AN ABSTRACT OF THE THESIS OF Luke D. Whitman for the degree of Master of Science in Fisheries Science presented on November 18, 2010 Title: Variation in the Energy Density of Forage Fishes and Invertebrates from the Southeastern Bering Sea Abstract approved: Scott A. Heppell The quality and availability of forage fishes and invertebrates can affect the behavior and productivity of predators that rely on these resources. This study measures the proximate composition of forage fishes and invertebrates from the southeastern Bering Sea to estimate prey energy density (quality) using a method that is ecologically relevant. During the summers of 2008 and 2009, transect surveys were conducted near the Pribilof Islands in the southeastern Bering Sea to collect forage fishes and environmental data during the time when seabirds and fur seals were rearing their young on those islands. All forage species caught in sufficient numbers by trawling were analyzed for proximate composition to estimate prey quality, in terms of total energy density. Proximate composition of forage species from this area varied widely between years and among sampling locations. Much of the observed variation in prey energy density was explained by lipid content, which was inversely related to water content. Invertebrates exhibited greater variation in lipid content and energy density than did forage fishes, although their overall prey quality was similar to that of many forage fishes. Among forage fishes, northern lampfish (Stenobrachius leucopsarus) and northern smoothtongue (Leuroglossus schmidti) were found to have especially high lipid content and energy density. Estimates of total energy per individual for all prey types provide an alternate ranking of prey quality when compared to energy density, which may affect predators with certain foraging strategies. Juvenile walleye pollock (Theragra chalcogramma), of both age 0+ and age 1+, were the most numerically abundant forage fish species caught and are known to be an important prey type in this area. Age 0+ pollock caught in 2009 were larger and had higher energy density than those caught in 2008, however the relationship between fish size and energy density was the same during the two years. The energy density of individual pollock from both years varied among sampling locations, suggesting that the quality of individual prey items is affected by variation in the environment. However, point‐specific measurements of ocean habitat did not explain the observed variation in energy density of juvenile pollock. Model selection indicated that size was by far the most important determinant of total energy density in 0+ pollock. This study observed a wide range in energy density and variation in the quality of these forage fishes and invertebrates will likely affect habitat use by predators seeking to optimize their use of this important prey resource. © Copyright by Luke D. Whitman November 18, 2010 All Rights Reserved Variation in the Energy Density of Forage Fishes and Invertebrates from the Southeastern Bering Sea by Luke D. Whitman A THESIS submitted to Oregon State University In partial fulfillment of the requirements for the degree of Master of Science Presented November 18, 2010 Commencement June 2011 Master of Science thesis of Luke D. Whitman presented on November 18, 2010. APPROVED: Major Professor, representing Fisheries Science Head of the Department of Fisheries and Wildlife Dean of the Graduate School I understand that my thesis will become part of the permanent collection of Oregon State University libraries. My signature below authorizes release of my thesis to any reader upon request. Luke D. Whitman, Author ACKNOWLEDGEMENTS First, I thank my graduate committee; Scott Heppell, Kelly Benoit‐Bird, and Daniel Roby. They all provided guidance and support beyond my expectations. From the beginning, my entire committee was involved in helping me form hypotheses and plan analyses. Scott, as my advisor, offered advice and guidance seemingly every day. His advice as an editor was crucial to my success and he supported me in all aspects of my thesis. Kelly gave me a great deal of advice on how this study fit into ecosystem research on a broader scale and patiently explained to me the fundamentals of oceanography. Dan too provided invaluable advice on how to form the hypotheses for this study in addition to giving me tremendous support with the laboratory analysis. I thank the North Pacific Research Board who funded this entire project through a joint effort with the National Science Foundation. This agency provided gracious travel support so that I could attend meetings and conferences that contributed greatly to this study and my understanding of how it fit into the “big picture”. In addition, I thank the Department of Fisheries and Wildlife at Oregon State University for scholarships and administrative support. This research was part of a much larger ecosystem study and, as such, could not have been possible without a great deal of cooperation from the many good people involved. In particular, the field collections would not have been possible or nearly as enjoyable without everyone working together in often difficult conditions. I thank Kelly Benoit‐Bird and Chad Waluk for putting up with me for two months at sea while providing support in all aspects of my field collections. Nathan Jones and Brian Hoover were the bird observers in 2008 and 2009, respectively, and I thank them both for their friendship and support while at sea. Also, I thank Greg Kowalke for always being there to lend a helping hand when I was sampling fish or working through problems. I thank Neal McIntosh for also helping to sample fish while at sea, her help with analysis and in putting together presentations to share this work at various conferences. I thank Allison Evans for her great support as a friend, statistical consultant, and editor, even when I had frantic questions for her at the last minute. I thank Stephanie Archer for all her help at sea and, especially, with proximate composition analysis. She was always there when I needed help preparing samples at the last minute and I could not have completed so much laboratory analysis without her help. I thank Don Lyons for his advice on my energy density calculations, his help checking my data, and guidance on the laboratory techniques for proximate composition analysis. Finally, I thank my partner Alison Dauble for her love and support in all aspects of my life, including my research. She gave me great advice on both my statistical analysis and this manuscript. In addition, I thank my mother, Sandra Dudley, and my brother, Leif, for always being there to listen to me when I needed to talk about my work as a graduate student, even though they were both on the other side of the country. They both continue to offer their great love and support no matter where my career takes me. CONTRIBUTION OF AUTHORS Scott Heppell, Kelly Benoit‐Bird, and Daniel Roby contributed to the overall study design and edited all chapters of this thesis. Specifically, Daniel Roby and Scott Heppell helped form the hypotheses on differences in proximate composition and contributed most to the development of chapter 1. Dan Roby provided space, materials, and support for laboratory analysis. Scott Heppell and Kelly Benoit‐Bird helped me develop the hypothesis on the effects of temperature and other environmental variables on energy density of walleye pollock in chapter 2 and helped with analysis to test these hypotheses. Scott Heppell and Kelly Benoit‐Bird both led the sampling conducted at sea. TABLE OF CONTENTS CHAPTER 1: GENERAL INTRODUCTION .......................................................... References ...................................................................................................... CHAPTER 2: PROXIMATE COMPOSITION OF FORAGE FISHES AND INVERTEBRATES FROM THE SOUTHEASTERN BERING SEA ................ Introduction ................................................................................................... Methods ........................................................................................................... Results .............................................................................................................. Discussion ....................................................................................................... References ...................................................................................................... CHAPTER 3: VARIATION IN THE ENERGY DENSITY OF JUVENILE WALLEYE POLLOCK (THERAGRA CHALCOGRAMMA) FROM THE SOUTHEASTERN BERING SEA .............................................................................. Introduction .................................................................................................... Methods ............................................................................................................ Results ............................................................................................................... Discussion ........................................................................................................ References ....................................................................................................... CHAPTER 4: GENERAL CONCLUSIONS ............................................................ References ....................................................................................................... Page 2 5 8 8 12 19 32 37 41 41 42 50 55 61 64 67 Figure LIST OF FIGURES Page Figure 2.1. Study area and sampling coverage in the southeastern Bering Sea during the 2008 and 2009 field seasons. Shading indicates the three sampling regions of Middle domain, Outer Shelf, and Slope while the hatch marks show each 10‐km sampling transect. The Pribilof Islands and Bogoslof Island are indicated in white and labeled ………………………………............................................................. 14 Figure 2.2. Linear relationship between % lipid content and total energy density for all forage fish and invertebrate species measured from trawls conducted in the southeastern Bering Sea ……………………. 23 Figure 2.3. Linear relationship between % lipid content and % water content for all forage fish and invertebrate species combined from trawls conducted in the southeastern Bering Sea …………………… 24 Figure 2.4. Simple linear relationship between lipid content (% Lipid content) as a percent of dry mass and ash‐free lean dry matter content (% Ash‐free lean dry matter) of total dry mass for all forage types from 2008 and 2009 analyzed for proximate composition………………. 26 Figure 2.5. Mean proximate composition as a percent of total dry mass with standard deviation for all forage species collected in 2008 and 2009 in the southeastern Bering Sea and measured for proximate composition ………………………..................................................... 27 Figure 2.6. Linear relationship between fork length and total energy density for northern lampfish (Stenobrachius leucopsarus) collected in 2008 and 2009 in the southeastern Bering Sea ………………................... 29 Figure 2.7. Linear relationship between fork length and mean total energy density for northern smoothtongue (Leuroglossus schmidti) caught at different locations in the southeastern Bering Sea during 2008 and 2009…………………………………………………………............................... 30 Figure 3.1. Study area and sampling coverage in the southeastern Bering Sea during both 2008 and 2009. Shading indicates the three sampling regions of Middle domain, Outer Shelf, and Slope, while the hatch marks show each 10‐km sampling transect. The Pribilof Islands and Bogoslof Island are indicated in white and labeled accordingly.....................................................................………………………………… 45 Figure 3.2 Simple linear regression of average standard length and average total energy density by year, with 95% confidence intervals, for juvenile walleye pollock (Theragra chalcogramma) collected in the southeastern Bering Sea …….................................................. 52 Figure 3.3. Linear relationship between total length and total energy density of age 1+ juvenile walleye pollock (Theragra chalcogramma). Each point represents an individual measurement of energy density from a different sampling location in the southeastern Bering Sea ……………………………………………………….............. 55 LIST OF TABLES Table Page Table 2.1. Mean total energy density, lipid content, and ash‐free lean dry matter (AFLDM) content for forage species sampled from the southeastern Bering Sea in 2008 and 2009 ……………………………………. 20 Table 3.1. Initial explanatory variables used in stepwise regression to predict total energy density of age 0+ walleye pollock (Theragra chalcogramma) ……………..................................................................... 52 Table 3.2. Best‐fit models to predict total energy content of age 0+ pollock (Theragra chalcogramma) collected in the southeastern Bering Sea ….................................................................................................................... 53 Table 3.3 Coefficients and statistics for explanatory variables used in Model 1 to predict total energy content of age 0+ pollock (Theragra chalcogramma) collected in the southeastern Bering Sea ……………….... 