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Body Size Regression Formulae, Proximate Composition and Energy Density of Eastern Bering Sea Mesopelagic Fish and Squid

  • Elizabeth H. Sinclair ,

    Contributed equally to this work with: Elizabeth H. Sinclair, William A. Walker, James R. Thomason

    Affiliation National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic Atmospheric Administration, Seattle, Washington, United States of America

  • William A. Walker ,

    Contributed equally to this work with: Elizabeth H. Sinclair, William A. Walker, James R. Thomason

    Affiliation National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic Atmospheric Administration, Seattle, Washington, United States of America

  • James R. Thomason

    Contributed equally to this work with: Elizabeth H. Sinclair, William A. Walker, James R. Thomason

    Affiliation National Marine Mammal Laboratory, Alaska Fisheries Science Center, National Marine Fisheries Service, National Oceanic Atmospheric Administration, Seattle, Washington, United States of America


12 Jul 2016: Sinclair EH, Walker WA, Thomason JR (2016) Correction: Body Size Regression Formulae, Proximate Composition and Energy Density of Eastern Bering Sea Mesopelagic Fish and Squid. PLOS ONE 11(7): e0159353. View correction


The ecological significance of fish and squid of the mesopelagic zone (200 m–1000 m) is evident by their pervasiveness in the diets of a broad spectrum of upper pelagic predators including other fishes and squids, seabirds and marine mammals. As diel vertical migrators, mesopelagic micronekton are recognized as an important trophic link between the deep scattering layer and upper surface waters, yet fundamental aspects of the life history and energetic contribution to the food web for most are undescribed. Here, we present newly derived regression equations for 32 species of mesopelagic fish and squid based on the relationship between body size and the size of hard parts typically used to identify prey species in predator diet studies. We describe the proximate composition and energy density of 31 species collected in the eastern Bering Sea during May 1999 and 2000. Energy values are categorized by body size as a proxy for relative age and can be cross-referenced with the derived regression equations. Data are tabularized to facilitate direct application to predator diet studies and food web models.


Mesopelagic (200 m–1000 m depth) fishes and cephalopods play a central role in marine ecology as vertically migrating planktivores and principal prey to a wide range of top predators [1,2]. It is widely recognized that the biomass of mesopelagic micronekton is greatly underestimated due to limitations in catching them [2]. Even so, the biomass of mesopelagic fishes alone is estimated to exceed that of worldwide commercial fish catches [3,4]. Their great biomass, diel vertical migration from ocean depths, high consumption of zooplankton and ubiquity in upper pelagic predator diets indicates significant carbon capture and energy transferal by mesopelagic micronekton throughout the water column [5,6,7] resulting in a prominent contribution to the surface-to-depth nutritional circulation (‘biological pump’) of the world oceans [8, 9]. Therefore, assessing the size-related energetic value of dominant species of the mesopelagic zone is relevant to interpreting the ecological linkages and dynamics of the pelagic system as a whole.

Increasing research focus on the structure of the marine food web underscores the need for detailed information on prey size and energetic value. Prey length and weight are primary variables in calculations of biomass consumption by individual predators and also indicate broader ecological patterns of predator foraging location and habitat use, since spatial distribution varies with age/body size for many marine species [10,11]. Therefore, the size-related energetic value of prey can point to the energetic potential of different foraging depths or regions specific to predator foraging habitat [12]. Prey size distribution and biomass consumption estimates are also integral to deciphering the trophic interactions and structure of marine communities through the use of ecosystem models [13], and bio-energetic modeling relies on specific values to project realistic ecosystem profiles [14]. Accordingly, estimates of the size and energetic value of prey serve as baseline values in models applied to ecosystem management [15].

In the absence of whole remains, the measurement of fish sagittal otoliths and squid beaks are long-standing tools to estimate prey size [1620]. The body size reconstruction of fish based on otolith measurement has become progressively available for mesopelagic [2126] and benthy-mesopelagic species [27,28] of the world’s oceans. Length and weight regressions based on beak measurements have been developed for numerous families of squid [29,30], particularly the Gonatidae which frequent the mesopelagic [30,31,27].

