Steller Sea Lion Synthesis Paper

The Climate-Ocean Regime Shift Hypothesis of the Steller Sea Lion Decline

Andrew W. Trites, Arthur J. Miller, Herbert D. G. Maschner,

Michael A. Alexander, Steven J. Bograd, Antonietta Capotondi, Kenneth O. Coyle,

Emanuele Di Lorenzo, Thomas C. Royer, Edward J. Gregr, Chester E. Grosch,

Bruce P. Finney, Lowell Fritz, George L. Hunt, Jaime Jahncke, Nancy B. Kachel,

Hey-Jin Kim, Carol Ladd, Nathan J. Mantua, Caren Marzban, Wieslaw Maslowski,

Douglas J. Neilson, James E. Overland, Stephen R. Okkonen, and Julian X. L. Wang

To be submitted to Fisheries Oceanography

First Rough Draft, March 29, 2004 Abstract. The decline of the Steller sea lion populations in the western Gulf of Alaska has been hypothesized to be a consequence of physical oceanographic changes due to the

1976-77 climate shift. New evidence is discussed here that substantiates this idea and links the physical environmental changes to their consequent effects on the health and fecundity of Steller sea lions. Spatial and temporal variations in the ocean climate system are creating adaptive opportunities for high trophic levels, which is the underlying mechanism for the decline of the Steller sea lion populations in the western Gulf of

Alaska. The climate forcing mechanism and the details of the mean and eddy oceanic response can explain both the temporal issue (populations decreased after the late 1970’s) and the spatial issue (western, but not eastern, populations decreased) of the decline. The basin-wide climate changes have regionally sensitive impacts due to the biogeographic structures in the Gulf, which includes a transition point from coastal to open-ocean conditions at Samalga Pass westward along the Aleutian Islands. Upscaling from local complexities to broadscale regularities is an important consideration in assessing these climate impacts on the ecosystem.

2 1. Introduction

Large-scale changes in the physical environment of North Pacific Ocean have the potential to influence Steller sea lion populations directly or indirectly by several processes, including altering the food web. A major change in both the physical state and the ecology of the North Pacific Ocean occurred near the end of 1976, with basin-wide changes in temperature, mixed layer depth, primary productivity, fisheries, etc. (e.g.

Mantua et al. 1997).

CIFAR sponsored a suite of projects to address the hypothesis that the climate shift induced the decline in the Steller sea lion populations. The projects covered a variety of topics, including physical and biological data analysis, ocean modeling experiments, and paleological evidence. Our objective here is to interpret these results in the context of our current understanding of what caused the decline of the Steller sea lion populations.

2. Physical Ocean Data Analysis

Observations of the changes in the physical oceanographic environment are sparsely sampled in space and time. The most complete observations are of sea-surface temperature (SST), and many previous efforts have linked broad-scale changes in SST to ecosystem response (e.g., Mantua et al., 1997). Large-scale surface-derived indices (such as PDO) are insufficient for understanding climate impacts on local populations. We need to understand the morphodynamics of climate regimes and the transitions between

3 regimes in order to develop ecologically relevant, mechanistic-based indicators of climate state.

Towards this goal, Bograd et al. (2004) show that there are regional and depth- dependent differences in the timing and amplitude of important climate events in the eastern Subarctic Pacific, which in turn may lead to local differences in ecosystem response. Figure 1 shows that a cluster analysis dendrogram of SST reveals five different regions in the Gulf of Alaska (GoA). These regional differences are also evident in the common trends (similar to EOFs but with the means included) shown in Figure 2. Trend

1 has loadings that increase from west to east and has a warming that commenced in the early 1970’s and accelerated after the 1976-77 climate shift (Miller et al., 1994). Trend 2 accentuates trend 1 with a strong warming after 1972. Trend 3 has a strong warming in the eastern GoA during the 1957-58 ENSO and a broad warming after 1976-77. Trend 4 exhibits strong interannual variability from ENSO events with strong loadings in coastal

British Columbia and WWD. Subsurface observations of temperature are sufficiently dense in the GoA to address some aspects of the vertical structure of physical oceanographic changes, and these tend to be in phase with the SST. Changes in the seasonality (phase and amplitude) of important environmental processes (evident from other analyses) may have a large ecosystem impact, by leading to mismatches in biophysical coupling.

Non-linear statistical analysis provides a complementary view to the linear analyses.