53 Variation in the Energy Density of Forage Fishes and Invertebrates from the Southeastern Bering Sea 2 Chapter 1: General Introduction Populations of invertebrates and forage fishes are thought to be strongly influenced by ocean conditions in the Bering Sea, which may in turn affect the upper trophic level predators that rely on this prey base. Fish populations in the North Pacific fluctuate across annual and decadal scales in response to shifting temperature regimes (Beamish et al. 2004) and these changes influence the composition of fish communities (Hollowed and Wooster 1995; Litzow et al. 2006). Warmer ocean regimes can increase populations of groundfish and salmon in Alaskan waters, while decreasing the abundance of lipid‐rich forage fishes such as capelin (Mallotus villosus) and herring (Clupea pallasi; Francis and Hare 1994). In addition, the water temperature associated with different climate regimes can affect the energetic demands of both predators and prey (Hoof and Peterson 2006). Ocean conditions may cause mismatches between marine predators and the prey they require for survival and reproduction, although highly abundant prey may compensate for these mismatches (Cushing 1980; Cushing 1990; Wassmann 1998). Furthermore, changes in prey distribution can alter the time and energy predators spend foraging, potentially affecting their ability to reproduce successfully (Litzow et al. 2006; Rosen et al. 2007). Energy density, or prey quality, of forage fishes can vary greatly within a species or among different species (Van Pelt et al. 1997; Payne et al. 1999; Anthony et al. 2000; Kitts et al. 2004; Ball et al. 2007). These differences affect the 3 energy available from the ecosystem, which can limit the productivity of predator populations, including seabirds (Roby 1991; Litzow et al. 2002; Romano et al. 2006). Moreover, low prey quality, not just quantity, has been studied intensively as a possible cause of declines in marine mammal populations in the North Pacific Ocean (Rosen and Trites 2000; Benson and Trites 2002; Osterblom et al. 2008). This so called “junk food” hypothesis proposes that low prey quality (i.e., low lipid content) can limit or reduce populations of predators at higher trophic levels because their prey does not provide the energy needed for growth and reproduction. If this is the case, changes in species composition brought about by shifting ocean regimes may greatly affect the predators in the ecosystem, including commercially important fishes that rely on these prey species. The Pribilof Islands are located near the edge of the continental shelf in the southeastern Bering Sea where the deep water off the shelf causes a transition zone, or front, that limits the movement of water in this area (Kachel et al. 2002). This front increases primary productivity by holding nutrients that support large amounts of phytoplankton (Hunt et al. 1996; Brodeur et al. 2002). As a result, the ocean currents around the Pribilof Islands offer abundant zooplankton as food to larval and forage fishes (Ciannelli et al. 2002; Flint and Sukhanova 2002), which in turn support large breeding colonies of seabirds and rookeries of northern fur seals (Callorhinus ursinus) on the Pribilof Islands. Because of the productivity of this area and the unique location of the islands, almost the entire world population of northern fur seals returns to the Pribilof 4 Islands in summer to breed and forage in the southeastern Bering Sea (Angliss and Lodge 2004). However, in recent years, the numbers of fur seals and some populations of seabirds on the Pribilof Islands have undergone sharp declines (Byrd et al. 2007). A number of different hypotheses have been suggested to explain these observed declines and, in particular, changes in prey quality are thought to strongly influence the abundance of these predators, especially during reproduction. Recent studies have begun to address the potential effects of climate change on the marine ecosystem of the Bering Sea; however, no study has previously measured energy densities of all forage species in this area in a way that will allow researchers to determine local differences in total available energy, as well as overall variation in available energy across the region. My thesis research is intended to determine the prey quality of forage fishes and invertebrates from the southeastern Bering Sea in a way that can be related to the rest of the ecosystem. This information is fundamental to the understanding of how this marine ecosystem functions and may have utility for predicting and detecting future ecosystem changes. 5 References: Angliss, R.P., and K.L. Lodge. 2004. Alaska Marine Mammal Stock Assessments, 2003. In U.S. Department of Commerce, NOAA Technical Memorandum, NMFS‐
AFSC‐144. NOAA Technical Memorandum. NMFS‐AFSC‐124, 230 pp. Anthony, J.A., D.D. Roby, and K.R. Turco. 2000. Lipid content and energy density of forage fishes from the northern Gulf of Alaska. Journal of Experimental Marine Biology and Ecology 248: 53‐78. Ball, J.R., D. Esler, and J.A. Schmutz. 2007. Proximate composition, energetic value, and relative abundance of prey fish from the inshore eastern Bering Sea: implications for pisciviorous predators. Polar Biology 30: 699‐708. Beamish, R.J., A.J. Benson, R.M. Sweeting, C.M. Neville. 2004. Regimes and the history of the major fisheries off Canada’s west coast. Progress in Oceanography 60: 355‐385. Benson, A.J. and A.W. Trites. 2002. Ecological effects of regime shifts in the Bering Sea and eastern north Pacific ocean. Fish and Fisheries 3: 95‐113. Brodeur, R.D., M.T. Wilson, L. Ciannelli, M. Doyle, and J.M. Napp. 2002. Interannual and regional variability in distribution and ecology of juvenile pollock and their prey in frontal structures of the Bering Sea. Deep‐Sea Research II 49: 6051‐6067. Byrd, G.V., W.J. Sydeman, H.M. Renner, and S. Minobe. 2007. Responses of piscivorous seabirds at the Pribilof Islands to ocean climate. Deep Sea Research II 55: 1856‐1867. Ciannelli, L., A.J. Paul, and R.D. Brodeur. 2002. Regional, interannual, and size‐
related variation of age 0 walleye pollock whole body energy content around the Pribilof Islands, Bering Sea. Journal of Fish Biology 60: 1267‐1279. Cushing, D.H. 1980. The decline of the herring stocks and the gadoid outburst. Journal du Conseil International de l’Exploration de la Mer 39: 70‐81. Cushing, D.H. 1990. Plankton production and year‐class strength in fish populations: an update of the match/mismatch hypothesis. Advances in Marine Biology 26: 250‐293. 6 Flint, M.V., and I.N. Sukhanova. 2002. The influence of the coastal fronts around the Pribilof Islands (Bering Sea) on the distribution and dynamics of zooplankton. Oceanology 42(S1): 63–78. Francis, R.C. and S.R. Hare. 1994. Decadal‐scale regime shifts in large marine ecosystems of the northeast Pacific: a case for historical science. Fisheries Oceanography 3: 279‐291. Hollowed, A.B., and W.S. Wooster. 1995. Decadal‐scale variations in the eastern subarctic Pacific II: Response of northeast Pacific fish stocks. In Climate change and northern fish populations. Canadian Special Publications in Fisheries and Aquatic Sciences 121: 373‐385. Hoof, R.C., and W.T. Peterson. 2006. Copepod biodiversity as an indicator of changes in ocean and climate conditions of the northern California current ecosystem. Limnology and Oceanography 51: 2607‐2620. Hunt, G.L., A.S. Kitaysky, and M.B. Decker. 1996. Changes in the distribution and size of juvenile walleye pollock, Theragra chalcogrammu, as indicated by seabird diets at the Pribilof Islands and by bottom trawl surveys in the eastern Bering Sea, 1975 to 1993. In U.S. Department of Commerce, NOAA Technical Memorandum, NMFS 126. Kachel, N.B., G.L. Hunt, Jr., S.A. Salo, J.D. Schumacher, P.J. Stabeno, and T.E. Whitledge. 2002. Characteristics and variability of the inner front of the southeastern Bering Sea. Deep‐Sea Research II: Topical studies in Oceanography 49: 5889‐5909 Kitts, D.D., M.D. Huynh, C. Hu, and A.W. Trites. 2004. Season variation in nutrient composition of Alaskan walleye pollock. Canadian Journal of Zoology 82: 1408‐
1415. Litzow, M.A., J.F. Piatt, A.K. Prichard, and D.D. Roby. 2002. Response of pigeon guillemots to variable abundance of high‐lipid and low‐lipid prey. Oecologia 132: 286‐295. Litzow, M.A., K.M. Bailey, F.G. Prahl, and R. Heintz. 2006. Climate regime shifts and reorganization of fish communities: the essential fatty acid limitation hypothesis. Marine Ecology Progress Series 315: 1‐11. Osterblom, H., O. Olsson, T. Blenckner, and R.W. Furness. 2008. Junk‐food in marine ecosystems. Oikos 117: 967‐977. 7 Payne, S.A., B.A. Johnson, and R.S. Otto. 1999. Proximate composition of some northeastern Pacific forage fish species. Fisheries Oceanography 8: 159‐177. Roby, D.D. 1991. Diet and postnatal energetics in two convergent taxa of plankton‐feeding seabirds. Auk 108: 131‐146 Romano, M.D., J.F. Piatt, and D.D. Roby. 2006. Testing the junk‐food hypothesis on marine birds: effects of prey type on growth and development. Waterbirds 29: 407‐414. Rosen, D.A., and A.W. Trites. 2000. Pollock and the decline of Steller sea lions: testing the junk food hypothesis. Canadian Journal of Zoology 78: 1243‐1250. Rosen, D.A., A.J. Winship, and L.A. Hoopes. 2007. Thermal and digestive constraints to foraging behavior in marine mammals. Philosophical Transactions of the Royal British Society 362: 2151‐2168. Van Pelt, T.J., J.F. Piatt, B.K. Lance, and D.D. Roby. 1997. Proximate composition and energy density of some North Pacific forage fishes. Comparative Biochemical Physiology 118A: 1393‐1398. Wassmann, P. 1998. Retention versus export food chains: processes controlling sinking loss from marine pelagic systems. Hydrobiologia 363: 29–57. 8 Chapter 2: Proximate composition of forage fishes and invertebrates from the southeastern Bering Sea Abstract The energy density of prey species can vary greatly and such differences may affect the predators that rely on these prey. The Pribilof and Bogoslof Islands in the southeastern Bering Sea are home to large colonies of seabirds and northern fur seals (Callorhinus ursinus), although no study has measured the energy density of forage fishes and invertebrates available to these predators. This study estimates the energy density of all forage species caught around these islands in a way that will allow for comparison among prey groups and within species. Lipid content accounted for most of the variation in total energy density, while differences in proximate composition were observed among the different groups of prey. Also, the relationship between size and energy density was different among different types of prey fishes. Certain prey types had much higher energy density than others, although estimates of energy content per individual prey item may influence their value depending on the feeding strategy of different predators. This study demonstrates a wide range of prey quality in forage fishes and invertebrates in the southeastern Bering Sea and estimates provided here may be combined with other data to better understand the foraging patterns of predators in this area. 9 Introduction A forage species is defined as a prey that is commonly consumed by higher trophic levels, which follows the definition of Anthony et al. (2000). The forage fishes and invertebrates in the waters around the Pribilof Islands in the southeastern Bering Sea support large colonies of seabirds and northern fur seals (Callorhinus ursinus), and also support large commercial fisheries, including walleye pollock (Theragra chalcogramma). In addition, these species support other marine mammals and piscivorous fishes in the waters around the islands. Fish recruitment in the Bering Sea and North Pacific Ocean can vary greatly on both annual and decadal time scales depending on the prevailing ocean conditions (Beamish and Bullion 1995; Megrey et al. 2009). Ocean condition in these areas tends to oscillate between regimes of predominately warm or cold temperatures. Such regimes can affect abundance as well as quality of forage fishes and invertebrates that are the primary prey of upper trophic level predators (Braun and Hunt 1983; Springer et al. 1986; Litzow et al. 2002). The breeding seasons of top predators, such as seabirds and fur seals, coincide with seasonal increases in their food supply, primarily forage fishes and invertebrates, as reproduction can exert high energy demands on these predators. Seabird populations can easily become energy limited because of the high energy requirements of both flight and reproduction (Drent and Daan 1980; Roby 1991). Growth and reproduction of marine mammals may also be reduced 10 by continued reliance on low‐quality prey (Rosen and Trites 2000). For these reasons, prey distributions have the potential to strongly affect the foraging success and population abundance of marine predators (Furness and Barrett 1985; Payne et al. 1986; Kappes et al. 2010). Prey species vary greatly in their abundance and distribution on multiple time scales, which requires predators to balance the costs and benefits of targeting certain prey types (Stephens and Krebs 1986). Patches of prey, defined as significant spatial variation in biomass (Downes 1990), can vary greatly in the marine environment, adding another dimension to how predators make foraging decisions. Patches can vary by size (Davis et al. 1991, Fauchald et al. 2000) and in the amount of time they persist (Cushing 1961). Understanding patches in terms of distribution and overall prey quality may help explain how predators make these complex foraging decisions. Differences in prey quality can also affect selection by top predators, as has been demonstrated through changes in foraging behavior of thick‐billed murres (Uria lomvia) in Hudson Bay (Elliott et al. 2009) and black‐headed gulls (Larus ridibundus) in the North Sea (Schwemmer and Garthe 2008). It has been shown that food quality can affect not only individual fitness but also top predators at a population