The proximate composition and energetic value of mesopelagic fish and squid has been extensively researched in the Gulf of Mexico [12], the southwest Atlantic Ocean [32] and Antarctica [3336] but, less so in the northeastern North Pacific Ocean or its associated waters. Research there has focused on the energetics of epipelagic and benthic species important in the diet of marine mammals and birds [3740], but not on the mesopelagic fishes and cephalopods that transit between zones.

In this study, we develop regression formulae to determine the length and weight of mesopelagic fish and squid based on otolith and beak measurements from species that dominated our directed catch in the southeastern Bering Sea. We then evaluate the proximate contribution of fat and protein to their energetic potential relative to body size. Our findings are provided in tabular format intended for direct application to diet studies of higher trophic level predators, and to growing efforts towards refining the details of ecosystem modeling.

Materials and Methods

Length-weight regression analyses

We developed length-weight regression analyses for 19 species of fish and 13 species of squid that dominated catch numbers in a dedicated mesopelagic survey effort in the eastern Bering Sea, May 1999 and 2000 [41]. Fish regressions were developed between otolith length (OL) or height (OH) and standard length (SL) and weight (WT) using measurements from either left- or right-sided otoliths. Otolith length is the greatest distance between anterior and posterior otolith margins and OH is the greatest distance from the ventral to the dorsal otolith margin [23]. Dentary anterior tooth length (DATL) was used in the case of (Chauliodus macouni), in lieu of measuring the very tiny otoliths typical of the Stomiidae. Standard length was selected as the best size parameter for fish since the caudal fin is so frequently damaged in specimens trawled from mesopelagic depths. However, pre-anal fin length (PAFL) was used instead of SL for grenadiers (Albatrossia pectoralis) and (Coryphaenoides cinereus) following the recommended standard for the Macrouridae [42,43]. Differences between left and right otoliths are rare and when reported are small [44,28] or suspect due to small sample sizes [19]. In our study, we investigated differences between left and right otoliths only if the R2 regression value was less than 0.90, and in the 10 species for which this was the case, we calculated separate regressions for both left and right otoliths. Potential differences between the regressions were checked by t-test and in all 10 cases no significant differences (P≤0.05) were indicated, so a single regression was employed.

Squid regressions were calculated using lower beak rostral length (LRL) or upper beak rostral length (URL) relative to dorsal mantle length (DML) or pen length (PL) and weight (WT). Both LRL and URL are defined as the length of the beak cutting edge between the rostral tip and the notch at the base of the wing insertion [30]. We used dorsal mantle length as the best measure of overall body size for squid following the prevailing standard [30]. The length of the pen, or gladius, is a very close approximation to dorsal mantle length and in samples with damaged mantle margins, we substituted PL for DML [45].

Depending on size, fish otolith and squid beak measurements were made with either optical micrometer or vernier calipers to the nearest 0.1 mm. Fish and squid were weighed to the nearest 0.1 g. With the exception of 5 cephalopod species, the relationship between hard part measurements to body length was best determined by least-squares linear regression function y = ax+b. For cephalopods Eogonatus tinro, Gonatus berryi, Gonatus sp. Z, Chiroteuthis calyx and Taonius borealis the LRL to DML or PL relationships were nonlinear and in these cases, we adopted the equation y = axb. The length-weight relationships for both squid and fish were determined using a least-squares regression of the log of the length and weight with subsequent transformation back to arithmetic units and presented as the function y = axb Transformation back to arithmetic units may result in underestimating weight, however these errors are typically small [46].

Proximate composition and energetic analysis

Proximate composition analyses were conducted on 23 species of fish and 9 species of squid. The energetic potential of prey can vary with region and season of collection as well as size (age) of specimen. Consequently, samples analyzed for proximate composition and energetic value were collected within a very narrow seasonal and temporal band in a localized area of the southeastern Bering Sea between 53°–56°N and 166°–170°W during May 15–22, 1999 and 2000 [41]. Body size and in some cases, reproductive condition served as our proxy for assigning individual age categories of juvenile (JUV), sub-adult (SA) or adult (A) in the laboratory. Samples were frozen (-40°F) in water immediately following collection at sea and then transferred to a -20°F freezer in the laboratory. Body lengths and weights were measured on pristine near frozen samples then organized by species into biologically significant size-stratified groups (JUV, SA, A) prior to refreezing and storage for up to two years preceding eventual full thawing for energetic analyses.