Following the earlier work of Hare and Mantua (2000), Marzban et al. (2004) have created a multivariate data set with 45 fishery and survey records from the Bering Sea and Gulf of Alaska for the period 1965-2001. These data contain time series for variables

4 like annual salmon landings for five species and three regions in Alaska, rockfish and herring recruitment indices, herring biomass, and zooplankton biomass estimates for subregions of the Gulf of Alaska and Bering Sea. A neural-net based principal component analysis is applied to identify the leading non-linear Principal Components. The results

(Figure 3a) are similar to those found by Hare and Mantua (2000), where the leading PC indicates a pattern with all positive scores from 1965-1979, and all negative scores from

1980-2000. The time series that load most strongly on this pattern include Alaska salmon landings, many rockfish recruitment records, while records for GoA shrimp catches load negatively on NL PC1. The explained variance in NL PC1 from this analysis is 39%, while PC1 from a linear analysis accounts for 27% of the variance in these data.

Figure 3b shows analogous nonlinear PC’s based on 22 physical indices representing both large and local environmental processes. These data contain time series for large scale climate processes like the PDO, an Aleutian Low index, indices for ENSO (Nino

3.4), the Arctic Oscillation, as well as local measures for things like alongshore upwelling winds, coastal SSTs, and Alaska and BC annual river discharge. Note that the loadings on NLPC1 for the abiotic series have more interannual variations than those for the fishery/survey (biotic) data. The time series that load most strongly onto NLPC1 for the physical data include the PDO index, the Aleutian Low index, and coastal SST data.

The explained variance in NL PC1 from this analysis is 32%, while PC1 from a linear analysis accounts for 25% of the variance in these data.

NLPC2 has more interannual variability than NLPC1 and no hint of interdecadal climate regime shifts.

5 Marzban et al. (2004) also computed the non-parametric “Kendall’s Tau” statistic to assess the statistical significance of trends in the SSL, climate and fishery data used in the analysis. A “trend" often means a linear trend. Kendall's tau assesses trend nonlinearly

(though monotonically). The Z-statistic shown in Figure 4 assesses the statistical significance of that trend; if Z >= 2.575, then the hypothesis that the true tau is zero can be rejected with 99% confidence.

The Adult SSL data show statistically significant negative trends in 5 of the 7 regions

(Figure 4a). There are many positive trends in the Alaska fishery and survey data for the period 1965-2001, with a few negative trends (Figure 4b). Most of the very strong trends are in the Gulf of Alaska records, including many of the salmon catch records.

The two series with large negative trends are for indices tracking Eastern Bering Sea

Turbot recruitment and Gulf of Alaska Shrimp catch per unit effort. The Kendall’s Tau statistic was also computed for climate/environmental data, but they find no statistically significant trends for 1965-2001 period (Figure 4c).

The previous statistical analyses reiterate the significance of the 1976-77 climate shift in the physical and biological variables of the GoA. The changes observed across this temporal boundary are shown in Figure 5 for winter SST

, sea level pressure and surface wind anomalies before and after the 1976-77 regime shift

(Peterson and Schwing, 2003). Also shown for comparison are changes across the 1999 temporal boundary. Note the similarities in the latest period to the earlier cool regime.

However, significant differences, such as a strong, displaced Aleutian Low with a strengthened North Pacific High, are evident. This implies more than two stable climate

6 states can exist. This is related to Bond et al’s (2003) arguments for the second SST PC, as opposed to the PDO, becoming more important in the recent period.

Changes in the Aleutian Low are associated with several oceanic forcing functions, including Ekman pumping (Figure 6), coastal upwelling, surface heat fluxes and surface fresh-water fluxes. These undoubtedly drive significant changes in the oceanic circulation and density fields. Additionally, streamflow variations along the GoA are linked to density driven changes in the Alaska Coastal Current (Royer, 1982). Gulf of Alaska coastal discharge is shown in Figure 7, based on Royer’s (2004) model using National

Weather Service temperature and precipitation data since 1980 and the same model using

NCEP Reanalysis data. From the early 1970’s to late 1980’s, the rate of freshwater discharge with glacial ablation into the Alaska Coastal Current (ACC) has increased by about 70%, suggesting a significant increase in the ACC. The ACC is a critical habitat for Steller sea lions in the Northeast Pacific. Presently it is unclear how an increase in the density stratification and the flow in the ACC affect the biological productivity of this coastal system.