level as well. This is the proposed “junk food” hypothesis, which suggests that the energy density of low quality prey is not sufficient to meet the energetic demands of reproduction, even if the prey are particularly abundant (Merrick et al. 1997; Rosen and Trites 2000; Litzow et al. 11 2002). For these reasons, prey quality is expected to affect how these predators select prey, in addition to the availability of the various prey species. This study measures the energy density of all forage species captured, including both fishes and invertebrates, in the southeastern Bering Sea from the area around the Pribilof Islands, which support large fur seal rookeries and seabird breeding colonies. For the purpose of this study, prey quality is defined as energy density (kJ g‐1 wet mass) while energy density per gram dry mass is provided to allow for comparison with other studies. Energy density on a per unit wet mass basis was selected as the metric of prey quality because this value is more ecologically relevant than energy density per gram dry mass when considering the energetic returns of foraging to top predators (Montevecchi and Piatt 1987). The amount and type of lipid strongly affects overall energy density of prey (Anthony et al. 2000), as lipid has roughly twice the energy density of protein and carbohydrate (Schmidt‐Nielsen 1997). High lipid intake by a predator provides energy that is easily metabolized for maintenance and thermoregulation, leaving dietary protein for use in growth of tissues and body protein turnover (Roby 1991). Energy density of the diet is expected to have an even stronger effect on breeding predators, when in addition to their own energy demands parents must also support those of their growing young. Bering Sea walleye pollock are a well‐studied species because of the importance of juvenile pollock as prey for many predators and the large fishery 12 that the Bering Sea pollock population sustains. However, while other forage species may be of great importance to particular predators in the Bering Sea, few studies have looked at the energy density of these other prey. Furthermore, there appear to be no published energy density values for euphausiids or amphipods from the southeastern Bering Sea. Squid (Gonatus spp.) are also considered an important prey type of marine mammals and seabirds in the Bering Sea (Sinclair et al. 2008; Ito et al. 2010), although few studies have measured the energy density of this prey. Finally, few studies have integrated the prey quality of both forage fishes and invertebrates from the Bering Sea in a way that is ecologically relevant to top predators. Prey sampling covered a large area, including distinct regions based on local oceanography, as well as different life stages. Four primary hypotheses were tested: 1. Proximate composition and energy density vary among and within the different forage species 2. Differences in energy density among prey types are largely determined by differences in lipid content, as described by Anthony et al. (2000) and Ball et al. (2007) 3. Individual prey size, sampling area, and year explain a significant proportion of observed intra‐specific variation in energy density 13 4. Proximate composition and energy density of forage fishes differs from that of invertebrate prey types, such as euphausiids and squid Methods Site description and timing Sampling took place in the summers of 2008 and 2009 during the seabird nesting season and while northern fur seals were at their rookeries (mid‐July to mid‐August). In 2008, the study area was defined by a 200‐km radius circle around the Pribilof Islands with its center at approximately 57000’00” N, 170000’00” W (Figure 1). The Pribilof Islands include St. Paul and St. George Islands, both of which are home to large rookeries of northern fur seals, and large breeding colonies of black‐legged kittiwakes (Rissa tridactyla) and thick‐billed murres (Angliss and Lodge 2004; Byrd et al. 2007). In 2009, the sampling area was expanded to include the waters north of Bogoslof Island, which also supports a fur seal rookery and breeding colonies of kittiwakes and murres (Figure 1). The study area was divided into three zones based on depth and proximity to the shelf edge because these zones have different hydrographic features that influence nutrient mixing in the water column (Stabeno et al. 1999; Hunt and Stabeno 2002). Seafloor depths of 50‐100 m were designated as the Middle Shelf, depths of 100 – 200 m as the Outer Shelf, and depths greater than 200 m as the 14 Slope. The latter zone included areas that are part of the deep‐water basin of the Bering Sea. 2008: 110 transects
2009: 141 transects
Figure 2.1. Study area and sampling coverage in the southeastern Bering Sea during the 2008 and 2009 field seasons. Shading indicates the three sampling regions of Middle Domain, Outer Shelf, and Slope while the hatch marks show each 10­km sampling transect. The Pribilof Islands and Bogoslof Island are indicated in white and labeled. 15 Sample collection Collection was based on transects that were randomly distributed, both in starting location and direction, throughout the study area, with an equal number of random transects assigned to each zone in 2008. The number of random transects in each zone was not equal in 2009 due to logistic constraints of covering a larger sampling area and inclement weather. In addition, adaptive transects were used to sample areas that exhibited interesting biological or physical features, as determined during the normal course of sampling, and were not necessarily distributed equally among the three zones. Each transect was 10 km in length and each was sampled in the same manner throughout the study area. To collect fishes and invertebrates, a mid‐water Marinovich trawl was fished for the first 20 min of each sampling transect, or approximately the first 1.5‐2 km of the transect length. The Marinovich trawl measured approximately 10.5 m in length, with a mouth opening that was an 8‐m square. The cod end was lined with a 0.3‐cm mesh liner and the trawl was pulled behind 1.5 m X 2.1 m mid‐water trawl doors. The fishing depth of each trawl was informed by hydroacoustics; typically the trawl was set at a depth identified as having interesting acoustic scattering. In the absence of a layer worthy of being targeted, trawl tows were stair‐stepped from a depth of 100 m (or within 5 m of the bottom) up to the surface, fishing at 10‐m intervals for approximately 2 min each. The trawl was fished to a maximum depth of 100 m. 16 Fishes and invertebrates caught in the trawl were identified to the lowest possible taxonomic level (typically to species for fishes, but to general taxonomic groups for invertebrates), sorted, enumerated, and measured at sea. All fishes were measured in mm standard length (SL). After sampling, specimens were immediately frozen to ‐20 degrees Celsius (oC) for further analysis. As needed, species verifications occurred in the laboratory following the conclusion of the sampling period. Walleye pollock were divided into the appropriate size‐age classes before proximate composition analysis, which was based on a length frequency distribution of all pollock measured. No pollock were measured between 66 and 98 mm SL, suggesting that this was the cut‐off between age groups. As a result, all pollock less than 66 mm SL were considered age 0+, while pollock greater than 98 mm SL were considered age 1+. This relationship was generally supported by published data (Nishimura and Yamada 1988; Brown and Bailey 1992). Other fish species were sampled only as age 0+, age 1+, or adults because generally the number of individuals present precluded fine scale divisions for analysis. Invertebrates were pooled by general taxonomic groupings for analysis regardless of size, as there is currently no method to determine the age of these species. 17 300
Frequency
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Standard Length (mm)
Fig. 2.1. Length frequency distribution of all walleye pollock caught in the trawl during both 2008 and 2009. Proximate composition analysis Lipid content and energy density of all prey species were determined following the methods of Anthony et al. (2000) and Reynolds and Kunz (2001). Many species were too small to be analyzed individually, so individuals were pooled to reach a wet mass of approximately 12 g, which typically provides 2.0 – 3.5 g of dry mass for analysis. Larger adult fishes and squid were analyzed as whole individuals. Proximate composition analysis was conducted on three replicates of each sample, when sufficient sample mass was available. Otherwise, 18 the available wet mass of each prey type was divided into as many 12‐g aliquots as possible and analyzed in the same manner. Samples were thawed and then weighed on an analytical balance to the nearest 0.0001 g to determine wet mass. The samples were then dried to a constant mass in a convection oven set at 60oC to determine water content, which is calculated as the difference between wet and dry mass. The dried sample was then completely homogenized with mortar and pestle to prepare the sample for lipid extraction. Total lipids were extracted from the dried samples using a Soxhlet apparatus and a solvent system of 7:2 (v/v) hexane/isopropyl alcohol over a period of 12 h. Extracted samples were placed in convection ovens for 24 h to remove solvents and were re‐weighed on an analytical balance to ± 0.0001 g to determine lipid content by subtraction. Lean dry samples were then incinerated for 12 hours in a muffle furnace set at 550oC to ash the remaining sample. Finally, samples were re‐weighed on an analytical balance to ± 0.0001 g to determine ash‐free lean dry mass by subtraction. Water content (the difference between wet mass and dry mass) was expressed as a percentage of total fresh mass by divided by fresh sample mass and multiplying by 100. Total lipid content was expressed as a percent of dry mass by subtracting lean dry mass of the sample (after solvent extraction) from the sample dry mass, dividing by dry sample mass, and multiplying by 100. Ash‐
free lean dry matter (AFLDM) was expressed as a percent of lean dry mass by taking the difference between lean dry mass of the sample and ash mass, dividing 19 by lean dry mass, and multiplying by 100. AFLDM is used to estimate the amount of protein in the sample. The ashing process allows the subtracting of the non‐
protein material from lean dry mass to provide a more accurate estimate of total protein content of the sample. The ash is composed of minerals, primarily from the endoskeleton or exoskeleton of the organism. Based on previous work (Sidwell et al. 1974, Stansby 1976, Craig et al. 1978), the amount of carbohydrates was assumed to be a negligible proportion of overall sample mass. Published average energy equivalents of lipids (39.3 kJ g‐1) and proteins (17.8 kJ g‐1, assuming uricotelic predators) were used to calculate the energy density of all forage species (Schmidt‐Nielsen 1997). The following equation was used to calculate the energy density of individual or pooled samples of forage fishes and invertebrates, based on total wet mass: Energy density (kJ g‐1 wet mass) = (1 – WF)*([LF * 39.3] + [AFLDMF * 17.8]), where the water fraction of wet mass is represented by WF and the lipid fraction of the dry mass is represented by LF. Ash‐free lean dry matter as a fraction of total dry mass of the prey sample is represented by the abbreviation AFLDMF. Energy density based on dry mass of the same samples was calculated by: Energy density (kJ g‐1 dry mass) = (LF * 39.3) + (AFLDMF * 17.8) 20 Throughout the following sections, “energy density” refers to total energy density expressed as kJ g‐1 wet mass while “lipid content” refers to the percent total lipids of dry mass of the sample. Energy density based on dry mass is used at times for comparison to published data on energy values of certain prey species. Energy density values were multiplied by the mass of prey sample in each aliquot to calculate the total energy per aliquot. This value was then divided by the number of individual prey in the aliquot to calculate estimates of total energy density per individual fresh prey. Analysis Data were analyzed in R (http:/r‐project.org) and S‐PlusTM (TIBCO Software Inc.). A level of α = 0.05 was used as the level of statistical significance for all tests. All data were tested for normality and equality of variance before further statistical analyses. One‐way analysis of variance (ANOVA) was used to determine if annual and regional groups of forage species were different and, if significant, Tukey’s multiple comparison tests were used to distinguish specific differences among the groups. Simple linear regression was used to determine relationships between the different measures of proximate composition and between length and energy density in certain prey species. 21 Results Energy Density Mean total energy density differed among the 9 prey species sampled in numbers that allowed for meaningful comparison (ANOVA, p‐value < 0.001), varying from as low as 1.75 kJ g‐1 for age 0+ Greenland halibut (Reinhardtius hippoglossoides) to a high of 10.28 kJ g‐1 for northern lampfish (Stenobrachius leucopsarus, Table 2.1). The majority of prey types (n = 6) had a mean total energy density between 3.30 kJ g‐1 and 4.94 kJ g‐1. Overall, mean energy density was different among the larger taxonomic groups of fish, squid, and euphausiids (ANOVA, p < 0.001). The squid (Gonatus spp.) and euphausiids (Euphausiidae) had higher total energy density than fish species averaged together (Tukey’s multiple comparison, p = 0.006 and < 0.001, respectively; Table 2.1).