Whole frozen samples were thawed at the analytical laboratory (Food Products Laboratory Inc., 12003 Ainsworth Circle, Suite 105, Portland, OR 97220) prior to homogenization in a blender either singly or by species within similar body size groups. Excess water retained in squid body cavities was drained after thawing to avoid variation in moisture content values. Three gram portions of homogenate were sampled for proximate composition analysis according to the Association of Analytical Chemists (AOAC) recommended methods [47]. Duplicate samples and standard reference samples were run as quality control measures for each analysis. Samples were reanalyzed if the deviation between duplicates was greater than 15% of the mean or if the standard reference sample was not within 2.5% (or 1.2% for ash) of the derived value.

A test of distillation efficiency during protein analysis was run with ammonium sulfate. If ammonium sulfate recovery was less than 95% the samples were retested. Protein was analyzed using the Kjeldhal method [47] and the nitrogen produced was converted to percent protein with a conversion factor of 5.65. Lipid values were obtained through acid hydrolysis [47]. Moisture content (or percent moisture loss) was determined by heating samples in an oven at 130°C for two hours and then subtracting the resulting dry weight from the original wet weight [47]. Ash content, a measure of vitamins and minerals in animal tissue, was determined by combusting samples at 550°C for up to 12 hours then measuring resulting weight loss [47]. Carbohydrates are calculated as the residual number after the measured values (which are expected to add to 100%) of lipid, protein, moisture and ash are subtracted from 100. As such, carbohydrates represent the additive error inherent in each separate proximate value which is generally less than 2% or, as a measure of quality control, the samples are re-run. Carbohydrate values are not reported here since in addition to negligible error rates, fish and squid have little or no carbohydrates [48]. Energy density was calculated in calories (cal/100g) from proximate composition by multiplying the wet weight values of lipid and protein by their energy equivalents, 9.5 and 5.65, respectively. Neither ash nor moisture has caloric value and carbohydrates have a minimal effect on caloric measurements [47].


Fish and squid were collected for research purposes only from standard annual bottom trawl surveys and a pilot midwater trawl survey conducted by the National Oceanic and Atmospheric Administration (NOAA) Alaska Fisheries Science Center (AFSC; Seattle, Washington) groundfish assessment program. Collection of biological data in the US Exclusive Economic Zone by federal scientists to support fishery research is granted by the Magnuson—Stevens Fishery Conservation and Management Act. No protected species were sampled during the course of this study.

Results and Discussion

Regression formulae, proximate composition values and energy (caloric) calculations are tabulated for direct application to predator diet studies and ecosystem modeling (Tables 14).

Table 1. Fish length and weight regression equations.

Otolith height (OH), otolith length (OL) or dentary anterior tooth length (DATL) were measured (mm) and regressed on standard length (SL) or pre-anal fin length. Standard length or PAFL were regressed on weight (WT) (g).

Table 2. Cephalopod length and weight regression equations.

Lower beak rostral length (LRL) and upper beak rostral length (URL) were measured (mm) and regressed on dorsal mantle length (DML) or pen length (PL). Dorsal mantle length or PL was regressed on weight (WT) (g).

Table 3. Fish proximate analyses.

Maturity status was approximated by body size and classified as adult (A), sub-adult (SA) or juvenile (JUV). All lengths are standard length except where noted.

Table 4. Squid proximate analyses.

Maturity status was based on reproductive condition and body size and classified as juvenile (Juv) or sub-adult (SA). All lengths are dorsal mantle length except where noted.

