the Aleutian Islands (AI) 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. 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 AI. The fishing gear used aboard the Japanese vessels that participated in all AI 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. 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 western and eastern AI than in other areas (Figure 22). The frequency of occurrence in trawl catches is generally from 20-60% across all areas, but has been variable. The 2006 AI survey experienced peak biomasses in all areas, whereas the 1992 survey had high abundance in the western AI only. Jellyfish catches and frequency of occurrence in the AI bottom trawl survey have been steadily increasing since the 2012 survey in all areas, but still have not reached the peak abundance from 2006.

    Factors influencing observed trends: Unknown

    Implications: The steady increase in the last three surveys in both frequency of occurrence and abundance of jellyfish has coincided with warming temperatures found during the AI survey. These data indicate that jellyfish are becoming more common in the Aleutian Islands, although an “outbreak” of jellyfish, such as happened in 2006 across all areas is not apparent.

    Figure 22

    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 2016

  • There are no ichthyoplankton indicators in this year’s report. See the contribution archive for previous indicator submissions at: http://access.afsc.noaa.gov/reem/ecoweb/index.php

  • There are no individual contributions with forage fish indicators in this year’s report, other than the pelagic foragers guild and the puffin indicators in the Report Card. See the contribution archive for previous indicator submissions at: http://access.afsc.noaa.gov/reem/ecoweb/index.php

  • 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 Aleutian Islands bottom trawl survey data was utilized to acquire lengths and weights of individual fish for walleye pollock, Pacific cod, arrowtooth flounder, southern rock sole, Atka mackerel, northern rockfish, and Pacific ocean perch. Only standard survey stations were included in analyses. Data were combined by INPFC area; Southern Bering sea, Eastern Aleutian Islands, Central Aleutian Islands, and Western Aleutian Islands. Length-weight relationships for each of the seven species were estimated with a linear regression of log-transformed values over all years where data was available (during 1984-2016). 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 AI and for the 3 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 23). Residuals for most species where there was data were negative from 2000 to 2006. Residuals were positive for all species but southern rock sole in 2010. In 2012-2014 length68 weight residuals were negative across most species, and the trendline has been negative since 2010. For northern rockfish, Pacific cod and Pacific ocean perch there has been a declining trend in residuals over the years covered by the survey.

    Spatial trends in residuals were also apparent for some species (Figure 24). Most species were generally in better condition in the southern Bering Sea (with the exception of Pacific cod). Species generally exhibited the worst condition in the Western Aleutians (with the exception of pollock and southern rock sole) Even in years where length weight residuals were positive overall (such as the early years in the northern rockfish time series), length weight residuals were lower (although still positive) in the western Aleutian Islands relative to other areas.

    Factors influencing observed trends: One potential factor causing the observed temporal variability in length-weight residuals may be population size. The species that appear to exhibit declining trends over the time series, have generally been increasing in abundance throughout the Aleutians (northern rockfish, Pacific Ocean perch and Pacific cod). In the western Aleutians, this may be especially magnified, due to the overall high level of population abundance in the area.

    Other factors that could affect length-weight residuals include temperature, 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 east to west. Therefore, it is impossible to separate the in-season time trend from the spatial trend in this 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 Aleutian Island groundfish, may therefore 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 the fact 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.

    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 2016

  • Miscellaneous Species – Aleutian Islands

    Description of indicator: RACE bottom trawl surveys in the Aleutian Islands (AI) 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. 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 AI. The fishing gear used aboard the Japanese vessels that participated in all AI surveys prior to 1991 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. Apparent abundance trends for a few of these groups are shown in Figure 26. For each species group, the largest catch over the time series was arbitrarily scaled to a value of 100 and all other values were similarly scaled. The standard error (± 1) was weighted proportionally to the CPUE to get a relative standard error.

    Status and trends: Echinoderms are frequently captured in all areas of the AI surveys occurring in 80-90% of all bottom trawl hauls. Echinoderm mean catch per unit effort (CPUE) is typically higher in the central and eastern AI than in other areas, although frequency of occurrence in trawl catches is consistently high across all areas. The lowest echinoderm CPUE has usually been in the southern Bering Sea, but has been increasing for the last two surveys. Eelpout CPUEs have generally been highest in the central and eastern AI. There has been a decline in eelpout biomass in the western Aleutian Islands over the last three surveys. Eelpouts generally occur in less than 10% of survey hauls across all areas. Poachers occur in a relatively large number of tows across the AI survey area (about 30-40% consistently), but mean CPUE trends are unclear and abundance appears low. A new shrimp time series has been calculated for 2016. The shrimp time series shows generally increasing trends in frequency of occurrence across all areas except the western Aleutian Islands since ∼1990. However, the CPUE is dominated by a single peak in 2006 in the western Aleutian Islands.