22 22 Table 2.1. Mean water content, lipid content, AFLDM, total energy density per gram of both wet and dry mass for forage species sampled from the southeastern Bering Sea in 2008 and 2009. Species Age 0+ Walleye pollock Average % Water Content of wet Mass (±SD) Average % Lipid of Dry Mass (±SD) Average % AFLDM of Dry Mass (±SD) Average Total Energy Density, kJ g‐
1
dry mass (±SD) Average Total Energy Density, kJ g‐1 wet mass (±SD) Average Total Energy per fresh Individual, kJ (±SD) 80.71 (±0.932) 10.60 (±2.768) 83.07 (±4.254) 17.228 (±0.716) 3.363 (±0.285) 1.735 (±0.0891) 76.93 (±0.985) 21.47 (±3.602) 86.19 (±1.269) 19.872 (±1.125) 4.732 (±0.387) 99.482 (±17.355) 80.68 (±1.630) 18.63 (±1.377) 74.92 (±7.200) 18.171 (±1.145) 3.522 (±0.466) 0.783 (±0.071) 80.47 (±1.527) 9.19 (±3.069) 84.77 (±2.700) 17.308 (±0.982) 3.388 (±0.402) 1.192 (±0.082) (n = 210) Age 1+ Walleye pollock (n = 44) Amphipods (n = 25) Arrowtooth flounder (n = 31) 23 23 Euphausiids (n = 124) 75.69 (±2.988) 27.12 (±10.366) 83.02 (±2.740) 21.426 (±1.225) 5.259 (±1.225) 0.504 (±0.131) 83.49 (±1.709) 11.57 (±1.483) 82.87 (±0.757) 17.608 (±0.368) 2.913 (±0.280) 0.775 (±0.048) 65.59 (±0.759) 49.17 (±6.615) 85.88 (±0.606) 26.335 (±3.404) 9.324 (±0.623) 11.366 (±2.020) 76.60 (±4.073) 36.04 (±8.191) 85.73 (±1.001) 23.897 (±1.989) 5.672 (±1.440) 26.141 (±3.974) 77.18 (±2.571) 20.22 (±9.117) 88.92 (±1.033) 20.762 (±2.514) 4.746 (±1.100) 18.258 (±3.572) Greenland halibut (n = 24) Northern lampfish (n = 31) Northern smoothtongue (n = 33) Squid (n = 97) 24 Lipid content Lipid content was the driver for differences in observed total energy density among prey types, explaining 86% of the variation observed in energy density across all prey types (SLR, p < 0.001, R2 = 0.861, Fig. 2). The lipid content of dry mass differed among the different prey types (ANOVA, p < 0.001; Table 1), from a low of 4% in some arrowtooth flounder (Atheresthes stomias) to as high as 55% in northern lampfish from certain locations. Most species or groups had total lipid content between 10% and 25%. Lipid content differed among the three taxonomic groups of fishes, squid, and euphausiids (ANOVA, p < 0.001). Euphausiids had a higher average lipid content than either squid or fish (Tukey’s multiple comparison, p = 0.001 and < 0.001, respectively). Northern lampfish and northern smoothtongue (Leuroglossus schmidti), both deepwater species, had the highest lipid content among all forage species. Northern lampfish collected at different locations (n = 5) had lipid contents that ranged from 34% to 55%, while northern smoothtongue had lipid contents between 22% and 49% among the different sampling sites (n = 7) where they were caught in the trawl. Age 0+ walleye pollock, arrowtooth flounder, and Greenland halibut all had similarly low lipid contents that varied between 4% (Greenland halibut) and 16% (Age 0+ walleye pollock). Overall, Age 1+ pollock had higher lipid content that averaged 22%. 25 The widest range of lipid contents was observed in the invertebrate samples. Euphausiids varied between 9% and 50% lipid content, while squid varied between 9% and 52%. The amphipod species Themisto libellula was the only amphipod caught in large numbers and analyzed for proximate composition. This invertebrate species had a much narrow range of lipid contents than euphausiids, between 12 and 17% lipid (Table 1). Total energy density (kJ g-1 wet mass)
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Fig. 2.3. Linear relationship between % lipid content and total energy density for all forage fish and invertebrate species measured from trawls conducted in the southeastern Bering Sea 26 Water content Water content of prey samples was inversely related to lipid content (SLR, p < 0.001, R2 = ‐0.743, Fig. 3). Water content is expressed as a percentage of fresh mass because it indicates the amount of water in muscle and other tissue of the fresh forage species. The mean water content of all prey types varied significantly (ANOVA, p < 0.001), from a low of 64% (of fresh mass or lean mass?) in northern lampfish to a high of 86% in larval Greenland halibut. Most prey samples had water contents between 76% and 81%. In addition, water content was different across the three taxonomic groups (ANOVA, p < 0.001). Fishes had higher average water content than either euphausiids or squid (Tukey’s multiple comparison, p < 0.001 and = 0.001, respectively). 27 % Lipid content of dry mass
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Fig. 2.4. Linear relationship between % lipid content and % water content for all forage fish and invertebrate prey types combined from trawls conducted in the southeastern Bering Sea. Ash­free lean dry matter The majority of species, including invertebrates, averaged between 82% and 86% AFLDM of the total dry mass of the prey sample, but varied significantly by prey type (ANOVA, p < 0.001), ranging from a minimum of 70% in the amphipod T. libellula to 91% for arctic squid (Gonatus borealis). Euphausiids were pooled together and treated as one prey type in this analysis. Total lipid content failed to explain the observed variation in AFLDM as a percent of lean dry mass (SLR, p = 0.438, R2 = 0.034, Fig. 4). In addition, there was not a strong 28 relationship between AFLDM as a percent of lean dry mass and water content (SLR, p = 0.371, R2 = 0.095). When combined into the major taxonomic groups of squid, fishes, and euphausiids, AFLDM as a percent of lean dry mass was significantly different among the groups (ANOVA, p < 0.001). Squid had the highest AFLDM content as a percent of lean dry mass (Tukey’s multiple comparison, p < 0.001), while fish had higher AFDLM contents as a percent of lean dry mass compared to euphausiids (Tukey’s multiple comparison, p < 0.001). % Lipid content of dry mass
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Fig. 2.5. Simple linear relationship between lipid content (% Lipid content) as a percent of dry mass and ash­free lean dry matter content (% Ash­free 29 lean dry matter) of total dry mass for all forage types from 2008 and 2009 analyzed for proximate composition. Mean proximate composition (% dry mass)
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Fig. 2.6. Mean proximate composition as a percent of total dry mass with standard deviation for all prey types collected during 2008 and 2009 in the southeastern Bering Sea and analyzed for proximate composition. Individual species analysis Northern lampfish and northern smoothtongue: Both these species are forage fishes with high lipid content that were only caught over the deep‐water of the slope region during this study. In 2008, northern lampfish were abundant 30 during night trawls, while too few northern smoothtongue were caught to provide enough mass for accurate proximate composition analysis. In 2009, northern lampfish were rarely caught in the study area, with two trawls providing enough individuals for proximate composition analysis. Conversely, northern smoothtongue were more abundant at night over the slope region in 2009 and proximate analysis of multiple samples was possible. Northern lampfish (Myctophidae) had very high lipid content and total energy density, averaging 51% lipid of dry mass for fish caught in both years. As noted above, most lampfish were caught in 2008 and total energy density of this species was significantly higher in 2008 than in 2009 (Welch’s two‐sample t‐test, p = 0.001), averaging 9.54 kJ g‐1 in 2008 compared to 8.67 kJ g‐1 in 2009. Energy density of this species did not vary by size, however, and length was unable to explain a significant proportion of the observed variation in energy density (SLR, p = 0.521, R2 = 0.045, Fig. 2.6). Northern smoothtongue were caught in both 2008 and 2009, although higher numbers were caught at more sampling stations in 2009. As with the myctophids, northern smoothtongue were only found in the deep water habitat of the slope region, and although they were of lower average energy density than northern lampfish, they averaged 36% lipid of dry mass with a total energy density of 5.67 kJ g‐1 wet weight. Total energy density of northern smoothtongue did not differ between the two years of sampling (Welch’s two‐sample t‐test, p = 0.291). Unlike northern lampfish, total energy density of northern smoothtongue 31 was strongly and positively correlated with standard length (SLR, p < 0.001, R2 = 0.741, Fig. 2.7). Total energy density (kJ/g-1 wet mass)
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Fig. 2.7. Linear relationship between fork length and total energy density for northern lampfish from both years combined. 32 Total energy density (kJ/g-1wet mass)
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Fig. 2.8. Linear relationship between fork length and mean total energy density for northern smoothtongue caught at different locations in 2008 and 2009. Euphausiids and Amphipods: Euphausiids were widely distributed and were caught in every region of the study area in both years. For both years combined, euphausiids had an average lipid content of 27% of dry mass and an average energy density of 5.28 kJ g‐1 wet mass. Total energy density of euphausiids was higher in 2008 than 2009 (Welch’s two sample t‐test, p < 0.001) with averages of 5.99 kJ g‐1 in 2008 and 4.68 k g‐1 in 2009. In addition, the average total energy density of euphausiids differed among the three sampling 33 regions (ANOVA, p < 0.001). Euphausiids caught in the Outer Shelf region had higher energy densities compared to those caught in the Middle Shelf and Slope regions (Tukey’s multiple comparison; p = 0.005 and < 0.001, respectively), with both years combined. Amphipods were only caught in the large numbers needed for proximate composition analysis in 2009 in the Middle and Outer shelf regions of the study area. Amphipods were also caught in low numbers, which could not be analyzed for proximate composition, close the Pribilof Islands. All ampipods in midwater trawl samples were subsequently identified as T. libellula, and had an average lipid content of 19% and an average total energy density of 3.52 kJ g‐1. There were no significant differences in total energy density between the two regions where amphipods were caught (ANOVA, p = 0.105). Squid (Gonatus spp.): Squid were caught in all sampling regions of the study area, although most were caught over the deep water of the slope region. Also, more squid were caught in the trawl during 2008 (n=2038) than in 2009 (n=1990) (Welch’s two sample t‐test, p < 0.001). For both years combined, squid had an average lipid content of 20% and an average total energy density of 4.76 kJ g‐1. In addition to more squid being caught in 2009, the total energy density of squid was higher in 2009 than in 2008 (Welch’s two sample t‐test, p < 0.001). The energy density of squid varied by region (ANOVA, p < 0.001), as squid caught in the Slope region had higher average total energy density than both the Middle 34 and Outer shelf regions (Tukey’s multiple comparison, p = 0.014 and < 0.001 respectively). Discussion There were significant differences in the energy density of the major prey types captured during this study. This was expected, and is similar to results found in other studies (Van Pelt et al. 1997; Payne et al. 1999; Anthony et al. 2000; Ball et al. 2007). Differences were also found in the energy density between life stages within species, such as ages 0+ and 1+ walleye pollock. This supports the first hypothesis that proximate composition and energy density vary among and within different prey types. Such variation within certain prey types and across different life stages has been described previously (Anthony et al. 2000, Cianelli et al. 2002). Inter‐ and intra‐specific variation in energy density was driven by differences in lipid content, in support of the second hypothesis, that differences in energy density among prey types are largely determined by differences in lipid content. Anthony et al. (2000) and Ball et al. (2007) both found that lipid content