Trends in Gulf of Alaska coastal freshwater discharge show seasonal changes in the amplitude and phase of the discharge (Figure 8). The spring submaximum that was present beginning in the mid-1930s, disappeared in about 1970, appeared weakly from

1978 to 1989, reappearing again 1994 – 2002. The timing of this freshening might be critical to the spring blooms in the Alaska Coastal Current and hence biological productivity. The low frequency changes in the amplitude of the seasonal signal might also affect the productivity. Wavelet analysis of the discharge (Figure 9) reveals changes in the low frequency variations of the signal with strong 18-22 year periodicities in the

7 1930s-1950s, a 50-year periodicity during the middle of the record and ENSO type variations (< 10 years) in the 1970s and 1980s.

3. Ocean Modeling Results

The sparseness of observations in space and time, especially before the 1976-77 climate shift, motivates modeling studies to decipher the physical processes that may have lead to changes in the food web. These studies include hindcasts forced by observed atmospheric variations to determine the magnitude of phasing of oceanic events in the water column. They also include processes studies in which the effects of eddies and their interactions with topography and mean conditions are explored. Coarse resolution models allow a broad-scale perspective of the physical oceanographic changes induced by climate forcing, while eddy-permitting models can suggest roles for eddies in altering the mean background states of the ocean.

A coarse-resolution hindcast of the GoA was analyzed by Capotondi et al. (2004) to determine how pycnocline depth may have changed after 1976-77. Pycnocline depth changes can be very relevant for biological productivity in the northeast Pacific. First of all, changes in pycnocline depth can be an indicator of changes in upwelling, a process responsible for exchange of nutrients from the deep ocean to the upper ocean. Second, the northeast Pacific is characterized by a well-mixed fresh surface layer bounded at the bottom by a halocline. Since at high-latitudes density is controlled by salinity, the halocline tends to coincide with the pycnocline. Thus, pycnocline depth is a good approximation for winter mixed layer depth in this area.

8 The changes in pycnocline depth associated with the 1976-77 climate regime shift, as diagnosed from the output of the NCAR ocean model driven by NCEP winds over the period 1958-1997 show a shoaling of the pycnocline in the central part of the Gulf of

Alaska after the mid-seventies, and a deepening in a broad band that follows the coast

(Figure 10). The deepening is particularly pronounced in the western part of the Gulf of

Alaska, to the southwest of Kodiak Island, where the pycnocline deepened by 25-30m after 1976. The surface forcing responsible for these changes is the local Ekman pumping

(Figure 6), which can account for a large fraction of the pycnocline depth changes as a local response (Cummins and Lagerloef, 2002).

Associated with these pycnocline depth changes is a strengthening of the Alaskan

Stream in the western GoA. This is a result that one would intuitively expect following an intensification of the Aleutian Low, which is main driver of the mean GoA ocean circulation. The data analysis of Lagerloef (1995), however, suggests that the Alaskan

Stream weakened after the shift. The objective analysis procedure used by Lagerloef may have been affected by the sparseness of data over these time intervals and further analysis of observations and model results is warranted.

Changes in the distribution of mesoscale eddies in the Gulf of Alaska after the 1976-

77 climate shift were studied by Miller et al. (2004). An eddy-permitting ocean model is forced over the 1950-1999 time period by monthly-mean wind stresses taken from the

NCEP/NCAR re-analysis. After the shift, the Alaska Stream is strengthened considerably in the northwest part of the GoA and weakened in the southwestern domain.

The increase in the strength of the Alaska Stream is consistent with coarse resolution model results of Capotondi et al. (2004). This change in the mean strength of the Alaska

9 Stream over decadal timescales alters the stability properties of the flow field and changes the mesoscale eddy variance distribution.

Figure 11 shows the surface current velocity variance for two 10-yr epochs, along with the difference in variance, before and after the climate shift. Before the shift, mesoscale eddy variance is highest southeast of Kodiak Island and along the Alaska

Stream to the southwest of Kodiak. After the shift, mesoscale eddy variance increases precipitously in the northwestern GoA and decreases precipitously to the south and west of Kodiak Island. The consequences of this change will include altering the cross- shelf/slope mixing of water masses of the open ocean and shelf regions. Since mesoscale eddies provide a mechanism for transporting nutrient rich open-ocean waters to the productive nearshore shelf region, the fundamental flow of energy through the food web may have been altered due to this physical oceanographic change. This mechanism may help to explain the changes in forage fish quality in the diets of the Steller sea lions whose western populations have declined precipitously since the mid-1970’s.