A regression formula for one species of fish (Diaphus theta) presented here has been evaluated in the past [22] as have formulae for the gonatid squid: Berryteuthis anonychus, Berryteuthis magister, Gonatopsis borealis, Gonatus middendorffi and Gonatus onyx [30,31,49,50]. We present new regression formulae for these based on enhanced sample sizes and body size ranges with consequently tighter R2 values than those previously published. All energetic data presented here are new to the published literature in the region of collection.

It is notable that the families of fish (Myctophidae, Bathylagidae) and squid (Gonatidae) that dominated our trawl catch [41] also dominate the mesopelagic portion of marine bird and mammal diets in the Bering Sea and North Pacific Ocean [1,51,10]. The numerically dominant species of fish (Stenobrachius leucopsarus, Leuroglossus schmidti) and squid (G. borealis, B. magister) that were caught also rank numerically highest in predator diets compared to other family members and were either comparable to, or ranked energetically highest among family mean values in this study (Fig 1; Tables 3 and 4).

Fig 1. Size-related energetic content.

Relative size related energy content of dominant fish and squid families and species caught in Bering Sea research trawls during 1999 and 2000.

The Myctophidae were significantly (P≤ 0.05) higher in mean energy, fat and protein values than the Bathylagidae or the Gonatidae (Figs 1 and 2; Tables 3 and 4). Two exceptions to family patterns among fishes were the myctophid Protomyctophum thompsoni (Table 3) and the bathylagid L. schmidti with respectively lower and higher caloric value compared to the rest of their families (Fig 1; Table 3). Sub-adult L. schmidti were comparable in proximate composition and energy value to juvenile S. leucopsarus, a species with high measures of protein, fat and subsequent energy values that are typical of the myctophid family (Figs 1 and 2; Table 3). Gonatid squid were significantly (P≤ 0.05) higher in protein than Bathylagidae but, generally lower in fat and as a result, comparable in overall energy values. Eogonatus tinro was an exception among the Gonatidae with significantly higher fat and lower protein values making it comparable to L. schmidti, and contributing towards overall energy values that are the highest among the Gonatidae.(Figs 1 and 2; Tables 3 and 4).

Fig 2. Percent contribution of fat and protein to energetic composition.

Relative contribution of fat and protein to energy content of dominant fish and squid families and species caught in Bering Sea research trawls during 1999 and 2000.

Myctophid fishes provide more energy in terms of both fat and protein than either bathylagid fishes or gonatid squids, but these results may be variably influenced by specimen age (as estimated by body size) and reproductive condition which was determined for only a subset of all taxa sampled in this study (Fig 1; Tables 3 and 4). In cases where sample sizes were large enough for analysis of proximate composition according to body size and reproductive condition, we found that energy values increased with age, as determined by body length, for all but one gonatid squid species (E. tinro) (Fig 1; Table 4). Eogonatus tinro is significantly (P≤ 0.05) higher in measures of fat and energy (but, not protein) than any other member of the gonatid family in both juvenile and sub-adult stages (Table 4). This could be a factor of sampling or sample size and it should be noted that B. magister does not increase in energy value until reaching a DML of over 20 cm (Fig 1; Table 4).

This paper is meant to serve as a resource guide for those wishing to incorporate mesopelagic fish and squid body size regression formulae and size-related energetic value in their own work.

We have accounted for several of the variables that influence intraspecific energy composition. Large samples were collected in the same place at the same time of year and were evaluated by body size as a proxy for age wherever possible. If not for limited life history information on most mesopelagic species, our analysis would have been further improved by directly aging each individual sample since interspecific energetic value is known to increase by size within age categories, particularly for batch spawners [36]. We emphasize the importance of evaluating fat and protein separately by size/age category wherever possible for several reasons: 1) both protein and fat drive energetic value; 2) intraspecific protein and fat values vary with relative life history stages and collection location [34,36]; and 3) protein and fat are variably important to predators at different life stages [52]. Ultimately, age-related proximate composition values are important variables in describing the energetic map and energy flux in the world’s oceans.