    Factors influencing observed trends: Unknown

    Implications: AI survey results provide limited information about abundance or abundance trends for these species due to problems in catchability. Therefore, the indices presented are likely of limited value to fisheries management. These species are not typically commercially important, but the trends in shrimp especially should be monitored as these are an important prey base for benthic commercial species.

    Figure 26

    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 2016

  • There are no seabird indicators in this year’s report, with the exception of those in the Report Card. See the contribution archive for previous indicator submissions at: http://access.afsc. noaa.gov/reem/ecoweb/index.php

  • The Marine Mammal Protection Act requires stock assessment reports to be reviewed annually for stocks designated as strategic, annually for stocks where there are significant new information available, and at least once every 3 years for all other stocks. Each stock assessment includes, when available, a description of the stock’s geographic range, a minimum population estimate, current population trends, current and maximum net productivity rates, optimum sustainable population levels and allowable removal levels, and estimates of annual human-caused mortality and serious injury through interactions with commercial fisheries and subsistence hunters. The most recent (2014) Alaska Marine Mammal stock assessment was released in August 2015 and can be downloaded at http://www.nmfs.noaa.gov/pr/sars/region.htm.

    There are no updates to marine mammal indicators in this year’s report, with the exception of those in the Report Card. See the contribution archive for previous indicator submissions at: http://access.afsc.noaa.gov/reem/ecoweb/index.php

  • There are no ecosystem or community indicators in this year’s report. See the contribution archive for previous indicator submissions at: http://access.afsc.noaa.gov/reem/ecoweb/index.php

  • There are no disease ecology indicators in this year’s report. See the contribution archive for previous indicator submissions at: http://access.afsc.noaa.gov/reem/ecoweb/index.php

 
  • Time Trends in Groundfish Discard

    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: November 2015

    Time Trends in Non-Target Species Catch

    Description of indicator: We monitor the catch of non-target species in groundfish fisheries in the Eastern Bering Sea (EBS), Gulf of Alaska (GOA) and Aleutian Islands (AI) ecosystems. In previous years we included the catch of “other” species, “non-specified” species, and forage fish in this contribution. However, stock assessments have now been developed or are under development for all groups in the “other species” category (sculpins, unidentified sharks, salmon sharks, dogfish, sleeper sharks, skates, octopus, squid), some of the species in the “non-specified” group (giant grenadier, other grenadiers), and forage fish (e.g., capelin, eulachon, Pacific sand lance, etc.), therefore we no longer include trends for these species/groups here (see AFSC stock assessment website at http://www.afsc.noaa.gov/refm/stocks/assessments.htm). Invertebrate species associated with habitat areas of particular concern, previously known as HAPC biota (seapens/whips, sponges, anemones, corals, and tunicates) are now referred to as structural epifauna. Starting with the 2013 Ecosystem Considerations Report, the three categories of non-target species we continue to track here are:

    1. Scyphozoan jellyfish 2. Structural epifauna (seapens/whips, sponges, anemones, corals, tunicates) 3. Assorted invertebrates (bivalves, brittle stars, hermit crabs, miscellaneous crabs, sea stars, marine worms, snails, sea urchins, sand dollars, sea cucumbers, and other miscellaneous invertebrates).

    Total catch of non-target species is estimated from observer species composition samples taken at sea during fishing operations, scaled up to reflect the total catch by both observed and unobserved hauls and vessels operating in all FMP areas. Catch since 2003 has been estimated using the Alaska Region’s Catch Accounting System. This sampling and estimation process does result in uncertainty in catches, which is greater when observer coverage is lower and for species encountered rarely in the catch.