The mean flows of the Alaska Current in the eastern GoA, in contrast, are nearly unchanged after the shift. Likewise, the surface velocity variance is only weakly altered, being reduced slightly compared to pre-shift conditions. Hence, an east-west asymmetry occurs in the GoA circulation response to the strengthening Aleutian Low. This is consistent with eastern populations of Steller sea lions remaining stable across the temporal boundary of the 1976-77 climate shift.

While the physical ocean models can give us an idea of how the basic ocean environment changed after the shift, an ecosystem model is needed to understand how the

10 physical changes can alter the food web. Towards this end, Alexander et al. (2004) studied a coarse-resolution physical-ecosystem ocean model hindcast (Chai et al. 2003) to examine whether changes in the physical environment associated with the 1976-77 transition, influence the lower trophic levels of the food web in the northeast Pacific.

The physical component simulates ocean temperatures, salinity, horizontal currents and upwelling, while the biological component consists of ten compartments with small and large classes of phytoplankton and zooplankton, two forms of dissolved inorganic nitrogen, detrital nitrogen, silicate, detrital silicate, and CO2. Processes simulated by the model include: primary productivity through new and regenerated production, grazing, predation, excretion, and sinking of organic matter (Chai et al. 2002, 2003). The model domain extends from 45ºS to 65ºN in the Pacific, with a horizontal resolution of 2º longitude by 2º latitude (north of 20ºN) and 40 vertical levels. A simulation was performed where the model was forced with observed atmospheric fields over the period

1960-1999.

The model simulation indicates that the large zooplankton biomass is substantially reduced during May in the Gulf of Alaska and eastern Bering Sea in 1977-1998 relative to 1960-1976 (Figure 12). A similar decrease occurs in April for the large phytoplankton and small zooplankton classes, representing about a 20% decrease in plankton during the spring bloom. This decrease in plankton biomass, could reduce the food available to higher trophic levels and thereby negatively impact Steller sea lions. The difference in the plankton concentration between the two epochs, however, is relatively unchanged in seasons other than spring and is negligible for the small phytoplankton class throughout

11 the year. Brodeur and Ware (1992) found instead that an increase in zooplankton occurred in the Gulf of Alaska after the 1976 transition, opposite to these results, although the observations they used were only collected during summer and were widely spaced in time and location. While no direct link was found between any single physical process (e.g. upwelling) and the biological changes seen in Figure 12, the increased mixed layer depth in late winter along the southern coast of Alaska (Figure 13) could reduce the light available for photosynthesis in the northern during 1976-1998 relative to

1960-1976 in that region.

The previous model was designed to study oceanic processes on a basin-scale and is therefore not of sufficient resolution to investigate coastal ecosystem dynamics. The use of limited domain models of ocean circulation allows employing higher resolution (as compared to global ocean general circulation models) and focused studies of critical processes and circulation in a region. Such an approach provides means for proper representation of the complex air-sea-bottom interactions and their influence on exchanges between the North Pacific Ocean and the Bering Sea, which occurs through the straits and passes of the Steller Sea lion populated Aleutian-Komandorskii Island arc.

A pan-Arctic coupled sea ice–ocean model, developed at the Naval Postgraduate

School (NPS), can improve understanding of the circulation and exchanges between the sub-arctic and arctic basins (Maslowski et al., 2004, Maslowski and Lipscomb, 2003,

Maslowski and Walczowski, 2002). The model domain extends from about 30oN in the

North Pacific, through the Bering Sea, Arctic Ocean, into the North Atlantic to about

45oN. The numerical grid is configured at 1/12o and 45 levels using rotated spherical coordinates. The model has been integrated with realistic daily-averaged 1979-1993

12 reanalyzed data and 1994-2001 operational products from the European Centre of

Medium-range Weather Forecast (ECMWF) to investigate interannual-to-interdecadal variations in transport through and properties within the passes of the central and eastern

Aleutian Islands.