Thanks to the crew of now retired NOAA RV Miller Freeman and to the Alaska Fisheries Science Center (AFSC) research team Dennis Benjamin, Kate Call, Carolyn Kurle and Tonya Zeppelin for their exceptional contributions to field collection efforts. Jay Orr (AFSC) and Eric Hochberg, Santa Barbara Museum of Natural History, were helpful in confirming the identification of fishes and cephalopods respectively. Jeff Laake (AFSC) provided statistical recommendations that greatly enhanced the structure of the regression tables. Reviews from researchers Thomas Gelatt, Harriet Huber, Libby Logerwell and Nate Raring of the AFSC and two anonymous reviewers improved the quality of this contribution.

Author Contributions

Conceived and designed the experiments: ES WAW JRT. Performed the experiments: ES WAW JRT. Analyzed the data: ES WAW JRT. Contributed reagents/materials/analysis tools: ES WAW JRT. Wrote the paper: ES WAW JRT.


  1. 1. Beamish RJ, Leask KD, Ivanov OA, Balanov AA, Orlov AM, Sinclair B. The ecology, distribution, and abundance of midwater fishes of the subarctic Pacific gyres. Progr. Oceanogr. 1999; 43: 399–442.
  2. 2. Brodeur R, Yamamura O. Micronekton of the North Pacific. PICES Working Group 14 Final Report. PICES Sci. Rep. 2005; No. 30.
  3. 3. Kaartvedt S, Staby A, Aksnes DL. Efficient trawl avoidance by mesopelagic fishes causes large underestimation of their biomass S Mar Ecol Prog Ser 2012; 456: 1–6.
  4. 4. Irigoien X, Klevjer TA, Rostad A, Martinez U, Boyra G, Acuna JL, et al. Large mesopelagic fishes biomass and trophic efficiency in the open ocean. Natl. Comm. 2014; 5: 3271,
  5. 5. Childress JJ, Nygard MH. The chemical composition of midwater fishes as a function of depth of occurrence off Southern California. Deep-Sea Res. 1973; 20: 1093–1109.
  6. 6. Childress JJ, Taylor SM, Cailliet GM, Price MH. Patterns of growth, energy utilization and reproduction in some meso- and bathypelagic fishes off Southern California. Mar. Biol. 1980; 61: 27–40.
  7. 7. Longhurst AR, Harrison WG. Vertical nitrogen flux from the oceanic photic zone by diel migrant zooplankton and nekton. Deep-Sea Res. 1988; 35: 881–889.
  8. 8. Hidaka K, Kawaguchi K, Murakami M, Takahashi M. Downward transport of organic carbon by diel migratory micronekton in the western equatorial Pacific: its quantitative and qualitative importance. Deep-Sea Res. I. 2001; 48: 1923–1939.
  9. 9. Radchenko VI. Mesopelagic fish community supplies “biological pump”. The Raffles Bull. Zool. Suppl. 2007; 14: 265–271.
  10. 10. Sinclair EH, Vlietstra LS, Johnson DS, Zeppelin TK, Byrd GV, Springer AM, et al. Patterns in prey use among fur seals and seabirds in the Pribilof Islands. Deep-Sea Res. II. 2008; 55: 1897–1918.
  11. 11. Robinson C, Steinberg DK, Anderson TR, Arístegui J, Carlson CA, Frost JR, et al. Mesopelagic zone ecology and biogeochemistry—a synthesis. Deep-Sea Res. II. 2010; 57: 1504–1518.
  12. 12. Stickney DG, Torres JJ. Proximate composition and energy content of mesopelagic fishes from the eastern Gulf of Mexico. Mar. Biol. 1989; 103: 13–24.
  13. 13. Anderson TR. Progress in marine ecosystem modelling and the “unreasonable effectiveness of mathematics”. J. Mar. Sys. 2010; 81 (1–2): 4–11.
  14. 14. Aydin K, Mueter F. The Bering Sea—a dynamic food web perspective. Deep-Sea Res. II. 2007; 54: 2501–2525.