    Status and trends: In the AI, the catch of Scyphozoan jellies has been variable and shows no apparent trend over time (Figure 27). The catch in 2015 was ∼25% of the catch in 2014. The catch of structural epifauna has been variable over time in the AI and peaked in 2015. The catch of structural epifauna in the AI is driven primarily by sponges caught in fisheries for Atka mackerel, rockfish and Pacific cod. Assorted invertebrate catches have generally trended upward from 2005 to a peak in 2013, with the exception of 2011 where the catch dropped back to nearly the 2005 level. The catch of assorted invertebrates dropped considerably from 2013 to 2014 and has remained low in 2015. Over that same span the assorted invertebrate catch has been dominated by sea stars and unidentified invertebrates. Assorted invertebrates are primarily caught in fisheries for Atka mackerel, Pacific cod, and rockfish.

    Factors influencing observed trends: The catch of non-target species may change if fisheries change, if ecosystems change, or both. Because non-target species catch is unregulated and unintended, if there have been no large-scale changes in fishery management in a particular ecosystem, then large-scale signals in the non-target catch may indicate ecosystem changes. Catch trends may be driven by changes in biomass or changes in distribution (overlap with the fishery) or both. Fluctuations in the abundance of jellyfish in the EBS are influenced by a suite of biophysical factors affecting the survival, reproduction, and growth of jellies including temperature, sea ice phenology, wind-mixing, ocean currents, and prey abundance (Brodeur et al., 2008).

    Implications: The catch of structural epifauna and assorted invertebrates in all three ecosystems is very low compared with the catch of target species. Structural epifauna may have become less available to the EBS fisheries (or the fisheries avoided them more effectively) since 2005. The interannual variation and lack of a clear trend in the catch of scyphozoan jellyfish in all three ecosystems may reflect interannual variation in jellyfish biomass or changes in the overlap with fisheries. Abundant jellyfish may have a negative impact on fishes as they compete with planktivorous fishes for prey resources (Purcell and Sturdevant, 2001), and additionally, jellyfish may prey upon the early life history stages (eggs and larvae) of fishes (Purcell and Arai, 2001; Robinson et al., 2014).

    Contributed by Andy Whitehouse1 , Sarah Gaichas2 , and Stephani Zador3 1Joint Institute for the Study of the Atmosphere and Ocean (JISAO), University of Washington, Seattle WA, 2Ecosystem Assessment Program, Northeast Fisheries Science Center, National Marine Fisheries Service, NOAA, Woods Hole MA, 3Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA Contact: andy.whitehouse@noaa.gov Last updated: September 2016

    Seabird Bycatch Estimates for Groundfish Fisheries off the Aleutian Islands, 2007-2015

    Contributed by Stephani Zador1 , Shannon Fitzgerald1 and Jennifer Mondragon2 1Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA 2 Sustainable Fisheries Division, Alaska Regional Office, National Marine Fisheries Service, NOAA Contact: shannon.fitzgerald@noaa.gov Last updated: October 2016

    Description of indicator: This report provides estimates of the numbers of seabirds caught as bycatch in commercial groundfish fisheries operating in federal waters off the Aleutian Islands of the U.S. Exclusive Economic Zone for the years 2007 through 2015. Estimates of seabird bycatch from earlier years using different methods are not included here. Fishing gear types represented are demersal longline, pot, pelagic trawl, and non-pelagic trawl. These numbers do not apply to gillnet, seine, or troll fisheries. Data collection on the Pacific halibut longline fishery began in 2013 with the restructured observer program, although some small amounts of halibut fishery information were collected in years previous when an operator had both halibut and sablefish individual fishing quota.

    Estimates are based on two sources of information, (1) data provided by NMFS-certified Fishery Observers deployed to vessels and floating or shoreside processing plants (AFSC, 2011), and (2) industry reports of catch and production. The NMFS Alaska Regional Office Catch Accounting System (CAS) produces the estimates (Cahalan et al., 2010). The main purpose of the CAS is to provide near real-time delivery of accurate groundfish and prohibited species catch and bycatch information for inseason management decisions. It is also used for the provision of estimates of non-target species (such as invertebrates) and seabird bycatch in the groundfish fisheries. At each data run, the CAS produces estimates based on current data sets, which may have changed over time. Changes in the data are due to errors that were discovered during observer debriefing, data quality checks, and analysis. Examples of the possible changes in the underlying data are: changes in species identification; deletion of data sets where data collection protocols were not properly followed; or changes in the landing or at-sea production reports where data entry errors were found.