was highly correlated with energy density, with lipids explaining much of the observed variation in total energy density. Furthermore, Anthony et al. (2000) found that water content was negatively correlated with lipid content of forage fishes, similar to the relationship found in this study. Anthony et al. (2000) found lipid content to be positively correlated with AFLDM, while in this study a 35 weaker, non‐significant relationship was observed. Sample sizes were variable among prey types for both this study and that of Anthony et al. (2000), while this work relied heavily on large sample sizes from only a few different prey types, including invertebrate prey types. In addition, Anthony et al. (2000) included more fish species in their analysis of the relationship between AFLDM content and lipid content. The broader range of fish species measured by Anthony et al. (2000) and differing sample sizes of prey types may explain the different results. Prey size, region, and year explained some of the differences observed in energy density, in support of the third hypothesis, that prey size, sampling area, and year explain a significant proportion of observed intra‐specific variation in energy density. These relationships were not consistent across all prey types, however, which underscore the potential importance of local ocean conditions and different life history strategies amongst prey types. For example, significant variation in energy density has been documented in walleye pollock sampled within the same region around the Pribilof Islands (Cianelli et al. 2002) and many studies have discussed the variable feeding and growth in this species in different areas (e.g., Meuter et al. 2007). The energy content of fishes at certain life stages may be influenced environmental conditions at the local level, because of possible effects these conditions have on growth and development. Northern lampfish are often cited as one the highest quality prey fishes in the North Pacific Ocean (Van Pelt et al. 1997; Anthony et al. 2000; Sigler et al. 36 2004) and, specifically, as an important prey fish in the Bering Sea (Sobolevsky et al. 1996). Van Pelt et al. (1997) measured myctophids from the Gulf of Alaska, which were most likely northern lampfish, with an average energy density of 8.05 kJ g‐1 wet mass, and Anthony et al. (2002) sampled northern lampfish from the Gulf of Alaska with an average energy density of 8.49 kJ g‐1 wet mass. These values for energy density are lower than the average total energy density of 9.324 kJ g‐1 wet mass measured for fish collected in the Bering Sea during this study. It is assumed that northern lampfish are important prey because of their high energy content, although this prey species is not always available to certain predators. Cailliet and Ebeling (1990) described the distinct vertical migrations of northern lampfish as they come close to the surface at night to forage on zooplankton. These diel migrations bring a lipid‐rich prey close to the surface over the deep water habitat of the Bering Sea, allowing top predators to take advantage of a prey resource that is at‐depth and inaccessible during the day. This may prove important to seabirds and fur seals that feed extensively at night to meet their own energy demands during the nesting and breeding seasons. There are no previously published values for the energy density of northern smoothtongue, although this species has been identified as an important prey species for northern fur seals (Zepplin and Ream 2006). Northern smoothtongue were one of the highest energy prey fish found in this study, with energy density values almost as high as those measured in northern lampfish, the 37 most energy rich group observed in this study. Northern smoothtongue were caught in greater abundance in 2009, during a time when very few myctophids were caught. Northern smoothtongue may serve as an alternative prey when myctophids are not available to predators that rely on them as prey. Based on size distributions, the northern smoothtongue measured in this study were likely mature females, which tend to grow larger and live longer than males (Mason and Phillips 1984). Northern smoothtongue grow quickly during a short life span and are thought to live in deep water habitat to avoid predators (Mason and Phillips 1984). In addition, this species is abundant over the deep‐water habitat in the eastern Bering Sea(Sobolevsky and Sokolovskaya 1996; Sobolevsky et al. 1996), which may cause this species to be an important prey of seals and seabirds nesting on the Pribilof and Bogoslof Islands. Finally, there was support for the fourth hypothesis that the proximate composition and energy density of forage fishes differs from that of invertebrate prey types, such as euphausiids and squid. There were significant differences in proximate composition between the invertebrate and vertebrate prey types sampled during this study. No studies were found that reported on prey quality of euphausiids from the Bering Sea, which is a surprise, as euphausiids are considered an important prey of many different species of seabirds and marine mammals (Hunt et al. 1988, Decker et al. 1996, Croll et al. 1998). Euphausiids taken from seabirds in Prince William Sound and Cook Inlet, Alaska had energy densities between 2.5 and 6.3 kJ g‐1 wet mass (P. Jodice, personal 38 communication). This range falls within the range of values measured during this study, although samples in this study exhibited a wider range of energy densities. This may reflect the different life histories of these euphausiid species as well as the availability of high‐energy copepod prey in the Bering Sea. Furthermore, the prey consumed by these birds may not reflect the energy density of euphausiids in this area. Energy values have not been reported for squid from the Bering Sea, either. Van Pelt et al. (1997) measured squid from the Gulf of Alaska and reported an average energy density of 3.81 kJ g‐1 wet mass. Squid sampled and measured by this study had a higher average energy density of 4.76 kJ g‐1. Squid are considered an important prey of seals and seabirds in the Bering Sea (Byrd et al. 2008; Zepplin and Ream 2006), but outside of this study and Van Pelt et al. (1997), few data are available to quantify the prey quality of these invertebrates. This study presents data on the total energy content of individuals from an number of different species and this information may be important to understanding consumption by predators that only take one prey item at a time. These estimates show a number of differences that were not seen at the level of total energy density. Of the samples measured in this study, age‐1+ walleye pollock had the highest total energy per individual, when compared to all other prey types. Also, individual Northern smoothtongue were larger and, as a result, had higher individual energy content than Northern lampfish, although lampfish 39 had higher energy density. Individual amphipods and euphausiids had the lowest total energy content, although these prey types are likely important to predators because of their high numbers and density. Also, this ranking indicates that squid have higher individual energy content than many other fish species in the southeastern Bering Sea, further supporting their importance as prey. Because of the differences in individual energy content and energy density, these prey resources may vary in their importance depending on the foraging strategy of various predators. This study demonstrates the wide range in prey quality of forage fishes and invertebrate prey types from the Bering Sea, while confirming some general trends in proximate composition among these prey types. 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Diet diversity of Steller sea lions (Eumetopias jubatus) and their population decline in Alaska: a potential relationship. Canadian Journal of Aquatic and Fisheries Sciences 54: 1342‐1348. 42 Montevecchi, W.A., and J. Piatt. 1984. Composition and energy contents of mature inshore spawning capelin (Mallotus villosus): Implications for seabird predators. Comparative Biochemistry and Physiology 78: 15–20. Nishimura, A., and J. Yamada. 1988. Geographic differences in early growth of walleye pollock, Theragra chalcogramma, estimated by back‐calculation of otolith daily growth increments. Marine Biology 97: 459‐465. Radin, N.S. 1981. Extraction of tissue lipids with a solvent of low toxicity. Methods in Enzymology 72: 5–7. Reynolds, S., and T. Kunz. 2001. Standard methods for destructive composition analysis. pp. 242. In: J.R. Speakman (ed.) Body composition analysis of animals: a handbook of non‐destructive methods, Cambridge University Press, Cambridge; New York. Roby, D.D. 1991. Diet and postnatal energetics in two convergent taxa of plankton‐feeding seabirds. Auk 108: 131‐146. Rosen, D.A., and A.W. Trites. 2000. Pollock and the decline of Steller sea lions: testing the junk food hypothesis. Canadian Journal of Zoology 78: 1243‐1250. Schmidt‐Nielsen, K. (ed.) 1997. Animal Physiology: Adaptation and Environment, 5th Edition, Cambridge University Press, New York. Sidwell, V.D., P.R. Foncannon, N.S. Moore, and J.C. Bonnet. 1974. Composition of the edible portion of raw (fresh or frozen) crustaceans, finfish, and mollusks. I. Protein, fat, moisture, ash, carbohydrate, energy value, and cholesterol. Marine Fisheries Review 36: 21–35. Sigler, M.F., J.N. Womble, and J.J. Vollenweider. 2004. Availability to Steller sea lions (Eumetopias jubatus) of a seasonal prey resource: a prespawning aggregation of eulachon (Thaleichthys pacificus). Canadian Journal of Aquatic and Fisheries Sciences 61: 1475‐1484. Sinclair, E.H., L.S. Vlietstra, D.S. Johnson, T.K. Zepplin, G.V. Byrd, A.M. Springer, R.R. Ream, and G.L. Hunt Jr. 2008. Patterns in prey use among fur seals and seabirds of the Pribilof Islands. Deep Sea Research II 55: 1897‐1918. Smith, S. L. (1991), Growth, development and distribution of the euphausiids Thysanoessa raschi (M. Sars) and Thysanoessa inermis (Krøyer) in the southeastern Bering Sea. Polar Research, 10: 461–478 43 Springer, A.M. 1992. A review: walleye pollock in the North Pacific‐how much difference do they really make? Fisheries Oceanography 1: 80‐96. Springer, A.M., D.G. Roseneau, D.S. Lloyd, C.P. McRoy, and E.C. Murphy. 1986. Seabird responses to fluctuating prey availability in the eastern Bering Sea. Marine Ecology Progress Series 32: 1‐12. Stabeno, P. J., J.D. Schumacher, S.A. Salo, G.L. Hunt and M. Flint. 1999. The physical environment around the Pribilof Islands. pp. 193–216 In: T.R. Loughlin and K. Ohtani (eds.) The Bering Sea: physical, chemical and biological dynamics, Alaska Sea Grant Press, Fairbanks. Schwemmer, P., and S. Garthe. 2008. Regular habitat switch as an important feeding strategy of an opportunistic seabird species at the interface between the land and the sea. Estuarine, Coastal and Shelf Science 77: 12‐22. Wanless, S., M.P. Harris, P. Redman, and J.R. Speakman. 2005. Low energy values of fish as a probable cause of a major seabird breeding failure in the North Sea. Marine Ecology Progress Series 294: 1‐8. 44 Chapter 3: Variation in the energy density of walleye pollock (Theragra chalcogramma) from the southeastern Bering Sea Abstract Walleye pollock (Theragra chalcogramma) are an important prey of seabirds and marine mammals in the southeastern Bering Sea. Many different environment factors are thought to affect their growth from year to year, which may in turn affect their value as prey to higher predators. This study estimates the energy density of both age 0+ and age 1+ walleye pollock sampled around the Pribilof and Bogoslof Islands during 2008 and 2009. Total energy density of age 0+ pollock varied greatly by location sampled and, overall, those caught in 2009 were larger with higher energy density than those caught in 2008. Age 1+ pollock were caught in meaningful numbers only in 2009 and were of consistently high prey quality. Measurements of physical ocean conditions were poor predictors of total energy density of age 0+ pollock from both years of study, while size appeared to be the most important determinant of total energy density. In 2009, the overall density of pollock and other prey fishes seemed to influence total energy density of age 0+ pollock from certain locations. This result suggests that prey patches may have been important to the growth of pollock in 2009. This study demonstrates the variability in energy density of this important prey item and provides insight into factors that may influence overall prey quality. 