  15. 15. Col LA, Link JS, Cadrin SX, Palka DL. Marine mammal consumption on the northeast US continental shelf. Int.Whaling Comm. Rep. 2010; SC/64/EM/2.
  16. 16. Clarke MR. The identification of cephalopod beaks and the relationship between beak size and total body weight. Bull. Br. Mus. (Nat. Hist.). 1962; 8: 419–480.
  17. 17. Fitch JE, Brownell RL. Fish otoliths in cetacean stomachs and their importance in interpreting feeding habits. J. Fish. Res. Bd. Can. 1968; 25(12): 2561–2574.
  18. 18. Anderson RO, Neumann RM. Length, weight and associated structural indices. In: Murphy BR, Willis DW, editors. Fisheries techniques. 2nd ed. Bethesda: American Fisheries Society; 1996. p. 447–481.
  19. 19. Harvey JT, Loughlin TR, Perez MA, Oxman DS. Relationship between fish size and otolith length for 63 species of fishes from the eastern North Pacific Ocean. U.S. Dep. Commer., NOAA Tech. Rep. NMFS. 2000; 150, 36 p.
  20. 20. Longenecker K. Relationships between otolith size and body size for Hawaiian reef fishes. Pac. Sci. 2008; 62 (4): 533–539.
  21. 21. Olsson CO, North AW. Diet of the king penguin Aptenodytes patagonicus during three summers at South Georgia. Ibis. 1997; 139: 504–512.
  22. 22. Ohizumi H, Watanabe H, Moku M, Kawahara S. Species identification for otoliths of myctophid fishes in the western North Pacific. Aquabiology. 2001; 137: 626–637.
  23. 23. Smale MJ, Watson G, Hecht T. Otolith atlas of southern African marine fishes. J. L. B. Smith Institute of Ichthyology, Grahamstown, South Africa, 1995.
  24. 24. Spear LB, Ainley DG, Walker WA. Foraging dynamics of seabirds in the eastern tropical Pacific Ocean. Stud. Avian Biol. 2007; p. 35–99.
  25. 25. Battaglia P, Malara D, Romeo T, Andaloro F. Relationships between otolith size and fish size in some mesopelagic and bathypelagic species from the Mediterranean Sea (Strait of Messina, Italy). Sci. Mar. 2010; 74(3): 605–612.
  26. 26. Yonezaki S, Kiyota M, Baba N, Koido T, Takemura A. Prey size reconstruction based on myctophid otoliths in scats of northern fur seals (Callorhinus ursinus). Mamm. Study. 2011; 36: 159–163.
  27. 27. Walker WA, Mead JG, Brownell RL Jr. Diets of Baird’s beaked whales, Berardius bairdii, in the southern Sea of Okhotsk and off the Pacific coast of Honshu, Japan. Mar. Mamm. Sci. 2002; 18(4): 902–919.
  28. 28. Bilge G. Otolith size-fish size relations in the jewel lanternfish Lampanyctus crocodilus (Actinopterygii:Myctophiformes:Myctophidae), from deepwater environment of the southern Aegean Sea. ACTA Ichthyologica et Piscatoria. 2013; 43 (4): 293–296,
  29. 29. Wolff GA. Identification and estimation of size from the beaks of 18 species of cephalopods from the Pacific Ocean. U.S. Dep. Commer., NOAA Tech. Rep. NMFS. 1984; 17, 50 p.
  30. 30. Clarke MR, editor. A handbook for the identification of cephalopod beaks. Oxford: Clarendon Press; 1986.
  31. 31. Kubodera T. Relationships between abundance of epipelagic squids and oceanographical-biological environments in the surface waters of the subarctic Pacific in summer. Bull. Intl. N. Pac. Fish. Comm. 1986; 47: 215–228.
  32. 32. Eder EB, Lewis MN. Proximate composition and energetic value of demersal and pelagic prey species from the SW Atlantic Ocean. Mar. Ecol. Progr. Ser. 2005; 291: 43–52.
  33. 33. Croxall JP, Prince PA. Calorific content of squid (Mollusca: Cephalopoda). Br. Antarct. Surv. Bull. 1982; 55: 27–31.