    Figure 27

    Status and trends: The numbers of seabirds estimated to be bycaught in Aleutian Islands fisheries in 2015 is the highest in the time series, which began in 2007 (Table 3). This follows four years (2011-2014) with relatively low numbers caught. The majority of those estimated to be caught were Northern fulmars and Laysan albatross, both numbers which were the highest in the time series. In contrast, shearwaters, which were the most numerous species group bycaught in 2007, had the second lowest numbers caught in 2015. The estimated numbers of birds bycaught in the Aleutians exceeded that in the Gulf of Alaska, which typically has a greater number of estimated bycaught birds (Figure 28).

    Figure 28

    table 3

    Factors influencing observed trends: A marked decline in overall numbers of birds caught after 2002 reflected the increased use of seabird mitigation devices. A large portion of the freezer longline fleet adopted these measures in 2002, followed by regulation requiring them for the rest of the fleet beginning in February 2004. There are many factors that may influence annual variation in bycatch rates, including seabird distribution, population trends, prey supply, and fisheries activities. Work has continued on developing new and refining existing mitigation gear (Dietrich and Melvin, 2008). The longline fleet has traditionally been responsible for about 91% of the overall seabird bycatch in Alaska, as determined from the data sources noted above. However, standard observer sampling methods on trawl vessels do not account for additional mortalities from net entanglements, cable strikes, and other sources. Thus, the trawl estimates are biased low (Fitzgerald et al., in prep). For example, the 2010 estimate of trawl-related seabird mortality is 823, while the additional observed mortalities (not included in this estimate and not expanded to the fleet) were 112. Observers now record the additional mortalities they see on trawl vessels and the AFSC Seabird Program is seeking funds to support an analyst to work on how these additional numbers can be folded into an overall estimate. The challenge to further reduce seabird bycatch is great given the rare nature of the event. For example, Dietrich and Fitzgerald (2010) found in an analysis of 35,270 longline sets from 2004 to 2007 that the most predominant species, northern fulmar, only occurred in 2.5% of all sets. Albatross, a focal species for conservation efforts, occurred in less than 0.1% of sets. However, given the vast size of the fishery, the total bycatch can add up to hundreds of albatross or thousands of fulmars (Table 3).

    Implications: The large increase seen in seabird bycatch in 2015 reverses a general declining trend seen since the new estimation procedures began in 2007. There is some concern that the mortality could be colony-specific, particularly for Northern fulmars, possibly leading to local depletions (Hatch et al., 2010). However as an increase in bycatch was noted in the AI, GOA and EBS, there is reason to believe that there was a widespread change in seabird distribution, fishing effort and/or seabird prey supply, all of which could impact bycatch. The recent warm oceanic conditions, the “Blob”, have been linked to changes in the ecosystem and lower productivity. It is difficult to determine how seabird bycatch numbers and trends are linked to changes in ecosystem components because seabird mitigation gear is used in the longline fleet. There does appear to be a link between poor ocean conditions and the peak bycatch years, on a species-group basis. Fishermen have noted in some years that the birds appear “starved” and attack baited longline gear more aggressively. In 2008 general seabird bycatch in Alaska was at relatively low levels (driven by lower fulmar and gull bycatch) but albatross numbers were the highest at any time between 2002 and 2013. This could indicate poor ocean conditions in the North Pacific as albatross traveled from the Hawaiian Islands to Alaska. Broad changes in overall seabird bycatch, up to 5,000 birds per year, occurred between 2007 and 2013. This probably indicates changes in food availability rather than drastic changes in how well the fleet employs mitigation gear. A focused investigation of this aspect of seabird bycatch is needed and could inform management of poor ocean conditions if seabird bycatch rates (reported in real time) were substantially higher than normal.

  • Areas Closed to Bottom Trawling in the EBS/ AI and GOA

    Contributed by John Olson, Habitat Conservation Division, Alaska Regional Office, National Marine Fisheries Service, NOAA Contact: john.v.olson@noaa.gov Last updated: October 2016

    Description of indicator: Many trawl closures have been implemented to protect benthic habitat or reduce bycatch of prohibited species (i.e., salmon, crab, herring, and halibut) (Figure 29, Table 4) Some of the trawl closures are in effect year-round while others are seasonal. In general, year-round trawl closures have been implemented to protect vulnerable benthic habitat. Seasonal closures are used to reduce bycatch by closing areas where and when bycatch rates had historically been high.