45 Introduction The fishery for walleye pollock (Theragra chalcogramma) in the eastern Bering Sea is considered one of the most productive fisheries in the world (NRC 1996). Furthermore, age 0 – 1+ walleye pollock are a key prey type for a variety of seabirds (Decker et al. 1995) and northern fur seals (Callorhinus ursinus), which have large breeding colonies or rookeries on several islands in the southeastern Bering Sea (Perez and Bigg 1986; Sinclair et al. 1994). In addition to their role as prey, age 0+ walleye pollock are major consumers of zooplankton in the southeastern Bering Sea and take advantage of the favorable conditions around the Pribilof Islands, using this area a nursery ground during early development (Swartzman et al. 1999; Brodeur et al. 2002) The development and growth of age 0+ walleye pollock is likely to be influenced by environmental conditions, which may ultimately affect their prey quality. For example, Brodeur et al. (2002) found highly variable growth patterns in age 1+ pollock from the Bering Sea, which may influence prey quality of these fish. Also, Feeding rates of age 1+ pollock are known to vary among years, even when their abundance remains relatively constant (Winter et al. 2005) and their feeding rate probably affects how these fish grow and store energy. On a larger scale, regime shifts in ocean climate have been shown to affect populations of walleye pollock (Brodeur et al. 2002; Winter and Swartzman 2006), suggesting that temperature and other environmental variables affect the growth of age 0+ pollock. Furthermore, Hoof and Peterson (2006) demonstrated that climate 46 change has influenced the biodiversity of copepod species, an important prey of larval and juvenile fishes, which may have consequences on feeding and growth rates of age 1+ walleye pollock and possibly their energy content. If these effects occur on large scales, local ocean conditions may have a similar effect on different groups of pollock in the southeastern Bering Sea. Understanding how the ocean environment can affect the growth and energy content of age 1+ walleye pollock is important overall because these effects have the potential to significantly influence the growth, survival, and reproductive success of place‐based predators in the Bering Sea. The goal of this research was to examine differences in energy density of age 0 – 1+ walleye pollock from the southeastern Bering Sea to better understand how their quality as prey for upper trophic level predators is influenced by local ocean conditions. To do so, the following hypotheses were tested: 1. Total energy density of age 1+ pollock varies between years and among different habitat zones in the southeastern Bering Sea. 2. The relationship between size and total energy density of age 0+ pollock differs among years and habitat zones in the southeastern Bering Sea. 3. Oceanographic variables explain a significant proportion of the observed variation in of total energy density for age 0+ pollock. Methods 47 Site description and timing Sampling took place in the summers of 2008 and 2009 during the seabird nesting season and while northern fur seals were at their rookeries (mid‐July to mid‐August). In 2008, the study area was defined by a 200‐km radius circle around the Pribilof Islands with its center at approximately 57000’00” N, 170000’00” W (Figure 1). The Pribilof Islands include St. Paul and St. George Islands, both of which are home to large rookeries of northern fur seals, and large breeding colonies of black‐legged kittiwakes (Rissa tridactyla) and thick‐billed murres (Uria lomvia, Angliss and Lodge 2004; Byrd et al. 2007). In 2009, the sampling area used in 2008 was expanded to include the waters north of Bogoslof Island, which also supports a fur seal rookery and breeding colonies of kittiwakes and murres (Figure 1). The study area was divided into three zones based on depth and proximity to the edge of the continental shelf and because these zones have different hydrographic features that influence nutrient mixing in the water column (Stabeno et al. 1999; Hunt and Stabeno 2002). Seafloor depths of 50‐100 m were designated as the Middle Shelf, depths of 100 – 200 m as the Outer Shelf, and depths greater than 200 m as the Slope. The latter zone included areas that are part of the deep‐water basin of the Bering Sea. 48 2008: 110 transects
2009: 141 transects
Figure 2.1. Study area and sampling coverage in the southeastern Bering Sea during the 2008 and 2009 field seasons. Shading indicates the three sampling regions of Middle domain, Outer Shelf, and Slope while the hatch marks show each 10­km sampling transect. The Pribilof Islands and Bogoslof Island are indicated in white and labeled. Sample collection Sample collection was based on transects that were randomly distributed throughout the study area, both in starting location and direction. In 2008, an equal number of transects were assigned to each zone. Logistic constraints of covering a larger area in 2009 did not allow for equal coverage among the zones. 49 In addition to the randomly selected transects, adaptive transects were used in both years to sample areas that exhibited interesting biological or physical features, as determined during the normal course of sampling, and were not necessarily distributed equally among the three zones. Each transect was 10 km in length and each was sampled in the same manner throughout the study area. A conductivity, temperature, and depth (CTD) profiler was cast at the beginning of each transect to sample ocean conditions down to 100 m in the water column. Following the CTD cast, a vertical zooplankton net was pulled from the same depth to sample the plankton community. This net was 0.75 m in diameter with a 333 µm mesh ring net at the end. All zooplankton from this net were measured for total wet weight. To collect walleye pollock and other forage species, a mid‐
water Marinovich trawl was fished for the first 20 min of each sampling transect, or approximately the first 1.5‐2 km of the transect length. The Marinovich trawl measured 10.5 m in length, with a mouth opening that was an 8‐m square. The cod end was lined with 0.3‐cm mesh and the trawl was pulled behind 1.5 m X 2.1 m mid‐water trawl doors. The fishing depth of each trawl was informed by hydroacoustics; typically the trawl was set at a depth identified as having large acoustic scattering. In the absence of a layer worthy of being targeted, trawl tows were stair‐stepped from a depth of 100 m (or within 5 m of the bottom) up to the surface, fishing at 10‐m intervals for approximately 2 min each. The trawl was fished to a maximum depth of 100 m or 5 m above the bottom if the depth was less than 100 m. Acoustic sampling was done for the entire length of the transect, 50 to collect data on the overall distribution of forage fishes. These data were used to estimate the abundance of forage fishes and euphausiids by integrating all acoustic signals of these prey types down to 100 m of depth, for the entire transect. Walleye pollock caught in the trawl were sorted by size, sorted, enumerated, and measured at sea in mm standard length (SL). Samples were immediately frozen to ‐20 degrees Celsius (oC) for further analysis. Walleye pollock were divided into the appropriate size‐age classes before proximate composition analysis, which was based on a length frequency distribution of all pollock measured. No pollock were measured between 66 and 98 mm SL, suggesting that this was the cut‐off between age groups. As a result, all pollock less than 66 mm SL were considered age 0+, while pollock greater than 98 mm SL were considered age 1+. In addition, this relationship was supported by published data (Nishimura and Yamada 1988; Brown and Bailey 1992). 51 300
Frequency
250
200
150
100
50
0
8
24
40
56
72
88
104
120
136
152
168
Standard Length (mm)
Fig. 2.1. Length frequency distribution of all walleye pollock caught in the trawl during both 2008 and 2009. Proximate composition analysis Lipid content and energy density of all walleye pollock were determined following the methods of Anthony et al. (2000) and Reynolds and Kunz (2001). Age 0+ pollock were too small to be analyzed individually, so individuals were pooled to reach a wet mass of approximately 12 g, which typically provides 2.0 – 3.5 g of dry mass for analysis. Larger age 1+ pollock were analyzed as whole individuals. Proximate composition analysis was conducted on three replicates of 52 each sample, when sufficient sample mass was available. Otherwise, the available wet mass of each prey type was divided into as many 12‐g aliquots as possible and analyzed in the same manner. Pollock were thawed and then weighed on an analytical balance to the nearest 0.0001 g to determine wet mass. The samples were then dried to a constant mass in a convection oven set at 60oC to determine water content, which is calculated as the difference between wet and dry mass. The dried sample was then completely homogenized with mortar and pestle to prepare the sample for lipid extraction. Total lipids were extracted from the dried samples using a Soxhlet apparatus and a solvent system of 7:2 (v/v) hexane/isopropyl alcohol over a period of 12 h, which does not extract non‐lipids from the sample (Radin 1981). Extracted samples were placed in convection ovens for 24 h to remove solvents and were re‐weighed on an analytical balance to ± 0.0001 g to determine lipid content by subtraction. Lean dry samples were then incinerated for 12 hours in a muffle furnace set at 550oC to ash the remaining sample. Finally, samples were re‐weighed on an analytical balance to ± 0.0001 g to determine ash‐free lean dry mass by subtraction. Water content (the difference between wet mass and dry mass) was expressed as a percentage of total fresh mass by divided by fresh sample mass and multiplying by 100. Total lipid content was expressed as a percent of total dry mass by subtracting lean dry mass of the sample (after solvent extraction) from the sample dry mass, dividing by dry sample mass, and multiplying by 100. 