  34. 34. Donnelly J, Torres JJ, Hopkins TL, Lancraft TM. Proximate composition of Antarctic mesopelagic fishes. Mar. Biol. 1990; 106: 13–23.
  35. 35. Tierney M, Hindell MA, Goldsworthy S. Energy content of mesopelagic fish from Macquarie Island. Antarctic Sci. 2002; 14(3):225–230.
  36. 36. Van de Putte A, Flores H, Volckaert F, Van Franeker JA. Energy content of Antarctic mesopelagic fishes: implications for the marine food web. Polar Biol. 2006; 29: 1045–1051.
  37. 37. Van Pelt TI, Piatt JF, Lance BK, Roby DD. Proximate composition and energy density of some North Pacific forage fishes. Comparative Biochemistry and Physiology Part A: Physiology. 1997; 118(4): 1393–1398.
  38. 38. Payne SA, Johnson BA, Otto RS. Proximate composition of some north-eastern Pacific forage fish species. Fish. Oceanogr. 1999; 8(3): 159–177.
  39. 39. Anthony JA, Roby DD, Turco KR. Lipid content and energy density of forage fishes from the northern Gulf of Alaska. J. Exp. Mar. Biol. Ecol. 2000; 248: 53–78. pmid:10764884
  40. 40. Logerwell EA, Schaufler LE. New data on proximate composition and energy density of Steller sea lion (Eumetopias jubatus) prey fills seasonal and geographic gaps in existing information. Aquat. Mamm. 2005; 31(1): 62–82.
  41. 41. Sinclair EH, Stabeno PJ. Mesopelagic nekton and associated physics of the southeastern Bering Sea. Deep-Sea Res. II. 2002; 49: 6127–6145.
  42. 42. Atkinson DB. Relationships between pre-anal fin length and total length of roughhead grenadier (Macrourus berglax Lacépède) in the Northwest Atlantic. J. Northw. Atl. Fish. Sci. 1991;11: 7–9.
  43. 43. O’Hea B, Johnston G, White J, Dransfeld L. Length-weight relations for seven grenadier species (Actinopterygii: Gadiformes:Macrouridae) to the west of Ireland. ACTA Ichthyologica et Piscatoria. 2013; 43 (4): 285–291,
  44. 44. Waessle JA, Lasta CA, Favero M. Otolith morphology and body size relationships for juvenile Scianidae in the Rio de la Plata estuary (35–36°S). Sci. Mar. 2003; 67 (2): 233–240.
  45. 45. Young RE. Vertical distribution and photosensitive vesicles of pelagic cephalopods from Hawaiian waters. Fish. Bull., U.S. 1978; 76 (3): 583–618.
  46. 46. Saila SB, Recksiek CW, Prager MH. Basic fishery science programs: a compendium of microcomputer programs and manual of operation. New York: Elsevier Science Publishing Co., 1988.
  47. 47. AOAC (Association of Official Analytical Chemists). Official Methods of Analysis; 2002.
  48. 48. Murray J, Burt JR. The composition of fish. Torry Advisory Note 38. FAO in partnership with support unit for International Fisheries and Aquatic Research, SIFAR, 200.
  49. 49. Gudmundson CJ, Zeppelin TK, Ream RR. Comparison of two methodologies for determining diet in northern fur seals (Callorhinus ursinus). Fish. Bull. 2006; 104(3):445–455.
  50. 50. Mori J, Kubodera T, Baba N. Squid in the diet of northern fur seals, Callorhinus ursinus, caught in the western and central North Pacific Ocean. Fish. Res. 2001; 52: 91–97.
  51. 51. Sinclair EH, Balanov AA, Kubodera T, Radchenko VI, Fedorets YA. Distribution and ecology of mesopelagic fishes and cephalopods. In: Loughlin TR, Ohtani K, editors. Dynamics of the Bering Sea. Fairbanks: University of Alaska Sea Grant. 1999; AK-SG-99-03. p. 485–508.
  52. 52. Fritz LW, Hinckley S. A critical review of the regime shift—“junk food"-nutritional stress hypothesis for the decline of the western stock of Steller sea lion. Mar. Mamm. Sci. 2005; 21: 476–518.