    Figure 29

    Table 4

    Status and trends: Additional measures to protect the declining western stocks of the Steller sea lion began in 1991 with some simple restrictions based on rookery and haulout locations; in 2000 and 2001 more specific fishery restrictions were implemented. In 2001, over 90,000 nm2 of the Exclusive Economic Zone (EEZ) of Alaska was closed to trawling year-round. Additionally, 40,000 nm2 were closed on a seasonal basis. State waters (0-3 nmi) are also closed to bottom trawling in most areas. A motion passed the North Pacific Management Council in February 2009 which closed all waters north of the Bering Strait to commercial fishing as part of the development of an Arctic Fishery management plan. This additional closure adds 148,300 nm2 to the area closed to bottom trawling year round.

    In 2010, the Council adopted area closures for Tanner crab east and northeast Kodiak. Federal waters in Marmot Bay are closed year round to vessels fishing with nonpelagic trawl. In two other designated areas, Chiniak Gully and ADF&G statistical area 525702, vessels with nonpelagic trawl gear can only fish if they have 100% observer coverage. To fish in any of the three areas, vessels fishing with pot gear must have minimum 30% observer coverage.

    Substantial parts of the Aleutian Islands were closed to trawling for Atka mackerel and Pacific cod (the predominant target species in those areas) as well as longlining for Pacific cod in early 2011 as part of mitigation measures for Steller sea lions. Management area 543 and large sections of 542 are included in this closure. The western and central Aleutian Islands were subsequently reopened to trawling in 2014.

    Implications: With the Arctic FMP closure included, almost 65% of the U.S. EEZ of Alaska is closed to bottom trawling. For additional background on fishery closures in the U.S. EEZ off Alaska, see (Witherell and Woodby, 2005). Steller Sea Lion closure maps are available here: http://www.fakr.noaa.gov/sustainablefisheries/sslpm/atka_pollock.pdf http://www.fakr.noaa.gov/sustainablefisheries/sslpm/pcod_nontrawl.pdf http://www.fakr.noaa.gov/sustainablefisheries/sslpm/cod_trawl.pdf

  • 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 assigning a score for each fish stock based on the following rules:

    1. Stock has known status determinations:
    (a) overfishing = 0.5
    (b) overfished = 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 (Tables 5 and 6). 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 (Tables 5 and 7).

    Status and trends: As of June 30, 2016, 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 5). The only crab stock considered to be overfished is the Pribilof Islands blue king crab stock, which is in year 2 of a rebuilding plan. None of the non-FSSI stocks are subject to overfishing, known to be overfished, or known to be approaching an overfished condition.

    The current overall Alaska FSSI is 132.5 out of a possible 144, or 92%, based on updates through June 2016 (Table 6). The overall Bering Sea/Aleutian Islands score is 85.5 out of a maximum possible score of 92. The BSAI groundfish score is 59 (including BSAI/GOA sablefish, see Endnoteg in Box A) of a maximum possible 60 and BSAI king and tanner crabs score is 26.5 out of a possible 32. The Gulf of Alaska groundfish score is 47 of a maximum possible 52 (excluding BSAI/GOA sablefish). Overall, the Alaska total FSSI score decreased slightly from 92.7% 2015 to 92.0% in 2016 (Figure 30).

    Table 5

    Figure 30

    Factors influencing observed trends: One point was lost from last year’s FSSI to this year for the St. Matthew Island blue king crab stock having their biomass drop below 80% of BMSY. This one point loss accounts for the 0.7% drop in the overall Alaska FSSI score. Other crab groups in the BSAI region with FSSI scores less than 4 are golden king crab-Aleutian Islands (FSSI=1.5) and blue king crab-Pribilof Islands (FSSI=2). Neither of these king crab stocks are subject to overfishing. The Pribilof Islands blue king crab stock is considered overfished and is in year 2 of a rebuilding plan. Biomass for this stock is less than 80% of BMSY. It is unknown if the golden king crab-Aleutian Islands stock is overfished and BMSY is not estimated.

    The only BSAI groundfish stock with an FSSI score less than 4 is the Greenland halibut, which loses a point for biomass being less than 80% of BMSY.