One of the important features of ocean circulation in the northern North Pacific is the

Alaskan Stream, its interannual variability, and effects on the mass and property transport through the Aleutian Island passes. A comparison of transport estimates of the Alaskan

Stream (Onishi, 2001; Reed and Stabeno, 1999; Roden, 1995; Warren and Owens, 1988;

Reed, 1984; Favorite, 1974; Thomson, 1972) with those through the eastern and central passes (Schumacher et al, 1982; Reed, 1990; Reed and Stabeno, 1997; Stabeno et al.,

1999) suggests that even small variations in the magnitude and position of the Alaskan

Stream could have significant consequences on the dynamics and hydrographic conditions within and to the north of the passes. Analyses of model output suggest that the dominant mechanism of interannual variability in volume transport is related to anticyclonic mesoscale eddies (100-250 km diameter) propagating westward along the

Alaskan Stream with mean speed of a few km per day. Similar eddies have been observed from satellites (Crawford et al., 2000; Okkonen, 1992, 1996) and in field observations

(Reed et al., 1980; Musgrave et al., 1992).

Model simulated eddies along the Alaskan Stream have significant influence on both the circulation and water mass properties across the eastern and central Aleutian Island passes. Figure 14 shows depth-averaged (0-100 m) velocity snapshots and salinity differences across the Amukta Pass for eddy and no eddy conditions in 1984. In March

(Figure 14a) no eddy is present in the Alaskan Stream and the dominant flow in the

13 region to the south of Amukta Pass is westward and parallel to the pass. Two months later (Figure 14b) when a mesoscale eddy enters the region, the flow of the Alaskan

Stream is significantly modified down to well over 1000 m, with a strong northward velocity component into Amukta Pass and a strong southward component some 200 km to the east. This has implications both on the transport of Alaskan Stream and on the flow through and conditions in Amukta Pass (Maslowski et al. 2004, submitted).

The salinity difference between the model sections marked “Cross Slope” when the eddy is (Figure 14b) or is not (Figure 14a) present in the region is shown in Figure 14c.

Eddy-related upwelling of salty water along the southern slope affects the water column down to about 1000 m. A salinity increase of 0.1 ppt extends all the way to the surface within the Amukta Pass region when the eddy is present. Given the high correlation between salinity and nutrient content at depths, the increased salinity in the upper ocean over the pass can represent nutrient input for enhanced and/or prolonged primary productivity. Since modeled eddies along the Alaskan Stream occur throughout a year, their contribution to high surface nutrient concentrations within the Aleutian Island passes could be especially significant during otherwise low primary productivity seasons.

This effect would be most important during years with mesoscale eddies frequently propagating along the Alaskan Stream. An overall net increase of salinity in the upper water column is experienced within the region adjacent to Amukta Pass after the eddy moves further to the west.

4. Biogeography

14 The oceanographic studies described herein provide evidence of medium and long- term changes in the physical dynamics in the northern Gulf of Alaska and Aleutian

Islands. It is therefore reasonable to expect these changes to be reflected in the biogeography of the regional fauna. Several studies addressed these issues.

A series of cruises revealed the biogeography of the region around the passes of the eastern Aleutian Islands. Samalga Pass was found to be a boundary between shelf waters to the east and open-ocean waters to the west. This can be seen in the surface salinity distribution along the island chain (Figure 15). Sharp fronts in surface salinity were observed associated with Unimak and Samalga Passes. The fresher water east of Samalga

Pass suggests that the Alaska Coastal Current influences the waters east of Samalga Pass while Alaskan Stream water influences the waters further west (Ladd et al., 2004). The difference in source waters in the two regions influences distributions of nutrients and biota in the different passes.

The changes in physical environment seen along the island chain result in changes in zooplankton populations (Coyle, 2004). The seasonal influences and the water mass influences on zooplankton species composition is shown in Figure 16a. The declines in abundance of Neocalanus plumchrus and N. flemingeri at Akutan2 and Unimak2 are due to their annual life cycle. They leave the surface waters and migrate down to depths over

300 m in May and June. Elevated abundances of Calanus marshallae and Acartia spp. at

Umnak, Akutan and Unimak Passes are due to their preference for warmer neretic conditions. Abundance of two species of euphausiid along the islands is shown in Figure

16b. Thysanoessa inermis prefers neretic water of Akutan, Unimak and Samalga Passes.

Euphausia pacifica prefers the open ocean conditions of the passes west of Samalga Pass.

15 The broad error bars are due to the very patchy distribution of these organisms, especially in the passes where tidally generated eddies can physically concentrate or disperse zooplankton.