53 Ash‐free lean dry matter (AFLDM) was expressed as a percent of total dry mass by taking the difference between lean dry mass of the sample and ash mass, dividing by total dry mass, and multiplying by 100. AFLDM is used to estimate the amount of protein in the sample. The ashing process allows the subtracting of the non‐protein material from lean dry mass to provide a more accurate estimate of total protein content of the sample. The ash is composed of minerals, primarily from the endoskeleton or exoskeleton of the organism. Based on previous work (Sidwell et al. 1974, Stansby 1976, Craig et al. 1978), the amount of carbohydrates was assumed to be negligible proportion of overall sample mass. Published average energy equivalents of lipids (39.3 kJ g‐1) and proteins (17.8 kJ g‐1, assuming uricotelic predators such as seabirds) were used to calculate the energy density of all forage species (Schmidt‐Nielsen 1997). The following equation was used to calculate the energy density of individual or pooled samples of forage fishes and invertebrates, based on total wet mass: Energy density (kJ g‐1 wet mass) = (1 – WF)*([LF * 39.3] + [AFLDMF * 17.8]), where the water fraction of wet mass is represented by WF and the lipid fraction of the dry mass is represented by LF. Ash‐free lean dry matter as a fraction of total dry mass of the prey sample is represented by the abbreviation AFLDMF. Energy density based on dry mass of the same samples was calculated by: 54 Energy density (kJ g‐1 dry mass) = (LF * 39.3) + (AFLDMF * 17.8) Throughout the following sections, “energy density” refers to total energy density expressed as kJ g‐1 wet mass while “lipid content” refers to the percent total lipids of dry mass of the sample. Energy density based on dry mass is used at times for comparison to published data on energy values of walleye pollock. Physical ocean conditions Pressure, temperature, and conductivity were measured directly by sensors on the CTD profiler. These data were processed and filtered with SEASOFT‐Win32 software (Sea‐Bird Electronics, Inc.) to smooth points in the profile that changed quickly and to eliminate anomalies caused by movement of the ship. Also, data were aligned relative to pressure and the time they were collected, to ensure that they were taken from the correct location in the water column. Density, total chlorophyll a, and maximum chlorophyll a were calculated by SEASOFT‐Win32 software after the initial processing. Density values were averaged for the 100 m profile. Also, the entire profile was used to calculate the level of total chlorophyll a and to determine values of maximum chlorophyll a. Depth sensors on the trawl were used to determine the likely depth that pollock were caught, so that corresponding temperature data could be taken from the profile. 55 Analysis Data were analyzed in R (http:/r‐project.org) and S‐PlusTM (TIBCO Software Inc.). A level of α = 0.05 was used as the level of statistical significance for all tests. All pollock data were tested for normality and equality of variance before further statistical analyses. One‐way analysis of variance (ANOVA) was used to determine if the energy density of age 0+ pollock varied between years and sampling regions, while a Tukey’s multiple comparison test was used to distinguish specific differences among the regional groups of pollock. Simple linear regression (SLR) was used to determine the relationship between total energy density and the length of age 0+ and age 1+ pollock. A regression equation including an interaction variable for the two years was used in an ANOVA to determine if the length and energy density relationship was different between 2008 and 2009. Stepwise regression, using Cp statistics, was used to select the initial variables to build models explaining the energy density of age 0+ walleye pollock with oceanographic variables. Multiple linear regression was used to model these data, while f‐tests were used to compare the predictive ability of the different models. Integrated abundance of all forage fishes was transformed with a logarithmic function to meet the normality assumption of SLR, which was then used to compare forage fish density to total energy density of pollock. 56 Results Age 0+ pollock The total energy density of age 0+ pollock varied among the various sampling locations and between the two years of sampling. Energy content of 0+ pollock ranged from 2.95 kJ g‐1 to 3.84 kJ g‐1 wet weight in 2008 and between 3.04 kJ g‐1 and 3.97 kJ g‐1 wet weight in 2009. Overall age 0+ pollock had higher average energy density in 2009 than in 2008 (Welch’s two sample t‐test, p = 0.005); average energy density was 3.18 kJ g‐1 in 2008 and 3.66 kJ g‐1 in 2009. The total energy densities of age 0+ pollock were different among the sampling regions (“habitat zones”) (ANOVA, p < 0.001) when years were combined. Pollock caught in the Outer Shelf zone had higher average energy density than those caught in the Middle Shelf or Slope zones (Tukey’s multiple comparison test, p‐
values < 0.001). As expected from the analysis of all species (Chapter 2), lipid content was variable and largely determined the differences in total energy density of age 0+ pollock (SLR, p = 0.392, R2 = 0.796). The linear relationship between the average standard length of pollock from a given sample and total energy density was not significantly different between 2008 and 2009 (F‐test, p = 0.391, Figure 3.2). Potential explanatory variables were chosen from metrics directly collected during or calculated from the at‐sea sampling; some variables were not used if they were found to co‐vary with other variables (Table 3.1). For both 57 years combined, Model 1 (Table 3.2) was selected initially by a Cp stepwise procedure, which indicates that average standard length, maximum chlorophyll, density, and overall abundance of forage fishes at the sampling location were the variables that best explain variation in total energy density of age‐0+ walleye pollock. After selecting the initial model with the lowest Cp statistic, F‐tests were used to compare the different models. For 2008 data, Model 1 did not explain any additional variation compared to Model 2 (Table 2; F‐test, p = 0.188), suggesting that chlorophyll levels and water density were not especially strong predictors of total energy density of age 0+ pollock in 2008. For 2009 data, Model 1 did not explain any additional variation when compared to Model 3 (Table 3.2; F‐test, p = 0.221), again suggesting that chlorophyll levels and water density could be dropped from the initial model. However, total abundance of all forage fishes remained a significant predictor of the total energy density of age 0+ pollock in 2009 (Table 3.2; F‐test, p = 0.035). Abundance of all forage fishes was positively correlated with total energy density of age 0+ pollock in 2009 (SLR, p = 0.006, R2 = 0.305). Forage fishes were not as widely and evenly distributed in 2009, so the influence of this variable suggests that patches of forage fishes may have been important to growth and survival of age 0+ and age 1+ pollock sampled in 2009 (Figure 3.3). All models suggested that the size of 0+ pollock is by far the strongest predictor of total energy density, although this relationship may change as these fish grow (Figure 3.2). 58 Average total energy density (kJ g-1 wet mass)
4.1
3.7
2008
2009
3.3
2.9
2.5
20
25
30
35
40
Standard Length (mm)
45
50
Fig. 3.2. Simple linear regression of average standard length and average total energy density by year with 95% confidence intervals for age 0+ walleye pollock collected in the southeastern Bering Sea during 2008 and 2009. 59 Table 3.1. Initial explanatory variables used in stepwise regression to predict total energy density of age 0+ pollock. Measurement (units) Count of all age‐0+ pollock from the Average length (mm SL) trawl catch Sea surface temperature (oC) Average temperature at depth caught (oC) Minimum temperature at depth caught Maximum temperature at depth caught (oC) (oC) Density (Sigma‐t) Abundance of all forage fishes (m2/nmi2) Total chlorophyll a (mg/m3) Maximum chlorophyll a (mg/m3) Abundance of all euphausiids Zooplankton wet weight (g/m3) (m2/nmi2) Table 3.2. Best­fit models to predict total energy density of age 0+ pollock collected in the southeastern Bering Sea. Model Model 1 Model 2 Model 3 Equation Pollock Total Energy Density ~ Average Length + Chlorophyll Maximum + Water Density + Forage fish Abundance Pollock Total Energy Density ~ Average Length Pollock Total Energy Density ~ Average Length + Forage Fish Abundance Cp 2.234 2.301 2.313 60 Table 3.3. Coefficients and statistics for explanatory variables used in Model 1 to explain variation in average total energy density of age 0+ walleye pollock collected in the southeastern Bering Sea. Variable Average length Chlorophyll maximum Density Forage fish abundance Coefficient Std. Error 0.0234
0.0050
0.0035
0.0024
‐0.0234
0.0910
0.0000
0.0000
t­value 4.6411 1.4377 ‐0.2570 0.5902 p­value 0.0001
0.1599
0.7988
0.5591
Average total energy density (kJ g-1 wet weight)
4.0
3.8
3.6
3.4
3.2
3.0
1.0
1.5
2.0
2.5
3.0
2
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2
Log(abundance of all forage fishes (m /nmi ))
Fig. 3.3. Simple linear regression of log transformed abundance of all forage fishes and average total energy density for age 0+ walleye pollock collected in the southeastern Bering Sea during 2009. 61 Age 1+ pollock Age 1+ pollock were caught primarily in the Middle and Outer Shelf regions of the study area during 2009. Only two age 1+ individuals were captured in 2008 from the Outer Shelf region. In 2009, the total energy density of age 1+ pollock differed across habitat zones (ANOVA, p = 0.002). Age 1+ pollock caught close to the Pribilof Islands in the Outer Shelf zone had lower energy density than those caught in either the Middle Shelf or the rest of the Outer shelf regions (Tukey’s multiple comparison test, p = 0.001 and 0.002, respectively). No differences in average energy density of 1+ pollock were found between the Middle and Outer Shelf regions during 2009 (Tukey’s multiple comparison test, p = 0.962). Total energy content of age 1+ pollock could not be compared between the two years because so few age 1+ pollock were collected in 2008 and there was insufficient mass to accurately determine proximate composition. Age 1+ pollock caught in 2009 had total energy densities that ranged from 4.10 kJ g‐1 to 5.19 kJ g‐1, with an average of 4.64 kJ g‐1. No linear relationship was found between average length of age 1+ pollock and total energy density (SLR, p = 0.978, R2 = ‐ 0.041; Figure 3.4), although differences in total energy density were strongly associated with differences in lipid content as a percent of dry mass (SLR, p = 0.565, R2 = ‐ 0.903). 62 Total energy density (kJ g-1 wet mass)
5.1
4.6
4.1
3.6
3.1
120
140
160
180
Standard Length (mm)
200
220
240
Fig. 3.3. Linear relationship between total length and total energy density of age 1+ and 2+ walleye pollock. Each point represents an individual measurement of energy density from different sampling locations. Discussion Energy density varied between years and among sampling regions for age 0+ pollock. This supports the first hypothesis of this study, that total energy density of age‐0+ pollock varies between years and among different habitat zones in the southeastern Bering Sea. Energy density of age 1+ pollock also varied by region; however, sample sizes from 2008 did not allow for proximate analysis and therefore no between comparisons could be made between years. Cianelli et al. (2002) sampled age 0+ pollock from the Middle and Outer Shelf regions of the 63 same study area and found that age 0+ pollock had higher energy density when they were closer to the frontal region along the shelf break of the Middle Shelf zone. This result is similar to the findings of this that age 0+ pollock had higher energy density in the Outer Shelf region. Cianelli at al. (2002) did not sample pollock from the Slope region west of the Pribilof Islands and it should also be noted that the sample size was limited in this zone. For these reasons, it is difficult to tell if this relationship of higher energy pollock continues into the slope region farther from the islands. Cianelli at al. (2002) sampled pollock over a longer period of time within a given field season, allowing them to put together a more complete picture of how energy density changes with length for age 0+ pollock. They report a non‐linear relationship between energy density and length that levels off as pollock approach 100 mm in length. In this study the relationship was linear for the range of age 0+ pollock lengths that were encountered, while for age 1+ pollock energy density did not increase with length (Figure 2). This result agrees with the model presented by Cianelli et al. (2002), indicating that the observed relationship does not hold up for a broader range of lengths. As pollock develop they begin to store more energy as lipid to give them a better chance of surviving the winter months (Paul and Paul 1998; Sogard and Olla 2000), which may explain why the relationship between length and energy density would change at this point in their growth. 