    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 of these groups are because the overfished status determination is 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. A single stock is considered to be overfished (Pribilof Islands blue king crab), no stocks are subject to overfishing, and no stocks or stock complexes are known to be approaching an overfished condition.

    table 6

    table 7

     

     

    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 2016

  • Trends in Human Population and Unemployment in the Aleutian Islands

    Description of indicator: Human population and unemployment, the social indices presented in this report, are significant factors in the Aleutian Islands (AI) ecoregion, and groundfish fishery management, as many communities in the region rely upon fisheries to support their economies and to meet subsistence and cultural needs. As with other areas neighboring the Arctic, population and unemployment are important indicators of community viability (Rasmussen et al. 2015). Advancements in socio-ecological systems (SES) research has demonstrated the importance of incorporating social variables in ecosystem management and monitoring, and these indices reflect aspects of the social (population) and economic (unemployment) settings of a SES (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 report summarizes trends in human population and unemployment rates over time in the Aleutian Islands chain including the eastern, central, and western areas. The 7 AI fishing communities included in analysis comprise the population that resides along the chain. Population was calculated by aggregating community level data between 1890 and 1990 (DCCED 2016) and annually from 1990-2015 (ADLWD 2016a). Unemployment data was also aggregated and weighted to account for varying community populations across Alaska Boroughs. Estimates are presented annually from 1990-2015 (ADLWD 2016a).

    Status and trends: As of 2015 the total population of all AI communities was 5,939. The total population of the AI has fluctuated since 1880 with the greatest population increase of 374.0% occurring between 1960 and 1970 (Table 8 and Figures 31-32). Population trends of the AI are not consistent with State trends where the greatest increase of 75% was between 1950 and 1960. Population of the AI increased from 1920 to 1940, and from 1960 to 1990. Between 1990 and 2015 the population declined by 30.2%. The Aleutian Islands overall has had sporadic population cycles. Notable decreases occurred between 1900 and 1910 between 1940 and 1950 and between 1990 and 2000. The eastern AI has had the most steady population increase between 1880 and 2015, whereas the central and western AI experienced fluctuations. The western AI had a population of zero in 2015. Most of the population increase of Alaska was in urban areas, such as Anchorage, where 40% of Alaskas population currently resides (ADLWD 2016a; 2016b).

    table 8

    Figure 31

    Figure 32

    The population of most AI communities decreased between 1990 and 2015. Adaks (central AI) population decreased by 94.0%. Attu Station (western AI) had zero residents as of 2015. Akutan and Unalaska (eastern AI) had steady population increases during this time period. Although 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), only 42% of the AI population identified as Native American alone or combination with another race (DCCED 97 2016). The higher proportion of Native Americans was in Atka and Nikolski. There has been increased migration of Alaska Natives from rural to urban areas (Goldsmith et al. 2004; Williams 2004) and the majority of population growth that has occurred in Alaska is of the Caucasian demographic (ADLWD 2016b).

    Unemployment rates in the AI, between 1990 and 2015, were lower than State and national rates (Figures 33-34). The eastern AI had higher unemployment rates than central AI, and western AI data was insufficient to interpret any trends. In the eastern AI, unemployment peaked in 1998 (4.0%), 2004 (4.5%), 2009 (4.8%), and 2012 (4.5%) which is consistent with State and national trends. The central AI maintained rates less than 1.0% which is lower than all regions of Alaska.

    Figure 33

    Figure 34

    Factors influencing observed trends: The population decrease of the AI between 1990 and 2015 (30.2%) was inconsistent with State trends (increase of 34.1%). Alaska has high rates of population turnover because of migration, and population growth has occurred mainly in urban areas (ADLWD 2016b). 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 2016b). In 2010, 61% of Alaskas population was born out of State (Rasmussen et al. 2015). In terms of natural growth, from 2013 to 2014 the birth rate in Alaska was 1.5 per 100 people which was higher than the national rate of 1.3. The Aleutian chain had the lowest natural increase (0.0- 0.5%) whereas the NBS area had the highest (1.5- 3.0%). In regard to migration, the net annual migration of the AI was low (less than 0- 100) (Williams 2004; ADLWD 2016b). The highest net migration occurs in the GOA region and the Matanuska-Susitna Borough has the highest growth rate in the State (ADLWD 2016b).

    Population trends in Alaska are largely 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 (ADLWD 2016b). However, the population of some communities declined in the 1990s because of Coast Guard cut-backs and military base closures (Williams 2006). For example, the closure of a Coast Guard base in Attu Station in western AI has left the community abandoned explaining the zero population in 2015. The fishing industry also influences population and this is evident in the AI with Unalaska and Akutan, the most populous communities of the AI, being landings for substantial volumes of seafood. The Aleutian Islands, and Kodiak, have the most transient populations because of the seafood processing industry (Williams 2004). Factors that influence population shifts and migration include employment, retirement, educational choices, cost of living, climate, and quality of life, (Donkersloot and Carothers 2016).