The extent of the Alaska Coastal Current also operates as a biogeographical boundary for seabirds around Samalga Pass (Jahncke et al., 2004). Seabirds depending on coastal food webs (shearwaters and puffins) are more abundant east of Samalga whereas seabirds depending on oceanic food webs (fulmars and auklets) are more abundant west of

Samalga (Figure 17). West of Samalga fulmars and shearwaters consume oceanic copepods and shelf break species of euphausiids, east of Samalga both seabirds consume shelf species of euphausiids.

The effects of broad scale changes in ocean climate on Steller sea lion habitat are moderated through a number of indirect mechanisms. For example, increased storm activity may reduce the suitability of certain haulouts and rookeries, while bottom-up effects mediated through at least 3 trophic levels (i.e. phytoplankton, zooplankton, forage fish) have the potential to affect the distribution of Steller sea lion prey species.

Given the spatial distribution of the species, and the potential foraging distances of individuals (Figure 18), it is only through range-wide studies, encompassing areas of both decreased and increased suitability, that the effects of a changing climate can be fully understood (Gregr et al., 2004). The synthesis presented here provides the physical framework upon which a more detailed understanding of the processes affecting species distributions and habitat use can now be built.

5. Paleological Perspectives

16 Paleological studies provide a long-term perspective to changes seen in recent decades. Finney (2004) studied indicators of oceanic productivity in two sediment cores in the GoA, one from the GAK-4 site in the central Gulf shelf and one from the Bering

Sea. (Skan Bay). Opal represents diatom productivity and delta 13C organic matter represents all organic productivity. Increases in productivity would be indicated by an increase in either indicator. Variability occurred over the last 300 years for each region

(Figure 19). In the GoA productivity increased since the 1976-77 regime shift, while in the Bering Sea the signals are mixed. These regional differences need to be rationalized in the context regional changes of Steller sea lions.

Long-term changes in the north Pacific and southern Bering Sea ecosystems have also been the subject of intensive investigations using archaeological and anthropological data

(Maschner, 2004). The archaeological data indicate that there have been significant variations in the distributions of key species over the last 5000 years that only loosely correlate with broad changes in regional climatic regimes (Figure 20). The most notable is the Steller Sea Lion. While it is impossible to use archaeological data to determine absolute abundances of individual species, the hundreds of thousands of bones from archaeological sites allow the reconstruction of relative abundances because the ancient

Aleut were optimal foragers – species were harvested in numbers relative to their actual abundance on the landscape. Thus, changes in the proportions of phocid and otariid bones, for example, are indicators of actual changes in the proportions of these animals on the natural landscape. Centennial scale changes in species abundance are a measure of long-term and region-wide fluctuations in the marine environment.

17 Decadal scale changes in the marine ecosystem spanning nearly 150 years are identifiable using both ethnohistoric data and traditional ecological knowledge of local

Aleut fishermen. Based on Russian and early American accounts of the region, there have been two periods in the last 250 years, one in the 1870s and another in the 1790s, when there were few or no Steller Sea lions in many areas of the north Pacific, leading to widespread starvation for the indigenous peoples who depended on them. These depressions in the population levels cannot be correlated with any human-based harvesting of either the sea lions or their food sources. We have also found that Steller sea lions have undergone local extinctions due to catastrophic environmental events such as earthquakes, volcanic eruptions, and tsunami. A number of these are documented for

Unalaska, Sanak Island, the Shumagin Islands, and the Attu area over the last 200 years.

Traditional knowledge of local fishermen have demonstrated that the north Pacific ecosystem has undergone a series of disruptions over the last 100 years that appear to be a product of commercial fishing activities (Figure 21). For example, the north Pacific was heavily fished for cod between the 1880s and the mid 1930s, when the fishery collapsed.

This collapse created a cascade of events that led to the complete absence of cod in many areas south of the Alaska Peninsula between 1945 and 1970. During the period of few or no cod, shrimp and crab came to dominate the ecosystem to the point that cod where unable to recover because of crab predation on roe. After the shrimp and then later the crab stocks were decimated by overfishing, the cod were able to return to dominance in the system. Cod predation on crab has kept the crab from returning even though commercial fishing is no longer allowed. These events appear to be completely independent of changes in the Steller sea lion population indicating that there is little to

18 no relationship between fishing and sea lion mortality. This scenario is supported by numerous interviews and historical data.