64 Anthony et al. (2000) collected age 0+ pollock from the Gulf of Alaska in the summer that had an average length of 57mm and an average energy density of 3.47 kJ g‐1, similar to the average reported in this study from both years of sampling. Interestingly, they report lower prey quality for age 1+ pollock, which averaged 3.24 kJ g‐1 wet mass. In this study, age 1+ pollock from the Bering Sea were of similar lengths, but had much higher energy density, on average 4.64 kJ/g‐1. Van Pelt et al. (1997) measured age 0+ pollock from the Gulf of Alaska with a mean length of 74.8 mm and lower average energy density of 2.73 kJ g‐1. These values are also within the same range as the present study, although Van Pelt et al. (1997) measured age 0+ pollock with comparatively lower energy density at larger sizes than encountered in this study. Together these studies demonstrate that pollock are of lower energy density when compared to many other forage fishes because of their low lipid content (Van Pelt et al. 1997; Payne et al. 1999; Anthony et al. 2000). Although Anthony et al. (2000) demonstrated that energy density of other forage fishes can vary by year and location, they did not conduct this analysis on walleye pollock. Cianelli et al. (2002) did not find a difference in the energy density of pollock between the Middle and Outer shelf regions and they suggested that differences in oceanography within one of the sampling regions was more important in determining total energy content. However, in the present study age 0+ pollock from the slope region had higher energy densities during both years, suggesting that the oceanography of the Outer Shelf zone provides 65 better conditions for growth and, in turn, accumulating and storing lipids. Results of this study, as well as Cianelli et al. (2002), indicate that the frontal area of the Outer Shelf zone is important to pollock development, which is likely connected to the primary productivity generated by upwelling in this area (Hunt and Stabeno 2002). In addition, the energy density of a large number of age 1+ pollock from the outer shelf was measured and their abundance and relatively high energy density may make these juvenile pollock an important prey resource for seabirds and marine mammals, as has been reported by Zeppelin and Ream (2006). The relationship between length and energy density of age 0+ pollock was not different between the two years of the study, indicating that these fishes stored lipids to a similar size‐dependent extent in both years of the study. However, this relationship may change if additional years were included and as pollock attain greater lengths, as suggested by Cianelli et al. (2002). Age 0+ pollock were larger and had higher average energy density in 2009, suggesting that ocean conditions leading to growth and development may have been different between the two years. The different mean sizes between the two years of this study may reflect earlier hatch dates of pollock in 2009, which would allow these fish more time to grow. The similar relationship between length and energy density suggests that growth rates were similar during 2008 and 2009, supporting the argument for an earlier hatch data in 2009. However, sampling took place over a relatively short window of time and many other factors may 66 have influenced the energy density of these fish. Predator avoidance behaviors and the ability of these fish to maintain their place in the water column likely affect the energy demands of age 0+ pollock and subsequently their growth. Both of these factors may have influenced the growth and energy density of pollock during the two years of this study, even if the hatch dates were the same. Studies have described the effect of ocean conditions on juvenile pollock (Winter and Swartzman 2006; Logerwell et al. 2007); however, the present study was unable to link ocean conditions to the variation in energy density of age 0+ walleye pollock, and providing no support the third hypothesis, that oceanographic variables explain a significant proportion of the observed variation in of total energy density for age 0+ pollock. These results indicate that more complex interactions or additional oceanographic variables that were not encompassed within this study’s design likely determine the total energy density of these fish. An explanation for this might be the nature of the sampling, as these were point‐specific measurements and did not attempt to describe ocean conditions over a larger scale. While the local oceanography during the summer did not explain the differences in energy density, conditions during spring, when pollock hatch, may better explain the growth and body condition of age 0+ pollock. Studies have indicated the importance of spring bloom conditions to growth and survival of young‐of‐the‐year pollock (Meuter et al. 2006; Winter and Swartzman 2006 ), although these ocean conditions would be difficult to measure 67 in a way that captures the large amount of fine‐scale variation in energy density of juvenile pollock demonstrated by this study. The abundance of all forage fishes at a given sampling location was found to be a significant predictor of total energy density of age 0+ pollock in 2009. This result suggests that conditions that help pollock grow and store lipids may benefit other forages species as well. Although this relationship was not observed with zooplankton that are prey for pollock and other fishes, a grazing effect may be taking place that would reduce our ability to sample large numbers of zooplankton. Also, these areas may serve as a refuge for the fishes from higher predators, allowing them to use energy for growth instead of behaviors needed to avoid predators. Many complex factors likely explain this observation, although provides a need for future research on these effects. This study demonstrated the importance of understanding spatial distribution and its relationship with large‐scale oceanography, as seen in the differences among sampling zones of the study area. In addition, annual variation appears to affect the energy density and hence quality as prey of age 0+ pollock, as well as the spatial distribution of age 1+ pollock, which may influence predators the rely heavily on these fish as prey. The relationship between energy density and size indicates how age 0+ pollock allocate energy, which can also affect their quality as prey. Many other factors during certain times of year may ultimately determine energy density of these fish, although this analysis begins to 68 demonstrate some of the variables that affect the energy density of age 0‐1+ walleye pollock in the southeastern Bering Sea. 69 References Angliss, R.P., and K.L. Lodge. 2004. Alaska Marine Mammal Stock Assessments, 2003. In U.S. Department of Commerce, NOAA Technical Memorandum, NMFS‐
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ICES Journal of Marine Science 56: 545‐560. Van Pelt, T.J., J.F. Piatt, B.K. Lance, and D.D. Roby. 1997. Proximate composition and energy density of some north Pacific forage fishes. Comparative Biochemical Physiology 118A: 1393‐1398. Winter, A.G., and G.L. Swartzman. 2006. Interannual changes in distribution of age‐0 walleye pollock near the Pribilof Islands, Alaska, with reference to the prediction of pollock year‐class strength. ICES Journal of Marine Science 63: 1118‐1135. Winter, A., G. Swartzman, and L. Ciannelli. 2005. Early to late‐summer population growth and prey consumption by age‐0 pollock (Theragra chalcogramma) in two years of contrasting pollock abundance near the Pribilof Islands, Bering Sea. Fisheries Oceanography 14: 207‐320. 72 Chapter 4: General Conclusions This study was able to measure the energy density and energy content of a number of different forage species in a way that will allow researchers to assess their quality as prey for top predators in the Bering Sea. Moreover, many of these reported values of proximate composition are novel data on species or from regions where prey types have not been previously analyzed. A wide range of variation was documented in lipid content and energy density both within and among the different species sampled, which may affect both the spatial and temporal foraging patterns of fur seals and seabirds. In addition, a detailed analysis on a key prey type, juvenile walleye pollock, was completed to better understand the variation in prey quality for this important forage fish. This work also presented a measure of total energy content of individual prey items, which may help understand the value of the prey for different types of predators. Juvenile walleye pollock were found to be the most numerically abundant species in trawl samples from the study area, although age 0+ pollock did not exhibit particularly high energy density. However, they are an important prey type and their numbers would account for a great deal of the available energy in this ecosystem. Mean values of proximate composition from this study were similar to those reported by Van Pelt et al. (2007) and Anthony et al. (2000) for age 0+ pollock collected in the Gulf of Alaska. Age 0+ pollock sampled from locations in the Outer Shelf zone had especially high energy densities and lipid 73 contents, suggesting that this area provides better conditions for growth. A similar relationship with the frontal region of the outer shelf zone was reported by Cianelli et al. (2002). Age 0+ pollock were larger and had higher energy densities in 2009 compared to 2008, suggesting that overall conditions in 2009 were better for growth of these juveniles. Based on the two years of sampling, no difference was found in the relationship between size and energy density, suggesting that pollock had longer to grow or experienced more favorable conditions for growth during 2009 compared to 2008. This may have resulted from earlier hatch dates that may have coincided with better ocean conditions earlier in the year. Point‐specific measures of environmental conditions were unable to explain the variation observed in energy density of age 0+ pollock, also suggesting that ocean conditions at particular times of year or on larger scales may influence energy density of these fish. Few other studies have measured proximate composition of invertebrates and a great deal of variation was observed in these prey types. Some of the euphausiids and squid sampled had energy densities similar to those of fishes considered high‐lipid, high‐quality prey. For this reason, certain areas with euphausiids and squid may prove to be an important prey resource to top predators in the Bering Sea. Van Pelt et al. (1997) reports similar measurements of proximate composition of squid (Gonatus spp.), although no published data was found on euphausiids or amphipods. 74 This study documented differences in total energy density and energy content of prey types both among sampling locations and between years, although patterns of such differences were not consistent among prey types. These results demonstrate that the marine ecosystem in the southeastern Bering Sea is extremely dynamic and difficult to predict, although the wide range of variation both within and among species may greatly affect the seabirds and marine mammals foraging in this area. Furthermore, these predators may alter their foraging strategy depending on differences in the total energy content of individual prey items. This study provides a number of different measures of prey quality that can be used to understand their value to predators in the southeastern Bering Sea. 75 References: Anthony, J.A., D.D. Roby, and K.R. Turco. 2000. Lipid content and energy density of forage fishes from the northern Gulf of Alaska. Journal of Experimental Marine Biology and Ecology 248: 53‐78. Ciannelli, L., A.J. Paul, and R.D. Brodeur. 2002. Regional, interannual, and size‐
related variation of age 0 walleye pollock whole body energy content around the Pribilof Islands, Bering Sea. Journal of Fish Biology 60: 1267‐1279. Van Pelt, T.J., J.F. Piatt, B.K. Lance, and D.D. Roby. 1997. Proximate composition and energy density of some north Pacific forage fishes. Comparative Biochemical Physiology 118A: 1393‐1398.