    Alaska State 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, whereas unemployment peak occurred following completion of the pipeline, during the oil bust of the late 1980s, and during the great recession of 2007-2009 (ADLWD 2016c) . However, during the great recession, Alaskas employment decreased only 0.4% whereas the national drop was 4.3% partly because of the jobs provided by the oil industry (ADLWD 2016d). Between 1990 and 2015, the eastern AI had the lowest unemployment rate in 1990 and highest in 2009 during the great recession (Figures 33-34).

    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. Population change in Alaska is largely fueled by increased net migration rather than natural increase, and there has been increased migration from rural to urban areas. AI communities are among the most transient with in-migration of foreigners working in processing plants, yet employment in fisheries is what maintains these communities, such as Unalaska and Akutan. Fisheries contribute to community vitality and changes in groundfish policy and management, such as increased regulations, may have implications for small communities of the Aleutian and Pribilof Island Community Development Association entity. Also, with almost half of the population of the AI being Native Alaskans, resource managers may benefit from working with communities holding traditional ecological knowledge (TEK) to incorporate TEK into ecosystem management (Huntington et al. 2004).

    Contributed by Anna Santos Resource Ecology and Fisheries Management Division, Alaska Fisheries Science Center, National Marine Fisheries Service, NOAA Contact: anna.santos@noaa.gov Last updated: September 2016

  • Description of indicator: Fishing vessels participating in federally-managed groundfish fisheries off Alaska principally use trawl, hook and line, and pot gear. Vessel counts were compiled from NMFS Alaska Region’s blend and Catch-Accounting System estimates and from fish ticket and observer data through 2014. These figures count vessels only for trips where groundfish is targeted.

    Status and trends: Figure 35 shows the number of vessels by gear type off the Aleutian Islands. The total number of vessels participating in federally-managed fisheries Alaska-wide has generally decreased since 1992, though participation has remained relatively stable in recent years. Vessels using hook and line or jig gear have accounted for most of the participating vessels from 1992 to 2015. Approximately 600 such vessels participated in 2015, compared to over 1,000 vessels annually from 1992 to 1994. The number of active trawl-gear vessels has decreased steadily from over 250 annually in the period from 1992 to 1999 to around 180 in each of the last 5 years. Pot-gear activity has steadily declined since a peak of 343 vessels in 2000, with 154 pot vessels active in 2015.

    Vessel counts before and after 2003 may not be directly comparable due to changes in fishery monitoring and reporting methods. The Catch Accounting System (CAS), implemented in 2003 for in-season monitoring of groundfish catch, registers the Federal Fisheries Permit number of catcher vessels delivering to motherships and shoreside processors, thus giving a more complete accounting of participating vessels than the previous “blend” system. The increase in 2003 in hook and line/jig vessel counts, in particular, is likely attributable this change.

    Figure 35

    Factors influencing observed trends: Participation in groundfish fisheries off Alaska since the early 1990s has been driven by a number of interacting factors. These include fluctuations in market conditions, stock levels, and allowable catch quotas; the availability of fishing opportunities in alternative fisheries; and the introduction of management measures intended to address issues such as bycatch, protected species, and overcapitalization.

    Steller sea lion protection measures in the Aleutian Islands have primarily affected fisheries for prey species (pollock, cod, and Atka mackerel). The AI trawl pollock fishery was closed for Steller sea lion recovery from 1999 to 2004, and participation has been limited since the fishery reopened in 2005 with additional area restrictions and full allocation of the TAC to the Aleut Corporation.

    Participation in the trawl Atka mackerel fishery declined sharply in 1994 following the implementation of Amendment 28 to the BSAI Groundfish FMP, which divided the Aleutian Islands into three districts for spatially allocating TAC.

    In the fixed gear sablefish fishery, participation by hook and line vessels has declined gradually since implementation of the IFQ program (from 66 vessels in 1995 to 8 vessels in 2015). As in the Bering Sea, sablefish fishing with pot gear increased beginning in 2000 and has leveled off in recent years.

    Implications: Monitoring the numbers of fishing vessels provides general measures of fishing effort, the level of capitalization in the fisheries, and the potential magnitude of effects on industry stakeholders caused by management decisions.

    Contributed by Jean Lee, 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: jean.lee@noaa.gov Last updated: September 2016

 

Aluetian Islands