Thus, archaeological and anthropological analyses have provided data for time scales that are currently not available in any other form of analysis. They demonstrated that the north Pacific and southern Bering seas have been dynamic, volatile, and subject to great fluctuations over the last hundreds to thousands of years. This requires that we evaluate our current models in order to determine where in one of these large cycles we are now positioned.

6. Summary

The decline of the Steller sea lion populations in the western Gulf of Alaska has been hypothesized to be a consequence of physical oceanographic changes due to the 1976-77 climate shift. New evidence is discussed here that substantiates this idea and links the physical environmental changes to their consequent effects on the health and fecundity of

Steller sea lions. Spatial and temporal variations in the ocean climate system are creating adaptive opportunities for high trophic levels, which is the underlying mechanism for the decline of the Steller sea lion populations in the western Gulf of Alaska. The climate forcing mechanism and the details of the mean and eddy oceanic response can explain both the temporal issue (populations decreased after the late 1970’s) and the spatial issue

(western, but not eastern, populations decreased) of the decline. The basin-wide climate changes have regionally sensitive impacts due to the biogeographic structures in the Gulf, which includes a transition point from coastal to open-ocean conditions at Samalga Pass westward along the Aleutian Islands. Upscaling from local complexities to broadscale

19 regularities is an important consideration in assessing these climate impacts on the ecosystem.

These are leftover bits and pieces………

(Waslowski) In summary, strong evidence exists for mesoscale eddy activity along the Alaskan Stream and for their contribution to circulation and hydrographic variability across Aleutian Island passes. However, long-term observations and more modeling studies of the Aleutian Island passes are needed to fully understand impacts of eddies, tides, and wind forcing on the biological environment not only related to primary productivity but also to higher trophic levels including Steller Sea Lions.

(Hunt) I am not certain that I have a specific bit of writing to contribute. We need to think about how climate might have affected the flow of water to and between the

Aleutians, as well as in the Gulf. Also, if there was a significant change in atmospheric circulation patterns, deposition of iron containing dust may have changed and thereby cut production in iron limited waters. I am concerned about not losing the information in the spatial patterns of population trajectories in the SSL. The temporal signal only tells part of the story. Our section here will be easier to put together when we see what others have come up with.

ACKNOWLEDGMENTS

The Cooperative Institute for Arctic Research (CIFAR) of the National Oceanic and

Atmospheric Administration provided funding for the suite of research efforts described here as well as for the synthesis workshop held December 4-5, 2004, in Newport Beach,

CA. The views expressed herein are those of the authors and do not necessarily reflect the views of NOAA or any of its subagencies.

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Legends

Figure 1. Cluster dendogram and map showing regional differences in long-term sea surface temperature variability in the Gulf of Alaska and eastern Bering Sea. These data are from COADS 1° summaries at the colored boxes (colors = clusters) over the period

1950-97. From Bograd et al. (2004).

25 Figure 2. Time series and factor loadings of the first four common trends (similar to principal components, but without the constraint of orthogonality) from a state-space decomposition of COADS sea surface temperature. These data are from COADS 1° summaries at the colored boxes over the period 1950-97. From Bograd et al. (2004).

Figure 3. (top) Nonlinear neural-net based PCA results from a multivariate dataset of 45 biotic indices (fishery and survey records) from the Bering Sea and Gulf of Alaska from

1965-2001. The explained variance in NL PC1 from this analysis is 39%, while PC1 from a linear analysis accounts for 27% of the variance in these data. (bottom) As in (top), but for 22 physical indices representing both large and local environmental processes.

The explained variance in NL PC1 from this analysis is 32%, while PC1 from a linear analysis accounts for 25% of the variance in these data.

The error bars for the NLPC scores are obtained from a “jittering” technique that involves the introduction of small errors into the data matrix prior to computation of the NL PCs.

From Marzban et al. (2004).

Figure 4. Non-parametric “Kendall’s Tau” statistic to assess the statistical significance of trends in (a) Steller sea lion data, (b) Pacific fishery data and (c) Pacific climate data. and

While a “trend" often means a linear trend, Kendall's tau assesses the trend nonlinearly

(though monotonically). The Z-statistic shown in this figure assesses the statistical significance of that trend such that if Z >= 2.575 then the hypothesis that the true tau is zero can be rejected with 99% confidence. From Marzban et al. (2004).

26 Figure 5.

Sea surface temperature (top panels) and sea level pressure and surface wind anomalies

(bottom panels) for winter periods before and after the 1976-77 regime shift, and for the most recent period. From Peterson and Schwing (2003).

Figure 6. (top) Mean winter (Dec-May) Ekman pumping in the Gulf of Alaska for the period 1964-75 from the NCAR/NCEP reanalysis. (bottom) Change in mean winter

Ekman pumping for the period 1977-97 relative to 1964-75. From Capotondi et al.

(2004).

Figure 7. Modeled Gulf of Alaska coastal discharge based on National Weather Service temperature and precipitation data (blue) with glacial ablation (red) since 1980 and the same model using NCEP Reanalysis data (magenta). A 5-year butterworth filter is applied to the data. From Royer (2004).

Figure 8. Trends in Gulf of Alaska coastal freshwater discharge, showing seasonal changes in the amplitude and phase of the discharge. From Royer (2004).

Figure 9. Wavelet analysis of the Gulf of Alaska coastal discharge, revealing changes in the low frequency variations of the signal. From Royer (2004).

27 Figure 10. Modeled pycnocline depth changes for the period 1977-97 relative to the period 1964-75, based on the depth of the sigma=26.4 isopycnal of the model hindcast.

From Capotondi et al. (2004).

Figure 11. Modeled surface velocity variance changes for the 10-year epochs 1967-1976

(top), 1979-1988 (middle), and the difference between the two epochs (bottom). Monthly mean anomalous surface current variance is contoured at 100 cm2/s2 intervals. From

Miller et al. (2004).

Figure 12. (a) Large zooplankton (mmol m-3) during 1977-1998 minus 1960-1976 in a physical-ecosystem ocean model’s top layer during May when the large zooplankton amount peaks. (b) The biomass of the four plankton classes (mmol m-3) in each calendar month during 1960-1976 (solid lines) and 1977-98 (dashed lines) in the Gulf of Alaska region [46ºN-58ºN, 160ºW-140ºW, indicated by the box in (a)]. From Alexander et al.

(2004).

Figure 13. Model March mixed layer depth (m) in 1977-1998 minus 1960-76. From

Alexander et al. (2004).

Figure 14. Depth-averaged (0-100 m) model velocity (cm/s) snapshots near the Amukta

Pass from the end of (a) March and (b) May of 1984 showing effect of a mesoscale eddy modeled within the Alaskan Stream on the flow across the Amukta Pass. The green solid contours represent bathymetry (m). (c) The salinity difference (ppt) along the section

28 marked “Cross Slope” between eddy (May) and no-eddy (March) conditions. From

Maslowski and Okkonen (2004).

Figure 15. Underway sea surface salinity (psu) during 2001 cruise. (a) Salinity plotted against latitude. (b) Salinity represented by colored line on map. Average salinity in the regions east of Unimak Pass, between Unimak and Samalga Passes, and between

Samalga Pass and Amukta Pass are noted. From Ladd et al. (2004).

Figure 16. Mean abundance of zooplankton species versus station seasonal influences and water mass influences on zooplankton species composition. Pass

Names: Um1 = Unimak (May), Ak1 = Akutan (May), Tng = Tananga (June), Sgm =

Seguam (June), Smg = Samalga (June), Un2 = Unimak (June), Ak2 = Akutan (June).

The error bars are 95% confidence intervals for power transformed mean abundance in the respective passes. From Coyle (2004).

Figure 17. Seabird abundances along the Aleutian Islands. From Jahncke et al. (2004).

Figure 18.

Predicted foraging distribution for adult female Steller sea lions in (a) summer and (b) winter, based on distance from rookeries (summer) and haulouts (winter), average counts, and depth. Foraging probability is shaded from high (red) to low (black). From Gregr

(2004).

29 Figure 19. Paleoproductivity indicators over the last 300 years derived from two cores in the Gulf of Alaska (GAK 4 site central gulf shelf) and Bering Sea (Skan Bay). Map in lower right shows locations. Two productivity indicators are plotted for each core: opal, which represents diatom productivity (blue) and delta 13C of organic matter (red) which represents all organic productivity. Increases in productivity would be indicated by an increase in either indicator. From Finney (2004).

Figure 20. Relative abundance of Steller sea lions based on archaeological data. From

Maschner (2004).

Figure 21. Schematic of traditional Aleut knowledge of recent North Pacific climate cycles. From Maschner (2004).